On Predicting ESG Ratings Using Dynamic Company Networks

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

Authors: Ang, Gary; Guo, Zhiling; Lim, Ee-Peng

Abstract: Environmental, social and governance (ESG) considerations play an increasingly i ... Expand

Abstract: Environmental, social and governance (ESG) considerations play an increasingly important role due to the growing focus on sustainability globally. Entities, such as banks and investors, utilize ESG ratings of companies issued by specialized rating agencies to evaluate ESG risks of companies. The process of assigning ESG ratings by human analysts is however laborious and time intensive. Developing methods to predict ESG ratings could alleviate such challenges, allow ESG ratings to be generated in a more timely manner, cover more companies, and be more accessible. Most works study the effects of ESG ratings on target variables such as stock prices or financial fundamentals of companies, but few works study how different types of company information can be utilized to predict ESG ratings. Previous works also largely focus on using only the financial information of individual companies to predict ESG ratings, leaving out the different types of inter-company relationship networks. Such inter-company relationship networks are typically dynamic, i.e., they evolve across time. In this paper, we focus on utilizing dynamic inter-company relationships for ESG ratings prediction, and examine the relative importance of different financial and dynamic network information in this prediction task. Our analysis shows that utilizing dynamic inter-company network information, based on common director, common investor and news event-based knowledge graph relationships, can significantly improve ESG rating prediction performance. Robustness checks over different time-periods and different number of time-steps in the future further validate these insights. Collapse

Topics: shapley additive explanation knowledge graph database system missing data network effect

Methods: machine learning econometric modeling longitudinal research Granger causality robustness check

The Role of Followers and Followees in the Adoption of Innovations

2023 | HICSS | Citations: 0

Authors: Atlas, M.; Gavious, A.; Ravid, G.

Abstract: An online social network is a key platform through which innovation diffuses. T ... Expand

Abstract: An online social network is a key platform through which innovation diffuses. To learn about innovativeness, we simultaneously investigate two Twitter networks, the relationships network, following-follower relationships, and the activity network, the flow of tweets. Specifically, the innovativeness relations to the networks' indegree and outdegree, the volume of platform use, and the profile's age. The more active and central the user, the earlier the adoption. Innovativeness increases with the number of followers only when at least several of them adopt the innovation. Surprisingly, having more followees is linked to later engagement with the innovation. This association is mediated by the number of adopters' followees. Those who created a Twitter profile later are also more likely to adopt innovations later. This study is novel in distinguishing between the two networks and analyzing their interactions. Its contribution lies in identifying the innovativeness of users in an online social network platform diffusion. Collapse

Topics: Twitter mindfulness social network innovation management user acceptance

Methods: descriptive statistic regression analysis method

Formation and Action of a Learning Community with Collaborative Learning Software

2023 | Journal of Management Information Systems | Citations: 0

Authors: Eryilmaz, Evren; Thoms, Brian; Ahmed, Zafor; Lee, Howard

Abstract: This paper explores the formation of a learning community facilitated by custom ... Expand

Abstract: This paper explores the formation of a learning community facilitated by custom collaborative learning software. Drawing on research in group cognition, knowledge building discourse, and learning analytics, we conducted a mixed-methods field study involving an asynchronous online discussion consisting of 259 messages posted by 50 participants. The cluster analysis results provide evidence that the recommender system within the software can support the formation of a learning community with a small peripheral cluster. Regarding knowledge building discourse, we identified the distinct roles of central, intermediate (i.e., middle of three clusters), and peripheral clusters within a learning community. Furthermore, we found that message lexical complexity does not correlate to the stages of knowledge building. Overall, this study contributes to the group cognition theory to deepen our understanding about collaboration to construct new knowledge in online discussions. Moreover, we add a much-needed text mining perspective to the qualitative interaction analysis model. Collapse

Topics: recommender system collaborative filtering pandemic online learning system open source

Methods: qualitative content analysis descriptive statistic sociometry synthesis mixed method

Theories: cognitive theory

From Seeker to Provider: User Ability and Role Switching in Online Support Communities

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Lee, Michael; Liao, Ruochen; Wang, Xunyi; Kishore, Rajiv

Abstract: Online health communities offer a platform for patients to seek support from ot ... Expand

Abstract: Online health communities offer a platform for patients to seek support from other users with similar conditions. As those support seekers' situation improves, they may ultimately want to give back to the community and provide support to others. This study explores how seekers' ability to provide support, operationalized using their cognitive state and social network structure, can influence the seekers' propensity to switch roles and become support providers. We leverage the Dual Perspective Model of Agency and Communion and Social Networks literature to theorize a seeker's ability to provide support. We apply survival analysis to study seekers' first role switch to a provider and the duration from seekers' initial post to role switch. We find that while seekers who have higher positive agency and closeness centrality are more likely to switch roles to a provider, those having higher positive communality, degree centrality, and eigenvector centrality are less likely to switch. Collapse

Topics: social network Python

Methods: survival analysis proportional hazards model theory development robustness check word2vec

Differential Impacts of Technology-Network Structures on Cost Efficiency: Knowledge Spillovers in Healthcare

2023 | Journal of Management Information Systems | Citations: 0

Authors: Nwafor, Onyi; Ma, Xiao; Johnson, Norman A.; Singh, Rahul; Aron, Ravi

Abstract: We examine how hospital cost efficiency can improve because of knowledge spillov ... Expand

Abstract: We examine how hospital cost efficiency can improve because of knowledge spillover effects, arising from the experiences of members of a healthcare system. We identify two types of functional technology networks—that we term repository-type and workflow-type networks—which are defined by the adoption patterns of different types of electronic health record (EHR) systems application functions by hospitals in a health system. Additionally, we examine how structural features of these two network types—namely, network centrality and interconnectedness—impact such knowledge spillover effects. By analyzing seven years of panel data for 1,420 U.S. hospitals across 216 health systems, and data obtained from surveys of healthcare experts, we found that greater centrality of nodes in repository-type networks enhances the influence of partners' experiences on cost efficiency but has the opposite effect in workflow-type networks, while greater interconnectedness of workflow-type networks enhances the influence of partners' experiences on cost efficiency. Interestingly, we found the opposite effect in repository-type networks, where greater interconnectedness diminishes the influence of partners' experiences on cost efficiency. We discuss reasons for this surprising and counterintuitive finding. Overall, our results demonstrate that at least in healthcare settings where information technologies enable knowledge-sharing networks to be formed, benefits can accrue through knowledge spillover effects; moreover, variations across functional categories of technologies adopted in a network context can result in different technology-network structures with differential impacts on hospital cost efficiency because of their influence on knowledge spillover at both the hospital and system levels. Not only can these results explain mixed findings in the literature that has been dominated by a focus on cost efficiency outcomes dependent solely on the EHR adoption choices of individual hospitals, but they also provide the basis for guidelines for a network-based perspective of technology adoption. We discuss other possible settings to which our empirical findings can be reasonably generalized, including supply chain technologies and the Internet of Things. Collapse

Topics: health information system knowledge sharing knowledge spillover technology adoption IT manager

Methods: longitudinal research robustness check descriptive statistic parametric test ANOVA

Theories: social network theory

"Digital Sanctums of Empowerment": Exploring Community and Everyday Resilience-Building Tactics in Online Professional Communities for Women

2023 | HICSS | Citations: 0

Authors: Sengupta, S.; Tacheva, Z.

Abstract: Work and learning are essential facets of our existence, yet women have and may ... Expand

Abstract: Work and learning are essential facets of our existence, yet women have and may continue to face multiple restrictions that hinder and impede their professional outcomes. These restrictions are especially pronounced in the technical domains of Information technology and Computer science.This paper explores the power of informal online communities to act as collective shields of care in the context of professional development, especially for women. Using a mixed-methods comparative investigation of 400,268 conversational traces from three professional development communities on Reddit, we report resilience and communal empathy-building tactics, as well as calls for inclusivity and belongingness, which drive the collective identity of these online channels. The long-term goal of this work is to address the way in which such channels can be designed and curated to offer spaces for enrichment, empowerment, and advocacy with a focus on the professional development of women, especially those engaged in technical domains. Collapse

Topics: online community IT career Reddit Python application programming interface

Methods: BERT linguistic analysis mixed method qualitative content analysis cross sectional research

A Responsible AI Framework for Mitigating the Ramifications of the Organ Donation Crisis

2023 | Information Systems Frontiers | Citations: 0

Authors: Tutun, Salih; Harfouche, Antoine; Albizri, Abdullah; Johnson, Marina E.; He, Haiyue

Abstract: Thousands of people die while waiting for organ transplants due to a significant ... Expand

Abstract: Thousands of people die while waiting for organ transplants due to a significant gap between demand and supply. This gap often leads to illegal activities and ethical issues such as illicit organ trade and auctions. Therefore, to increase the organ supply and procure more organs, organizations must understand the causes of families who refuse to consent to donate their loved one's organs. Furthermore, such organizations must better identify those families most likely to consent to organ donation. We propose a responsible AI framework that integrates network science and artificial intelligence to identify consent outcomes for organ donation. The proposed framework includes three phases: (1) collecting and pre-processing data, (2) creating new features and identifying root causes of family refusal, and (3) training and testing models to predict the probability of families granting consent for organ donation. The designed artifact included collaborative decisions and network measures, increasing explainability through network science. It integrated human reviews and assessment of risks which increases correct and interpretable predictions. Results can help encourage organ donations and reduce the illegal organ trade. The experimental results show that the designed artifact outperformed previous studies identifying factors affecting consent outcomes. This framework integrates network science and artificial intelligence to reduce maleficence, solve the lack of transparency (i.e., increase trustworthiness) and improve accountability of the model that aims to predict consent outcomes. Collapse

Topics: responsible artificial intelligence artificial intelligence explainable artificial intelligence procurement management authorization

Methods: machine learning computational algorithm support vector machine experiment classification method

Theories: justice theory

An AI-based Decision Support System for Predicting Mental Health Disorders

2023 | Information Systems Frontiers | Citations: 0

Authors: Tutun, Salih; Johnson, Marina E.; Ahmed, Abdulaziz; Albizri, Abdullah; Irgil, Sedat; Yesilkaya, Ilker; Ucar, Esma Nur; Sengun, Tanalp; Harfouche, Antoine

Abstract: Approximately one billion individuals suffer from mental health disorders, such ... Expand

Abstract: Approximately one billion individuals suffer from mental health disorders, such as depression, bipolar disorder, schizophrenia, and anxiety. Mental health professionals use various assessment tools to detect and diagnose these disorders. However, these tools are complex, contain an excessive number of questions, and require a significant amount of time to administer, leading to low participation and completion rates. Additionally, the results obtained from these tools must be analyzed and interpreted manually by mental health professionals, which may yield inaccurate diagnoses. To this extent, this research utilizes advanced analytics and artificial intelligence to develop a decision support system (DSS) that can efficiently detect and diagnose various mental disorders. As part of the DSS development process, the Network Pattern Recognition (NEPAR) algorithm is first utilized to build the assessment tool and identify the questions that participants need to answer. Then, various machine learning models are trained using participants’ answers to these questions and other historical data as inputs to predict the existence and the type of their mental disorder. The results show that the proposed DSS can automatically diagnose mental disorders using only 28 questions without any human input, to an accuracy level of 89%. Furthermore, the proposed mental disorder diagnostic tool has significantly fewer questions than its counterparts; hence, it provides higher participation and completion rates. Therefore, mental health professionals can use this proposed DSS and its accompanying assessment tool for improved clinical decision-making and diagnostic accuracy. Collapse

Topics: artificial intelligence decision support system explainable artificial intelligence information portal decision making

Methods: computational algorithm support vector machine design process lasso regression ridge regression

Phishing Susceptibility in Context: A Multilevel Information Processing Perspective on Deception Detection

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Wright, Ryan; Johnson, Steven; Kitchens, Brent

Abstract: Despite widespread awareness of risks, significant investments in cybersecurity ... Expand

Abstract: Despite widespread awareness of risks, significant investments in cybersecurity protection, and substantial economic incentives to avoid security breaches, organizations remain vulnerable to phishing attacks. Phishing research has informed effective practical interventions to address phishing susceptibility that emphasize the importance of broadly applicable IT security knowledge. Yet employees still frequently fall victim to phishing attempts. To help understand why, we conceptualize phishing susceptibility as the failure to differentiate between deceptive and legitimate information processing requests that occur within the context of an employee's typical job responsibilities. We apply this contextual lens to identify characteristics of knowledge workers' organizational task and social context that may enhance or diminish performance in detecting deception in phishing email attempts. To test our hypotheses, we conducted a study in which employees of the finance division of a large university encountered simulated email-based phishing attempts as part of their normal work routine. We found evidence supporting our hypotheses that an individual's susceptibility to phishing attacks is influenced by their position in the knowledge flows of the organization and by the impact of workgroup responsibilities on their cognitive processing. We contend that phishing susceptibility is not merely a matter of IT security knowledge but is also influenced by contextualized, multilevel influences on information processing. As phishing attacks are increasingly targeted to specific organizational settings, it is even more important to incorporate this contextualized information processing view of phishing susceptibility. Collapse

Topics: phishing electronic mail system support IT security email phishing

Methods: theory development literature study simulation logistic regression survey

Optimization of Dynamic Product Offerings on Online Marketplaces: A Network Theory Perspective

2023 | Journal of Management Information Systems | Citations: 0

Authors: Zuo, Meihua; Angelopoulos, Spyros; Ou, Carol Xiaojuan; Liu, Hongwei; Liang, Zhouyang

Abstract: The fierce competition among brands on online marketplaces makes the optimizatio ... Expand

Abstract: The fierce competition among brands on online marketplaces makes the optimization of offerings within this context a significant challenge. To address this challenge, we draw upon network theory and model the degree of competition through consumers' consideration sets. We use a large empirical dataset from one of the biggest online marketplaces to explore the dynamic relationship between network position and the degree of competition, and we depict the redistribution of market share of related offerings after adjusting their array. In doing so, we provide a theoretical reference on when and how brands should optimize their product offerings on online marketplaces. We further demonstrate that intra-brand cannibalization relations have a significantly greater impact on the degree of competition compared to inter-brand ones, while intra-brand cannibalization relations represent the main reason for fluctuations in the degree of competition. Hence, contrary to existing theoretical insights and practical intuitions, our findings demonstrate that brands should minimize the number and heterogeneity of their offerings within a market segment to increase their sales on online marketplaces. Collapse

Topics: electronic market organizational productivity marketing management decision making business model

Methods: spatial autocorrelation statistic descriptive statistic cross sectional research instrumental variables estimation longitudinal research

Theories: social network theory