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Journal ArticleDOI

Internet privacy concerns: an integrated conceptualization and four empirical studies

01 Mar 2013-Management Information Systems Quarterly (Society for Information Management and The Management Information Systems Research Center)-Vol. 37, Iss: 1, pp 275-298
TL;DR: In this article, the authors identify alternative conceptualizations of Internet privacy concerns (IPC) based on multidimensional developmental theory and a review of the prior literature and examine the various conceptualizations with four online surveys involving nearly 4,000 Internet users.
Abstract: Internet privacy concerns (IPC) is an area of study that is receiving increased attention due to the huge amount of personal information being gathered, stored, transmitted, and published on the Internet. While there is an emerging literature on IPC, there is limited agreement about its conceptualization in terms of its key dimensions and its factor structure. Based on the multidimensional developmental theory and a review of the prior literature, we identify alternative conceptualizations of IPC. We examine the various conceptualizations of IPC with four online surveys involving nearly 4,000 Internet users. As a baseline, study 1 compares the integrated conceptualization of IPC to two existing conceptualizations in the literature. While the results provide support for the integrated conceptualization, the second-order factor model does not outperform the correlated first-order factor model. Study 2 replicates the study on a different sample and confirms the results of study 1. We also investigate whether the prior results are affected by the different perspectives adopted in the wording of items in the original instruments. In study 3, we find that focusing on one's concern for website behavior (rather than one's expectation of website behavior) and adopting a consistent perspective in the wording of the items help to improve the validity of the factor structure. We then examine the hypothesized third-order conceptualizations of IPC through a number of alternative higher-order models. The empirical results confirm that, in general, the third-order conceptualizations of IPC outperform their lower-order alternatives. In addition, the conceptualization of IPC that has the best fit with the data contains a third-order general IPC factor, two second-order factors of interaction management and information management, and six first-order factors (i.e., collection, secondary usage, errors, improper access, control, and awareness). Study 4 cross-validates the results with another data set and examines IPC within the context of a nomological network. The results confirm that the third-order conceptualization of IPC has nomological validity, and it is a significant determinant of both trusting beliefs and risk beliefs. Our research helps to resolve inconsistencies in the key underlying dimensions of IPC, the factor structure of IPC, and the wording of the original items in prior instruments of IPC. Finally, we discuss the implications of this research.
Citations
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Journal ArticleDOI
TL;DR: An integrated model proposed and empirically tested an integrated model to better understand the determinants of consumers' continued intention to purchase on mobile sites indicated that information quality, and privacy and security concerns are the main factors affecting trust, whereas service quality is the main factor affecting flow.

285 citations

Journal ArticleDOI
TL;DR: An initial trust theoretical model for user adoption of m-payment systems is proposed and tested and shows that initial trust positively affects perceived benefit and perceived convenience, and these three factors together predict usage intention.
Abstract: User adoption of mobile payment (m-payment) is low compared to the adoption of traditional forms of payments. Lack of user trust has been identified as the most significant long-term barrier for the success of mobile finances systems. Motivated by this fact, we proposed and tested an initial trust theoretical model for user adoption of m-payment systems. The model not only theorizes the role of initial trust in m-payment adoption, but also identifies the facilitators and inhibitors for a user's initial trust formation in m-payment systems. The model is empirically validated via a sample of 851 potential m-payment adopters in Australia. Partial least squares structural equation modelling is used to assess the relationships of the research model. The results indicate that perceived information quality, perceived system quality, and perceived service quality as the initial trust facilitators are positively related to initial trust formation, while perceived uncertainty as the initial trust inhibitor exerts a significant negative effect on initial trust. Perceived asset specificity is found to have insignificant effect. In addition, the results show that initial trust positively affects perceived benefit and perceived convenience, and these three factors together predict usage intention. Perceived convenience of m-payment is also found to have a positive effect on perceived benefit. The findings of this study provide several important implications for m-payment adoption research and practice.

268 citations

Journal ArticleDOI
TL;DR: A U.S. representative sample was used to test the privacy calculus' generalizability and extend its theoretical framework by including both self-withdrawal behaviors and privacy self-efficacy, and results confirmed the extended privacy calculus model.
Abstract: The privacy calculus established that online self-disclosures are based on a cost-benefit tradeoff. For the context of SNSs, however, the privacy calculus still needs further support as most studies consist of small student samples and analyze self-disclosure only, excluding self-withdrawal e.g., the deletion of posts, which is essential in SNS contexts. Thus, this study used a U.S. representative sample to test the privacy calculus' generalizability and extend its theoretical framework by including both self-withdrawal behaviors and privacy self-efficacy. Results confirmed the extended privacy calculus model. Moreover, both privacy concerns and privacy self-efficacy positively predicted use of self-withdrawal. With regard to predicting self-disclosure in SNSs, benefits outweighed privacy concerns; regarding self-withdrawal, privacy concerns outweighed both privacy self-efficacy and benefits.

215 citations

Journal ArticleDOI
TL;DR: The results suggest that service providers should address the issues of social influence and privacy concern to encourage mobile SNS continuance usage.

199 citations

Journal ArticleDOI
TL;DR: Some of the economic, technical, social, and ethical issues associated with personal data markets, focusing on the privacy challenges they raise, are outlined.
Abstract: Personal data is increasingly conceived as a tradable asset Markets for personal information are emerging and new ways of valuating individuals’ data are being proposed At the same time, legal obligations over protection of personal data and individuals’ concerns over its privacy persist This article outlines some of the economic, technical, social, and ethical issues associated with personal data markets, focusing on the privacy challenges they raise

190 citations

References
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TL;DR: The SIMPLIS language shifts the focus away from the technical question "How to do it", so that researchers can concentrate on the question, "What does it all mean?"
Abstract: This text introduces the SIMPLIS command language for structural equation modelling. It is written for students and researchers with limited mathematical and statistical training who need to use structural equation models to analyze their data, and for those who have tried but failed to learn the LISREL command language. It is not a textbook on factor analysis, structural equations or latent variable models, although there are many examples of such in the book. Rather, it is assumed that the reader is already familiar with the basic ideas and principles of these types of analyses and techniques. The main objective is to demonstrate that structural equation modelling can be done easily without the technical jargon with which it has been associated. The SIMPLIS language shifts the focus away from the technical question "How to do it", so that researchers can concentrate on the question, "What does it all mean?" Although the SIMPLIS language makes it easier to specify models and to carry out the analysis, the substantive specification and interpretation remain the same as with the LISREL command language.

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Journal ArticleDOI
TL;DR: A model is presented that allows partial correlation analysis to adjust the observed correlations for CMV contamination and determine if conclusions about the statistical and practical significance of a predictor have been influenced by the presence of CMV.
Abstract: Cross-sectional studies of attitude-behavior relationships are vulnerable to the inflation of correlations by common method variance (CMV). Here, a model is presented that allows partial correlation analysis to adjust the observed correlations for CMV contamination and determine if conclusions about the statistical and practical significance of a predictor have been influenced by the presence of CMV. This method also suggests procedures for designing questionnaires to increase the precision of this adjustment.

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TL;DR: This paper presents a meta-modelling framework for testing the factorial Validity of a Theoretical Construct and the Invariant Factorial Structure of a theoretical construct using LISREL, PRELIS, and SIMPLIS through Windows.
Abstract: Contents: Preface. Part I: Introduction. Structural Equation Models: The Basics. Using LISREL, PRELIS, and SIMPLIS. Part II: Single-Group Analyses. Application 1: Testing the Factorial Validity of a Theoretical Construct (First-Order CFA Model). Application 2: Testing the Factorial Validity of Scores From a Measuring Instrument (First-Order CFA Model). Application 3: Testing the Factorial Validity of Scores From a Measuring Instrument (Second-Order CFA Model). Application 4: Testing for Construct Validity: The Multitrait-Multimethod Model. Application 5: Testing the Validity of a Causal Structure. Part III: Multiple Group Analyses. Application 6: Testing for the Invariant Factorial Structure of a Theoretical Construct (First-Order CFA Model). Application 7: Testing for Invariant Factorial Structure of Scores From a Measuring Instrument (First-Order CFA Model). Application 8: Testing for Invariant Latent Mean Structures. Application 9: Testing for Invariant Pattern of Causal Structure. Part IV: LISREL, PRELIS, and SIMPLIS Through Windows. Application 10: Testing for Causal Predominance Using a Two-Wave Panel Model.

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Journal ArticleDOI
TL;DR: The results of this study indicate that the second-order IUIPC factor, which consists of three first-order dimensions--namely, collection, control, and awareness--exhibited desirable psychometric properties in the context of online privacy.
Abstract: The lack of consumer confidence in information privacy has been identified as a major problem hampering the growth of e-commerce. Despite the importance of understanding the nature of online consumers' concerns for information privacy, this topic has received little attention in the information systems community. To fill the gap in the literature, this article focuses on three distinct, yet closely related, issues. First, drawing on social contract theory, we offer a theoretical framework on the dimensionality of Internet users' information privacy concerns (IUIPC). Second, we attempt to operationalize the multidimensional notion of IUIPC using a second-order construct, and we develop a scale for it. Third, we propose and test a causal model on the relationship between IUIPC and behavioral intention toward releasing personal information at the request of a marketer. We conducted two separate field surveys and collected data from 742 household respondents in one-on-one, face-to-face interviews. The results of this study indicate that the second-order IUIPC factor, which consists of three first-order dimensions--namely, collection, control, and awareness--exhibited desirable psychometric properties in the context of online privacy. In addition, we found that the causal model centering on IUIPC fits the data satisfactorily and explains a large amount of variance in behavioral intention, suggesting that the proposed model will serve as a useful tool for analyzing online consumers' reactions to various privacy threats on the Internet.

2,597 citations