The Impact of Use of Big Data-Driven Systems on Decision-Makers’ Power

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Abouei, Mahdi; Ansari, Kimia; Ghasemaghaei, Maryam

Abstract: Modern organizations are adopting Big Data-Driven (BDD) systems to obtain and s ... Expand

Abstract: Modern organizations are adopting Big Data-Driven (BDD) systems to obtain and sustain their competitive advantage through improved decisions. However, the changes in the organizational decision structure and processes due to the establishment of these systems raised the question of how becoming data-driven will affect the traditional role of human decision-makers in organizations. Specifically, this research-in-progress study investigates the mechanisms through which the use of BDD systems impacts organizational decisionmakers' power. To address this research question, the study draws on the premises of the strategic contingencies theory of intra-organizational power and the literature on BDD decision-making in organizations and develops a theoretical model to be tested empirically. Ultimately, the potential contributions to theory and practice are also discussed. KeywordsBig data-driven systems, decision-making, strategic contingencies theory of intra-organizational power. Collapse

Topics: decision making IS impact business process management

Methods: survey theory development cross sectional survey structural equation modeling cross sectional research

The Interplay of Information Processing, Mindfulness, and Affective State in Phishing Detection

2023 | International Conference on Information Systems | Citations: 0

Authors: Bera, Debalina; Kim, Dan

Abstract: This study investigates the effect of individuals' information processing modes ... Expand

Abstract: This study investigates the effect of individuals' information processing modes on phishing email detection mechanisms and accuracy. Drawing on the heuristic-systematic model of processing, mindful decision-making, and affect-and-persuasion literature, this study examines the antecedents (i.e., heuristic, systematic processing, general and domain mindfulness, and affective state) and behavioral consequences (i.e., detection accuracy) in phishing identification. Using a scenario-based survey experiment this study shows mindfulness and affectivity navigate information processing mechanisms, thus influence phishing detection accuracy. It aims to contribute to information security literature by examining novel associations between general, domain mindfulness and its effect on online users' information processing strategy and phishing detection accuracy. Furthermore, this study contributes to phishing training and awareness by identifying the function of cognitive-affective (affective states, trait mindfulness) and cognitive-behavioral (domain mindfulness) factors on the choice of information processing modes and phishing detection accuracy. Finally, the affective states might help improve the efficacy of automatic filters. Collapse

Topics: phishing mindfulness electronic mail email phishing data security

Methods: experiment survey partial least squares regression machine learning laboratory experiment

Theories: protection motivation theory

Artificial Intelligence applications in diagnostic medicine: a decade of expectations

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Cabral, Bernardo; Castro, Leonardo; Fornazin, Marcelo; Penteado, Bruno; Silva, Sandro Luís Freire de Castro; Braga, Luiza Amara Maciel; Filho, Carlos Gilbert Conte; Mota, Fabio Batista

Abstract: Considered a new paradigm in healthcare, AI is expected to change diagnostic me ... Expand

Abstract: Considered a new paradigm in healthcare, AI is expected to change diagnostic medicine in the coming decades. However, its future is still uncertain -partly because it is an emerging technology being gradually applied to a mature field of medicine. We conducted a global cross-sectional survey with more than 1,400 authors of recent scientific publications indexed by the Web of Science to foresee the future of AI in diagnostic medicine. Most respondents expect AI to change diagnostic medicine in this decade radically. In this period, the two most likely outcomes are reduced screening costs and increased diagnostic reliability. X-ray diagnosis and heart rhythm interpretation are the two diagnostic tools most likely to be integrated with AI. The two main barriers are the difficulty of incorporating it into clinical practice and the ethical-regulatory issues. Respondents' expectations align with the literature and suggest that AI may substantially change diagnostic medicine within this decade. Collapse

Topics: artificial intelligence electronic mail database system

Methods: survey cross sectional survey cross sectional research literature study magnetic resonance imaging

Unveiling the paradox of technostress: Impacts of technology-driven stressors on the elderly's avoidance behaviors

2023 | Information & Management | Citations: 0

Authors: Cheng, Xusen; Huang, Xiaowen; Yang, Bo; Xiao, Yuting

Abstract: This study reveals the paradox of technostress on the elderly due to the large-s ... Expand

Abstract: This study reveals the paradox of technostress on the elderly due to the large-scale COVID testing during the postCOVID-19 period. Drawing upon the stressor-strain-outcomes framework, we examined how technology-driven stressors affect the elderly’s avoidance behaviors. Results indicate that two types of technology-driven stressors exert opposite influences on the elderly’s avoidance behaviors through the mediating effect of technology anxiety. Specifically, the elderly’s technology-driven challenge stressors exert a positive influence on their avoidance behaviors, while the elderly’s technology-driven hindrance stressors exert a negative influence. Moreover, stigma consciousness plays a moderating role in the relationship between the elderly’s technologydriven hindrance stressors and technology anxiety by strengthening the positive influence of the elderly’s technology-driven hindrance stressors on technology anxiety. Our findings deepen the understanding of the good and bad of technostress and provide implications for older adults, technology developers, and the government. Collapse

Topics: smartphone technostress pandemic self efficacy anonymity

Methods: qualitative interview survey SEM tool mixed method qualitative coding

Automated Deductive Content Analysis of Text: A Deep Contrastive and Active Learning Based Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: De Oliveira Silveira, Alysson; Ray, Arindam; Ebrahimi, Mohammadreza; Bhattacherjee, Anol

Abstract: Content analysis traditionally involves human coders manually combing through t ... Expand

Abstract: Content analysis traditionally involves human coders manually combing through text documents to search for relevant concepts and categories. However, this approach is time-intensive and not scalable, particularly for secondary data like social media content, news articles, or corporate reports. To address this problem, the paper presents an automated framework called Automated Deductive Content Analysis of Text (ADCAT) that uses deep learning-based semantic techniques, an ontology of validated construct measures, a large language model, human-in-the-loop disambiguation, and a novel augmentation-based weighted contrastive learning approach for improved language representations, to build a scalable approach for deductive content analysis. We demonstrate the effectiveness of the proposed approach to identify firm innovation strategies from their 10-K reports to obtain inferences reasonably close to human coding. Collapse

Topics: knowledge representation human in the loop human computer interaction innovation management organizational innovation

Methods: ontological modelling machine learning language model large language model qualitative content analysis

Impact of Using Online Health Management Tools on Patient Perception of Healthcare Quality: A Multiple Chronic Conditions and Generational Perspective

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

Authors: Ghosh, Kaushik; Deokar, Amit; Sen, Sagnika

Abstract: Please cite this article as: Ghosh, K., Deokar, A. V., & Sen, S. (in press). Im ... Expand

Abstract: Please cite this article as: Ghosh, K., Deokar, A. V., & Sen, S. (in press). Impact of using online health management tools on patient perception of healthcare quality: A multiple chronic conditions and generational perspective. Abstract:While access and adoption issues related to online health management tools (OHMT) have been studied in healthcare contexts, questions remain about whether and how their use impacts patients' perceptions of healthcare. Drawing on technology affordance and media synchronicity frameworks, we explore how the existence of multiple chronic conditions (MCC) and differences in usage pattern due to patient's generation impact these relationships. Utilizing HINTS data, this study provides empirical support for a positive relationship between utilization of electronic personal health records (e-PHRs) and healthcare quality perceptions, albeit with a caveat that patients with greater healthcare needs as well as millennial and younger generations do not seem to enjoy the same benefits from increased use of e-PHRs. Furthermore, asynchronous patient-provider electronic communication is yet to achieve positive perceptions of better healthcare quality for most users. This research bears implications for personalization and customization of OHMT to account for variations in patients' healthcare needs and usage patterns. Collapse

Topics: self efficacy affordance communication service infrastructure healthcare data health information system

Methods: survey multiple regression statistical hypothesis test Levene's test descriptive statistic

Theories: media synchronicity theory

Examining the impacts of fitness app features on user well-being

2023 | Information & Management | Citations: 0

Authors: Hu, Jiang; He, Wei; Zhang, Jie; Song, Jaeki

Abstract: Drawing on the self-regulation theory, the current paper explores the impacts of ... Expand

Abstract: Drawing on the self-regulation theory, the current paper explores the impacts of two types of fitness app feature sets (i.e., personal-oriented and social-oriented features) on users’ health behavior and well-being. The results from fitness app users show that both personal-oriented features and social-oriented features of fitness apps can significantly improve exercise adherence and social engagement of users. Users’ exercise proficiency level negatively moderates the relationship between social-oriented features and (a) exercise adherence and (b) social engagement. High levels of social engagement promote users’ physical adherence to exercises. Exercise adher­ ence and social engagement both enhance users’ subjective well-being, but their impacts on different dimensions of well-being vary. Furthermore, regardless of specific features, sufficient use of fitness apps, in general, can significantly help users lead more positive and healthier lives by maintaining exercise adherence, reducing emotional exhaustion, and improving their satisfaction with the overall quality of life. Our findings offer important insights into the underlying mechanisms that help explain fitness app features on users’ well-being, and on a practical level, provide suggestions for mobile app developers in designing better fitness app prod­ ucts and for exercisers in optimizing the benefits of fitness technology adoption. Collapse

Topics: mobile application pandemic technology adoption covid 19 usage intention

Methods: survey cross sectional research survey design PLS tool statistical hypothesis test

Privacy Policy and Hosts’ Concerns on Accommodation Sharing Platforms

2023 | HICSS | Citations: 0

Authors: Hu, Jiang; Zhang, Jie; Liang, Huigang

Abstract: Accommodation-sharing services are gaining great popularity via online community ... Expand

Abstract: Accommodation-sharing services are gaining great popularity via online community platforms in recent years. Meanwhile, users’ privacy concerns over social interactions and online transactions on these platforms are escalating. This study investigates whether and how privacy policy can properly mitigate hosts’ privacy concerns, enhance perceived benefits, and subsequently encourage their information disclosure on the accommodation sharing platforms (ASPs). Through a scenario-based survey and a controlled experiment, we find that the hosts are more concerned about the other users’ misappropriating the private information that the hosts disclose on the platform than the platforms’ privacy invasion behaviors. However, this major concern is not significantly mitigated by the current privacy policy. Moreover, privacy policy engenders two types of perceived benefits, among which the perceived social benefit has a stronger effect than economic benefit on the hosts’ intentions to disclose information on ASPs. Collapse

Topics: information privacy concern privacy data breach decision making electronic commerce

Methods: experiment survey parametric test factorial experiment two-way ANOVA

Theories: privacy calculus theory

Can Broadband Help Curb Pollution? Implications for Marginalized Communities

2023 | International Conference on Information Systems | Citations: 0

Authors: Jabr, Wael; Dhanorkar, Suvrat

Abstract: When stakeholders are unaware of information that is of relevance to them and w ... Expand

Abstract: When stakeholders are unaware of information that is of relevance to them and when this information is costly to obtain (due to costs associated with information awareness, acquisition, and integration), those stakeholders suffer from information asymmetry visà-vis the producer and owner of this information. To overcome those conditions, mandated disclosure has been used in a number of settings, requiring the owner of information such as a chemical manufacturer to disclose the types and level of toxic releases it produces. However, without the ease of information acquisition and subsequent dissemination, such disclosure's value remains limited. In this paper, we study broadband penetration in the United States as an enabler of the public to be better informed about manufacturers' pollution and its implications on curbing toxic releases. Because some marginalized communities have been documented to suffer more from such toxic releases, we also study the disproportionate impact of broadband on Black communities. Collapse

Topics: internet technology cloud computing mobile phone accounting green IS

Methods: instrumental variables estimation survey longitudinal research regression analysis method theory development

How Do Users Feel When They Use Artificial Intelligence for Decision Making? A Framework for Assessing Users’ Perception

2023 | Information Systems Frontiers | Citations: 0

Authors: Kushwaha, Amit Kumar; Pharswan, Ruchika; Kumar, Prashant; Kar, Arpan Kumar

Abstract: Artificial intelligence (AI) transits from merely adopted technology to fueling ... Expand

Abstract: Artificial intelligence (AI) transits from merely adopted technology to fueling everyday decision-making systems from medication to navigation. With this combination of AI in decision-making systems (ADMS), the present study explores how text-based users' data from social media helps organize the users' perspectives of ADMS? To investigate our research questions, we used a framework consisting of three phases, exploratory, confirmatory, and validatory. We applied hierarchy clustering and topic modeling in the exploratory study, hypothesis building, and empirical analysis during the confirmatory study and support vector machine (SVM) in the validatory study. Our findings suggest that users are primarily concerned about the risk involved in using ADMS. Factors like accountability, self-efficacy, knowledge of ADMS individuals' attitudes towards ADMS impact the perception of ADMS among individuals. This study's theoretical and practical implications have great scope as ADMS is still in its elementary stage. Collapse

Topics: artificial intelligence Twitter social media decision making user attitude

Methods: survey support vector machine topic model regression analysis method statistical hypothesis test