석사학위논문 Study on Customer Satisfaction of Online Shopping in Uzbekistan
Download 0.89 Mb.
|
25.06 Thesis
- Bu sahifa navigatsiya:
- Question Gender
- Responses Percentage (%) Frequency
- Estimation of Parameters
- Parameters Result 10
- 3. Model Fit Summary Figure 5> Research Framework Result 11 Delivery Speed
- Web Design Design v
- Root mean square error of approximation (RMSEA) Result 12
- PCLOSE Default model
- Root mean square residual (RMR) and Goodness-of-fit index (GFI) Result 13
- Conclusion and limitations
- Appendix Abstract
|
Descriptive Result
In
This questionnaire had 293 valid responses including all Uzbek respondents inside and outside of Uzbekistan. Based on the table 2 Data Statistical Result, 67.6% of total respondents are male and 32.4% of total is male. Looking insight to the Age groups are between the 19-24range old about 23.6% of total respondents following the majority age group is 25-29 years old about 44.5%of total respondents. 30-34 years old and 35-39 years old respondents can be shown as 13.9% and 8.2% of total respondents as in order. In Uzbekistan older people rarely use from the internet, that’s why 40-45 range old and above 46 years old respondents have very little votes with 4.7% and 2.4%. Coming to the education level, Uzbek people are trying to get high diploma that’s what middle level of education is seen very low degree as 3.1%of all respondents. High School students as the respondents’ latest education reached 16.4% of total respondent while looking for the profession statistics result, 70% of total numbers are students, following 10.5% of total respondents are post university masters. Estimation of Parameters In this study, we use Maximum likelihood and Scalar estimates for testing the hypothesis in order to reach reliably approach and data information were normally dispersed (James B, et al., 2006). The model was evaluated by determining the fit indexes. While the Goodness of Fit (GFI) index is approaching to 0.9 (Bentler C, et al., 1990). The hypothesis verification can illuminate in the SPSS AMOS estimates section and it can be shown as below;
The
The RMSEA value of about 0.08 or less would indicate a close fit of the model in relation to the degrees of freedom (Arbukle at al, 2005). In this study RMSEA result is 0.057 and it means that RMSEA result is good. And the model is fit.
The RMR is the square root of the discrepancy between sample matrix and model matrix. The RMR can be difficult to analysis, however, as its range is belong to the scales of the indicators in this model. The smaller, the better; if the result shows 0, the model indicates perfect fit. In this study, RMR is 0.055; it means that the model is fit. GFI (goodness-of-fit index) was first used by Jöreskog (1984) for measuring Maximum Likelihood and Un-weighted Least Squares estimation. GFI always shows less than 1 or equal to that. If GFI is close to 1 it will indicate a perfect fit. In this study, GFI result is 0.893, it means that the model is fit. Conclusion and limitations Online customer satisfaction is the basic object to the marketing concept. This study was managed with the aim of measuring customer satisfaction. First one is to identify the factors affected on customer satisfaction. The latter is to determine the connection between online customer satisfaction and product quality, web design, delivery speed, e-payment, security and trust. Prior researches on online shopping documented that product quality, web design, delivery speed, e-payment, security and trust are related to customer satisfaction on online shopping. This study was an attempt to find out whether given factors also plays an important part in satisfying online buyers in Uzbekistan. Findings show that product quality, web-design, delivery speed, e-payment, security and trust play an essential role in customer satisfaction among internet shoppers in Uzbekistan. In order to testing hypothesis is accepted or not, we use AMOS statistical estimates. The result of testing hypothesis as mentioned earlier that use 11 hypotheses were 2 hypothesis which determine web design, security were not accepted because the value it is not significant enough and the finding reinforce from previous findings (Grewal, et al., 1998). This may be happened in Uzbekistan customers do not pay more attention to delivery speed, web design and they have very little impact on online security as well as online trust. While this research is not the best study and need more improvements in the future directions. Therefore, readers should be aware of some parts. First, the lack of total 37 numbers of data information can be impact on statistical data-set results. It needs more data and the lack awareness of Uzbek consumers while participating into this online survey made some irrational data that cannot be accepted in statistical analytics. Second, the research framework can be more develop in size with new trends and ideas will establish in upcoming periods.
Alba et al., J. (1997). Interactive home shopping: Consumer, retailer, & manufacturer incentives to participate in electronic marketplaces. Journal of Marketing, 38-53. Anderson, E.W., Fornell, C., Lehmann, D.R., (1994). Customer satisfaction, market share, and profitability: Findings from Sweden. Journal of Marketing, 58, 53-66. Anderson, R.E. and Srinivasan, S.S. (2003) ‘E- satisfaction and e-loyalty: a contingency framework’, Psychology and Marketing, Vol. 20, No. 2, pp.123–138. Abbott, M., Chiang, K.P., Hwang, Y.S., Paquin, J. and Zwick, D. (2000) ‘The process of online store loyalty formation’, Advance in Consumer Research, Vol. 27, pp.145–150. Adams, Dennis A., R.R. Nelson, and Peter A. Todd (1992), “Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication,” MIS Quarterly, 16 (2), 227–47. Brayen, P., (2012) The Loyalty Leap: Turning Customer Information into Customer Intimacy, Journal of consumer research, pp. 154-171 Brown, T. J., Barry, T. E. Dacin, P. A., and Gunst, R. F., 2005, “Spreading the word: Investigating antecedents of consumers’ positive word-of-mouth intentions and behaviors in a retailing context,” Journal of the Academy of Marketing Science, Vol. 33, No. 2, pp. 123-138. Bitner, M. J., Booms, B. H. and Tatreault, M. S.,1990, “The service encounter diagnosing favorable and unfavorable incidents,” Journal of Marketing, Vol. 54, No. 1, pp. 71-84. Baron, R.M., Kenny, D.A., (1986). The moderator-mediator variable distinction in psychological research: Conceptual, strategic and statistical considerations. Journal of Marketing, 33(11/12), 1082-1106. Bolton, R., (1998). A dynamic model of the duration of the customer’s relationship with a continuous service provider: The role of satisfaction. Marketing Science, 17(1), 45-65. Brucks et al., (2000), “Price and Brand Name As Indicators of Quality Dimensions for Consumer durables” Journal of the Academy of Marketing Science 28(3):359-374. Chandon, P., Morwitz, V.G., Reinartz, W.J., (2005). Do intentions really predict behavior? Self-generated validity effects in survey research. Journal of Marketing, 69, 1-14. Cronin, J.J., Brady, M.K., Hult, G.T., (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of Retailing, 76(2), 193-218. Davis, F.D. (1989) “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology” MIS Quarterly 13(3), pp. 319-340.
Forbes, L. P., Kelly, S. W. and Hoffman, K. D, (2005), “Typologies of e-commerce retail failures and recovery strategies,” Journal of Service Marketing, Vol. 19, No. 5, pp. 280-292. Google Scholar https://scholar.google.co.kr/scholar?q=www.scholar.google.com&hl=en&as_sdt Han et al., (2001). A framework for analyzing customer value of Internet business. Journal Information Technology Theory and Application, 25-38. Hoffman, K. D., Kelley, S. W. and Rotalsky, H. M., (1995), “Tracking service failures and employee recovery efforts,”Journal of Service Marketing, Vol. 9, No. 2, pp. 49-61. Holloway, B. B. and Beatty, S. E., (2003), “Service failure in online retailing: A recovery opportunity,” Journal of Service Research, Vol. 6, No. 1, pp. 92-105. Harris, L. C., & Goode, M. M. (2004). The four levels of loyalty and the pivotal role of trust: a study of online service dynamics. Journal of Retailing, 80(2), 139-158. Household Assets Mobilization, (2010) https://www.sapling.com/7817240/household-assets Internet Live Stats, (2016) https://www.internetlivestats.com Jin, B., & Park, J. Y. (2006). The moderating effect of online purchase experience on the evaluation of online store attributes and the subsequent impact on market response outcomes. Advances in Consumer Research, 33, 203. International Labor Organization, (2016) https://www.ilo.org/global/research/global-reports/weso/2016/lang--en/index.htm Journal Storage (JSTOR) https://www.jstor.org/stable/41675301?Search=yes& resultItemClick=true&searchText=hoffman&searchText=1998&searchUri=%2Faction%2FdoBasicSearch%3FQuery%3Dhoffman%2B1998&ab_segments=0%2Fdefault-2%2Fcontrol&refreqid=search%3Aa04d195c5e1166940d71303e48ec5124&seq=1#page_scan_tab_contents Kelly, S. W., Hoffman, K. D. and Davis, M. A., (1993), “A typology of retail failures and recoveries,” Journal of Retailing, Vol. 69, No. 4, pp. 429-453. Keeney et al., (1999), “The Value of Internet Commerce to the Customer” Management Science 45(4):533-542. Kotler, P. & Armstrong, G. (2008). Principles of Marketing. 12th Ed., New Jersey: Pearson Education Ltd. Kotler, P. & Keller, L. K. (2009). Marketing Management. 13th Ed., New Jersey: Pearson Education Ltd. Ling, K. C., D. P., Keoy, K. H., & Hassan, P. (2011). Perceived risk perceived technology, online trust for the online purchase intentions. International Journal of Business and Management 6(6) , 167-182. Mason et al., (1998), “Assessing the Effects of Product Quality and Environmental Management Accounting on the Competitive Advantage of Firms” Journal of Finance, Article 3. Markey, R. G., & Hopton, C. (2000). “E-customer loyalty–applying the traditional rules of business for online success”. European Business Journal, 12(4), 173-79. Mukherjee A., Nath P., (2007) “Role of Electronic Trust in Online Retailing” European Journal of Marketing, vol. 41, no. 9/10, pp. 1173-1202. Oliver, R. L. and Swan, J. E., 1989, “Consumer perceptions of interpersonal equity and satisfaction in transactions: A field survey approach,” Journal of Marketing, Vol. 53, No. 2, pp. 21-35. Parasuraman et al., (1985), “A Conceptual Model of Service Quality and its Implication for Future Research” Journal of Marketing 49:41-50. Research Gate https://www.researchgate.net/directory/publications/A-1-1102 Raturi et al., (2004), ”Sources of volume flexibility and their impact on performance” Journal of Operations Management 20 (2002) 519–548. Robert, D., & Rossiter, J. (1982). “Store atmosphere: an environmental psychology approach”. Journal of retailing 58.1, 34-57. Science Direct https://www.sciencedirect.com/journal/information-and-management Smith, A. K., Bolton, R. N. and Wagner, J., 1999, “A model of customer satisfaction with service encounters involving failure and recovery,” Journal of Marketing Research, Vol. 36, No. 3, pp. 356-372. Smith, J., & Barclay, D. (1997), The effects of organizational differences and trust on the effectiveness of selling partner relationships. Journal of Marketing, 61, 3-21. Tax, S. S., Brown, S. W. & Chandrashekaran, M. (1998). Customer evaluations of service complaint experiences: Implications for relationship marketing. Journal of Marketing, 62(2), 60-76. The United Nations Population Division's World Urbanization Prospects, (2016). https://www.un.org/development/desa/publications/2016-revision-of-world-urbanization-prospects.html Uz Daily, (2018) https://www.uzdaily.com/en Verhoef et al., (2003), “Understanding the Effect of Customer Relationship Management Efforts on Customer Retention and Customer Share Development”, Journal of Marketing, Vol. 67 (October 2003), 30–45. Wolfinbarger et al., (2003), “Measuring the quality of e-service: Scale development and initial validation” Journal of Electronic Commerce Research 10(1):13-28. Zeithmal, V.A. (2000), ‘Service quality, profitability, and the economic worth of customers: what we know and what we need to learn’, Journal of the Academy of Marketing Science, Vol. 28, No. 1, pp.67–85. Appendix Abstract Study on Customer Satisfaction of Online Shopping in Uzbekistan by Ibrohim Akhmedov Dept. of Business Administration Graduate School, Dong-A University Busan, South Korea In present times, the digital users have been increased significantly and shopping pattern of Uzbek consumers has changed simultaneously. E-commerce is also growing rapidly in last decade. Online shopping is becoming common with its penetration nowadays. Due to the rapid growth of the digital and its use as a channel for shopping, today’s consumers are able to shop from anywhere at any time with just a few clicks of their fingers (Lee, 2004). Online shopping provides easy accessibility, more information and choice compared to traditional way of shopping. The aim of the research is to investigate the relationship of customer satisfaction and customer loyalty on online shopping mall in Uzbekistan. The focus of the assessment was on the six-dimension variable of customer satisfaction and loyalty which are: product quality, delivery speed, e-payment, web design, security and trust. The online survey-questionnaire method was used for data-collecting. 330 respondents were participated in this online survey. This study uses structural equation model (SEM) to analyze data between dependent and independent variables with the help of the SPSS AMOS statistical program. The questionnaire was distributed among Uzbeks inside and outside of Uzbekistan. Questionnaire was given to respondents aimed to find out which factors are affect customer satisfaction and customer loyalty into purchase online. Among the entire hypothesis, there are nine hypotheses from eleven which is accepted. Hypothesis that product quality, delivery speed, e-payment and perceived usefulness give impact to customer satisfaction are significantly accept. E-payment, trust and perceived ease of use give positive impact to customer loyalty are normally accepted. Among all of indicators which was tested to customer satisfaction only security and web design that not significant. According to this study, the hypothesis product quality and delivery speed are positively give impact to customer satisfaction. Perceived usefulness, perceived ease of use and customer satisfaction give impact to customer loyalty. From this study can conclude that Uzbek shoppers have a willing to do purchase through online even they come across some difficulties. 1 2 The United Nations Population Division's World Urbanization Prospects, 2016 3 Household Assets Mobilization–2010 4 < Source :> Calculated based on «Household Assets Mobilization – 2010» survey 5 6 Euromonitor International, 2016 7 Uz Daily, 2018 8 9 10 11 12 13 Download 0.89 Mb. Do'stlaringiz bilan baham: 
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||