See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/330992972 Examining the Customer Experience of Using Banking Chatbots and Its Impact on Brand Love: The Moderating Role of Perceived Risk Article in Journal of Internet Commerce · February 2019 DOI: 10.1080/15332861.2019.1567188 CITATIONS READS 32 693 1 author: Jay P Trivedi Mudra Institute of Communications, Ahmedabad (MICA) 27 PUBLICATIONS 83 CITATIONS SEE PROFILE Some of the authors of this publication are also working on these related projects: Viral Marketing Messages View project Celebrity Endorsements View project All content following this page was uploaded by Jay P Trivedi on 17 September 2020. The user has requested enhancement of the downloaded file. Main Document (Blinded) Investigating the Factors that Make a Fashion App Successful: The Moderating Role of Personalization Abstract Online fashion retailers promote usage of fashion apps among their consumers. However, dissatisfaction towards using these apps is one of the reasons consumers uninstall them. This research studies the influence of information quality, service quality, and system quality on app satisfaction. Further, the influence of app satisfaction on purchase intentions is also studied. Additionally, the moderating role of personalization between the three quality dimensions and app satisfaction is observed. Primary data were collected from 268 respondents. The results established system quality as the most significant factor influencing app satisfaction. Further, personalizing the app increased influence of the three quality dimensions on app satisfaction. Keywords: Mobile applications(apps), app satisfaction purchase intentions Introduction The online market in India is growing rapidly backed by aggressive marketing efforts (SivaKumar and Gunasekaran, 2017). It is projected to value US$ 100 billion by 2020 (IBEF, 2017). The smartphone revolution has given an impetus to the rising popularity of the internet. Sale of smartphones in India grew by 18% in 2016, as compared to the global average of 3 % (Chopra, 2016). The rapid rate at which Indian consumers started using smartphones, motivated marketers from industries like travel, banking, e-commerce, and fashion & lifestyle to offer mobile applications (apps) to their consumers (Alavi and Ahuja, 2017). Almost 60 % of Indian urban consumers use the internet, and 77 % of them access it using a mobile phone (Chopra, 2016). It is notable that 64 % of India’s internet users fall in the age group of 20 to 35 years and are referred to as Gen Y in popular literature (Ramanathan, 2015). Gen Y consumers shop more fashion products online (Honigman, 2013). The fashion industry is growing at a rapid rate in India (Singh, 2016). Higher adoption of smartphone enables fashion retailers to sell products and services to young consumers by offering mobile applications (Cecilo, 2015). On average, Indians have installed 32 apps on their mobile phone (Business Standard, 2016). Owing to the large population and fast-growing acceptance of mobile internet, India is expected to be the next big app market in the world (Business Standard, 2016). Fashion and lifestyle industry is one of the largest sectors banking on the success of mobile apps (Cecilo, 2015). This because, some retailers find mobile apps more efficient in converting consumers as compared to mobile browsers (Criteo, 2015). The flip side of this story is that most consumers who have used fashion apps are not satisfied with the experience and hence uninstall them (Meola, 2016). This research provides a direction for fashion marketers in India to understand Gen Y’s shopping behavior through fashion apps. In this study, the researchers have looked at fashion apps as a form of information system (IS). There are multiple explanations of the term information system (Paul, 2010). However, the one that most suits this research work, has been proposed by Huber, Piercy, and Mckeown (2007) which describes IS as “an organized collection of people, information, business processes, and information technology designed to transform inputs into outputs to achieve a goal.” Information system success model (Delone and Mclean, 2003) established three quality dimensions which determine the success of any information system. The three dimensions are information quality, system quality, and service quality. From a marketers’ perspective, it is essential that an online consumer is satisfied, as satisfaction influences future purchase intentions (Zeithaml, Berry, and Parasuraman, 1996). This research focused on fashion apps. One of the objectives of this research was to explore the relationship between the three quality dimensions and app satisfaction. The researchers studied the influence of three quality dimensions viz. information quality, system quality, and service quality on app satisfaction. Further, the impact of app satisfaction on purchase intentions was also studied. The importance of personalization is growing in marketing (Mpinganjira, 2014). On these lines, the role of personalization, as a moderating variable between the three quality dimensions and app satisfaction was observed by the researchers. This research is necessary because even though the Indian online market is growing at a rapid rate, online shopping behavior is still underexplored (Prashar, Vijay, and Prashad, 2017), and shopping behavior through fashion apps is even less explored. The manuscript is structured as follows: Literature review discussed the variables involved in the study. The review helped the researchers propose a model reflecting the research hypotheses. The research design, its implementation, and the statistical tools used are elaborated in the methodology section. The data analysis section describes the execution of the statistical tools to test the hypotheses. Further, the results are discussed, which helps academia and fashion marketers understand Indian consumer’s behaviour while purchasing through fashion apps. The section on limitations and future research lists the limitations of this study and proposes further research possible in this area. Literature Review and Conceptual Framework The researchers studied relevant literature from information systems, web satisfaction, personalization, and the Theory of Reasoned Action. This study helped the researchers propose a model reflecting the research objectives. The independent and dependent variables are discussed in the review laying a theoretical platform for the proposed hypotheses. Model of Information System Success Delone and Mclean (2003) established the updated information system (IS) success model which brought out that three quality dimensions viz. information quality (INFQ), system quality (SYSQ), and service quality (SERQ) play a crucial role in predicting the success of an IS. Their research also established that this model could be used to measure challenges of the modern B2C ecommerce world. The present study is focused on fashion apps. The researchers have applied the IS success model to observe the influence of INFQ, SYSQ, and SERQ on app satisfaction. The information system success model was chosen over other models like the Technology Acceptance Model (Davis, 1989) as it captures an essential commercial factor like service quality along with the technical factors like information quality and system quality. The Technology Acceptance Model focused only on studying the perceived ease of use and perceived usefulness of the technology under consideration. However, it did not focus on studying the effect of the service quality offered by the technology firm. As explained by Delone and Mclean (2003), information quality refers to the content dimension of an IS. In this study, the researchers have looked at a fashion app as a form of an IS. Hence, information on the app should be complete, relevant and easy to understand. Elaborating on information quality, Lee (2002) explained that an IS must present information in a manner that users find comprehensible and concise. From the fashion app perspective, this implies that a fashion marketer must not overload users with information but provide relevant details about the product through the app. If a fashion marketer provides incomplete and irrelevant information, the user may get frustrated and uninstall the app. System quality refers to usability, availability, reliability, adaptability, and response time of the app (Delone et al., 2003). Usability refers to the ease with which an individual can use a fashion app (ISO 1998). Availability of the app refers to the ability of a user to access information in a specific place and also in the correct format (Schou, 1996). Users expect 100% availability of the app which is difficult to achieve. (Martin and Khazanchi, 2006). This expectation presents a challenge for fashion marketers because, in a situation of frequent unavailability, consumers start forming a negative attitude towards the app. Reibman and Veeraraghavan (1991) explained reliability as the possibility of an information system performing appropriately in a given period under different operating conditions. There might be an instance when a consumer is using a slow net connection or located in an area where network signals are weak. Even in such circumstances, the users expect the app to perform optimally, presenting a challenge for the fashion marketer. Adaptability is the extent to which an information system can adapt itself to changes as needed and expected by various stakeholders (Nechkoska, 2015). For example, Indians use budget smartphones with low random access memory (RAM). This situation presents a challenge for fashion marketers as the app needs to be developed in the smallest file size possible. The amount of time taken by the IS to process an input and respond to it is referred to as the response time. Usually, this period is expressed in seconds (Hoxmeier and DiCesare, 2000; Joseph and Pandya, 1986). Consumers expect fashion apps to respond quickly, enabling them to complete the desired task faster. Service quality refers to responsiveness, assurance, and empathy offered by the IS provider (Delone et al., 2003). This dimension was added by Delone and Mclean in 2003 as an update to the IS model proposed by them earlier. Delone and Mclean (2003) argued that the emergence of end-user computing made IS assume two different roles viz. that of an information provider and the other of a service provider. They established that the quality of service provided by an IS also determined its success or failure. Responsiveness was described as the promptness in providing service to its users (Delone et al., 2003). At times, backend operations support to mobile apps may not be good enough, leading to dissatisfaction. Assurance was explained as the knowledge possessed by the employees to accomplish their task (Delone et al., 2003). For example, most online companies have a customer service call center. If a customer service associate is not trained well, the customer will be quick to spread the word in the market and abandon using the app. Delone et al. (2003) explained empathy as keeping user interests at heart. For instance, a consumer might not be aware of the company's loyalty program. The company then needs to reach out the consumers and make her aware of the multiple benefits available under their loyalty program. This gesture will ensure the consumer that the company keeps her interests at heart. Research on the influence of the three quality dimensions on user satisfaction has exhibited varying results when tested for different industries. Research papers, which focused on measuring IS success in service sectors like insurance posited that service quality was the primary dimension influencing customer satisfaction (Browne and Cudeck, 2009; Zhou and Huang, 2009). Zhou, Nakatani, and Chuang (2011) studied mobile website adoption and established that all three quality dimensions viz. information quality, system quality, and service quality exhibit a significant influence on customer satisfaction. As consumers increasingly shop through fashion apps, it is crucial for marketers to study the factors leading to app satisfaction. App Satisfaction (APS) Consumer satisfaction is crucial to success in any business. Satisfaction is defined as "consumer's evaluation of a product or a service concerning their needs and expectations.” (Oliver, 1980). From the perspective of online shopping, e-satisfaction can be defined as “the contentment of the customer for her prior purchasing experience with a given electronic commerce firm (Anderson and Srinivasan, 2003). In this study, the researchers have adopted the same definition from the perspective of a mobile application selling fashion apparel. Zeithaml et al. (1996) studied the role of satisfaction in motivating purchase intentions for an online portal. The results showed that when consumers were satisfied with the services of an online portal, they were more likely to purchase through that portal. Other researchers too, confirmed the relationship between satisfaction and purchase intention through their empirical results. Shankar, Smith, and Rangaswamy (2003) established that customer’s intention to purchase from an ecommerce portal is influenced by the attitude towards that site, which is a result of their overall satisfaction with the shopping experience. Bai, Law, and Wen, (2008) explored the relationship between customer satisfaction and purchase intentions. The results established satisfaction as a crucial determinant of purchase intentions in the online environment. Kumar and Mukherjee (2013) studied mobile device user's attitude towards shopping and their purchase intentions. The study revealed that user's attitude towards technology and perceptions for shopping through mobile devices played a crucial role in forming purchase intentions. Jiradilok (2014) studied the impact of web satisfaction on consumers purchase intention. The study was done for an e-commerce portal. The results established a significant relationship between satisfaction and purchase intentions. However, most of the above studies lack exclusive focus on mobile applications. Bellman, Potter, Treleaven, Robinson, and Varan (2011) established that apps differentiate themselves from websites as they are comparatively more engaging. This difference makes it essential to study consumer behavior while purchasing through apps. Hedonic and Utilitarian shopping values motivate buying behaviour, which increases web satisfaction (Prashar et al., 2017). Wolfinbarger and Gilly (2001) posited that online shoppers with hedonic motivation look for enjoyment while shopping online. On the other hand, online shoppers with utilitarian motivation, look for product price, functionality, and usability (Sorce, Perotti, and Widrick, 2005). However, in this study, the researchers focused only on observing the influence of the three quality dimensions on app satisfaction directly and then, moderated by personalization. Hence the researchers did not include the two shopping values in the model. Based on the above discussion, the researchers proposed the following hypotheses: H1: Perceived information quality has a significant influence on app satisfaction H2: Perceived system quality has a significant influence on app satisfaction H3: Perceived service quality has a significant influence on app satisfaction Personalization(PER) Mpinganjira (2014) posited that the concept of personalization had been interchangeably used with customization. The variable explains the level up to which a marketer can tailor make a product or service for their consumers. Vasanen (2007) established that personalization has a significant and positive influence on consumer satisfaction. Personalization has been addressed in several ways like retailers mailing consumers using their name, providing personalized recommendations and also by giving preferential access to the website (Chung and Shin, 2008). Simonson (2005) established that success of personalization efforts depends on the stability of choices made by a consumer and established personalization as an important moderating variable. Halima, Chavosh, and Choshalyc (2011) reported a significant relationship between personalization and consumer satisfaction. Personalization for online platforms can be achieved through the use of data mining techniques (Mehtaa, Parekh, Modi, and Solanki, 2012). The above studies indicate that personalization could moderate the relationship between the three quality dimensions and app satisfaction leading to the formation of the following hypothesis: H4: Personalization moderates the relationship between the three independent variables viz. information quality, system quality, service quality and the dependent variable app satisfaction. Purchase Intentions (PI) The Theory of Reasoned Action (TRA) coined by Ajzen and Fishbein (1980) established the importance of behavioral intentions as an essential research construct. Researchers in the area of marketing have used this variable to measure acceptance of a product or a service in the market (MacKenzie, Scott, and Lutz, 1989). Purchase intention, the dependent variable in this study, was described as the consumer’s judgment if she will consider purchasing an available and advertised product in the near future (MacKenzie et al., 1989). In this study, the researchers focused on mobile apps for fashion apparel. Wu et al. (2009) established that success of M-commerce industry depends on consumers' regular and long-term intention to use the platform. Chong (2013) established that M-commerce users are not very consistent in their action and might not return if they leave dissatisfied. However, their satisfaction level will determine the consistency of shopping through mobile devices. Organizations spend millions of dollars on developing state of the art mobile applications. This investment is justified if the consumers are satisfied with the app and make regular purchases. Hence studying the relationship between app satisfaction and purchase intentions is crucial. The above discussion leads to the formation of hypothesis H5 and a model as proposed in Figure-1: H5: App satisfaction has a significant influence on purchase intentions Figure 1: Authors’ Proposed Model -------- = Moderating Information Quality (INFQ) System Quality (SYSQ) App Satisfaction (APS) Purchase Intentions (PI) Service Quality (SERQ) Personalization (PER) Methodology Data and Sample The descriptive research design was used to conduct the research. A structured questionnaire was created and administered to Gen Y respondents falling in the age group of 21- 25 years. This cohort was chosen as they form the largest proportion of online fashion shoppers. Moreover, this cohort is digitally literate and can share important insights into the research topic. A group of 350 postgraduate respondents (a mix of male and female) living in an Indian metro city were asked if they had purchased apparel using fashion apps. 310 respondents answered in affirmative. Only these respondents were sent the questionnaire. Finally, 268 responses were found eligible to be included for data analysis as other responses were incomplete. The respondents were informed that the purpose of the survey is academic and not commercial. The respondent characteristics are exhibited below. Respondent Characteristics (n=310) Age(in Years) Under 20 20-25 26-30 Gender Male Female in % 0% 56% 44% 53% 47% in % Marital Status Single Married Seperated 95% 5% 0% Household Income(in INR) Upto 5,00,000 5,00,001-10,00,000 10,00,001 and above 4% 57% 39% Instrument Design A seven-point Likert scale ranging from “strongly disagree” to “strongly agree” was used to design the questionnaire. The language of the instrument was English. The literature review identified information quality, service quality, system quality, personalization, web satisfaction, and purchase intentions as the variables forming the proposed research model. The scale used to measure information quality, system quality, and service quality were adapted from a study done by Brown and Jayakody (2009). Personalization was measured using a scale adapted from Harris and Goode (2010) and Srinivasan et al. (2002). The scale used to measure app satisfaction was adapted from a study done by Bhattarcherjee (2001). The scale for measuring purchase intentions was adapted from the study done by Dodds (1991). The instrument consisted of 23 items. Statistical Tools SPSS, AMOS, and MS-Excel were the statistical software used for data analysis. Cronbach’s alpha was the statistical tool used to measure the internal consistency of the questionnaire. Exploratory and confirmatory factor analysis was conducted to measure instrument validity. Structural model measurement was conducted to test the hypotheses. Hierarchical regression analysis was conducted to test the moderation effect. Average variance extraction and composite reliability were calculated using Excel. Data Analysis Measurement Model and Validity The internal consistency of the instrument was established using Cronbach’s alpha. Alpha values obtained were above 0.70 and hence acceptable (Nunnally, 1978). The alpha values for each variable is exhibited in Table-1. KMO and Bartlett’s test was conducted to observe the need for conducting factor analysis. Value for KMO was 0.862 and Bartlett’s test was significant, thus justifying the need for factor analysis. Exploratory factor analysis (EFA) was executed to establish the validity of the instrument. Factor loadings obtained for every item of different variables were above 0.60. EFA values are exhibited in Table-2. To test for multicollinearity issues, VIF values were observed. Maximum VIF value obtained was 1.811, indicating no problem of multicollinearity. Post this, confirmatory factor analysis (CFA) was executed using maximum likelihood estimation. Table-3 exhibits result of model fit tests. Chin and Todd (1995) established that CMIN/DF value of less than 3 indicates a good model fit. Hair, Anderson, Tatham, and Black, (1998) established that values of TLI, GFI, and CFI if found greater than 0.90, indicate an excellent model fit. The SRMR value should be less than 0.07 for a good model fit. RMSEA value of less than 0.1 indicates a good model fit (Browne and Cudeck, 1992). The results obtained in this research comply with the established standards, and hence the model demonstrated an acceptable fit. Factor loadings of three items post the CFA was below 0.50. These items were removed from further analysis. Further, average variance extracted (AVE) and composite reliability (CR) were tested to establish consistency and validity. Table-4 exhibits values obtained for Cronbach alpha, factor loadings, AVE and CR satisfying conditions for convergent validity. Bhattacherjee and Premkumar (2004) posited that to establish discriminant validity, the square root of the AVE of a variable should be higher than its correlation values with other variables. The results establish the discriminant validity and are exhibited in Table-5. Common Method Variance Test The only instrument used for collecting data was a questionnaire. Hence, post examining the measurement model, common method variance test was conducted. Harman’s single factor test was executed to identify the possibility of errors due to the common method. For this, all the factors were loaded as a single factor, and EFA was executed without any rotation. The result showed that a single factor accounted for around 23 % of the variance, indicating an absence of common method issues. Structural Model Measurement The proposed model was tested using structural equation modeling. The results of the tests conducted for establishing a model fit are exhibited in Table-6 and indicate an acceptable model fit. Regression analysis was conducted to study the effect of independent variables on dependent variables as exhibited in Table-7 and also in Figure-2. All three independent variables viz. INFQ, SERQ, and SYSQ exhibit a significant influence on APS. However as observed in the results, SYSQ exhibited the strongest influence on APS (p= 0.000, β= 0.423). APS exhibited a very strong influence on PI (p=0.000, β= 0.943). Figure 2: Relationship between variables with Beta Values Information Quality (INFQ) System Quality (SYSQ) S= Significant S, 0.285 S,0.423 S,0.943 S, 0.378 Service Quality Hierarchical Regression Analysis – Moderation Test (SERQ) Purchase Intentions (PI) App Satisfaction (APS) The researchers studied the moderating role of personalization between the three quality dimensions and APS. Hierarchical regression analysis was executed to test the moderating effect of personalization between the three quality dimensions and APS. Holmbeck (2002) established that moderation analysis tests if an interaction between the moderating variable and independent variable is a significant predictor of the dependent variable. Kim, Kaye, and Wright (2001) established that a moderating variable might increase or decrease or change direction between independent and dependent variables. The moderated regression technique proposed by Baron and Kenny (1986) and Sharma et al. (1981) was conducted. A three-step hierarchical regression was conducted. In the first model, the effect of INFQ, SYSQ, and SERQ is observed on APS. In second regression PER is introduced in the model as an additional independent variable predicting APS. In the last step interaction between three quality dimensions viz. INFQ, SYSQ, SERQ, and PER is created after standardizing the variables and then computing the interacting variable, which is added to the model as a predicting variable. APS is the dependent variable here. Regression results exhibited in Table-8 and Figure-3, establish the moderating effect of PER between the three quality dimensions and APS. The increase of R squared values in model 3 in comparison to model 1 also indicate the moderating effect. The increase is also statistically significant suggesting a moderating effect (Aiken et al. 1991). Figure 3: Moderating Role of Personalization Information Quality (INFQ) System Quality (SYSQ) S= Significant S, 0.323 S,0.528 S, 0.453 Service Quality (SERQ) App Satisfaction (APS) S, 0.403 Personalization (PER) Discussion As observed in Table-7, all three quality dimensions viz. information quality, system quality, and service quality exhibit a significant influence on app satisfaction. This validates hypotheses H1, H2, and H3. These findings match the results obtained by Zhou et al. (2011). However, looking at beta values obtained from the regression, the influence of system quality is strongest on app satisfaction. Hence, this research helps establish that better system quality increases APS (app satisfaction). APS exhibited a significant influence on PI, validating hypothesis H5. This result matches the results obtained by Zeithaml et al. (1996), Bai et al. (2008) and Jiradilok (2014). However, the above studies did not focus on mobile applications, making the current study a unique contribution. The result obtained in this research helps establish that satisfied Gen Y fashion app users develop strong purchase intentions, increasing the possibility that the firm will succeed financially. As observed in Table-8, personalization increases the strength of the relationship between the three quality dimensions and app satisfaction, thus playing the role of a moderating variable in the model (Kim et al., 2001). This validates hypothesis 4 (H4). This result matches with results obtained by Simonson (2005) and Halima et al. (2011). However, their research did not study personalization from fashion app perspective, hence making this research necessary. These results help establish that fashion marketers should ensure a personalization experience in addition to a strong system quality to ensure app satisfaction. The results further establish a significant influence of app satisfaction on purchase intentions. The increased purchase intentions may lead to the financial success of the firm. Contribution to Theory and Practice Usage of fashion apps is growing in India. Retail companies are launching fashion apps to improvise their consumers’ shopping experience. However, consumer dissatisfaction with the usage of fashion apps is one of the reasons leading to app uninstalls. This research attempted to study the impact of three quality dimensions viz. information quality, service quality, and system quality on app satisfaction which further leads to purchase intentions. The role of personalization as a moderating variable between the three quality dimensions and app satisfaction was also studied. The results obtained demonstrate that all three quality dimensions viz. INFQ, SYSQ, and SERQ significantly influence APS. Also, APS exhibits a significant influence on the PI. However, system quality exhibited the most significant influence on app satisfaction. This is a significant contribution towards understanding Indian Gen Y’s shopping behaviour through fashion apps. System quality emphasizes usability, availability, reliability, adaptability and response time of the app. Gen Y wants fashion apps which are easy to use and give relevant information. They also want the app to perform optimally under different operating conditions. Gen Y appreciates the fashion apps which are quick to respond. This requires fashion marketers to offer apps which are small in file size. Users want to complete the intended action with minimum possible effort indicating affinity towards apps which require minimum clicks for accomplishing the desired action. Users also value factors like uptime of the app and its ability to provide easy, consistent, quick and apt operational performance. This establishes that if an app does not operate when needed, app satisfaction may be negatively affected. Personalization was observed to moderate relationship between the three quality dimensions and app satisfaction. Personalization increases the strength of the relationship between independent variables INFQ, SYSQ, SERQ and dependent variable APS, thus moderating the relationship. Existing studies have posited a strong correlation between personalization and online user satisfaction for website shopping. This study reaffirms the proposition with a focus on fashion apps. Drawing from these results, fashion marketers can employ data-led strategies which enable the app to learn from the user’s profile and provide customized information, service, and system performance, leading to higher user satisfaction, which further leads to purchase intentions. Hence we can say that fashion apps offering good system quality and a personalized experience are more likely to satisfy the Gen Y users in India. Once satisfied, the users are more likely to purchase through the app. Limitations and Future Research This research has several limitations. The data collected for this research is cross-sectional. A longitudinal work can capture changing user expectations with time. Data is collected from Gen Y members currently residing in the metro cities in India. More inclusive research involving metro and non-metro consumers may present a better picture of the research objective. Also, the researchers did not attempt to bifurcate responses on the basis of gender. Such an attempt can be made in future to strengthen the body of knowledge in this area. More variables like hedonic and utilitarian shopping values can bring fresh insights into understanding Gen Y’s shopping behavior through apps. 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Appendix Table 1: Cronbach Alpha Values Variable System Quality Service Quality Information Quality Personalization App Satisfaction Purchase Intention Total No.of Items 4 4 5 3 3 4 23 Alpha Value 0.909 0.796 0.797 0.824 0.742 0.827 Table 2: Exploratory Factor Analysis Factors Information Quality Fashion m-commerce apps provide me with the precise information I need Fashion m-commerce apps provide responses to questions and queries that are exactly what I need Fashion m-commerce apps provide sufficient information to enable me to do my tasks I am satisfied with the accuracy of information provided by fashion m-commerce apps The information provided by fashion m-commerce apps is helpful regarding my questions or problems System Quality I find it easy to become skillful at using fashion mcommerce apps I believe that fashion m-commerce apps are easy to use Using fashion m-commerce apps require very little mental effort It is satisfying to use fashion m-commerce apps Service Quality I am satisfied with the customer support provided by fashion m-commerce apps I am satisfied with the after-sales service provided by fashion m-commerce apps Fashion m-commerce apps understand my problems and requests Fashion m-commerce apps respond to my requests fast enough INFQ SYSQ SERVQ PER 0.792 0.707 0.761 0.702 0.756 0.921 0.918 0.841 0.868 0.775 0.791 0.726 0.867 APS PI Personalization The services of fashion m-commerce apps are often personalized for me The fashion m-commerce apps treat me as an individual unique customer When communicating with the fashion m-commerce apps I am often addressed using my name App Satisfaction While shopping on fashion m-commerce apps I feel like I am exploring new worlds I am pleased with the experience of using fashion mcommerce apps The fashion m-commerce apps do a good job of satisfying my needs Purchase Intention My likelihood of purchasing apparel products from fashion m-commerce apps is high The probability that I would consider buying apparel through fashion m-commerce apps is high My willingness to buy apparel through fashion mcommerce apps is high I intend to purchase apparel through fashion mcommerce apps 0.883 0.88 0.817 0.864 0.805 0.779 0.814 0.856 0.869 0.704 Table 3: Results of Model Fit test of CFA FIT Test CMIN/DF CFI TLI GFI RMSEA SRMR Values 1.879 0.911 0.907 0.897 0.078 0.0725 Table 4: Post CFA, Cronbach alpha values, AVE, CR and Factor Loadings Factor Post CFA Alpha AVE Factor Value Loadings Information Quality -INFQ Fashion m-commerce apps provide me with the precise 0.834 information I need Fashion m-commerce apps provide sufficient information 0.785 to enable me to do my tasks Composite Reliability The information provided by fashion m-commerce apps is helpful regarding my questions or problems System Quality -SYSQ I find it easy to become skillful at using fashion mcommerce apps I believe that fashion m-commerce apps are easy to use Using fashion m-commerce apps require very little mental effort It is satisfying to use fashion m-commerce apps Service Quality - SERQ I am satisfied with the customer support provided by fashion m-commerce apps I am satisfied with the after-sales service provided by fashion m-commerce apps Fashion m-commerce apps understand my problems and requests Fashion m-commerce apps respond to my requests fast enough Personalization -PER The services of fashion m-commerce apps are often personalized for me The fashion m-commerce apps treat me as an individual unique customer When communicating with the fashion m-commerce apps I am often addressed using my name App Satisfaction - APS While shopping on fashion m-commerce apps I feel like I am exploring new worlds I am pleased with the experience of using fashion mcommerce apps The fashion m-commerce apps do a good job of satisfying my needs Purchase Intention -PI My likelihood of purchasing apparel products from fashion m-commerce apps is high The probability that I would consider buying apparel through fashion m-commerce apps is high My willingness to buy apparel through fashion mcommerce apps is high 0.756 0.802 0.637 0.713 0.909 0.724 0.701 0.796 0.532 0.740 0.824 0.619 0.718 0.742 0.615 0.715 0.835 0.618 0.717 0.907 0.902 0.779 0.808 0.746 0.707 0.789 0.792 0.822 0.801 0.736 0.843 0.665 0.626 0.717 0.821 0.815 Table 5: Discriminant Validity Factor INFQ SYSQ SERQ PER APS PI INFQ 0.798 0.392 0.395 0.468 0.423 0.447 SYSQ SERQ PER APS PI 0.85 0.608 0.561 0.425 0.616 0.729 0.426 0.487 0.615 0.786 0.468 0.443 0.784 0.683 0.785 Table 6: Results of Model Fit test of structural model FIT Test CMIN/DF CFI TLI GFI RMSEA SRMR Values 1.853 0.923 0.915 0.891 0.076 0.0721 Table 7: Structural Model Assessment (IV = independent variable, DV= Dependent variable) IV DV Beta Value t Value p Value INFQ APS 0.285 2.631 0.009 SERQ APS 0.378 4.189 0.001 SYSQ APS 0.423 5.505 0 APS PI 0.943 6.101 0 Table 8: Regression 2 – Hierarchical Regression Analysis for Moderation Testing Beta Value t Value p Value R2 0.298 0.431 0.373 2.631 4.189 3.897 0.009 0.000 0.002 0.53 APS 0.315 0.510 0.437 0.402 3.569 4.283 3.654 4.138 0.003 0.000 0.002 0.001 0.59 INFQ APS SYSQ SERQ PER Moderator (INFQ*SYSQ*SERQ*PER) IV = Independent Variables, DV = Dependent Variable 0.323 0.528 0.453 0.403 0.421 2.113 4.897 3.993 4.129 4.561 0.013 0.000 0.001 0.003 0.000 0.61 Models Model 1 IV INFQ SYSQ SERQ DV APS Model 2 INFQ SYSQ SERQ PER Model 3 View publication stats
