Classification of Devices and Contact Points of Electronic Channels, Regarding the Behavior of Online Retail Customers in the E-Commerce Environment

Document Type : Original Article

Authors

1 Assistant Professor, Department of Management, University of Qom, Qom, Iran

2 Master, Industrial Management, Department of Management, Electronic Branch, Islamic Azad University, Tehran, Iran

Abstract

Purpose: This research aims to enhance understanding of online retailing through electronic channels (such as mobile devices) and touch points of electronic channels (such as mobile shopping software) from the customer's perspective.
Methods: This research is applied for the purpose and descriptive in terms of survey and library data collection methods. The statistical population for this research consists of students at Tehran Azad University. The community consists of more than 4000 people, so according to Morgan's table, the number of samples should be 384. Sampling has been conducted using the available method, and data analysis has been performed using the LSD test. The dependent variable in online shopping is purchase intention, while the independent variables include usefulness, ease of use, pleasure, privacy, and satisfaction.
Findings: The research findings indicate that customers currently use eight different devices, including laptops/notebooks, personal computers (PCs), smartphones, tablets, internet-equipped TVs, and in-store kiosks. Additionally, the research findings revealed that purchasing devices can be categorized into four electronic channel categories from the perspective of online retail customers. The floors are categorized as A, B, C, and D, and the coordinates of each are listed in the article.
The research findings indicate that both the technological quality and the situational benefits
of the context influence consumers' use of electronic channels. Also, customers engage in
online shopping through various electronic channels (sets of Internet-enabled devices, such as mobile devices) and multi-channel touchpoints (digital shopping formats, such as mobile shopping apps).
Conclusion: The results indicate that there is no significant difference between the first cluster (A), which involves shopping using personal computers (PCs), laptops, and netbooks, and the second cluster (B), which involves shopping using smartphones and tablets, in terms of usefulness, ease of use, shopping pleasure, privacy, satisfaction, and purchase intention. There is no significant difference between the first cluster (A) and the third cluster (C), i.e., Internet TV (IE TV), in terms of usefulness, ease of use, shopping pleasure, satisfaction, and purchase intention. There is a significant difference between the first cluster (A) and the fourth cluster (D), specifically in-store kiosks, in terms of usefulness, ease of use, shopping pleasure, privacy, satisfaction, and purchase intention. There is a significant difference between the second cluster (B) and the fourth cluster (D) in terms of usefulness, ease of use, shopping enjoyment, privacy, satisfaction, and purchase intention. Also, there is no significant difference between the third cluster (C) and the fourth cluster (D) in terms of usefulness, ease of use, shopping enjoyment, privacy, satisfaction, and purchase intention.
 

Keywords

Main Subjects

References

Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 25(3): 351-370. https://doi.org/10.2307/3250921
Blumler, J.G. & Katz, E. (1974). The Uses of Mass Communications: Current Perspectives on Gratifications Research. Sage Annual Reviews of Communication Research, Vol. 3.
Brasel, S.A. & Gips, J. (2014). Tablets, touchscreens, and touchpads: How varying touch interfaces trigger psychological ownership and endowment. Journal of Consumer Psychology, 24(2):
226-233.‏
Davis, F.D. (1989). A technology acceptance model for empirically testing new end-user information systems: Theory and results. Doctoral dissertation. Massachusetts Institute of Technology.
Dennis, C., Bourlakis, M., Alamanos, E., Papagiannidis, S. & Brakus, J.J. (2017). Value co-creation through multiple shopping channels: The interconnections with social exclusion and well-being. International Journal of Electronic Commerce, 21(4): 517-547.‏
Dunyadost, M. & Keshavarzpour, M.S. (2018). Gender, fashion innovation and opinion leadership and need for touch in multi-channel shopping choice. In: Tehran: The 5th International Conference on Accounting, Management and Innovation in Business. [in persian]
Fekur Thaghieh, A.M., Qavi Hikal, M. & Ilani, R. (2019). Examining the effect of consumers' need to be unique on their buying behavior through self-expression and self-expression. Modern Marketing Research, 10(4): 17-36. [in persian]
Gao, L., Waechter, K.A. & Bai, X. (2015). Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study–A case of China. Computers in Human Behavior, 53: 249-262.
Grewal, D., Roggeveen, A.L. & Nordfalt, J. (2017). The future of retailing. Journal of Retailing, 93(1): 1–6.
Ha, S. & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of business research, 62(5): 565-571.‏
Hedayat Nazari, F. & Dehdashti Shahrokh, Z. (2017). Backgrounds and consequences of customer experience in Iranian online retail stores. Public Management Research, 11(41): 313-336.
[in persian]
Kampstra, P. (2008). Beanplot: A Boxplot Alternative for Visual Comparison of Distributions. Journal of Statistical Software, 28(1): 1–9.
Loiacono, E.T., Watson, R.T. & Goodhue, D.L. (2007). WebQual: An instrument for consumer evaluation of web sites. International Journal of Electronic Commerce, 11(3): 51-87.‏
Maity, M., Dass, M. & Kumar, P. (2018). The impact of media richness on consumer information search and choice. Journal of Business Research, 87: 36–45.
Mansur, D.M., Sule, E.T., Kartini, D., Oesman, Y.M., Putra, A.H.P.K. & Chamidah, N. (2019). Moderating of the Role of Technology Theory to the Existence of Consumer Behavior on
e-commerce. The Journal of Distribution Science, 17(7): 15-25.‏
McLean, G., Al-Nabhani, K. & Wilson, A. (2018). Developing a mobile applications customer experience model (MACE)-implications for retailers. Journal of Business Research, 85: 325-336.‏
Mehrani, H. & Mandegari, M.J. (2019). Investigating the impact of security perception, information quality and user interface on consumer buying behavior in online shopping of digital goods. Sari: Sari: The first international conference on new challenges and solutions in industrial engineering and management and accounting. [in persian]
Mital, M., Chang, V., Choudhary, P., Papa, A. & Pani, A.K. (2018). Adoption of Internet of Things in India: A test of competing models using a structured equation modeling approach. Technological Forecasting and Social Change, 136: 339-346.
Mollahosseini, A. & Tajalddini, F. (2014). Investigating the effect of diversity of foreign luxury brand distribution channels on brand value and consumer loyalty in Kerman clothing market. Business Administration, 7(1): 187-208. [in persian]
Monczka, R.M., Handfield, R.B., Giunipero, L.C. & James L. (2021). Patterson. Purchasing and supply chain management. Cengage Learning.
Özpolat, K., Gao, G., Jank, W. & Viswanathan, S. (2013). Research note—The value of third-party assurance seals in online retailing: An empirical investigation. Information Systems Research, 24(4): 1100-1111.‏
Rapp, A., Baker, T.L., Bachrach, D.G., Ogilvie, J. & Beitelspacher, L.S. (2015). Perceived customer showrooming behavior and the effect on retail salesperson self-efficacy and performance. Journal of Retailing, 91(2): 358-369.‏
Safari, F. & Azami, M. (2018). Examining the relationship between the factors affecting online retail sales on the purchase intention in social networks. In: National conference of economy, development management and entrepreneurship with the approach of supporting Iranian goods. [in persian]
Saghiri, S., Wilding, R., Mena, C. & Bourlakis, M. (2017). Toward a three-dimensional framework for omni-channel. Journal of Business Research, 77: 53-67.
Seret, A., Vanden Broucke, S.K., Baesens, B. & Vanthienen, J. (2014). A dynamic understanding of customer behavior processes based on clustering and sequence mining. Expert Systems with Applications, 41(10): 4648-4657.
Sheikhi, M., Absalan, A. & Khudavandegar, P. (2017). Investigating the factors affecting the buying behavior of Iranian consumers in the field of online shopping. In: Second International Conference on Management and Business. Tabriz: Tabriz University Management Department. [in persian]
Shirmohammadi, M. & Rabiei, M. (2016). Evaluation of customers' perceived value of the online channel of multi-channel retailers. In: Tehran: Third International Conference on Industrial Engineering and Management. [in persian]
Venkatesh, V., Thong, J.Y. & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1): 157-178. https://doi.org/10.2307/41410412
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.‏
Wagner, G., Schramm-Klein, H. & Steinmann, S. (2020). Online retailing across e-channels and
e-channel touchpoints: Empirical studies of consumer behavior in the multichannel e-commerce environment. Journal of Business Research, 107: 256-270.‏
Zhang, J., Farris, P.W., Irvin, J.W., Kushwaha, T., Steenburgh, T.J. & Weitz, B.A. (2010). Crafting integrated multichannel retailing strategies. Journal of Interactive Marketing, 24(2): 168-180.‏
Send comment about this article
CAPTCHA Image
Sciences and Techniques of Information Management
Volume 9, Issue 3
July 2023
Pages 74-55

Files

Share

How to cite

Statistics