Which Privacy Policy Works, Privacy Assurance or Personalization Declaration? An Investigation of Privacy Policies and P
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ORIGINAL PAPER
Which Privacy Policy Works, Privacy Assurance or Personalization Declaration? An Investigation of Privacy Policies and Privacy Concerns Fue Zeng1 · Qing Ye1 · Zhilin Yang2 · Jing Li3 · Yiping Amy Song4 Received: 22 September 2019 / Accepted: 19 September 2020 © Springer Nature B.V. 2020
Abstract This study focuses on two specific privacy policies, namely privacy assurance and personalization declaration. Specifically, we investigate how these distinct privacy policies affect customers’ privacy concerns and subsequent purchase responses. We have developed a conceptual model that addresses the independent effects of privacy assurance and personalization declaration, as well as the mechanism (i.e., privacy concerns) of these effects. Our model is grounded in motivation theory and supported by a field experiment and a controlled experiment. Our study demonstrates that privacy assurance that claims security protection negatively affects customers’ purchase probability and purchase amount. By contrast, personalization declaration that addresses personalized benefits positively affects customers’ purchase probability and purchase amount. Privacy concerns significantly mediate the negative effects of privacy assurance on purchase responses and the positive effects of personalization declaration on purchase responses. Overall, our findings inform managers of how to deploy privacy policies to reduce customers’ privacy concerns and boost purchase responses. Keywords Privacy policy · Privacy assurance · Personalization declaration · Privacy concerns · Purchase responses
Introduction The current proliferation of big data analytics has empowered companies to transform customer’s online traces (e.g., browsing and purchasing data) into valuable content (e.g., * Jing Li [email protected] Fue Zeng [email protected] Qing Ye [email protected] Zhilin Yang [email protected] Yiping Amy Song amy.song@neoma‑bs.fr 1
Department of Marketing, Economics and Management School, Wuhan University, Wuhan 430072, China
2
School of Management, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
3
Department of Marketing, College of Business, City University of Hong Kong, Hong Kong 852, China
4
Department of Marketing, NEOMA Business School - Campus de Reims, Reims 51100, France
one-to-one personalized promotions) that can enhance customers’ experiences and increase conversion rates (Martin et al. 2017). To inform customers of the details about how firms gather, use, disclose and manage their personal data, diverse privacy policies are implemented (Awad and Krishnan 2006), among which privacy assurance and personalization declaration are the most commonly used in practice. For example, as Cnblogs highlights, “This software will not disclose this information to third parties or provide it to third parties without your permission”.1 This privacy policy indicates the assurance of little personal data leakage. In contrast, Splayer claims, “SPlayer and its affiliates may also combine this informatio
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