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

Is There More Than Pokémon Go? -Exploring the State of Research on Causal Modeling in the Field of Augmented Reality

2023 | HICSS | Citations: 0

Authors: Bräker, J.; Semmann, M.

Abstract: The paper explores how scholars apply causal modeling to gain an understanding ... Expand

Abstract: The paper explores how scholars apply causal modeling to gain an understanding of augmented reality as innovative technology and its potential for application. To do so, we conducted a structured literature review and applied a graph database-driven approach to analyze how scholars research augmented reality. Such an approach enables in-depth analysis of the body of knowledge that is not accessible in traditional ways of exploring literature. The results help to understand where we as a community stand and how directions for future research can help reshape the understanding of augmented reality and its application. Collapse

Topics: augmented reality smart device graph database perceived usefulness Neo4j

Methods: structural equation modeling structured literature research literature study literature sample synthesis

Theories: technology acceptance model cue utilization theory extended unified theory of acceptance and use of technology

Distant and Local Knowledge: Investigating the Effect of Changing Interest in Knowledge Generation

2023 | International Conference on Information Systems | Citations: 0

Authors: Feiter, Tim; Resch, Christian; Kock, Alexander

Abstract: This study examines how changes between drawing inspiration from distant knowle ... Expand

Abstract: This study examines how changes between drawing inspiration from distant knowledge and focusing on local knowledge affect contributions in online communities. The research compares two theoretical frameworks for understanding knowledge generation: The tension-based view highlights the tensional perspective of initially engaging with distant knowledge before narrowing the focus to specific domains to foster creative behavior. Conversely, the foundational view posits that creative behavior requires local expertise before it is combined with insights from distant knowledge domains. We collected data from 15 Q&A forums hosted by Stack Exchange and used natural language processing to analyze users' contributions and changes in interest. Our findings suggest that both theories explain knowledge generation. Individuals need to engage with more distant knowledge over time but also streamline their interests between local and distant knowledge domains to generate more valuable and novel contributions. The study enriches understanding of knowledge generation in online communities and offers insights into how to support creative individuals. Collapse

Topics: online community social network open innovation innovation management database system

Methods: BERT natural language processing transformer model archival research linear regression analysis

Theories: creativity theory

Alternative Dispute Resolution Research Landscape from 1981 to 2022

2023 | Group Decision and Negotiation | Citations: 0

Authors: Mota, Fabio Batista; Braga, Luiza Amara Maciel; Cabral, Bernardo Pereira

Abstract: Alternative dispute resolution (ADR) is an important means of resolving disputes ... Expand

Abstract: Alternative dispute resolution (ADR) is an important means of resolving disputes outside of traditional legal frameworks. It is usually adopted because of its flexibility, cost-effectiveness, and ability to preserve relationships that a contentious court battle might damage. This study aims to evaluate the scientific publication related to ADR. To do so, we used metadata of ADR-related articles from 1981 to 2022 collected in the Web of Science Core Collection and carried out a bibliometric, network, and latent Dirichlet allocation analysis. Our results indicate that ADR research is concentrated in North America, with research organizations from the United States accounting for most publications. At the same time, recent years have seen a shift to a more diverse group of countries, with the Chinese City University of Hong Kong and the Australian Victoria University leading in the last five years. The five main topics in ADR research include online dispute resolution for consumer protection, mediation for family law, arbitration in medical malpractice cases, ADR in construction projects, and ADR in employment law. The most frequent research areas assigned to ADR publications are Government & Law and Business & Economics. Network results show keywords ADR, Mediation, and Arbitration as central nodes, while the Chinese and North American organizations established the most frequent collaborations, addressing ADR applications in various sectors. The findings underscore the interdisciplinary nature of ADR research, its adaptability across industries, and the importance of cross-cultural research partnerships. Collapse

Topics: Python information system use accounting

Methods: action design research network analysis literature sample bibliometric analysis latent dirichlet allocation

On Technology Ecosystems: De-Jure Standards Setting by Firms

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Shams, Reza; Saraf, Nilesh; Ruckman, Karen

Abstract: We study how social structure, strategic heterogeneity, and competition impact ... Expand

Abstract: We study how social structure, strategic heterogeneity, and competition impact collaboration efforts among firms in a technological standard-setting organization. Our findings reveal that a higher level of social capital among firms in the standard committee leads to a quicker consensus over the current standard. However, more competition among committee firms and larger committees delays the time to consensus. Our study emphasizes that supporting open standard development and consensus-based decision making is a double-edged sword for SSOs. Our research has some implications for value cocreation in open innovation alliances. Collapse

Topics: open innovation decision making social capital Java participatory design

Methods: proportional hazards model statistical hypothesis test qualitative coding design methodology

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

Investigating Churn in Physical Activity Challenges: Evidence from a U.S. Online Social Network

2023 | HICSS | Citations: 0

Authors: Zhu, Y.; Mani, A.

Abstract: Physical activities have been found to be positively contagious, as active exer ... Expand

Abstract: Physical activities have been found to be positively contagious, as active exercisers tend to motivate their friends to do more exercise. However, it is not clearly understood if inactive exercising behaviors are also socially contagious. As insufficient physical activity is a huge threat to people's health, understanding the potential negative contagion in physical activities is crucial. We approach this problem by studying the effect of individuals' churn of online physical activity challenges relying on the physical activity and a large social network data from a renowned U.S. fitness platform. The underexplored online physical activity challenges provide a natural setup to measure churn and opportunities to study the contagion heterogeneities. Consistent with previous findings, we confirm that physical activity churn is socially contagious. Interestingly, unlike the insideout positive contagion, our analyses reveal that the contagion of churn happens outside-in on the social network. Implications of such findings are discussed. Collapse

Topics: social network accounting social influence

Methods: instrumental variables estimation longitudinal research causality analysis statistical hypothesis test econometric modeling

Theories: social contagion theory

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

Algorithmic Processes of Social Alertness and Social Transmission: How Bots Disseminate Information on Twitter

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Alves de Lima Salge, Carolina; Karahanna, Elena; Thatcher, Jason

Abstract: Despite increased empirical attention, theory on bots and how they act to dissem ... Expand

Abstract: Despite increased empirical attention, theory on bots and how they act to disseminate information on social media remains poorly understood. Our study leverages the conduit brokerage perspective and the findings of a multiple case study to develop a novel framework of algorithmic conduit brokerage for understanding information dissemination by bots and the design choices that may influence their actions. Algorithmic conduit brokerage encompasses two intertwined processes. The first process, algorithmic social alertness, relies on bot activity to curate and reconfigure information. Algorithmic social alertness is significant because it involves action triggers that dictate the kinds of information being searched, discovered, and retrieved by bots. The second process, algorithmic social transmission, relies on bot activity to embellish and distribute the information curated. Algorithmic social transmission is important because it can broaden the reach of information disseminated by bots through increased discoverability and directed targeting. The two algorithmic conduit brokerage processes we offer are unique to bots and distinct from the original conceptualization of conduit brokerage, which is rooted in human activity. First, since bots lack the human ability of sensemaking and are instead fueled by automation and action triggers rather than by emotions, algorithmic conduit brokerage is more invariant and reliable than human conduit brokerage. Second, automation increases the speed and scale of information curation and transfer, making algorithmic conduit brokerage not only more consistent but also faster and more extensive. Third, algorithmic conduit brokerage includes a set of new concepts (e.g., action triggers and rapid scaling) that are specific to bots and therefore not applicable to human conduit brokerage. Collapse

Topics: Twitter boundary spanning social media website social network

Methods: multiple case study computational algorithm case study theory development machine learning