Reducing the Service Deficit in M-Commerce: How Service-Technology Fit Can Support Digital Sales of Complex Products

Abstract

Mobile commerce still experiences high drop-out rates throughout the sales process. This is especially evident for complex products such as insurance, which consumers often initially explore online but complete the purchase offline to enjoy direct human assistance, helping them to better understand the product and reduce associated risks. To facilitate complex product purchases online, this article highlights the need for fit between the type of service and the type of technology used for the purchase, termed as service-technology fit (STF). In particular, the article proposes a comprehensive technological service (CTS); that is, the combination of a technology-mediated service (TMS) and a technology-generated service (TGS) as a central element of a face-to-screen service (FtoS). This experimental study found a significant superiority for CTS in terms of service quality and information quality as well as concerning consumers’ perceived financial risk, psychological risk, ease of use, and purchase intention. In contrast, relying solely on either TMS or TGS reduces the perceived service and information quality and reinforces the perception of several risks. These findings extend the theoretical knowledge on the mechanisms that are inherent to an FtoS service and give concrete practical implications for the future design of service in mobile commerce.

KEY WORDS AND PHRASES:

• Complex products
• comprehensive technological service
• mobile commerce
• perceived risk
• online risk
• online service
• technology-generated service
• technology-mediated service

Supplemental File

Supplemental data for this article can be accessed on the publisher’s website.

Notes

¹ A summary of all acronyms can be found in online Appendix 1.

² We later repeated the comparisons regarding the group homogeneity for our exploratory analysis with the age-reduced dataset. The results revealed no significant difference between the groups.

³ Because 4 is the scale mean, this can be seen as critical threshold for purchases to take action. This is in line with findings from Gefen and Pavlou [40].

Additional information

Notes on contributors

Jörg Heinze

JÖRG HEINZE ([email protected]) completed his Ph.D. at the Department of Psychology at the University of Regensburg, where he investigated the resistance of consumers toward complex products in e- and m-commerce. He is a cofounder of a tech startup and consults companies on the digital transformation of their businesses. He formerly headed a research and development department of a leading German insurance company, with focus on the digitization of financial services. His work has appeared in Computers in Human Behaviors as well as in conference proceedings and academic monographs.

Christian Matt

CHRISTIAN MATT ([email protected], corresponding author) is assistant professor at the University of Bern, Switzerland. He holds a Ph.D. in management from Ludwig-Maximilians-University, Munich, Germany. His research focuses on digital operations and digital transformation as well as on the design and use of personal information systems. His work has appeared in Journal of Management Information Systems, MIS Quarterly Executive, Electronic Markets, Business and Information Systems Engineering, and in various international conference proceedings.