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Explainable artificial intelligence in information systems: A review of the status quo and future research directions
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2023 | Electronic Markets | Citations: 4
Authors: Brasse, Julia; Broder, Hanna Rebecca; Förster, Maximilian; Klier, Mathias; Sigler, Irina

Abstract: The quest to open black box artificial intelligence (AI) systems evolved into an ... Expand

Abstract: The quest to open black box artificial intelligence (AI) systems evolved into an emerging phenomenon of global interest for academia, business, and society and brought about the rise of the research field of explainable artificial intelligence (XAI). With its pluralistic view, information systems (IS) research is predestined to contribute to this emerging field; thus, it is not surprising that the number of publications on XAI has been rising significantly in IS research. This paper aims to provide a comprehensive overview of XAI research in IS in general and electronic markets in particular using a structured literature review. Based on a literature search resulting in 180 research papers, this work provides an overview of the most receptive outlets, the development of the academic discussion, and the most relevant underlying concepts and methodologies. Furthermore, eight research areas with varying maturity in electronic markets are carved out. Finally, directions for a research agenda of XAI in IS are presented. Collapse

Semantic filters: information boundary theory

Topics: explainable artificial intelligence artificial intelligence user requirement electronic market user experience

Methods: literature study literature sample cluster analysis synthesis machine learning

Theories: information systems theory activity theory agency theory attribution theory cognitive fit theory
AIoT-enabled smart surveillance for personal data digitalization: Contextual personalization-privacy paradox in smart home
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2023 | Information & Management | Citations: 0
Authors: Zhang, Fengjiao; Pan, Zhao; Lu, Yaobin

Abstract: Artificial intelligence of things technology provides smart surveillance capabil ... Expand

Abstract: Artificial intelligence of things technology provides smart surveillance capability for personal data digitalization. It will invade individuals’ information, physical, and social spaces and raise contextual privacy concerns while providing personalized services, which has not been explored in previous research. We theorize three types of smart surveillance and identify three subdimensions of contextual personalization and privacy concerns. Grounded in surveillance theory and personalization-privacy paradox, we examined the different trade-offs of contextual personalization and privacy concerns underlying the three types of smart surveillance on users’ behavioral intention in smart home context. The results also indicated that transparency can lessen the trade-off effects. Collapse

Semantic filters: information boundary theory

Topics: information privacy concern usability personalization behavioral intention smart home

Methods: archival research survey partial least squares regression SEM tool theory development

Theories: information boundary theory
Privacy and the Internet of Things−An experiment in discrete choice
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2021 | Information & Management | Citations: 0
Authors: Goad, David; Collins, Andrew T.; Gal, Uri

Abstract: The Internet of Things (IoT) is the concept that everyday devices are connected ... Expand

Abstract: The Internet of Things (IoT) is the concept that everyday devices are connected to the Internet generating data about us and the world around us. With the number of devices connected directly to the Internet expected to be three times the number of people by 2020, the potential for a reduction in personal privacy is evident. This research fills a gap in the literature by conducting a quantitative analysis of people’s privacy preferences as it relates to the IoT. Our findings provide potential guidance to practitioners in their IoT architectural design and increase our understanding of privacy preference overall. Collapse

Semantic filters: information boundary theory

Topics: privacy mobile system internet of things information privacy concern fitness tracker

Methods: survey statistical hypothesis test experimental design literature study experiment

Theories: privacy calculus theory personality theory social cognitive theory endowment effect information boundary theory
AI for Depression Treatment: Addressing the Paradox of Privacy and Trust with Empathy, Accountability, and Explainability

2021 | International Conference on Information Systems | Citations: 4
Authors: Yan, Aihua; Xu, David

Abstract: Personal healthcare information (PHI) disclosure is vital in leveraging artific ... Expand

Abstract: Personal healthcare information (PHI) disclosure is vital in leveraging artificial intelligence (AI) technology for depression treatment. Two challenges for PHI disclosure are high privacy concern and low trust. In this study, we integrate three theoretical lenses, i.e., information boundary theory, trust, and AI principles to investigate whether AI principles of empathy, accountability, and explainability can address these two challenges. We propose that AI empathy can increase depression patients' privacy concern and trust simultaneously. This paradox of high privacy concern and high trust has to be addressed for successful AI deployment in depression treatment. The proxies of AI accountability such as AI company reputation and government regulation can help reduce this paradox. Further, we argue that explainability can moderate the relationships between this paradox (i.e., privacy concern and trust) and patient's intention to disclose PHI. Overall, our expected results can provide significant implications to IS literature and practitioners. Collapse

Semantic filters: information boundary theory

Topics: artificial intelligence information privacy concern privacy healthcare data usability

Methods: experiment qualitative interview survey chatbot experimental design

Theories: information boundary theory trust theory
The Influence of Privacy Dispositions on Perceptions of Information Transparency and Personalization Preferences
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2017 | HICSS | Citations: 0
Authors: Hauff, Sabrina; Dytynko, Olga; Veit, Daniel

Abstract: To attract customers, firms offer personalized services. This is perceived benef ... Expand

Abstract: To attract customers, firms offer personalized services. This is perceived beneficial by many customers as it enhances the purchase experience and addresses customers’ needs. However, to offer personalized services, customer data has to be collected and analyzed. This practice gives rise to privacy concerns and can inhibit the usage of such services. Our research aims to address the tension between personalization and privacy by applying information boundary theory to investigate how respondents’ disposition to value privacy and the availability of information transparency features influences individuals’ intention to disclose information to personalized services. Based on an experimental study, we find a significant interaction between disposition to value privacy and personalization, while the implementation of transparency features does not yield substantial changes in information disclosure. Thus, in order to successfully offer personalized services, we recommend that practitioners take individuals’ privacy preferences into account for their service design. Collapse

Semantic filters: information boundary theory

Topics: personalization usability privacy data breach website

Methods: experiment analysis of covariance survey factor analysis data transformation

Theories: information boundary theory theory of bounded rationality communication privacy management theory
Beyond the Personalization–Privacy Paradox: Privacy Valuation, Transparency Features, and Service Personalization
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2017 | Journal of Management Information Systems | Citations: 0
Authors: Karwatzki, Sabrina; Dytynko, Olga; Trenz, Manuel; Veit, Daniel

Abstract: Digital services need access to consumers’ data to improve service quality and t ... Expand

Abstract: Digital services need access to consumers’ data to improve service quality and to generate revenues. However, it remains unclear how such services should be configured to facilitate consumers’ willingness to share personal information. Prior studies discuss an influence of selected individual traits or service configurations, including transparency features and service personalization. This study aims at uncovering how interactions among individuals’ privacy valuation, transparency features, and service personalization influence their willingness to disclose information. Building oninformation boundary theory, we conducted an experimental study with 286 participants on a data-intense digital service. In contrast to our expectation, we found no indication that providing transparency features facilitates individuals’ information disclosure. Relative to the personalization–privacy paradox, individuals’ privacy valuation is a strong inhibitor of information provision in general, not only for personalized services. Personalization benefits only convince consumers who exhibit little focus on privacy. Thus, service providers need to align their service designs with consumers’ privacy preferences. Collapse

Semantic filters: information boundary theory

Topics: personalization usability privacy data breach personality

Methods: experiment analysis of covariance experimental group archival research descriptive statistic

Theories: information boundary theory theory of bounded rationality
Drivers of Health Information Privacy Concern: A Comparison Study

2016 | Americas Conference on Information Systems | Citations: 24
Authors: Kenny, Grace; Connolly, Regina

Abstract: There has been an exponential increase in the utilization of health ICTs by both ... Expand

Abstract: There has been an exponential increase in the utilization of health ICTs by both health professionals and individual citizens. Despite the many benefits these technologies offer, citizens’ health information privacy concerns are commonly cited as a barrier to their sustained growth. In order to address these concerns and ensure the continued success of health ICTs, the factors driving concern must be understood. At present there is a lack of understanding of what factors can affect citizens’ health information privacy concerns (HIPC). This paper addresses this gap in our knowledge by developing and testing a framework which examines the antecedents to HIPC. The findings indicate that individual characteristics such as age, individual perceptions such as perceived sensitivity, and individual experiences can all influence health information privacy concerns. This paper represents an important initial step in unraveling the role the labyrinthine privacy construct plays in the health context. Collapse

Semantic filters: information boundary theory

Topics: privacy health information system healthcare data individual characteristic information privacy concern

Methods: qualitative interview survey statistical hypothesis test confirmatory factor analysis data transformation

Theories: information boundary theory protection motivation theory
Sharing Personal Health Information: Personalization versus Privacy

2015 | Americas Conference on Information Systems | Citations: 2
Authors: Jena, Rishikesh

Abstract: Personal health information (PHI) is increasingly becoming a critical component ... Expand

Abstract: Personal health information (PHI) is increasingly becoming a critical component of personalized healthcare. However, investment in PHI capabilities may be severely undermined if consumers are unwilling to share PHI. While consumers are concerned about potential compromise of PHI, they also value the personalization benefits of PHI use. In light of the limited understanding of this tension, this research develops a parsimonious model to predict consumer’s willingness to share PHI as a result of the tradeoff between concern for privacy and value for personalization. Grounded on theoretical positions of Information Boundary Theory, we examine the mechanism through which privacy concern and personalization influence willingness to share PHI. Results of the study suggests that the attitude towards sharing PHI is shaped by two competing forces of privacy concerns and value for personalization. Insights from the study can be used to craft future healthcare implementation policies. Collapse

Semantic filters: information boundary theory

Topics: usability personalization information privacy concern privacy electronic health

Methods: survey regression analysis method descriptive statistic

Theories: information boundary theory privacy calculus theory
Healthcare Professionals’ Attitudes towards Privacy in Healthcare Information Systems

2015 | Americas Conference on Information Systems | Citations: 0
Authors: Lapke, Michael; Garcia, Christopher; Henderson, David

Abstract: As a result of the Health Information Technology for Economic and Clinical Healt ... Expand

Abstract: As a result of the Health Information Technology for Economic and Clinical Health Act (HITECH), healthcare organizations are finding themselves scrambling to digitize their Electronic Medical Records (EMRs). A concern of the widespread implementation of digital health records is the assurance of privacy for the sensitive information held by healthcare organizations (Greenberg, 2010; Hoffmann, 2009). There have been many studies that examine patient’s perspectives and expectations of privacy (Agarwal & Anderson, 2011; Cannoy & Salam, 2010) but the literature is lacking analysis of healthcare providers’ privacy perspectives with regards to EMRs. Using Information Boundary Theory (Stanton, 2002) as a theoretical framework, this study seeks to determine healthcare providers’ attitudes towards privacy with regards to EMRs. Analysis of survey data collected from healthcare providers found that healthcare providers do not value patient privacy over personal or organizational gain. Collapse

Semantic filters: information boundary theory

Topics: privacy information exchange healthcare data information privacy concern

Methods: survey survey design statistical hypothesis test scale reliability Student's t-test

Theories: information boundary theory
An empirical analysis of users’ privacy disclosure behaviors on social network sites
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2015 | Information & Management | Citations: 0
Authors: Li, Kai; Lin, Zhangxi; Wang, Xiaowen

Abstract: Users’ privacy on social network sites is one of the most important and urgent i ... Expand

Abstract: Users’ privacy on social network sites is one of the most important and urgent issues in both industry and academic fields. This paper is intended to investigate the effect of users’ demographics, social network site experience, personal social network size, and blogging productivity on privacy disclosure behaviors by analyzing the data collected from social network sites. Based on two levels of disclosed privacy sensitivity information, the textual information of a user's blog postings can be converted into a 4-tuple to represent their privacy disclosure patterns, containing the breadth and depth of disclosure, and frequencies of highly and less sensitive disclosures. Collections of a user's privacy disclosure patterns in social network sites can effectively reflect the user's privacy disclosure behaviors. Applying the general linear modeling approach to blogging data converted with a coding scheme, we find that males and females have significantly differentiated privacy disclosure patterns in dimensions related to the breadth and depth of disclosure. In addition, age has a significant negative relationship with the breadth and depth of disclosure, as well as with highly sensitive disclosure. We also find that social network site experience, personal social network size, and blog length are not significantly related to users’ privacy disclosure patterns, while blog number always has positive associations with privacy disclosure patterns. Collapse

Semantic filters: information boundary theory

Topics: privacy blog social network productivity privacy behavior

Methods: qualitative coding generalized linear model robustness check

Theories: information boundary theory

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Found 13 articles

Explainable artificial intelligence in information systems: A review of the status quo and future research directions

AIoT-enabled smart surveillance for personal data digitalization: Contextual personalization-privacy paradox in smart home

Privacy and the Internet of Things−An experiment in discrete choice

AI for Depression Treatment: Addressing the Paradox of Privacy and Trust with Empathy, Accountability, and Explainability

The Influence of Privacy Dispositions on Perceptions of Information Transparency and Personalization Preferences

Beyond the Personalization–Privacy Paradox: Privacy Valuation, Transparency Features, and Service Personalization

Drivers of Health Information Privacy Concern: A Comparison Study

Sharing Personal Health Information: Personalization versus Privacy

Healthcare Professionals’ Attitudes towards Privacy in Healthcare Information Systems

An empirical analysis of users’ privacy disclosure behaviors on social network sites