Counteracting consumer subversion: Organizational privacy ethical care as driver of online information sharing

Abstract

With an ever‐increasing hunger for consumer data by firms, and despite many organizational efforts to reduce consumer privacy concerns, consumer subversion behavior towards information provision persists. Organizational privacy ethical care, an organizational behavior that goes beyond legislative action and moral codes, provides a new theory of how to overcome this issue. Across three studies, we develop and test theory which suggests an organizational ethic of care approach to privacy will have a positive impact on reducing consumer subversion behavior (i.e. increase consumers' willingness to share information and the accuracy of information they share). The correlational and causal results indicate that perceived organizational privacy ethical care is a positive driver of the amount and the accuracy of information consumers are willing to share with firms. The results also suggest partial support that this relationship is mediated through perceived information control and trust towards the organization. Thus, we provide some support for a better corporate approach to privacy, beyond previously suggested legislative and social responsibility standards, which allows for the reduction of consumer privacy concerns and subsequent subversion behaviors.

Why do consumers subvert brands? investigating the influence of subjective well‐being on brand avoidance

“…Therefore, in this study, we integrate self‐control theory (Baumeister et al, 2018) and the literature related to subjective well‐being (Diener, 2009; Zhong & Mitchell, 2012) as our overarching theoretical framework, to explore why and when subjective well‐being influences brand avoidance. In this paper, we contribute to the literature on subjective well‐being and its capacities (Ifcher & Zarghamee, 2011; Zhong & Mitchell, 2012) as well as expand the current understanding of consumer subversion behaviors (Mattison Thompson, 2021).…”

Explaining how algorithms work reduces consumers' concerns regarding the collection of personal data and promotes AI technology adoption

“…With these findings, we build on investigations of consumers' privacy and data collection concerns (Cloarec et al, 2022; Maseeh et al, 2021) and identify new ways to overcome them (Martin & Murphy, 2017; Martin et al, 2017; Mattison Thompson & Siamagka, 2022). Our novel empirical evidence that mechanistic explanations can inhibit data collection concerns represents a response to calls for further insights into consumers' responses to AI algorithms that collect and use their personal data to provide personalized recommendations (Mariani et al, 2022).…”

“…In prior attempts, scholars and practitioners have proposed various business factors and marketing strategies (Martin & Murphy, 2017; Mattison Thompson & Siamagka, 2022), often related to the seemingly essential need to grant consumers control over their personal data management (e.g., Tucker, 2014; Xu et al, 2012). However, it may be equally relevant to provide consumers with explanations of AI algorithms that detail the uses of their data (Bhalla, 2020).…”

“…Therefore, in this study, we draw on prior studies of data collection concerns (Martin & Murphy, 2017; Martin et al, 2017; Mattison Thompson & Siamagka, 2022), mechanistic explanations (Craik, 1943; Glennan, 1996), and consumers' subjective understanding of product functioning (Cadario et al, 2021; Fernbach et al, 2013) to propose that providing consumers with detailed mechanistic explanations of AI algorithms can reduce their data collection concerns (Studies 1a and 1b). When exposed to such explanations, consumers gain an increased subjective understanding of how AI algorithms work and express diminished data collection concerns (Study 2).…”

“…In turn, we make three main contributions to the literature on privacy (e.g., Scarpi et al, 2022), data collection concerns (e.g., Hsu & Lin, 2016; Malhotra et al, 2004; Smith et al, 1996), and AI technology adoption (e.g., Mariani et al, 2022). First, rather than privacy inhibitors ensconced in a firm's privacy policies or characteristics (e.g., control, organizational privacy ethical care, brand credibility; Jain et al, 2022; Martin & Murphy, 2017; Mattison Thompson & Siamagka, 2022), we address privacy inhibitors as they relate to explaining AI algorithms. With this novel approach, we conceive of and empirically test mechanistic explanations of AI algorithms as a crucial inhibitor and thereby demonstrate that providing consumers with these explanations significantly reduces their data collection concerns.…”