Privacy Regulation and Its Unintended Consequence on Consumption Behaviors: Evidence From CCPA

2023 | International Conference on Information Systems | Citations: 0

Authors: Bae, Donghwa; Mayya, Raveesh; Nian, Tingting

Abstract: This study explores the unintended consequences of data protection regulations ... Expand

Abstract: This study explores the unintended consequences of data protection regulations on consumer behavior. Specifically, we examine the California Consumer Privacy Act, which restricts companies from collecting Californians' personal information. Privacy regulations may limit firms' ability to suggest products aligned with consumers' taste predicted from data, potentially influencing subsequent consumer behavior. These regulations especially pose a challenge to digital platforms, as their success hinges on seamless data flow between firms and consumers. Using a difference-in-differences approach with transaction data, we find that post-CCPA, Californians reduce purchase by 4.1%, increase returns by 2.9%. Moreover, we find that Californians spend more time online and visit more webpages, potentially indicating increased search efforts. Mechanism analysis suggests that firms under CCPA proactively alter their data collection strategy to reduce the liability. These results reveal the complex interplay between privacy regulation and consumer behavior, highlighting the need for a nuanced understanding of the trade-offs between privacy protection and economic outcomes. Collapse

Topics: information privacy law legal context consumer behavior advertising management website

Methods: experimental group longitudinal research natural experiment archival research difference in differences

CAPABILITIES FOR DATA ANALYTICS IN INDUSTRIAL INTERNET OF THINGS (IIOT)

2023 | European Conference On Information Systems | Citations: 0

Authors: Bagheri, Samaneh; Dijkstra, Jesse

Abstract: ... Expand

Abstract: Collapse

Topics: analytical information system internet of things mobile system organizational value cloud computing

Methods: qualitative interview machine learning literature study personal interview computational algorithm

Promise not fulfilled: FinTech, data privacy, and the GDPR

2023 | Electronic Markets | Citations: 0

Authors: Dorfleitner, Gregor; Hornuf, Lars; Kreppmeier, Julia

Abstract: This article analyzes how the General Data Protection Regulation (GDPR) has affe ... Expand

Abstract: This article analyzes how the General Data Protection Regulation (GDPR) has affected the privacy practices of FinTech firms. We study the content of 276 privacy statements respectively before and after the GDPR became binding. Using text analysis methods, we find that the readability of the privacy statements has decreased. The texts of privacy statements have become longer and use more standardized language, resulting in worse user comprehension. This calls into question whether the GDPR has achieved its original goal—the protection of natural persons regarding the transparent processing of personal data. We also link the content of the privacy statements to FinTech-specific determinants. Before the GDPR became binding, more external investors and a higher legal capital were related to a higher quantity of data processed and more transparency, but not thereafter. Finally, we document mimicking behavior among FinTech industry peers with regard to the data processed and transparency. Collapse

Topics: legal context General Data Protection Regulation privacy electronic finance website

Methods: parametric test paired t-test descriptive statistic Student's t-test qualitative content analysis

Theories: privacy calculus theory

Law, Economics, and Privacy: Implications of Government Policies on Website and Third-Party Information Sharing

2023 | Information Systems Research | Citations: 0

Authors: Gopal, Ram D.; Hidaji, Hooman; Kutlu, Sule Nur; Patterson, Raymond A.; Yaraghi, Niam

Abstract: Widespread abuse of internet users’ privacy online has prompted user advocacy gr ... Expand

Abstract: Widespread abuse of internet users’ privacy online has prompted user advocacy groups to implore governments to intervene and protect consumer rights. To study such interventions’ effects, we examine data-protection policies that policy makers and governments can enforce on websites, including consent-based user information sharing and subsidizing competing websites. We use a stylized analytical model to examine such policies’ impact on the decisions and outcomes of websites, users, and third-parties. Interestingly, we find that even though a consent-based policy may improve user surplus, in the absence of market entry and exit (static market), it has the unintended consequence of increasing the number of third-parties and, thus, sharing of user information. We also determine that both consent-based and website subsidization policies may reduce competition by driving websites out of the market—to the detriment of user surplus and social welfare. To validate our analytical model’s findings, we empirically investigated the impact of a consent-based policy on third-parties in a natural experiment of the California Consumer Privacy Act. These findings raise significant implications for policy making surrounding online privacy. History: Giri Kumar Tayi, Senior Editor; Nachiketa Sahoo, Associate Editor. Funding: Financial support from the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged. R. D. Gopal gratefully acknowledges support by the Gillmore Centre for Financial Technology at the Warwick Business School. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1178. Collapse

Topics: website information privacy concern data protection privacy legal context

Methods: mathematical model natural experiment experimental group longitudinal research time series analysis

Taking the Person Seriously: Ethically Aware IS Research in the Era of Reinforcement Learning-Based Personalization

2023 | Journal of the Association for Information Systems | Citations: 0

Authors: Greene, Travis; Shmueli, Galit; Ray, Soumya

Abstract: Advances in reinforcement learning and implicit data collection on large-scale ... Expand

Abstract: Advances in reinforcement learning and implicit data collection on large-scale commercial platforms mark the beginning of a new era of personalization aimed at the adaptive control of human user environments. We present five emergent features of this new paradigm of personalization that endanger persons and societies at scale and analyze their potential to reduce personal autonomy, destabilize social and political systems, and facilitate mass surveillance and social control, among other concerns. We argue that current data protection laws, most notably the European Union's General Data Protection Regulation, are limited in their ability to adequately address many of these issues. Nevertheless, we believe that IS researchers are well-situated to engage with and investigate this new era of personalization. We propose three distinct directions for ethically aware reinforcement learning-based personalization research uniquely suited to the strengths of IS researchers across the sociotechnical spectrum. Collapse

Topics: usability personalization legal context General Data Protection Regulation information privacy law

Methods: reinforcement learning computational algorithm simulation language model experiment

Theories: social sciences theory

Designing a cross-organizational identity management system: Utilizing SSI for the certification of retailer attributes

2023 | Electronic Markets | Citations: 0

Authors: Guggenberger, Tobias; Kühne, Daniela; Schlatt, Vincent; Urbach, Nils

Abstract: The introduction of blockchain offers new opportunities to rethink enterprise id ... Expand

Abstract: The introduction of blockchain offers new opportunities to rethink enterprise identity management. Recently, a new concept has emerged in the blockchain community called self-sovereign identity. Self-sovereign identity combines several existing decentralized identity management approaches, promising new ways to promote more convenient, connected, and secure identity services for the private and public sector. Nevertheless, research in this area is still in its infancy. Most of the very few articles focus either on the opportunities self-sovereign identity might offer or on very specific technical features. Studies on real-world applications of organizations using modern self-sovereign identity implementations and design theory are very rare. To fill this gap, we follow the design science research approach to design, implement, and evaluate a self-sovereign identity system to present tax attributes of online retailers. We present four design principles and conclude that the use of self-sovereign identity and blockchain offers opportunities to improve verification processes. Collapse

Topics: blockchain Hyperledger electronic commerce government system privacy

Methods: design principle design artifact qualitative interview design theory literature study

Design Knowledge for GAIA-X-compliant Ecosystems: A Literature Review

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Hefft, Daniel; Große, Nick

Abstract: Integrating digital technologies offer companies a range of potentials for opti ... Expand

Abstract: Integrating digital technologies offer companies a range of potentials for optimizing business processes, exploring new business models and collaborating along value chains to co-create value. However, this integration increases complexity, especially within SMEs that lack extensive resources to drive data transformation. GAIA-X offers potential solutions in form of federated services as a low-threshold way to participate in federated data ecosystems. Thus, in this paper we survey the current state of research on GAIA-X by means of a literature review extracting and collecting design knowledge as well as to prepare further implementation within the scope of our research project. Therefore, we build a concept matrix in which we differentiate the identified body of knowledge by three concepts with 18 characteristic expressions in total. Our analysis of the identified papers highlights architectural approaches for designing a GAIA-X compatible data ecosystem, augmented by additional factors to consider when designing these ecosystems. Collapse

Topics: digital ecosystem database system organizational value General Data Protection Regulation artificial intelligence

Methods: literature study literature sample case study design artifact survey

IMPACT OF EXPLAINABLE AI ON COGNITIVE LOAD: INSIGHTS FROM AN EMPIRICAL STUDY

2023 | European Conference On Information Systems | Citations: 0

Authors: Herm, Lukas-Valentin

Abstract: While the emerging research field of explainable artificial intelligence (XAI) ... Expand

Abstract: While the emerging research field of explainable artificial intelligence (XAI) claims to address the lack of explainability in high-performance machine learning models, in practice, XAI targets developers rather than actual end-users. Unsurprisingly, end-users are often unwilling to use XAI-based decision support systems. Similarly, there is limited interdisciplinary research on end-users' behavior during XAI explanations usage, rendering it unknown how explanations may impact cognitive load and further affect end-user performance. Therefore, we conducted an empirical study with 271 prospective physicians, measuring their cognitive load, task performance, and task time for distinct implementationindependent XAI explanation types using a COVID-19 use case. We found that these explanation types strongly influence end-users' cognitive load, task performance, and task time. Further, we contextualized a mental efficiency metric, ranking local XAI explanation types best, to provide recommendations for future applications and implications for sociotechnical XAI research. Collapse

Topics: explainable artificial intelligence task productivity artificial intelligence decision support system shapley additive explanation

Methods: descriptive statistic statistical hypothesis test literature study machine learning survey

Theories: theory of abduction

GDPR Privacy Type Clustering: Motivational Factors for Consumer Data Sharing

2023 | European Conference On Information Systems | Citations: 7

Authors: Hanneke, Björn; Baum, Lorenz; Hinz, Oliver

Abstract: The GDPR introduced restrictive privacy-preserving measures, affecting the dail ... Expand

Abstract: The GDPR introduced restrictive privacy-preserving measures, affecting the daily life of (online) consumers. Moreover, literature shows that privacy preferences are constantly evolving. This is the first study introducing a GDPR exercising-oriented approach to identify consumer privacy types. Based on a representative sample of the German online population, we cluster consumers according to their privacy importance ("intention to act") and GDPR knowledge ("ability to act") and derive four consumer privacy type clusters: fundamentalists, amateurs, pragmatists, and unconcerned. We investigate motivational factors for changing privacy settings and find significant differences between consumers' intentions and actions for selected factors. This provides evidence for the privacy paradox. Contrarily, intentions and actions align for other factors, which supports the hypothesis that actionbased consent might lower the privacy paradox. Finally, we suggest the development of standardized scales and corresponding clustering methodologies for consumer privacy type clustering to increase comparability over time and across populations. Collapse

Topics: privacy legal context General Data Protection Regulation privacy behavior advertising management

Methods: cluster analysis survey archival research k-means clustering survey design

Consumer Preferences for Privacy Management Systems

2023 | International Conference on Information Systems | Citations: 0

Authors: Hanneke, Björn; Baum, Lorenz; Schlereth, Christian; Hinz, Oliver

Abstract: This work presents insights into consumer preferences regarding Privacy Managem ... Expand

Abstract: This work presents insights into consumer preferences regarding Privacy ManagementSystems in the context of the General Data Protection Regulation (GDPR). The authors perform a Choice-Based Conjoint experiment with consumers (n = 589) to elicit preferences over four attributes and compute usage likelihoods for all product configurations. Results show that data sharing for marketing purposes and discounts are the most important attributes for consumers. Furthermore, consumers prefer digital access to privacy-related information, detailed rights management for data sharing and no data sharing for marketing purposes. Moreover, a cluster analysis reveals differing importance weights across clusters. The study concludes that incorporating consumer preferences into the design and development process of Privacy Management Systems could increase their use and effectiveness, ultimately strengthening consumers' privacy rights and companies' legal compliance. The authors suggest researching legal, business, and consumer requirements more holistically to converge these perspectives to improve Privacy Management Systems adoptions. Collapse

Topics: privacy legal context General Data Protection Regulation marketing management digital dashboard

Methods: experiment cluster analysis survey literature study computational algorithm