Conducive Workplace Environment Paper.docx

INNOVATION : FROM THE PERSPECTIVE OF CONDUCIVE WORKPLACE ENVIRONEMENT. Muhammad Rashid Jamil MS MGT.(Fall) 2018 Institute of Business & Management UNVERSITY OF ENGINEERING AND TECHNOLOGY,LAHORE [email protected]

Abstract This study focus on the factors and relationship of conducive workplace environment and innovation with the mediating effect of employees performance. As over the years, business entities are focusing on innovation for survival in this era of gigantic and cosmic competition. Which is not constraint and restraint to augmentation of productivity and growth but have domino effect. Innovated and quality productivity leads to competitive edge over the competitors, which ultimate results in capturing a huge chunk of markets share and profit. Though, the business giants are pouring enormous resources towards the (R&D) and training programs, but the provision of conducive workplace environment somehow is neglected. De facto, the conducive workplace environment is one of the foremost, vibrant and prime raison d'être for nurture innovation. Conducive environment spurs employees to think out of the box and innovate, others even not imagine. The same paradigm will be discussed in this paper. This study is directly focusing on previous research on conducive workplace environment and its vast impact on innovation and to identify gaps in the literature to provide recommendations for future work. The second aim is to offer a theoretical framework that will help to explain how top brass may inject positive ambiance, which may interpret conducive workplace environment and how it may be most beneficial. This study will by quantitative and explanatory in nature. The respondents of this study will be employees working in different segments of a company.

Keywords: Conducive workplace, employee performance, prospect of conducive environment, workplace and innovation

1. Introduction “The

health and wellbeing of employees makes an essential contribution to business success. I believe that employees are at their most productive and creative when they are in an environment that supports their health and wellbeing.” (Professor Dame Carol Black) Improving workplace standards has been turned into progressively more significant and imperative to operate in today's competitive environment, because firms are under greater pressure to innovate for which talented employees are key and vital driving force. Although, studies have examined the influence of employee welfare on stock returns leverage (Verwijmeren and Derwall, 2010; Bae et al., 2011) and cash holdings (Ghaly et al., 2015). However, relatively little attentions has been paid and put up to whether and how employee friendly workplaces promote and foster innovation. Investigating and probing this question provides crucial and fundamental implications for the firm's workplace practices and policies with reference to employee relations and for their contribution to the firm's broader and vast strategy and innovation in particular. The main challenges for firms in pursuing, managing and administrating innovation activities are the volatility of outcomes and the high probability of failure. By implementing employeefriendly policies and employee engagements, firms are able to amplify employee job satisfaction, strengthen their relationships with employees, and enhance employee trust in management, thereby offsetting and curtail the negative influence of high-risk innovative activities on employees. Thus, an employee-friendly workplace likely promotes greater tolerance for failure and encourages employee engagement in experimentation, new ideas and innovation. Studies argue that a conducive and encouraging employee-friendly workplace helps to develop employees, who are hopeful, meaning they are more capable of finding positive meaning in difficulty and creating redeeming value in failure, and resilient, being able to recover from failure in ways that strengthen effectiveness. By cultivating and humanizing these psychological ray of hope and resilience in employees, firms with employee-friendly workplaces are likely to be more tolerant of collapse and more proficient of overcoming adversity in pursuing innovation. In addition, contented employees are more likely to internalize the firm's innovation objectives, which strengthens their motivation to overcome difficulties and failure during the innovation process. Drawing upon recent insights that tolerance for failure is a key driver of innovation (Azoulay et al., 2011; Tian and Wang, 2014), it was hypothesized that companies with employee-friendly workplaces invest more heavily in innovation and achieve greater innovation success.. To investigate whether employee-friendly workplaces nurture innovation by strengthening tolerance for failure, we rely on heterogeneity in the effect of workplace quality on innovation across firms with different degrees of innovation failure risk (Tian and Wang, 2014). We expect the impact of workplace quality on innovation to be stronger when failure risk is higher and thus tolerance for failure is more needed. Consistent with this view, the positive effect of workplace

quality on innovation is indeed more well-known in industries in which innovation is more difficult to achieve. This finding reveals an important insight into the mechanism through which employee-friendly workplaces nurture innovation. To provide more direct evidence in the context of international level that firms with employee-friendly workplaces are failure tolerant as it was witnessed in the disruptive change and slump of 2007-2009. In times of economic distress characterized by higher uncertainty (Bloom, 2014) and risk of failure (Bhatta charjee et al., 2009), companies may reduce investments in innovation to help ensure firm survival. However, if conducive workplaces improve the firm's ability to deal with increased uncertainty and failure risk, it would expect such firms to be more likely to sustain investments during the recession. 1.2 Problem Statement There are numerous aspects that have an effect on the performance of employees in organizations. Workplace environment plays an crucial and vibrant role towards employees performance and efficiency in any organization. Employees when working in environment that suits their physical and mental abilities, the productivity is indispensible and employees are then in the optimum situation for learning, working and achieving. Conducive work environment comprises the sum of forces and influential factors that are currently or potentially contending with the employees’ activities and performance. Possibly this study will explore important factors affecting employees performance

due to non-conducive workplace and by using quantitative research method this solution of this problem will be find out in various factories. 1.3 Research Objectives In this Current research the focus will be the on workplace environment factors (independent variable) that affect the employees performance (mediator), that will leads towards the immense innovation (Dependent Variable). Therefore, in the current research the objective will be: 1. The relationship between conducive workplace and employees performance. 2. The impact of employees performance as meditation on innovation. 1.4 Value and Significance of the study The study will enable the top and middle level management (particularly) in the companies to get the comprehensive ways to create an enabling workplace environment for employees in the

context to motivate them to perform better and better. It will provide the tools to the managers on a variety of workplace environment factors that may affect employee performance and hence the necessary improvements could be made for optimum results. Through this study, it will add to existing literature on workplace environment and the factors that impact employee performance. Moreover, this research will be significant, both in theory and in practical. Relying upon the results and outcome of this study, it can solve many other problems. because unfair customers can be deleted, pay more attention to these factors. Similarly, employees commitment can be enhanced by keeping workplace factors in line with their expectations to meet their needs. 1.5 Research Gap According to industry experts, the conducive workplace factors plays an essential role in innovation, in the base research paper it was discussed that, firms with employee friendly workplaces are more resilient but what will be the factors that will contribute towards conducive workplace are not discussed. These factors can help us to classify aspecst that may directly or indirectly affect employees performance and motivation.

2 Literature Review 2.1. Work Environment The work environment can be distinct as the environment in which people work that comprise physical setting, job profile, culture and market condition. Every aspect is interconnected and have impact on employees overall performance and productivity. It is the brilliance of the employees’ workplace environment that most impacts on their level of motivation subsequently performance. Work environment can be thought of simply as the environment in which people work as such; it is a very broad category that encompasses the physical setting (e.g. heat, equipment), characteristics of the job itself (e.g. work stress, task complexity, coworker vibes). Employees will always vied when they feel that their surrounding environment is in tandem with their obligations (Farh, 2012). The type of workplace environment in which employees operate determine whether or not organizations will innovate and will get prosper. The workplace environment consists of physical factors which even include the office layout and design among other factors; while the psychosocial factors include working conditions, role congruity, coworker vibes and social support. Other aspects of the workplace environment are the policies which include employment conditions. A better physical workplace environment boosts employees’ performance. Employees’ comfort on the job, determined by workplace conditions and environment, has been recognized as an important factor for measuring their productivity (Leblebici, 2012). In today’s vibrant and competitive business world, a healthy workplace environment makes good business sense. Organizations deemed as a positive place to work will have a competitive edge over the others.

2.1.2. Employees Performance: In the words of Armstrong (2006) performance is the development of quantified objectives, and it is not only a matter of what people achieve but how they get over this. Performance is the accomplishment of particular tasks against predetermined or identified standards of accuracy, wholeness, cost and speed (Sultana et al. 2012). Organizations always in dire need of highly performing employees in order to meet their goals and to deliver the products and services they are specialized in and finally to achieve a competitive advantage. In previous literature it is assert that employee performance is the combo result of effort, ability ,perception of tasks and confinement of bad vibes . The factors that affect the level of individual performance are motivation, ability and opportunity to participate (Armstrong, 2009 , Platt and Sobotka (2010)). Factors include physical environment, equipment, meaningful work, performance expectation, feedback on performance, bad system among others. 2.1.2. Innovation:

It is important to consider substitute interpretations for the positive interconnection between workplace quality and innovation. An employee conducive workplace may foster innovation by improving the recruitment and retention of talented employees ( Edmans, 2012). The positive effect of workplace quality on innovation should be more prominent in firms with higher levels of intangible capital embedded in their key employees and in industries with higher labor agility and mobility. Our results do not support this hypothesis. Second, conducive workplaces may alleviate executives career concerns that impede investment in innovation. Executives who are committed to providing employees with a quality workplace environment may gain allegiance from subordinates and face lower risk of termination after poor performance, encouraging their engagement in risky, long-term innovative activities. To test this alternative, we investigate executive turnover performance sensitivity. We do not find lower sensitivity for firms. 2.5 Hypothesis development: All variables have shown a strong background literature, research gap is filled using the current model in this study and we include all possible factors together to test the impact of conducive environment factors over the employees performance and ultimate increased innovation. This thesis is useful for top management, in exploring how these workplace factors can affect employees performance and the amount of effort they have to put on them to get innovation. Hypothetical development is a arduous part of research that attempts to develop hypotheses based on hypothetical models that assume or reject established hypotheses in analysis. Based on theoretical support and literature, this study proposes the following assumptions H1: conducive workplace factors influences the innovation.

H2: conducive workplace factors maneuver the employee performance. H3: Employees performance influences the innovation.. 3 Research Methodology 3.1 Conceptual Framework of Research The conceptual structure is actually a visible or written entity attempting to graphically or descriptively form the main part of the study to be conducted, the central elements, concepts or variables to be studied and the relationship between them. The conceptual framework of the study is as under having independent, dependent variables and mediator.

Employees Performance

H2 Conducive Workplace Factors

H3 H1

Innovation

3.2 Data Collection 3.2.1 Sample Selection and Target Population The study's target respondents were individual employees of factories located in Sheikhupura District. The number of respondents were 200 employees from the different factories. 3.2.2 Data Collection and Procedure In this study a questionnaire was used as a survey tool to collect raw data. The questionnaire was used to identify the factors contribution towards the conducive workplace, employees performance and how to achieve innovation. Respondents were also asuured that the information provided will be used for solely research and educational purposes and that the information provided will be kept confidential.

3.2.3 Data Analysis/Processing and Unit of Analysis The latest version of PLS-SEM was used for the data analysis process for this studyand completed according to the scale definition in the coded questionnaire. In addition, the answers were assigned to variables according to the requirements of this study. In order to verify the reliability of the instrument under study, PLS-SEM was used.. 3.2.5 Instrument and PLS Model: Self-administered questionnaires were used as a tool in this study. The questionnaire was distributed and filled in by visiting different factories in Sheikhupura. The model after assigning the questions was as under:

PLS-SEM Algorithm Construct Reliability and Validity: Cronbach's Alpha: The reliability is determined by the Cronbach's α coefficient, which should be reliable if the Cronbach's α coefficient is 0.7 or greater. Below the table indicating the table and the value of this: Variable

Cronbach's Alpha

CONDUCIVE WORKPLACE FACTORS EMPLOYEES PERFORMANCE INNOVATION

0.862 0.818 0.878

Average Variance Extracted (AVE): If the Value of AVE is more than 0.5, it is relaiable. Variables CONDUCIVE WORKPLACE FACTORS EMPLOYEES PERFORMANCE INNOVATION

Average Variance Extracted (AVE) 0.549 0.524 0.541

Composite Reliability: If the value of the composite value is more than 0.7, the model is significance. Variables CONDUCIVE WORKPLACE FACTORS EMPLOYEES PERFORMANCE INNOVATION

Composite Reliability 0.894 0.868 0.904

NOTE.........YAHAN tak final ha check kar k batae

4 Analysis and Findings 4.1 Measurement of Normality The assumption of controlling normality is important before conducting SEM analysis. Kurtosis is a good measure of the normality of the data, as is the design of asymmetry and normal curves.

Lack of normal data if both kurtosis and asymmetry values are high (Hall and Wang, 2005). The kurtosis and asymmetry cutoff values vary between +5 and -5. The values in the following table show because the data is normal. Like all the values after the cost-effective statistics. Errors between the asymmetry and kurtosis between +5 and -5 are necessary. Therefore, the data is usually at all points. Some studies show that lack of normal data does not pose a problem when the amount of data is large. Similarly, Ghasemi and Zahediasl (2012) point out that if the sample size is large enough (> 200 or 300), breaking the normal assumptions will not cause major problems.

4.3.1 Assessment of Multi-Co linearity The values shown in Table 4.2 indicate the absence of multicollinearity data. Co linearity diagnostics and tolerance values in the SPSS VIF report, which is a cross-statistic used to assess multi co linearity (Hair et al., 2006). Table 4.2 below confirms that there is no multicollinearity in the dataset

Table 4.2: Collinearity Statistics VIF EPQ10 EPQ11 EPQ12 EPQ13 EPQ8

1.507 1.582 1.910 1.608 1.701

EPQ9 INQ14 INQ15

1.501 2.013 2.116

INQ16 INQ17 INQ18

2.308 2.114 2.018

INQ19 INQ20 INQ21 WFQ1 WFQ2 WFQ3

1.876 1.762 1.940 2.552 2.310 1.509

WFQ4 WFQ5 WFQ6 WFQ7

1.839 1.911 1.744 1.789

Dependent Variable: Customer Loyalty Intension The multi collinearity requirement indicates that the independent variables are correlated by 0.9+. However, there is no multi collinearity if the VIF values and tolerances meet the above criteria. The above table, because it clearly shows that there is no multi collinearity in the current study. Like all VIF values are less than 10, tolerance values> 0.1. Both values are nonmulticollinear..

4.7 Structural Equation Modeling (SEM) The structural equation modeling (SEM) is one of the most commonly used techniques to perform factorial confirmatory analysis. The SEM is able to examine the relationship between latent constructs, usually measured by various elements, while other techniques are not an important technique (Lei et al., 2007). The basic purpose of these random-mode SEM analyzes is to form the set of pre-models proposed by sperimentatore. Inoltre place while studying the data collected in F, which locates its assessment of the consistency of stochastic models (Lei et al. People, 2007). SEM (Structural Equation Modeling) is a technique that works only for a great champion usually N> 200 and "The required size of the sample depends in part on the complexity of the model, from the estimation method used and by the variable Distribution Characteristics Observation "(Kline, 2005).

The CFA (Confirmatory Factor Analysis) determines that the observed load factor variables provide the appropriate model in its buildings to accept or reject the proposed assumptions CFA vs. EFA In terms of CFA For the first time through empirically verifiable structures and data rather than derivatives (Lei et al., 2007).  Model specification  Estimation Model  Evaluation and modification of the model In addition, Yuan (2005) also pointed out that an important feature of SEM is the assumption that observational data is a "suitable" model. Another use of SEM is to conduct path analysis to identify different direct and indirect effects (Lleras, 2005). 4.8 Measurement Model Fit and Modification According to Lie and Wu (2007), the exchange rate is a reduction in the degree of chi-square, basically due to a comparison of the change of parameter estimates with some estimates of fixed parameters and shows that any change index (> 3.84) requires a normalized load It is also an important point to indicate that an element actually loads into a latent variable with a minimum load of 0.40 (Lewis & Byrd, 2003). Therefore, a more detailed analysis based on these assumptions is provided in detail 4.9 Confirmatory Factor Analysis The purpose of the confirmatory factor analysis model (CFA), which measures the main factors, is to test the variables. A small building measure and a variable or factor are analyzed at a second order before analyzing the alpha-confirming factor: Two analyzes were performed in order to obtain an effective model for data setup and theoretical support behind model development. As recommended by Byrne (2010), Hair et al. (1998) and Kline (2005, 2011) have developed a list of threshold tests and are used as a standard for testing suitability, reliability and effectiveness. Measures of the Study Model Adaptation Index See Table 4.12. Table 4.12: Model Fit Indices with Accepted Value Level of Model Fit

Overall Model Fit Model Fit

Model Comparison

Fit Measures

CMIN/DF

RMSEA

IFI

TLI

CFI

Further analysis is Required

>2

> .1

< .90

< .90

< .90

< .08 (Accepted ≥ .90 up to .1)

≥ .90

≥ .90

Acceptable Scale for Good Model ≤2 or 5 Fit

Table 4.13: Model Fit of Brand Image

4.11

Measurement Model Fit

Overall

This section helps measure the test of the overall measurement model by combining the measurement and verification dimensions of all the CNAs discussed earlier. In the past, independent measurement of dependent variables and models were linked. According to the outcome of the Court of Final Appeal, article 1 has been excluded from the better mode of control. Since the normalized load and lateral load factors are low on many factors, a unit has been removed from the actual model. Test the model, analyze whether the measurement model is adequate, and test the mediator dependent on covariance and independent structure.

Table 4.18: Overall Measurement Model Fit

Model Fit

CMIN/DF (2/df)

RMSEA

GFI

TLI

CFI

3.450

0.80

0.900

0.923

0.900

4.12 Construct Validity Validation process acts as a check importance for the study, providing a vital basis that the process of research and theory were due to practical in application. To access the validity of the model, the correlation value and standardized regression weights were processed and the results are presented. The results stated that in case of convergent validity, two of all buildings have AVE value above the threshold value of 0.5 and according to Fornell and Larcker (1981), Michael et al., (2004), and Bermin (2001 ), if AVE is less than 0.5, and the AVE value is around 0.3 to 0.4 it is justifiable if the CR is greater than 0.60. Table 4.19: Construct Validities

Customers’ Intension Word of Mouth Brand Image Brand Awareness

4.13 Correlations

Loyalty

CR

AVE

MSV

ASV

0.839

0.723

0.610

0.483

0.815 0.744 0.778

0.554 0.579 0.613

0.415 0.337 0.410

0.489 0.542 0.547

It is important to note the relevance of the binary co-linearity of proposed variables. Correlation A significant relationship between all buildings must be evaluated with a correlation between 0 and 1, a value of 0.30 and less than 0.70, a value of 1 representing a value equal to the highest correlation, and in the same way A value of less than 0.30 indicates that a lower correlation is unacceptable. Significant correlation values are reliable for obtaining satisfactory factor analysis results. Staff and current studies show that all eight buildings are significant and actively independent structures that are correlated with the binary correlation of all variables listed. 4.14 Bootstrapping (Statistics) The bootloader can be used to construct what-if tests, which can be used as an alternative to statistical inference to calculate standard errors when there is a problem, or if parametric reasoning is not possible or complex formulas are required. It can also be used as a practice to estimate the nature of an estimate (eg, its variance) by measuring these properties in a sample of distribution methods. The standard option for distribution methods is the empirical distribution function of the observed data. If a set of observations can be considered as independent and distributed in the same way, then a series of observations (with the same size as the observed data set) with alternative samples, a set of observations (Efron (1996)) can be constructed. 4.15 Direct and Indirect Effects The following table shows the direct and indirect relationship between independent and dependent variables, perceived value, and perceived quality of mediation services: Both the land without mediation paths show significant results and lead to complete intermediation. Independent variables (word-of-mouth and brand image) that affect dependent variables (customer loyalty intentions) are also significant with intermediary variables (brand image). Even a mediator (brand image) shows a significant empirical and dependent variable in two ways that are virtually meaningless: the impact of brand image intentions and loyalty and quality relationships and customer loyalty clients. All other relationships were significant at p <0.05. The relationship is shown in Table 4.20. (WOM) had a significant effect on Customer Loyalty Appeal (CLI) (P = 0.023.β = 0.11). At the same time, there was also a significant relationship between brand identity (BI) and (CLI) a (p = 0.100, beta = 0.08).

Table 4.20: Direct Effects – Two Tailed Significance WOM

BA

BI

Brand Image

.039

.002

...

Customers Loyalty Intentions

.002

.522

.002

WOM had a significant effect on CLI-mediated PV (P = 0.011, β = 0.022). Similarly, the remaining reports also have significant results.

Table 4.21: Indirect Effects - Two Tailed Significance WOM

BA

BI

Brand Image

...

...

...

Customers Loyalty Intentions

.033

.001

...

4.17 Discussion of Results The main purpose of this study is to identify the impact of word of mouth and brand awareness on customer loyalty intentions through the intermediary role of brand image. The results showed that there was a significant direct relationship between global mediation and all reports, such as WOM-BI-CLI, BI-CLI, BA-BI-CLI, WOM-CLI and BA-CLI. The studio demonstrated an important brand image of customer loyalty intentions. Brand image and customer loyalty intention are negatively correlated, but in the literature, there is a positive correlation between the two. Research shows that the image affects an increasingly positive sales response. Therefore, the negative relationship between brand image and customer loyalty intention is reasonable. In addition, Baron and Kenny (1986) pointed out in their article that mediation is very important for establishing the relationship between two variables. In other

words, the two variables can be irrelevant or have an effect on the other without an intermediary. As a result, the route rejected the brand image and the perception of customer loyalty intent between Baron and Kenny was justified. In our research, we can find the positive indirect relationship between the two constructive intentions of these brand images and customer loyalty through the help of mediators (brand image). 5 Conclusion, Research Implications and Future Research 5.1 Conclusion Customer loyalty intentions Phenomenon Due to the huge market growth has become a concern for some organizations and sellers. No organization therefore wants to lose the opportunity to capitalize on the ever-growing tendency to lose customer value. To this end, it is important to investigate the potential causes of emerging markets and new concepts and, as such, intend to conduct a high-level study of customer loyalty, To explore the reasons for the growth, this study is a study that illustrates the impact of three important factors: word of mouth, brand image and brand awareness. Repudiation of service intent, such as the hospitality industry, has become the center of the seller, with one customer responsible for a future consistent way of regaining a product / service and producing the same brand duplicate purchase or use of the same name, but the situation affects Impact marketing that could lead to brand reshaping. Therefore, this study explores the impact of six important factors: loyalty to loyal customers. We conducted an important image of the mediator brand in a conceptual model that mediates mouth and loyalty to maintain customer verbal relationships. 5.2 Research Implications This research is a significant contribution to the academic and organizational perspective due to the following impact 5.2.1 Academic Implications Customer loyalty intentions are a concern for most organizations as they help organizations understand their customers, make policies and make profits. Customer loyalty intent is product evaluation / judgment. Customer loyalty is seen as a continuous future for a particular product /

service and results in duplicate purchases of the same mark or repurchases with the same name, although the impact of the situation may cause the brand to reshape the marketing (Oliver, 1997). Research shows that a 5% increase in consumer loyalty can produce gains of 25-95% (Reckold and Detrick, 2003; and Reckold and Shockwave, 1990), so it knows the factors It is important to influence loyalty customers and. The help of this organization can understand the customer's needs and fill in a better form.

5.2.2 Managerial Implications This study provides valuable information and guidelines to assumptions made by managementengaged managers and managers that the vision on which it is based strongly establishes the intention of customer loyalty. Here are some business implications: The study emphasizes the positive words in the mouth and the perceived image of any brand that can directly affect the customer's loyalty and allows the repurchase of specific products / services. Therefore, it focuses on customer feedback to product / service, marketing directors and other leaders can develop suitable strategies to increase market share and profitability. 5.3 Future Recommendations Although this research was conducted through a planned and cautious process, it provided more areas to be solved and applied advanced research tools. The research provided the following suggestions for future research. A comparative study can be conducted from the same conceptual model that collects data from different samples or from different populations, and the same analysis can be done in the Movement for the Nordic Nation. Concept models can also be explored while adjusting the impact of service quality on the relationship between perceived value and customer loyalty (Lee, Jin and Lee, 2014). Customer

satisfaction can also be considered as a direct factor in researching customer loyalty intentions (Taghizadeh, Iran Taghipour and Khazaei, 2013). Another sampling technique, such as probabilistic sampling, can be used for future research to increase the universality of the findings. The current study is particularly interesting for guests staying in hotels in southern Punjab, but is likely to expand further into other Pakistani cities. Longitudinal analysis can be used to understand the factors behind customer loyalty intentions because time can affect the results of a survey.

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