Skip to main content
Log in

Adoption and Performance of Mobile Sales Channel for e-Retailers: Fit with M-Retail Characteristics and Dependency on e-Retailing

  • Published:
Information Systems Frontiers Aims and scope Submit manuscript

Abstract

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 via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. 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.

  2. 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.

References

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bell, D. R., Gallino, S., & Moreno, A. (2014). How to win in an omnichannel world. MIT Sloan Management Review, 56(1), 45–53.

    Google Scholar 

  • Brynjolfsson, E., Hu, Y. J., & Rahman, M. S. (2013). Competing in the age of omnichannel retailing. MIT Sloan Management Review, 54(4), 23–29.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Famoe, F. (1993). Restricted generalized poisson regression model. Communications in Statistics, Theory and Methods, 22(5), 1335–1354.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ferraria, S., & Cribari-Neto, F. (2004). Beta regression for modelling rates and proportions. Journal of Applied Statistics, 31(7), 799–815.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Geyskens, I., Gielens, K., & Dekimpe, M. G. (2002). The market valuation of internet channel additions. Journal of Marketing, 66(2), 102–119.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kim, S., & Garrison, G. (2008). Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers, 11(3), 323–333.

    Article  Google Scholar 

  • Lee, Y., & Benbasat, I. (2003). Interface design for mobile commerce. Communications of the ACM, 46(12), 49–52.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Mallat, N., & Tuunainen, V. K. (2008). Exploring merchant adoption of mobile payment systems: An empirical study. e-Service Journal, 6(2), 24–57.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Ozarslan, S., & Eren, P. E. (2018). MobileCDP: A mobile framework for the consumer decision process. Information Systems Frontiers, 20(4), 803–824.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Porter, M. (2001). Strategy and the internet. Harvard Business Review, 79(3), 63–78.

    Google Scholar 

  • Shankar, V., & Balasubramanian, S. (2009). Mobile marketing: A synthesis and prognosis. Journal of Interactive Marketing, 23(2), 118–129.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Xia, Y., & Zhang, G. P. (2010). The impact of the online channel on retailers’ performances: An empirical evaluation. Decision Sciences, 41(3), 517–546.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Benjamin B. M. Shao.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chou, YC., 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 23, 681–694 (2021). https://doi.org/10.1007/s10796-020-09989-0

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10796-020-09989-0

Keywords

Navigation