While the Internet drives the first transition of sales channels from physical stores to web storefronts, it is mobile devices like smartphones that provide the mobility and ubiquity wired desktop computers lack and that enable the second transition from e-retailing to m-retailing. Unlike the first transition that has been well studied in the literature, the follow-up transition from e-retailing to m-retailing has been under-explored. In this paper, we examine this transition by studying the timing of e-retailers’ initiation of m-retail sales channels (as years of adoption) and the performance of such adoption (as business value). We employ a theoretical contingency framework that classifies firms by the fit between characteristics of merchants and capabilities of the mobile sales channel (i.e., ubiquitous access capability and limited information search capability). We find that firms which sell time critical products and hence benefit from ubiquitous access are inclined to adopt m-retailing early. Interestingly, those firms that adopt early do not necessarily show the greatest values at all times. Instead, the type of performance metrics used matters. Apart from the distinct capabilities of the mobile sales channel, our finding suggests that dependency on existing e-retailing also has a positive effect on a firm’s m-retailing performance. Especially, the influence of e-retailing varies with the fit of a merchant with the mobile sales channel as well as the type of performance metric used.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
We are aware that m-retail ranks come after adoption. Yet, it is indeed a measure to reflect firms’ heterogeneities that pertain to m-commerce.
We use log of m-retail traffics for the estimation in Table 7. Thus, we apply the exponential function to the estimated coefficients when comparing traffics among the three types of merchants.
Anckar, B., & D’Incau, D. (2002). Value creation in mobile commerce: Findings from a consumer survey. Journal of Information Technology Theory and Applications, 4(1), 43–64.
Ayanso, A., & Yoogalingam, R. (2009). Profiling retail web site functionalities and conversion rates: A cluster analysis. International Journal of Electronic Commerce, 14(1), 79–113.
Baabdullah, A. M., (2020). Factors influencing adoption of mobile social network games (M-SNGs): The role of awareness. Information Systems Frontiers, forthcoming.
Balasubramanian, S., Peterson, R. A., & Jarvenpaa, S. L. (2002). Exploring the implications of m-commerce for markets and marketing. Journal of the Academy of Marketing Science, 30(4), 348–361.
Bang, Y., Lee, D. J., Han, K., Hwang, M., & Ahn, J. H. (2013). Channel capabilities, product characteristics, and the impacts of mobile channel introduction. Journal of Management Information Systems, 30(2), 101–125.
Bell, D. R., Gallino, S., & Moreno, A. (2014). How to win in an omnichannel world. MIT Sloan Management Review, 56(1), 45–53.
Brynjolfsson, E., Hu, Y. J., & Rahman, M. S. (2013). Competing in the age of omnichannel retailing. MIT Sloan Management Review, 54(4), 23–29.
Chou, Y. C., Chuang, H. H. C., & Shao, B. B. M. (2016). The impact of e-retail characteristics on initiating mobile retail services: A modular innovation perspective. Information and Management, 53(4), 481–492.
Chuang, H. H. C., Lu, G., Peng, X. D., & Heim, G. R. (2014). Impact of value-added service features in e-retailing processes: An econometric analysis of website functions. Decision Sciences, 45(6), 1159–1186.
Consul, P. C. (1989). Generalized Poisson distributions: Properties and applications. New York: Marcel Dekker.
Dahlberg, T., Mallat, N., Ondrus, J., & Zmijewska, A. (2008). Past, present and future of mobile payments research: A literature review. Electronic Commerce Research and Applications, 7(2), 165–181.
Famoe, F. (1993). Restricted generalized poisson regression model. Communications in Statistics, Theory and Methods, 22(5), 1335–1354.
Fang, X., Chan, S., Brzezinski, J., & Xu, S. (2005). Moderating effects of task type on wireless technology acceptance. Journal of Management Information Systems, 22(3), 123–157.
Ferraria, S., & Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics, 31(7), 799–815.
Fichman, R. G. (2004). Going beyond the dominant paradigm for information technology innovation research: Emerging concepts and methods. Journal of the Association for Information Systems, 5(8), 314–355.
Gao, L., & Waechter, K. A. (2015). Examing the role of initial trust in user adoption of mobile payment services: An empirical investigation. Information Systems Ftrontiers, 19(3), 525–548.
Geyskens, I., Gielens, K., & Dekimpe, M. G. (2002). The market valuation of internet channel additions. Journal of Marketing, 66(2), 102–119.
Guo, X., Y. Zhao, Y. Jin, and N. Zhang, 2010. Theorizing a two-sided adoption model for mobile marketing platforms. International Conference on Information Systems (ICIS) Proceedings, paper 128.
Han, K., Oh, W., Im, K. S., Chang, M. R., Oh, H., & Pinsonneault, A. (2012). Value cocreation and wealth spillover in open innovation alliances. MIS Quarterly, 36(1), 291–305.
Hilbe, J. M. (2014). Modeling count data. Cambridge University Press.
Hong, S. J., & Tam, K. Y. (2006). Understanding the adoption of multipurpose information applicances: The case of mobile data services. Information Systems Research, 17(2), 162–179.
Hulland, J., Michael, W. R., & Kersi, A. D. (2007). The impact of capabilities and prior investments on online channel commitment and performance. Journal of Management Information Systems, 23(4), 109–142.
Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111–126.
Kim, S., & Garrison, G. (2008). Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers, 11(3), 323–333.
Lee, Y., & Benbasat, I. (2003). Interface design for mobile commerce. Communications of the ACM, 46(12), 49–52.
Lee, S., Shin, B., & Lee, H. G. (2009). Understanding post-adoption usage of mobile data services: The role of supplier-side variables. Journal of the Association for Information Systems, 10(12), 860–888.
Lin, H. H. (2012). The effect of multi-channel service quality on mobile customer loyalty in an online-and-mobile retail context. The Service Industries Journal, 32(11), 1865–1882.
Mallat, N., & Tuunainen, V. K. (2008). Exploring merchant adoption of mobile payment systems: An empirical study. e-Service Journal, 6(2), 24–57.
McElheran, K. S. (2015). Do market leaders lead in business process innovation? The case(s) of e-business adoption. Management Science, 61(6), 1197–1216.
Min, S., & Wolfinbarger, M. (2005). Market share, profit margin, and marketing efficiency of early movers, bricks and clicks, and specialists in e-commerce. Journal of Business Research, 58(8), 1030–1039.
Ozarslan, S., & Eren, P. E. (2018). MobileCDP: A mobile framework for the consumer decision process. Information Systems Frontiers, 20(4), 803–824.
Ozturk, A. B., Nusair, K., Okumus, F., & Singh, D. (2017). Understanding mobile hotel booking loyalty: An integration of privacy calculus theory and trust-risk framework. Information Systems Frontiers, 19(4), 753–767.
Picoto, W. N., Belanger, F., & Palma-dos-Reis, A. (2014). An organizational perspective on m-business: Usage factors and value determination. European Journal of Information Systems, 23(5), 571–592.
Porter, M. (2001). Strategy and the internet. Harvard Business Review, 79(3), 63–78.
Shankar, V., & Balasubramanian, S. (2009). Mobile marketing: A synthesis and prognosis. Journal of Interactive Marketing, 23(2), 118–129.
Sharma, S. K. (2017). Integrating cognitive antecedents into TAM to explain mobile banking behavioral intention: A SEM-neural network modeling. Information Systems Frontiers, 21(4), 815–827.
Sheng, H., Nah, F. F. M., & Siau, K. (2008). An experimental study on ubiquitous commerce adoption: Impact of personalization and privacy concerns. Journal of the Association for Information Systems, 9(6), 344–376.
Swilley, E., Hofacker, C. F., & Lamont, B. T. (2012). The evolution from e-commerce to m-commerce: Pressures, firm capabilities and competitive advantage in strategic decision making. International Journal of E-Business Research, 8(1), 1–16.
Tsai, J. Y., Raghu, T. S., & Shao, B. B. M. (2013). Information systems and technology sourcing strategies of e-retailers for value chain enablement. Journal of Operations Management, 31(6), 345–362.
Witcher, F., (2016). Announcing the Forrester wave: Mobile commerce and engagement platforms, Q1 2016. Forrester Blogs. Available at https://go.forrester.com/blogs/16-01-19-announcing_the_forrester_wave_mobile_commerce_and_engagement_platforms_q1_2016/
Wu, J. H., & Wang, S. C. (2004). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719–729.
Xia, Y., & Zhang, G. P. (2010). The impact of the online channel on retailers’ performances: An empirical evaluation. Decision Sciences, 41(3), 517–546.
Xu, J., Forman, C., Kim, J. B., & Van Ittersum, K. (2014). News media channels: Complements or substitutes? Evidence from mobile phone usage. Journal of Marketing, 78, 97–112.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Chou, Y., Shao, B.B.M. Adoption and Performance of Mobile Sales Channel for e-Retailers: Fit with M-Retail Characteristics and Dependency on e-Retailing. Inf Syst Front (2020). https://doi.org/10.1007/s10796-020-09989-0
- Mobile retailing
- Dependency on e-retailing
- Capabilities of mobile channel
- Contingency theory
- Omnichannel retailing