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- P-ISSN1738-3110
- E-ISSN2093-7717
- SCOPUS, ESCI
ISSN : 1738-3110
Omnichannel's Perception Effect on Omnichannel Use and Customer-Brand Relationship
Han, Sang-Seol
Abstract
Purpose - This study focuses on new type distribution channel that named as Omnichannel. Omnichannel is developed from Multichannel which is used in many distribution channels to buy or selling goods. Omnichannel basically needs an Information and Communications Technologies(ICT) to use, so researcher conduct a Technology Acceptance Model(TAM) to research model. Customer-brand relationship was used as dependent variable to focus on the role of Omnichannel. Research design, data, and methodology - The subject of this study is customer who purchase goods or service through omnichannel. Based on the literature from the preceding research analysis of TAM and customer-brand relationship, this study was constructed by the reference to previous studies, final research model design for figure out casual relationship among perceived ease of use, perceived usefulness, omnichannel use and customer-brand relationship. From 2016 February 3 to March 17, questionnaire survey targeted customers who use online and offline channels. 273 questionnaire survey had conducted, then, 252 survey data were available for empirical analysis. Researcher provide descriptive statistics for checking generality. Cronbach's alpha value was used to check the reliability of data. Exploratory factor analysis was used for purification of values and eigenvalue checking. After EFA, Confirmatory factor analysis was used to prepare structural equation modeling with executing structural equation modeling for confirming hypothesis which developed by researcher. Results - The main results of this empirical study are as follows. First, omnichannel's perceived ease of use has positive significant effect on perceived usefulness(estimate: 0.579). Moreover, omnichannel's perceived ease of use and perceived usefulness has positive significant effect on omnichannel use(estimate: 0.325,0.648). Second, using omnichannel has positive significant effect on brand-customer relationship(estimate: 0.521). Every hypothesis adopted as researcher designed. This study found out the intermediate relationship between perceived ease of use and omnichannel use by investigating hypothesis. Conclusions - Base on the empirical result, this study confirmed that TAM theory perceived has relation with omnichannel. First, factors of TAM has positive effect on omnichannel use, so it highlights the important role of customer based interface and usefulness. Especially, perceived usefulness has high indirect influence on ease of use and use of omnichannel. It seems that when customers try to decide use or not use omnichannel, customers focus on percept benefits from omnichannel. Thus, a provider should applicate attractive price table, accurate product or service information and high switching cost strategy to emphasize the usefulness of omnichannel. Second, using omnichannel enhances the relationship between customers and brand, because there are more time and frequency to serve customers. It is important because good relationship between customers can increase the future's financial performance through word of mouse, positive brand image and loyalty to brand or company. Finally, despite of empirical result and implications, this study has limitations. First, there are only a few previous studies about omnicahnnel, so literature reviews are restricted. While set up the factors which can affect the use of omnichannel, next study should be considered with broader theories or models(ex: contingency theory). Second, omnichannel has developed from multichannel, so comparative analysis is needed between these methods because there is a possibility about different forte character of each distribution system on customer's consuming patterns.
- keywords
- Technology Acceptance Model, Omnichannel, Customer-Brand Relationship
Reference
Aaker, D. A., & Joachimsthaler, E. (2000). The brand relationship spectrum: The key to the brand architecture challenge. California management review, 42(4), 8-23.
Baird, N., & Raj, W. (2012). Customer-Centricity Drives Successful Omni-Channel Retailing: Insights from a webinar presented by Retail Systems Research (RSR) and SAS. SAS Institute Inc.
Beck, N., & Rygl, D. (2015).Categorization of multiple channel retailing in Multi-, Cross-, and Omni‐Channel Retailing for retailers and retailing. Journal of Retailing and Consumer Services, 27, 170-178.
Bell, D. R., Gallino, S., & Moreno, A. (2014). How to win in an omnichannel world. MIT Sloan Management Review, 56(1), 45.
Bell, D. R., Gallino, S., & Moreno, A. (2013). Inventory showrooms and customer migration in omni-channel retail: The effect of product information. Available at SSRN 2370535.
Bhalla, R. (2014). The omni-channel customer experience:Driving engagement through digitisation. Journal of Digital & Social Media Marketing, 1(4), 365-372.
Brynjolfsson, E., Hu, Y. J., & Rahman, M. S. (2013). Competing in the age of omnichannel retailing. MIT Sloan Management Review, 54(4), 23.
Choi, S. S., Kim, P. J., & Lee, S. Y. (2011). A research on private apparel brand's product strategy in discounted stores. Journal of Industrial Distribution & Business, 2(2), 25-38.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 11(2), 319-340.
Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & management, 36(1), 9-21.
Fei, L. I. (2013). Connation, Cause and Countermeasures of Omni Channel Retailing: Further Discussion on How to Meet China Multi Channel Retailing Revolution Storm. Journal of Beijing Technology and Business University (Social Science), 2(2), 23-34.
Fournier, S., Dobscha, S., & Mick, D. G. (1998). The premature death of relationship marketing. Harvard business review, 76(1), 42-51.
Frazer, M., & Stiehler, B. E. (2014, January), Omnichannel Retailing: the merging of the online and off-line environment. In Global Conference on Business &Finance Proceedings, 9(1), 655-665.
Gabrielsson, P., & Gabrielsson, M. (2004). Globalizing internationals: business portfolio and marketing strategies in the ICT field. International Business Review, 13(6), 661-684.
Golombek, J. (2013). Omni-channel: The Future of Retailing. Working Paper, The Pennsylvania State University, Retrieved May 22, 2016, from http://www. personal. psu. edu/users/j/l/jlg5690/images/White Paper. pdf
Hollebeek, L. D. (2011). Demystifying customer brand engagement: Exploring the loyalty nexus. Journal of Marketing Management, 27(7-8), 785-807.
Kim, E. H., & Kim, M. J. (2011). The Effect of Distributor Private Brand Product Type on Consumer Attitude. The East Asian Journal of Business Management, 1(1), 13-20.
Kim K. B., & Kim B. G. (2016). Relationship among Brand Value Propositions, Brand Attitude and Brand Attachment considering Consumer Involvement. Journal of Distribution Science, 14(2), 103-111.
Kim, P. J., Kim, M. S., Kim, W., Mehyaoui, O., & Youn, M. K. (2014). Effects on the Consumer Buying Behavior of an Agricultural Brand in South Korea. The Journal of Industrial Distribution & Business, 5(2), 21-28.
Lazaris, C., & Vrechopoulos, A. (2014). From multi-channel to “omnichannel” retailing: review of the literature and calls for research. In 2nd International Conference on Contemporary Marketing Issues,(ICCMI).
Lee, J. S., Jung, S. H., & Shin, M. K. (2012). A study on multichannel consumer's choice of the information search and shopping channel. Journal of Consumer Custom, 15(2), 21-45.
Lee, S. J., & Jing D. (2015). Use Intentions of Mobile Tour Apps through Expansion of the Technology Acceptance Model. Journal of Distribution Science, 13(10), 135-142.
Madden, T. J., Ellen, P. S., & Ajzen, I. (1992). A comparison of the theory of planned behavior and the theory of reasoned action. Personality and social psychology Bulletin, 18(1), 3-9.
Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191.
Nam, Y. J. (2014). Consumer information search and consumer confusion under multi-channel environment. Seoul, Korea: Thesis for Doctorate in Catholic University.
Napolitano, M. (2013). Omni-channel distribution: moving at the speed of" now". Logistics management (Highlands Ranch, Colo.: 2002), 52(6), 212-222.
Oh, Y. S. (2013). Study on the Effect of the Usability and Usefulness of Mobile Application Programs on Buying Intention. Journal of Distribution Science, 11(11), 41-47.
Ooi, K. B., & Tan, G. W. H. (2016). Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications, 59, 33-46.
Story, J., & Hess, J. (2006). Segmenting customer-brand relations: beyond the personal relationship metaphor. Journal of Consumer Marketing, 23(7), 406-413.
Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of consumer Research, 15(3), 325-343.
Piotrowicz, W., & Cuthbertson, R. (2014). Introduction to the special issue information technology in retail: Toward omnichannel retailing. International Journal of Electronic Commerce, 18(4), 5-16.
Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management, 33(1), 1-11.
Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management science, 42(1), 85-92.
Verhoef, P. C., Kannan, P. K., & Inman, J. J. (2015). From multi-channel retailing to omni-channel retailing:Introduction to the special issue on multi-channel retailing. Journal of Retailing, 91(2), 174-181.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.
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