The Effects of Information Nudges on Consumer Usage of Digital Services under Three-Part Tariffs

ABSTRACT

We develop a dynamic model to examine how information nudges influence digital services’ consumer usage behavior and welfare under a three-part tariff structure. We study two types of information nudging: nudging through full and nudging through partial information provision. In the former, information nudges are provided to inform consumers of their usage status at every decision point in a billing cycle. In contrast, in the latter, consumers are nudged at one or more decision points within a billing cycle but not throughout the billing cycle. Our model considers two dimensions of consumer heterogeneity: inattentiveness and preference. Furthermore, our model investigates an important but under-investigated design element of information nudges, namely, the timing of the information nudges. We find that (1) nudging through information provision influences inattentive consumers’ usage decisions and improves consumption efficiency, (2) consumers’ welfare gains from full information nudging depict an inverted-U shape contingent on consumers’ preference heterogeneity, and (3) the timing of nudging matters. Our findings provide managerial implications for the design of information nudging strategies and procedures. Finally, we empirically illustrate the analytical results in the context of consumers’ mobile data usage behavior.

KEYWORDS:

• Digital nudge
• behavioral economics
• limited attention
• preference heterogeneity
• service tariffs
• three-part tariff
• digital services
• information nudge

Supplementary information

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

Acknowledgement

We are especially grateful for the constructive comments and suggestions from the JMIS Editor-in-Chief, Dr. Vladimir Zwass, and three anonymous reviewers. Ping Xiao wants to thank the Department of Marketing, Deakin Business School, Deakin University to support her when she worked on the paper there.

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

1. While it would be ideal to test every analytical finding empirically, we can only focus on some analytical findings in our empirical demonstration as we lack certain relevant data.

2. Note that we also explored the algorithm, solution and numerical illustrations for a general model with $y=1,2,3,\dots Y\ge 3$. The results are available upon request.

3. The discrete demand assumption is only for algebra simplification, and it does not impact the model inference. It shares the same decision process as the continuous demand assumption. Regardless of the demand is discrete or continuous, keeping track of the past consumption (even simple events such as consuming or not) and staying attentive costs time and effort. The discussion on the case of continuous demand is available upon request.

4. This means that $\in >\bar{\in }$ in the parameter space. We indicate $\bar{\in }$ in the Online Technical Supplemental Appendix. In this parameter interval, the user’s optimal usage decisions are interior solutions.

5. The results are available upon request.

6. We conducted a survey on the Amazon MTurk in December 2020. The survey result shows that 140 out of 200 respondents are, to some extent, inattentive to their mobile data consumption activities. Only about 30 percent of respondents are always attentive to their data usage status.

7. The usage monitoring cost is an example to rationalize the four consumer segments. Users’ costs to monitor their usage could be heterogeneous across consumers and periods. For example, consumers in Segment “att” could always take initiatives to track their usage because monitoring costs in all periods are low. In contrast, consumers in the inattentive segments may not find it beneficial when such cost is high in some or all periods. We thank an anonymous reviewer for the inspiration.

8. We present the details of ${\hat{\in }}_n$ in the Online Technical Supplemental Appendix.

9. Testing the other two propositions is out of scope due to the lack of relevant data.

10. Though the service provider canceled FDN services during the sample period in our empirical context, FDN service is still widely used in the mobile service industry. China Mobile (the largest mobile service provider) offers information nudges about the allowance balance, sent on the specified dates in a billing cycle.

11. The R-squared is generally low, as shown in many other quasi-experimental studies [43] because the individual fixed effects are differenced out instead of estimated. The statistical power and magnitude of the estimated parameters are the focus of the analysis in our study.

12. The focus of our empirical analysis is not to separate the impacts of the two FDN messages but to provide an empirical illustration of the overall impact of nudging on consumer usage behavior. The identified effect of FDN2 may include the carry-over effect from FDN1.

13. The analyses and results on how our analytical propositions may hold under usage-based nudging are available upon request.

Additional information

Notes on contributors

Ping Xiao

Ping Xiao ([email protected]) is an Associate Professor at Melbourne Business School, University of Melbourne. She holds a Ph.D. from Washington University in Saint Louis. Her main research interests are strategic interactions in business (e-commerce) expansion, big data analytics, and policy evaluation, social network/media and consumer analytics, and the social impact of corporate social responsibility. Dr. Xiao’s work has appeared in Marketing Science, Management Science, Journal of Marketing Research, Journal of Econometrics, International Journal of Research in Marketing, Journal of Industrial Economics, and other journals.

Yuanyuan Chen

Yuanyuan Chen ([email protected]; corresponding author) is an Assistant Professor in the Department of Information Systems, Statistics and Management Science at the University of Alabama. She received her Ph.D. and Master of Law degrees from Emory University. Her research focuses on the economics of IT and IT-enabled services, including sharing economies, economics of cloud computing; cybersecurity and data protection; and IT law and policy. Dr. Chen’s research has been published in journals such journals as Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Information & Management, and Journal of Strategic Information Systems.

Anandhi Bharadwaj

Anandhi Bharadwaj ([email protected]) serves as the Vice Dean of Faculty and Research of Goizueta Business School at Emory University. She received her Ph.D. degree in Management Information Systems with a minor in Computer Science at Texas A&M University. Previously, Dr. Bharadwaj was an information systems consultant at NIIT, a world-wide IT consulting firm, where she was responsible for IT systems development and executive training for clients world-wide. She is Department Editor for the IS track at Management Science. She has also served as Senior Editor for Information Systems Research, and Associate Editor of MIS Quarterly and Journal of the AIS. Her research has been published in such journals such as Management Science, Information Systems Research, Journal of Management Information Systems, MIS Quarterly, Organization Science, Production and Operations Management, and IEEE Transactions on Engineering Management.

Weining Bao

Weining Bao ([email protected]) is Assistant Professor of Marketing at the University of Connecticut. He received his Ph.D. in Economics at Johns Hopkins University. His research focuses on empirical and analytical modeling of strategic interactions between firms and consumers. Dr. Bao’s work is interdisciplinary, spanning marketing, economics, and finance. He is particularly interested in the firms’ strategic decisions in the context of information asymmetry and moral hazard and their implications for digital marketing, marketing of financial services, education marketing, and emerging markets. His research has been published in the leading marketing journals such as Marketing Science.