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

Effectiveness of Health Communication Technology on Compliance Disposition of Covid-19 Guidelines

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

Authors: Bui, Quang “Neo”; Moriuchi, Emi

Abstract: Recent research has mostly examined the role of health communication technology ... Expand

Abstract: Recent research has mostly examined the role of health communication technology (HCT) in patient empowerment and in producing patient-focused outcomes. This study examines HCT in a larger context where it is used as a tool to improve public health. The objective is to examine how HCT is used to monitor Covid-19's spread, and how social factors affect individual assessment of HCT and individual compliance disposition of Covid-19 guidelines. Analyzing data collected from 360 HCT users suggests that the information and system quality of HCT indeed impact users' assessment of its effectiveness and their compliance disposition. However, such effects are strongly mediated by social factors: Peer influence and government trust can sway an individual's cognitive judgments of Covid-19 regardless of HCT's impacts. The findings highlight the importance of social factors in pandemic management and the need to socialize health informatics to make them more effective. Collapse

Topics: social influence data quality digital dashboard system quality mobile application

Methods: survey mediation analysis partial least squares path modeling post analysis path analysis

Theories: health belief model information systems success model

Selecting the Optimal Number of Crowd Workers for Forecasting Tasks

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Carvalho, Arthur; Karimi, Majid

Abstract: Crowdsourcing platforms enable requesters to elicit information from thousands ... Expand

Abstract: Crowdsourcing platforms enable requesters to elicit information from thousands of workers worldwide. A question that arises when outsourcing a task to a crowd is how many crowd workers a requester should hire. Focusing on forecasting tasks, we provide a methodological way of answering that question by formally describing how the number of crowd workers relates to the expected forecast error. Specifically, we discuss how an error curve associates a number of hired crowd workers with a fixed and a variable error. We then suggest an optimality concept that enables requesters to select an optimal number of crowd workers based on the tolerable variable error. In an illustrative study on Amazon Mechanical Turk, we demonstrate how highly prescriptive our approach is. In particular, we show how requesters can perform ex-ante and ex-post analyses by determining the optimal number of workers before running a task and by determining the fixed error after running the task and collecting forecasts. Collapse

Topics: crowdsourcing decision support system

Methods: computational algorithm post analysis optimization model experiment mathematical model

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

Reorganization and Participation in Decentralized Platform Ecosystems: Evidence from Blockchain Forking

2023 | HICSS | Citations: 0

Authors: Hsieh, Ying-Ying; Andersen, Jonas

Abstract: Like any organizational system, platform ecosystems reorganize to update its ali ... Expand

Abstract: Like any organizational system, platform ecosystems reorganize to update its alignment with the internal and external environments. However, unlike reorganizations of centrally managed platforms performed by the owners, reorganizations of decentralized platforms ecosystems do not rely on formal authority. Instead, the network self-reorganizes to renew the structure, rules, and information to evolve. Little is known about how self-reorganizations influence the participation of various types of networks. In this study, we investigate nine reorganization events on Ethereum, a blockchain-based decentralized smart contract platform, to unpack how self-reorganization related to hard forking influence participation in the development, validation, transaction, and complementor networks. We find that, while participation increases across all networks show a small increase after hard forking events, more complex dynamics are at play within each network that builds on delicate trade-offs between participation structure, configuration, and incentives. Our findings have implications for blockchain research as well as for start-ups building decentralized applications on top of decentralized smart contract platforms. Collapse

Topics: blockchain smart contract Ethereum digital ecosystem organizational change

Methods: paired t-test post analysis longitudinal research

Time Series Prediction Using Deep Learning Methods in Healthcare

2023 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Morid, Mohammad Amin; Sheng, Olivia R. Liu; Dunbar, Joseph

Abstract: Traditional machine learning methods face unique challenges when applied to heal ... Expand

Abstract: Traditional machine learning methods face unique challenges when applied to healthcare predictive analytics. The high-dimensional nature of healthcare data necessitates labor-intensive and time-consuming processes when selecting an appropriate set of features for each new task. Furthermore, machine learning methods depend heavily on feature engineering to capture the sequential nature of patient data, oftentimes failing to adequately leverage the temporal patterns of medical events and their dependencies. In contrast, recent deep learning (DL) methods have shown promising performance for various healthcare prediction tasks by specifically addressing the high-dimensional and temporal challenges of medical data. DL techniques excel at learning useful representations of medical concepts and patient clinical data as well as their nonlinear interactions from high-dimensional raw or minimally processed healthcare data. In this article, we systematically reviewed research works that focused on advancing deep neural networks to leverage patient structured time series data for healthcare prediction tasks. To identify relevant studies, we searched MEDLINE, IEEE, Scopus, and ACM Digital Library for relevant publications through November 4, 2021. Overall, we found that researchers have contributed to deep time series prediction literature in 10 identifiable research streams: DL models, missing value handling, addressing temporal irregularity, patient representation, static data inclusion, attention mechanisms, interpretation, incorporation of medical ontologies, learning strategies, and scalability. This study summarizes research insights from these literature streams, identifies several critical research gaps, and suggests future research opportunities for DL applications using patient time series data. Collapse

Topics: missing data health information system database system healthcare data

Methods: longitudinal research time series analysis structured literature research ontological modelling literature study

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

How are Digital Micro-Influencers Driving the Social Commerce?

2023 | International Conference on Information Systems | Citations: 0

Authors: Hoor, Munsifa; Ilavarasan, Vigneswara

Abstract: Digital micro-influencers (DMIs) seem to drive social commerce as they connect ... Expand

Abstract: Digital micro-influencers (DMIs) seem to drive social commerce as they connect with customers in tier-II towns better than others. The extant literature on the efforts and work processes of DMIs is limited. The present study fills the gap through a qualitative study in India. Using the persuasion knowledge model as a guiding framework, nineteen qualitative in-depth interviews with the DMIs were conducted and analyzed using thematic analysis. The study finds that understanding the target (target knowledge) helps DMIs in selecting appropriate brands (topic knowledge) and creating compelling content (persuasion knowledge), leading to social commerce. Also, topic knowledge assists persuasion knowledge. The study further offers insights into the nature of the motivators on the DMIs in the light of organismic integration theory. Theoretical and managerial implications for future research are provided. Collapse

Topics: social commerce Instagram social media video conference user interface

Methods: qualitative interview personal interview qualitative content analysis post analysis literature study

Posting versus Replying: The Effects of Instructor Participation in MOOC Discussion Forums

2022 | HICSS | Citations: 0

Authors: Du, Zhao; Wang, Fang; Wang, Shan

Abstract: Previous research has suggested that the frequency of instructor participation i ... Expand

Abstract: Previous research has suggested that the frequency of instructor participation in online discussion forums can affect student participation and subsequently their learning results in online courses. However, the different approaches of instructor participation and their effects have not been well studied, particularly in MOOCs of very large class sizes. This research investigates how instructor participation via posting vs. replying in online discussion forums affects student participation in MOOCs. Our results show that instructors mainly participate through replying rather than posting. However, instructor posting is more effective than instructor replying on enhancing student participation, and this advantage of instructor posting over replying increases with class size. Class size negatively moderates the positive effect of instructor replying, but not that of instructor posting. These findings implicate the necessity of a well-planned participation strategy for instructors to engage in MOOC discussion forums. Collapse

Topics: online learning system

Methods: regression analysis method statistical hypothesis test forums posting post analysis descriptive statistic