本文精选了信息系统领域国际顶刊《Information Systems Research》近期发表的论文，提供信息系统研究领域最新的学术动态。

Impact of Telehealth and Process Virtualization on Healthcare Utilization

原刊和作者：

Information Systems Research Volume 35, Issue 1

Sezgin Ayabakan （Temple University）

Indranil R. Bardhan （The University of Texas at Austin）

Zhiqiang (Eric) Zheng （The University of Texas at Dallas）

Abstract

Technological advancements and the COVID-19 pandemic have catapulted process virtualization across many industries, including healthcare, where telehealth has enabled significant digital transformation of care delivery. Although telehealth has been proposed as a potential solution to improve access to care and restrain runaway healthcare costs, it can increase spending if telehealth use leads to new types of resource utilization. Drawing on the lens of process virtualization theory, we study the impact of telehealth on healthcare utilization by examining visit-level patient data of telehealth use in facilitating e-visits with healthcare providers. On average, a telehealth visit reduces the number of future outpatient visits by 13.6% (or 0.15 visits), equal to a reduction of $239 in total cost within 30 days after the visit. Our results suggest that the benefits of telehealth use are observed primarily among diseases with high virtualization potential. Specifically, patients with mental health, skin, metabolic, and musculoskeletal diseases exhibit a significant reduction of 0.21 outpatient visits per quarter (an equivalent cost reduction of $179) when they are treated via telehealth, suggesting a substitution effect with respect to traditional clinic visits. Our research identifies the boundary conditions that determine the nuanced impact of telehealth on care utilization and shows that its effectiveness depends on the process virtualization potential of different diseases. Our findings have several practical and theoretical implications for fostering telehealth use in a value-based healthcare environment, especially for diseases with high virtualization potential where telehealth use should be promoted to bend the cost curve.

Link: https://doi.org/10.1287/isre.2023.1220

Atrophy in Aging Systems: Evidence, Dynamics, and Antidote

原刊和作者：

Information Systems Research Volume 35, Issue 1

Amrit Tiwana （University of Georgia）

Hani Safadi （University of Georgia）

Abstract

A pervasive, unbroached phenomenon is how once-modern systems age into unmaintainable albatrosses. We conceptualize this phenomenon from first-principles as system atrophy. We construct a trace data set from 190 million lines of evolving code in 1,354 systems spanning 25 years to corroborate it. Our middle-range theory introduces system atrophy into the conversation on information systems evolution, showing how small increments in modularity slow atrophy but lose potency with age. Atrophy eventually stunts systems, increases bugginess, and disengages developers.

Link: https://doi.org/10.1287/isre.2023.1218

The Effect of Gender Expectations and Physical Attractiveness on Discussion of Weakness in Online Professional Recommendations

原刊和作者：

Information Systems Research Volume 35, Issue 1

Rohit Aggarwal （University of Utah）

Vishal Midha （Illinois State University）

Nicholas Sullivan （University of Mississippi）

Abstract

In the current professional environment, recruiters are using online professional networks at an increasing rate to find qualified candidates for job openings. Online professional recommendations on these sites can provide valuable information; however, because of the medium by which they are provided, their effectiveness may suffer from lower levels of trust. We theoretically conjecture why including a discussion of a candidate’s weakness (termed, scope of improvement) can lead to a higher likelihood of a recruiter being willing to interview a candidate. We theorize that the effect of scope of improvement will depend on the nature of the weakness discussed and the physical attractiveness of the candidate, which is only relevant in an online context where that information is known. Analysis of the data we collected from industry professionals indicates that for average candidates, scope of improvement has a positive effect when the scope discussed does not counter expectations derived from common gender stereotypes (referred to as gender-expected scope), and a negative effect when it does. For attractive candidates, any discussion of scope has a negative effect. A theoretical contribution is our explaining the process of how different types of scope effect the likelihood of interview across different levels of attractiveness. Another theoretical contribution was the counterfactual performed in a second study to show how reinforcement of gender-expected strengths can attenuate the penalty of gender-expected scope for attractive candidates. Findings indicate that the penalty observed for attractive candidates can be avoided by reinforcing traits commonly associated with their gender. Data for both studies were collected from industry professionals involved in the hiring process, allowing us to offer practical guidelines to users of online professional networks.

Link: https://doi.org/10.1287/isre.2021.1032

Effect of Online Professional Network Recommendations on the Likelihood of an Interview: A Field Study

原刊和作者：

Information Systems Research Volume 35, Issue 1

Rohit Aggarwal （University of Utah）

Vishal Midha （Illinois State University）

Nicholas Sullivan （University of Mississippi）

Abstract

Online professional networks (OPNs) are an increasingly common tool used by recruiters to find and vet qualified job candidates for open positions. These sites allow users to publish recommendations given by other users to supplement their profile information and add credibility to the information provided. OPN recommendations provide a rich source of information to recruiters. Unlike recommendations shared in other ways (non-OPN recommendations), OPN recommendations are publicly accessible, and candidates have full control over which recommendations they show to others. Despite the role OPN profiles play in the recruiting process, there has been a lack of research into the specific effect of OPN recommendations, how the impact of OPN recommendations differs from non-OPN recommendations, and how they can best be used to maximize the likelihood of candidates receiving interviews. First, we theoretically conjecture why recommendations that reveal a candidate’s weakness will have different effects on the likelihood of interview depending on whether the weakness is expected or unexpected and how that difference depends on how the recommendation is presented (OPN versus non-OPN). Then, we establish that the effectiveness of a recommendation is higher when presented as a non-OPN recommendation than as an OPN recommendation and show that OPN recommendations benefit more from discussing an expected weakness than non-OPN recommendations. Data were collected from a field study leveraging the candidate tracking system of a large recruitment firm.

Link: https://doi.org/10.1287/isre.2021.1053

Smart Testing with Vaccination: A Bandit Algorithm for Active Sampling for Managing COVID-19

Information Systems Research Volume 35, Issue 1

Yingfei Wang （University of Georgia）

Inbal Yahav （University of San Francisco）

Balaji Padmanabhan （University of San Francisco）

Abstract

This paper presents methods to choose individuals to test for infection during a pandemic such as COVID-19, characterized by high contagion and presence of asymptomatic carriers. The smart-testing ideas presented here are motivated by active learning and multi-armed bandit techniques in machine learning. Our active sampling method works in conjunction with quarantine policies, can handle different objectives, and is dynamic and adaptive in the sense that it continually adapts to changes in real-time data. The bandit algorithm uses contact tracing, location-based sampling and random sampling in order to select specific individuals to test. Using a data-driven agent-based model simulating New York City we show that the algorithm samples individuals to test in a manner that rapidly traces infected individuals. Experiments also suggest that smart-testing can significantly reduce the death rates as compared with current methods, with or without vaccination. While smart testing strategies can help mitigate disease spread, there could be unintended consequences with fairness or bias when deployed in real-world settings. To this end we show how procedural fairness can be incorporated into our method and present results that show that this can be done without hurting the effectiveness of the mitigation that can be achieved.

Link: https://doi.org/10.1287/isre.2023.1215