It’s All about Timing: Captive Targeting through Mobile Ads

Abstract

How would consumers respond to mobile ads in spatially and temporally bounded captive environments? We theorize that consumers who are forced to wait are likely to perceive an abundance of time and ultimately become more responsive to mobile ads. To test our theory, we conducted three studies following a multimethod approach. In Study 1, a large-scale field study (n = 66,473), we found a relationship between captivity and receptiveness to mobile ads in transit. In Study 2, a lab experiment, we extended our findings by revealing that time perception mediated the relationship between captivity and intention to click on mobile ads. Last, in Study 3, we tested boredom and individual propensities to use time efficiently as underlying mediating and moderating conditions for captivity’s effect. Overall, the results have implications for advertising practitioners and researchers examining context-based mobile targeting.

Acknowledgments

This research was developed from initial findings presented at the International Conference on Information Systems (ICIS) 2018.

Notes

1 In complementary analyses presented in the Supplemental Online Appendix, we further rigorously classified nonriding users into prospective passengers before riding or past passengers who alighted from public transit. The results collectively enhance the validity of the findings.

2 Empirical results remained robust for both the 5-minute and 1-hour thresholds.

3 Routine routes were also measured by comparing familiar, previously traveled routes with unfamiliar ones. Results remained robust regardless.

Additional information

Funding

This research was supported by the National Chengchi University’s Office of Research and Development (108KY01225) and the National Science and Technology Council in Taiwan (109-2410-H-004-066-MY3). This work was partially supported by the Brain Korea 21 Program for Leading Universities and Students Fostering Outstanding Universities for Research in 2022 and the Yonsei University Research Fund of 2022 (2022-22-0068)

Notes on contributors

Jinpyo Hong

Jinpyo Hong (PhD, Korea Advanced Institute of Science and Technology [KAIST]) is currently an AI business strategy lead, Techlabs Co., Ltd., Seoul, Republic of Korea. At the time of this research the author was affiliated with Korea Advanced Institute of Science and Technology.

Il Im

Il Im (PhD, University of Southern California) is a professor, School of Business, Yonsei University.

Sungjun (Steven) Park

Sungjun (Steven) Park (PhD, Korea Advanced Institute of Science and Technology) is full-time faculty, Department of Business Administration, National Chengchi University.