Issue 1

International Journal of Entrepreneurship and Innovative Competitiveness – IJEIC Volume 1 – Issue 1, February 2019

https://www.nup.ac.cy/gr/hephaestus-research-repository/

Publisher: Research Institute for Entrepreneurship Development (RIED) – Neapolis University, Pafos

Volume: 1 - Issue: 1 February 2019

Consumers’ perceptions toward E-Service Quality, Perceived Value, Purchase and Loyalty Intentions Sofia D. Anastsasiadou University of Western Macedonia [email protected], Kapetanidi 21, Thessaloniki, Greece, 55131, +302310410000 Zafeiria E. Papadaki International Hellenic University

ABSTRACT Purpose: Customers’ Perceptions and Attitudes are significant aspects of consumer behavior for Marketing. Such perceptions and attitudes are measured as advantages, carrying special weight for the company. Furthermore they shape beliefs strongly relating to the Service Quality, while maximizing the magnitude of customer satisfaction. This paper will explore customer behavior in the light of customers’ intentions towards EService Quality, Perceived Value, Purchase and Loyalty Intentions, with the view to provide information and feedback to enterprises. Methodology: To test the research hypotheses, a survey was carried out on 302 Greek customers of 85 Greek e-shops. The data of the survey were analysed using the Implicative Statistical Analysis technique. The Similarity Diagram and the Implicative Diagram were utilized to interpret the data. The instrument used to measure customers’ Loyalty in relation to E-Service Quality, is E-S-QUAL, while that measuring their perception vis-a-vis the Web Site’s Performance is E-RecS-QUAL. Their attitudes with regard to Perceived Value and Loyalty Intentions were measured utilizing four 4- and five 5-point likert scale questions. And Overall Perceived Quality is measured by one 5-point likert scale question. In addition, Customer Satisfaction is measured by one 5-point likert scale question. Findings: The results of the study demonstrate that all four dimensions of customers’ EService Quality, namely Efficiency, Fulfillment, System Availability and Privacy do not affect Perceived Value as well as Purchase and Loyalty Intentions. In addition dimensions relating to the Web Site’s Performance, namely Responsiveness, Compensation and Contact do not have a direct effect on Overall E-Service Quality. Research Limitations/Implication: The paper calls for more research on how customers 1

Volume: 1 - Issue: 1 February 2019 influence e-service quality and satisfaction for a Web Site’s Performance and Web based services. Originality/value: The paper expands existing literature, focusing on e-shopping, while using a multi-dimensional construct to measure customers’ perceptions. Key words: E-Service Quality, Perceived Value, Loyalty Theoretical Frame work Overall Service Quality: Deming, is regarded as a “guru” in the field of Total Quality (Stefanatos, 2000; Anastasiadou, 2015; Anastasiadou & Zirinoglou, 2015; Anastasiadou, 2016). He considers Quality to be a sign of customer contentment and advocates that it must be focused on the satisfaction of immediate and future customer needs (Steiakakis & Kofidis, 2010). Deming et al. (1994) considered expected service quality as the level of quality customers demand and expect from service providers. According to Feigenbaum (1986) the concept of “Quality” is strongly related to Cost. He claimed that “Quality” is unswerving with customer satisfaction at the lowest possible Cost (Feigenbaum, 1986). Ishikawa, another “guru” in the field of Total Quality (Stefanatos, 2000; Anastasiadou, 2016). Ishikawa (1976, 1985) claimed that a necessary and sufficient condition for Improving Quality is the knowledge of those customer demands that need to be satisfied. Grönroos (1982) associated service quality with customers’ perceived expectations. Parasuraman et al. (1988) defined perceived quality as “global judgment or approach to the superiority of the service”. Zeithaml et al. (1996) declared that perceived service quality can be portrayed as the customers’ outlook of a service that leads to their satisfaction and future buying intentions. Jiang and Rosenbloom (2005) suggest that in the era of technology, where one can perform purchases and other transactions with a click of a button, service quality constitutes a competitive advantage for businesses and organizations. Eshghi et al. (2008) argued that service quality has been defined as the overall appraisal of a service by customers. Furthermore, Culiberg and Rojsck (2010) proposed that service quality should be correlated with customers’ preferences. It is calculated as the difference between perceived/expected service and the service actually rendered (Parasuraman et al., 1985). Parasuraman et al. (1988) designated perceived quality to be the “global judgment or attitude with respect to the service’s superiority’’.

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Volume: 1 - Issue: 1 February 2019 Zeithalm et al. (1996) and Zeithaml et al. (1988) suggest that the perceived quality of a service can be described as the prospect of a service’s customers leads to their satisfaction and guides their future purchase intentions. Parasuraman et al. (1985) have identified five distinct gaps between customers’ expectations and perceptions: (Gap 1). The knowledge gap, which refers to the difference between what customers expect of a service and what management perceives that customers expect (Musaba et al., 2014). (Gap 2). The standards gap, which refers to the difference between what management perceives that customers expect and the quality and specifications set for service delivery (Musaba et al., 2014). (Gap 3). The delivery gap, referring to the difference between the quality specifications set for a service delivery and the actual quality of service delivery. (Gap 4). The communications gap which refers to the difference between the actual quality of service delivered and the quality of service described in the firm’s external communications, such as brochures and mass media advertising (Musaba et al., 2014). (Gap 5). The service gap which summarizes all the other gaps and describes the difference between customers’ expectations and their perceptions of the service they receive (Musaba et al., 2014). Gap 5 between the expected and the perceived service is considered to be the most significant one.

Service Quality and e-Service Quality: argued that traditional service quality is connected with all non-Internet debased customers’ exchanges with firms. Service quality represents the comparison between what customers believe a firm should and could offers in relation to firm’ actual service performance. E-service quality (E-SQ) seems to exhibit differences as well as similarities with respect to traditional service quality, due to the fact that customers’ satisfaction depends on their reaction to the use of technology, such as their technological readiness, or their beliefs regarding issues relating to security, reliability and trust towards technology. Zeithaml et al. (2000) argued that the evaluation a Web site’s quality by consumers includes, besides their personal experiences deriving from their interactions with the site, also post interaction service aspects such as fulfillment etc. Parasuraman et al. (2005) argued that E-SQ

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Volume: 1 - Issue: 1 February 2019 is defined in such a way that it encompasses of all phases of a customer’s interactions with a web site. It takes into account the extent to which the Web Site facilitates efficient and effecting purchasing and delivery. Web site reliability, responsiveness, access, flexibility, navigation easiness, efficiency, assurance and trust, security and privacy, system availability, contact and compensation are some of the major attributes connected with e-SQ (Parasuraman et al., 2005).

Purchase Intentions: Customer satisfaction is the approach that proceeds from comparing the expectations for performance and the perceived performance after familiarising oneself with the service (Oliver, 1980). Customer satisfaction consigns to both tangible and intangible supplies and its definition contains both transactional as well as accumulative measures (Jones and Suh, 2000) and is the resulting attitude of the assessment of the service by the consumer. Repurchase intention is defined as the judgment by an individual to purchase a product or use a service all over again, the choice to take part at a future activity with the same service provider or in the form of a repurchase (Hellier et al., 2003; Zeithalm et al., 1996). Customer Satisfaction: Spreng et al. (1995) define customer satisfaction as one of marketing’s core concepts. Customer satisfaction is the key objective of every enterprise (Anastasiadou, 2014; Anastasiadou, 2015; Anastasiadou, 2016; Anastasiadou et al., 2016a; Anastasiadou et al., 2016b). It captures very important needs and many organizations have understood the value of satisfied customers, in the sense that they will be positively inclined towards their product offers, there will be more positive word of mouth, a repurchase of their products and loyalty toward their organization, their products and their services. Customer satisfaction is an estimation of the fulfilment of customer expectations with respect to the quality of the product or service and the price paid. Morgan et al. (2005) consider customer satisfaction is the key objective of every firm. Business performance is strongly related to the satisfaction of its customers. Scope of the study For Marketing, the Perceptions and Attitudes of consumers are significant aspects of their behavior. They are measured as advantages carrying special weight for a firm. Furthermore

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Volume: 1 - Issue: 1 February 2019 they shape beliefs relating to Service Quality and maximize the magnitude of customer satisfaction. This paper will explore customer behavior in the light of their intentions towards E-Service Quality, Perceived Value, Purchase and Loyalty Intentions, aiming to provide information and feedback to firms. The Instruments/ Measures The first group relates to conceptual construct Efficiency and comprises of 8 statements (EFFi) (e.g. EFF5: It loads pages fast) while the second group regards conceptual construct System Availability (SYSi) and comprises of 4 statements (e.g. SYS1: This site does not crash). The third group regards conceptual construct Fulfilment (FULi) and comprises of 7 statements (e.g. FUL3: It quickly delivers what I order, and, finally, the fourth and last group regards conceptual construct Privacy (PRIi) and comprises of 3 statements (e.g. PRI3: This site protects information about my credit card). These four conceptual constructs contribute to the creation of Latent Variable, E-S-QUAL that measures service quality delivered by Web Tites (Parasuraman et al., 2005). E-RecS-QUAL was measured using the multidimensional and hierarchical scale by Parasuraman et al. (2005), consisting of 11 items, rated on a five-point Likert format, ranging from 1 (strongly disagree) to 5 (strongly agree). Customers rated the Web Site’s Performance, on the basis of the constructs of Responsiveness, Compensation and Contact. The first group relates to conceptual construct Responsiveness and comprises of 5 statements (RESi) (e.g. RES2: This site handles product returns well, while the second group regards conceptual construct Compensation (COMi) and comprises of 3 statements (e.g. COM1: This site compensates me for issues that may arise). Finally, the third and last group regards conceptual construct Contact (CONi) and comprises of 3 statements (e.g. CON3: It offers the ability to speak to a representative if there is a problem). These three conceptual constructs contribute to the creation of Latent Variable, E-RecS-QUAL. E-RecS-QUAL relates to the handling of service problems and inquires by the Web sites. Perceived Value was measured by four items (PERi). Customers rated the Web Site on each item using a scale of 1 (poor) to 10 (excellent) (e.g. PER2. The overall convenience of using this site).

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Volume: 1 - Issue: 1 February 2019 Loyalty Intentions was measured using five items (LOYi). Customers rated their likelihood of engaging in each behavior on a five-point Likert format, ranging from 1 (very unlikely) to 5 (very likely). The assessment of the overall quality of the e-shop’s services was evaluated using another statement of the five -point Likert scale, which investigates the extent by which the overall view of the respondent on the services extended by the e-shop is very positive (GPO) (e.g. I am positively dispositioned towards the services offered by the e-shop). The assessment of the customer’s degree of satisfaction is evaluated based on another fivepoint on the Likert scale statement, investigating the extent by which the respondent is satisfied from the purchasing experience he had with the e-shop (CSF) (e.g. I am satisfied from my purchasing experience with the e-shop).

Research Hypotheses The present study will examine the following hypotheses: Ηο1: Factors Efficiency, Availability, Fulfilment and Privacy contribute to the conceptual construct E-S-QUAL. Ηο2: Web Site’s Efficiency is related to Perceived Value Ho3: Web Site’s Efficiency is related to Loyalty Intentions Ho4: Web Site’s Efficiency is related to Overall Perceived Quality Ho5: Web Site’s Efficiency is related to Customer Satisfaction Ηο6: Web Site’s Availability is related to Perceived Value Ho7: Web Site’s Availability is related to Loyalty Intentions Ho8: Web Site’s Availability is related to Overall Perceived Quality Ho9: Web Site’s Availability is related to Customer Satisfaction Ηο10: Web Site’s Fulfilment is related to Perceived Value Ho11: Web Site’s Fulfilment is related to Loyalty Intentions Ho12: Web Site’s Fulfilment is related to Overall Perceived Quality Ho13: Web Site’s Fulfilment is related to Customer Satisfaction Ηο14: Web Site’s Privacy is related to Perceived Value Ho15: Web Site’s Privacy is related to Loyalty Intentions Ho16: Web Site’s Privacy is related to Overall Perceived Quality

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Volume: 1 - Issue: 1 February 2019 Ho17: Web Site’s Privacy is related to Customer Satisfaction Ηο18: Perceived Value is related to Loyalty Purchase Intentions Ηο19: Perceived Value is related to Overall Perceived Quality Ηο20: Perceived Value is related to Customers Satisfaction Ηο21: Loyalty Purchase intentions is related to Overall Perceived Quality Ηο22: Loyalty Purchase intentions is related to Customers Satisfaction Ηο23: Overall Perceived Quality is related to Customer Satisfaction Ηο24: Factors Responsiveness, Compensation and Contact contribute to the conceptual construct E-RecS -QUAL. Ηο25: Web Site’s Responsiveness is related to Perceived Value Ho26: Web Site’s Responsiveness is related to Loyalty Intentions Ho27: Web Site’s Responsiveness is related Overall Perceived Quality Ho28: Web Site’s Responsiveness is related to Customer Satisfaction Ηο29: Web Site’s Compensation is related to Perceived Value Ho30: Web Site’s Compensation is related to Loyalty Intentions Ho31: Web Site’s Compensation is related to Overall Perceived Quality Ho32: Web Site’s Compensation is related to Customer Satisfaction Ηο33: Web Site’s Contact is related to Perceived Value Ho34: Web Site’s Contact is related to Loyalty Intentions Ho35: Web Site’s Contact is related to Overall Perceived Quality Ho36: Web Site’s Contact is related to Customer Satisfaction Ηο37: Perceived Value is related to Loyalty Purchase Intentions Ηο38: Perceived Value is related to Overall Perceived Quality Ηο39: Perceived Value is related to Customers Satisfaction Ηο40: Loyalty Purchase intentions is related to Overall Perceived Quality Ηο41: Loyalty Purchase intentions is related to Customers Satisfaction Ηο42: Overall Perceived Quality is related to Customer Satisfaction

Methodology To test the research hypotheses, a survey was conducted using 302 Greek customers of 85 Greek e-shops. The data of the survey were analysed using the Implicative Statistical Analysis

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Volume: 1 - Issue: 1 February 2019 technique. To interpret the data the Similarity Diagram and Implicative Diagrams were employed. The sample: The sample comprises of 302 respondents, of whom 171 (56.6%) were men and 131 (43.4%) were women. With respect to the respondents’ age, 157 (52%) were from 18 to 24 years old; 71 (23.5%) from 25-34; 43 (14.2%) from 35 to 44 years; and finally 31 (10.3%) from 45-54 years old. With respect to their marital status, 213 (70.5%) were single; 81 (26.8%) were married and 8 (2.6%) were separated or divorced. As for the respondents’ education, one (0.3%) answered that he has completed elementary education, 137 (45.4%) secondary, 120 (39.7%) tertiary and, finally, 42 (13.9%) hold a postgraduate or doctoral title. 180 of the 302 respondents (59.6%) stated that their income is less than €10.000; 84 (27.8%) from €10.000 to €24.999; 25 (8.3%) from €25.000 to €49.999; 3 (1%) from €50.000 to €74.999 and, finally, 10 (3.3%) did not respond to this question. Table 1: Demographics Demographic

Category

Frequency

data Sex

Age

Family status

(N=111)

(%)

Male

171

56.6

Female

131

43.4

18-24

157

52.0

25-34

71

23.5

35-44

43

14.2

45-54

31

10.3

Single

213

70.5

81

26.8

Divorced/Separated

8

2.6

Elementary education

1

0.3

Secondary education

137

45.4

Tertiary education

120

39.7

42

13.9

Married

Education

Relevant frequency

Postgraduate studies /

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Volume: 1 - Issue: 1 February 2019 Doctorate Income

<€10.000

180

59.6

€10.000-€24.999

84

27.8

€25.000-€49.999

25

8.3

€50.000-€74.999

3

1

10

3.3

Did not respond

Implicative Statistical Analysis: Gras (1979) notes the need to use a method of data analysis which will constitute “a precise mechanism for the collection and processing of data that are appropriate to reinforce or refute a hypothesis, to draw conclusions.” A characteristic example of this is a method of analysis that prioritizes and connects factors. The method proposed by Gras (1979) is deemed to be appropriate in cases where one seeks: (a) the principal distinguishing factors for a population vis-a-vis its variables; (b) a partitioning of the variables; (c) a typology or a classification–a hierarchical classification of similarities and (d) an implication between variables or classes of variables–an implication tree or implication hierarchy and so on. The implicative method allows monitoring the creation of a skill and permits the finding of unadulterated or fixed (items) (variables) in the thoughts of social subjects (Gras, & Kuntz, 2008). These are not causality relations, but, rather, an index of quality, and allows one to assert that success in an item entails success in some other item, with which the first is connected. Correspondingly, failure in some item entails failure in some other item connected to the first one. Thus, one gets: (a) the Implicative Diagram and (b) the Similarity Diagram. The Implicative Diagram shows the different implicative relations that exist between variables. The Similarity Diagram presents the similarity relations holding between various items. Items which, when encountered by social subjects, the latter appear to behave in the same manner, are grouped together (Lerman, 1981). The horizontal connections in accented black denote the existence of similarity at a significance level of 99%. The data were analysed by chic software (Coutourier, & Gras, 2005; Couturier, 2008).

Results

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Volume: 1 - Issue: 1 February 2019

Ε-S-QUAL_Similarity Diagram: The similarity diagram presents groupings of statements based on customer behavior when completing the questionnaire. Similarities in emphasized black are significant, at a significance level of 99%. The similarity diagram (Figure 1) presents three distinct similarity groups (Group A, Group B) (Diagram 1). The first similarity group (Group A) refers to similarity relations between variables between two sub-groups. The first one PRI1-PRI2-PRI3 (similarity: 0.713066) that regard construct Privacy and the second one SYS4-SYS1-SYS2-SYS3 that regard construct System Availability (similarity: 0.752447) and show the similar tactic employed by the interviewees to treat and perceive the implicit latent variables/Constructs Privacy and System Availability. Specifically, similarity PRI1-PRI2 (similarity: 0.877793) shows the similar tactic adopted by interviewees with respect to their perception whether Wed site protects information about their Web-shopping behaviour (PRI1) as well as whether it shares or not their personal information with other sites (PRI2). This group PRI1-PRI2 is connected to a third variable, PRI3, which belongs to the conceptual construct Privacy and relates to the whether the site protects information about customers cards, with a similarity relation which, however, appears to be of quite special significance (PRI1-PRI2-PRI3) (similarity: 0.713066). Similarity SYS4-SYS1 (similarity: 0.880147) illustrates the parallel approach adopted by interviewees with respect to their perception whether pages at the site do not freeze after customers enter their order information (SYS4) as well as the site availability for business (SYS1). Similarity SYS2-SYS3 (similarity: 0.744794) demonstrates the analogous approach adopted by interviewees with respect to their perception whether the site launches and runs right away (SYS2) as well as the site prospects avowing crash to (SYS3). Similarity SYS4 SYS1-SYS2SYS3 is of a medium importance (similarity: 0.537773). Similarity PRI1-PRI2-PRI3-SYS4-SYS1-SYS2-SYS3 (similarity: 0.0673247) makes it clear that the two constructs Privacy and System Availability are independent. The second similarity group (Group B) refers to similarity relations between variables between three sub-groups. The first one EFF4-EFF1-EFF2 (similarity: 0.388431) that regards part of the construct Efficiency, the second one FUL5-FUL6-FUL2-FUL4-FUL7-FUL1-

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Volume: 1 - Issue: 1 February 2019 FUL3 (similarity: 0.891837) that regards construct Fulfilment and the third one EFF7-EFF8EFF3-EFF5-EFF6 (similarity: 0.356499) that regards the last part of the construct Efficiency show the similar tactic employed by the interviewees to treat and perceive the implicit latent variables/constructs Efficiency and Fulfilment. Specifically, similarity EFF1-EFF2 (similarity: 0.627968) shows the similar tactic adopted by interviewees with respect to their perception whether the site makes it easy to find what customers need (EFF1) as well as whether it makes it easy to get anywhere on the site (EFF2). This group EFF1-EFF2 is connected to a third variable, EFF4, which belongs to the conceptual construct Efficiency and relates to the weather information at the site is well organized, with a similarity relation which, however, appears to be of a minimum significance EFF4-EFF1-EFF2 (similarity: 0.388431). The second sub-group of group B, FUL5-FUL6-FUL2-FUL4-FUL7-FUL1-FUL3 (similarity: 0.891837) regards construct Fulfilment. Specifically, similarity FUL5-FUL6 (similarity: 0.997263) shows the similar tactic adopted by interviewees with respect to their perception whether the site has in stock the items the company claims to have (FUL5) as well as whether it is truthful about its offerings (FUL6). Similarity FUL2-FUL4 (similarity: 0.999998) (almost 1) shows the similar tactic adopted by interviewees with respect to their discernment whether the site makes items available for delivery with a suitable time frame (FUL2) as well as whether it sends out the item ordered (FUL4). Similarity FUL5-FUL6-FUL2-FUL4 (similarity: 0.97595) appears to be of an important significance. This group FUL5-FUL6-FUL2-FUL4 is connected to a fifth variable, FUL7, which belongs to the conceptual construct Fulfilment and relates to the weather site makes accurate promises about delivery of products FUL5-FUL6-FUL2-FUL4-FUL7 (similarity: 0.933838). Similarity FUL1-FUL3 (similarity: 0.989351) shows the similar tactic adopted by interviewees with respect to their perception whether the site delivers orders when promised (FUL1) as well as whether it quickly delivers when they order (FUL3). The group FUL5-FUL6-FUL2-FUL4-FUL7 is connected to similarity FUL1-FUL3 and regards construct Fulfilment. It appears to be of special significance (similarity: 0.891837).

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Volume: 1 - Issue: 1 February 2019 The third sub-group of group B, EFF7 EFF8-EFF3-EFF5 EFF6 (similarity: 0.356499) regards

P R I P 1 R I P 2 R I S 3 Y S S 4 Y S S 1 Y S S 2 Y S E 3 F F E 4 F F E 1 F F F 2 U L F 5 U L F 6 U L F 2 U L F 4 U L F 7 U L F 1 U L E 3 F F E 7 F F E 8 F F E 3 F F E 5 F F 6

part of construct Efficiency.

Arbre de s similarite s : C:\Use rs\Use r\De sktop\z e fh\ZEFH INPLICAT IVE_3.csv

Group Β Group A Diagram 1: E-S-QUAL_Similarity Diagram Specifically, similarity EFF7-EFF8 (similarity: 0.990579) shows the similar tactic adopted by interviewees with respect to their perception whether the site enables customers to get on to it quickly (EFF7) as well as whether the site is well organized (EFF8). Similarity EFF5-EFF6 (similarity: 0.949668) shows the similar approach adopted by interviewees with respect to their perception whether the site loads its pages fast (EFF5) as well as whether the site is simple to use (EFF6). Variable EFF3 is connected to EFF5-EFF6 with a strong similarity EFF3-EFF5-EFF6 (similarity: 0.838882). The group EFF7-EFF8 is connected to similarity EFF3-EFF5-EFF6 and regards part of the construct Efficiency. It appears to be of minor significance (similarity: 0.356499). The group FUL5-FUL6-FUL2-FUL4-FUL7-FUL1-FUL3 is connected to EFF7-EFF8- EFF3EFF5-EFF6 (similarity: 0.150454). Their similarity appears to be unimportant (similarity: 0.150454).

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Volume: 1 - Issue: 1 February 2019 Finally, second similarity group (Group B), EFF4-EFF1-EFF2-FUL5-FUL6-FUL2-FUL4FUL7-FUL1-FUL3-EFF7-EFF8-EFF3-EFF5-EFF6 (similarity: 0.027464) has unimportant similarity. Thus, it is crystal clear that the implicit latent variables Efficiency and Fulfilment are independent. Therefore the null hypothesis Ηο1 is rejected.

Ε-S-QUAL_Implicative diagram: The implicative diagram shows the implicative relations between the variables (Diagram 2). In more detail, the first leg of the implicative chain PRI3>PRI2-> PRI1 shows the belief that the site protects information about customers’ cards, with a similarity relation (PRI3) leads the customers to think that it does not share personal information with other sites (PRI2) and it protects information about their Web-shopping behaviour (PRI1). The second leg of the implicative chain SYS3->SYS4->SYS1->SYS2 shows that the belief that the site does not crash (SYS3) leads the customers to think that pages in the site do not freeze after customers enter their order information (SYS4), that the site is always available for business (SYS1) and that it properly launches and operates (SYS2). The third leg of the implicative chain has the following parts: FUL5->FUL6, FUL5->FUL2-> FUL4->FYL7,FYL1, FYL1->FUL3->EFF2, FYL5->SYS4. The implicative chain FUL5>FUL6 shows that the belief that the site has in stock the items the company claims to have (FUL5) leads the customers to think that it is truthful about its offerings (FUL6). The implicative chain FYL5->FUL2->FUL4->FYL7,FYL1, shows that the belief that the site has in stock the items the company claims to have (FUL5) leads the customers to think that the site makes items available for delivery within a reasonable time frame (FUL2), it sends out the item ordered (FUL4), it makes accurate promises about delivery of products (FUL7). The implicative chain FUL1->FUL3->EFF2, shows that the belief that the site delivers orders when promised (FUL1) leads the customers to consider it quickly delivers when they order (FUL3) and it makes it easy to get anywhere on the site (EFF2). The implicative chain FUL5->SYS4 illustrates that the belief that the site has in stock the items the company claims to have (FUL5) leads the customers to think that the site does not freeze after customers enter their order information (SYS4).

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Volume: 1 - Issue: 1 February 2019 FUL 5

FUL 2

FUL 6

FUL 4

PRI 3

PRI 2

PRI 1

SYS3

FUL 7

FUL 1

E FF7

E FF6

FUL 3

E FF8

E FF5

E FF3

E FF2

SYS4

SYS1

SYS2

Graphe implicatif : C:\Use rs\Use r\De sktop\z e fh\ZEFH INPLICAT IVE_3.csv99 95 90 85

Diagram 2: E-S-QUAL_Implicative Diagram There also a few more implicative relations: EFF7->EFF8, FUL3->EFF2, EFF6->EFF3,EFF5 and PRI3->EFF5. The implicative chain EFF7->EFF8 points up that the belief that the site enables customers to get on to it quickly (EFF7) leads the customers to think that the site is well organized (EFF8). The implicative chain FUL3->EFF2 illustrates that the belief that the site quickly delivers when they order (FUL3) leads the customers to think that it makes it easy to get anywhere on the site (EFF2). The implicative chain EFF6->EFF3,EFF5 points up that

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Volume: 1 - Issue: 1 February 2019 the belief that the site is simple to use (EFF6) leads the customers to think that the site enables customers to complete a transaction quickly (EFF3) and the site loads its pages fast (EFF5). Finally, the implicative chain PRI3->EFF5 points up that the belief that the site protects information (PRI3) leads the customers to think that site loads its pages fast (EFF5). In order to test the hypotheses Ηο2-Ηο23, an implicative chain involved the constructs Efficiency, System Availability, Fulfilment and Privacy, Perceived Value, Loyalty Intentions, Overall Perceived Quality and Customer Satisfaction is evaluated below. Ε-S-QUAL_Perceived Value_Loyalty Intetions_GPO_CSF_Implicative Diagram: The Ε-SQUAL_Perceived Value_Loyalty Intentions_GPO_CSF_Implicative Diagram shows the implicative relations between the variables (Diagram 3). In more detail, the implicative chain LOY2->LOY3,LOY4 shows implicative relations only between variables of a specific construct named Loyalty Intentions. The implicative chain LOY4->PER4 shows implicative relations only between the two variables, one related to Loyalty Intentions construct and one related to Perceived Value construct. Thus, the null hypotheses Ηο18 and Ho10 could not be accepted. The implicative chain FUL2->FUL4->FUL7,FUL1 proves implicative relations only between variables of a specific construct named Fulfilment. The implicative chain FUL5->FUL2-> PER4 establishes implicative relations only between two variables of a specific construct named Fulfilment and a variable related to Perceived Value. The implicative chain PER3-> PER2->FUL1->FUL3->EFF2 demonstrates implicative relations between two variables related to Perceived Value and two variables related to Fulfilment and one variable related to Efficiency construct. In addition the implicative chain PER3->PER2->PER1->EFF5 demonstrates implicative relations between two variables related to Perceived Value and one variable related to Efficiency construct. In consequence, the null hypotheses Ηο2 and Ho10 could not be accepted. The implicative chain FUL2->LOY5 verifies implicative relations only between the two variables, one related to Fulfilment and one related to Loyalty Intentions. Thus, the null hypothesis Ho11 could not be accepted. The implicative chain FUL1->EFF6,EFF8, verifies implicative relations only between the here variables, one related to Fulfilment and two related to Efficiency construct. The

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Volume: 1 - Issue: 1 February 2019 implicative chains EFF7->EFF8 and EFF6->EFF5,EFF3 exhibit implicative relations between the same constrict named Efficiency. The

implicative

chain

PER3->PER2->PER1->EFF5->SUS4->SUS1->SUS2

reveals

implicative relations between three variables related to Perceived Value, one variable related to Efficiency construct and three variables related to System Availability and one variable related to Efficiency construct. Accordingly, the null hypothesis Ηο6 could be accepted. The implicative chain EFF5->SUS4->SUS1->SUS2 reveals implicative relations between one variable related to Efficiency and three variables related to System Availability. The implicative chain SUS3->SUS4->SUS1->SUS2 exhibits implicative relations between the same construct named System Availability. The last implicative chain FUL5->FUL6,SUS4 exhibits implicative relations between two variables related to Fulfilment and one related to System Availability. There are no implicative relations between Web Site’s Efficiency and Loyalty Intentions, between Web site’s Efficiency and Overall Perceived Quality, and between Web site’s Efficiency and Customer Satisfaction. Hence, the null hypotheses Ho3, Ho4 and Ho5 could not be accepted. There are no implicative relations between Web Site’s System Availability and Loyalty Intentions, between System Availability and Overall Perceived Quality, and between System Availability and Customer Satisfaction. Consequently, the null hypotheses Ho7, Ho8 and Ho9 could not be accepted.

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Volume: 1 - Issue: 1 February 2019 L O Y2

L O Y4

PE R4

FUL 5

L O Y3

FUL 2

PE R3

FUL 4

L O Y5

PE R2

PE R1

FUL 7

FUL 1

E FF7

FUL 3

PRI 3

E FF6

E FF8

E FF2

PRI 2

SYS3

E FF5

PRI 1

FUL 6

E FF3

SYS4

SYS1

SYS2

Graphe implicatif : C:\Use rs\Use r\De sktop\z e fh\ZEFH INPLICAT IVE_3.csv 99 95 90 85

Diagram 3: Ε-S-QUAL_Perceived Value_Loyalty Intetions_GPO_CSF _Implicative Diagram

There are no implicative relations between Fulfilment and Overall Perceived Quality, and between Fulfilment and Customer Satisfaction. Accordingly, the null hypotheses Ho12 and Ho13 could not be accepted. There are no implicative relations between Web Site’s Privacy and Perceived Value, between Web site’s Privacy and Loyalty Intentions, between Web site’s Privacy and Overall Perceived

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Volume: 1 - Issue: 1 February 2019 Quality, and between Web site’s Privacy and Customer Satisfaction. Thus, the null hypotheses Ho14, Ho15, Ho16 and Ho17 could not be accepted. There are no implicative relations between Perceived Value leads and Overall Perceived Quality and Perceived Value leads and Customers Satisfaction. Therefore, the null hypotheses Ho19 and Ho20 could not be accepted. There are no implicative relations between Loyalty Intentions and Overall Perceived Quality and Loyalty Intentions and Customers Satisfaction. Therefore, the null hypotheses Ho21 and Ho22 could not be accepted. Finally there is no implicative relation between Overall Perceived Quality and Customer Satisfaction. In the matter of fact those variables are not even appeared in the implicative diagram. Hence, the null hypothesis Ho23 could not be accepted.

E-RecS-QUAL_Similarity Diagram: The similarity diagram presents groupings of statements based on customer behavior when completing the questionnaire. Similarities in emphasized black are significant, at a significance level of 99%. The similarity diagram (Diagram 4) presents two distinct similarity groups (Group A, Group B). The first similarity group (Group A) refers to similarity relations between variables RES1RES2-RES3-RES4-RES5 that regard factor Responsiveness shows the similar tactic employed by the interviewees to treat and perceive the implicit Latent Variable E-RecSQUAL. More specifically, the most powerful similarity in the first group, Group A, is that between variables RES2-RES3 (similarity: 0.647661) which refer to whether the Web site handles product returns well and whether the site offers a meaningful guarantee. A third variable, RES1 of conceptual construct Responsiveness, comes to complete this similarity group, group RES2-RES3. In a line is the similarity between the variables RES4-RES5 (similarity: 0.640068) which refer to whether the Web site informs the customer what to do if his/her transaction is not processed and whether it takes care of problems promptly. Although the similarity of the whole Group A is extremely weak, RES1-RES2-RES3-RES4-RES5 (similarity: 0.066533). Thus the consistency of the conceptual construct Responsiveness is disputed.

18

Volume: 1 - Issue: 1 February 2019 The second and third construct, Compensation and Contact, contribute towards a second similarity group, Group B, which is an independent group. The second similarity group, Group B refers to similarity relations between variables COM1-COM3-COM2-CON3-CON1CON2 (similarity: 0.295759). More specifically, the most powerful similarity in the second group, Group B, is that between variables CON1-CON3 (similarity: 0.973242), which refer to whether the site provides a telephone number to reach the company (CON1) and it offers the ability to speak to a lone person if there is a problem (CON3). The similarity between the variables in the subgroup CON1-CON3-CON2 (similarity: 0.594072) is of a medium importance. Thus, the consistency of the conceptual construct Contact is not disputed. The similarity between variables COM1-COM2 (similarity: 0.928392), is also very significant, which refers to whether this site compensates the customer for the problems it creates and it compensates him/her when what he/she order doesn’t arrive on time. Overall, the entire similarity of the subgroup COM3-COM1-COM2 is very significant (similarity: 0.831234). Items COM1 and COM2 are connected to COM3 which refers to the possibility that the site picks up items the customer wants to return from the house or business. Thus, the consistency of the conceptual construct Compensation is not disputed. Overall, the entire similarity of the group B, COM1-COM3-COM2-CON3-CON1-CON2, is insignificant (similarity: 0.295759). Consequently, Group B depicts a tiny connection between

Arbre de s similarite s : C:\Use rs\Use r\De sktop\z e fh\ZEFH INP LICAT IVE_3.csv

Group Β Group A 19

C O M 2

C O M 1

C O M 3

C O N 2

C O N 3

C O N 1

R E S 5

R E S 4

R E S 3

R E S 2

R E S 1

the Compensation and Contact latent variables/ constructs.

Volume: 1 - Issue: 1 February 2019 Diagram 4: E-RecS-QUAL_Similarity Diagram

E-RecS-QUAL_Implicative Diagram: The E-RecS-QUAL_Implicative Diagram shows the implicative relations between the variables (Diagram 5). In more detail, the first leg of the implicative chain RES5->RES4->RES2->RES1,RES3 shows that the belief that the site takes care of problems promptly (RES5), is that that leads the customers to think that it tells what to do in case that a transaction is not processed (RES4) and it handles product returns well (RES2), additionally it provides customers with convenient options for returning items (RES1) and consequently it offers a meaningful guarantee (RES3). The second leg of the implicative chain CON1->CON3>CON2,RES2,COM2 makes the relation between construct Content and RES2 and COM2. Specifically, the implicative chain CON1->CON3>CON2 shows the implicative relation between the items of the construct Content. The implicative chain CON1->CON3->CON2 renders it clear that the belief that when the site provides a telephone number to reach the company, CON1, then it also offers the ability to speak to a live person if there is a problem, CON3, and it leads to the site’ opportunity to have customer service representatives or availability online, CON2. In addition, the second leg of the implicative chain CON1->CON3-> CON2,RES2,COM2 shows the implicative relations between the items CON3 and RES2 and COM2. These implicative relations demonstrate some kind of relation between items of the three constructs named Content, Compensation and Responsiveness. It renders it clear that the belief that when it also offers the ability to speak to a live person if there is a problem, CON3, and it leads to belief that it handles product return well, RES2, and it compensates the customers when what they ordered delay, COM2.

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Volume: 1 - Issue: 1 February 2019 RE S5

CON1

RE S4

CON3

RE S2

CON2

RE S1

RE S3

COM 2

COM 1

COM 3

Graphe implicatif : C:\Use rs\Use r\De sktop\z 99e fh\ZEFH 95 90 85 INPLICAT IVE_3.csv

Diagram 5: E-RecS-QUAL_Implicative Diagram

The third leg of the implicative chain COM2->COM1->COM3 shows the implicative relation between the items of the construct Compensation. It is notable that construct Responsiveness has not any kind of connection with Compensation construct. Therefore, the null hypothesis Ηο24 (Ηο24: Factors Responsiveness, Compensation and Contact contribute to the conceptual construct E-RecS–QUAL) cannot be accepted. In order to test the hypotheses Ηο25-Ηο42 an implicative chain involved the constructs Responsiveness, Compensation and Contact, Perceived Value, Loyalty Intentions, Overall Perceived Quality and Customer Satisfaction is evaluated below.

E-RecS-QUAL_Perceived Value_Loyalty Intetions_GPO_CSF_Implicative Diagram: The implicative diagram shows the implicative relations between the above variables (Diagram 6). In more detail, the first leg of the implicative chain RES5-> RES4->RES2->RES1,RES3

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Volume: 1 - Issue: 1 February 2019 shows implicative relations only between the variables of a specific construct Responsiveness. There is no implicative relation between the constructs Responsiveness and Perceived Value, Loyalty Intentions, Overall Perceived Quality and Customer Satisfaction. Thus, the null hypotheses Ηο25-Ho28 are rejected. The second leg of the implicative chain has the following parts, CON1->PER3->PER2-> PER1->CON3->RES2,CON2, CON1->PER4, CON3->RES2->RES1,RES2 and CON3-> COM2->LOY5,COM1,COM3. As a consequence the second leg, shows implicative relations between some specific variables of the constructs Compensation and Contact with variables of the constrict Perceived Value. More especially all variables of the construct Contact have implicative relations with variables of the construct Perceived Value. Thus the null hypothesis Ho33 can be accepted. There are no implicative relations between Contact and Overall Perceived Quality, and between Contact and Customer Satisfaction. Thus, the null hypotheses Ho35 and Ho36 could not be accepted. The third leg of the implicative chain has the following parts, CON1->LOY2->LOY3-> LOY4->PER4, LOY3->COM2->LOY5, COM2->COM1->COM3, CON1->LOY3->COM2-> LOY5. As a consequence the third leg shows implicative relations between the specific variable CON1 of the construct Contact and the variables of the construct Loyalty Intentions. Accordingly, the null hypothesis Ho34 could be accepted. The third leg also shows implicative relations between the specific variables COM2 and LOY3 and LOY5. Thus, the null hypothesis Ηο29 could not be accepted. The third leg shows implicative relations between variables LOY3 and LOY5 of the construct Loyalty Intentions and the specific variable COM2 of the construct Contact. Accordingly, the null hypothesis Ho30 could not be accepted. The third leg also shows implicative relations between the specific variable CON1 of the constructs Contact and the variables of the construct Loyalty Intentions. Accordingly, the null hypothesis Ho34 could not be accepted. There are no implicative relations between Compensation and Overall Perceived Quality, between Compensation and Customer Satisfaction and between Compensation and Perceived Value. Thus, the null hypotheses Ho31, Ho32 and Ho29 could not be accepted.

22

Volume: 1 - Issue: 1 February 2019 In the chain PER3->PER2->PER1->CON3->COM2->LOY5 Perceived Value variables, PER3, PER2 and PER1, have some kind of implicative relation with LOY5 though CON3 and COM2. Hence, the null hypothesis Ho37 could not be accepted. CO N1

L O Y2

PE R3

PE R2

RE S5

PE R1

RE S4

CO N3

RE S2

CO N2

RE S1

RE S3

L O Y3

G PO

CSF

L O Y4

PE R4

CO M 2

L O Y5

CO M 1

CO M 3

Graphe implicatif : C:\Use rs\Use r\De sktop\z e fh\ZEFH 99 INP95 LICAT 90 85 IVE_3.csv

Diagram 6: E-RecS-QUAL_Perceived Value_Loyalty Intetions_GPO_CSF_Implicative Diagram There are no implicative relations between Perceived Value and Overall Perceived Quality, Perceived Value and Customers Satisfaction Loyalty Purchase intentions and Overall Perceived Quality, relations between Loyalty Purchase intentions and Customers Satisfaction, hence, the null hypotheses Ηο38, Ηο39, Ηο40 and Ηο41 could not be accepted. The fourth leg shows implicative relations between the variables GPO and CSF, GPO->CSF. Thus the null hypothesis Ηο42 could be accepted.

23

Volume: 1 - Issue: 1 February 2019 Conclusions Ε-S-QUAL as designed to comprise of 4 constructs, namely Efficiency, System Availability, Fulfilment, and Privacy. The findings from the Similarity Analysis showed constructs System Availability, Fulfilment, and Privacy have significance homogeneity and internal consistence and similarity, but these traits were not exhibited by Efficiency. Efficiency construct was dived into parts; the first one consisted of items EFF4, EFF1 and EFE2 and the second one consisted of items EFF7, EFF8, EFF3, EFF5 and EFE6. The findings from the Similarity Analysis showed that none of the four constructs, namely Efficiency, System Availability, Fulfilment, and Privacy exhibit similarity relations between them. More specifically, it was established by the Similarity Diagram that constructs Efficiency, System Availability, Fulfilment, and Privacy are not connected with one another with similarity relations that constitute conceptual construct Ε-S-QUAL relating to Web-Site’s Performance. Concomitantly, null hypothesis Ho1 is rejected. This result is of significant importance to Marketing, since it shows that these constructs are differentiated from each other. In addition, there are no implicative relations connecting conceptual constructs Perceived Value, Loyalty Intentions with Overall Perceived Quality and Customer Satisfaction. Furthermore, there are no implicative relations that connect conceptual constructs Efficiency, System Availability, Fulfilment, and Privacy with Overall Perceived Quality and Customer Satisfaction. Also, there is no implicative relation that connects conceptual construct Overall Perceived Quality with Customer Satisfaction. It is notable that there are no implicative relations connecting conceptual construct Privacy with Perceived Value and Loyalty Intentions. There are, however, some implicative relations which connect some items of conceptual constructs Efficiency, System Availability and Fulfilment with Perceived Value, but the connection is not so powerful, since the whole constructs Efficiency, System Availability and Fulfilment do not constitute part of those relations. Further, Loyalty Intentions is only connected with Fulfilment, but the implicative relation is also powerful. The implicative relation connecting Loyalty Intentions with Perceived Value is also powerful one.

24

Volume: 1 - Issue: 1 February 2019 Ε-RecS-QUAL was designed to comprise of 3 constructs, namely Responsiveness, Compensation and Contact. The findings from Similarity Analysis showed that constructs Compensation and Contact have significance homogeneity and internal consistence and similarity, but Responsiveness does not. Responsiveness construct was dived into parts; the first one consisted of items RES1, RES2 and RES3 and the second one consisted of items RES4 and RES5. None of these three constructs named Responsiveness, Compensation and Contact are related in pairs. Concomitantly, null hypothesis Ho24 is rejected. This result is of significant importance to Marketing, since it shows that these constructs are differentiated from one another. In addition, there are implicative relations connecting conceptual constructs Perceived Value, Loyalty Intentions with Overall Perceived Quality and Customer Satisfaction. According to Ε-RecS-QUAL there is a powerful implicative relation connecting conceptual constructs Overall Perceived Quality and Customer Satisfaction. In addition, there are powerful implicative relations connecting Perceived Value with Compensation and Contact. Furthermore, there is a powerful implicative relation that connects conceptual construct Loyalty Intentions with Compensation. There is also an implicative relation that connects some items of conceptual construct Responsiveness with Perceived Value, but such connections are not so powerful, since the whole construct Responsiveness is not part of this relation. There is no implicative relation that connects Loyalty Intentions with Responsiveness.

Managerial Implications The research findings lead to important managerial implications which expand the capacity of e-shops to attain a positive perceived quality for their services and high levels of satisfaction of their customers. Attention should be paid on the effects of Efficiency and Fulfilment on Customer Satisfaction and Overall Service Quality. The effects of Loyalty Intentions and Perceived Value on Customer Satisfaction and Overall Service Quality must also be considered. Ε-RecS-QUAL dimensions such as Responsiveness, Compensation and Contact can offer a strong assessment instrument for improving service quality. Ε-RecS-QUAL dimensions can assess the Wed site’s quality through issues that customer face. The significance of Privacy on customers’ higher evaluation pertaining Wed sites should be also be taken into account.

25

IJEIC Im",no!""'.1 Journal 01 Entrepreneurship and 10""". 11".. CompedtlYe ....... Volume: 1 - Issue: 1 February 2019

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Volume: 1 - Issue: 1 February 2019

REFERENCES Anastasiadou, S., 2014. A structural equation model describes factors affecting Greek students’ consumer behavior. Procedia Economics and Finance. Volume 9, pp. 402– 406. Anastasiadou, S., 2016. Evaluation of the application of TQM principles to Tertiary Level Education, using the EFQM Excellence Model- Research in Departments of Primary Education of Greek Universities. Postgraduate thesis in Quality Assurance, Hellenic Open University. Anastasiadou, S., Fotiadis, T., Anastasiadis, L., Iakovidis, G., Fotiadou, X. and Tiliakou, C., 2016a. Estimate and Analysis of Vocational Training School (Iek) Students’ Satisfaction Regarding the Quality of Studies Provided by These Schools. Scientific Bulletin-Economic Sciences, 15(2), pp.38-45. Anastasiadou, S., Fotiadou, X., and Anastasiadis, L., 2016b. Estimation of Vocational Training School (IEK) students’ contentment in relation to quality of their studies. New Trends and Issues Proceedings on Humanities and Social Sciences, 2(6), pp.09-18. Anastasiadou, S.D. and Zirinoglou, P.A., 2015. EFQM dimensions in Greek Primary Education System. Procedia Economics and Finance, 33, pp.411-431. Anastasiadou, S.D., 2015. The Roadmaps of Total Quality Management in the Greek Education System According to Deming, Juran, and Crosby in light of the EFQM Model. Procedia Economics and Finance, 33, pp.562-572. Coutourier, R., & Gras, R. 2005. CHIC: traitement de données avec l’analyse implicative. Extraction et Gestion des Connaissances, Volume II, RNTI, Cepadues, Paris, p.679-684. Couturier, R., 2008. Chic: Cohesive hierarchical implicative classification. In Statistical implicative analysis (pp. 41-53). Springer, Berlin, Heidelberg. Culiberg, B. and Rojsck, I. 2010. Indentifying service quality dimensions as antecedents to customer satisfaction in retain banking. Economic and Business Review, 12(3), pp. 151-166. Deming, W. E. 1994. The New Economics for industry.. Government Education, MIT Press: Cambridge, MA.

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Volume: 1 - Issue: 1 February 2019 Eshghi, A., Roy, S. K. & Ganguli, S. 2008. Service quality and customer satisfaction: an empirical investigation in Indian mobile Telecommunications service. The Marketing Management Journal, Vol. 18, Issue 2, pp. 119-144. Feigenbaum, A.V., 1986. Total Quality Control, (3rd ed.). New York: McGraw-Hill. Gras, R. and Kuntz, P., 2008. An overview of the Statistical Implicative Analysis (SIA) development. In Statistical implicative analysis (pp. 11-40). Springer, Berlin, Heidelberg. Gras, R., 1979. Contribution étude expérimental et l’analyse de certaines acquisitions cognitives et de certains objectifs en didactique des mathématiques, Thèse de doctorat, l’Université de Rennes 1. Grönroos, C., 1982. An applied service marketing theory. European journal of marketing, 16(7), pp.30-41. Hellier, P.K., Geursen, G.M., Carr, R.A. and Rickard, J.A., 2003. Customer repurchase intention: A general structural equation model. European journal of marketing, 37(11/12), pp.1762-1800. Ishikawa, K., 1976. Guide to Quality Control. Tokyo: Asian Productivity Organization. Ishikawa, K., 1985. What is total quality control? USA: Prentice-Hall. Jiang, P. and Rosenbloom, B., 2005. Customer intention to return online: price perception, attribute-level performance, and satisfaction unfolding over time. European Journal of Marketing, 39(1/2), pp.150-174. Jones, M.A. and Suh, J., 2000. Transaction-specific satisfaction and overall satisfaction: an empirical analysis. Journal of services Marketing, 14(2), pp.147-159. Lerman, C., 1981. Classication et analyse ordinale des donnes, Paris. Morgan, R.M. and Hunt, S.D., 1994. The commitment-trust theory of relationship marketing. The journal of marketing, pp.20-38. Morgan, N. A., Anderson, E. W. 2005. Understanding firms; customer satisfaction information usage. Journal of Marketing, 69(3), pp. 131-151.

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Volume: 1 - Issue: 1 February 2019 Musaba, C, N., Musaba, E. C. and Hoabeb S.I.R. 2014. Employee perceptions of service quality in the Namibian hotel industry: A SERVQUAL approach. International journal of Asian Social Science, 4(4), ll. 533-543. Oliver, R.L., 1980. A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of marketing research, pp.460-469. Parasuraman, A., Zeithaml, V. A. and Berry, L. L. 1985. A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), pp. 41-50. Parasuraman, A., Zeithaml, V. A. and Berry, L. L. 1988. SERVQUUAL: A multi-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), pp. 12-40. Parasuraman, A., Zeithaml, V.A., Malhotra, A. 2005. E-S-Dual. A Multiple-Item Scale for Assessing Electronic Service Quality. Journal of Service Research, Vol. 7, No X., pp. 1-21. Spreng, R. A., Gilbert, D. H., Mackoy, R. D. Service recovery Impact on satisfaction and intentions. Journal of Services Marketing, Vol. 9. Issue 1, pp. 15-23. Stefanatos, S.,2000. Quality Planning, vol. 2. Hellenic Open university pubs, Patras, Greece. Stiakakis, E. and Kofidis N., 2010. Management and Quality Control. Tziola Pubs, Thessaloniki, Greece. Zeithaml, V.A., 1988. Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. The Journal of marketing, pp.2-22. Zeithaml, V.A., Berry, L.L. and Parasuraman, A., 1996. The behavioral consequences of service quality. the Journal of Marketing, pp.31-46. Zeithaml, V.A., Parasuraman, A., Malhotra, A. 2000. A Conceptual Framework for Understanding e-Service Quality: Implications for Future Research and Managerial Practice, working paper, report No. 00-115. Marketing Science Institute, Cambridge. MA.

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  Volume: 1 ‐  Issue: 1 February 2019   

Contemporary advanced statistical methods for the science of marketing: Principal Components Analysis vs Analysee Factorielle des Correspondances Thomas A. Fotiadis Post Doc Candidate, University of Western Macedonia Sofia D. Anastasiadou University of Western Macedonia  Introduction: There is substantial growth and employment of pattering methods in statistics, although a direct comparison of multivariate methods in group/cluster identification in the field of Consumer Behavior in relation to Perceived Risk of e-Services Adoption Intentions has not yet been undertaken. Objective: This study analyses two different statistical techniques: i.e Principal Components

Analysis (PCA) and Analysee Factorielle des Correspondances (AFC). The main objective is to compare patterns derived from Principal Components Analysis (PCA) and Analysee Factorielle des Correspondances (AFC) procedures with respect to the Perceived Risk relating to the e-Services Adoption Intentions. Design: A survey was carried out using a structured questionnaire for a sample of 335 adults, customers of 125 Greek e-shops. These were conventionally approached by the Marketing Laboratory of a major public University in Northern Greece. Information Seeking, Information Sharing and Responsible Behavior subscales are related to the Perceived Risk of e-Services Adoption  Intentions. These subscales were measured by 25 items, rated on a seven-point Likert scale. Methods: The study focuses on the presentation of the two main types of clustering methods, Principal Components Analysis (PCA) and Analysee Factorielle des Correspondances (AFC). Results: PCA’s results verified the construct validity of Perceived Risk of e-Services Adoption

Intentions multidimensional and hierarchical scale (Featherman & Pavlou, 2003). It demonstrated the existence of seven Components, amongst which are the Financial Risk,

Performance Risk, Privacy Risk, Psychological Risk, Social Risk, Time Risk and Overall Risk. Analysee Factorielle des Correspondances (AFC) revealed the first factorial axis which expresses a negative attitude with respect to Privacy Risk, Performance Risk, Overall Risk

  Volume: 1 ‐  Issue: 1 February 2019    and Financial Risk on its left side and a positive attitude with respect to Privacy Risk, Performance Risk and part of Overall Risk on its right side. Analysee Factorielle des Correspondances (AFC) revealed the second factorial axis a neutral attitude to a part of the conceptual construct Overall Risk, a neutral attitude to part of the conceptual construct Financial Risk, to part of conceptual construct Performance Risk and to conceptual construct named Privacy Risk. In addition, the second factorial axis detects those respondents who did not have a crystal clear view as to whether they get Overall Service Quality also with respect to their Purchase Intentions. The first factorial axis juxtaposes the extreme cases while the second one, those in-between of the extreme ones. On the first factorial level, at the first quadrant e1 +,e2 + the group of respondents may be distinguished by their positive attitude with respect to Privacy Risk, Performance Risk and part of Overall Risk. On the first factorial level, at the second quadrant e1  ,e2 + the group of respondents may be distinguished by their negative attitude with respect to Privacy Risk, Performance Risk, Overall Risk and Financial Risk. Finally, on the fourth factorial level and at the second quadrant e1  ,e2   the group of respondents may be distinguished by their neutral attitude with respect to a part of the conceptual construct Overall Risk, to a part of conceptual construct Financial Risk, to a part of conceptual construct Performance Risk and to conceptual constructs Privacy Risk, Overall Service Quality and their Purchase Intentions. Psychological Risk and Social Risk seemed to be unimportant factors - their role in determination of customers’ behavior is insignificant. AFC’s results related to the customers psychological aspects regarding the specific scale dimensions that determined their behaviour. Key words: Principal Components Analysis, Analysee Factorielle des Correspondances,

Perceived Risk, e-Services Adoption Intention

Theoretical Framework

  Volume: 1 ‐  Issue: 1 February 2019    E-services are interactive software-based information systems received via the Internet which provide on-demand solutions while on the provider end they are seen as a means of driving new revenue streams and creating efficiencies. Unlike decisions for one-time purchases over the Internet, the adoption of an e-Service is a more complex decision on the part of the consumer, since it initiates a long-term relationship with a distant and faceless service provider to purchase what essentially is the functionality offered by a web-portal. Thus, the decision to adopt an e-Service is typically more complex and involves the evaluation of the perceived risks, or adoption barriers. As Koller (1988) puts it, the degree of importance of the situation determines the potential effect of risk. Given that the adoption of e-Services is an important decision for most consumers with long-term implications, the role of risk is likely to become prominent. Discussions and analyses of the barriers to technological adoption in an on-line context usually utilize the Technology Acceptance Model (TAM, Davis, 1989) to gauge user perceptions of system use and the probability of adopting an on-line system (Teo et al., 1999; Gefen and Straub, 2000; Moon and Kim, 2001; Pavlou, 2001). The variable relating to perceived risk is initially modeled as a singular one within TAM and afterwards, following Cunningham’s theorization, it is decomposed into its sub-facets, so as to offer insight as to the salient risk facets for potential consumers of e-Services. It is common to think of perceived risk (PR) as the uncertainty with respect to possible negative effects from using a service or product. Bauer (1967) defines it as “a combination of uncertainty plus seriousness of outcome involved’’, while Peter and Ryan (1976) augment this definition by including ‘‘the expectation of losses associated with purchase and acts as an inhibitor to purchase behavior’’. The Instruments/ Measures Perceived Risk of e-Services Adoption Intention multidimensional and hierarchical scale by Featherman & Pavlou (2003) consisting of 25 items, rated on a seven-point Likert format. Perceived Risk of e-Services Adoption Intentions, in particular, contains the following constructs: Financial Risk, Performance Risk, Privacy Risk, Psychological Risk, Social Risk, Time Risk and Overall Risk. The first group regards conceptual construct Financial Risk, and is comprised of 4 items (PRFi) (e.g. PRF1: There are chances that I stand to lose money if I use the e-shop?), while

  Volume: 1 ‐  Issue: 1 February 2019    the second group regards conceptual construct Performance Risk and comprises of 5 items (PRPi) (e.g. PRP1: The e-shop might not perform well and create problems with my credit). The third group regards conceptual construct Privacy Risk and includes 3 items (PRVi) (e.g. PRV1: What are the chances that using an e-shop will cause me to lose control over the privacy of your payment information), and the forth group regards conceptual construct Psychological Risk and contains 2 items (PRCi) (e.g. PRC1: The e-shop will not fit in well with my self-image or self-concept). The fifth group relates to conceptual construct Social Risk and includes 2 items (PRSi) (e.g. PRS1: There are chances that using the e-shop will negatively affect the way others think of me?), and the sixth group regards conceptual construct Time Risk and is comprised of 4 items (PRTi) (e.g. PRT2: My signing up for and using an e-shop would lead to a loss of convenience for me because I would have to waste a lot of time fixing errors in payments). Finally, the seventh group regards conceptual construct Overall Risk and contains 5 items (PRAi) (e.g. PRA5: Using e-shop exposes me to an overall risk). These seven conceptual constructs contribute to the creation of latent Variable Perceived Risk of e-Services Adoption Intention. E-service quality was measured through the use of a scale developed expressly for this purpose by Lee and Lin (2005). Lee and Lin’s (2005) model, contains a one-item scale developed to measure overall service quality, and a one-item scale for customer satisfaction. The assessment of the overall quality of the e-shop’s service is evaluated through another statement investigating the extent by which the overall view of the respondent on the services extended by the e-shop is very positive (GPO). The assessment of the customer’s satisfaction degree is evaluated based on another seven-step on the Likert scale statement, investigating the extentt by which the respondent is satisfied from the purchasing experience he had with the e-shop (CSF). Finally, two further statements of a seven-step Likert scale constitute conceptual construct Purchase Intentions (ITBi) (eg. ITB1: If I proceed with the purchase of some product in the coming 30 days, then I shall realize such purchase from this particular e-shop). Methodology

  Volume: 1 ‐  Issue: 1 February 2019    Analysee Factorielle des Correspondances or AFC: In the course of the research, absolute and relative frequencies were recorded for the 29 statement variables, using classic statistics methods. The 29 statement variables were then classified into three classes each, resulting in all of the data to be described by 87 classes, namely by a logical table (0-1). By means of the categorization of the variables a double entry table was created for the relative and absolute frequencies with dimensions 87x87. This table is a Burt table and each column in this Burt table is considered a vector with a dimension of 105. The Burt table allowed for each class and each variable to be surveyed individually and then for the classes of variables to be crossexamined. The objective being to determine these relations employed were the nxn double entry tables, the Burt tables containing all the classes, to which variables have been divided, in their columns and lines. Consequently, each element in the Burt table exclusively depends on two variables, thus revealing the relationship that connects them. Data Analysis techniques were employed for the processing of the data, since this paper necessitated that no a priori hypotheses be made. This convention was totally covered by Data Analysis methods or, more precisely, by Multivariate/Multidimensional Statistical Analysis without models. The selection of the methods rests on the fact that traditional statistical hypotheses as to the behavior of the phenomenon described by the table under analysis were not employed, but a more specific determination of their structure is attempted. The detection of the characteristics of the variables affecting the behavior and attitudes of respondents makes it possible to approach the real dimensions that customers’ attitudes take with respect to e-shop services. The approach consisting of an a posteriori categorization of e-shop customers’ attitudes, as such is presented via the questionnaires, is expedited with the help of factorial axes, namely the complex variables, and the factorial levels providing a more complete supervisory view. It is through these that the qualitative relationships between all variables are accentuated and designated. From the Data Analysis methods, cited above, Analysee Factorielle des Correspondances (Correspondence Factor Analysis) (AFC) technique was employed to analyse the data. Analysee Factorielle des Correspondances (Correspondence Factor Analysis) (AFC) technique allows for the simultaneous statistical processing of categorized qualitative and quantitative

variables

(Benzecri,

1973;

Karapistolis,

2015;

Papadimitriou,

2007;

  Volume: 1 ‐  Issue: 1 February 2019    Anastasiadou, 2016). The grouping of dominant observation groups is effected through this and thus attained is an almost universal description of the phenomenon which is expressed by the table analysed with the help of a smaller number of new complex variables-factors (Papadimitriou, 1994). These factors, independent per couple between them, are created from the synthesis of groups of the initial variables, fact that simplifies the process for probing the relations between the variables, thus offering a full and more complex image of the phenomenon under examination. The factors can assume the form of axes and form the factorial levels in pairs, which will allow the graphic representation of the variables. The contribution and cohesion of the indexes are then presented, constituting the criteria for the selection of the variables for constructing and interpreting the axes and, consequently, the factorial levels.

1.

The contribution of a point, line and column, towards the construction of a factorial axis. If λk is the total inertia along axis k and if λk is the total inertia along part of axis k and

f i Fk2 i  is the inertia of point i in cloud N I on each axis k , then contribution, which is symbolized as Ctrk i  is given from relation (4), Ctrk i  =

f i Fk2 i  λκ

(4)

where

n

 Ctr i  = 1 (5) for each axis k. k

i=1

The contribution of points j in cloud N J is correspondingly defined. As defined, contribution gives the inertia percentage of the point with respect to the inertia explained by the factorial axis. Since the contribution index reveals the points that principally contribute towards the construction of the axis, we seek points with high Ctrk i  and on which the interpretation of the axis may possibly rest, a fact that is significant for the interpretation of the phenomenon (Drosos, 2004; Papadimitriou, 2007).

2.

The square of cosine cos

k2

i  (or relevant contribution) signifies the representation

quality of a point by the factorial axis and essentially depicts a form of correlation

  Volume: 1 ‐  Issue: 1 February 2019    between point i and factorial axis k , while it is symbolized as Cork i  and given from relation (6), Cork i  =

F 2 i  k 2

d G,i 

= cos2ω (6), where d 2 G,i  is the distance of i from

the centroid (center of gravity) (Drosos, 2004). High value for Cork i  means a small angle ω namely high correlation of point i with the axis, that is good quality for the projection of i with the axis, namely good projection quality of i axis. Pursuant to the above, index Cork i  expresses the percentage of inertia at point i which is interpreted by axis k. Points with very high Cor also exhibit high Ctr . In the case where they exhibit high values for Cor and low values for Ctr  this means that they have good projection quality on the axis but do not participate in the construction thereof (Papadimitriou, 2007). In the case where they exhibit low values for Cor and high values for Ctr , this means that they contribute towards the construction of the axis but are better projected on some other axis towards the construction of which they may potentially contribute more (Drosos, 2004; Papadimitriou 2007; Anastasiadou, 2016). Principal component analysis or PCA is a method for the analysis of multivariate data, considered as constituting a part of factor Analysis. The principal objectives of PCA are: 

Data Reduction. PCA aims to replace highly correlated variables with a small number of correlated variables (Dafermos, 2013).



To detect and establish a structure/model. The goal of PCA is, namely, to accentuate structures or fundamental relations existing between the existing variable (Dafermos, 2013). Moreover, PCA aims to bring to light and assess latent variables, and to detect and assess latent sources of variability and co-variability in observable measurements.



To detect patterns. The goal of PCA is to detect prototype correlations which may potentially determine causality relations between the examined variables (Dafermos, 2013).

  Volume: 1 ‐  Issue: 1 February 2019    PCA is a descriptive or explanatory method and does not rest on conditions. In reality, PCA rests on the spectrum analysis of the variance or correlation matrix. Principal Components Analysis is by far the most widespread pattern recognition tool. It is a method for compressing a lot of data into patterns that capture the essence of the original data. Specifically, it constitutes a multivariate statistical analysis that is often used to reduce the dimension of data for easy exploration. Its objectives include: 1) to reduce the original into a lower number of orthogonal (uncorrelated), synthesized variables; 2) to visualize correlations among and between the original variables and the components, and 3) to visualize proximities among statistical units. Furthermore, PCA is considered to be a change of variable space. It rests on the study of eigenvalues and eigenvectors in the correlations or Covariance matrix.   As a multivariate analysis technique for dimension reduction, PCA aims to compress the data without losing much of the information contained in the original data. This process explains the variance-covariance structure of a set of variables through a few new variables. All principal components are specific linear combinations of the p random variables exhibiting three important properties: 1. The principal components are uncorrelated. There are also orthogonal uncorrelated, linear combinations of standardized variables. 2. The first principal component has the highest variance; the second principal component has the second highest variance, and so on. 3. The total variation in all the principal components combined is equal to the total variation in the original variables. In reality, PCA converts data into a set of linear components and, as it is characteristically alluded by Field (2009), converts them to measurable ones. Each component has the form: Componenti=b1X1+ b2X2+…. bnXm.. It is evident that PCA forecasts components based on measured variables. It is rendered clear that PCA breaks down the original data to a model of linear variables. PCA brings to light which linear components exist in the data and the manner by which one particular variable contributes to the shaping of each component (Field, 2009). PCA rests on the overall variance of the variables in descending order. The first Principal Component (PC1) captures the most variance of the data; the second Principal Component (PC2), which is not correlated with PC1, captures the second variance etc.

  Volume: 1 ‐  Issue: 1 February 2019    The number of the components extracted is equal to the original variables and the sum of their variance is the sum of the variance of the original variables. The sum of the squares of loadings to a principal component signifies the participation of the component to the overall variance of the variables. The value of the sum for each principal component is called eigenvalue. Eigenvalues are presented in descending order and allow for the exclusion of these components that do not interpret a satisfactory percentage of the overall variance, resulting only in only components interpreting a satisfactory percentage of the overall variance to be employed for the interpretation of the results. Selected are components whose eigenvalues are equal or greater than one (Kaiser, 1960, 1974) or equal or greater than 0.70 (Jolliffe, 1972, 1986).

Data Collection and Sample Data Collection: A survey was carried out using a structured questionnaire for a sample of 335 adults, customers of 125 Greek e-shops. These were conventionally approached by the Marketing Laboratory of a major public University in Eastern Greece. Two post-graduate students were carefully trained in order to perform their duties as interviewers. The questionnaire was originally developed in English and then it was translated to Greek using the translation and back translation procedure, while tutors of English who speak fluent Greek assumed to provide the relevant translations. The sample: The sample comprised of 335 interviewees, of whom 185 (55.2%) were men and 150 (44.8%) were women. With respect to the ages of participants, 67 (20%) of them were between 18 to 24, 67 (20%) of them were between 25-34, 68 (20.3%) of them were between 35-44, 67 (20%) of them were between 45-54 and, finally, 66 (19.7%) were between 55 to 64. With respect to their family status, 143 (42.7%) were single, while 180 (53.7%) were married and 12 (3.6%) were separated or divorced. 288 of 335 interviewees, or a percentage of 86%, stated that they live in an urban setting, while 47 (14%) in a rural one. Regarding the education of interviewees, one (0.3%) stated that he has completed elementary education, 124 (37%) secondary, 160 (47.8) tertiary, while 50 (14.9%) hold a postgraduate diploma or doctorate. Out of the 154 interviewees, 137 (40.9%) declared that their income

  Volume: 1 ‐  Issue: 1 February 2019    was less than €10,000 per year, 154 (46%) declared that their income was between € 10,000 and €24.999, while the income for 35 interviewees (10.4%) ranged between €25.000 to €49.999. According to 5 participants, (1.5%) their income ranged from €50.000 to €74,999. Finally, 4 interviewees (1.2%) declined to answer the question relating to their income. Findings Analysee Factorielle des Correspondances (AFC) results: The indexes employed to interpret the results of this particular correspondence factor analysis are the well-known indexes “inertial” and “contribution” (Benzécri, 1980; Papadimitriou, 2007). These indexes allow one to immediately distinguish the most important and determinative variables or objects that contribute to the creation of factorial axes. The results of this factorial analysis were interpreted with the help of inertia, which is explained by each factorial axis, of correlation and of the contribution. The data table analysis using AFC initially produces Table 1, which presents the eigenvalues of the Burt table as well as the inertia percentages for each factorial axis. Table 1 offers the capacity to distinguish the number of the most significant factorial axes, which are the most appropriate in order to interpret the results. The inertia percentage of each factorial axis denotes the significance percentage expressed by each one. According to the values complemented by the histogram (Table 1), the significance percentage of the first factorial axis is 52.92%, while that of the second amounts to 9.08%, the third 4.37%, the fourth 3.72% etc. The total information offered by the 12 factorial axes amounts to 83.27%, as can be seen from the table below (Table 1). Table 2: Inertia – Eigenvalues TOTAL INERTIA 0.16572  AXIS INERTIA %INTERPRETATION

SUM

| EIGENVALUES HISTOGRAM 

01

0.0876958

52.92

. 52.92

|***************************** 

02

0.0150499

9.08

62.00

|*********

03

0.0072446

4.37

66.37

|****

04

0.0061599

3.72

70.09

|***

05

0.0043896

2.65

72.74

|***

  Volume: 1 ‐  Issue: 1 February 2019    06

0.0036697

2.21

74.95

|**

07

0.0029736

1.79

76.75

|**

08

0.0025851

1.56

78.31

|**

09

0.0023805

1.44

79.74

|**

10

0.0021107

1.27

81.02

|*

11

0.0019320

1.17

.

82.18

|*

12

0.0017978

1.08

.

83.27

|*

  Based on cumulative frequency, the first three factorial axes interpret 66.37% of the total data variance (Table 1). This percentage is deemed satisfactory to interpret the data (Karapistolis, 2015). Moving on and from the table of the results of the factorial analysis of correspondences, pursuant to the aforementioned criteria that were chosen (inertia, correlation and contribution), the variables contributing to the shaping of the two first factorial axes were detected, using MAD software (Karapistolis, 2000). The aforementioned variables are deduced in compliance with two criteria, correlation ( Cor  200 , criterion 2) and contribution ( Ctr 

1000  11.4  12 , criterion 3) (Karapistolis, 2015). 87

Interpretation of the first factorial axis e1: More specifically, based on the responses by the respondents and as follows from factor analysis, the first axis – factor e1, with eigenvalue 0.0876958 explaining 52.92% of the total variance is constructed from classes PRV11, PRV31, PRV21, PRP11, PRP21, PRP31, PRP51, PRP41, PRA41, PRA21, PRA41, PRA31, PRA11, PRF11, PRF41, PRF21, PRF31, PRV33, PRV23, PRV13, PRP13, PRF13, PRP23, PRP53, PRP33, PRP43, PRA43, PRA53, PRA13. More specifically, the factorial axis e1, is constructed from those variable classes, that project a negative attitude with respect to Privacy Risk, Performance Risk, Overall Risk and Financial Risk and which are quoted on its left side and the positive attitude with respect to Privacy Risk, Performance Risk and part of Overall Risk on its right side (Figure 1).

  Volume: 1 ‐  Issue: 1 February 2019   

Figure 1: First factorial axis e1 We initially come across the respondents’ views with respect to conceptual construct Privacy, which support that the chances of them losing control of the privacy of their payment information when using an e-shop are probable (PRV11), (Cor=863, Ctr=18), Internet hackers (criminals) might assume control of their checking account if they use an e-shop (PRV31) (Cor=847, Ctr=16) and finally that it is probable for their signing up and using an e-shop to lead them to lose their privacy because their personal information would be used without their knowledge (PRV21) (Cor=821, Ctr=16). We then come across the respondents’ views with respect to conceptual construct Performance Risk. It supports that The e-shop might not perform well and lead to problems with their credit card (PRP11) (Cor=832, Ctr=16), due to the fact that the security systems built into the E-SHOP are not strong enough to protect their checking account (PRP21) (Cor=821, Ctr=15) and the risk of the likelihood that there will be something wrong with the performance of the e-shop or that it will not work properly (PRP31) (Cor=815, Ctr=15) is a high functional risk. Considering the expected level of service performance of the e-shop, it would be risky for them to sign up for and use it (PRP41) (Cor=879, Ctr=14). Additionally, the respondents claimed that e-shop servers may not perform well and process payments incorrectly (PRP51) (Cor=899, Ctr=15). We then come across the respondents’ views on conceptual construct Overall Risk. The respondents supported that Using thje e-shop would

  Volume: 1 ‐  Issue: 1 February 2019    add great uncertainty to their bill paying (PRA41) (Cor=940, Ctr=16) and using e-shop to pay their bills would be risky (PRA21) (Cor=891, Ctr=14) and, finally, that using the e-shop exposes them to an overall risk (PRA51) (Cor=940, Ctr=15). Furthermore they claimed that e-shops are perilious to use (PRA31) (Cor=918, Ctr=14).  On the whole and considering all sorts of factors combined, about how risky they would say using an e-shop is, they suggested that it is very risky to sign up for and use the services of an e-shop (PRA11) (Cor=940, Ctr=16).  Lastly, classes of variables quoted on its left side express views with respect to conceptual construct named Financial Risk. Responders considered that there are high chances of losing money if they use the e-shop (PRF11) (Cor=783, Ctr=12) and thus Using an Internet billpayment service subjects their checking account to financial risk (PRF41) (Cor=771, Ctr=13) and to potential fraud (PRF21) (Cor=757, Ctr=15). Accordingly, their signing up for and using an e-shop would lead to a financial loss for them (PRF31) (Cor=904, Ctr=12).  The variables projecting a positive attitude with respect to Privacy Risk, Performance Risk and part of Overall Risk are quoted to the right of the factorial axis. We initially come across the views by respondents expressing a positive attitude with respect to the conceptual construct Privacy Risk and more specifically claiming that Internet hackers (criminals) might not take control of their checking accounts if they used an e-shop (PRV33) (Cor=795, Ctr=19); that their signing up for and using an e-shop would probably not lead to a loss of privacy for them due to their personal information being used without their knowledge and permission (PRV23) (Cor=749, Ctr=18) and that there are no chances that using an e-shop will cause them to lose control over the privacy of their payment information (PRV13) (Cor=776, Ctr=21) because the e-shop might perform well and not create problems with their credit card (PRP13) (Cor=743, Ctr=19) and thus their signing up for and using an e-shop would not lead to a financial loss for them (PRF13) (Cor=704, Ctr=18). In addition, variables that are quoted on its right side express views with respect to the conceptual construct Performance Risk. Respondents claimed that the security systems built into the e-shop are strong enough to protect their checking account (PRP23) (Cor=755, Ctr=20), e-shop servers may perform well and process payments correctly (PRP53) (Cor=778, Ctr=22), that there is low functional risk that there will be something wrong with the performance of the e-shop or that it will not work properly (PRP33) (Cor=879, Ctr=14)

  Volume: 1 ‐  Issue: 1 February 2019    and thus that there is no risk at all involved in the expected level of service performance of the e-shop, for them to sign up for and use it (PRP43) (Cor=805, Ctr=25), while this is then followed by a positive attitude to part of the conceptual construct Overall Risk. More specifically, respondents considered that using e-shop would not encumber their bill paying with great uncertainty (PRA43) (Cor=725, Ctr=26); using the e-shop will probably not expose them to an overall risk (PRA53) (Cor=737, Ctr=27). On the whole and considering the combination of factors relevant to risk, these respondents would claim that it is not risky to sign up and use the e-shop (PRA13) (Cor=801, Ctr=34). It is, therefore, relatively easy to draw the conclusion that in the first factorial axis e3 and to its left one comes across those variable classes expressed by a group of respondents that project a negative attitude with respect to Privacy Risk, Performance Risk, Overall Risk and Financial Risk, while variable classes quoted to the right of the first factorial axis that represent a group of respondents who have a positive attitude with respect to construct Privacy Risk, the construct Performance Risk and part of the construct Overall Risk. Interpretation of the second factorial axis e2: Based on the answers given by the respondents and as follows from factor analysis, the second axis – factor e2, with an eigenvalue οf 0.0150499 and explaining 9.08% of total variance, is constructed from classes GPO2, PRA12, PRA42, PRV32, PRF22, PRV12, PRA22, PRF42, PRP22, ITB22, PRF12, PRP32, PRP52, PRV22 and PRP12 (Figure 2). To the left of the second factorial axis e2 one finds those respondents who did not have a crystal clear view with respect as to whether they get Overall Service Quality (GPO2) (Cor=272, Ctr=37); with regards to their Purchase Intentions (Cor=209, Ctr=19); and, taking into account all combinations of factors, about how risky they would say it is to sign up and use the e-shop (PRA12) (Cor=292, Ctr=33). Their views were also unclear as to whether by using the e-shop they would add great uncertainty to their bill paying (PRA42) (Cor=212, Ctr=30) and as to how risky it would be for them to use the e-shop to pay their bills (PRA22) (Cor=243, Ctr=27). Thus we came across a neutral attitude to part of the conceptual construct Overall Risk. In addition, variables that are quoted on its left side express views with respect to conceptual construct Privacy Risk. Respondents did not seem to have a crystal clear view with respect to whether Internet hackers (criminals) might take control of their checking accounts if they

  Volume: 1 ‐  Issue: 1 February 2019    used an e-shop (PRV32) (Cor=386, Ctr=29); what are the chances that using an e-shop will result in them losing control over the privacy of their payment information (PRV12) (Cor=322, Ctr=27) and whether their signing up for and using an e-shop would lead to a loss of privacy for them because their personal information would be processed and shared without their knowledge PRV22 (Cor=206, Ctr=15). Moving forward, to the left side of the second factorial axis e2 we came across a neutral attitude to part of conceptual construct Financial Risk. Respondents exhibited a neutral attitude with respect to whether using an Internet-bill-payment service subjects their checking account to potential fraud (PRF22) (Cor=261, Ctr=19); whether using an Internet bill-payment service subjects their checking account to financial risk (PRF42) (Cor=313, Ctr=31) and, finally, whether the chances for them to lose money because they used the services of an e-shop are low or high (PRF12) (Cor=235, Ctr=19). Finally, variables classes PRP22, PRP32, PRP52 and PRP12 that are quoted on its left side relate to part of conceptual construct Performance Risk. Respondents had a neutral attitude as to whether the security systems built into the e-shop are strong enough to protect their checking account (PRP22) (Cor=397, Ctr=34); if there is a low or high functional risk for something to go wrong with the performance of the e-shop, i.e. that it will not work properly (PRP32) (Cor=331, Ctr=26); whether the e-shop servers may not perform well and, thus, incorrectly process payments (PRP52) (Cor=253, Ctr=27) and finally whether the e-shop as a whole may not perform well and, thus, create problems with their credit cards (PRP12) (Cor=220, Ctr=15).

  Volume: 1 ‐  Issue: 1 February 2019   

Figure 2: Second factorial axis e2 The first factorial level e1  e2 : The variables which are most significant for the first factorial level e1  e2 and pursuant to the criteria of inertia, contribution and correlation are analysed in what follows. The first factorial level e1  e2 (Figure 3) interprets 62% of total inertia– information, a satisfactory percentage. The first factorial axis juxtaposes the extreme cases and the second those in-between of the extreme ones. On the first factorial level and at the first quadrant e1+, e2 + the group of respondents may be distinguished vis-a-vis their positive attitude with respect to Privacy Risk, Performance Risk and part of Overall Risk. On the first factorial level and at the second quadrant e1  , e2 + the group of respondents may be distinguished as to their negative attitude with respect to Privacy Risk, Performance Risk, Overall Risk and Financial Risk. Finally,  on the fourth factorial level and at the second quadrant e1  , e2   the group of respondents may be distinguished by reference to their neutral attitude with respect to part of conceptual construct Overall Risk, to part of conceptual construct Financial Risk, to part of

  Volume: 1 ‐  Issue: 1 February 2019    conceptual construct Performance Risk and to conceptual constructs Privacy Risk, Overall Service Quality and their Purchase Intentions. 

Figure 3: First factorial level

e 1× e2

Principal Component Analysis (PCA) results: Kaiser-Meyer-Olkin (ΚΜΟ) Measure of the Sampling Adequacy and Bartlett's Test of Sphericity, and Measure for the suitability of the method were tested before the analysis of the factor analysis results (Table 2). Both the Kaiser-Meyer-Olkin (ΚΜΟ) factor, equal to 0.929 and deemed very satisfactory, as it exceeds the acceptable value of 0.60, and Bartlett's Test of Sphericity (x2=5739.637, df=300, p<0.001) have shown that the application of the Principal Component Analysis with varimax rotation method is permitted (Kaiser, 1974). Table 2: KMO and Bartlett's Test KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of

Approx. Chi-Square

.929  5739.637 

  Volume: 1 ‐  Issue: 1 February 2019    Sphericity

df

300 

Sig.

.000 

  The application of Principal Component Analysis with varimax rotation for all variables on the basis that the characteristic root or eigenvalue criterion is over one (eigenvalue 1 ), was verified for 8 Components. These specific factors explained 75.462% of the variance. Similarly, according to the Scree Plot criterion, the steep descending trend of eigenvalues begins after the 8th Principal Components (PC8) (Cattel, 1996). Consequently, the existence of the 8 Components was verified. The first Principal Component (PC1), with an eigenvalue equal to 11.852, interprets 11.773% of the total variance of data, a percentage deemed satisfactory (Hair, 2005) and gathers values for variables PRT2, PRT3, PRT4 and PRT1 with very high loadings. These gathered values amount to 0.827, 0.772, 0.743 and 0.728, respectively (Table 3). The values of the Communalities of items PRT2, PRT3, PRT4 and PRT1, take on values 0.739, 0.790, 0.753, 0.728 and 0.639, exceeding the 0.40 value criterion posed as the limit for the verification of the satisfactory quality for the variables of the First Component (PC1). The First Component (PC1) is constructed and interpreted by PRT2, PRT3, PRT4 and PRT1. The First Component (PC1) is shown to essentially be the Component of Time Risk. The Second Component (PC2) refers to PRP3, PRP2, PRP4, PRP5 and PRP1, related to Information Sharing. This Component has an eigenvalue of 2.545 and interprets 11.305% of total data variance. The eigenvalue criterion, eigenvalue over one, verifies that the 5 variables/items PRP3, PRP2, PRP4, PRP5 and PRP1 which exhibit very high loadings 0.790, 0.698, 0.653, 0.595 and 0.572 correspondingly, are represented by the same conceptual construct (Table 3). The values for the Communalities of PRP3, PRP2, PRP4, PRP5 and PRP1 take on prices 0.796, 0.668, 0.711, 0.608 and 0.599 respectively, and exceed the 0.40 value criterion posed as the verification limit for the satisfactory quality of statements of Second Component (PC2) named Performance Risk. The Third Component (PC3) (Table 3) refers to Information Seeking, which is represented by items PRA2, PRA3, PRA, PRA4 and PRA1 and exhibit high loadings of 0.745, 0.733, 0.709, 0.697 and 0.494 respectively, with an eigenvalue of 2.188, that interprets 11.167% of total

  Volume: 1 ‐  Issue: 1 February 2019    data variance, a percentage deemed satisfactory (Hair et al., 2005), while falling under it are, in order, elements PRA2, PRA3, PRA, PRA4 and PRA1. The values of the Communalities of PRA2, PRA3, PRA, PRA4 and PRA1take on prices 0.796, 0.841, 0.802, 0.800 and 0.709 exceeding the 0.40 value criterion posed as the limit for the verification of the satisfactory quality of Third Component (PC3). The Third Component (PC3) is essentially shown to be the Component of Overall Risk. The Fourth Component (PC4) (Table 3) refers to Information Seeking, which is represented by items ITB2, Customer Satisfaction, Overall Service Quality and ITB1 and exhibit high loadings of 0.850, 0.832, 0.766 and 0.705 respectively, with an eigenvalue of 1.344, that interprets 9.953% of total data variance, a percentage deemed satisfactory (Hair et al., 2005), while falling under it are, in order, elements ITB2, Customer Satisfaction, Overall Service Quality and ITB1. The values of the Communalities of ITB2, Customer Satisfaction, Overall Service Quality and ITB1 take on prices 0.0.792, 0.799, 0.736 and 0.633 exceeding the 0.40 value criterion posed as the limit for the verification of the satisfactory quality of Fourth Component (PC4). The Fourth Component (PC4) is essentially shown to be the Component of Overall Service Quality Customer, Satisfaction and Purchase Intentions. The Fifth Component (PC5) (Table 3) refers to Information Seeking, which is represented by items PRF2, PRF4, PRF1 and PRF3 and exhibit high loadings of 0.807, 0.769, 0.661 and 0.607 respectively, with an eigenvalue of 1.167, that interprets 9.892% of total data variance, a percentage deemed satisfactory (Hair et al., 2005), while falling under it are, in order, elements PRF2, PRF4, PRF1 and PRF3. The values of the Communalities of PRF2, PRF4, PRF1 and PRF3 take on prices 0.809, 0.792, 0.682 and 0.682 exceeding the 0.40 value criterion posed as the limit for the verification of the satisfactory quality of Fifth Component (PC5). The Fifth Component (PC5) is essentially shown to be the Component of Perceived Risk. The Sixth Component (PC6) (Table 3) refers to Information Seeking, which is represented by items PRV2, PRV1 and PRV3 and exhibit high loadings of 0.834, 0.762 and 0.644 respectively, with an eigenvalue of 1.077, that interprets 8.303% of total data variance, a percentage deemed satisfactory (Hair et al., 2005), while falling under it are, in order, elements PRV2, PRV1 and PRV3. The values of the Communalities of PRV2, PRV1 and PRV3 take on prices 0.847, 0.887 and 0.634 exceeding the 0.40 value criterion posed as the

  Volume: 1 ‐  Issue: 1 February 2019    limit for the verification of the satisfactory quality of Sixth Component (PC6). The Sixth Component (PC6) is essentially shown to be the Component of Privacy Risk. The Seventh Component (PC7) (Table 3) refers to Information Seeking, which is represented by items PRS2and PRS1 and exhibit high loadings of 0.848 and 0.817 respectively, with an eigenvalue of 1.046, that interprets 6.987% of total data variance, a percentage deemed satisfactory (Hair et al., 2005), while falling under it are, in order, elements PRS2and PRS1. The values of the Communalities of PRS2and PRS1 take on prices 0.874 and 0.846 exceeding the 0.40 value criterion posed as the limit for the verification of the satisfactory quality of Seventh Component (PC7). The Seventh Component (PC7) is essentially shown to be the Component of Social Risk. The Seventh Component (PC7) (Table 3) refers to Information Seeking, which is represented by items PRS2and PRS1 and exhibit high loadings of 0.848 and 0.817 respectively, with an eigenvalue of 1.046, that interprets 6.987% of total data variance, a percentage deemed satisfactory (Hair et al., 2005), while falling under it are, in order, elements PRS2and PRS1. The values of the Communalities of PRS2and PRS1 take on prices 0.874 and 0.846 exceeding the 0.40 value criterion posed as the limit for the verification of the satisfactory quality of Seventh Component (PC7). The Seventh Component (PC7) is essentially shown to be the Component of Social Risk. The Seventh Component (PC8) (Table 3) refers to Information Seeking, which is represented by items PRC1 and PRC2 and exhibit high loadings of 0.865 and 0.749 respectively, with an eigenvalue of 1.016, that interprets 6.082% of total data variance, a percentage deemed satisfactory (Hair et al., 2005), while falling under it are, in order, elements PRC1 and PRC2. The values of the Communalities of PRC1 and PRC2 take on prices 0.890 and 0.843 exceeding the 0.40 value criterion posed as the limit for the verification of the satisfactory quality of eighth Component (PC8). The eighth Component (PC8) is essentially shown to be the Component of Psychological Risk. Financial Risk, Performance Risk, Privacy Risk, Psychological Risk, Social Risk, Time Risk and Overall Risk constructs constitute the latent variable named Perceived Risk of e-Services Adoption Intentions. The construct validity of the scale is evident from this fact. Additionally, variables related to Overall Service Quality Customer, Satisfaction and Purchase Intentions contribute to another independent construct.

  Volume: 1 ‐  Issue: 1 February 2019   

Table 3: Rotated Component Matrix Rotated Component Matrixa Component 1 PRT2

,827

PRT3

,772

PRT4

,743

PRT1

,728

2

PRP3

,790

PRP2

,698

PRP4

,653

PRP5

,595

PRP1

,572

3

PRA2

,745

PRA3

,733

PRA5

,709

PRA4

,697

PRA1

,494

4

ITB2

,850

Customer

,832

5

6

7

Satisfaction Overall

,766

Service Quality ITB1

,705

PRF2

,807

PRF4

,769

PRF1

,661

PRF3

,607

PRV2

,834

PRV1

,762

PRV3

,644

PRS2

,848

PRS1

,817

8 

  Volume: 1 ‐  Issue: 1 February 2019    PRC1

,865 

PRC2

,749 

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 7 iterations.

Conclusion The current study presents two different statistical techniques: i.e the Analysee Factorielle des Correspondances (AFC) and the Principal Components Analysis (PCA). The main objective is to compare the outcomes derived from Analysee Factorielle des Correspondances (AFC), Principal Components Analysis (PCA) procedures with respect to Consumer Behavior and specifically with respect to the Perceived Risk of the Adoption Intention of e-Services. The two methods operate complementary, each one accentuating a different dimension for the interpretation of data, the interpretation of which would not have been determinative without the import of Marketing Scientists. Analysee Factorielle des Correspondances (AFC) application unveils factors, independent per couple between them, which are created from the synthesis of groups of the initial variables, simplifying the process for probing the relations between the variables and thus offering a full and more complex image of the phenomenon under examination. The factors can assume the form of axes and form factorial levels in pairs, which will then allow for the graphic representation of the variables. Analysee Factorielle des Correspondances (AFC) is a method where no a priori hypothesis is made. Principal Components Analysis (PCA) is an unsupervised pattern recognition method. It is based on the principle that there is no a priori information about the membership of the sample examined. PCA falls under this category, since the Principal Components are not known beforehand, but ensue from the application of the method (Anastasiadou, 2018). Principal Components are hierarchically calculated (Anastasiadou, 2018). Perceived Risk of e-Services Adoption Intentions multidimensional and hierarchical scale by Featherman & Pavlou (2003) consists of seven constructs: Financial Risk, Performance Risk, Privacy Risk, Psychological Risk, Social Risk, Time Risk and Overall Risk. The application of Analysee Factorielle des Correspondances (AFC) made it evident that only Financial Risk,

  Volume: 1 ‐  Issue: 1 February 2019    Performance Risk, Privacy Risk, Time Risk and Overall Risk constructs are shaped attitudes. Psychological Risk and Social Risk constructs seem to be unimportant because none of their dimensions play a role to respondents mind. The application of Analysee Factorielle des Correspondances (AFC) based on the three criteria, inertia (criterion 1) correlation ( Cor , criterion 2) and contribution ( Ctr 2 , criterion 3) reveal the latent dimension of respondents psychological attributes towards Perceived Risk of e-Services Adoption Intentions. The application of Principal Components Analysis (PCA) creates patterns for Perceived Risk of e-Services Adoption Intentions scale and made it evident that the specific scale constitutes a seven diminution scale containing the constructs Financial Risk, Performance Risk, Privacy Risk, Psychological Risk, Social Risk, Time Risk and Overall Risk. References

Anastasiadou, S. (2016). Evaluation of the Implementation of TQM principles in Tertiary Education using the EFQM Excellence Model -Research in Educational Departments of Greek Universities. Published Master’s thesis, Greek Open University, Patra, Greece. Anastasiadou, S. (2018). Comparison of multivariate methods in group/cluster identification. Published Master’s thesis, Faculty of Medicine, University of Thessaly. Bauer, R. (1967). Consumer behavior as risk taking. In: Cox, D. (Ed.), Risk Taking and Information Handling in Consumer Behavior. Harvard University Press, Cambridge, MA. Benzécri J. P. (1980). Pratique de l’Analyse des données T. 2 : Analyse des Correspondances, exposé élémentaire, Dunod, Paris. Benzécri J. P. (1973). Analyse des Données, Paris, France. Dafermos, B. (2013). Factor Analysis, Thessaloniki: Ziti. Davis, F. 1989. Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly 1, pp. 319-340.  

Drosos, B. (2004). Statistical Analysis Linguistics Information. PhD Thesis. University of Macedonia.

  Volume: 1 ‐  Issue: 1 February 2019    Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-Services adoption: a perceived risk facets perspective. International journal of human-computer studies, 59(4), 451-474. Gefen, D., Straub, D. (2000). The relative importance of perceived ease-of-use in IS adoption: a study of e-commerce adoption. JAIS 1 (8), pp. 1-20. Jollife, I. T (1972). Discarding variables in the principal components analysis, I: Artificial data. Applied Statistics, 1, 57-93. Jollife, I. T (1986). Principal components analysis, New York: Springer. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurements, 20, 141-151. Kaiser, H. F. (1974). In order of factorial simplicity, Psychometrika, 39, 31-36. Karapistolis, D. (2000). Data Analysis Software MAD. Altitzi Eds. Thessaloniki, Greece. Karapistolis, D. (2015). Multivariate Satistical Analysis. Altitzi Eds. Thessaloniki, Greece.

Koller, M. (1988). Risk as a determinant of trust.Basic and Applied Social Psychology 9(4), pp.256-276. Lee, G.G. and Lin, H.F. (2005). Customer perceptions of e-Service quality in online shopping. International Journal of Retail & Distribution Management, 33(2), pp.161-176. Papadimitriou Ι. (2007). Data Analysis. Tipothito Eds. Athens, Greece.

Moon, J., Kim, Y. (2000). Extending the TAM for a world-wide-web context. Information and Management, 28, pp. 217-230. Pavlou, P. (2001). Integrating trust in electronic commerce with the technology acceptance model: model development and validation. AMCIS Proceedings, Boston, MA. eter, J., Rayn, M. (1976). An investigation of perceived rosk at the brand level. Jornal of Marketing Research, 13. Pp. 184-188. Teo, S., Lim, V., Lai, R. 1999. Intrinsic and extrinsic motivation in Internet usage. Omega International Journal of Management Studies, 27, pp. 25-37.  

Volume: 1 - Issue: 1 February 2019

Contemporary advanced statistical methods for the science of marketing: Implicative Statistical Analysis vs Principal Components Analysis Thomas A. Fotiadis Post Doc Candidate, University of Western Macedonia Sofia D. Anastsiadou University of Western Macedonia

Introduction: Even though there is a substantial development and utilization of pattering methods in the science of marketing, a direct comparison of multivariate methods in group/cluster identification in the field of Consumer Behavior has not been carried out. Objective: This study analyses two different statistical techniques: i.e the Principal Components Analysis (PCA) and the Implicative Statistical Analysis (ASI). The main objective is to compare patterns derived from Principal Components Analysis (PCA) and Implicative Statistical Analysis (ASI) procedures with respect to Consumer Behavior. Design: A survey was carried out using a structured questionnaire for a sample of 335 adults, customers of 125 Greek e-shops. These were conventionally approached by the Marketing Laboratory of a major public University in Northern Greece. Information Seeking, Information Sharing, Responsible Behavior subscales are related to Customer Participation Behavior. These subscales were measured by 15 items, rated on a seven-point Likert format, ranging from 1 (strongly disagree) to 7 (strongly agree). Methods: The study focuses on the presentation of the two main types of clustering methods, Implicative Statistical Analysis (ΑSI) and Principal Components Analysis (PCA). Results: PCA’s results showed the existence of 3 Component, amongst which the first is shown to be the Component of Responsible Behavior, the second is shown to be the Component of Information Sharing, and the third is shown to be the Component of Information Seeking. ASI results release a similarity tree and a cohesive tree. Similarity tree showed that Information Seeking is the par excellence most powerful constituent of the creation of Customer Participation behaviour values and Information Sharing is the next similarity tree also showed that customers’ Responsible Behaviour is the weakest constituent for the creation of Customer Participation Behaviour values.

Volume: 1 - Issue: 1 February 2019 Hierarchical group of the items in conceptual construct Information Seeking exhibits the externally significant cohesion. Beliefs on conceptual construct Information Sharing imply beliefs on Responsible Behavior with exceptionally high cohesion. Key words: Principal Components Analysis, Implicative Statistical Analysis, Consumer, Behavior

The Instruments/ Measures Customer Participation Behavior scale was measured using Customer Value Co-creation Behavior multidimensional and hierarchical scale of Yi & Gong (2013) that consists of 15 items, rated on a seven-point Likert format, ranging from 1 (strongly disagree) to 7 (strongly agree). Customer citizenship behavior, in particular, contains the constructs of Information Seeking, Information Sharing, and Responsible Behavior. The group regards conceptual construct Information Seeking, and comprises of 3 statements (INFi) (eg. INF1: I have asked others for information on what this service offers), while the second group regards conceptual construct Information Sharing (FDBi) and comprises of 4 statements (eg. FDB1: I clearly explained what I wanted the employee and the e-shop to do). The third group regards conceptual construct Responsible Behavior (INSi) and comprises of 4 statements (eg. INS1: I performed all the tasks that are required). These three conceptual constructs contribute to the creation of Latent Variable Customer Participation Behavior.

Data Clustering Techniques This section is dedicated to the presentation of the three main types of clustering methods that is Implicative Statistical Analysis (ASI), and Principal Components Analysis (PCA). Implicative Statistical Analysis (ASI): Implicative Statistical Analysis (ASI) was initiated and developed by Régis Gras to be applied in the Didactic of Mathematics (Gras, 1979). Since the doctoral dissertation of Régis Gras, a great deal of research has been published concerning different paths of theory development (Gras et al., 1997; Gras, & Couturier, 2013; Gras et al., 2004; Gras, et al., 2008 ; Gras, Regnier, & Guillet, 2009; Gras, Régnier, Marinica, & Guillet, 2013). Consequently, the method has been advanced noticeably and has been applied to a wide range of data, such as mathematics education; psychology; physics, medicine, etc (Nikolaou et al., 2017). According to Coutourier (2008) the initial objective of this method is to define an

Volume: 1 - Issue: 1 February 2019 approach that adequately confronts the question “if an object has a property, does it also have another one”. This is seldom accurate although a tendency seems to emerge. ASI aims at highlighting such tendencies in a set of properties. According to Coutourier (2008), ASI can be regarded as a method used to generate association rules. Furthermore, it is considered to be a wide theoretical framework, a theory connected with causality due to the fact that it responds to the weakness regarding other multivariate methods, as well as highlighting formal tools and practical methods of data representation, evaluation and interpretation. It is of a major importance to note that compared to other association rule methods; ASI distinguishes itself by providing a non linear measure that satisfies some important criteria. In order for the implicative association rules to be extracted, the ASI assigns a numerical value between zero to R rules and one according to following form: If the variable a is observe then it is possible for the variable b to be observed. Consequently, if the variable a gets a specific value, then variable b possibly gets a higher value. The measure assigned is a probability, well known now as intensity of involvement. Consequently, causal and predictive relations are influenced by the intensity of involvement. The principle of determining the intensity of involvement as a probability of a random event and it is defined as follows: if there was a non a priori asymmetric link between a and b, the number of counterexamples to the rule R, is under the unique effect of chance, usually higher than the number of counterexamples observed in the contingency. Thus, the method is based on implication intensity that measures the degree of astonishment inherent in a rule. For example, the set of items B, then it is legitimate and intuitive to expect that the counter part is and the set of non-B items is strongly associated with the set of non A-items. According to Coutourier (2008), the implication intensity maybe reinforced by the degree of validity that is based on Shannon’s entropy, in case that a researcher chooses this comparison approach. The implicative representation of the associations is presented in figure 1 by a weighted graph without cycle where each edge corresponds to a rule, and in figure 2 by an ascending hierarchy oriented by meta-rules.

Volume: 1 - Issue: 1 February 2019

Figure 1

Figure 2 Source: Wikipaideia

Similarity: a symmetrical analysis according to the algorithm of the I.C. Lerman (Lerman, 1978) link brings together in a large class practically all the items whatever their a priori taxonomic classification maybe (Gras & Bodin, 2017). Similarity tree is based on the similarity indexes, defined by Lerman (1981). Similarity indices are used in data analysis to study objects described by binary variables. According to Blanchard (2009), they allow one to assess the likeness between two objects and two variables. The likelihood index is based on Likelihood Linkage Analysis (LLA) (Lerman, 1981) and it is given by Lerman (1993) in Blanchard (2009) as: Likelihood Linkage Index of Lerman P(Nabnab*), where the hypothesis tested is Ho: there is independence between a and b, and Nab and Nab* are random variables for the numbers of examples and counterexamples nab the number of examples and nab* the number of counterexamples. Cohesive hierarchy: It can now be expected that the cohesive hierarchy, always obtained by CHIC Software, which structures successes in groups guided by implication, respects, within them, the presumed taxonomic order. For the analysis of the data Implicative Statistical Analysis is used. Specifically the Cohesion tree (Gras et al., 1997) as well as the Similarity tree (widely known as dendrogram (Lerman, 1981) resulted by CHIC Software (Couturier, 2008). Principal component analysis or PCA is a method for the analysis of multivariate data, and it is considered to constitute a part of factor Analysis. The principal objectives of PCA are:

Volume: 1 - Issue: 1 February 2019 

Data Reduction. PCA aims to replace highly correlated variables with a small number of correlated variables (Dafermos, 2013).



To detect and establish a structure/model. The goal of PCA is, namely, to accentuate structures or fundamental relations existing between the existing variable (Dafermos, 2013). Moreover, PCA aims to bring to light and assess latent variables, and to detect and assess latent sources of variability and co-variability in observable measurements.



To detect patterns. The goal of PCA is to detect prototype correlations which may potentially determine causality relations between the examined variables (Dafermos, 2013).

PCA is a descriptive or explanatory method and does not rest on conditions. In reality, PCA rests on the spectrum analysis of the variance or correlation matrix. Principal Components Analysis is by far the most widespread pattern recognition tool. It is a method for compressing a lot of data into patterns that capture the essence of the original data. Specifically, it constitutes a multivariate statistical analysis that is often used to reduce the dimension of data for easy exploration. Its objectives include: 1) to reduce the original into a lower number of orthogonal (uncorrelated), synthesized variables; 2) to visualize correlations among and between the original variables and the components, and 3) to visualize proximities among statistical units. Furthermore, PCA is considered to be a change of variable space. It rests on the study of eigenvalues and eigenvectors in the correlations or covariance matrix. As a multivariate analysis technique for dimension reduction, PCA aims to compress the data without losing much of the information contained in the original data. The process regards explaining the variance-covariance structure of a set of variables through a few new variables. All principal components are specific linear combinations of the p random variables exhibiting three important properties: 1. The principal components are uncorrelated. There are also orthogonal uncorrelated, linear combinations of standardized variables. 2. The first principal component has the highest variance; the second principal component has the second highest variance, and so on. 3. The total variation in all the principal components combined is equal to the total variation in the original variables.

Volume: 1 - Issue: 1 February 2019 In reality, PCA converts data into a set of linear components and, as it is characteristically alluded by Field (2009), it converts them to measurable ones. Each component has the form: Componenti=b1X1+ b2X2+…. bnXm.. It is evident that PCA forecasts components based on measured variables. It is rendered clear that PCA break down the original data to a model of linear variables. PCA brings to light which linear components exist in the data and the manner by which one particular variable contributes to the shaping of each component (Field, 2009). PCA rests on the overall variance of the variables in descending order. The first Principal Component (PC1) captures the most variance of the data; the second Principal Component (PC2), which is not correlated with PC1, captures the second variance etc. The number of the components extracted is equal to the original variables and the sum of their variance is the sum of the variance of the original variables. The sum of the squares of loadings to a principal component signifies the participation of the component to the overall variance of the variables. The value of the sum for each principal component is called eigenvalue. Eigenvalues are presented in descending order and allow for the exclusion of these components that do not interpret a satisfactory percentage of the overall variance, resulting only in only components interpreting a satisfactory percentage of the overall variance to be employed for the interpretation of the results. Selected are components whose eigenvalues are equal or greater than one (Kaiser, 1960) or equal or greater than 0.70 (Jolliffe, 1972, 1986). The following table (Table 1) presents some of the basic differences of the two methods. Table 1: Differences of the two methods

ASI

PCA 

ASI rests on rules.



PCA does not rest on conditions.



It is based on a probabilistic model.



It is based on metric space distances



It highlights tendencies in a set of 

properties (Coutourier, 2008). 

It

generates

association

It provides a non linear measure Coutourier, 2008).

Its patterns are based on correlation between variables.

rules

Coutourier, 2008). 

Coutourier, 2008).



It provides a linear measure.

Volume: 1 - Issue: 1 February 2019 

It visualizes correlations among the original variables and between these variables and the components.



It visualizes proximities among statistical units.



It is a frequently employed statistical technique

for

unsupervised

dimension reduction. Properties between the variables 

Relationship between variables is

Properties between the variables 

dissymmetrical. 

The association measures are not

Relationship between variables is symmetrical.



The association measures are linear.



Data Reduction (Dafermos, 2013).



Data detection and establishment of

linear and are based on probabilities.

a structure/model (Dafermos, 2013). 

Establishment of latent variables.



Detection of latent sources of variability and co-variability in observable

measurements

(Dafermos, 2013). 

Detection of patterns (Dafermos, 2013).



Represented by the similarity tree (lerman, 1981).



Represented by the implication tree (Gras et al., 1997).



Represented by the cohesion tree (Gras et al., 1997).



Represented by factorial plane.

Volume: 1 - Issue: 1 February 2019

Data Collection and Sample Data Collection: A survey was carried out using a structured questionnaire for a sample of 335 adults, customers of 125 Greek e-shops. These were conventionally approached by the Marketing Laboratory of a major public University in Northern Greece. Two post-graduate students were carefully trained in order to perform their duties as interviewers. The questionnaire was originally developed in English and then it was translated to Greek using the translation and back translation procedure, while tutors of English who speak fluent Greek assumed to provide the relevant translations.

The sample: The sample comprised of 335 interviewees, of whom 185 (55.2%) were men and 150 (44.8%) were women. With respect to the ages of participants, 67 (20%) of them were between 18 to 24 years old, 67 (20%) of them were between 25-34, 68 (20.3%) of them were between 35-44, 67 (20%) of them were between 45-54 and, finally, 66 (19.7%) were between the ages of 55 to 64 years old. With respect to their family status, 143 (42.7%) were single, while 180 (53.7%) were married and 12 (3.6%) were separated or divorced. 288 of 335 interviewees, or a percentage of 86%, stated that they live in an urban setting, while 47 (14%) in a rural one.

Regarding the education of interviewees, one (0.3%) stated he graduated elementary education, 124 (37%) secondary, 160 (47.8) tertiary, while 50 (14.9%) hold a postgraduate diploma or doctorate. Out of the 154 interviewees, 137 (40.9%) declared that their income was less than €10,000 per year, 154 (46%) declared that their income was between € 10,000 and €24.999, while the income for 35 interviewees (10.4%) ranged between €25.000 to €49.999. According to 5 participants, (1.5%) their income ranged from €50.000 to €74,999. Finally, 4

interviewees (1.2%) declined to answer the question relating to their income. Results The similarity diagram: The similarity diagram presents groupings of statements based on customer behaviour as it is captured on the questionnaire. Similarities in emphasized black are significant, at a significance level of 99%. The similarity diagram (Figure 3) presents two distinct similarity groups (Group A, Group B). The first similarity group (Group A) refers to similarity relations between variables (((INF1 INF3) INF2) ((FDB1 FDB2) (FDB3 FDB4))) (similarity: 0.0172892) that regard the factor Information Seeking and the factor Information

Volume: 1 - Issue: 1 February 2019 Sharing, and represent the similar tactic employed by the interviewees to treat and perceive the implicit variable Customer Participation Behaviour. Specifically, this similarity is

extremely weak because its value is equal to 0.0172892, almost 2%. Specifically, similarity (INF1-INF3) (similarity: 0.918299), the most forceful in the first group, it is also the most forceful compared to all other similarity groups. A third variable, INF2 of the conceptual construct Information Seeking, completes this similarity group ((INF1 INF3) INF2) similarity: 0.768218). This reflects the degree of information searched regarding the eshop location. This Similarity between variables INS3-INS4- INS1-INS2 shows that Information Seeking is the par excellence most powerful constituent of the creation of Customer Participation Behaviour values. The second most forceful similarity is the one between variables FDB1-FDB2 (similarity: 0.850801) that refer to the possibility interviewees clearly explained what they wanted the employee and the e-shop to do and consequently have provided the e-shop with the proper information. The similarity FDB3-FDB4 (similarity: 0.775505) is equally important and refers to the necessary information given by customers to the shop so that the employee could perform his or her duties by answering all the employee's service-related questions. These two similarity groups form an equally forceful relation between the four items FDB1-FDB2 and FDB3-FDB4 (((FDB1 FDB2) (FDB3 FDB4)) similarity: 0.502155) which also approximates the amount 0.50 and, consequently, is a similarity of a medium importance. This specific similarity group refers to Information Sharing.

IN S 4

IN S 3

IN S 2

IN S 1

F D B 4

F D B 3

F D B 2

F D B 1

IN F 2

IN F 3

IN F 1

Volume: 1 - Issue: 1 February 2019

Arbre des similarites : C:\Users\User\Desktop\New folder (4)\ICME_2018 CONFERENCE\CO_CREATIONA_COM_SAT_PURS_2_CHIC.csv

Figure 3: Similarity Tree

A third construct, Responsible Behaviour, contributes towards a second similarity group, Group B, which is an independent group. More specifically, the most powerful similarity in the second group, Group B, is that between variables INS3-INS4 (similarity: 0.716318), which refer to the possibility that customers followed the employee's or e-shop’s directives and fulfilled their responsibilities to the business or e-shop. Similarity INS1-INS2 (similarity: 0.711894) shows the similar tactic adopted by the interviewees to perform all the tasks required and adequately completed all the expected behaviors (similarity: 0.711894). These two similarity groups form an equally forceful relation between the four items INS1INS2 and INS3-INS4 ((INS1 INS2) (INS3 INS4)) (similarity: 0.256839) which also approximates value 0.27≈ 0.30 and, consequently, is of a limited acceptance accepted similarity. This Similarity between variables INS3-INS4-INS1-INS2 shows that customers’ Responsible Behaviour is the weakest constituent for the creation of Customer Participation Behaviour values.

Volume: 1 - Issue: 1 February 2019

The hierarchical diagram: The hierarchical diagram named cohesive tree (Figure 4) presents the implicative relations between the variable in order of significance.

IN S 2

IN S 3

IN S 4

IN S 1

F D B 4

F D B 3

F D B 2

F D B 1

IN F 2

IN F 3

IN F 1

Additionally, the cohesive tree also shows the direction of such relations.

Arbre cohesitif : C:\Users\User\Desktop\New folder (4)\ICME_2018 CONFERENCE\CO_CREATIONA_COM_SAT_PURS_2_CHIC.csv

Figure 4: Cohesive Tree With respect to the first hierarchical group, this refers to items INF1-INF3 (cohesion: 0.999) where the response to INF1 entails the response to INF3. Responses to items INF1 and INF3 entail the response to INF2. The hierarchical group (INF1-INF3)-INF2 exhibits the externally significant cohesion (cohesion: 0.994). Specifically, when customers ask for information regarding the e-shop’s offers and they pay attention on how others behave to use this service well, then they have search for information on where this e-shop is located. The conclusion that this first hierarchical group is a hierarchy of the items in conceptual construct Information Seeking ensues effortlessly. There are three hierarchical structures in the second hierarchical group. More specifically, the first refers to the three out of four items of the conceptual construct Information Sharing and its

Volume: 1 - Issue: 1 February 2019 cohesion equals to 1, which constitutes a perfect cohesion [FDB1- (FDB2-FDB3)) cohesion: 1]. The figure renders it clear that the behaviour of customers who adequately explained what they wanted the e-shop to do (FDB1) the e-shop with proper information, so it could respond in a satisfactory manner. With respect to the hierarchical relation (FDB2 FDB3) (cohesion: 1- which constitutes the maximum degree of cohesion), it is shown that when the customers provide the e-shop with proper information (FDB2), the e-shop is able to perform its duties flawlessly (FDB3). Items FDB4 and INS1 [(FDB4 INS1) cohesion: 1] form another hierarchical group, with maximum cohesion. With respect to hierarchical relation FDB4-INS1 (cohesion: 1), it is shown that when customers answer all the employee's service-related questions (FDB4), then all required tasks are performed (INS1). Items NS4, INS3 and INS2 [((INS4 INS3) INS2) cohesion: 0.998] form another hierarchical group whose cohesion is almost perfect. With respect to hierarchical relation INS4-INS3 (cohesion: 0.999) whose cohesion is, again, perfect, it is shown that when followed the e-shops’ directives or orders (INS4) then they fulfilled responsibilities to the e-shops (INS3). This implication in turn also implies the adequately complement of all the expected behaviors towards e-shop (INS2).

The hierarchy between these two groups cited above, [((FDB4 INS1) ((INS4 INS3) INS2))] is almost perfect (cohesion: 0.994). Τhe first hierarchical structure, appears between the one item out of four of the construct Information Sharing and items comprising the construct Responsible Behavior. The entire second hierarchical group [((FDB1 (FDB2 FDB3)) ((FDB4 INS1) ((INS4 INS3) INS2))) cohesion: 0.982] exhibits exceptionally high cohesion (cohesion: 0.982) and shows that beliefs on conceptual construct Information Sharing implies beliefs on Responsible Behavior.

Principal Component Analysis (PCA) results Principal Component Analysis (PCA) results: Kaiser-Meyer-Olkin (ΚΜΟ) Measure of the Sampling Adequacy and Bartlett's Test of Sphericity, and Measure for the suitability of the method were tested before the analysis of the factor analysis results (Table 2).

Volume: 1 - Issue: 1 February 2019 Both the Kaiser-Meyer-Olkin (ΚΜΟ) factor, equal to 0.857 and deemed very satisfactory as it exceeds the acceptable value of 0.60, and Bartlett's Test of Sphericity (x2=1408.907, df=55, p<0.001) have shown that the application of the Principal Component Analysis with varimax rotation method is permitted (Table 2) (Kaiser, 1974). Table 2: KMO and Bartlett's Test KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy. Bartlett's Test of Sphericity

Approx. Chi-Square Df Sig.

,857 1408,907 55 ,000

The application of Principal Component Analysis with varimax rotation for all variables on the basis that the characteristic root or eigenvalue criterion is over one (eigenvalue≥ 1), was verified for 5 Components. These specific factors explained 65.527% of the variance. Similarly, according to the Scree Plot criterion, the steep descending trend of eigenvalues begins after the 3rd Principal Components (PC3) (Cattel, 1996). Consequently, the existence of the 3 Components was verified. The first Principal Component (PC1), with an eigenvalue equal to 3.114, interprets 28.309% of the total variance of data, a percentage deemed satisfactory (Hair, 2005), gathers values for variables INS3, INS2, INS4, INS1 and FDB4 with very high loadings. These gathered values amount to 0.829, 0.821, 0.790, 0.763 and 0.483, respectively (Table 3). The values of the Communalities of items INS3, INS2, INS4, INS1 and FDB4, take on values 0.739, 0.716, 0.636, 0.710 and 0.410, exceeding the 0.40 value criterion posed as the limit for the verification of the satisfactory quality for the variables of the First Component (PC1) (Table 3). The First Component (PC1) is constructed and interpreted by INS3, INS2, INS4, INS1 and FDB4. The First Component (PC1) is shown to essentially be the Component of Responsible Behavior and with a spot of Information Sharing. The Second Component (PC2) refers to FDB1, FDB2 and FDB3 related to Information Sharing. This Component has an eigenvalue of 2.382 and interprets 2.658 % of total data variance. The eigenvalue criterion, eigenvalue over one, verifies that the 3 variables FDB1, FDB2 and FDB3,

Volume: 1 - Issue: 1 February 2019 which exhibit very high loadings 0.833, 0.775 and 0.770 correspondingly, are represented by the same conceptual construct (Table3). The values for the Communalities of FDB1, FDB2 and FDB3 take on prices 0.711, 0.749 and 0.733 respectively, and exceed the 0.40 value criterion posed as the verification limit for the satisfactory quality of statements of Second Component (PC2). The Third Component (PC3) (Table 3) refers to Information seeking, which is represented by items INF1, INF2 and INF3 and exhibit high loadings of 0.827, 0.730 and 0.679 respectively, with an eigenvalue of 1.712, that interprets 15.560% of total data variance, a percentage deemed satisfactory (Hair et al., 2005), while falling under it are, in order, elements INF1, INF2 and INF3. The values of the Communalities of INF1, INF2 and INF3 take on prices 0.628, 0.591 and 0.585 exceeding the 0.40 value criterion posed as the limit for the verification of the satisfactory quality of Third Component (PC3). The Third Component (PC3) is essentially shown to be the Component of Information Seeking.

Table 3: Rotated Component Matrix Rotated Component Matrixa Component 1

2

INS3

,829

INS2

,821

INS4

,790

INS1

,763

FDB4

,483

3

,397

FDB1

,833

FDB2

,775

FDB3

,770

INF1

,784

INF3

,756

INF2

,694

Volume: 1 - Issue: 1 February 2019 Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 5 iterations.

Conclusion-Discussion This study presents two different statistical techniques: i.e the Principal Components Analysis (PCA) and the Implicative Statistical Analysis (ASI). The main objective is to compare the

outcomes derived from Principal Components Analysis (PCA) and Implicative Statistical Analysis (ASI) procedures with respect to Consumer Behavior and specifically with Customer Participation Behavior. In addition, they showed that the two methods operate complementary, each one accentuating a different dimension for the interpretation of data, the interpretation of which would not have been determinative without the import Marketing Scientists. Principal Components Analysis is an unsupervised pattern recognition method. It is based on the principal that there is no a priori information about the membership of the sample examined. PCA also falls under this category, since the Principal Components are not known beforehand, but ensues from the application of the method (Anastasiadou, 2018). Principal Components are hierarchically calculated (Anastasiadou, 2018). Implicative Statistical Analysis (ASI), is connected with Implication Intensity of Gras (Gras, 1996; Gras & Kuntz, 2008). Specifically, similarity Likelihood Linkage Index of Lerman is connected with Likelihood Linkage Analysis (LLA) (Lerman, 1981). It is based on rules and especially on a probabilistic model. It highlights tendencies in a set of properties and generates association rules (Coutourier, 2008). ASI measure is assigned as a probability, named Intensity of Involvement. Regarding the data analysisof the present research example connected with Customer citizenship behavior, that contains the constructs of Information Seeking, Information Sharing, and Responsible Behavior the similarity tree showed that Information Seeking is the par excellence most powerful constituent of the creation of Customer Participation behaviour values as similarity Likelihood Linkage Index amounts for 0.768218. Similarity Likelihood Linkage Index regarding Information Sharing amounts 0.502155 and shows is a similarity of a

Volume: 1 - Issue: 1 February 2019 medium importance. Finally, similarity tree also showed that customers’ Responsible Behaviour is the weakest constituent for the creation of Customer Participation behaviour values. Its similarity Likelihood Linkage Index amounts for 0.256839. Similarity tree identify the Similarity Intensity. In addition, similarity tree present an extremely weak Similarity Intensity between factors Information Sharing and Information Seeking amounts for 0.0172892. Implication Intensity of Gras express as Likelihood Linkage Index of Gras or intensity of involvement determined the implicative relations between the variables in order of significance. Additionally, the cohesive tree showed the direction of such relations. Hierarchical group of the items in conceptual construct Information Seeking exhibits the externally significant cohesion, amounts for 0.994 and revealed the direction of its items.

The hierarchy between the items FDB4, INS1, INS4, INS3, and INS2 whose intensity of involvement is almost perfect cohesion: 0.994. Τhis hierarchical structure, appears between the item of the construct Information Sharing and items comprising the construct Responsible Behavior, implies cohesion between them.

The hierarchy between the items FDB1, FDB2, FDB3, FDB4, INS1, INS4, INS3, INS2 that exhibits exceptionally high cohesion amounts for 0.982 revealed that beliefs on conceptual construct Information Sharing implies beliefs on Responsible Behavior. Finally, the intensity of involvement between the items INS4, INS3, INS2 related to beliefs on conceptual construct Responsible Behavior is almost perfect as it amounts for 0.998. One can concisely cite that the application of PCA resulted to a data reduction and showed that there are three Principal Components (Latent Variables) which interpret all of the total variability/information of data, as well as their structure. It is worth noting that the First Principal Component is in a line with hierarchy structure between the all items comprising the construct Information Seeking and an item of the construct Information Sharing (INS3, INS2, INS4, INS1 and FDB4). Thus, the First Principal Component is a Latent Variable immerged by these items based on their loadings. These gathered values amount to 0.829, 0.821, 0.790, 0.763 and 0.483, respectively highlighting items INS3, INS2 as the most significant variables as the values of the corresponding loadings are over 0.820. First Principal Component is a Latent Variables constituted from items FDB1, FDB2 and FDB3 whose loadings amounts for 0.833, 0.775 and 0.770 correspondingly highlighting item FDB1

Volume: 1 - Issue: 1 February 2019 as the most significant variable as its loading value is higher than 0.830 and it is higher regarding all loadings’ values to three Principal Components. Finally, the third is a latent variables constituted from items emerged as the component Information Seeking comprises of variables INF1, INF2 and INF3 whose loadings amounts for 0.784, 0.756 and 0.694 correspondingly highlighting item INF1 as the most significant variable as its loading value is higher for this Component. The results from the application of the methods have pointed at their differences and similarities but also their complementarity. One can concisely cite that the application of PCA resulted to a data reduction and showed that there are three Principal Components (Latent Variables) which interpret all of the total variability/information of data, as well as their structure and the of ASI result in hierarchy and cohesive structures based on similarity and Intensity of Involvement.

References Anastasiadou, S. (2018). Comparison of multivariate methods in group/cluster identification. Dissertation thesis of the degree of MSc. in Research Methodology in Biomedicine, Biostatistics and Clinical Bioinformatics, Fuculty of Medicine, University of Thessaly.

Blanchard, J., Guillet, F.,& Kuntz, P. (2009). Semantics-based classification of rule interestingness measures. Yanchang Zhao, Chengqi Zhang, Longbing Cao. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction, IGI Global, pp.56-79, 2009. . Coutourier, R. (2008). CHIC: Cohensive Hierarchical Implicative Classification. Studies in Computational Intelligence (SCI), pp.41-53. Springer-Verlag Berlin Heidelberg. Dafermos, B. (2013). Factor Analysis, Thessaloniki: Ziti. Field. A. (2009). Discovering statistic using SPSS. SAGE Publications India Pvt Ltd. Gras, R., P. Peter, H. Briand, & J. Philippé. (1997). Implicative Statistical Analysis. In C. Hayashi, N. Ohsumi, N. Yajima, Y. Tanaka, H. Bock, Y. Baba (Eds.). Proceedingsofthe5th Conference of the International Federation of Classication Societies, Volume 2, pp.412-419. Tokyo, Berlin, Heidelberg, New York : Springer-Verlag. Gras, R., & Bodin, A. (2017 ). L’A.S.I., Analyseur et révélateur de la complexité cognitive taxonomique. 9ème Colloque International sur Analyse Statistique Implicative, Belfort – France, In Jean-Claude Régnier, Régis Gras, Raphaël Coutourier, Antoine Bodin (edus) pp. 128-142.

Volume: 1 - Issue: 1 February 2019 Gras, R. (1996). The statistical implication-A new method for data esploration (in french). La Pensse Sauvage, editor. Gras, R & Kuntz, P. (2008). An overview of the Statitical Implicative Analysis (ASI) development, In Gras, R., Suzuki, E., Guillet,F and Spanolo, F. (2008). Statistical Analysis: Theory and Applications, Studies in Computational Intelligence Volumr No. 127, Berlin & Heidelberg: Springer- Verlag. Gras, R. (1979). Contribution étude expérimental et l’analyse de certaines acquisitions cognitives et de certains objectifs en didactique des mathématiques, Thèse de doctorat, l’Université de Rennes 1. Gras, R., & Couturier, R. (2013). Spécificités de l'Analyse Statistique Implicative par rapport à d'autres mesures de qualité de règles d'association. Educação Matemática Pesquisa, 15(2). Gras, R., Couturier, R., Blanchard, J., Briand, H., Kuntz, P., & Peter, P. (2004), Quelques critères pour une mesure de qualité de règles d’association. Revue des nouvelles technologies de l’information RNTI E-1, 3-30 Gras, R., Regnier, J. C., & Guillet, F., (2009). Analyse statistique implicative : Une méthode d'analyse de données pour la recherche de causalités (p. 510). Cépaduès Editions. Gras, R., Régnier, J. C., Marinica, C., & Guillet, F., (2013). L'analyse statistique implicative Méthode exploratoire et confirmatoire à la recherche de causalités (p. 522). Cépaduès Editions Gras R., Suzuki E., Guillet F. and Spagnolo F. (Eds) (2008). Statistical Implicative Analysis. Springer-Verlag, Berlin-Heidelberg. Jollife, I. T (1972). Discarding variables in the principal components analysis, I: Artificial data. Applied Statistics, 1, 57-93. Jollife, I. T (1986). Principal components analysis, New York: Springer. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurements, 20, 141-151. Kaiser, H. F. (1974). In order of factorial simplicity, Psychometrika, 39, 31-36. Lerman, I. C. (1981). Classification et Analyse Ordinale des Données, Dunod, Paris. Lerman, I. C. (1978). Formes d’ aptitude et taxinomie d’ objectifs en mathematiques. In: Revue française de pédagogie, Vol. 44, pp. 5-53.

Volume: 1 - Issue: 1 February 2019 Lerman, C. (1993). Likelihood linkage analysis (LLA) classification method: An example treated by hand. Biochimie. Vol. 75, Issue 5, pp.379-397. Yi, Y. and Gong, T., 2013. Customer value co-creation behavior: Scale development and validation. Journal of Business Research, 66(9), pp.1279-1284.

Volume: 1 - Issue: 1 February 2019

Tracing the concept of entrepreneurship and the role of an entrepreneur: A critical review Giossi Styliani Adjunct Lecturer and Scientific Collaborator, Department of Educational and Social Policy, University of Macedonia, Greece Anastasiadou Sofia Professor, Department of Early Childhood Education, University of Western Macedonia, Greece Gamanis Achilleas Undergraduate Student, Department of Electrical Engineer and Computer Science, University of Patras, Greece Gamanis G. George Undergraduate student, School of Mathematics, Aristotle University of Thessaloniki, Greece

Abstract This study provides a critical examination of how different theoretical perspectives present the concept of entrepreneurship and its relative issues evident in the creation and development of some theories, trends and strategies. As an entrepreneur is the most important factor either to the development of new ventures or to the majority of the theories of entrepreneurship, the present analysis highlights his/her roles and underlies the differences and similarities in various reviews and how he/she designated in the past and present days. Core issues related to entrepreneurship are also presented with the aim of developing insights that would advance the concept of entrepreneurship and accentuate types of entrepreneurship where different entrepreneurial skills, such as opportunity recognition and risk-taking, are apparent and help educators interested in the entrepreneurial education. Keywords: entrepreneurship, entrepreneur, types of entrepreneurship, risk-taking, opportunity. Introduction There is a continuous rise in interest on entrepreneurship and entrepreneurial issues, like entrepreneurial spirit, entrepreneurial skills and entrepreneurial behavior, among the scholarly investigations and empirical surveys in an attempt to respond to economic crisis challenges such as unemployment, brain drain, refugees’ flows and immigrant employability as well as low levels of economic growth. To this vein, European Commission formulates strategies that advocate the empowerment of entrepreneurship through teaching entrepreneurship in all levels of formal education, non-formal and informal education in a lifelong process and in all disciplines of learning. Entrepreneurship education mainly aims to help learners to develop skills, mindset and behavior in order to be able to turn creative ideas into entrepreneurial action

with or without a commercial objective (European Commission/EACEA/Eurydice, 2016, p. 121). Entreneurship as a key competence was first announced in the 2006 Recommendation of the European Parliament and the Council on Key Competences for Lifelong Learning (where it was identified as initiative and entrepreneurship) among the eight key competences and more precisely there it refered to an individual’s ability to turn ideas into action, to be innovative, take the initiative, take risks, plan and manage projects aiming to achieve objectives (European Commission, 2006, p. 4). A special focus on entrepreneurship is also evident in the strategic framework for Education and Training 2020 with three important programmes “Youth on the Move”, “An Agenda for New Skills and Jobs”, and the “Innovation Union” having as main objective to enhance creativity and innovation, and entrepreneurship in all levels of education and training. It is encouraging that education in recent years and the youth strategies have succeeded in promoting and developing entrepreneurship among the other key competences especially in Northern Europe (Eurydice, 2012, pp. 9-10) Under the belief that European strategies want to promote entreprenurship as a remedy to unemployment and economic growth the conceptualization of entrepreneurship and the clarification of the roles of an entrepreneur are of great importance. Furthermore, the increase of entrepreurial capital which is designated as the link of entrepreneurship, economic performance and regional development (Audretsch & Keilbach, 2004) follows the same path. It is easily understandable that neither entrepreneurs nor entrepreneurship are new concepts of human experience (Hebert & Link, 1989, p. 39). But the intensive need of increasing the entrepreneurial capital raise the interest and the attention to what is entrepreneuship and which are the roles of an entrepreneur. For this reason, a concise literature search for the evolution of the concept of entrepreneurship and other relevant issues was taken place seeking to clarify the meaning of entrepreneurship as well as the roles of an entrepreneur. Methodology approach How the concept of entrepreneurship and the roles of an entrepreneur are presented in different theories, trends and strategies were the main questions the present literature review pursues to give answers. This review was created by searching different scientific data bases, mainly by using as search keywords “entrepreneurship”, “entrepreneur”, “theories of entrepreneurship” and some other relevant issues. After the collection of a serious amount of articles, papers, reports and books, a selection was taken place. The basic criteria of that selection were to stand out the most important of the existing explanations and definitions of entrepreneurship, entrepreneur and relative issues which could be easily understood by anyone interested. For this reason, a further selection was made in order to find out comparisons of different approaches to meanings, definitions and descriptions since these could provide a better understanding and clarification of concepts through similarities and differences. Finally, some important definitions and explanations gathered in two tables, which were formed in order to emphasize some meaningful explanations and definitions and are expanded in detail on below. Different perspectives about entrepreneurship Entrepreneurship is described in various and different ways among which it is regarded as a factor, as a function, as an initiative, as a spirit and as a behaviour (Cuervo, et al., 2007, p. 3). More precisely, as a factor it is presented as a new factor except from the three classical factors of production-land, labour, caputal. As a function it refers to the creation of a business or the

discovery and exolitation of new opportunities. As an initaitive it covers the creation, risktaking, renewal or innovation inslide or outside an existing organization. As a spirit it covers exploration, search and innovation. As a behaviour, it is the one that manages to combine innovation, risk-taking and proactiveness (Miller, 1983). Different theories offer different meanings, explanations and definitions to the concept of entrepreneurship. The majority of the traditional economic theories describe the meaning of entrepreneurship indirectly in the creation of a new venture where the entrepreneur is the leading actor. Other theories and trends introduce some entrepreneurial skills or entrepreneurial attributes in order to describe the role of an entrepreneur. Entrepreneurship initially started as a theme to economics, to sociology, to management science and thus, it was faced under the triptych economics-sociology-management (Hebert & Link, 2009). Then, psychology follows which puts emphasis on entrepreneurial behaviour and creativity. With evolution of technology and the emergence of the digital economy, things have changed and greater emphasis has been put on innovation based on technological achievements. This indicates that entrepreneurship is studied through different interdisciplinary approaches and no one can predict which scientific field researchers interest will stimulate in future (Dagdilelis & Giossi, 2014). Different perspectives and theories embrance entrepreneuship and are named correspondingly economic theories, sociological theories, psychological theories, entreprenurial innovation theory, resource-based theory, opportunity-based theory, theory of high achievement, according to their reference field. As it is not the scope of the present research study to present and analyze all these theories and perspectives, but to give some basic knowledge to those who are interested to have a first contact with the content of entrepreneurship and the roles of entrepreneur, a brief presentation of the characteristics of some important theories and perspectives was considered adequate. The economic theories of entrepreneurship are based on the link of entrepreneurship and economy and emphasize the crucial role of the economic conditions and economic incentives like taxation policies, financial resources, market opportunities, availability of information and access to technology. In these theories the risk-taking of the entrepreneur is narrowly connected to the economic conditions. In sociological theories of entrepreneurship the prominent interest of entrepreneurship is the public good, the needs of society and the growth mainly in social settings. In this case an entrepreneur can carry out a business to provide public goods such as a not-for profit organization and in this way he/she appear their ideological point of view, their values, their ethics, their preferences in cultural activities and their sensitivity in social problems. Contemporary theories of this kind deal with a new kind of entrepreneurship named as social entrepreneurship where the focus is not on the economic profit but on the benefit of the society. This orientation does not exclude entrepreneurs from risk-taking, innovative initiatives, discovering and exploiting opportunities but limits their actions to those for social wealth. The psychological theories of entrepreneurship pay attention to the personality of the entrepreneur. Psychology can give answers to questions and explain decisive actions (behaviours), perceptions and implementations of opportunities (perception, cognition, emotions, motivation) concerning entrepreneurship as it has this role by definition (Frese, 2009, p. 439). Entreprenurial innovation theory highlights the value of innovation which is the vital part of entrepreneurship. Schumpeter (1934) characterized entrepreneur as an innovator who destroys equilibrium in a creative way. Innovation includes the production of a new product, the creation

of a new method and a new idea, the entrance in a new market and all new things that can create value. Another theory is the resource-based theory where entrepreneurship is the driver of economic growth and progress (Bosma, Wekkeners & Amoros, 2011) and creates information, knowledge, and even economic wisdom (Holcombe, 2007). As entrepreneurs need resources for their entrepreneurial activities, the resources are of great importance and it is up to their capability to find and take advantage of them either these are capital, labor and time or access to information, education, leadership and other capabilities. Concerning the perspective of opportunities, entrepreneurship deals with how, by whom, and what effects opportunities which are discovered, evaluated and exploited in order to create future goods and services (Venkataraman, 1997). The emphasis given to opportunities is the main point of opportunity-based theories. The approach of opportunities is based on the work of the Austrian economists (Hayek, 1945; Kirzner, 1973; Von Mises, 1949) Furthermore, concerning opportunities the form of de novo startups can arise which they have very uncertain opportunities (Casson, 1982), they face opportunities which they do not require complementary assets hintering in this way the imitators and others followers to benefit at the expense of innovators (Teece, 1986) and opportunities where the information cannot be protected by the laws of intellectual property and thus, impeding the sale of opportunities (Cohen & Levin, 1989). Concerning the startups of recent times, there is a different aspect of opportunities. In this case, opportunities are dealt with technology, teamwork, open innovation and immediate and high profit. The theory of high achievement or theory of achievement motivation is referred mostly to the McClelland theory of needs (McClelland, 1961; 1975;1985), which is one of the most eminent and pragmatic theory in personality and organizational schorarship (Royle & Hall, 2012, p. 25). In this theory there are three kinds of needs, the achievement needs, the power needs and the affiliation needs. In this theory, the characteristics of a person with prevailing the needs of achievement is his desire to excel, his seach for situations where he/she can obtain personal responsibilities for finding new solutions to different problems. Indded, achievement motivation is very important to entrepreneurship as entrepreneurs always want to success. The need for achievement stands out among the other two kinds of needs, where both of these kinds are dealt with relationships with other people, the former to gain dominance and the later to develop and keep friendships with other people. Table 1 presents four types of entrepreneurship and their explanations found in the literature which were chosen based on the easy understanding of their meanings. To this vein, the ‘explanation’ regarded as suitable to this case, as there are many different definitions about the different types of entreprenurship and in this study there is no need to encompass different definitions. Further, five entreprenurial issues were selected to be explained as they are crucial to a better understanding of entrepreuship as a whole. Table 1. Types of entrepreneurship and other relevant issues Types of entrepreneurship Explanation and other relevant issues Entrepreneurship that is Intrapreneurship related to an entrepreneurial employee activity Cooperative entrepreneurship Entrepreneurship which refers to a group of people who manage the venture

References (Bosma, Wekkeners & Amoros, 2011, p. 7)

(Diaz-Focea & Marcuello, 2013)

Corporate entrepreneurship

Social entrepreneurship

Entrepreneurial capital

Entrepreneurial capacity

creation process, take risk, and make judgmental decisions to create a business in a participatory way with the objective of obtaining mutual benefit to be distributed with equity among them Entrepreneurship which is based on the combination of new resources internally generated in a firm, in order to extend firm’s competence and the corresponding set of opportunities Social entrepreneurship is the creation of viable socio-economic structures, relations, institutions, organizations, and practices that yield and sustain social benefits. The capital which is derived from the link of entrepreneurship, economic performance and regional development It is related to: -structural formality, -structural differentiation and decentralization -control mechanisms -number of layers in the organizational hierarchy It is affected by: -multiple facets organization structure

(Burgelman, 1984, p.154)

(Fowler, 2000)

(Audretsch & Keilbach, 2004)

(Stevenson & Gumpert, 1985) (Miller, 1983) (Zahra, 1986) (Peters, 1987)

of

(Covin & Slevin, 1991) A process with factors which include: -the identification and evaluation of objective opportunities -the establishment of goals to exploit identified opportunities -the analysis of alternative means to fulfill goals and constrains due to environmental conditions. indicator which Employee An includes the development of new activities for an individual’s main employer, such as

Entrepreneurial process

Entrepreneurial Activity (EEA)

(Shane & Vebkataraman, 2000) (Sarasvathy, 2001)

(Sarasvathy, 2001)

(GEM 2017, p. 25)

Entrepreneurial learning

developing or launching new goods or services, or setting up a new business unit, a new establishment or subsidiary. Learning which refers to (Rae, 2005) the recognition and exploitation of opportunities; social interaction with the aim to initiate, organize and manage new ventures. Learning which is based on creativity, informality, curiosity, emotion, real world problems and opportunities and takes place through entrepreneurial ways.

(Penaluna & Penaluna, 2015)

The roles of an entrepreneur Entrepreneurs discover, evaluate and exploit opportunities; they initiate and motivate the process of change. Entrepreneurs consider change normal and healthy and they always search for change, respond to it and exploit it as an opportunity and this is the deep meaning of both entrepreneurship and entrepreneurs (Drucker, 1985, p. 27). Αn important record of the entrepreneur's roles in the economic literature was carried out by Hebert & Link (1989). According to their reference twelve are the roles of an ertrpreneur and these are: risk-taking role associated with uncertainty; supplier of financial capital; innovator; decision maker; industrial leader; manager or superintendent; coordinator of economic resources; owner of an enterprise; employer of production factors; contractor; arbitrageur; and allocator of resources among alternative uses (Hebert & Link, 1989, p. 42; Hebert & Link, 2009, p. xviii). Different types of entrepreneurs have different synthesis of these roles. For example, a cooperative entrepreneur is risk-bearing, decision-maker, owner of the enterprise and contractor; a collective entrepreneur is financial capital supplier, decision-maker, manager, coordinator of economic resources, allocator of resources (Diaz-Focea & Marcuello, 2013, p. 245) while a nonprofit entrepreneur has only one of the roles described by Herbert and Link (1989) the risk-bearing role, and another different role which is an actor with ideological commitment and altruistic motives (Rose-Ackerman, 1997, p. 120) Indeed, there are many theories which descibe the roles of an entrepreneurs but a selection of three representatives were chosen to be presented in the following table (Table 2). The main reasons for the selection of these three theories and not others are: Firstly, the main aim of the present review which was to present some definitions and explantion about entrepreneurship and entrepreneur in brief, especially either to those having a poor understanding of these concepts or to those wanting to read a brief overview of the evolution of the entrepreneurship in order to begin a new research or further reading. Secondly, these three theories are essential for the creation of others and also represent the main points of the evolution of the entrepreneurship and the role of the entrepreneur. Cantillon’ s theory is one of the most important theory presented early in the 18th century. Cantillon showed the earliest interest in entreprenurship and focused on the economic role of

the entrepreneur. Also, he recognized three classes of economic agents -the landoweners, the entrepreneurs and the hirelings-, and characterized the entreprenur as the central economic actor. He stated that “entrepreneurs and nonentrepreneurs are joined in reciprocal trade agreements and therefore entrepreneurs become cosumers and customes one in rgard to the other” (Hebert & Link, 1989, p. 42). Additionally, Cantillon’ s theory consisted the basis for the distinction of the three traditions which present entrepreneur in functional terms and these are: the German tradition having as main representatives Thunen and Schumpeter; the Chicago tradition having as main representatives Mises and Kirzner and the Austrian tradition having as main representatives Knight and Schultz (Hebert & Link, 1989, p. 41). The focus of traditional economic theories the search, evaluation and exploitaion of opportunities based on the demand and supply status and market needs, risk taking through a new venture and the requirement of return on investment with the entrepreneur to play a protagonist role in the economic growth. One of the emerging theories of entrepreneurship refers to the use of resources not in long run plans and without paying attention to the environmental limitations. Table 2 presents three different theories and eight perspectives of the roles of an entrepreneur found in the literature with the aim of clarifying them in a simple and easily understandable way. Table 2. Roles of an entrepreneur in three different theories and other perspectives Roles of an entrepreneur Theory -he has a pivotal role in the Cantillon’s theory economy of the entrepreneur -he lives on uncertain income -he is responsible for the production, circulation, and exchange of goods -he acts on perceived arbitrage opportunities -he/she seeks, evaluates and Traditional economic exploits opportunities by theory searching where the demand exceeds supply -he/she establishes an entity in order to develop and deliver a product or service in a market with the aim to have return on investment -he/she focuses on his/her Emerging theory of resources and ignore market entrepreneurship needs -he/she focuses on what he/she is willing to lose while chasing an opportunity -he/she does not pay attention to the resource limitations given by the environment -he/she avoids long run goals and plans Perspectives on the roles of an entrepreneur According to Mises, he is Entrepreneur uncertainty-bearer, who

References (Brown & Thornton, 2013)

(Shane & Vebkataraman, 2000) (Venkataraman, 1997)

(Casson, 1982) Khilstrom & Laffont, 1979

(Sarasvathy, 2001)

References (Rothbard, 1985, p. 281)

receives profit in case of his successful future forecasting and suffers losses in case of failed future forecasting. According to Kirzner, he is Entrepreneur characterized by alertness which means the perception of opportunities and then the exploitation of them. According to Schumpeter, he Entrepreneur is an innovator According to Schumpeter, he creates a dynamic disequilibrium through his innovation. According to Knight, he Entrepreneur takes risks and occupies a position of uncertainty According to Knight, he is paid for taking risks. The ideal type of the entrepreneur is the top manger of the corporation and not the stock-holder. According to Mises, he is Entrepreneur uncertainty-bearer, who receives profit in case of his successful future forecasting and suffers losses in case of failed future forecasting. He is a major change agent Social entrepreneur whose core values center on identifying, addressing and solving societal problems. Social bricoleur Social entrepreneur (typology build on work of He focuses on discovering Hayak, Kirzner and and addressing small-scale Schumpeter local social needs Social Constructionist He exploits opportunities and market failures by filling gaps with the aim to introduce reforms and innovation to the broader social system Social Engineer He recognizes systemic problems within existing structure and address them by introducing revolutionary change Someone who specializes in Entrepreneur (synthetic definition) taking responsibility for and making judgmental decisions that affect the location, the form, and the use of goods, resources, or institutions Limitations and implications of the research

(Kirzner, 1973) (Rothbard, 1985)

(Drucker, 1985, p. 27) (Schumpeter, 1934)

(Knight, 1921) (Langlois & Cosgel, 1993)

(Rothbard, 1985, p. 281)

(Drayton, 2002)

(Zahra, et al., 2009, p. 519)

(Hebert & Link, 1989, p. 39)

One of the limitations is the selection of a very limited amount of theories for describing the roles of an entrepreneur. The analysis of some types of entrepreneurship by presenting only one definition or explanation for each is also another limitation. Also, the focus of this research mainly on those who have adequate knowledge about entrepreneurship and the roles of the entrepreneur can be included to the limitations. This literature review could be useful and essential for all who are about to teach entreprenurship in any level of education and with any interdisciplinary approach as well as for aspiring entrepreneurs as they could be well informed about what is the meaning of entrepreneurship, the role of an entrepreneur and the skills which are needed to have before they begin acting as entreprenurers. Additionally, it can help policy makers to form strategies for promoting entrepreurial spirit and mindsets and create a climate flourish for taking entrepreneurial intiatives such as the beginning of a start-up, some kind of cooperative entrepreneurship and self-employment. Finally, the value of this research study could be its contribution to more clearly frame future research and the possibility to motivate other researchers to investigate entreprenurhip, entrepreneur and relevant issues in depth rather than to be evaluated as an complete and thorough review of relevant literature since it has a specific targeting. Conclusions This investigation of the concept of entrepreneurship is advocated by the designation of entrepreneurial capital introduced by Audretsch & Keilbach (2004), where the entrepreneurship is the one of the three main elements of the entrepreneurial capital while the other two are economic performance and regional development. Thus, the understanding of the meaning of entrepreneurship and the roles of entrepreneur through this literature review was considered to be the starting point firstly in understanding and secondly in developing entrepreneurial capital which is concerned to give solutions to current economic crisis problems. As the role of the entrepreneur has changed, it is vital for those who attracted by entrepreneurial activities, to be prepared for recognizing opportunities, exploiting them and find the resources to begin a new venture. In terms of entrepreneurial skills, it seems that in future they would not be related solely to entrepreneurs but also to other professionals. Thus, as entrepreneurial skills refer to abilities the demand of which is increasing in existing or new occupations can be characterized as emerging/new skills (CEDEFOP, 2014, p.75). But the development of the entreprenurial skills can not be achieved if there are no opportunities. The existence or the creation of opportunities is of great value because when entrepreneurs take advantage of opportunities this could have an impact on the economic environment and as a consequence it will offer additional opportunities and therefore entreprenurship could lead to more entrepreneurship (Holcombe, 1998, p. 54) Under the belief that the entrepreneurship can not be mainly effective due to economic reasonsas it was the case from the economists’ point of view in the past-, but due to the changes in values, perspectives, attitudes, demographics, institutions and education (Drucker, 1985, p. 13) emphasis should be given to policies, strategies and especially to education in order to support the entrepreneurial spirit which in simple words means the recognition, the evaluation and the exploitation of opportunites. As a result, everyone could be able to think and/or act as an entrepreneur either for his own benefit or for the economy and sociey’ benefits. References

Audretsch, D. & Keilbach, M., 2004. Entrepreneurship capital and economic performance. Regional Studies, 38(8), pp. 949-959. Bosma, N., Wennekers, S. & Amoros, E., 2011. Global Entrepreneurship Monitor 2011 Extended Report: Entrepreneurs and entrepreneurial employees across the globe, s.l.: Global Entreprenurship Research Association. Brown, C. & Thornton, M., 2013. How entrepreneurship theory created economics. Quarterly Journal of Austrian Economics, 16(4), pp. 401-420. Burgelman, R. A., 1984. Designs for corporate entrepreneurship. California Management Review, Volume 26, pp. 154-166. Casson, M., 1982. The entrepreneur. Totowa: Barnes & Noble Books. CEDEFOP, 2014. Terminology of European education and training policy. 2nd ed. Luxembourg: Publications Office of European Union. Cohen, W. & Levin, R., 1989. Empirical studies of innovation and market structure. In: R. Schmalensee & R. Willig, eds. Handbook of industrial organization. New York: Elsevier, pp. 10601107. Covin, J. G. & Slevin, D. P., 1991. A conceptual model of entrepreurship as firm behavior. Entrepreneurship theory and Practice, pp. 7-25. Cuervo, A., Ribeiro, D. & Roig, S. eds., 2007. Entrepreneurship: Concepts, theory and perspective. Heidelberg: Springer. Dagdilelis, V. & Giossi, S., 2014. Entrepreneurship 2.0 and its didactics. Thessaloniki: Publications of University of Macedonia. Diaz-Focea, M. & Marcuello, C., 2013. Entrepreneurs and the context of cooperative organizations: A definition of cooperative entrepreneur. Canadian Journal of Administrative Sciences, Volume 30, pp. 238-251. Drayton, B., 2002. The citizen sector: Becoming as entreprenurial and competitive as business. California Management Review, 44(3), pp. 120-132. Drucker, P. F., 1985. Innovation and entrepreneurship: Practice and principles. New York: Harper & Row. European Commission/EACEA/Eurydice, 2016. Entrepreneurship education at school in Europe, Luxemburg: Publications Office of the European Union. European Commission, 2006. Implimenting the Community Lisbon Programme: Fostering entreprenurial mindsets through education and learning, s.l.: s.n. Eurydice, 2012. Entrepreneurship education at school in Europe: National strategies, curricula and learning outcomes, Brussels: Education, Audiovisual and Culture Executive Agency. Fowler, A., 2000. NGDOs as a moment in history: Beyond aid to social entrepreneurship or civic innovation?. Third World Quarterly, 21(4), pp. 637-654. Frese, M., 2009. Toward a psychology of entrepreneurship- An action theory perspective. Foundation and Trends in Entrepreneurship, 5(6), pp. 435-494. GEM-Global Entrepreneurship Monitor , 2017. Global Report 2016/17, s.l.: Global Entrepreneurship Research Association (GERA).

Hayek, F. A., 1945. The use of knowledge in society. The American Economic Review, 35(4), pp. 519530. Hebert, R. F. & Link, A. N., 1989. In search of the meaning of entrepreneurship. Small Business Economics, 1(1), pp. 39-49. Hebert, R. F. & Link, A. N., 1989. In search of the meaning of entrepreneurship. Small Business Economics, Volume 1, pp. 39-49. Hebert, R. F. & Link, A. N., 2009. A history of entrepreneurship. Oxon: Routledge. Holcombe, R. G., 1998. Entrepreneurship and economic growth. Quartery Journal of Austrian Economics, 1(2), pp. 45-62. Holcombe, R. G., 2007. Entrepreneurship and economic progress. London: Routledge. Kirzner, I., 1973. Competition and entrepreneurship. Chicago: University of Chicago Press. Knight, F., 1921. Risk, uncertainty and profit. New York: Augustus Kelley. Langlois, R. N. & Cosgel, M. M., 1993. Frank Knight on risk, uncetainty, and the firm: A new interpretation. Economic Inquiry, Volume XXXI, pp. 456-465. McClelland, D., 1961. The achieving society. Princeton: Van Nostrand Company. McClelland, D., 1985. Human motivation. Glenview: Scott, Foresman. McClelland, D. C., 1975. Power: The inner experience. Irvington ed. s.l.:New York. Miller, D., 1983. The correlates of entrepreneurship in three types of firms. Management Science, 29(7), pp. 770-791. Penaluna, A. & Penaluna, K., 2015. Entrepreneurial Education in Practice, Part, 2 – Building Motivations and Competencies , s.l.: Organisation for Economic Co-operation and Development (OECD, LEED Programme) and the Europea. Peters, T. J., 1987. Thriving on chaos. New York: Alfred A. Knopf.. Rose-Ackerman, S., 1997. Altryuism, ideological entrepreneurs and the non-profit firm. VOLUTAS:International Journal of Voluntary and Nonprofit Organizations, 8(2), pp. 120-134. Rothbard, M. N., 1985. Professor Hebert on entrepreneurship. The Journal of Libertarian Studies, VII(2), pp. 281-286. Royle, M. T. & Hall, A. T., 2012. The relationship between McClelland's theory of needs, feeling individually accountable, and informal accountability for others. International Journal of Management and Marketing Research, 5(1), pp. 21-42. Sarasvathy, S. D., 2001. Causation and effectuation: Toward a theoretical shift from economic inevitability to entrepreneurial conthngency. Academy of Management Review, 26(2), pp. 243-263. Schumpeter, J. A., 1934. The theory of economic development. Cambridge: Harvard University Press. Shane, S. & Vebkataraman, S., 2000. The promise of entrepreneurship as a field of research. Academy of Management Review, 25(1), pp. 217-226. Stevenson, H. H. & Gumpert, D. E., 1985. The heart of entrepreneurship. Harvard Business Review, 63(2), pp. 85-94. Teece, D. J., 1986. Profiting from technological innovation: Implications for integration, collaboration, licensing and public policy. Research Policy, Volume 15, pp. 285-305.

Venkataraman, S., 1997. The distinctive domain of entrepreneurship research: An editor's perspective. In: J. Katz & R. Brochhaus, eds. Advances in entrepreneurship, firm, emergence, and growth. Greenwich: JAI Press, pp. 119-138. Zahra, S. A., 1986. A canonical analysis of corporate entrepreneurship antecedents and impact on performance. Proceedings of the National Academy of Management , Volume 46, pp. 71-75. Zahra, S. A., Gedajlovic, E., Neubaum, D. O. & Shulman, J. M., 2009. A typology of social entrepreneurs: Motives, search processes and ethical challenges. Journal of Business Venturing, Volume 24, pp. 519-532.

Volume: 1 - Issue: 1 February 2019

Evaluating citizens’ actual perceptions and expectations and assessing e-Service Quality Gap in Public Sector related to e-Government Services Anastasiadis Lazaros Political Sciences, University of Crete, Greece Christoforidis Christos Public Administration, University of Neapolis, Pafos, Cyprus [email protected] Abstract

Purpose - The main purpose of this article is to explore the inter-relationships of major constructs related to citizens’ satisfaction regarding e-Service Quality in Public Sector. The plan of the document is to evaluate the e-Service Quality in Public Sector of Greece. The paper examines the relationship or the Gap between the perceived and expected levels of e-Service Quality in public sector with respect to its dimensions, namely Tangibility related to Web site design, Reliability, Responsiveness, Security and Confidentiality and Personal Handling or Personalization and Privacy. Design/ Methodology/ Approach- The study intends to disclose the sources supporting the satisfaction of citizens as well as those holding back it. The instrument employed to assess citizens’ satisfaction regarding e-Service Quality in Public Sector related to eGovernment Services, is the SEVQUAL. Findings- The research findings draw our attention to the significant effects of Web site design/Tangibility,

Reliability,

Responsiveness,

Security/Confidentiality,

and

Personalization/Privacy on service quality related to Public Sector related to eGovernment Services. Adding, it places of interest citizens’ negative attitudes and obstacles or positive behaviors toward e-Government Services.

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Research limitations/ implications- The study was refereeing to Greek public sector citizens’ satisfaction related to e-Government Services. Future research could supply new empirical results in relation to the current new high tech area. Originality/ value- The document adds a total new situation’ presentation, e-Service Quality Gap in public sector related to e-Government Services. Key words: e-Service Quality, Gap Analysis, Public Sector, e-Government Services. Theoretical Framework Societies ask for highly educated citizens. According to Anastasiadou (2018) Education, training and culture of the youth is of the utmost importance for people, nations, and economies and cultures (Anastasiadou, 2016, Anastasiadou et al., 2016). In the era of technological revolution e-Government is well defined. According to Bardi & Alshare (2008) e-Government is highly used as a tool for prompting economic development due to the fact that it facilitates organization to effectively carry out in a more efficient conduct with the government. ΙΤ and Internet have opened new possibilities for government and governed (Moon, 2002). Melitski (2003) argued that e-Government has become a significant strategic tool for the Public Sector. eGovernment success related to e-Service Quality (Anastasiadou 2015; 2018b, 2018c). Service Quality and e-Service Quality in the high tech era can be evaluated in terms of the Gaps between customers’ expectations and perceptions (Hoffman and Bateson, 2006), while Parasuraman et al. (1985) recommend that customers’ assessment of service quality taken as a whole depends on the Gaps between the expected and the perceived service. Parasuraman et al. (1985) and Zeithaml et al. (1990) have recognized five separate Gaps between customers’ expectations and perceptions. These five Gaps are illustrated below (Figure 1). (a) Gap 1: The Knowledge Gap, which refers to the difference between what customers expect of a service and what management perceives that customers expect (Musaba et al., 2014). Gap 1 assigned as Positioning Gap, is strongly related both to managers’

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perceptions regarding customers’ expectations and the importance customers connect with the quality dimensions comparatively (Zeithaml et al. 1990; Anastasiadou, 2018a). Mohammand and Moghadam, (2016) argued that management might have an erroneous perception of customers’ actual perception. In addition they pointed out that this Gap has its pedigree in deficient in focus on customers or the market (Mohammand and Moghadam, 2016); (b)Gap 2: The Standards Sap, which refers to the difference between what managers perceives that customers expect and the quality and specifications set for service delivery (Musaba et al., 2014). Gap 2 assigned as Specification Gap, points out the actual difference between what the management believes regarding customers want and what is expected by customers related to the organization will provide (Zeithaml et al. 1990; Anastasiadou, 2018a). Mohammand and Moghadam, (2016) argued that the organization might not be capable of translating customers’ expectations into service specifications/ features. This Gap relates with aspects of service design (Mohammand and Moghadam, 2016); (c) Gap 3: The Delivery Dap, referring to the difference between the quality specifications set for a service delivery and the actual quality of service delivery. Gap 3 assigned as Delivery Gap points out the actual difference between the service made available by the organization employee and the specification that are allocated by the managers (Zeithaml et al. 1990; Zeithaml et al. 1996; Zeithaml et al. 1990; Zeithaml et al. 2000; Anastasiadou, 2018a). (Mohammand and Moghadam, (2016) argued that, with respect to services rendered; organizations do not offer high quality services. According to Anastasiadou (2018a) they argued that the organization might be faced with, personnel and communication problems, the unpredictability of frontline personnel and shortcomings regarding processes; (d) Gap 4: The Communications Cap refers to the difference between the actual quality of service delivered and the quality of service described in the firm’s external communications, such as brochures and mass media advertising (Musaba et al., 2014). Gap 4, assigned as Communication Gap points out the given promises by the

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organization to its customers but not truly be in a line with the expectations related to the external promises made by customers (Zeithaml et al. 1990; Anastasiadou, 2018a). Mohammand and Moghadam, (2016) argued that customers’ expectation might be strongly predisposed by the external relations of the organization. This Gap relates to unrealistic expectations formed by the encouragement of positive perceptions that the organization is not capable of supporting (Mohammand and Moghadam, 2016); (e) Gap 5: The Service Gap which summarizes all the other Gaps and describes the difference between customers’ expectations and their perceptions of the service they receive (Musaba et al., 2014). Gap 5, assigned as Perception Gap, points out the difference between the anticipation of the services and customers internal perceptions (Zeithaml et al. 1990; Anastasiadou, 2018a). Perceived quality of the service relates to difference between expectation and perception. A negative difference between customer’s perceptions and expectations shows a level of service quality below customers’ expectations (Mohammand and Moghadam, 2016).

Mouth-to mouth communication

Personal needs

Part experiences

Expected services Gap 5 Provided services Customer Provider Service delivery Gap 3 Gap 1

Service quality

Gap 4

External costumer communications

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Figure 1: Gap model (Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4963340/figure/Fig1/) Gap 5 between the expected and the perceived service is considered to be the most significant one (Katler and Armostrong, 2000; Musaba et al., 2014). According to Kumar et al., (2009), SERVQUAL instrument dimensions named Tangibility, Reliability, Responsiveness, Assurance and Empathy are strongly connected quality measurement (Zeitham, 1988; Parasuraman, Berry and Zeitham, 1988; 1990,). According to Grönroos (1982) SEVQUAL has been the predominant method used to measure consumers’ perceptions relating to Service Quality. The connection presented in the Figure 2 below.

External Factors Influencing expectation SERVQUAL Dimensions Tangibles Expectation Reliability

(Expected Service)

Responsivenes

Perceived Service Quality

s Assurance

Perception (Perceived

Empathy

Service)

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Figure 2: Measuring Service Quality with SERQUAL Model (Source: Kumar et al., 2009) Aim of the study The major intend of this paper is to investigate the inter-relationships of major constructs related to citizens satisfaction regarding e-Service Quality Gap in Public Sector related to e-Government services. The plan of the document is to appraise the eService Quality offered by the Greek e-Government services by evaluating Gaps between customers’ expectations and perceptions as they relate to SERVQUAL dimensions with respect to citizens’ trustworthiness (Parasuraman, Berry and Zeitham, 1988; 1990). Consequently this study will focus on Gap 5 between expected and perceived/actual e-Government services.

The instrument Proposed a Conceptual Model that is the based to measure e-Government Service Quality, is related to SERVQUAL dimensions. These dimensions named Web site design/ Tangibility, Reliability, Responsiveness, Security and Personalization were modified and paraphrased to be in a line with organization perspectives. The proposed new SERVQUAL instrument by Wesam Abdallat (2014) adapting the five dimensions of service quality secured by SERVQUAL to e-Government Service Quality (Ateeq et al., 2010) includes 22 items. This instrument consisted of two parallel sections: the expected e-Government services and the actual/ perceived e-Government services. The difference between them represents the Service’s Gap. This tool consists of 22 items referring to five different dimensions, as follows:

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(a) Web site design/ Tangibility’ dimension includes 7 items (E1/A1, E2/A2, E3/A3, E4/A4, E5/A5, E6/A6 and E7/A7). It refers to Web site design congeniality and suitability, functionality and appearance (e.g. E1. e-Government web site will be excellent with an attractive appearance, A1. The e-Customs Department web site has an attractive appearance to the viewer). (b) Reliability’ dimension includes 4 items (E8/A8, E9/A9, E10/A10, E11/A11). It refers to promised service performance regarding e-mailing, calling a customer, delivering the right products with right charges (e.g. E8. When the e-Government website undertakes to call me or send me an email message, I would like to commit them to this, A8. When the e-Customs Department web site undertakes to call me or send me an email message, they are committed to this). (c) Responsiveness’ dimension includes 3 items (E12/A12, E13/A13, E14/A14). It refers to e-Government service provision regarding adequate assistant to users with delays (e.g. E12. I think that the e-Government website provides prompt service, A12. I think that the e-customs department website provides prompt service). (d) Security/Confidentiality’ dimension includes 4 items (E15/A15, E16/A16, E17/A17, E18/A18). It refers to e-Government service provision regarding security and confidentiality and protection related to users’ personal information (e.g. E15. The eGovernment website must provide security and protection, A15. The e-Customs department website provides security and protection for users). (e) Personalization/Privacy’ dimension includes 4 items (E19/A19, E20/A20, E21/A21 and E22/A22). It refers to e-Government service virtual environment. It relates to eGovernment service services to convince individuals business’s needs (e.g. E19. I love the e-Government website that offers option to build a personal profile, A19. The eCustoms Department website provides options to build a personal profile). The sample

The sample comprises of 205 respondents, of whom 128 (62.4%) were men and 77 (37.6%) were women (Table 1).

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With respect to the respondents’ age, 110 (53.7%) were from 18 to 24 years old; 44 (21.5%) from 25-34; 24 (11.7%) from 35 to 44 years; and finally 27 (13.2%) from 4554 years old. With respect to their marital status, 157 (76.6%) were single; 43 (21%) were married and 5 (2.4%) were separated or divorced. As for the respondents’ education level, 2 (1%) answered that they have completed elementary education, 105 (51.2%) secondary, 72 (35.1%) tertiary and, finally, 26 (12.7%) hold a post-graduate or doctoral title. 127 of the 205 respondents (62%) stated that their income is less than €10.000; 56 (27.3%) from €10.000 to €24.999; 12 (5.9%) from €25.000 to €49.999; 2 (1%) from €50.000 to €74.999 and, finally, 8 (3.9%) did not respond to this question. Table 1: Demographics Demographic

Category

data Sex Age

Family status

Education

Frequency

Relevant frequency

(N=205)

(%)

128

62.4

Female

77

37.6

18-24

110

53.7

25-34

44

21.5

35-44

24

11.7

45-54

27

13.2

Single

157

76.6

Married

43

21.0

Divorced/Separated

5

2.4

Elementary education

2

1.0

Secondary education

105

51.2

Tertiary education

72

35.1

Postgraduate studies /

26

12.7

127

62.0

Male

Doctorate Income

<€10.000

Volume: 1 - Issue: 1 February 2019 €10.000-€24.999

56

27.3

€25.000-€49.999

12

5.9

€50.000-€74.999

2

1.0

Did not respond

8

3.9

Results Reliability test: Α reliability test was carried out to ensure that the reserve instrument that evaluates the data collected is reliable (Anastasiadou & Zirinolou, 2014). The coefficient Cronbach’s α is calculated to measure the reliability of the five dimensions, i.e. Web site design/Tangibility, Reliability, Responsiveness, Security/ Confidentiality and Personalization/ Privacy (Table 2). Table 2: Cronbach’s Alpha of all the items Dimensions

Expectation

Perception /actual

Web site design/ Tangibility

0.76

0.72

Reliability

0.69

0.84

Responsiveness

0.78

0.79

Security/ Confidentiality

0.83

0.88

Personalization/ Privacy

0.78

0.81

Analysis of Mean Scores and e-Service Quality Gap of Perception and Expectation in Public Sector related to e-Government Services: The following section presents the mean and the standard deviation of perceptions/actual and expectations and the eService Gap regarding e-Service Quality Gap in Public Sector related to e-Government Services on Tangibility. From the results presented in table 3 it can be observed that the mean expectation scores are greater than the mean actual/perception scores in relation to all seven attributes, fact that it can certify that citizens are dissatisfied.

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However in terms of magnitudes of the Gap scores, it was found the Gap scores ranged from -1.67 to -0.68. Attribute E6 referring to whether the Website of the e-Government should not be down permanently has the highest mean and attribute E2 referring to whether the user interface for e-Government website will be well-organized has the lowest mean in terms of expectation. Attribute A3 connected with whether the process of conducting transactions in the eCustoms Department web site is easy and fast has the lowest in the dimension of Tangibility. Attribute A1 refers to whether the e-Customs Department web site has an attractive appearance to the viewer in terms of actual/ perception has the highest mean in the dimension of Tangibility. It should also be noted that attribute E6 which refers to whether the Website of the eGovernment should not be down permanently, has the highest negative sign. Table 3: Mean Scores and e-Service Quality of Actual Perceptions and Expectations and e-Service Gap on Web site design/Tangibility The Expected E-

Mean (Std.

The actual E-

Mean (Std.

Government services

Deviation)

Government services

Deviation)

Actual

Expectation E1. e-Government web

3.64(0.564)

Gap

A1. The e-Customs

site will be excellent with

Department web site has an

an attractive appearance.

attractive appearance to the

2.35(0.636)

-1.29

2.21(0.909)

-0.68

2.04(0.498)

-1.28

2.23(0.486)

-1.42

viewer. E2. The user interface for

2.89(1.160)

A2. The user interface for e-

e-Government website will

Customs Department web

be well-organized.

site is well-organized.

E3. The process of

3.32(1.025)

A3. The process of

conducting transactions on

conducting transactions in

the e-Government website

the e-Customs Department

will be easy and fast.

web site is easy and fast.

E4. The e-Government website will be always

3.65(0.620)

A4. The e-Customs Department web site is

Volume: 1 - Issue: 1 February 2019 available to business

always available to business

companies.

companies

E5. The e-Government

3.59(0.692)

A5. The e-Customs

web must download and

Department web site is

run immediately.

downloaded and run

2.25(0.535

-1.34

2.10(0.409)

-1.67

2.22(0.617)

-1.45

immediately. E6. The Website of the e-

3.77(0.611)

A6. The e-Customs

Government should not be

Department web site is

down permanently.

rarely down.

E7. The pages in e-

3.67 (0.653)

A7. The pages in e-Customs

Government web site do

Department web site do not

not delay to emerge after

delay to emerge after the

the entry of a request for

entry of a request for

Information.

Information.

The following section presents the mean and the standard deviation of perceptions/actual and expectations and the e-Service Gap regarding e-Service Quality Gap in Public Sector related to e-Government Services on Reliability. From the results presented in table 4 it can be easily observed that the mean expectation scores are greater than the mean actual/ perception scores in relation to all four attributes. The results show that citizens are not satisfied as far as reliability is concerned. However, in terms of magnitudes of the Gap scores, these ranged from -1.13 to -0.77. It must be said at this point, that attributes E8 and E9 have the highest negative signs and state that citizens are dissatisfied with both when the e-Government website undertakes to call them or send them an email message, they would like to commit them to this and when the e-Customs Department web site delivers the services that they order it do it exactly. Attribute E10 refers to whether they can be sure that when e-Government website will ask them for payment, fits with the requested service submitted by they like paying taxes has the

highest mean in terms of expectation. Attribute E9 referring to whether they can be sure

Volume: 1 - Issue: 1 February 2019

when that when the e-Government web site will deliver the services that they order it do it exactly haw the lower mean in terms of expectation.

In addition, Attribute A11 refers to whether e-Customs Department web site insists on error-free records has the highest mean in terms of perceptions, while, Attribute A9 refers to whether The e-Customs Department web site delivers the services that they order exactly

has the lowest mean.

Table 4: Mean Scores and e-Service Quality of Actual Perceptions and Expectations and e-Service Gap on Reliability The Expected e-

Mean (Std.

The actual e-

Mean (Std.

Government services

Deviation)

Government services

Deviation)

Gap

Actual E8. When the e-

3.56(0.729)

A8. When the e-Customs

Government website

Department web site

undertakes to call me or

undertakes to call me or

send me an email message,

send me an email message,

I would like to commit

they are committed to this.

2.50(1.008

-1.06

2.38(0.762)

-1.13

2.72(0.973)

-0.85

them to this. E9. I like to be sure that

3.51(0.844)

A9. The e-Customs

the e-Government web site

Department web site

will deliver the services

delivers the services that I

that I order exactly.

order exactly.

E10. I like to be sure that

3.57(0.818)

A10. I like to be sure that

the e-Government website

the e-Customers Department

will ask me for payment,

web site will ask me for

fits with the requested

payment, fits with the

Volume: 1 - Issue: 1 February 2019 service submitted by me

requested service submitted

like paying taxes.

by me like paying fees.

E11. The excellent e-

3.56(0.736)

A11. e-Customs Department

Government to have error-

web site insists on error-free

free records.

records.

2.79(0.946)

-0.77

The following section presents the mean and the standard deviation of actual perceptions and expectations and Service Gap of citizens on Responsiveness. From the results presented in table 5 it can be effortlessly observed that the mean expectation scores are greater than the mean perception scores in relation to all three attributes, fact that it can again confirm citizens’ dissatisfaction. Nevertheless, in terms of the magnitudes of the Gap scores, it was found that Gap scores ranged from -1.21 to -0.45. It ought to be mentioned that attribute E14/A14 has the highest negative sign and signify citizens’ dissatisfaction in relation to e-Customs Department website’ delay in answering requests from companies. It should be noted that the highest mean in terms of expectations involve attributes E14 and E13 which shows that the citizens feel that these two are the attributes that matter the most to them. The highest mean in terms of expectation is observed in attribute E14, which relates to e-Government website busyness to answer requests from companies. The second highest has the attribute E13, which relates to e-Government website readiness to help companies. Attribute A13 also has the highest mean score in terms of perception. Nevertheless, attribute A14 which refers to whether that e-Customs Department website should delay in answering requests from companies scored the lowest mean in terms of actual perception. Table 5: Mean Scores and e-Service Quality of Actual Perceptions and Expectations and e-Service Gap on Responsiveness

Volume: 1 - Issue: 1 February 2019

The Expected e-

Mean (Std.

The actual e-

Mean (Std.

Government services

Deviation)

Government services

Deviation)

Expectation E12. I thing that the e-

3.49(0.711)

Gap

Actual A12. I think that the e-

Government website

Customs Department

provides prompt service.

website provides prompt

2.72(0.973)

-0.45

2.79(0.946)

-0.78

2.60(0.953)

-1.21

service. E13. I believe that e-

3.58(0.505)

A13. I believe that the e-

Government website must

Customs Department

be always ready to help

website must always be

companies.

ready to help companies.

E14. I think that e-

3.81(0.402)

A14. I think that e-Customs

Government website

Department website should

should not be too busy to

not delay in answering

answer requests from

requests from companies.

companies.

The following section presents the mean and the standard deviation of actual perception and expectations and the e-Service Gap regarding e-Service Quality Gap in Public Sector related to e-Government Services on Security and Confidentiality. From the results presented in table 6 it is manifest that the mean expectation scores are greater than the mean perception scores in relation to all four attributes on security and confidentiality, fact that once again confirms citizens’ dissatisfaction. Even so, in terms of magnitudes of the Gap scores, it was found that the Gap scores ranged from-1.32 to -1.03. It should be pointed out that attributes E15/A15, E16/A16, E17/A17 and E18/A18 have the quite high negative sign and thus were revealing of customers’ disappointment and dissatisfaction. It can be noted that the highest negative sign of the Gap, -1.32, is connected with attribute E17/A17, namely whether the e-customs department website shores their

Volume: 1 - Issue: 1 February 2019

personal information with other websites. Equally high was the negative Gap, -1.31, of attribute E18/A18 indicating that the protection of credit card information by the ecustoms department website is of a major importance. Table 6: Mean Scores and e-Service Quality of Actual Perceptions and Expectations and e-Service Gap on Security and Confidentiality. The Expected e-

Mean (Std.

The actual e-

Mean (Std.

Government services

Deviation)

Government services

Deviation)

Expectation E15. The e-Government

3.90(0.304)

Gap

Actual A15. The e-Customs

website must provide

Department website

security and protection.

provides security and

2.62(0.996)

-1.18

2.77(1.081)

-1.03

2.63(1.061)

-1.32

2.54(1.091)

-1.31

protection for users. E16. I want to be confident

3.80(0.397)

A16. I am confident of the

of the security of e-

security of the e-Customs

Government website.

Department website.

E17. The e-Government

3.95(0.226)

A17. The e-Customs

website does not share my

department website does not

personal information with

shore my personal

other websites.

information with other websites.

E18. The e-Government

3.85(0.579)

A18. The e-Customs

website will protect my

Department website is

credit card information.

protecting my credit card information.

The following section presents the mean and standard deviation of Actual perception and expectations and the e-Service Gap regarding e-Service Quality Gap in Public Sector related to e-Government Services on Personal handling and privacy. From the results presented in table 7 it can be observed without doubt that the mean expectation scores are greater than the mean perception scores with respect to all four

Volume: 1 - Issue: 1 February 2019

attributes on personal handling and privacy, fact that further verifies citizen dissatisfaction. It is ought to be mention that attribute E22/A22 has the highest negative sign and signifies the discord by citizens for e-Government website options’ provision for delivering services. Attribute E21, refers to whether the e-Government website will provide other eGovernment service options (e.g., payment methods) has the highest mean score in terms of expectation. Attribute E22 regards whether the e-Government website will provide options for delivering services scored the lowest mean in terms of expectation. Attribute A21, which refers to whether the e-Customs Department website provides other options of e-Governmental services (e.g. payment methods) has the highest mean score in terms of perception. Finally, attribute E22 which regards whether the eCustoms Department website provides options for delivering services in terms of perception. Table 7: Mean Scores and e-Service Quality of Actual Perceptions and Expectations and e-Service Gap on Personal handling and Privacy. The Expected e-

Mean (Std.

The actual e-Government

Mean (Std.

Government services

Deviation)

services

Deviation)

Expectation E19. I love the e-

3.74(0.718)

Actual A19. The e-Customs

Government website that

Department website

offers option to build a

provides options to build a

personal profile.

personal profile.

E20. The excellent e-

3.81(0.480)

Gap

A20. The e-Customs

Government website has

Department website has

links to other websites.

links to other websites.

That could be of interest

That could be of interest to

to companies (links with

companies (links with

similar companies and

similar companies and

other website branches of

other websites branches or other e-government sites)

2.47(0.993)

-1.27

2.67(1.087)

-1.14

Volume: 1 - Issue: 1 February 2019 other e-GOVERNMENT sites). E21. The e-government

3.85(0.406)

A21. The e-Customs

website will provide other

Department website

e-Government service

provides other options of e-

options (e.g., payment

Governmental services

methods).

(e.g. payment methods).

E22. The e-Government

3.50(1.065)

A22. The e-Customs

website will provide

Department website

options for delivering

provides options for

services.

delivering services.

2.75(1.125)

-1.10

2.27(0.991)

-1.33

Conclusions In conclusion, one could claim that the citizens are not satisfied with the quality of eService Quality Gap in public sector related to e-Government services. Above all, citizens are dissatisfied with respect to the possibility that the e-Customs Department web site can be down permanently and they point it out that e-Customs Department web site must be rarely down. In addition, citizens are dissatisfied with respect to the pages in e-Government website delay to emerge after the entry of a request for Information. It is worth observing that there was a negative Gap for all 22 attributes.

References Anastasiadou, S.D, Fotiadou, X.G, Anastasiadis, L. 2016. Estimation of Vocational Training School (IEK) students’ contentment in relation to quality of their studies. New Trends and Issues Proceedings on Humanities & Social Sciences, [On line].10, pp 0918.

Available

from:

https://sproc.org/ojs/index.php/pntsbs/article/view/1424

doi:https://doi.org/10.18844/gjhss.v2i6.1424

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Anastasiadou S., 2015. The Roadmaps of Total Quality Management in the Greek education system according to Deming, Juran, and Crosby in light of the EFQM model. Procedia

Economics

and

Finance,

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562-572.

doi:https://doi.org/10.1016/S2212-5671(15)01738-4 Anastasiadou S., Zirinolou, P., 2014. Reliability testing of EFQM scale: The case of Greek secondary teachers Procedia - Social and Behavioral Sciences Volume 143, pp. 990–994. doi:https://doi.org/10.1016/j.sbspro.2014.07.541 Anastasiadou, S. (2018a). Gap analysis between perceived and expected of service quality in Greek Tertiary Education. 10th annual International Conference on Education and

New

Learning

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Palma de Mallorca, Spain, pp. 8373-8382. doi:10.21125/edulearn.2018.1951. Anastasiadou S. (2018b). Leadership according to EFQM Model in Tertiary education: Τhe case of Greek Universities10 th International Conference EBEEC 2018 - The Economies of the Balkan and the Eastern European Countries in the changing world’, EBEEC 2018, Warsaw, Poland, pp. 20-24. Anastasiadou S. (2018c). Total quality management in Greek Tertiary Educational System: Τhe case of Greek Universities. 10 th International Conference EBEEC 2018 - The Economies of the Balkan and the Eastern European Countries in the changing world, Warsaw, Poland, pp. 59-64. Ateeq, M., Kamil, A. & Basri, S. (2010). A proposed instrument dimensions for measuring E-government service quality. International Journal of U- and E-Service, Service AND Techology, vol 4 (4), pp. 1-17. Bardi, M. &Alshare, K. (2008). A Path Analytic Model and Measurement of the Business Value of E-government: An International Perspective International Journal of Information Management, vol 28(6), pp. 524-535. Grönroos, C., 1982. An applied service marketing theory. European journal of marketing, 16(7), pp.30-41.

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Hoffman, K.D and Bateson, J.E.G. (2006). Services marketing: Concepts, strategies, and cases. 3rd Edn., Ohio: Thomson South-Western. Katler, P. and Armostrong, G. (2000). Marketing Principles. Adabestan Publication, Tehran, Iran. Kumar, M., Kee, F. & Manshour, A. (2009). Deterring the relative importance of critical factors in delivering service quality of banks: an application of dominance analytics in SERVQUAL Model. Managing Service Quality, Vol. 19, No. 2, pp.211-228. Melitski, J. (2003). Capacity and E-government Performance: An Analytics Based on Early Adopters of Internet Technologies in New Jercey. Public Performance and Management Review, vol 26 (4), pp.376-390. Mohammand, G. and Moghadam, N.S. (2016). The Reviews of Gap between Customers Expectations and Perceptions of Electronic Service Quality Saderat Bank in Zahedam. International Business Management 10(10), pp. 2017-2022. Moon, M. J. (2002). The evolution of E-Government among municipalities rhetoric or reality? Public Adm. Rev., 62 (4), pp. 424-433. Musaba, C, N., Musaba, E. C. and Hoabeb S.I.R. (2014). Employee perceptions of service quality in the Namibian hotel industry: A SERVQUAL approach. International journal of Asian Social Science, 4(4), ll. 533-543. Parasuraman, A., Zeithaml, V. A. and Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), pp. 41-50. Parasuraman, A., Berry, L. L. & Zeithaml, V. A. (1990). Guideliness for Conducting Service Quality Research. Marketing Research, pp. 34-44. Zeithaml, V. A. and Berry, L. L. (1988). SERVQUUAL: A multi-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), pp. 1240.

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Zeithaml, V.A., 1988. Consumer perceptions of price, quality, and value: a means-end model and synthesis of evidence. The Journal of marketing, pp.2-22. Zeithaml, V.A., Berry, L.L. and Parasuraman, A., 1996. The behavioral consequences of service quality. the Journal of Marketing, pp.31-46. Zeithaml, V.A., Parasuraman, A., Malhotra, A. 2000. A Conceptual Framework for Understanding e-Service Quality: Implications for Future Research and Managerial Practice, working paper, report No. 00-115. Marketing Science Institute, Cambridge. MA. Zeithaml, V.A., Parasuraman, A. & Berry, L.L (1990). Delivering Quality Service, The Free Press, New York, NY. Wesam Abdallat (2014). Evaluation of E-Government Services Quality: A Business Perspective. Thesis, Scholl of Business Administration, Brunel University, United Kingdom,

Volume: 1 - Issue: 1 February 2019

Voting Consuming Behaviour, Political communication campaigns and Ideological Clarity - a parallel review of academic/empirical evidence Harry Ph. Sophocleous 1 1

Neapolis University Paphos, 2 Danais Avenue, 8042 Paphos, Cyprus [email protected]

Abstract. This paper combines the notions of consuming behaviour and ideological clarity in relation to the political Marketing and more specifically the production and consumption of political campaigns and examines the empirical evidence concerning the proposed topic, by focusing on some basic conceptual and methodological issues, as they are arising from previous research. Accordingly, earlier research has shown that visible political attitudes approximate electoral choice (i.e., actual votes), demonstrating that voters are able to give explanation concerning voting decisions. Other studies, though, have indicated that the attitudes of which we may not be aware, such as our implicit (e.g., subconscious) preferences, determine voting choice. Additionally, previous research was dealing with the campaigns effects and made attempts in measuring the impact of society and media upon electoral campaigns. In a similar manner, earlier studies, gave some directions in the notions of political marketing and voting decision making process. Accordingly, the paper highlights the gap that is presented in the sufficient interlink of those concepts. In the same manner, the paper reviews the methodological impact and the research paradigm of earlier work, in order to identify any possible research gap and limitations and to facilitate the ground for further research. Keywords: Political Communication, Voting Behaviour, Pre Election Campaigns, Ideological clarity.

1

1

Introduction

This paper connects the ideas of political communication, voting behaviour and ideological clarity and attempts to identify their linking points and their relevance with the proposed study. Accordingly, after giving an insight view at the general concept and the essence of communication, the paper proceeds to a parallel analysis of the models and patterns of political communication, political campaigns (Cohen, 1963, McCombs & Shaw, 1972), voting/ electoral behaviour (Berelson, Lazarsfeld, and McPhee 1954; Katz 1987) and Ideological clarity(Lo, Proksch and Slapin 2014). In this manner, the paper attempts to outline the way in which the wider theoretical field evolved through the years, as well as to build the ground for further analysis and evaluation concerning the functional and academic interaction of those aspects, in relation to the scope of the proposed study.

2

The essence of Communication

The parallel consideration of production and consumption of political communication and in particular pre-election campaigns it may consist the most fundamental issue of the proposed study and it would be its basic differentiator from the existing research. Therefore, in order to examine the production and consumption of political communication and therefore to interlink the notions of voters’ perceptions and campaigns Agenda-Setting, priming and Framing (Scheufele, 2000), it is considered as essential to take an insight theoretical assessment on the essence of communication and clarifying its basic functions, attributes and complexities as the appear in the relevant theoretical context. Thus, communication can be simply described as the act of transferring information from one position to another. Although this is a uncomplicated definition, when we consider about how we may communicate, the subject turns out to be a lot more complex and complicated.

The study of communication phenomena since the mid-1930s has provided the following trends: The identification of communication research with the study of mass media Krone (2007). The identification of methods of communication research with those of

2

wider Social theory. The view that the bulk the main volume of communication research was a branch of American social theory and that the main pursuit of social research was the exploration of the processes through which the messages were influential to members of the public. This fact is important for the scope of the study and is reflected in the following sections of the chapter, especially if we consider the notion of the “Americanization of Political Communication (Negrine and Papathanassopoulos, 1996). “Americanization indicates that both the electoral campaigns and therefore the research of the electoral behaviour, all over the cavilled word is mainly based and is influenced primarily by the electoral action in America, and in particular in the US. The term “Americanization” originally emerged in the early 19th century and referred to ‘…the real or purported influence of one or more forms of Americanism on some social entity, material object or cultural practice’ (Van Elteren, 2006: 3). In the field of political communication, the term refers to the worldwide proliferation of American campaign techniques. It implies that the U.S. is leading trends in a direct way by exporting American style campaigning, through American consultants working abroad and through a global acceptance of the U.S. as the most vital role model of how to run campaigns (Scammell, 1998). For Swanson and Mancini (1994; 1996), the term is a good starting point for comparing campaign practices in different countries, and for Butler and Ranney (2005), it is a suitable description of campaign innovations that have emerged and are continuing to surface in many democracies around the world. However, the term has been challenged in academic writing. According to Swanson and Mancini (1994: 4) ‘The appropriateness of the term is contested, nevertheless, by some who argue surface similarities obscure important national adaptation and variations’. The Basic Components of the Communication Process There are distinct categories of communication and more than one may occur at any period. Moreover, thorough the years we came across various conceptual models used to explain the human communication process. Communication comprises of 8 major components (Shannon and Weaver, 1949), which are the objects of study of Communication Theory, and therefore are all linked with the central scope of the proposed study, which overall is concerning the processes of sending and receiving information

3

and perceptions. These are interdependent and are considered as basic elements of any communication process. They include Source, Sender Channel, Receiver, Destination, Message, Feedback, and Context.

2.1 Models of communication

Models of communication refer to the conceptual models used to describe the human communication process. The origin of the word ‘Model’ could be traced to the French word modèle; Italian modello, diminutive of modo, form, and Latin modus, measure, standard; Model refers to representation/replica of the original. A model is thus a schematic description of a system, theory, or phenomenon that accounts for its known or inferred properties and may be used for further study of its characteristics. Communication models seek to represent the structure and key elements of the communication process.

There are many models of communication developed by noted theorists of different disciplines. Since it would be impracticable to make a thorough reference to all those models, it has been considered and essential outline only a few of them in order to use them as the basis for the theoretical discussion upon political communication which is follows and is related to the scope of the proposed study. Among the theorists are: Lasswell (1948), Shannon and Weaver (1948), Gerbner (1956), are some of the renowned ones. Some important and well-known contributions are highlighted below Aristotle, a great philosopher was the first (300 B.C.) to develop a communication model called ‘Aristotle’s Model of Communication’. This model is more focused on public speaking than interpersonal communication. Aristotle Model is mainly focused on speaker and speech. It can be broadly divided into 5 primary elements: Speaker, Speech, Occasion, Audience and Effect. The Aristotle’s communication model (Figure 1.0) is a speaker cantered model as the speaker has the most important role in it and is the only one active. It is the speaker’s

4

role to deliver a speech to the audience. The role of the audience is passive, influenced by the speech. This makes the communication process one way, from speaker to receiver. The speaker must organize the speech beforehand, according to the target audience and situation (occasion). The speech must be prepared so that the audience be persuaded or influenced from the speech.

Figure 1.0

Aristotle has given 3 elements that must be present in a good communicator or public speaker. These elements are related to each other and they reinforce the other elements. Ethos is the characteristic which makes you credible in front of the audience. If there is no credibility, the audience will not believe in you and will not be persuaded by you. Pathos. If what you say matters to them and they can connect with it, then they will be more interested and they will think you are more credible. Emotional bonds will make the audience captivated and they feel the speaker is one of their own people. Logos is logic. People believe in you only if they understand what you are trying to say. People find logic in everything. If there is no logic behind the speaker’s work or time, they do not want to get involved. Nowadays, the Aristotelian model of communication is still broadly applied and acknowledged. In this model of communication, the sender sends the message to the 5

receiver in an attempt to influence them to respond accordingly. The message must be very impressive and convincing. Therefore, the sender must know and understand their audience well. In this model, the sender is an active participant and the receiver is passive. This concept is used in public speaking, seminars, and lectures. Lasswell (1948), a political scientist and communication theorist, was a member of the Chicago school of sociology. Lasswell’s (1948) work 'The Structure and Function of Communication in Society', defined communication process as Who (says) What (to) Whom (in) What Channel (with) What Effect. The distinct model he propounded was known as Dance Model. Shannon &Weaver (1949) and others, encouraged research on new models of communication from other scientific perspectives like Psychology and Sociology. Shannon and Weaver’s information theory had a notable influence on the development of communication theories and models. These first studies on communication's models promoted more research on the subject. Shannon's model of communication marks, in important ways, the beginning of the modern field. It provided, for the first time, a general model of the communication process that could be treated as the common ground of such diverse disciplines as journalism, rhetoric, linguistics, and speech and hearing sciences. Newcomb (1953) and Katz and Lazarsfeld (1955), are the other major contributors. Other models, including a helical-spiral model developed by Dance (1967). The basic premise of the transactional model of communication is that individuals are simultaneously engaging in the sending and receiving of messages. Communication is viewed as a conduit in which information travels from one to another and the information is separate from the communication. The evolution of communication theories and models leap from 1970 to 2003. The aforementioned evolution has been toward theories of communication that emphasize the active and powerful influence of receivers as well as senders, meanings as well as messages, and interpretations as well as intentions. The sender and message are among these factors, as are others, such as the channel, situation, relationship between sender and receiver, and culture. (Shannon and Weaver, 1949; Schramm, 1954; Katz and Lazarsfeld 1955; Westley and MacLean, 1957)

6

The two-step flow of communication hypothesis (Figure 2) was first introduced by Lazarsfeld, Berelson, and Gaudet (1944) in The People's Choice, a study focused on the process of decision-making during a Presidential election campaign. (Katz, Lazarsfeld, Pand Roper, E., 2017). These researchers expected to find empirical support for the direct influence of media messages on voting intentions, a fact that makes it directly relevant to the scope of the current study. They were surprised to discover, however, that informal, personal contacts were mentioned far more frequently than exposure to radio or newspaper as sources of influence on voting behaviour. Armed with this data, Katz and Lazarsfeld developed the two-step flow theory of mass communication. Figure 2

3.0

Political Communication

A basic and very commonly applied type of communication, as this is conceptualized by models such as those we have outlined in the previous section, is Political Communication. “…The study of political communication has come a long way. If we take Aristotle’s Rhetoric and Politics written in 350 B.C. as a starting point, political messages have been noted, considered and analysed about for well over 2,000 years. So where are we

7

now, in the 21st century of the Christian era, and where should we be heading?...” (Graber, 2005) The essence of politics is dialog and interaction. In this manner, political communication can be defined as the role of communication in the political process. It can take place in a variety of methods (formal or informal), in a variability of locations (public and private) and through a variety of medium (mediated or unmediated content). In other words, political communication involves the production and generation of messages by political actors, the transmission of political messages through direct and indirect channels, and the reception of political messages (Marland and Giasson, 2014). Political communication is a process that includes political institutions and actors, the news media and, importantly, citizens. Political Communication is an interdisciplinary filed and in contrast to mainstream political sociology, which lays its grounds basically on the grand sociological tradition of theorist such as Marx, Weber, Simmel, etc, goes further the grounds of political science and is extended in the fields of sociology, anthropology, psychology, public relations, economics and even more, linguistics and journalism (Bennet & Lyengar, 2010) Models of Political communication should consider the transformations of society and technology, as well as their behavioral impact. Therefore, as it will be seen in the following paragraphs, the theoretical outcome of the field involves a continuously evolved process. Every action of political communication shaped by parties, interest groups, or the media is communicated toward citizens, to inform them and to influence them. In this sense political communication can be defined as the interaction between these three groups that matter in political communication.

The approach in which theorists approached the area of political communication, indicates that the field is not something static but is something that evolves and develops according to the changing social trends and the continuously changing socio-economic and behavioral environment and the way that this is reflected on social structures (Bennett and Iyengar, 2010).More specifically, through the years, Scholars increasingly are sensing that reflective changes in both society and the media may lead to a new system of political communication that is qualitatively different from its predecessors (Norris, Curtice, Sanders, Scammell, & Semetko, 1999; Wyatt, 1998). 8

4.0

Voting /Electoral Behaviour

4.1 The Classic Voting Studies The first indication of studding voting behaviour, based on the perception that citizens had limited capacity to reason and decide indecently about politics and thus they shaped their views though their participation in groups. Moreover, as we will see in the following sections between the early 1940s and the late 1960s, four basic theoretical schools of voter behaviour have been proposed on which almost all studies of electoral behaviour draw (Campbell et al. 1954; 1960; 1966)

Columbia Studies The classic voting studies in sociology can also be outlined to earlier interdisciplinary influences. For example, Tarde’s (1903) theories of diffusion, imitation, and interpersonal influence clearly formed the study of Lazarsfeld et al. These early political communication theorists endorsed the concept that average citizens had little capacity to reason or decide independently about politics Moreover, the modern history of academic voting research started in 1940 at Columbia University, where a team of social scientists assembled by Paul Lazarsfeld pioneered the application of survey research to the study of electoral behaviour. Lazarsfeld and his colleagues surveyed 600 prospective voters in a single community (Erie County, Ohio) as many as seven times over the course of the 1940 presidential campaign, with a complex mixture of new and repeated questions in each successive interview, and with additional fresh cross-sections to serve as baselines for assessing the effects of repeated interviewing on the respondents in the main panel. The results of the 1940 Columbia study were published in The People’s Choice: How the Voter Makes Up His Mind in a Presidential Campaign (Lazarsfeld, Berelson, and Gaudet 1944). A second panel study conducted by the Columbia team in Elmira, New York, in 1948 provided

9

the basis for an even more influential book, Voting: A Study of Opinion Formation in a Presidential Campaign (Berelson, Lazarsfeld, and McPhee 1954).

As a result, they found themselves concluding (Berelson et al. 1954, 310-311) that the usual analogy between the voting “decision” and the carefully calculated decisions of consumers or businessmen or courts … may be quite incorrect. For many voters’ political preferences may better be considered analogous to cultural tastes in music, literature, recreational activities, dress, ethics, speech, social behaviour. Both have their origin in ethnic, sectional, class, and family traditions. Finally Lazarsfeld and his colleagues turned more detailed attention to the role of political issues, stressing the frequency with which respondents ignored or misperceived their favorite candidates’ issue stands 5 when these were in conflict with the respondents’ own views. The “Michigan Model” The study of Lazarsfeld and Columbia researches proved the potential of election surveys as data for understanding campaigns and elections. The following significant, movement in election studies came out in the following decade at the University of Michigan. Sarcastically, the Michigan team, in the vein of their counterparts at Columbia, did not originally set out to study voting behaviour. The Michigan data suggested that “many people know the existence of few if any of the key issues of policy,” and that “major shifts of electoral strength reflect the changing association of parties and candidates with general societal goals rather than the detail of legislative or administrative action” (Campbell et al. 1960, 170, 546). As the authors summarized their own argument (Campbell et al. 1960, 543)/ Moreover, Michigan's most important differentiation from the school of Columbia and Lazarsfeld is that it gives more weight to individual psychology and the structure of people's political perceptions, and less to social inclusion and social characteristics of voters. According to Michigan, it is right to note that social characteristics affect political preference, but it is not enough to see the relationship between these two parameters, but to find the way that it forms and reproduces it. The Michigan model thus introduces the notion of party identification as the basic element of the constitution of the cohesion of the social integration relationship - a political 10

preference. Party membership is the firm attachment of the voter to a political party, a commitment that includes acceptance of the party's ideology and values, political program, history of the charge, the persons (candidates, executives, leaders) who constitute it. Depending on the intensity of this identification, we can separate the voters into "absolutely identifiable" and "less identifiable / circumscribed").

4.2 The Macro-Sociological Model and the contemporary view In contrast to the Columbia and Michigan studies, the macro-sociological approach emphases its clarifications on processes at the level of the entire society. In Germany this approach was initially forwarded by Lepsius (1966) who was primarily occupied with “social-moral milieus”, a key characteristic of German society in the Imperial and Weimar periods. Internationally Lepsius (1966) had little impact, while even within the German literature his approach was soon displaced by a competing macro-sociological model that argued from the outset with abstract categories, was tailored to explain a larger area (Western Europe) and was easily portable to other contexts. Lipset and Rokkan (1967) getting the association between social structures and the party system is highly internally consistent and constitutes a powerful analytical frame, in that prior findings on voting behavior are easily integrated into a cleavage theory. An apparent lack in their model, though, is the failure to reflect the individual level and the role of communication. Lipset and Rokkan (1967) are not concerned on why individual voters usually behave empirically as elites expect them to. Social Choice Theory Another school of thought derived from social choice theory (Arrow, 1951; Cyert & March, 1963; Olson, 1971; Simon, 1955) helped develop a signalling approach to political communication (Lupia & McCubbins, 1998; Popkin, 1991). Olson’s The Logic of Collective Action has more recently been challenged as new technologies have

11

changed both the costs and the processes of political organization (Lupia & McCubbins, 1998). Arrow (1950; 1951) shaped the modern field of social choice theory, the study of how society should make group decisions based on individuals’ preferences. There had been scattered contributions to this field before Arrow (1950;1951), going back (at least) to Borda (1781). But earlier writers all focused on elections and voting, more specifically on the properties of voting rules. Arrow’s approach, by contrast, encompassed not only all possible voting rules (with some qualifications, discussed below) but also the issue of aggregating individuals’ preferences or welfares, more generally. Arrow’s first social choice paper was “A Difficulty in the Concept of Social Welfare” (Arrow 1950), which he then expanded into the celebrated monograph Social Choice and Individual Values (Arrow, 1951; Cyert & March, 1963, 1972; Simon, 1955).

Spatial Models, Retrospective Voting, and Rational Choice The growing interest in “issue voting” that was part of the broader wave of revisionism in the voting research of the late 1960s and 1970s also drew upon a quite distinct source of intellectual ferment—the emerging “rational choice” paradigm, which applied the hypothesis of utility maximization developed in economics to political decision-making. Rational choice theory likewise played a vital role in the incorporation of the empirical insights of Stokes (1963), Key (1966), Kramer (1971), and others regarding the electoral significance of “perceptions and appraisals of policy and performance” (Key 1966, 150) into the mainstream of voting research under the rubric 17 of “retrospective voting” (Fiorina 1981).

4.3

Ideological Clarity and Consumption of Campaigns

Another research issue that might be essential to review for assisting the scope of the specific study is that of ideological clarity and the way in which influences the consumption of electoral campaigns. According to Lo, Proksch and Slapin (2014) “…Parties in advanced democracies use ideological declarations in anticipating voting rivalry, nevertheless some parties are 12

able to communicate their spot more obviously than others…” According to their study, Parties may present voters with an obvious message, or they may suggest a program that holds a diversity of perspectives, maybe muddying its ideological content. This doubt may arise in a party’s program for a variety of motives. Contradictory ideological opinions might occur within the party leadership, who must then determine how best to accommodate differing opinions in the party program. Alternatively, the party may attempt to attract a wide array of voters by pitching different and possibly incompatible messages to different electoral audiences. Finally, new parties may need to study which messages reverberate best with their voters, and they may attempt different pitches before settling on a message that works. In spite of the principal cause, parties face choices over how to stand for their programs to the public when multiple viewpoints exist. Much recent work has used election campaign documents written by parties at the start of an election campaign state a core platform to approximately calculate party positions. However, a small number of studies clearly acknowledge that parties must combine an assortment of strategy proposals into a particular manuscript.

Moreover, Lo, Proksch and Slapin (2014) hypothesize that changes in ideological clarity may adjust how position shifts affect party vote split. In particular, parties are likely to find unclear positions more gainful as they moderate their ideological stance; they can reach out to a larger segment of the electorate at the centre of the political space. Equally, they conclude that parties moving to the extremes may win more votes as they stake out clearer positions. At the boundaries/poles, there are less additional voters for parties to pick up through broadening their ideological appeal. Rather, the relatively few extreme voters may view ideological uncertainty as a sign of weakness, or insufficient commitment to their cause.

5.0

Suggestions and Recommendations

As we have seen through the various sections of the paper, previous research occurred and has shape a considerable level of understanding, the specific research ground; however, it remains unfortunate that our research questions have been more often dictated 13

by data rather than theoretical expectations. A clearer understanding of campaign effects, not only needs more coherent data, it also demands a theoretical acknowledgment that campaign dynamics mirror an interaction linking voters and candidates. Be aware of this association and taking into consideration the concern, capacity, and incentive of the related actors in a political campaign must assist us in developing broader theoretical expectations about when, why, how, and for whom campaigns matter (Hillygus, 2010).

Moreover, the review of previous research work, led us on the conclusion that the specific research question and the specific research focus that is attempted by the current paper remains unanswered, since its various components might be examined individually, but the mutual spot that is linking these ideas, is misplaced. In this manner, our further research attempts to fill the noticed research gap, by obtaining a more transparent illustration regarding the decision‐making process of both candidates and voters in a political campaign and to evaluate the factors influencing the balance between the production and consumption of political communication.

Accordingly, our projected research aims to further knowledge in the areas discussed above in order to connect the concepts and the theoretical background of voting behaviour and political propositions, with the notions and the theoretical context of political marketing and consuming behaviour and thus to examine voter under the prospect of the potential customer of the so-called political market as this is shaped in the specific context of Cyprus and far away from the so called “Americanization” which dominates the evolution and the study of the wider discipline.

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Volume: 1 - Issue: 1 February 2019

New generation of consumers in the tourism industry: Secondary Research Ioannis Komodromos Neapolis University Paphos, 2 Danais Avenue, 8042, Paphos, Cyprus [email protected]

Abstract: Marketing communication has taken a new approach towards the consumers due to the vast transition and interaction of technology in our daily lives. After introducing the relevant factors stating the importance of mobile friendly website and app, the study will evaluate how it applies to the new generation of consumersthe millennials. This is key for businesses in capturing younger consumer base as well as understanding how technological trends are affecting the future of consumer’s behaviour. By understanding the new generation of consumer’s behaviour and their interaction with technology, an organisation can target a larger group of consumers and gain their loyalty. If a company wants to remain relevant in the minds of millennials, they must integrate themselves into the digital world, whether by creating an app, social media page or a mobile friendly website. Moreover, millennia’s interaction with social media platforms and technological innovations affect the tourism market and tourist trends across the globe. It has been proven that consumers involve social media platforms at all 3 stages of their journey (journey preparation, during the journey, after the journey). This article aims to highlight the most common communication channels of the millennial generation and the level of influence that each channel has during the decision making process.

[1]

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Key-Words: Millennial, Gen-Z, Young Travellers, Digital Marketing, Communication Channels, Tourism Marketing, Destination Marketing, Hospitality Marketing.

1.0 Introduction In recent years marketing communication has taken a new approach towards consumer due to rapid technological interaction in our daily lives. New technologies have managed to simplify our daily life’s and evolve the way of doing business by introducing alternative and more time effective solutions, either by using the Internet as source of data collection and clarification or either by the creation of new applications for a mobile device. Thus, marketing communication had to evolve and adjust to new technologies since it is the main source of contacting the appropriate group of consumers. After introducing relevant factors stating the importance of mobile friendly website and applications, the article will evaluate how it applies to the ‘’new generation of consumers’’. The ‘’new generation of consumers’’ is a combination of millennials and Generation-Z consumers that will be introduced later on at Chapter 2. New generation of consumers are interacting with their mobile devices daily and use the Internet for a sufficient amount of time, thus communication channels of corporations must adjust their strategy and content in order to get their attention. This is key in capturing younger consumer base as well as understanding how technological trends are affecting the future of consumer’s behaviour. Moving forward, the article will introduce how the tourism industry is being affected by the new generations of consumers and how the new technological trends have benefited and simplified the industry. Moreover, a discussion will be made on how technology and social media are being used during the 3 phases of a journey (journey preparation, during the journey, after the journey). By the end of this article,

[2]

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the most common social media platforms will be introduced and how they affect the decision making process of young consumers. Overall, the article seems to indicate that if businesses want to succeed in the new trends of the market and approach greater number of consumers they must digitalise their presence and start targeting younger generations. By understanding the new generation of consumer’s behaviour and their interaction with technology, an organisation can target a larger group of consumers and gain their loyalty. If a company wants to remain relevant in the minds of millennials, they must integrate themselves into the digital world, whether by creating an app, social media page or a mobile friendly website. If an organisation wants to be a pioneer in the industry, it must master the above technological tools that will provide a competitive advantage over their competitors.

2.0 The influence of new technologies on Consumer Behaviour In recent years there have been discussions regarding crisis within the advertising sector due to upcoming new technological trends in the marketing communication. The constant use of the internet has introduced new means of communication with the consumers such as email, mobile friendly-websites, social media and applications (apps). This is the result of gradual generation change respectively emerging from the millennial demographic. Organisations that want to reach the ‘’new generation of consumers’’ will have to find other than traditional ways of advertising and promotion. As seen at Table 1, previews generations (baby-boomers, Gen-X) have different communication channels and media than the millennials and Gen-Z. Millennials are one of the most populated generations on the planet, and there are surrounded by digital influences daily, which is characterised by information technology. They are currently surrounded by the latest technologies and intentions, they carry out their work at any place by using the Internet and they have no limit of workplace. This generation is employing social media [3]

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information and communication technologies to a higher degree that other generations (Kavoura, 2015). Therefore, marketing and human resource management are looking for new ways of how to attract the ‘’new generation of consumers’’, the millennials(this also applies for the Gen-Z)

Table 1: Characteristics of Generations With the extreme acceptability of online information the first place that will likely be checked for product information is the internet. It is often said that millennials are the most connected generation and that the internet is the most convenient consumer tool available and will be the first point of contact when searching for a product. As stated by a research conducted by G/O Digital, 62% of the market makes an online research for any type of product before purchasing and 58% of consumers who want to buy or book a product, visits in advance the company’s online profile and finally [4]

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80% of users consult the reviews of other customers (Evisiontourism, 2014). This study has pointed out that the consumers before engaging into any product purchase they conduct an online research and check out the product’s online presence along with the reviews of other consumers. In a few words, consumer’s decision making process can be influenced by the online presence of the product. Another recent study conducted by Eurostat (2018) has shown that 67% of the participants who use the internet are logged in their Social Media account at least once o day, while 50% of individuals aged 16-74 use the internet to participate in social networks. A person’s identity is comprise of daily posts, shared photos, profile updates and post comments. In order to preserve personal relationships and be socially active as a person, a social media account is by far a necessity (Tsay-Vogel, 2016). According to the study’s findings, consumer’s use of internet mostly accounts for social media platforms thus, could be used by companies as an interactive communication channel to promote their product and communicate with their customers. Traditionally television once had the largest share in paid advertising and communication, but in 2017 digital advertising spending has surpassed it (Woods, 2017). Traditional word of mouth (WOM) has turned into E-WOM and influencer marketing has emerged. It is a form of advertising that focuses on specific individuals with a large number of followers. It is ‘’the art and science of engaging people who are influential online to share brand messaging with their audiences in the form of sponsored content’’ (Sammis, et. Al, 2016). Individuals who may act as social media influences are trusted based on their reputation, predictability and competence (Christou, 2015). Social media is a significant tool that influences trust to the young consumers and is directly linked to viral marketing. Viral marketing is the process of individuals marketing to each other (Subramani and Rajagopalan, 2003). Social media provide the tool to anyone that builds an audience to become an influencer and the marketing industry needed to cope with the growth of social media users and especially the generation with the largest purchasing power, the millennials. [5]

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The increasing share of the online environment in the consumption behaviour is recorded as a trend among the world population. A study conducted in 2015 by the National Authority for Management and Regulation in Communications shows that 75% of people visit daily the virtual environment via mobile internet and 15% use it 23 times a week (ANCOM, 2016). Another interesting fact is the frequency of mobile Internet daily use were in urban areas is slightly higher (80%) than in rural ones (72%). In addition, EY Romania (2015) has published a study conducted in the summer of that year, on a sample of 1,040 respondents, regarding the use of mobile devices in Romania (Lungu, 2016). The most important findings refer to the fact that the majority of respondents own a smartphone (87%) and check approximately 11 - 50 times a day their mobile devices (52%). The large number of used applications is explained by various categories: 66% of respondents use apps in the business category, 60% in the Photo & Video category, 46% in the category of music and entertainment; the lowest values of used applications are registered for the categories lifestyle (28%), health & fitness (25%) and sports (17 %). Another study also conducted in Romania (2016) shows the average daily usage among millennials in the country: 13% spend less than an hour, 44% spends at least 1-3 hours daily on social media, 26% 3-5 hours and 17% more than 5 hours. As the results have shown, millennials are spending a significant amount of time on social media in a daily basis, which can be translate that a direct channel of communicating with them is through the online means. Therefore, many companies have taken steps in creating an online presence. This could aid their brand’s awareness to the consumers and this could influence consumer’s purchasing decision making. Online sales in recent years are growing in developing countries and will likely continue to be a key factor in years to come (Barik, Pandey and Soni, 2015). Companies not only boost their online revenue by having better websites, but also enjoy a better overall return by enhancing their brand as a whole. It is shown that online shopping can help build a sustainable competitive [6]

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advantage in brand loyalty. Consumers often build a relationship with a company by the reduced costs that online shopping allows, for example superior goods & services and brand equity that is supported by awareness and perceived quality (Bilgihan and Bujsic, 2015). In general, this highlights why developing an online channel is important in order to gain e-loyalty. E-loyalty is the online ability of a company to earn a loyal customer over the web. As the demand of online businesses and mobile phones continue, so will the importance of e-loyalty. Factors that benefit the process of obtaining e-loyalty include the ease of ordering, an on time delivery system, accessible product information and selection, customer confidence, well maintained privacy policies and value for money in terms of the quality (Forman, Lester and Loyd, 2005). Websites are found to be effective when they are user friendly and have both hedonic and utilitarian benefits. In a more practical way of viewing the utilisation benefits, web designers should keep in mind the use of user-friendly tools that are functional and flow easily in order to create the most effective website possible. For example if a user is looking for specific information and it is easily available the utility of the website is increased, creating a better customer experience (Bilgihan and Bujisic, 2015). On the other hand, hedonic features are the ones that trigger pleasant sensations and feelings to a consumer and are focusing on the fun or light-hearted aspects of an application or an information system. This means that companies that take the time to make their online presence both aesthetically and functionally appealing, will most likely see increased usage which could lead to increased sales (Bilgihan and Bujisic, 2015). By creating an interactive web page and forcing the consumer to engage and interact, it creates a more value adding experience for the customer, which ensures that they will return and continue returning, which in the long term will translate as e-loyalty (Forman, Lester and Loyd, 2005).

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Moreover, the image of an app and its design can significantly influence the stickiness and its use. Stickiness is defined as the duration of time that a consumers spends on an app or website. If the design of an app is well structured it can even improve brand image and consumer attitude towards it. In order to build trust with the consumers a website must fulfil the informational need and update its content which could result in the increase of repeated level of use (Kim and Wang, 2015). Taking into consideration the above statements, in order for an organisation to have a solid online presence it must create user friendly and efficient website. The online presence could also be enhanced by the creation of an app, or through a cost effective solution- a mobile friendly website. For the new generation of consumers this channel of communication would be a more convenient option relatively to standard ones (Gilles, 2015). The introduction of new technologies and secure mobile devices in the market, allows the consumers to use their phones as portable shopping and booking engines. According to a study, market penetration has reached 72% in the U.S as of August 2014 and has only grown since then, making its importance in the private sector worldwide (Kim, Malthouse and Wang, 2015). Another important finding of the study states that 50% of time spent on digital media is on mobile friendly websites and apps, suggesting that the use of this mean of communication has penetrated into the daily lives of smartphone users. After introducing the relevant factors stating the importance of mobile friendly website and app, it is necessary to evaluate how it applies to the new generation of consumers, the millennials. This is key in capturing younger consumer base as well as understanding how technological trends are affecting the future of consumer’s behaviour.

3.0 The ‘’new generation of consumers’’ Each generation has its own values, expectations, skills and interests. Generation Y-demographics include people whose birth years are between 1982[8]

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2002. Generation Y could also be translated into Millennials (1982- 1999) and Generation Z (1999-2010). Millennials are the central part of Generation Y and Generation Z are the young consumers that grew up during the technological evolution period (Pendergast, 2010). One of the most common characteristic among millennials is reliance on cell phones. Not only do millennials use smartphones for standard uses such as calling and texting, but they also use them for social media, games, music, alarm clocks, pictures, email, and so much more. Millennials are a generation that came to life amid the internet boom, almost as if technology and millennials grew and evolved together. This has formed a strong relationship and, today, consumers’ cell phones act as phones, computers, gaming devices, televisions, and shopping tool. Phones are considered life lines, and when people are without them, a state of panic arises (Camarda, 2016). This is important for businesses since the millennia’s decisions regarding a product is influenced by the social media and digital appearance of a company. The millennials are the age group with the largest purchasing power and the most active group regarding social media interaction. Bennett (2014) claims that 74% of consumers make their purchasing decisions based on social media. It is the age group where connecting to others is highly important. A study by the Boston Consulting Group (2012) identified several inherent characteristics of Millennials – they are consumers who have confidence in their power to influence, they are natively digital in terms of how to use technology in a multitasking way, they show a strong personal interconnectivity and they share travel experiences (Benckendorff, Moscardo and Pendergast, 2010). Millennials are considered to be a progress factor, as they generate new ideas, and their creativity ensures sustainability and generally revives the economy (Okere, 2016). They represent an active audience that wants to collect experiences. Subsequently, they share the experiences with family and friends, online, and such experiences are also an inspiration for other people from their generation. They prefer active involvement in marketing campaigns and are willing to provide personal data only to brands that prove that they can provide [9]

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tangible benefits in exchange for this information: special offers, discount coupons corresponding in shopping preferences etc. (Buru, 2015). They also prefer to first consult with their friends, family or any other online communities they belong to. For them, shopping has become a social experience, as confirmed by numerous fashion blogs and the increased popularity of the app Pinterest. Young people represent a well-informed audience, endowed with critical sense, and knowing the rights they have as consumers (Nistoreanu, 2004). Moreover, millennials care less about specific brands that are available for purchase. This means that this kind of consumers prefer to place a greater emphasis on the value for money proposition (JD Power, 2016). Many believe that shopping for value means shopping for the cheapest option, when in reality this actually means they are more willing to pay any price asked, either high or low, if they believe that they are getting a worthwhile value. This gives the opportunity to less known brands to put themselves on the same level as well-known ones if they focus on their communication and the quality of the product in regards for the price asked (value for money). So far, studies have shown that we are witnessing a series of mutations in terms of consumption behaviour of young people. There is a shift regarding online privacy, users focus more on the benefits obtained from the online exchange of personal information than the possible risks. Also, they prefer social announcements that replace those that have become classics, like banners, due to the fact that the smaller size is better suited for smartphones (Fromm, 2014). In addition, millennials are also known as the ‘’opt-in’’ generation, meaning information comes directly to them rather than having to search for it themselves and if any research is needed the solution is already in their hands (Mobile device) (Camandra, 2016). For example well-known airplane companies such as EasyJet and Rynair, have created an app that not only allows to the user to book a flight or have a digital copy of their boarding pass, but also notifies them if any delays have occur.

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Another example is the case of booking engines such as Expedia, AirBnB that allow to their user to book an accommodation all over the world in a matter of minutes. This simple examples introduce information difference between the previews generations of consumers and the current ones. The ‘’new generation of consumers’’ have all the information needed at their fingertip at all times and have forced companies to start thinking about their next move in the digital era and introduces new ways of doing business (Migliaccio, 2017). An app or a mobile friendly-website is considered very useful to this generation, since they prefer to gets things done quickly and efficiently (Trop, 2015). As mentioned above, some companies have already created relevant apps that suit their businesses and are not only targeting younger generations but also making life easier for the oldones. This kind of customer targeting can also be viewed as a new way of attracting new customers, which could have as a result the increase of sales as well as building brand loyalty. In regards to customer loyalty, millennials are more likely to choose a product that is already integrated into their life; such as an app that could be downloaded at any time at their phone, or a mobile friendly website. If a young consumer has remote access to a company’s product from his/her phone it is more likely to engage to a purchase since efficiency is priority for them (Trop, 2015). Overall, the above chapter seems to indicate that if businesses want to succeed in the new trends of the market and approach greater number of consumers they must digitalise their presence and start targeting younger generations. By understanding the new generation of consumer’s behaviour and their interaction with technology, an organisation can target a larger group of consumers and gain their loyalty. If a company wants to remain relevant in the minds of millennials, they must integrate themselves into the digital world, whether by creating an app, social media page or a mobile friendly website. If an organisation wants to be a leader in in its industry, it must

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master the above technological tools that will provide a competitive advantage over their opponents.

3.1 Young Travellers Throughout the years, youth tourism has taken an upward trend with strong dynamics as a result of mutations in supply diversification and the increase in people mobility. In recent international studies (2012) it has been stated that young people travel in order to experience a different culture, learn a new language, volunteer, find a job familiarise themselves with new lifestyles and meet new people (YouthTourism, 2012) (Khoshpakyants and Vidischcheva, 2012). They constitute the new visitors in the tourism market (Pendergast, 2010). According to Iakovidou et al., (2005) the majority of young travellers comprises of young people (19-35 years old - millennials), highly educated who usually choose rural destination depending on the natural resources that the destination has to offer. Furthermore, at a study that the World Youth Student and Educational Travel Confederation (WYSETC) conducted in 2016, t was stated that youth tourism includes individual that travel for periods of less than a year, motivated by the desire to experience other cultures, to gain experience and to benefit from opportunities for formal and informal learning in a different environment than the usual one. At the same study it was estimated that the current size of the global tourism market for young people, which is represented by people between 19 and 35 years old, covers approximately 23% of the total number of arrivals and international tourists This percentage by 2020 will be equivalent to 300 million young travellers(WYSETC, 2015).. The most popular tourism forms fall mainly within the category of trips for knowledge, which bear the imprint of social and cultural particularities of demand of the emitting countries (Angel, 2015) and the average length of stay records higher values than in the case of traditional forms of tourism. Meanwhile, youth tourism [12]

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generates advances in technology, by encouraging innovation, given their consumer preferences and habits - online reservations, interaction with fellow travellers through social networks, use of mobile devices and applications for planning and conducting a journey etc. As millennials are the digital natives (Prensky, 2012), Internet - based booking platforms became a necessity for the survival of tourism businesses. The tourism industry could not afford to ignore this marketing trend bringing fundamental changes in tourism marketing (Gossling & Lane, 2014). Combining this necessity with Internet based booking platforms and social media, the owners of tourist properties have now the opportunity of an e-adoption ladder (Martin, 2004) including email campaigns, social media marketing (Facebook, Instagram, etc) and other form of digital marketing. Tourism consumption behaviour of young people has influenced decisively on a number of issues regarding the tourism products which are specifically designed by hotel chains and hostel accommodation (Evisiontourism, 2014). Also, youth preferences regarding the use of gadgets put their mark on the marketing policies of companies in the hospitality industry as well as some service sectors (e.g. air transport). Technology experts- the Millennials- can easily use online travel aggregators such as Expedia, AirBnB or booking.com to book a trip for leisure, but for choosing the location of their visit they prefer a travel agent, given the lack of experience in knowing tourist destinations. In 2014, 28% of Millennials (16-35 years) used agents, compared to 13% of baby boomers (50-65 years) and 15% of Generation X (36-49 years) (Evisiontourism, 2016). Moreover, as mentioned above the share of young travellers has increased in recent years and has been diversified due to the growing number of young people in emerging economies. In these circumstances, the recorded dynamics contributes to a sustainable price control of travel destinations which are less popular, preferred by young travellers, which justifies researching travel preferences in order to meet the growing requirements. Young people contribute to the development of specific [13]

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infrastructure consisting of hostels, restaurants, leisure centres, etc., and therefore their contribution to economies and communities is significant (Moisă, 2016). The Millennials have a definite impact on the economy in a period of globalization which is influenced by a variety of technological changes and economic difficulties (Goldman Sachs, 2016)

4.0 Importance of technology in consumer’s decision-making process Moving forward in identifying the technological involvement in young traveller’s decision making process, a study was conducted in Romania (2016) with 387 participants (Șchiopu, Pădurean, Țală & Nica, 2016). Findings of the study have introduced the involvement of online sources by millennials throughout the 3 phases of the journey (journey planning, reservation, actual journey), the importance of online sources during travel decision making and the online sharing platforms during the trip. Firstly, at Graph 1 it can be seen illustrated the involvement of social media platforms throughout the 3 phases of the journey (travel planning, reservation, actual travel). Consumers while planning their journey, use in a higher degree Twitter, Facebook and the Hotel’s Website. While on reserving they use booking.com and the Hotel’s Website. During the actual travel, the respondents post content to social media applications like Instagram and Facebook. This findings provide important information regarding the involvement of technology throughout the three stages of a journey and may also be considered vital tool in the future of travel planning. This must

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Travel Agencies WebSite Hotel WebSite

Trip Advisor

Booking

Instagram

Twitter

Facebook 0

20

40 Travel Planning

60 Reservation

80 100 Actual Travel

120

140

Graph1: Online sources used for travel and online planning Source: Șchiopu, A.F., Pădurean, A.M, Țală, M.L. and Nica, A.-M., (2016) Secondly, a part of the same study, investigates the importance given by young people to information obtained online through photos, comments, videos and ratings. Illustrated bellow at Graph 2, it can be viewed the importance granted to several online sources in travel decision making of young adults. It is noteworthy that these information play significant role in decision making process within the digital tourism. More than 40% of respondents consider photos, comments and ratings important and more than 35% very important during the purchasing process. Videos also wright in this decision with 45% of participants believing is an important factor when it comes to decision making. As discussed at previews chapters, it is proven that visual display is [15]

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an important influencing characteristic amongst the millennial generation, as well as, E-WOM which has also been proven to be equally important and influencing for young travellers. As it can be seen from the study’s findings the consumers before engaging into any purchasing decisions are consulting online sources, which should be taken into consideration by companies and make sure to create or frequently update their online presence.

Graph 2: Importance of online sources for travel decision-making of millennials Source: Șchiopu, A.F., Pădurean, A.M, Țală, M.L. and Nica, A.-M., (2016) Lastly, another finding of the study conducted by the Bucharest University of Economic Studies, is the online sharing of travel information by millennials. Travel experience sharing through photos, is mostly used by millennials with 85% of the [16]

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participants posting one at least time-time while on a journey. Particularly, 21% sometimes post photos, 30% almost every time and 34% every time. Another important part of the graph are the comment ratings, where more than half of the respondents share this type of information several times. Videos and ratings are preferred by a lower number of young travellers. As in previews chapters, E-WOM adds significantly during the process of a journey and people are interacting with this kind of information in an often basis, as well as, consumers trust the visual display posted through social media and are being influenced by them. Overall, this chapter has highlighted the importance of online sources during all 3 phases of the journey, including the decision-making process, which can be influenced by E-WOM, photos, videos, social media and the online presence of a company or a product.

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35%

30%

25%

20%

15%

10%

5%

0% Every Time

Almost Every Time Photos

Sometimes Comments

Rarely Videos

Almost Never

Never

Ratings

Graph 3: Online sharing of travel information by millennials from Romania Source: Șchiopu, A.F., Pădurean, A.M, Țală, M.L. and Nica, A.-M., (2016) Overall, after reviewing the above data it can be acknowledged that youth tourism market segment is growing and it can be said that is a trend accompanied by a series of changes when it comes to communication channels and decision-making process. The conceptual definitions aim towards delimiting the age range of Millennials/Gen-Z and their characteristics but also the changes in consumer [18]

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behaviour and trends that are currently emerging. In this regard, a particular importance is attributed to decisive factors in choosing a tourism product or service – the price and the possibility to book online and via mobile devices. Additionally, the collection of information required from the ‘’new generation of consumers’’ for selecting a holiday-destination contains several websites and social media applications. As seen at Graph 1 websites and applications are a big part of all 3 phases of the journey. Not least another widely known method used to collect and influence the consumers are the shared travel experiences by the use of photos, comments, videos and ratings. As it can be seen at Graph 2, the online content found can influence the decision making process of possible customers. Last but not least, as it can be seen in Graph 3 online sharing of travel experience through photos, videos and ratings is widely spread. A valid information will create realistic expectations, while incorrect information will cause negative reactions. In a sensory industry such as tourism, providers must be very careful. Dissatisfied tourists can attract negative image through postings, pictures, videos or bad reviews and rating. It is difficult to satisfy various requirements and demands of consumers. But this is the current customer profile and therefore, a reality.

5.0 Discussion New technologies have managed to simplify our daily life’s and evolve the way of doing business by introducing alternative and more time effective solutions, either by using the Internet as source of data collection and clarification or either by the creation of new applications for a mobile device. As it was presented at the above article, 67% of individuals who use the internet are logged in Social Media account at least once a day and a person’s identity is comprise of daily posts, photos, profile updates and comments (Eurostat, 2018). Traditional ways of advertising have been substituted with digital advertising, word of mouth (WOM) has turned into E-WOM and influencer marketing has emerged (Woods, 2017). Social media provide the tool to [19]

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anyone to become an influencer and the marketing industry needed to adjust to the social media growth. Thus, marketing communication had to evolve and adjust to new technologies since it has become the main source of contacting the appropriate target group of consumers. Secondly, the extreme acceptability of online information from the younger generations has had as a result internet being the first place that will be checked for any product information. As seen in a study at Chapter 2.0, 62% of the market makes an online research and 80% of them before engaging to a purchase consult the comments and reviews of others (Evisiontourism, 2014). The increasing share of the online environment is recorded as a trend among the global population, and companies need to adjust their methods of communication in order to reach the customers. Therefore, many companies have taken steps in creating an online presence. This strategic move will aid their brand’s awareness to the consumers and could even influence their decision making process. It is stated that online shopping can help build sustainable competitive advantage in brand loyalty. In general this highlights why developing an online channel is important in order to gain e-loyalty and have a competitive advantage over a competitor (Forman, Lester and Loyd, 2005). In order to build trust with the consumer a website must fulfil the informational need and update the content which could result in an increase of repeated level of use (Kim and Wang 2015).The image and design of an app or a website can significantly influence the stickiness and its use. If the design is well structured it can even improve brand image and the consumer’s attitude towards it, that’s why before developing any of the above must have both hedonic and utilitarian benefits

(Bilgihan

and

Bujsic, 2015). After introducing relevant factors stating the importance of mobile friendly website and applications, the article has evaluated how it applies to the ‘’new

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generation of consumers’’- Millennials and Gen Z. Young people interact daily with their mobile devices and use the Internet for a sufficient amount of time, thus communication channels of corporations must adjust their strategy and content in order to get their attention. The millennials are the age group with the largest purchasing power and the most active group regarding social media interaction. Bennett (2014) claims that 74% of consumers make their purchasing decisions based on social media. It is the age group where connecting to others is highly important. Millennials are consumers who have confidence in their power to influence, they are natively digital in terms of how to use technology in a multitasking way, they show a strong personal interconnectivity and they share travel experiences (Benckendorff, Moscardo and Pendergast, 2010). They represent an active audience that wants to collect experiences. Subsequently, they share the experiences with family and friends, online, and such experiences are also an inspiration for other people from their generation. They prefer active involvement in marketing campaigns and are willing to provide personal data only to brands that prove that they can provide tangible benefits in exchange for this information: special offers, discount coupons corresponding in shopping preferences etc. (Buru, 2015). The ‘’new generation of consumers’’ have all the information needed at their fingertip at all times and have forced companies to start thinking about their next move in the digital era and introduces new ways of doing business (Migliaccio, 2017). An app or a mobile friendly-website is considered very useful to this generation, since they prefer to get things done quickly and efficiently (Trop, 2015). This is key in capturing younger consumer base as well as understanding how technological trends are affecting the future of consumer’s behaviour. Then ‘’new generation of consumers’’ is certainly an important niche whose consumption habits should not be ignored. This scientific approach enables the development of suggestions for certain stakeholders in the tourism market such as travel agencies and tourism service providers. Tourism is highly mobile, influenced by [21]

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a multitude of extremely varied factors: technology, fashion, terrorist attacks, income levels etc. Consequently, research related to segments of demand is extremely useful for market factors. In this case, the Millennials generation consumers have provided relevant information, which can be integrated in the supply of service providers and of packaged travel. In a future, research, amongst several universities across Europe in order to examine how young travelers use technology at all 3 phases of a journey (travel planning, reservation, actual travel). The goal would be to detect the most common social media platform used by the young travelers in Europe and the most influencing mean when it comes to decision making (photo, video, and comments). This kind of information could be used by tourist operators in order to create a more effective marketing champagne in terms of communication channels and content. Moreover, this research could examine in detail if young travelers prefer to book their holidays through an app or a mobile friendly website or either choose to contact a travel agent and book a package with them. This could give a better understanding of young travelers purchasing tools and preferences. Overall, the article seems to indicate that if businesses want to succeed in the new trends of the market and approach greater number of consumers they must digitalise their presence and start targeting younger generations. By understanding the new generation of consumer’s behaviour and their interaction with technology, an organisation can target a larger group of consumers and gain their loyalty. If a company wants to remain relevant in the millennia’s minds, they must integrate themselves into the digital world, whether by creating an app, social media page or a mobile friendly website. If an organisation wants to be a pioneer in the industry, it must master the above technological tools that will provide a competitive advantage over their competitors. The digitalisation of tourism will enable organisations to create and use new communication channels (Social Media, Websites, etc.) making it easier for consumers to find what they are looking for and engaging into a purchase. [22]

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6.0 References ANCOM, (2016). Marketstudy. [online] Available at [Accessed 30 November 2018]. Angel, J.C. (2015). The International Law of Youth Rights. Second Revised Edition. [ebook] Boston: Law Books. Available at < http://www.brill.com/internationallawyouth- rights> [Accessed 30 November 2018]. Barik, P., Pandey, B., & Soni, V. (2015). Online Shopping Catching Up with the Trend. CLEAR - International Journal of Research in Commerce & Management. 6(4). 53-57.

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Benckendorff, P., Moscardo, G. & Pendergast, D. (2010). Tourism and Generation Y. [ebook] Boston: CAB International. Available at [Accessed 30 November 2018] Bennett, S. (2014). Social media business statistics, facts, figures & trends 2014. Social Times. Available at http://www.adweek.com/socialtimes/social-businesstrends (Accessed 30 November 2018). Bilgihan, A., & Bujisic, M. (2015). The Effect of Website Features in Online Relationship Marketing: A Case of Online Hotel Booking. Electronic Commerce Research And Applications. 14(Special section on e-selling and online engagement). 222-232. Buru, L. (2015). Millennials – cine sunt și cum îi abordăm în campaniile noastre? [online] Available at [Accessed 30 November 2018]. Camarda, S. (2016). They Can Hear You Now. Successful Meetings Magazine – a Northstar Meetings Group Publication providing strategies and solutions to increase workplace productivity. 65(8). Christou, E. (2015). Branding social media in the travel industry. Social and Behavioral Sciences, Vol. 175, pp 607-614. Eurostat (2018). Individuals who used the internet, frequency of use and activities Available at appso.eurostat.ec.europa.eu/nui/show.do?dataset=isoc_r_iuse _i&lang=en [Accessed 30 November 2018] Evisiontourism, (2014). Millennials ‒ targetul la modă în turism. [online] Available at [Accessed 30 November 2018]. Evisiontourism, (2015). Consumatorii millennials impulsionează reîntoarcerea la agenția tradițională. [Online] Available at
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millennialsimpulsioneaza- reintoarcerea-la-agentia-traditionala> [Accessed 30 November 2018]. Forman, A. M.,Lester, D. H.,& Loyd, D. (2005). Internet Shopping and Buying Behavior of College Students. Services Marketing Quarterly Journal. 27(2), 123–138. Fromm, J. (2014). Millennial trends. Available at [Accessed 30 November 2018]. Gilles, T. (2015). Cell phones a Harder Hack Target than Computers, FireEye’s President Says. CNBC News. Retrieved from http://www.cnbc.com/2015/04/19/cellphones-a-harder Pendergast, D. (2010). Getting to know the Y generation in tourism. In Tourism and Generation Y. Edited by Benckendorf, P., Moscardo G & Pendergast D. CABI. Goldman Sachs, (2016). Global Investment Research. [online] Available at [Accessed 30 November 2018]. Gossling, S. & Lane, B. (2014). Rural tourism and the development of Internet – based accommodation booking platforms: a study in the advantages, dangers and implications of innovation. Journal of Sustainable Tourism, DOI: 10.080/09669582.2014.909448. Iakovidou, O., Koutsou, S. & Vlahou, H. (2005). Mediterranena tourism beyond the shores: New trends in tourism and social organization of landscape (In Greek). Thessaloniki: Ziti Editions Kavoura, A., & Stavrianeas, K. (2015). The importance of social media on holiday visitors’ choice-the case of Athens, Greece. EUROMED, Journal of Business, 10(3), 360-374. Khoshpakyants, A. & Vidischcheva, E. (2012). Challenges of Youth Tourism. Sochi: State University for Tourism and Recreation.

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