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International Journal of Contemporary Hospitality Management Motivators and inhibitors in booking a hotel via smartphones Sangwon Park, Yiqun Huang,

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Article information: To cite this document: Sangwon Park, Yiqun Huang, (2017) "Motivators and inhibitors in booking a hotel via smartphones", International Journal of Contemporary Hospitality Management, Vol. 29 Issue: 1, pp.161-178, https:// doi.org/10.1108/IJCHM-03-2015-0103 Permanent link to this document: https://doi.org/10.1108/IJCHM-03-2015-0103 Downloaded on: 03 March 2019, At: 02:58 (PT) References: this document contains references to 87 other documents. To copy this document: [email protected] The fulltext of this document has been downloaded 2428 times since 2017*

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Motivators and inhibitors in booking a hotel via smartphones

Motivators and inhibitors

Sangwon Park Department of Hospitality and Food Management, University of Surrey, Guildford, UK, and

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Yiqun Huang Department of International Hotel Management, University of Surrey, Guildford, UK

161 Received 9 March 2015 Revised 23 September 2015 16 February 2016 Accepted 7 June 2016

Abstract Purpose – The purpose of this research is to identify motivators (i.e. self-efficacy, perceived behavioural control and perceived benefits) and inhibitors (i.e. perceived cost and anxiety) that affect behavioural intentions to book hotel rooms using smartphones. Design/methodology/approach – Utilising survey data collected from online consumers who have booked hotels in London, two stages of structural equation modelling were applied to estimate the proposed model. Findings – The results of this research indicate that perceived behavioural control appears to be the core motivator for the use of smartphones to book a hotel with perceived benefits, whereas anxiety plays a negative role in leading to mobile booking behaviours. It is also identified that self-efficacy indirectly influences intentions to reserve hotel accommodation. Practical implications – This study suggests that hospitality marketers should simplify the mobile purchasing process to enhance self-confidence in controlling the system during transactions, educate current and potential online consumers to become aware of the competitive benefits of using smartphones and create alliances with credit card companies to relieve anxiety when users are asked to provide personal or banking information. Originality/value – In light of the substantial literature regarding the adoption of technology in terms of user experience (i.e. TAM), this study integrates two theoretical foundations of understanding consumer behaviours (i.e. a concept of consumer values and theory of planned behaviour) to assess motivators and inhibitors in behaviours related to booking hotel accommodation via smartphones. Keywords Information technology, Decision-making, Hotel Paper type Research paper

Introduction Smartphones and tablets are revolutionizing consumers’ planning, researching and executing in the decision-making process. Nelson (2013) reported that about 67 per cent of South Koreans, 66 per cent of Chinese, 65 per cent of Australians, 61 per cent of British and 53 per cent of Americans use smartphones (with or without touch screens) in their daily lives. eMarketer (2013) expected 80 per cent of the Americans to be users of mobile devices by 2014. In terms of mobile device use for travel purposes, Targeting Innovation (2013) stated that about 75 per cent of the mobile users have used their smartphones when travelling domestically or overseas. This remarkable growth in the utilization of smartphones implies potential capacity of advanced information and communication technology (ICT) in the hospitality and tourism industry because the ubiquity of mobile information services enables people to gain advantage from spatial, temporal and contextual mobility (Rasinger et al., 2009). Recently, several scholars in hospitality and tourism have endeavoured to understand the usage of smartphones in information-search behaviours (Kim et al., 2011) and travel

International Journal of Contemporary Hospitality Management Vol. 29 No. 1, 2017 pp. 161-178 © Emerald Publishing Limited 0959-6119 DOI 10.1108/IJCHM-03-2015-0103

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planning process (Park and Wang, 2013), and ultimately to identify the structure of enhancing travel experiences (Brown et al., 2013; Wang et al., 2012; Tussyadiah and Zach, 2012). This suggests that the previous studies investigating advanced information technology (i.e. smartphones) focus primarily on the travel planning process in general and the aspect of information-search behaviours in particular. Importantly, however, there is scarcity of research to assess the stage of a purchase decision along with the advancement of information technology (Bouwman et al., 2007). Hotels and accommodations comprise a type of hospitality products that most online travellers are likely to access on smartphones (Expedia Media Solutions, 2014); however, these products seem to be less likely purchased via mobile phones. According to the HeBS Digital (2012) hotel client portfolio, about 14.0 per cent of visits to hotels’ websites occur via mobile phones, whereas just 2.6 per cent of them completed bookings on the device, generating 1.1 per cent of total revenues for hotels. These statistics propose an important research issue to identify those factors that drive or inhibit consumer’ hotel booking decisions via smartphones. There are a number of extant studies that investigate the adoption of technology in the hospitality area. Indeed, the dominant paradigm used to assess the adoption of mobile information services is the technology adoption model (TAM), originally developed to explain individuals’ adoption of technology in an organizational setting (Kim et al., 2008a, 2008b). The theory argues that the adoption of mobile service is largely determined by two factors: perceived usefulness and ease of use. However, from the consumer behaviour perspective, adopters of new ICTs are individuals who play the dual roles of technology user and service consumer, and thus, tend to evaluate the services in regard to perceived values. Thus, the authors of this paper argue that the concept of perceived value composing perceived benefits and sacrifice that includes perceived cost and risk (anxiety) is an important concern as one of the underlying concepts in this study (Kim et al., 2008a, 2008b). Furthermore, the theory of planned behaviour (TPB) is a well-developed model that has been shown to predict behaviours across a variety of settings. In particular, this theory suggests the influential roles of perceived behaviour control (Pavlou and Fygenson, 2006) and self-efficacy (Venkatesh et al., 2003) in understanding online consumer behaviours. Based on the integration of those two important theories relevant to consumer behaviours, this research aims to identify motivators (i.e. self-efficacy, perceived behavioural control and perceived benefits) and inhibitors (i.e. perceived cost and anxiety) that affect behavioural intentions to book hotel rooms using smartphones. Literature review The adoption of smartphones in hospitality and tourism Due to the nature of the hospitality products (inherently experiential, intangible and heterogeneous), assessment of the quality of products before purchasing is difficult for consumers. Hence, consumers seek recent and relevant information to reduce the level of information asymmetry referring to the difference of information between sellers and buyers (Pavlou et al., 2007). The highly innovative location-based services allow online consumers to obtain personalized and “last minute” information based on the user’s current location (Krum, 2010), which make travel behaviours adaptable to the specific context they confronted (Kramer et al., 2007). With recognizing the important role of smartphones, several researchers have attempted to identify factors that influence the adoption of smartphones for travel purposes. Oh et al. (2009) applied the expectance theory, comprising performance and effort expectancies, to understand behavioural intention to use mobile devices for future travel. Kim et al. (2008a,

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2008b) applied the TAM theory with two external variables, technology and trip experiences, to find out determinants leading to tourists’ acceptance of mobile devices. Recently, a series of studies conducted by Morosan (2014) proposed an extensive TAM in purchasing ancillary services in airline, and accepting biometric systems for the security in air travel and using mobile devices in private clubs. While TAM has been extensively used and provided significant contributions to understanding adoption behaviours, it includes limitations in accounting for the advanced information and communication technology. First, the theory suggests two types of beliefs (i.e. perceived usefulness and ease of use) to account for the likelihood to adopt a technology, which has restrictions of predicting behavioural intention from those two factors (Bagozi, 2007). Furthermore, the TAM model is developed in the organizational context where people use free-of-charge technology daily for work purposes. Importantly, however, individual users adopt the new technology for their personal purposes which requires to pay the usage fee of voluntary adoption. From the consumer behaviour perspective, perceived value regards as an important determinant of the behavioural intention. That is, consumers are likely to choose a behaviour by evaluating the way to maximize the value comprising the trade-off between total benefits and sacrifice, when making a decision to use a new technology (Kim et al., 2011). Wang and Wang (2010) examined the perceived value derived in using mobile devices to account for behavioural intentions to adopt mobile services for hotel reservations. The results of the study concluded that perceived benefits and sacrifices consisting of perceived cost and anxiety (or risk) form the perceived value inducing adoption behaviour. In addition, this study takes into account important factors in TPB that cause a behavioural intention: self-efficacy and personal behavioural control. Numerous researchers suggested that perceived capability to control a new technology are vital in leading to the adoption behaviour. The following section discusses the relationships between the theoretical factors examined in this research (Figure 1).

Hypothesis development Perceived behavioural control Ajzen (1991) developed the theory of planned behaviour (TPB) based on the theory of reasoned action (TRA) by adding perceived behavioural control as a crucial factor. In the view of Ajzen (1991), perceived behavioural control defined as “individual perception of the ease or difficulty of performing the behaviour of interest” (Ajzen, 1991, p. 183) is an extended determinant that affects the intentions which allow to understand the reasons for individual actions. Accordingly, in this study, perceived behavioural control refers to consumers’ perceptions of the ease or difficultly of obtaining information about a hotel and booking the hotel room via a smartphone’s application. Indeed, when individuals perceive technology easily manipulated and in their control, confidence for using a new technology and the outcome derived increases that lead to forming a favourable attitude towards purchasing products via a certain technological device (Hansen, 2008; Al-Swidi et al., 2012). Thus, the current study argues that for those travellers having sufficient confidence to use mobile devices (i.e. high level of perceived behavioural control), attitudes towards purchasing products or services via a smartphone become favourable, and increases the likelihood of booking hotel reservations (Maity, 2010). Thus, this study proposes the hypothesis: H1. Perceived behaviour control has a positive effect on the intention to reserve hotel accommodations using a smartphone.

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Figure 1. The proposed model

Perceived benefits The development of informational technology enables consumers to evaluate and use relatively convenient, effective and inexpensive channels to purchase travel products (Buhalis and Law, 2008) that brings about perceived benefit to technology uses (Kim et al., 2008a, 2008b). Smartphones facilitate for consumers obtaining ubiquitous, convenient, personalized and location-based benefits when searching for information to decide on a purchase (Akturan and Tezcan, 2012; Turban and King, 2003). More specifically, the mobile reservation channel allows travellers to receive location-based recommendations, corresponding to a certain moment when people seek to accomplish specific tasks by using smartphones. Liebermann and Stashevsky (2009) reviewed a substantial number of studies that discuss determinants of online shopping, and suggested that consumers who recognize the benefits of using the information technology, including in-depth information, social interaction, convenience, selection and availability, tend to increase the likelihood of online purchasing (Doolin et al., 2005; Wolfinbarger and Gilly, 2001). That is, consumers assess certain benefits from the solution for need that a product offers and consider the comprehensive attributes that deliver benefits (Kotler and Armstrong, 2003). Thus, it can be argued that when travellers use mobile phones to reserve hotel accommodations, they can reduce the time and costs for searching, and encounter a wider range of informational alternatives for products and services, thereby leading to increased likelihood of reserving hotel rooms via the smartphone. The proposed hypothesis is: H2. Perceived benefit has a positive effect on the intention to reserve hotel accommodations using a smartphone. Perceived costs Previous studies of consumer behaviour identified a trade-off between perceived benefits and costs that exist during the decision-making process for a purchase. The perceived costs

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consist of monetary and nonmonetary aspects (Hauser and Wernerfelt, 1990): for example, effort cost, difficulty of use and risk of privacy in using ICT for booking a hotel (Parra-López et al., 2011). Effort costs refer to money and time spent adapting ICT for searches, communication and transactional activities (Patterson and Smith, 2001). The difficulty of use indicates the extent to which the use of technological device is difficult for people to obtain customized information and produces unsatisfied search results. A number of MIS researchers have broadly suggested the importance of ease of use as a vital factor that leads to adopting behaviour for using new ICTs (Gefen et al., 2003; Venkatesh and Davis, 2000). Third element reflects anxiety and/or perceived risk, which is largely discussed as an important factor itself. Thus, the next section focuses on the notion of anxiety more details. In terms of mobile phones, while sifting and simplifying information and content on the smartphones would fit “relatively” small screens of the devices, there is a potential limitation that may not present sufficient information to meet user’s information-seeking needs (Chan et al., 2002). That is, when adopting new type of ICTs, consumers incur relatively higher levels of cognitive costs to use it and accomplish the specific tasks by the technology (Maity, 2010; Swartz, 2001). Therefore, the proposed hypothesis is: H3. Perceived cost has a negative effect on the intention to reserve hotel accommodations using a smartphone. Anxiety Based on the Çelik (2011)’s study, anxiety refers to transitory unpleasant and negative emotions in cognitive states evoked in actual or imaginary interactions in online purchasing experiences. These negative emotions include feeling fearful, worried, apprehensive and uneasy. Adopting a new technology, imaginations or physical experiences may engender uncertainty for outcomes that leads to negative emotions (Lazarus, 1991). More specifically, when online travellers receive requests to provide their personal information, such as demographic and banking details in the payment process, anxiety may arise from losing control if transactional errors occur (Kuisma et al., 2007). With regard to intangibility in m-commerce environment, consumers may also have perceived uncertainty in unexpected product performance. Several studies examined anxiety’s direct and indirect negative effects on the use of information technology (Beaudry and Pinsonneault, 2010; Lu and Su, 2009). Beaudry and Pinsonneault (2010) indicated that individuals who have a high level of anxiety behave more rigidly and cautiously during use of technology compared to those who are less likely to be anxious. In the mobile purchasing context, Lu and Su (2009) explored the relationship between anxiety and mobile shopping behaviour. The results of the study revealed that highly anxious and uncomfortable individuals are likely to avoid situations that trigger these feelings, and are thus, less likely mobile shopping. Therefore, the proposed hypothesis tests the effect of anxiety: H4. Anxiety has a negative effect on the intention to reserve hotel accommodations using a smartphone. Self-efficacy Based on the social cognitive theory (Bandura, 1994), self-efficacy refers to the level of confidence individuals have for successful completion of a task within their capacities to do so as a proximal determinant of one’s behaviour (Bandura, 1977). Accordingly, this research defines self-efficacy in the context of mobile technology as confidence in the capability to conduct an online hotel booking and confidence in accomplishing tasks based on perceived capability to use smartphones (Compeau and Higgins, 1995).

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Several previous studies examined the effect of self-efficacy in technological experiences (Kim et al., 2011; Iconaru, 2013; Islam et al., 2011). Pavlou and Fygenson (2006) demonstrated that judgment of consumers’ capabilities to gain information about a product online has a positive and indirect influence on consumers’ actual purchase behaviour, mediated by consumers’ perceptions of ability to control behaviour. That is, a greater level of perceived self-efficacy leads to an increasing degree of perceived ease of performing an action, thereby generating intentions to purchase products using certain technological devices (Manstead and van Eekelen, 1998). A recent study conducted by Yang (2012) concluded the consistent argument in mobile context. Indeed, when consumers become confident in using mobile phones, they are more comfortable using purchasing behaviour via the mobile channel and in turn, are likely to explore more functions and features that enhance outcomes from mobile shopping. Besides, studies suggest self-efficacy is an influential factor for alleviating anxiety (Bandura, 1994). Consumers with a high level of self-efficacy regard the adoption of purchasing via a smartphone as a challenge; in contrast, those with a low level of self-efficacy consider the technology to be a threat that causes a high level of cognitive stress and negative feelings, leading to unwillingness to use information technology. In this vein, Fagan et al. (2003) demonstrated that individual self-efficacy negatively relates to anxiety for using new technology. Hence, the proposed hypotheses can be proposed that: H5a. Self-efficacy has a positive effect on perceived behavioural control. H5b. Self-efficacy has a negative effect on anxiety. Research method Data collection An online survey, conducted from June to August in 2013, collected relevant data by contacting through a social media website, TripAdvisor that is the most popular social community websites to share travel experiences ranked top five apps of Apple store in travel category. Specifically, the randomly contacted participants were those who posted comments regarding hotel experiences in London, UK. The sample frame includes online travellers who have stayed hotels in London, UK. London is one of the most popular travel destinations that 160.2 million travellers visited in 2013 and numbers are expected to arise up to 179 million in 2018 (Mintel, 2013). According to the report by Mintel (2014), London provides 22 per cent of room supply in the entire UK. Of those who stayed in London hotels, 91 per cent of the people have used information technology (i.e. mobiles) when booking hotels (Mintel, 2014). These statistics indicate that London city is a relevant strength towards the specific setting of this research. Then, potential respondents were asked to approach every tenth user who posts the reviews of London hotels in TripAdvisor. The logic is that those people who have left online comments of their hotel experiences are accustomed to using advanced technology for other activities in everyday life (Gretzel and Yoo, 2008). Thus, the way to contact sample of this study is appropriate to address the purposes of this research. More specifically, the initial approach to potential participants included detailed information of the purposes of the research to request participation through the provided survey. Upon receipt of agreement from online travellers, they received a link to access the online questionnaire along with an explanation of the mechanics for responding. As a result, the total valid responses considered for data analysis were 295, excluding ten respondents who did not complete the entire survey (response rate: about 11 per cent). Further, to identify relevant samples, the process of data collection included filter questions:

• “Do you own a smartphone or mobile phone that can access to the internet?”; and • “Have you used a smartphone or mobile phone to search for information about hotels you stayed in hotels in London, UK?”.

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These questions allow determining whether respondents not only have access to the certain technological device related to the research’s context (i.e. smartphones) but also experience using mobile phones to search for accommodations. Measurement development The survey consists of three sections. The first section inquires of participants’ travel and smartphone experiences. Specifically, the questions asked for types and number of travel companions, length of stays in a hotel and travel budget per day. The second section includes multiple items to measure six theoretical variables, including perceived behavioural control, perceived benefits, perceived costs, anxiety, self-efficacy and behavioural purchasing intention, to address the purpose of this research. All of measurements used a five-point Likert scale (1 ⫽ strongly disagree; 5 ⫽ strongly agree), according to an accepted methodology for determining consumers’ attitudes, perceptions and/or intentions (Wilson, 2012). The scales of the variables are adaptations from previous studies which exhibited relatively high levels of reliability and validity. The items of perceived behavioural control include four questions derived from Taylor and Todd (1995) and Yang (2012). Five items on perceived benefits and six items of perceived costs are adaptations similar to those of Park and Kim (2006) and Parra-López et al. (2011). The current research used three items to measure anxiety (Compeau et al., 1999; Lu and Su, 2009; Thatcher and Perrewe, 2002) and three items, adapted from Compeau and Higgins (1995) and Kim et al. (2011), to measure self-efficacy. For behavioural intentions, three items relied on the suggestions of Lee et al., (2002), Pavlou and Chai (2002) and Yang (2012). The final section seeks demographic information: gender, age, marital status, level of education and monthly income. Data analysis Descriptive analysis determined the characteristics of the sample and identified the distributions of the data relevant to the variables in the theoretical model. Then, structural equation modelling (SEM) assessed the proposed relationships by estimating measurement, functional and predictive hypotheses (Bagozzi and Yi, 2012). Specifically, SEM consists of two steps: (1) assessment of latent variables along with levels of observations (i.e. measurement model); and (2) testing the proposed relationships between latent variables on the theoretical level (i.e. structural model). Confirmatory factor analyses (CFA) estimated the measurement model to check reliability and validity of the constructs using M-Plus software. A number of methods for the model’s fit considered factor loadings (or indicator reliability) (above 0.70), composite reliability of the latent constructs (above 0.70), chi-square (␹2), comparative fit index (CFI) (above 0.90), Tucker–Lewis Index (TLI) (above 0.90), root mean square error of approximation (RMSEA) (less than 0.05) and root mean square residual (RMSR) (less than 0.05) (Nunnally and Bernstein, 1994). Furthermore, guiding post-hoc analysis of the model used statistical output but in accordance with underlying theory to ensure maintaining the psychometric properties of each construct (Kline, 2011). Estimating the structural model, the same goodness-of-fit indexes such as CFI, TLI, RMSEA and

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RMSR included are concerned with R2. The indication from the R2 values of 0.75, 0.50 or 0.25 is that the endogenous constructs are high, moderate and weak, respectively. Results Profiles of respondents As shown at Table I, female respondents (n ⫽ 162, 54.9 per cent) are slightly more than males (n ⫽ 133, 45.1 per cent). Online travellers in this research appear with relatively high level of education: about 90 per cent of respondents completed college or above. Approximately, half of the people are married (51.2 per cent) and have monthly net income less than £1,000 (48.8 per cent). The average age was 31.68 with 10.86 of standard deviation. With regard to travel behaviours visiting London, 41 per cent of the respondents have travelled with spouse/partners, followed by with friends (31.9 per cent), colleagues (11.9 per cent), children (7.8 per cent) and alone (7.5 per cent). Majority (90.5 per cent) of the subjects went to the trip with more than one companion. Asking their behaviours about hotel stay, more than half (57.9 per cent) of the travellers have stayed in the hotel between one and three days while visiting the UK. Measurement model CFA was used to test the validity and reliability of the theoretical constructs by the latent variable approach using M-Plus software. Checking the factor loadings, two items below cut-off values are decided to be eliminated: one of perceived behavioural control (PBC_2 ⫽ 0.24), which is consistent with PCA result, and an item of perceived costs (PC_2 ⫽ 0.50). After the modification, all the factor loadings are over 0.68, which means that interrelations are significantly high in magnitude (p ⬍ 0.001) (Kline, 2011). The results of composite reliability also show reasonable values, including perceived behavioural control (0.93), perceived benefit (0.92), perceived cost (0.92), anxiety (0.92), self-efficacy (0.96) and behavioural intention (0.97) (Table II). The square root of average variance extracted (AVE) was calculated to test the convergent validity for six latent variables and then, the values were compared with other constructs to assess discriminant validity. The results of the analysis show that the AVEs (the mean-squared loading for each construct) of each construct are larger than the cross-correlations of other constructs, which suggests that each reflective construct is distinct from other constructs in the measurement model: the confirmation of discriminant validity. The squared AVE is also over 0.82, implying that the latent variables explain its indicators more than error variance: the confirmation of convergent validity (Table III). Furthermore, there is no correlation value over 0.90, which limits the collinearity between constructs. The various goodness-of-fit indexes for CFA reasonably fit well; the model chi-square (␹2) value was 515.18 with 194 df, which refers to that the value of ␹2/df (2.66) is lower than cut-off level 3.0 (Kline, 2011). The comparative fit index (CFI) and the TLI support the conclusion that the model including six theoretical constructs fits (CFI ⫽ 0.939 and TLI ⫽ 0.928) acceptably by indicating over cut-off values (Table IV). The RMSEA and standardized root mean square residual (SRMR) were also calculated to evaluate the model fit (RMSEA ⫽ 0.075 and SRMR ⫽ 0.046). While the values are slightly higher than the recommended levels (⬍ 0.05), Hu and Bentler (1999) suggested that values of RMSEA below 0.10 and SRMR as high as 0.07 are deemed acceptable (MacCallum et al., 1996). Thus, it can be concluded that the results of the model indexes show the reasonable values as well. Structural model Given confirming the measurement model, the structural model was assessed to examine the hypothesized relationships among the latent factors. SEM using maximum likelihood estimation was undertaken using M-Plus. The goodness-of-fit results show

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Variables Demographic variables Gender Female Male Education Less than high school Completed high school Some college, not completed Completed college Post graduate work started or completed Marital status Married Single or never married Divorced, separated, widowed Living with partner Do not wish to comment Monthly income Below £1,000 £1,001-2,000 £2,001-3,000 £3,001-4,000 Over £4,001 Do not wish to comment Age Travel behaviors Travel party in the most recent trip Alone With children With spouse/partners With friends With colleagues Number of travel company in the most recent trip One Two Three-five people Six or more people Length of stays in a hotel 1 day 2 days 3 days 4 days 5-10 days 11 or more days

Frequency

(%)

162 133

54.9 45.1

1 7 19 179 89

0.3 2.4 6.4 60.7 30.2

151 128 3 11 2

51.2 43.4 1.0 3.7 0.7

144 42 16 8 20 65 Mean 31.68

48.8 14.2 5.4 2.7 6.8 22.0 SD 10.86

22 23 121 94 35

7.5 7.8 41.0 31.9 11.9

28 140 87 40

9.5 47.5 29.5 13.6

39 65 67 45 77 2

13.2 22.0 22.7 15.3 26.1 0.7

that 2.72 of ␹2/df (p ⬍ 0.001), CFI (0.936), TLI (0.925), RMSEA (0.076) and SRMR (0.05), which suggests that the estimated model built on the covariance metrics is reasonably acceptable (Figure 2). To be more specific, the path relationships are statistically significant except for the relationship between perceived cost and purchasing intention (␤ ⫽ 0.06, p ⬎ 0.05). As this

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Table I. Profiles of respondents

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Variables PBC PBC_1: I have an internet-enabled mobile phone (or smartphone) to access the hotel booking sites PBC_3: It could be easy for me to use the system when booking a hotel via mobile devices PBC_4: I have the knowledge necessary for mobile hotel booking Perceived benefit PB_1: Keeping up to date with knowledge about the hotels of interest PB_2: Permitting to save costs and get the most from the resources invested in the trip PB_3: Giving the possibility to provide and to receive information about hotels of interest PB_4: Both pleasing and fun PB_5: I am proud of doing so Perceived costs PC_1: The personal effort and time is excessive and not worthwhile PC_3: Difficult to spend the time needed to monitor the mobile hotel websites PC_4: High risk of losing my privacy PC_5: Often involves processes that are too complicated or bothersome, which makes me abandon the idea PC_6: Difficult to know where to book a hotel via mobile devices Anxiety Anxiety_1: Feel apprehensive about using smartphones or mobile devices to book a hotel Anxiety_2: Hesitate to use smartphones or mobile devices to book a hotel because I could make mistakes Anxiety_3: Using smartphones or mobile devices to book a hotel is somewhat intimidating to me

Table II. The results of CFA

Self-efficacy SE_1: Feel comfortable booking hotels via mobile devices on my own SE_2: Easily to book a hotel via mobile devices on my own SE_3: Feel comfortable booking a hotel via mobile devices even if there is no one around me to tell me how to use it

Factor loadings

SE

t-values

Construct reliability 0.93

0.70

0.03

21.28***

0.92

0.02

61.98***

0.89

0.02

53.32*** 0.92

0.80

0.02

33.40***

0.80

0.02

32.88***

0.81 0.84 0.72

0.02 0.02 0.03

34.86*** 41.08*** 22.92*** 0.92

0.70

0.03

20.63***

0.70 0.69

0.03 0.03

20.80*** 20.32***

0.85

0.02

39.51***

0.80

0.03

31.87*** 0.92

0.88

0.02

45.54***

0.79

0.03

30.43***

0.77

0.03

27.63*** 0.96

0.89

0.02

58.52***

0.88

0.02

55.40***

0.93

0.01

79.30*** (continued)

Factor loadings

Variables

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Behavioral intention Intention_1: Intend to book a hotel via smartphones or mobile devices Intention_2: Expect my mobile hotel booking to continue in the future Intention_3: Intend to make a hotel reservation via smartphones or mobile devices

SE

Construct reliability

t-values

Motivators and inhibitors

0.97 0.87

0.02

53.99***

0.91

0.01

70.75***

0.97

0.01

119.02***

171

Note: *** p ⬍ 0.001

Table II.

study proposed, self-efficacy positively affects perceived behavioural control (␤ ⫽ 0.77, p ⬍ 0.001, R2 ⫽ 0.59) and negatively influences anxiety (␤ ⫽ ⫺0.62, p ⬍ 0.001, R2 ⫽ 0.38). With regard to the direct antecedents of intention to book hotels using smartphones, perceived benefit (␤ ⫽ 0.33, p ⬍ 0.001) and perceived behavioural control (␤ ⫽ 0.45, p ⬍ 0.001) positively as well as anxiety (␤ ⫽ ⫺0.25, p ⬍ 0.001) negatively affects the endogenous variable (purchasing intention), which accounts for 70 per cent of variance explained (R2 ⫽ 0.70) (Figure 2). The indirect effect of self-efficacy on purchasing intention was also estimated by concerning Sobel test. Table V presents that self-efficacy has statistically indirect influence via both perceived behavioural control (␤ ⫽ 0.31, p ⬍ 0.001) and anxiety (␤ ⫽ 0.14, p ⬍ 0.001) on intention to book a hotel room using smartphones. Then, this study calculated the post-hoc statistical power to test the insignificant relationship between perceived cost and behavioural intention reflecting H3 (Cohen, 1988). The observed statistical power indicates 0.99, given the probability of the relationship, 0.01, which suggests that the chance of a Type II error occurring for the specific hypothesized relationship is very restricted.

Variables

Mean

SD

1

2

3

4

5

6

3.86 3.71 3.76 2.61 2.50 3.66

1.07 1.07 0.94 0.98 1.03 1.05

0.90 0.77 0.64 –0.48 –0.48 0.75

0.94 0.84 –0.62 –0.62 0.74

0.87 –0.58 –0.52 0.71

0.83 0.39 –0.44

0.88 –0.61

0.95

1. PBC 2. Self-efficacy 3. Perceived benefit 4. Perceived cost 5. Anxiety 6. Behavioral intention

Note: The diagonal elements (in italic) represent the square root of AVE

Chi-square (␹2)

df

␹2/df

CFI

TLI

RMSEA

SRMR

515.175***

194

2.66

0.939

0.928

0.075

0.046

Table III. Latent correlation analysis

Table IV. Notes: *** p ⬍ 0.001; CFI refers to comparative fit index; TLI refers to Tucker–Lewis index; RMSEA refers Summary of model-fit indexes for CFA model to root mean square error of approximation; SRMR refers to standardized root mean square residual

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Figure 2. Structural model

Relationship Self-efficacy ¡ perceived behavioral control ¡ intention Table V. Self-efficacy ¡ anxiety ¡ intention Estimations of indirect effect Note: *** p ⬍ 0.001

␤

SE

t-values

0.31 0.14

0.04 0.04

7.16*** 3.55***

Discussion With recognition of the importance of smartphones to the hospitality business, previous researchers mainly discussed consumers’ adoption of smartphones with TAM theory emphasizing perceived usefulness and ease of use, as well as the roles of the technology in the information-search stage (Kim et al., 2008a, 2008b; Oh et al., 2009). However, efforts to identify factors that affect transaction behaviours using mobiles are limited despite the large potential of expanding the future market in hospitality industries. Accordingly, this study adapted the concept of consumer values that suggest the trade-off between perceived benefits and sacrifices (i.e. costs and anxiety) and TPB that proposes the importance of PBC and self-efficacy factors. As a result, this paper identifies that perceived benefit, PBC and self-efficacy play roles of direct or indirect motivators, whereas anxiety serve as an inhibitor to make a behavioural intention to use their smartphones for booking hotel accommodations. More specifically, perceived behavioural control has a positive influence on mobile hotel-booking behaviour. This finding is consistent with the results of previous studies (Hansen, 2008; Kidwell and Jewell, 2010), which argued that the behavioural intention increases when consumers perceive ease for performing a particular task. That is, online consumers are more likely to use smartphones for purchasing products when they can recognize that doing so via new technology is a simple, uncomplicated transaction. This

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study also identified that perceived benefit is one of the main motivators for inducing reservations via smartphones (Parra-López et al., 2011). That is, online travellers who recognize the benefits of using smartphones from past experiences or knowledge tend to have higher intentions to use mobile devices for booking hospitality products (Strader and Inapudi, 2004). While several prior studies stated that perceived costs negatively associate with behavioural intentions in e-commerce (Maity, 2010; Parra-López et al., 2011), the current study relating to the mobile purchasing aspect shows an insignificant relationship. It can be explained that, as penetration of smartphones in daily life increases, people may adjust the marginal levels of effort and time required to use the smartphones, and less suspend concern for the potential risk to the security of personal information. Consistently, the respondents in this current study showed the average values of perceived costs below the median (Mean ⫽ 2.60; SD ⫽ 0.98) (Table III). Additionally, Doolin et al. (2005) suggested an offset effect of negative factors for online purchasing processes, whereby elements that positively influence buying behaviour (i.e. behavioural control and benefits) alleviate the effect of perceived costs. A negative correlation between anxiety and intention to secure reservations via mobile devices appeared in this study. This finding is consistent with prior studies, such as Compeau et al. (1999), Beaudry and Pinsonneault (2010), and Lu and Su (2009). That is, consumers who purchase travel products using smartphones (advanced ICT) may provoke anxiety from potentially uncertain outcomes, an unsuccessful transaction and individual mistakes during the transaction, which may create reluctance to purchase products via smartphones. Last, self-efficacy directly affects perceived benefit and anxiety as well as indirectly influences intentions to reserve hotel accommodations using a mobile device (Yang, 2012; Pavlou and Fygenson, 2006). This result implies that the level of individuals’ confidence for capability to complete a task with a positive outcome influences perceptions for estimating behavioural control, and negatively influences anxiety. Consumers who have a high level of self-efficacy use smartphones with relative ease, and in turn, that characteristic improves confidence for purchasing hospitality products via smartphones. Contrarily, those who feel less confident in ability to accomplish an online reservation have a relatively higher level of discomfort, and therefore, less likely to use a smartphone for that purpose (Fagan et al., 2003). Implications and future research The implications of this study are twofold: theoretical and managerial aspects. This research provides a theoretical contribution to the hospitality literature by identifying the factors that have positive and negative influences on intentions to book hotel reservations via smartphones. Most of the previous studies have applied theories of TAM developed from the organizational context to assess the adoption and impact of smartphones on hospitality activities, which implicitly assume that behaviour is volitional. However, smartphone users confront several new constraints pertaining to use of new technology, such as uncertainty of outcomes from use, lack of control, etc. These issues induce a need to assess elements more relevant to the consumer behaviours. Accordingly, this study took into account consumer values and TPB model that propose five important factors. As a result, this research finds significant motivators and obstacles to mobile hotel booking behaviours. The findings of this research provide hotel marketers with important practical suggestions to motivate consumers reserve accommodations using smartphones. Marketers should simplify the process to improve mobile purchasing confidence through transactions that allow individuals to control systems personally. For example, development of a clear and concise mobile webpage or mobile application would enhance consumers’ experiences

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for the purchasing process. Moreover, marketers need to educate current and potential online consumers to become aware of competitive benefits (i.e. optimized recommendations and last-minute offers from location-based systems) by providing advertisements/promotions to assess the values of using smartphones compared to other information and communication resources. Furthermore, creating alliances with credit card companies could be a valuable solution for relieving anxiety when consumers must provide personal or banking information. These cooperatives may also reduce inconvenience for consumers and increase the efficiency, as online consumers would not need to input credit card information repeatedly. A number of suggestions for future research are apparent by concerning limitations in this study. First, building on the current study’s estimation of behavioural intention for mobile purchasing, future research should investigate the actual purchasing behaviour. Accordingly, longitudinal research should estimate and track the cause of motivators and inhibitors to actual mobile purchasing behaviours. Second, the model considering more contextually specific variables is a recommendation for future research; for example, types of smartphone applications, the individual characteristics, the features of products and etc. Venkatesh (2000) stated that as more contextual information becomes available, judgments within that context expand and improve.

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