Chatbot Advertising As a Double-Edged Sword: The Roles of Regulatory Focus and Privacy Concerns

Abstract

In this article, the authors examine the effect of regulatory focus and privacy concerns on consumers’ responses to highly personalized chatbot advertising. Findings from two experimental studies indicate that predominantly promotion-focused consumers are more receptive and respond more favorably to highly personalized chatbot advertising because they attend more to benefits they may gain from disclosing personal information. In contrast, consumers who are predominantly prevention focused are more attentive to the risks involved and feel unfavorably toward highly personalized chatbot advertising. In addition, consumers who are highly concerned about privacy disdain highly personalized ads, regardless of regulatory focus. Risk-benefit perceptions are shown to mediate interactions of ad personalization, regulatory focus, and privacy concerns.

Disclosure

No potential conflicts of interest were reported by the author(s).

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Notes

1 Participants answered two questions related to a short story we provided: (1) What is the dog’s name? (2) Why did the family love the dog? Participants who failed to answer one or more questions correctly (n = 15) were directed to the end page and excluded from the data, leaving 178 participants. The power of our samples is .806, which is above the general cutoff line (.8), suggesting that our sample sizes are sufficient based on focal effects (interaction), desired precision (5%), and experimental design (Faul et al. 2007).

2 Participants with higher product involvement indicated higher rent intentions, even when product involvement was excluded from the analysis.

3 J-N values represent units of standard deviation (SD) of the moderator measured as a continuous variable. To convert J-N values to benchmark scores on a 7-point scale, we use the following formula: [1SD * (J-N values * 100) / 100] + mean scores

4 Before the regulatory focus manipulation, we administrated attention checks as recommended for ensuring good respondent data (Smith et al. 2016). Specifically, consistent with Study 1, participants answered two questions related to a short story we provided: (1) What is the dog’s name? (2) Why did the family love the dog? Participants who failed to answer one or more questions correctly (n = 20) were directed to the end page and excluded from the data, leaving 430 participants.

5 Study 2 results remained the same even when we excluded product involvement and ad believability from the model.

Additional information

Notes on contributors

WooJin Kim

WooJin Kim (MA, University of Texas at Austin) is a doctoral student, Charles H. Sandage Department of Advertising, College of Media, University of Illinois at Urbana–Champaign.

Yuhosua Ryoo

Yuhosua Ryoo (PhD, University of Texas at Austin) is an assistant professor, School of Journalism and Advertising, College of Arts and Media, Southern Illinois University.

SoYoung Lee

SoYoung Lee (PhD, University of Texas at Austin) is an assistant professor, The Ric Edelman College of Communication and Creative Arts, Rowan University.

Jung Ah Lee

Jung Ah Lee (PhD, University of Texas at Austin) is a senior researcher, Institute of Communication Research, Seoul National University.