Understanding mediators in location-based mobile marketing and why commuting hubs are so effective—a mediation analysis of a randomized field experiment

ABSTRACT

Due to its inexpensiveness, speed of implementation and adaptation, push location-based mobile advertising (LBMA) can constitute a powerful, cost-effective marketing tool. Thus, in theory, LBMA bears considerable potential for conducting effective marketing campaigns. However, in reality, to tap into this potential, practitioners, and academics must first understand how targeting strategies, and in particular spatial targeting – determine LBMA’s success rates. While the role of location for the effectiveness of LBMA is well understood, the underlying mechanisms through which location can influence LBMA redemption rates remain widely unexplored. Based on a field experiment with same-day LBMA coupons, this study extends the existing research and shows that location determines LBMA redemption rates via a cognitive and affective component of consumers’ ad perception. Further, this study shows that surprisingly, and contrary to the prevailing mantra that location congruency yields the best LBMA redemption rates, commuting hubs are an appealing alternative to location congruent LBMA.

Acknowledgments

This work has been performed in the context of the DFG funded CRC 1053 MAKI.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

¹ Affective responses to an ad are “not evaluative responses to an advertisement, but represent the moods and feelings evoked by the ad” (Batra & Ray, 1986, p. 234).

² Originally composed of nine items measuring consumers’ affective reaction to an ad, this scale focuses on the positive or pleasurable types of feelings consumers experienced (Bruner, 2009, p. 29).

³ Odds-Ratios (OR) were computed using the estimated coefficients β, following the formula: OR = exp(β). For example, for LBMA perceived relevance (β = .219), the OR is exp(.219) = 1.2448 respectively 24.5%.

⁴ Further information is available here: https://www.processmacro.org/index.html

Additional information

Funding

This work was supported by the Deutsche Forschungsgemeinschaft [SFB 1053 MAKI].

Notes on contributors

Patrick Felka

Patrick Felka is postdoctoral researcher at the Chair of Information Systems and Information Management at Goethe University Frankfurt and researcher at the Collaborative Research Centre (CRC) 1053 “MAKI – Multi-Mechanisms Adaptation for the Future Internet” funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). He is conducting research on consumer behavior, data-mining, and context-aware applications.

Cristina Mihale-Wilson

Cristina Mihale-Wilson is a postdoctoral researcher at the Chair of Information Systems and Information Management at Goethe University Frankfurt. Her main focus lies on Artificial Intelligence-based Systems, Smart Services and Digital Ecosystems. As a team member of the ForeSight project funded by German Federal Ministry for Economic Affairs and Energy (BMWi) she is currently conducting research in the area of AI-based assistance for smart living.

Oliver Hinz

Oliver Hinz is Professor of Information Systems and Information Management at Goethe University Frankfurt. He is interested in research at the intersection of technology and markets. His research has been published in journals like Information Systems Research, Management Information Systems Quarterly, Journal of Marketing, Journal of Management Information Systems, Decision Support Systems, Business & Information Systems Engineering (BISE) and in a number of proceedings (e.g. ICIS, ECIS, PACIS).