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2018 | 1(7) | 4-27

Article title

Customers’ Perceptions as an Antecedent of Satisfaction with Online Retailing Services

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Abstracts

EN
The assessment of antecedents of customer satisfaction has become very important for the success of online retailing services. This paper reports the results of a study that investigated the antecedent role of customers’ perceptions vis-a-vis satisfaction with online retailing services. While the study model conceptualizes customers’ perceptions as a composite variable made up of three dimensions (perceived attributes, perceived risk and perceived value) prescribed by four established information systems (IS) and consumer behaviour frameworks, namely the Technology Acceptance Model (TAM), Perceived Risk Theory (PRT), Theory of Consumption Values (TCV) and Expectations-Artifact Model of Satisfaction (EAMS), it does not specify how the different perceptual factors infl uence online satisfaction; instead it aggregates all three dimensions into a higher-order construct called “customers’ perceptions” and tries to understand the nature of relationship between the composite independent variable and the dependent variable. It employed a descriptive, correlational survey design whereby the response data collected from 240 registered users of 6 online retailers was analyzed using both descriptive as well as inferential statistics. The linear regression analyses indicate that the model provides a statistically signifi cant explanation of the variation in consumers’ online retailing satisfaction. The study also found empirical support for customers’ perceptions as an antecedent of satisfaction with online retailing services.

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4-27

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Dates

published
2018

Contributors

  • School of Business, Kenyatta University, Kenya
  • School of Business, Kenyatta University, Kenya

References

  • Abadi, H.R.D., Hafshejani, S.N.A., & Zadeh, F.K. (2011). Considering factors that affect users’ online purchase intentions with using structural equation modeling. Interdisciplinary Journal of Contemporary Research in Business, 3(8), 463–471.
  • Adams, D.A., Nelson, R.R., & Todd, P.A. (1992). Perceived usefulness, ease-of-use and usage of information technology: A replication. MIS Quarterly, 16(2), 227–247.
  • Ahn, T., Ryu, S., & Han, I. (2004). The impact of the online & offl ine features on the user acceptance of Internet shopping malls. Electronic Commerce Research & Applications, 3(4), 405–420.
  • Ahuja, R. (2005). Research methods. New Delhi: Rawat Publications. Anderson, S., Pearo, L.K., & Widener, S.K. (2008). Drivers of service satisfaction linking customer satisfaction to the service concept and customer characteristics, Journal of Service Research, 10(4), 365–381.
  • Barnes, S.J., Bauer, H.H., Neumann, M.M., & Huber, F. (2007). Segmenting cyberspace: A customer typology for the internet. European Journal of Marketing, 41(1/2), 71–93.
  • Bauer, R.A. (1960). Consumer behavior as risk-taking. In R.S. Hancock (Ed.), Dynamic Marketing for a Changing World (pp. 389–98). Chicago, IL: American Marketing Association,
  • Bearden, W.O. & Teel, J. E. (1983). Selected determinants of consumers’ satisfaction and complaint reports. Journal of Marketing Research, (XX), 21–28.
  • Berelson, B. & Steiner, G.A. (1964). Human behaviour: An inventory of scientifi c fi ndings. New York: Harcourt Brace Jovanovich.
  • Bettman, J. (1973). Perceived risk and its components – A model and empirical test. Journal of Marketing Research, 10, 184–90.
  • Bhatnagar, A., Misra, S., & Rao, H.R. (2000). Online risk, convenience and internet shopping behavior. Communications of the ACM, 42(11), 98–105.
  • Bhattacherjee, A. (2001a). Understanding information systems continuance: An expectation-confi rmation model. MIS Quarterly, 25(3), 351–70.
  • Bhattacherjee, A. (2001b). An empirical analysis of the antecedents of electronic commerce service continuance. Decision Support Systems, 32(2), 201–214.
  • Bolton, R.N. & Drew, J.H. (1994). Linking customer satisfaction to service operations and outcomes. In R.T. Rust & R.L. Oliver (Eds.), Service quality: New directions in theory and practice (pp. 173 – 200). Newbury Park, CA: Sage Publications.
  • Boscheck, R. (1998). New media economics are transforming consumer relations. Long Range Planning, 31, 873–878.
  • Brocato, E.D., Voorhees, C.M., & Baker, J. (2012). Understanding the infl uence of cues from other customers in the service experience: a scale development and validation. Journal of Retailing, 88(3), 384–398.
  • Burns, R.P. & Burns, R. (2009). Business research methods and statistics using SPSS. London: Sage Publications. Carifi o, L. & Perla, R. (2008). Resolving the 50 year debate around using and misusing Likert scales. Medical Education, 42, 1150–1152.
  • Chen, Q. & Wells, W.D. (1999). Attitude toward the site. Journal of Advertising Research, September/October, 27–37.
  • Chew, K.-W., Shingi, P.M., & Ahmad, M.I. (2006). TAM derived construct of perceived customer value and online purchase behavior: An empirical exploration. In R. Suomi, R. Cabral, J.F. Hampe, A. Heikkilä, J. Järveläinen & E. Koskivaara (Eds.), Project e-society: Building bricks – 6th IFIP International Conference on e-Commerce, e-Business, and e-Government (13E 2006), October 11–13, 2006, 226 (pp. 215–227). IFIP Advances in Information and Communication Technology.
  • Cho, Y. (Upcoming). A consumer satisfaction model based on the integration of EDT and TAM: Comparative study of Korean and US consumers. Asia Pacifi c Journal of Marketing and Logistics, https://doi.org/10.1108/APJML-07-2016-0127.
  • Choi, J.H. & Jahng, J.J. (2009). Predictors of e-commerce use of the Internet: A multinational comparative study – the U.S., the Netherlands, and S. Korea. Seoul Journal of Business. 15(1), 65–90.
  • Churchill, G.A. & Surprenant, C. (1982). An investigation into the determinant of customer satisfaction. Journal of Marketing Research, (XIX), 491–504.
  • Cox, D.F., & Rich, S.U. (1964). Perceived risk and consumer decision making – the case of telephone shopping. Journal of Marketing Research, 1, 2–39.
  • Cronin, J.J. & Taylor, S.A. (1992). Measuring service quality: A reexamination and extension. Journal of Marketing, 56, 55–68.
  • Cronin, J.J. & Taylor, S.A. (1994). SERVPERF versus SERVQUAL: Reconciling performance-based and performance-minus-expectations measurement of service quality. Journal of Marketing, 58(1), 125–131. doi10.2307/1252256.
  • Cunningham, S.M. (1967). The major dimensions of perceived risk. In D. Cox (Ed.), Risk taking and information handling in consumer behavior (pp. 82–109). Harvard: Harvard University Press.
  • Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
  • Davis, F.D., Bagozzi, P.R., & Warsaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1003.
  • Davis, F.D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.
  • Davis, M.K. & Heineke, J. (1998). How disconfi rmation, perception and actual waiting times impact customer satisfaction. International Journal of Service Industry Management, 9(1), 64–73, https://doi.org/10.1108/09564239810199950
  • Day, E. & Crask, M.R. (2000). Value assessment: The antecedent of customer satisfaction. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behaviour, 13, 52–60.
  • Dholakia, R. & Uusitalo, O. (2002). Switching to electronic stores: Consumer characteristics and the perception of shopping benefi ts. International Journal of Retail and Distribution Management, 27(4), 154–165.
  • Doherty, N.F. & Ellis-Chadwick, F. (2010). Internet retailing: The past, the present and the future. International Journal of Retail & Distribution Management, 38(11/12), 943–965.
  • Erevelles, S. & Leavitt, C. (1992), A comparison of current models of consumer satisfaction/dissatisfaction. Journal of Consumer Satisfaction, Dissatisfaction and Complaining Behavior, 5, 104–14.
  • Forsythe, S., Chuanlan, N., Shannon, D., & Gardner, L.C. (2006), Development of a scale to measure the perceived benefi ts and risks of online shopping. Journal of Interactive Marketing, 20(2), 55–75.
  • Forsythe, S.M. & Shi, B. (2003). Consumer patronage and risk perceptions in internet shopping. Journal of Business Research, 56(11), 867–876.
  • Gimpel, G. (2011). Value-driven adoption and consumption of technology: Understanding technology decision making. Unpublished PhD thesis. Retrieved from: http://openarchive.cbs.dk/bitstream/handle/10398/8326/Gregory%20Gimpel.pdf?sequence=1 (August, 2012).
  • Gliem, J.A. & Gliem, R.R. (2003). Calculating, interpreting, and reporting Cronbach’s alpha reliability coeffi cient for Likert-type scales. Refereed Paper presented at the Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education on October 8–10, 2003. The Ohio State University, Columbus, OH.
  • Hansen, T. (2007). Determinants of consumers’ repeat online buying of groceries. The International Review of Retail, Distribution and Consumer Research, 16(1), 93–114.
  • Ho, C.-F. & Wu, W.-H. (1999). Antecedents of customer satisfaction on the Internet: An empirical study of online shopping. Proceedings of the 32nd Hawaii International Conference on System Sciences – 1999. Retrieved from: http://www.ieee.org (13/11/2017).
  • Hoyer, W.D. & MacInni, D.J. (2008). Consumer Behavior (5th Ed.). Mason, OH: South Western – Cengage Learning. Jarvenpaa, S.L. & Todd, P.A. (1996). Consumer reactions to electronic shopping on the world-wide web. International Journal of Electronic Commerce, 1(2), 59–88.
  • Jarvenpaa, S.L. & Tractinsky, N. (1999). Consumer trust in an internet store: A cross-cultural validation. Journal of Computer-Mediated Communication, 5(2).
  • Jacoby, J. & Kaplan, L. (1972). The components of perceived risk. In M. Venkatesan (Ed.), Proceedings, 3rd Annual Conference (pp. 383–393). Chicago, IL: Association for Consumer Research.
  • Jiang, P. & Rosenbloom, B. (2005). Customer intention to return online: Price perception, attribute-level performance, and satisfaction unfolding over time. European Journal of Marketing, 39(1/2), 150–174.
  • Jiradilok, T., Malisuwan, S., Madan, N., & Sivaraks, J. (2014). The impact of customer satisfaction on online purchasing: A case study analysis in Thailand. Journal of Economics, Business and Management, 2(1), 5–11.
  • Johnson, M.D. & Fornell, C. (1991). A framework tot comparing customer satisfaction across individuals and product categories. Journal of Economic Psychology, 12, 267–286.
  • Johnson, M.D., Nader, G., & Fornell, C. (1996). Expectations, perceived performance, and customer satisfaction for a complex service: The case of bank loans [Electronic version]. Retrieved from Cornell University, School of Hospitality Administration site: http://scholarship.sha.cornell.edu/articles/692 (15/11/2017).
  • Joines, J.L., Scherer, C.W., & Scheufele, D.A. (2003). Exploring motivations for consumer web use and their implications for e-commerce. Journal of Consumer Marketing, 20(2), 90–108.
  • Kalafatis, S.P., Ledden, L., & Mathioudakis, A. (n.d). Re-specifi cation of the theory of consumption values. Retrieved from: http://eprints.kingston.ac.uk/18098/1/Kalafatis-S-18098.pdf (28/02/2014).
  • Kau, A.K., Tang, Y.E., & Ghose, S. (2003). Typology of online shoppers. Journal of Consumer Marketing, 20(2/3), 139–154.
  • Kaye, B.K. & Johnson, T.J. (2001). A Web for all reasons: uses and gratifi cations of Internet resources for political information. Paper presented at the Association for Education in Journalism and Mass Communication Annual Conference, Washington, DC, August.
  • Ko, H., Cho, C.-H., & Roberts, M.S. (2005). Internet uses and gratifi cations. Journal of Advertising, 34(2), 57–70. Korgaonkar, P.K. & Wolin, L.D. (1999). A multivariate analysis of Web usage. Journal of Advertising Research, 39(2), 53–68.
  • Kotler, P., Cunningham, M.H., & Turner, R.E. (2001). Marketing Management. Pearson Education.
  • Lee, D., Park, J., & Ahn, J. (2000). On the explanation of factors affecting e-commerce adoption. Working Paper. Retrieved online from: http://misrc.umn.edu/workingpapers/fullpapers/2000/0025_120100.pdf. (23/09/2017).
  • Lemm, K. (2010). Stratifi ed sampling. In N.J. Salkind (Ed.), Encyclopaedia of research design (pp. 1451–1454). Thousand Oaks, CA.: Sage Publications.
  • Liebermann, Y. & Stashevsky, S. (2002). Perceived risks as barriers to internet and e-commerce usage. Qualitative Market Research: An International Journal, 5(4), 291–300.
  • Limayem, M., Hirt, S.G., & Cheung, C.M.K. (2007). How habit limits the predictive power of intention: The case of information systems continuance. MIS Quarterly, 31(4), 705–37.
  • Liu, C. & Forsythe, S. (2010). Post-adoption online shopping continuance. International Journal of Retail & Distribution Management, 38(2), 97–114.
  • Luo, X. (2002). Uses and gratifi cations theory and e-consumer behaviors: A structural equation modeling study. Journal of Interactive Advertising, 2(2), 44–54.
  • Mallet, D. (2006). Sampling and Weighting. In R. Grover & M. Vriens (Eds.), The handbook of marketing research (pp. 159–177). Thousand Oaks, CA: Sage.
  • McGuire, W.J. (1974). Psychological motives and communication gratifi cation. In J.G. Blumler & E. Katz (Eds.), The uses of mass communication. Beverly Hills, CA: Sage.
  • McQuitty, S., Finn, A. & Wiley, J. (2000), Systematically varying consumer satisfaction and its implications for product choice. Academy of Marketing Science Review. Retrieved from: www.amsreview.org/articles/mcquity10-2000.pdf (June, 2016).
  • Meeks, J.G.T. (1984). Utility in economics: A survey of the literature. In: C.F. Turner & M. Martin (Eds.), Surveying subjective phenomena (vol. 2, pp. 41–91). New York: Russell Sage Foundation.
  • Moore, G.C. & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2, 192–222.
  • Nimon, K. (2010). Regression commonality analysis: Demonstration of an SPSS solution. Multiple Linear Regression Viewpoints, 36(1), 10–17.
  • Nunnally, J.C. & Bernstein, I.H. (1994). Psychometric theory. New York, NY: McGraw-Hill.
  • Nusair, K. & Kandampully, J. (2008). The antecedents of customer satisfaction with online travel services: A conceptual model. European Business Review, 20(1), 4–19.
  • Oliver, R.L. (1980). A cognitive model of the antecedents and consequences of satisfaction judgments. Journal of Marketing Research, 17, 460–469.
  • Pallant, J. (2007). SPSS survival manual: A step by step guide to data analysis using SPSS for Windows (3rd Ed.). New York: McGraw Hill Open University Press.
  • Parasuraman, A., Zeithaml, V.A., & Berry, L.L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service. Journal of Retailing, 64, 12–40.
  • Peng, D. W.-J. (2007). Factors affecting consumers’ uses and gratifi cations of the Internet: A cross-cultural comparison among Taiwan, Hong Kong and China. International Journal of Computer Sciences and Network Security, 7(3), 233–242.
  • Pizam, A., Shapoval, V., & Ellis, T. (2016). Customer satisfaction and its measurement in hospitality enterprises: A revisit and update. International Journal of Contemporary Hospitality Management, 28(1), 2–35.
  • Rayburn, J.D. (1996). Uses and gratifi cations. In M.B. Salwen & D.W. Stacks (Eds.), An integrated approach to communication theory and research (pp. 145–63). Mahwah, New Jersey: Lawrence Erlbaum.
  • Razali, N.M. & Wah, Y.B. (2011). Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling Tests. Journal of Statistical Modelling and Analytics. 2(1), 21–33.
  • Rogers, E.M. (1995). Diffusion of innovations (4th Ed.). New York: The Free Press.
  • Rogers, E.M. (2003). Diffusion of innovations (5th Ed.). New York: The Free Press.
  • Roy, S.K. (2008). Determining uses and gratifi cations for Indian internet users. CS-BIGS, 2(2), 78–91. Retrieved online from: http://www.bentley.edu/centers/sites/www.bentley.edu.centers/fi les/csbigs/roy.pdf (20/07/2016).
  • Rubin, A. & Babbie, E.R. (2011). Research methods for social work (7th Ed). Belmonst, CA: Cengage Learning. Rugimbana, R. & Iversen, P. (1994). Perceived attributes of ATMs and their marketing implications. International Journal of Bank Marketing, 12(2), 30–35.
  • Sahaf, M.A. (2008). Strategic marketing: Making decisions for strategic advantage. New Delhi: Prentice Hall of India.
  • Severin, W.J. & Taknard, W.J. (1997). Communication theories origins, methods, and uses in the mass media (4th ed.). White Plains, NY: Longman.
  • Sharma, S., Durand, R.M., & Gurarie, O. (1981). Identifi cation and analysis of moderator variables. Journal of Marketing Research, 18(3), 45–57.
  • Sheth, J.N., Newman, B.I., & Gross, B.L. (1991a). Consumption values and market choices: Theory and applications. Cincinnati: South-Western Publishing Co.
  • Sheth, J.N., Newman, B.I., & Gross, B.L. (1991b). Why we buy what we buy: A theory of consumption values. Journal of Business Research, 22(2), 159–170.
  • Schiffman, L.G. & Kanuk, L.L. (2010). Consumer behavior: Global edition. London: Pearson Higher Education. Spencer, S.J., Zanna, M.P., & Fong, G.T. (2005). Establishing a causal chain: Why experiments are often more effective than mediational analysis in examining psychological processes. Journal of Personality and Social Psychology, 89, 845–851.
  • Sreejesh, S., Sarkar, J.G., Sarkar, A., Eshghi, A., & Anusree, M. R. (2017). The impact of other customer perception on consumer-brand relationships. Journal of Service Theory and Practice, 2055–6225. https://doi.org/10.1108/JSTP-11-2016-0207.
  • Swanson, R.A. & Holton, E.F. III. (Eds.). (2005). Research in organizations: Foundations and methods of inquiry. San Francisco: Berrett-Koehler.
  • Taylor, J.W. (1974). The role of risk in consumer behavior. Journal of Marketing, 38(2), 54–60
  • Teas, R.K. (1993). Expectations, performance evaluation, and consumers’ perceptions of quality. Journal of Marketing, 57(10), 18–34.
  • Ting, O.S., Ariff, M.S.M., Zakuan, N., Sulaiman, Z., & Saman, M.Z.M. (2016). E-service quality, e-satisfaction and e-loyalty of online shoppers in business to consumer market; Evidence form Malaysia. Paper presented at the IOP Conference Series: Materials Science and Engineering.
  • Van Raaij, W.F. (1981). Economic psychology. Journal of Economic Psychology, 1, 1–24.
  • Vrechopoulos, A., Siomkos, G., & Doukidis, G. (2001). Internet shopping adoption by Greek consumers. European Journal of Innovation Management, 4(3), 142–152.
  • Warmbrod, J.R. (2014). Reporting and interpreting scores derived from Likert-type scales. Journal of Agricultural Education, 55(5), 30–47, doi: 10.5032/jae.2014.05030.
  • Weltevrenden, J.W.J. & Boschma, R.A. (2008). Internet strategies and the performance of Dutch retailers. Journal of Retailing & Consumer Services, 15, 163–178.
  • Wilkie, W.L. (1994). Consumer behavior. New York: Von Hoffman Press.
  • Wilson, A. (2006). Marketing research: An integrated approach (2nd Ed.). Gosport: Prentice Hall.
  • Woodruff, R.B. & Gardial, S.F. (1996). Know your customer: New approaches to customer value and satisfaction. Cambridge, MA: Blackwell.
  • Yee, C.C. & Yazdanifard, R. (2014). How customer perception shape buying online decision. Global Journal of Management and Business Research: E Marketing, 14(2), 1–9.
  • Yen, Y.S. (2011). The impact of perceived value on continued usage intention in social networking sites. Proceedings of the 2nd International Conference on Networking and Information Technology, 17, 217–223.
  • Yunjie, X. & Shun, C. (2004). A conceptual model for customer value in e-commerce. Retrieved from: http://is2.lse.ac.uk/asp/aspecis/20040178.pdf (20/09/2016).
  • Zhang, L., Tan, W., Xu, Y., & Tan, G. (2012), Dimensions of consumers’ perceived risk and their infl uences on online consumers’ purchasing behavior. Communications in Information Science and Management Engineering, 2(7), 8–14.
  • Zhou, L., Dai, L., & Zhang, D. (2007). Online shopping acceptance model: A critical survey of consumer factors in online shopping. Journal of Electronic Commerce Research, 8(1), 41–62.
  • Zikmund, W.G & Babin, B.J (2007) Exploring marketing research (9th Ed.). Ohio: Thomson South-Western.

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Biblioteka Nauki
2011566

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bwmeta1.element.ojs-doi-10_7172_2449-6634_jmcbem_2018_1_1
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