The Role of Push-Pull Technology in Privacy Calculus: The Case of Location-Based Services

location-based services (lBS) use positioning technologies to provide individual users with reachability and accessibility that would otherwise not be available in the conventional commercial realm. While lBS confer greater connectivity and personalization on consumers, they also threaten users’ information privacy through granular tracking of their preferences, behaviors, and identity. To address privacy concerns in the lBS context, this study extends the privacy calculus model to explore the role of information delivery mechanisms (pull and push) in the efficacy of three privacy intervention approaches (compensation, industry self-regulation, and government regulation) in influencing individual privacy decision making. The research model was tested using data gathered from 528 respondents through a quasi-experimental survey method. Structural equations modeling using partial least squares validated the instrument and the proposed model. results suggest that the effects of the three privacy intervention approaches on an individual’s privacy calculus vary based on the type of information delivery mechanism (pull and push). results suggest that providing financial compensation for push-based lBS is more important than it is for pull-based lBS. Moreover, this study shows that privacy advocates and government legislators should not treat all types of lBS as undifferentiated but could instead specifically target certain types of services. keY words and pHrases: compensation, distributive justice, government regulation, industry self-regulation, information delivery mechanisms, location-based services (lBS), privacy calculus, procedural justice. In the new wireless era, we can go anywhere and still maintain intimate contact with our work, our loved ones and our real-time sports scores. To see how this might work, check out Worktrack, a tracking system used by a company in the heating and air-conditioning business. Workers have cell phones equipped with global Positioning System (gPS) that pinpoint their locations to computers in the back office. Their peregrinations can be checked against the “geo Fence” that employers draw up, circumscribing the area where their work is situated. THE rOlE OF PuSH–Pull TEcHNOlOgY IN PrIVacY calculuS 137 If they’re not in the right working area, a notification will be sent to the back office. Worktrack is only one of many services devoted to tracking humans. Parents use similar schemes to make sure their kids are safe; our buddies use location-based “friend-finder” services to make social lives more efficient and pleasurable. Sooner or later, the persistent connectedness may well lead us toward a future where our cell phones tag and track us like FedEx packages. Here’s a new battle cry for the wireless era: Don’t geo-Fence me in. [45] recenT advances in moBile communicaTion TecHnologies are spearheading the next generation of itinerant e-commerce applications. The development of positioning technologies, such as gPS and cellular triangulation techniques, has not only provided consumers with unprecedented accessibility to network services while on the move, but also enabled the localization of services [8, 61]. In the literature, commercial locationsensitive applications and services that utilize geographical positioning information to provide value-added services are generally termed location-based services (lBS) [6, 8]. These services include emergency and safety-related services, location-sensitive billing, entertainment, navigation, asset tracking, directory and city guides, traffic updates, and location-based advertising [6]. By bringing locatability and personalization to users, emerging lBS applications potentially offer significant value by placing information, transactions, and entertainment in a location-specific context [8]. The growth trajectory of lBS is striking. according to a recent report from allied Business Intelligence Inc., lBS revenues are expected to reach an annual global total of $13.3 billion by 2013, up from an estimated $515 million during 2007 [53]. unsurprisingly, the commercial potential and rapid growth of lBS have been accompanied by concerns over the collection and dissemination of personal information by service providers and merchants. The concerns center on the confidentiality of accumulated consumer location data and other personal information, and the potential risks that consumers experience over the possible breach of confidentiality [41]. location information often reveals the position of a person in real time, rendering the potential intrusion of privacy a critical and acute concern. Indeed, the Big Brother imagery [55] looms in the popular press where lBS are discussed [45]. To the degree that privacy concerns represent a major inhibiting factor in the adoption of lBS [41], we respond to the call of no LBS without L-privacy [34] by theoretically developing and empirically testing a model that addresses privacy issues in the context of lBS usage. Individual privacy decision making is often described in terms of a calculus where personal information is given in return for certain benefits [44, 67]. We extend the privacy calculus framework by modeling three privacy intervention approaches (compensation, industry self-regulation, and government regulation) as important variables that exert direct effects on the privacy calculus. We theoretically link these privacy intervention approaches with two types of justice provisions, and argue that justice provisions, through the forms of compensation, industry self-regulation, and government regulation, influence the outcomes of the privacy calculus. We manipulate these privacy intervention approaches in a quasi-experimental survey study and examine their effects on the privacy calculus in two types of information delivery mechanisms. In particular, we study pull-based lBS, where consumers initiate requests 138 Xu, TEO, TaN, aND agarWal for information and services based on their locations, and push-based lBS, where positioning technologies autonomously and proactively push information and services to consumers’ mobile devices based on their locations. We propose that the effects of the three privacy intervention approaches on an individual’s privacy calculus vary based on the type of information delivery mechanism (pull or push). The current study contributes to existing privacy literature in several important ways. First, in contrast to most privacy research that was conducted in the Internet context (e.g., [29, 48]), we develop and empirically test a research model in an understudied lBS context. Such a new ubiquitous computing environment offers consumers relatively higher levels of reachability and accessibility over the communication and exchange process than has been the case with the Internet [39]. accordingly, privacy concerns in such contexts become particularly salient as merchants and service providers may have access to a large volume of potentially sensitive consumer information. Second, following the call by chan et al. [15], this research explores the role of one particular technological attribute (information delivery mechanisms) in the theoretical development surrounding privacy. Particularly, we attempt to show if and to what extent the effects of privacy intervention strategies on privacy calculus are dependent upon different information delivery mechanisms (pull and push). Third, although scholarly efforts have been devoted to identify the dimensions of justice in the information privacy context (e.g., [27, 43, 66]), few studies have identified approaches to justice provision and examined the efficacies of these approaches. To address this gap, we theoretically differentiate three privacy intervention approaches (compensation, industry self-regulation, and government regulation) based on the types of justice components they provide. Theoretical Foundations THe calculus perspecTive of informaTion privacY interprets the individual’s privacy interests as an exchange where individuals disclose their personal information in return for certain benefits. We extend this perspective by integrating it with justice theory to study the efficacy of three privacy intervention strategies (compensation, industry self-regulation, and government regulation) in influencing perceptions of privacy benefits/risks. By theoretically linking three privacy intervention approaches with two types of justice provisions, we propose that the conventional understanding of privacy as a calculus can be explained within the framework of justice theory: on one hand, consumers may evaluate the fairness of the distribution of outcomes, which includes the tangible consequences of the information disclosure to both themselves and firms; on the other hand, they may attend to and evaluate the fairness of the manner in which they were treated in the information exchange. using the calculus Perspective to understand Information Privacy Information privacy refers to the ability of the individual to control the terms under which personal information is acquired and used [73]. although ostensibly the noTHE rOlE OF PuSH–Pull TEcHNOlOgY IN PrIVacY calculuS 139 tion of privacy appears straightforward, there are a great many ways in which the literature in diverse fields such as law, marketing, political science, psychology, and social sciences has treated this concept [49, 67]. Within the robust body of research that attempts to understand the nature of consumer privacy, it has been found that the calculus perspective of privacy is “the most useful framework for analyzing contemporary consumer privacy concerns” [27, p. 326]. This calculus perspective is especially evident in empirical studies of consumer privacy concerns (e.g., [23, 29, 36, 38, 52]). according to these studies, consumers perform a risk–benefit analysis of all the factors related to a particular information disclosure situation in order to assess privacy concerns. In contrast to most prior research that was conducted to apply the privacy calculus framework in the direct marketing or conventional Web context, we empirically test and extend the calculus model in an understudied lBS context. In a context marked by ubiquity and uniqueness where individuals engage more devices and systems in a real-time fashion [39], privacy concerns become particularly salient as service providers may have access to a larger volume of potentially sensitive information. Thus, the use of lBS often demands that individuals be continually engaging in a dynamic adjustment process in which privacy risks are weighed against benefits of information disclosure, rendering the privacy calculus very significant and highly relevant in this context. consistent with the core ideas of privacy calculus, exchange theory [5] may further help predict how individuals make decisions regarding the disclosure of personal information [27]. This theory describes the utilitarian exchange as an interaction whereby goods are given in return for money or other goods [5, p. 36], which is considered as the “first exchange” [27, p. 326]. The concept of the “second exchange” has been introduced by culnan and Bies [27, p. 326] to explain the privacy calculus, whereby consumers’ personal information is given in return for value such as higher-quality service and personalized offers or discounts [27]. applying the second exchange framework to the lBS context, we may interpret information disclosure in lBS as an exchange where consumers disclose their personal information and location data in return for value such as locatability and personalization provided by lBS providers. Specifically, consumers behave as if they are performing a risk–benefit analysis (i.e., privacy calculus) in assessing the outcomes they would receive as the result of providing personal information to lBS providers. We further integrate the privacy calculus model with justice theory to argue that the outcomes of risk–benefit analysis of personal information disclosure, at the individual level, could be differentiated according to the extent to which justice provisions are manifested in privacy interventions. using Justice Theory to understand Information Privacy Much has been written on the notion of justice or fairness, from a wide variety of disciplines, including ethics, economics, management, sociology, and psychology, resulting in a plethora of definitions and uses of the concept (see [20] for a review). recently, justice theory has seen a popular return in the privacy literature [3, 27, 62]. accordingly, scholarly efforts have been devoted to theoretical development 140 Xu, TEO, TaN, aND agarWal for analyzing privacy through a justice theoretical lens (e.g., [27, 43, 66]). a general conclusion from this stream of research is that the fairness perceptions of a firm’s information practices can have a major positive effect on consumers’ privacy decision making [26]. Specifically, the presence of justice, with the concerns for fairness, transparency, and accountability for privacy protection actions, provides consumers with the tangible processes and psychological benefits such as confidence and control that lead to a positive outcome of their privacy calculus and a greater willingness to disclose personal information [27]. However, in the justice literature, there remains considerable debate as to the dimensionality of justice. arguments have been made that justice consists of anywhere between one single underlying dimension [22] and up to five dimensions, including distributive, procedural, interactional, interpersonal, and informational justice [10, 27, 35, 66]. To address these confusions, colquitt et al. [20] conducted a meta-analytic review of 25 years of justice research and concluded that the various components of justice reflect two underlying dimensions—namely, distributive justice and procedural justice. In a more recent article on identifying dimensions of justice in the privacy context, ashworth and Free [3] echoed this conclusion by suggesting that distributive justice and procedural justice fundamentally reflect consumers’ privacy concerns (see [3] for a review). They further argued that “the other components of justice [such as interactional justice] in the literature reflect the same underlying concern as that voiced for procedural justice” [3, p. 113]. consequently, we focus on distributive justice and procedural justice in this research: (1) distributive justice refers to the perceived fairness of outcomes that one receives from providing personal information, and (2) procedural justice refers to the perceived fairness of the procedures that are enacted for information collection and use. In our research model, distributive justice provisions, predicting individuals’ attitudes toward material outcomes of an exchange [3], are mapped as the compensation which raises material outcomes of personal information disclosure. We argue that providing financial compensation constitutes an extra consumer outcome and an additional firm input, which is likely to increase the consumer’s judgments of the benefits of information disclosure. Procedural justice provisions, predicting “attitudes toward authorities” [42, p. 503], are mapped in our research model as industry self-regulation and government regulation which alleviate consumers’ perceived privacy risks through ensuring that their personal information is treated in a respectful and fair manner. We argue that these self-regulatory and legislative efforts specifically address how to enact the procedures to address consumer concerns regarding the fairness and accountability for privacy protection actions, thereby providing consumers with a sense of procedural justice.