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Research article: Provide a description of each article in a min of 100 words in APA format. the_role_of_privacy_assurance_.pdf information_privacy_and_correl.pdf the_influence_of_the_informal_.pdf why_security_and_privacy_resea.pdf examining_the_intended_and_uni.pdf Unformatted Attachment Preview European Journal of Information Systems (2015) 24, 624–644 © 2015 Operational Research Society Ltd. All rights reserved 0960-085X/15 www.palgrave-journals.com/ejis/ EMPIRICAL RESEARCH The role of privacy assurance mechanisms in building trust and the moderating role of privacy concern Gaurav Bansal1, Fatemeh ‘Mariam’ Zahedi2 and David Gefen3 University of Wisconsin – Green Bay, WI, U.S.A. University of Wisconsin – Milwaukee, WI, U.S.A. 3 Drexel University, PA, U.S.A. 1 2 Correspondence: Gaurav Bansal, University of Wisconsin – Green Bay, 2420 Nicolet Drive, Green Bay, WI, 54311, U.S.A. Tel: +9204652216; Fax: +9204652660; E-mail: bansalg@uwgb.edu Abstract Privacy policy statements and privacy assurance cues are among the most important website features that online providers use to increase individuals’ trust and willingness to disclose private information online. The focus of this study is a comprehensive examination of the process by which privacy assurance mechanisms influence trust and the moderating role of privacy concern in this process. We use the lens of the Elaboration Likelihood Model to investigate the way different individuals perceive and process privacy assurance mechanisms. We argue that the trust-enhancing role of these mechanisms depends on the individual’s privacy concern. The results of this study articulate the process by which various privacy assurance mechanisms operate in enhancing an individual’s trust, and show that there are distinct behavioral differences between individuals with high- vs low-privacy concern when forming their trust to disclose private information. The paper sheds new light on the role of elaboration in the trust building process, and shows why privacy assurance mechanisms have different impacts depending on individuals’ privacy concerns. European Journal of Information Systems (2015) 24(6), 624–644. doi:10.1057/ejis.2014.41; published online 17 February 2015 Keywords: information privacy concern; trust; elaboration likelihood model; privacy policy statements; privacy assurance cues Introduction Received: 7 February 2013 Revised: 5 October 2013 Accepted: 25 October 2014 Online businesses use customers’ private information in order to personalize their services (Lee et al, 2011), or to offer more services to those who have higher potentials, values, or loyalty. Year after year online privacy protection emerges as a major concern of customers in Digital Future Report surveys (The Center for the Digital Future, 2011). It is widely acknowledged that information privacy is a growing concern (Smith et al, 2011, Xu et al, 2012a, b). Privacy concerns continue to drive customers away from online businesses (Kukar-Kinney & Close, 2010) and there are several reasons for that. People are wary not only of government surveillance (Solove, 2011), but also of corporate surveillance. ‘Forget Big Brother – it’s Big Business that’s watching you’ (The Center for the Digital Future, 2011). Recent U.S. newspaper headlines suggest that ‘not much – if any – of our electronic communication is genuinely “private,” not even for the director of the world’s largest spy agency’ (Policinski, 2012). Data collection poses a threat to customers’ privacy (Li, 2011) and subsequent loss of trust in the website, leading to undesirable responses, such as refusal, misrepresentation, negative word-of-mouth, and complaints (Son & Kim, 2008). The most critical The role of privacy assurance mechanisms issue that Internet customers face is ‘fear and distrust regarding the loss of personal privacy in the electronic commerce markets’ (Luo, 2002, p. 111). Such concerns reduce consumers’ willingness to transact with a website (Lowry et al, 2012). Furthermore, privacy is value based in this study since it is viewed as a civil right and a commodity rather than the desire to be anonymous or live in solitude (Smith et al, 2011). We define privacy assurance as mechanisms that directly or indirectly provide customers with assurances and guarantees that their private information will be protected and kept private by the website (Lowry et al, 2012). Privacy assurance plays a fundamental role in lowering privacy concerns and building trust (e.g., Wu et al, 2012), and adds value by protecting customers’ right to privacy (Smith et al, 2011). Prior research (e.g., Milne & Culnan, 2002, 2004) has reported that privacy assurance mechanisms are among the most important website features for creating a trusted online environment. However there has been inadequate investigation into the simultaneous impacts of such mechanisms on customers’ trust to disclose personal information online. More importantly, there is little knowledge of the process by which these mechanisms influence trust. We argue that these mechanisms are comprised of direct and indirect features. Direct mechanisms include privacy policy statements which explicitly attempt to address the issues related to the handling of one’s private information. Although there are studies at the research level for introducing new elements that could increase users control over their privacy (e.g., Xu et al, 2012a), at present the universally accepted practice is to rely on privacy-policy statements to convey the direct information about the user’s control over their private data. Indirect mechanisms include website features that implicitly assure customers about their privacy (Kim & Benbasat, 2003; Lowry et al, 2012). These cues are in contrast to privacy policy statements that explicitly reference the protection of customers’ privacy. Individuals vary in their perception of threat to their privacy. People with a high level of privacy concern would seek a higher degree of privacy assurance to address their concern and might look deeper into websites’ contents for guarantees regarding the fair and safe treatment of their personal information than people with a low level of privacy concern. As discussed later, the existing literature on the impact of various assurance mechanisms on trust has ignored the role of privacy concern in the assurance process. To address this gap, we focus on a comprehensive set of privacy assurance mechanisms, and investigate the process by which these mechanisms influence the trust of individuals with different levels of privacy concern. We examine the role of the direct mechanism (privacy policy statement), as well as indirect mechanisms – websites’ reputation, perceived presence of company information, design appeal, and perceived information quality (PIQ). In doing so, we pose the following research questions: What is the role of privacy assurance mechanisms in enhancing trust Gaurav Bansal et al 625 for disclosing private information online? What is the role of privacy concern in moderating the assurance-to-trust process? The study formulates a conceptual model that examines assurances provided by various mechanisms in an integrated framework, thus allowing the comparative analysis of such mechanisms. The conceptualization of the model is based on the Elaboration Likelihood Model (ELM) as the overarching theory. ELM is a dual-process theory (Petty & Cacioppo, 1986a). It postulates that processing information within a message involves two possible routes. The route used depends on the individual’s motivation to invest cognitive efforts to process the message. People in the high elaboration likelihood state are more likely to engage in thoughtful processing of the message and, therefore, are more persuaded by the message’s argument quality. This is called the central route. Those in the low elaboration likelihood state lack the motivation to scrutinize the message, and tend to rely more on peripheral cues in making a judgment about the message. This is called the peripheral route. More specifically, ELM posits that persuasion outcomes (i.e. changes in consumers’ trusting beliefs) are a function of (1) strength of content, (2) peripheral cues (e.g., heuristic cues which are non-content stimuli that influence persuasion, such as the source of trustassuring arguments), and (3) elaboration likelihood (Petty & Cacioppo, 1986a; Kim & Benbasat, 2009–2010). In addressing the research questions, we argue that websites’ privacy policies are articulated in their privacy policy statements. Hence, contents of privacy policies are the main messages and arguments forwarded by websites to assure their visitors of the adequacy of the websites’ privacy-protection measures. However, there are other features in websites that do not contain direct arguments for privacy assurance, but visitors with less motivation may rely on them in forming their trust to disclose private information. We call such features privacy-assurance cues and define them as website features that implicitly assure visitors about their privacy (Kim & Benbasat, 2003; Lowry et al, 2012). Such cues include websites’ reputation, company information, design appeal, and perceived information quality (PIQ). In ELM, elaboration likelihood is based on the individual’s motivation or ability. We argue that privacy concern is an immediate motivator in assessing privacy-assurance messages since people with a high level of privacy concern are more reactive to any object related to their concern. Thus, a higher degree of privacy concern provides a stronger motivation for careful elaboration and more willingness to spend cognitive energy to process privacy policy statements, whereas those with low-privacy concern are less motivated to spend intellectual energy on such statements and instead rely more on peripheral cues. This study makes a number of contributions. (1) This paper extends trust literature by adding the dual-route process of trust formation, with privacy concern as the motivator. It reveals the process by which different privacy assurance mechanisms impact trust to disclose private European Journal of Information Systems 626 The role of privacy assurance mechanisms information online. (2) While prior studies have investigated the strength and presence of privacy policy statements, this paper conceptualizes the argument about the quality of a privacy policy as measured by adequacy. (3) This paper uses an integrative approach to study the simultaneous influence of various privacy assurance mechanisms and compares their relative contributions in building trust. (4) The study identifies the dual role of the known trust antecedents such as reputation, design appeal of the website, and PIQ as peripheral cues for privacy assurance. (5) Our findings also provide insights for managers who require the disclosure of private information online. Literature review Privacy Concern, Trust, and Privacy Assurance. Individuals’ information privacy refers to their claims ‘to determine for themselves when, how, and to what extent information about them is communicated to others’ (Westin, 1967, p. 7 as quoted by Malhotra et al, 2004, p. 337). We define information privacy concern in the online context as individuals’ concern about the ‘threat to their information privacy’ when submitting their personal information on the Internet (Son & Kim, 2008, p. 504). The importance of privacy concern stems from its impact on trust and eventual behavior intentions of Internet users (Pavlou, 2003). Trust is defined as ‘the willingness of a party to be vulnerable to the actions of another party’ (Mayer et al, 1995, p. 712). Higher privacy concern reflects perceived vulnerability and hence should reduce trust. The negative impact of privacy concern on trust has been examined in the literature (Malhotra et al, 2004; Eastlick et al, 2006; VanDyke et al, 2007; Kim, 2008). Angst & Agarwal (2009) have reported the moderating role of privacy concern on the adoption of electronic health records. However, the moderating role of privacy concern on factors influencing trust has not been examined in the IS literature. One set of factors impacting trust are privacy assurance mechanisms. Table 1 summarizes the salient past research examining the impact of privacy policy statements on trust and behavioral intentions. Literature on such impacts has examined privacy policy – trust and/or behavior intentions. The literature reports contradictory findings about the impact of presence and strength of privacy policies on trust – with some reporting positive effect, some reporting no effect, and some even reporting a negative effect (Table 1). The strength of privacy policy statements in most cases has been related to some of the FTC dimensions (discussed later) with equally mixed results. It is apparent that there is a gap in examining the quality of privacy policy statements in terms of their adequacy of coverage as well as in the integrative impacts of other privacy assurance mechanisms in promoting trust. Furthermore, studies of online privacy policy statements, with few exceptions, have paid little attention to the influence of privacy concern, indicating another gap in understanding how European Journal of Information Systems Gaurav Bansal et al privacy concern may moderate the privacy assurance–trust process. Thus, there are two significant gaps in the literature about the trust building influence of privacy policies. First, there is a lack of systematic analysis of privacy assurance mechanisms (comprised of adequacy of privacy policy statements and privacy assurance cues), on trusting intentions. Second, there is a gap in our knowledge about the moderating role of privacy concern in the trust building impact of privacy mechanisms. We address these gaps by relying on the ELM lens to conceptualize an integrative model for investigating the process by which various privacy assurance mechanisms influence trust, and the moderating role of privacy concern in this process. ELM. The dual-process approach of ELM has a rich history of development and extensive applications, as reviewed by Petty & Wegener (1999). Applications of ELM as an overarching theory in the IS literature have been relatively sparse (Table 2). ELM studies have used message attributes (quality or framing) and the environmental settings (decision setting, preferences, and quality of session) as the central route. Source credibility (credibility of the person or system that conveys the message) has been conceptualized as the peripheral cue in most studies. Several research papers (e.g., Yang et al, 2006; Kim & Benbasat, 2009–2010; Greiner & Wang, 2010–2011; Martín et al, 2011) have examined the role of persuasive arguments and heuristic cues on trusting beliefs using the ELM lens. Kim & Benbasat (2009–2010) show that when customers are more involved (when buying a high priced product), they tend to form trusting beliefs by scrutinizing argument content (argument quality/central route) rather than by judging the heuristic cues (peripheral cues) as the ELM would predict. Citing the suitability of ELM for studying the efficacy of trust related arguments, Yang et al (2006) noted ‘[f]rom the ELM perspective, these antecedents of trusting beliefs can be regarded as trust-related arguments and categorized as either central cues or peripheral cues’ (p. 433). Similarly, in the context of a P2P lending marketplace Greiner & Wang (2010–2011) found that trust behavior is influenced differently by the central route (economic status) and peripheral cues (social capital and listing quality). Martín et al (2011) found that in online shopping, cognitive signals (argument quality) have a greater effect on website trust when the user is highly involved in online buying. Our study is the first to apply ELM in the investigation of the trust-building impacts of privacy assurance mechanisms as moderated by privacy concern. ELM is a suitable overarching theory for this study because, as online vendors strive to increase customer trust in their websites, individuals seek to see if and how much they can trust them (Gefen et al, 2003). This corresponds with the first postulate of ELM that people strive to hold correct attitudes (Petty & Cacioppo, 1986b). Since individuals vary in their traits (postulate 2), they rely on different cues from the environment in forming correct attitudes (postulate 3), the process which is moderated by the extent of their The role of privacy assurance mechanisms Table 1 Source Summary of research on online privacy assurance mechanismsa PPS→Trust (T) and/or BI Operationalization Impact on: Hui et al (2007) Liu et al (2004) Liu et al (2005) McKnight et al (2002) PPS presence PPS FTC notice presence PPS FTC notice presence PPS presence BI: Positive T: Positive T: Positive T: Positive Metzger (2006) PPS presence PPS FTC compliant or not Peterson et al (2007) PPS strength (3 levels of guarantees related to notice and security) Wang et al (2004) PPS strength related to FTC dimensions (high vs low) Arcand et al (2007) 627 Gaurav Bansal et al PPS presence PPS self-reported reading PPS opt-in/out format (choice) Lauer & Deng (2007) PPS strength (FTC compliant or not) Meinert et al (2006) PPS strength (3 levels of guarantees related to notice and security) Milne & Culnan Comprehension of PPS in general (2004) Pan & Zinkhan PPS presence (2006) PPS length and simplicity Tsai et al (2011) PPS presence and strength displayed via icons Wu et al (2012) PPS Strength related to FTC dimensions BI: Positive T: None BI: None T: None BI: None BI: Positive T: None BI: Positive T: None T: Negative T: Positive T: Positive BI: Positive Context Research Method E-Commerce E-Commerce E-Commerce Field Experiment Laboratory Experiment Laboratory Experiment Theoretical E-Commerce Laboratory Experiment E-Commerce (B, C, F info) Survey E-Commerce Laboratory Experiment E-Commerce: two studies travel & music Laboratory Experiment E-Commerce Not specified (B, C, F info) Laboratory Experiment Survey T in PPS: Positive T: Positive Not specified Survey E-Commerce Laboratory Experiment T: None BI: Positive T: Positive E-Commerce Not specified Laboratory Experiment Survey a B = Biographical; BI = Behavioral Intention; C = Contact; FTC = Federal Trade Commission; F = Financial; H = Health; PPS = Privacy policy statement; T = Trust. motivations (Petty & Cacioppo, 1986b). In the context of privacy assurance, individuals seek to form the correct trust attitude in websites by relying on privacy assurance mechanisms that websites provide. Based on their traits and experiences, people vary in the extent of their privacy concern, which motivates them differently in spending cognitive energy on suitable privacy cues in forming their trust. Hence, ELM provides a suitable lens in studying the impact of various privacy assurance mechanisms on people’s trust attitude. Research model Based on the ELM as the overarching theory, we have developed the research model (Figure 1) as discussed below. Involvement ELM asserts that users that are highly involved engage in extensive elaboration, while those that are less involved elaborate less and are more likely to rely on peripheral cues (Petty et al, 1981). Several studies have since demonstrated that individuals who possess strong concerns about a particular issue require credible and persuasive arguments to influence their belief structure (e.g., Bassili, 1996; Angst & Agarwal, 2009). ‘The stronger the concern, the more persuasive a message needs to be in order to overcome the associated apprehension’ (Angst & Agarwal, 2009, p. 349). Hence it could be argued that high- and low-privacy concern users would rely on different privacy assurance mechanisms to influence their trust in a website when it comes to disclosing their private information. Thus, privacy assurance mechanisms could influence attitude change (trust to disclose private information online) via central and peripheral routes a ... Purchase answer to see full attachment