An Extensive Examination of Regression Models with a Binary Outcome Variable

Linear regression is among the most popular statistical models in social sciences research, and researchers in various disciplines use linear probability models (LPMs)—linear regression models applied to a binary outcome. Surprisingly, LPMs are rare in the IS literature, where researchers typically use logit and probit models for binary outcomes. Researchers have examined specific aspects of LPMs’ but not thoroughly evaluated their practical pros and cons for different research goals under different scenarios. We perform an extensive simulation study to evaluate the advantages and dangers of LPMs, especially with respect to big data, which is now common in IS research. We evaluate LPMs for three common uses of binary outcome models: inference and estimation, prediction and classification, and selection bias. We compare its performance to logit and probit under different sample sizes, error distributions, and more. We find that coefficient directions, statistical significance, and marginal effects yield results similar to logit and probit. In addition, LPM estimators are consistent for the true parameters up to a multiplicative scalar. This scalar, although rarely required, can be estimated assuming an appropriate error distribution. For classification and selection bias, LPMs are on par with logit and probit models in terms of class separation and ranking and is a viable alternative in selection models. LPMs are lacking when the predicted probabilities are of interest because predicted probabilities can exceed the unit interval. We illustrate some of these results by modeling price in online auctions using data from eBay

Coping with Self-harm in Elderly People: The Impact of Internet Use on Suicidal Ideation

Given the significant costs of suicidal behavior for society, suicide prevention is one of the most urgent issues for most countries. By considering suicidal ideation as a strong indicator of suicide, this paper examines how Internet use influences suicidal ideation and its underlying mechanisms in the context of older adults. Synthesizing the interpersonal theory of suicide with prior literature on Internet use, this study explains that Internet use can reduce suicidal ideation through enhanced social belongingness. Our results using data from 6,056 older adults show that Internet use is negatively associated with suicidal ideation in older adults. The present study further highlights the mediating role of social connectedness (i.e., perceived loneliness and social relationship satisfaction) as an underlying mechanism between Internet use and suicidal ideation. Contributions and practical implications for addressing elderly suicidal problems and future works are discussed

Explanation matters:An experimental study on explainable AI

Explainable artificial intelligence (XAI) is an important advance in the field of machine learning to shed light on black box algorithms and thus a promising approach to improving artificial intelligence (AI) adoption. While previous literature has already addressed the technological benefits of XAI, there has been little research on XAI from the user’s perspective. Building upon the theory of trust, we propose a model that hypothesizes that post hoc explainability (using Shapley Additive Explanations) has a significant impact on use-related variables in this context. To test our model, we designed an experiment using a randomized controlled trial design where participants compare signatures and detect forged signatures. Surprisingly, our study shows that XAI only has a small but significant impact on perceived explainability. Nevertheless, we demonstrate that a high level of perceived explainability has a strong impact on important constructs including trust and perceived usefulness. A post hoc analysis shows that hedonic factors are significantly related to perceived explainability and require more attention in future research. We conclude with important directions for academia and for organizations.</p

Doctors’ Orders or Patients’ Preferences? Examining the Role of Physicians in Patients’ Privacy Decisions on Health Information Exchange Platforms

Health information exchange (HIE) platforms could increase the efficiency of health care services by enabling providers to instantly access the medical records of their patients. However, these benefits cannot be realized unless patients disclose their information on HIE platforms. We examine actual privacy decisions made by patients on an HIE platform, study the influence of physicians’ recommendations on patients’ decisions, and explore the process through which this effect takes place. By analyzing a unique data set consisting of the privacy decisions of 12,444 patients, we show that contrary to common belief, patients do not simply follow physician recommendations, but rather carefully consider the risks and benefits of providing consent. We show that competition among medical providers does not hinder patient participation in HIEs, but that providers’ decisions to ask for consent are primarily driven by the potential benefits of HIE for themselves and their patients

Preschool participation and students’ learning outcomes in primary school: Evidence from national reform of pre-primary education in Ethiopia

This study examines whether a large expansion of pre-primary education in Ethiopia affected subsequent students’ learning outcomes during the national reform of pre-primary education. The study utilizes two comparable, representative early grade reading assessment data that straddle the reform period from 2010 to 2016, during which enrolment rates in pre-primary education soared by nearly ten times nationwide. We find that associations between preschool participation and literacy outcomes were positive and significant after the expansion, yet no such relationships were observed before the reform. However, there was little heterogeneity in the gains of the preschool participation by gender, urbanity, and parental literacy. We discuss implications for ongoing reform, including strategic and inclusive policy designed to close the learning gap between children from advantaged and disadvantaged backgrounds

Organizations and stakeholders : three papers on data leveraging for AI implementation

Leveraging data for AI implementation requires complex managerial approaches and sophisticated technology arrangements that ideally bridge the gaps between the needs of different stakeholders and an organization’s strategic objectives. Although organizations believe that they are in the “perfect” position to do so, in practice this is not good enough. The tandem between the managerial approaches and the technology catalyzes changes in the way an organization operates and distributes its services, keeping digital innovation at the center of the operation. However, the understanding of how different groups of stakeholders actually promote, support and participate in such innovation opens up a new path for further investigation. This thesis contributes to a better understanding of how organizations leverage data and implement AI within specific industry domains, promoting and maintaining digital innovation. It sheds light on how organizations leverage data and support AI implementation from a theoretical perspective on business analytics and AI, as well as an empirical perspective with two case studies of Lufthansa Industry Solutions and ARUP, and pulls together both the systematic literature review method and qualitative analysis to problematize the current scholars’ conversation on these topics. This thesis adds important insights to the current academic conversation on leveraging data for AI implementation in today’s business landscape. The insights do not emphasize failed attempts, but rather highlight how specific organizations face unique challenges compared to well-accepted and continuously discussed practices while working with data and AI. In doing so, the thesis also provides multiple future research directions, finally highlighting important theoretical and practical implications for organizations and practitioners