Identifying key factors affecting consumer purchase behavior in an online shopping context
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The study aims to identify key factors influencing consumer purchase behavior in online shopping, focusing on service quality dimensions, information satisfaction, and security perception. A sample of 107 respondents revealed that user interface quality, product information quality, and service information quality significantly affect consumer site commitment and relational benefits. Findings suggest that information features of online shopping sites are critical for determining consumer loyalty and purchasing decisions.
Identifying key factors affecting consumer purchase behavior in an online shopping context
- 1. Identifying key factors affecting consumer purchase behavior in an online shopping context in Gwalior Group members name Kratika Agnihotri Kuldeep Mathur Sakshi Mishra* Shivam Sharma* Sumit Arora* Research methodology Objective and hypothesis These are the following objectives of this study: The aim of this study is to identify the key service quality dimensions that affect relational benefit while choosing online shopping store. On what basis customer choose the online shopping stores? To know about the service provided by the online shopping stores. To study the level of satisfaction of the respondents about the various facilities. This study focuses on various satisfactory levels like quality, satisfaction, security available of the online stores. The study investigates the impact of product quality and security perception on relational benefit. In order to meet the study’s objectives and answer research questions, following Hypotheses were proposed: H1. There is a positive relationship between information satisfaction and product information quality.
- 2. H2. There is a positive relationship between information satisfaction and service information quality. H3. There is a positive relationship between information satisfaction and user interface quality. H4. There is a positive relationship between information satisfaction and security perception. H5. There is a positive relationship between the product information quality and relational benefit. H6. There is a positive relationship between the service information quality and relational benefit H7. There is a positive relationship between security perception and relational benefit. H8. There is a positive relationship between site awareness and relational benefit. H9. There is a positive relationship between relational benefit and site commitment. H10. There is a positive relationship between information satisfaction and site commitment. H11. There is a positive relationship between site commitment and purchasing behavior. Sample size: The sample size is 107 respondents. Sampling Design 107 respondents were randomly selected. Respondents were only students who filled a questionnaire the collected data were carefully assessed to the statistical software i.e. SPSS and the results were taken as they were required for the analysis of this research study. Data collection method: In this study both primary and secondary sources of data will be included. The primary data for this has been taken by the help of structured questionnaire that proved to be effective in collection the relevant information; the data of questionnaire was collected from 107 respondents which served as the primary source of data for the analysis of this research and that lead this research study to the exploration of the customer choice behavior and customer satisfaction towards restaurants. At the same time literature review of this research study will provided the secondary. Source of secondary data which is gathered from published research articles. Research Model
- 3. Data Collection The target population of this study consists of all customers who shop from online stores. It contains heterogeneous products and these response are reference on the basis of last visited online store of customer. The target customers was from Gwalior region. Measures When we developed the questionnaire, the multiple-item method was used and each item was measured based on a five-point Likert scale from ‘‘strongly agree’’ to ‘‘strongly disagree’’. All operational definitions of the constructs and instrument items are shown in Table . Table I Descriptive statistics of the respondent profile Measure items % Gender Male 70.1 Female 29.9 Age 16-25yrs 86.9 26-34yrs 10.3 35-44yrs 1.9 44+yrs 0.9 Time to use internet ½ hrs per day 15.9 1 hr per day 19.6 2 hrs per day 43 More 21.5
- 4. Preferred online site Flipkart 57.9 Amazon 30.8 Other 11.2 Variety of products Electronics 35.5 Appliances 3.7 Clothes & Accessories 54.2 Home & Furnitures 2.8 Books & more 2.8 Other 0.9 Table2: AII Measurements of instrument of key constructs Construct Items (anchors: strongly disagree/strongly agree) Independent variables User interface quality 1. This site is convenient to search for product 2. This site is convenient to order a product 3. This site is easy to navigate wanted product 4. This site is user friendly Product information quality 1. This site provides up-to-date product information 2. This site provides sufficient product information 3. This site presents product information easy to understand 4. The book information is consistent 5. The book information is playful 6. The book information is relevant Service information quality 1. This site provides up-to-date service information 2. This site provides sufficient service information 3. This site presents service information easy to understand 4. The service information is consistent 5. The service information is playful 6. The service information is relevant Site awareness 1. Neighbors know this site very well 2. This site is very famous as an Internet online store 3. This site is known through the advertising media (TV, newspaper,Internet, etc.) Security perception 1. My private information is managed securely on this site
- 5. 2. I am sure that payment information will be protected in this site 3. This site provides detailed information about security 4. I am afraid that my private information will be used in an unwanted manner. Mediators and dependent variable Variables Information satisfaction 1. I am satisfied with the information service of this site compared to other shopping sites 2. Information service of this site satisfies my expectations 3. I am satisfied with the overall information service of this site Relational benefit 1. At this site, I am able to reduce the time to purchase wantedproducts 2. At this site, I am able to reduce efforts to purchase wanted products 3. At this site, I am able to purchase wanted product that are hard to purchase at other stores 4. I will receive credible customer service from this site. Site commitment 1. I will not change my product shopping site in the future 2. I will continuously purchase products at this site in the future 3. I will recommend this site to other people 4. I will visit this site first when I want to buy products Purchasing behavior Please mark the frequency of product purchase at this site in a year .Reliability of measurement instrument The Cronbach alpha coefficient was used to assess reliability of the measures (Straub, 1989). As shown in Appendix 3, reliability coefficients were acceptable for all constructs, ranging from 0.8687 for service information quality to 0.6712 for relational benefit. While all the reliability figures were higher than 0.6, the lowest acceptable limit for Cronbach’s alpha suggested by Hair et al. (1998), variables with reliabilities lower than 0.8 deserve further refinement in future research.
- 6. REGRESSION ANALYSIS: 1. Impact of independent variables on relational benefit- The first table is the Model Summary table, as shown below. Model Summaryb Mode l R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square Change F Chan ge df1 df2 Sig. F Change 1 .890a .793 .780 1.75727 .793 63.70 1 6 100 .000 a. Predictors: (Constant), secpertotal, uiqtotal, sattotal, siqtotal, sitawatotal, piqtotal b. Dependent Variable: relbentotal This table provides the R and R2 values. The R value represents the simple correlation and is 0.890 (the "R" Column), which indicates a high degree of correlation. The R2 value (the "R Square" column) indicates how much of the total variation in the dependent variable, relational benefit, can be explained by the independent variable, user interface qualitys,product information quality,security perception,site awareness, reliability. In this case, 79.3% can be explained, which is very large. The next table is the ANOVA table, which reports how well the regression equation fits the data (i.e., predicts the dependent variable) and is shown below: ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 1180.247 6 196.708 63.701 .000b Residual 308.800 100 3.088 Total 1489.047 106 a. Dependent Variable: relbentotal b. Predictors: (Constant), secpertotal, uiqtotal, sattotal, siqtotal, sitawatotal, piqtotal
- 7. This table indicates that the regression model predicts the dependent variable significantly well. Look at the "Regression" row and the "Sig." column. This indicates the statistical significance of the regression model that was run. Here, p < 0.0005, which is less than 0.05, and indicates that, overall, the regression model statistically significantly predicts the outcome variable (i.e., it is a good fit for the data). The required table shows that our findings have supported the hypotheses that Hypothesis 1 proposed that information satisfaction has no impact on relational benefit. Hypothesis 2 proposed that security perception has no impact on relational benefit. A multiple regression analysis was conducted to verify this and explore how much variation in relational benefit could be explained by the variability in different dimensions. Such analysis is appropriate in the case that there a set of predictor variables (user interface quality, product information quality, service information quality, site awareness, security perception, information satisfaction and site commitment) and one response variable (relational benefit). The regression results shown in Table 3 indicate that the independent variables have a significant and information satisfaction and security perception have no effect on relational benefit. Therefore, hypothesis 1 and hypothesis 2 is rejected. The Coefficients table 5 provides us with the necessary information to predict satisfaction from independent variables, as well as determine whether independent variables contribute statistically significantly to the model (by looking at the "Sig." column). Furthermore, we can use the values in the "B" column under the "Unstandardized Coefficients" column, as shown below: Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig.B Std. Error Beta 1 (Constant) 1.191 .521 2.288 .024 sattotal .563 .110 .438 5.126 .000 uiqtotal -.057 .097 -.058 -.587 .559 piqtotal .027 .084 .041 .325 .746 siqtotal .055 .060 .085 .913 .364 sitawatotal -.024 .123 -.020 -.191 .849 secpertotal .435 .096 .454 4.511 .000 a. Dependent Variable: relbentotal
- 8. Conclusion We developed and empirically validated a model of consumers’ relational purchasing behavior in an online shopping context. The key affecting factors of user interface quality, product and service information quality ,security perception and site awareness were found to have significant effects on consumer’s site commitment. Furthermore, we investigated whether information satisfaction and relational benefit play a significant mediating role on consumers’ relationship purchasing behavior. In an online shopping context, the information feature of a shopping site was validated to be an important factor determining consumers’ site loyalty and decision-making in terms of whether or not they will shop at the store. This emphasizes the importance of product information quality and user interface design in the online shopping site development. Other attributes of an online store were also found to influence a consumer’s perceived relational benefits from online shopping. Service information quality was found to be the most important factor among them.