Does Analytics Help Resolve Equivocality in the Healthcare Context? Contrasting the Effects of Analyzability and Differentiation

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

Authors: Adjerid, Idris; Angst, Corey; Devaraj, Sarv; Berente, Nicholas

Abstract: Organizations are increasingly using data analytics to help make decisions and ... Expand

Abstract: Organizations are increasingly using data analytics to help make decisions and drive positive outcomes. But organizational scholarship has warned us that the sort of information processing associated with analytic capabilities, while effective for uncertainty reduction, may be less effective in equivocal contexts. Equivocality is evident when tasks are not easily analyzable (task analyzability) or when organizational departments are highly differentiated (differentiation). We hypothesize that analytics will be less effective in driving positive outcomes when equivocality is high because of low task analyzability. However, when an organization is more differentiated, resulting in high equivocality, we anticipate that analytics will be more effective in driving positive outcomes. To test this theory, we studied how clinical healthcare analytics influenced experiential quality (akin to patient satisfaction) in over 3,000 hospitals across nine years. Our results show that analytics capabilities, on average, do improve outcomes in terms of patient experiential quality, suggesting that analytics can reduce uncertainty, but we also found evidence for the moderating role of equivocality. Specifically, as task analyzability decreases (i.e., increasing equivocality), clinical healthcare analytics becomes less effective in improving experiential quality. However, when equivocality is high because of differentiation, there is a positive relationship between clinical healthcare analytics and experiential quality but only in larger hospitals. From a managerial perspective, this study has implications for boundary conditions of data analytics in organizations. Collapse

Topics: healthcare analytics technology adoption electronic health record health information system artificial intelligence

Methods: theory development post-hoc analysis experimental group longitudinal research machine learning

Theories: organizational information processing theory

An Investigation of Marketing Compensation in Medicine: The Impact of the Information Disclosure Regulations

2023 | HICSS | Citations: 0

Authors: Duan, Wenjing; Cheng, Sherri; Zhou, Wenqi

Abstract: Pharmaceutical scandals frequently occupy national media headlines, and these co ... Expand

Abstract: Pharmaceutical scandals frequently occupy national media headlines, and these controversial practices exacerbate the healthcare expenditure burden and cause ethical concerns. Big Pharma’s marketing strategies involve making various types and sizes of payments to healthcare providers to induce favorable prescribing behaviors for their drugs. Enacted in 2013, the Physician Payments Sunshine Act (PPSA) mandates company disclosures of payments to physicians. Its effectiveness has been explored in various settings, but there have been few rigorous academic studies, and a unified conclusion has not been reached. In this paper, we utilized unique datasets, including pre- and post-PPSA payment data from six major pharmaceutical companies, to examine the impact of state-level disclosure policies and the federal PPSA on pharmaceutical companies’ payments to physicians. Difference-in-difference analysis was adopted to study the dynamics of company payments to physicians before and after the PPSA. We found significant interactions for the state policies and the PPSA on all studied payment types for the six companies. The companies generally decreased their total payments to physicians and the number of paid physicians. However, the policy impacts differed across the payment types and companies. Collapse

Topics: marketing management database system missing data data breach

Methods: difference in differences self reported survey experimental group robustness check post analysis

Exploring Personality-based Heterogeneity in Metaknowledge and Human-AI Collaboration

2023 | International Conference on Information Systems | Citations: 0

Authors: Gass, Daniel F

Abstract: While artificial intelligence (AI) has great potential to transform how we work ... Expand

Abstract: While artificial intelligence (AI) has great potential to transform how we work, it's clear that in many contexts, humans and AI will work side by side. According to recent research, humans' ability to capture complementarities while working collaboratively with AI is critically constrained by our limited ability to assess what we know, i.e., metaknowledge. However, extant research has largely overlooked a critical difference between human and AI: Human behavior is shaped by personality. I explore how differences arising from personality traits impact metaknowledge and delegation behavior towards AI. Using an image classification experiment, I find that higher levels of conscientiousness improve, while higher levels extraversion harm humans' ability to work collaboratively with AI. Differences in metaknowledge help to explain this relationship. These findings motivate further research on personality in human-AI collaboration and the design of collaborative systems which foster metaknowledge and improved performance. Collapse

Topics: artificial intelligence personality computer supported cooperative work database system human computer interaction

Methods: experimental group experiment survey linear regression analysis descriptive statistic

Theories: big five model

A Real Utopia Under What Conditions? The Economic and Social Benefits of Workplace Democracy in Knowledge-Intensive Industries

2023 | Organization Science | Citations: 0

Authors: Young-Hyman, Trevor; Magne, Nathalie; Kruse, Douglas

Abstract: Given consistent evidence of its social benefits but questions about its market ... Expand

Abstract: Given consistent evidence of its social benefits but questions about its market viability, this paper examines the conditions under which workplace democracy can be understood as a “real utopia”; a viable form of organization that is both economically productive and socially welfare enhancing. Conceptualizing democratic firms as organizations with formally distributed authority and collectivist norms, we argue that democratic firms will operate more productively in knowledge intensive industries, compared with conventional firms in the same industries, because they give authority to those with relevant knowledge and encourage intrafirm information sharing. Next, focusing on intrafirm wage inequality as a key social welfare outcome, we argue that knowledge-intensive sectors are also settings where the benefits of workplace democracy are likely to be greater. Knowledge intensive industries tend to generate greater intrafirm inequality through the adoption of market-based employment policies and reliance on unique expertise, yet the formal structure and collectivist norms of democratic firms are likely to limit these mechanisms of inequality, generating inequality reductions. We test these hypotheses with longitudinal linked employer-employee data from French cooperatives and conventional firms, including firms that shift organizational structures over time. We find robust support for our hypothesis about economic performance and moderate support for our hypothesis about social performance. Funding: This research has been generously supported by the Berg Center for Ethics, the Katz International Business Center, and the Bill Nobles Fellowship, as well as a public grant overseen by the French National Research Agency (ANR) as part of the “Investissements d’Avenir” program [Reference ANR-10-EQPX-17-Centre d’accès sécurisé aux données]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1622. Collapse

Topics: productivity organization structure economic surplus IS economics human resource management

Methods: descriptive statistic longitudinal research event study statistical hypothesis test robustness check

Theories: sociological theory

An Information Systems Design Theory for Service Network Effects

2020 | Journal of the Association for Information Systems | Citations: 4

Authors: Janiesch, Christian; Rosenkranz, Christoph; Scholten, Ulrich

Abstract: Service platforms make software applications available as a service to end users ... Expand

Abstract: Service platforms make software applications available as a service to end users. Platforms enable noticeable economic benefits for scaling and transforming a business. Their long-term competitiveness is ensured in controlled cooperation with channel intermediaries and network partners. Hence, service platforms must be designed to harness self-enforcing effects of value generation, so-called network effects. In an exaptation of existing knowledge, we present an information systems design theory to inform the design of methods that analyze, describe, and guide the design of service platforms through the means of causal loops and control methods. We describe the theory’s purpose and scope as well as the underlying justificatory knowledge behind the constructs and principles of form and function. The design theory covers the design of all service platform participants and activities as well as their transactions and influences in areas of staged platform authority, using enforcing and incentivizing control methods. We demonstrate the principles of implementation with an expository instantiation and apply it to the M-Engineering service platform, which offers surveillance, control, and data acquisition solutions. Furthermore, we present and discuss testable propositions and a study design to evaluate our design principles. Collapse

Topics: network effect digital platform Facebook online learning system service quality

Methods: design principle design artifact design science design methodology survey

Theories: control theory critical mass theory social network theory network effect theory cognitive theory of multimedia learning