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

CO-CREATING VALUE FROM ELECTRIC VEHICLE DIGITAL SERVICES: EFFECT OF PERCEIVED ENVIRONMENTAL PERFORMANCE ON PERSONAL DATA SHARING

2023 | European Conference On Information Systems | Citations: 0

Authors: Alotaibi, Awatif; Barros, Alistair; Degirmenci, Kenan

Abstract: Electric vehicles (EVs) are considered a product rather than a mobility service ... Expand

Abstract: Electric vehicles (EVs) are considered a product rather than a mobility service. The expansion of EV services requires large amounts of personal information. Due to privacy concerns, EV users may deactivate these services, leading to the discontinuance of EV services and value destruction. Service dominant logic is improving insights into the roles of resources (e.g., personal data) and institutions (e.g., concerns) in influencing individual behaviours in value co-creation (e.g., disclosing personal data). This logic is a metaperspective that needs to be integrated with mid-range theories (e.g., the enhanced antecedents-privacy concerns-outcomes) to investigate how resources, institutions and value co-creation are integrated and how to quantify this integration. This study conducted an online survey of EV users. The findings show that privacy awareness, environmental concerns and privacy concerns influence privacy calculus. However, perceived environmental performance has a stronger impact than privacy risks on intentions to continue disclosing personal data for EV services. Collapse

Topics: privacy information privacy concern participatory design environmental sustainability decision making

Methods: survey archival research survey design design methodology partial least squares path modeling

Theories: privacy calculus theory service-dominant logic

Human-machine collaboration for feature selection and integration to improve congestive Heart failure risk prediction

2023 | Decision Support Systems | Citations: 0

Authors: Ben-Assuli, Ofir; Heart, Tsipi; Klempfner, Robert; Padman, Rema

Abstract: The issue of harnessing machine learning (ML) algorithms for the prediction of a ... Expand

Abstract: The issue of harnessing machine learning (ML) algorithms for the prediction of adverse medical events is important considering the prevalence of vast repositories of patient-level medical data, and the quest to reduce healthcare costs while elevating the quality of care. Of particular interest is identifying a set of features that can better predict the risk of unplanned readmissions of chronically ill patients within 30 days post discharge, among them patients with Congestive Heart Failure (CHF). While numerous studies have employed ML algorithms to identify sets of significant features, only a handful have compared features extracted by various methods, and even fewer have compared the predictive power of feature sets selected by human experts to those selected by ML. Hence, this research aimed to elicit a comprehensive feature set selected by four ML algorithms and compare its predictive performance to that of a feature set selected by experts. We then evaluated a merged list of the two feature sets, constructing a human-machine collaborative set to predict the likelihood of 30-day unplanned readmission of CHF patients using data on 10,763 CHF patients hospitalized during the years 2010–2017. Our models achieved Area under the ROC Curve (AUC) above 0.8 and an Accuracy of ~0.89, comparable to the best models in the extant literature. Moreover, the Human-Machine Collaborative model significantly outperformed (best AUC 0.836) other human-only selected models as well as the machine-selected set. This study contributes to a better understanding of the power of using ML for patient risk stratification with special attention to the benefits of human-machine collaboration, even when these two entities work separately or in parallel, for improved clinical decision making. Collapse

Topics: healthcare data decision making big data missing data data warehouse

Methods: machine learning computational algorithm XGBoost descriptive statistic random forest classification

Analyzing Patients’ EHR: Predicting and Explaining Admission Consequences for COPD and Liver Disease Patients

2023 | International Conference on Information Systems | Citations: 0

Authors: Ben-Assuli, Ofir; Geva, Gaya

Abstract: This study analyzed the admission outcomes in chronic patients (with COPD, and ... Expand

Abstract: This study analyzed the admission outcomes in chronic patients (with COPD, and Liver disease) to demonstrate the feasibility of applying prediction methods on EHR records while incorporating an explainable AI technique. We predicted three target variables: 30-day readmission, Medium&Long Length of Stay and Single-day admission and analyzed the features using an explainable AI technique, the SHapley Additive exPlanations (SHAP). The results show that Readmission had higher prediction scores than all other dependent variables. Some features affected all target variables with either positive or negative influence including: Age, Charlson comorbidity index, Day-Shift, Gender, using EHR screens and Insurance cover level. These findings thus point to the value of using Machine-Learning combined with an explainable AI method to understand and assess the risks factors. The assessment of the potential factors leading to multiple complications can bolster prevention-oriented medical decisions to groups of patients but can also be tailored to the patient level. Collapse

Topics: shapley additive explanation explainable artificial intelligence Python IT work artificial intelligence

Methods: machine learning XGBoost logistic regression artificial neural network classification method

Mechanical ventilation settings advisory system Odyn

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Grąbczewski, Krzysztof; Adamczak, Rafał; Grochowski, Marek; Joachimiak, Michal; Sokolov, Oleksandr; Kołodziejczak, Michalina; Kowalski, Piotr; Sierakowska, Katarzyna

Abstract: COVID-19 pandemic impaired function of healthcare systems globally by increased ... Expand

Abstract: COVID-19 pandemic impaired function of healthcare systems globally by increased workload at intensive care units in hospitals that underwent a massive influx of patients in critical condition in need of tailored and specialized care. Prevalent respiratory failure increased the demand for ventilators and respiratory therapists. Due to personnel shortages, doctors and nurses, not trained in the field of intensive care, were expected to provide adequate care for patients, supporting themselves with help of consulting specialists and navigating equipment shortages. To deal with this problem we present the research result, conducted in cooperation between computer scientists and anesthesiologists, namely the advisory system that supports physicians who do not have expertise in operating ventilators for typical COVID-19 patients. The system has been implemented at two hospitals. We present the system architecture and some internals of its recommendation modules based on decision graphs and recurrent neural networks. Further research will embrace analysis and optimization of the modules to provide yet more accurate support, as early as possible and in a broader spectrum of patient's conditions. Collapse

Topics: healthcare data application programming interface artificial intelligence systems development enterprise architecture

Methods: time series analysis longitudinal research artificial neural network computational algorithm data imputation

When Do Firms Crack Under Pressure? Legal Professionals, Negative Role Models, and Organizational Misconduct

2023 | Organization Science | Citations: 0

Authors: Gonsalves, Leroy

Abstract: Strain theory has long been invoked to explain organizational misconduct, with u ... Expand

Abstract: Strain theory has long been invoked to explain organizational misconduct, with underperformance creating pressure for firms to engage in morally objectionable activities. In this paper, I examine whether underperformance increases the risk of organizational misconduct. Drawing on institutional arguments about professions and social learning, I further predict that when experiencing performance strain, legal professionals will push the boundaries of the law, increasing the risk of misconduct if they have influence over decision making. However, industry peers caught engaging in misconduct should serve as negative role models, reducing the risk of the firm resorting to misconduct to overcome performance shortfalls. I test and find support for these predictions using longitudinal data on material legal claims filed against S&P 1500 firms between 2000 and 2017. The study extends the strain theory of organizational misconduct, identifying how legal professionals and negative role models shape firms’ strategic responses to performance pressure. Collapse

Topics: social media Slack decision making database system missing data

Methods: longitudinal research descriptive statistic robustness check SEC EDGAR Compustat

Theories: general strain theory

Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing

2023 | Information Systems Frontiers | Citations: 1

Authors: Johnson, Marina; Albizri, Abdullah; Harfouche, Antoine

Abstract: It is estimated that one out of seven health insurance claims is rejected in the ... Expand

Abstract: It is estimated that one out of seven health insurance claims is rejected in the US; hospitals across the country lose approximately $262 billion annually due to denied claims. This widespread problem causes huge cash-flow issues and overburdens patients. Thus, preventing claim denials before claims are submitted to insurers improves profitability, accelerates the revenue cycle, and supports patients’ wellbeing. This study utilizes Design Science Research (DSR) paradigm and develops a Responsible Artificial Intelligence (RAI) solution helping hospital administrators identify potentially denied claims. Guided by five principles, this framework utilizes six AI algorithms – classified as white-box and glass-box – and employs cross-validation to tune hyperparameters and determine the best model. The results show that a white-box algorithm (AdaBoost) model yields an AUC rate of 0.83, outperforming all other models. This research’s primary implications are to (1) help providers reduce operational costs and increase the efficiency of insurance claim processes (2) help patients focus on their recovery instead of dealing with appealing claims. Collapse

Topics: artificial intelligence ADA missing data partial dependence plot responsible artificial intelligence

Methods: machine learning computational algorithm experiment support vector machine design artifact

Tacit knowledge acquisition & sharing, and its influence on innovations: A Polish/US cross-country study

2023 | International Journal of Information Management | Citations: 0

Authors: Kucharska, Wioleta; Erickson, G. Scott

Abstract: This study measures the relationship between tacit knowledge sharing and innovat ... Expand

Abstract: This study measures the relationship between tacit knowledge sharing and innovation in the Polish (n = 350) and US (n = 379) IT industries. Conceptually, the study identifies the potential sources of tacit knowledge devel­ opment by individuals. That is, the study examines how “learning by doing” and “learning by interaction” lead to a willingness to share knowledge and, as a consequence, to support process and product/service innovation. This study empirically demonstrates that tacit knowledge internalization and externalization (awareness and sharing) significantly mediate between tacit knowledge experimentation and socialization (acquisition) and its final combination (knowledge in action). While such theoretical assumptions already exist, they have not yet been empirically explained and revealed in a single structural model. Further, this empirical approach enabled a demonstration that internalization and externalization of tacit knowledge may occur consciously or uncon­ sciously with equal success. Even so, the study also showed conscious tacit knowledge’s greater impact on innovation. Therefore, an organizational effort to manage autonomous, informal, and strongly contextual tacit knowledge is worthwhile and creates the capacity for superior competitive advantage. Finally, this study also demonstrates that national context influences tacit knowledge acquisition. In the US, “learning by doing” is dominant, whereas in Poland, “learning by interaction” and critical thinking are more common. This might be related to factors such as risk acceptance that could be studied in more detail and provide opportunities for future research. Collapse

Topics: innovation management implicit knowledge knowledge acquisition business process innovation knowledge sharing

Methods: survey data imputation descriptive statistic SEM tool conceptual modelling

Handling Missing Values in Information Systems Research: A Review of Methods and Assumptions

2023 | Information Systems Research | Citations: 0

Authors: Peng, Jiaxu; Hahn, Jungpil; Huang, Ke-Wei

Abstract: In today’s big data environment, missing values continue to be a problem that ha ... Expand

Abstract: In today’s big data environment, missing values continue to be a problem that harms data quality. The bias caused by missing values raises the highest concern, as it cannot be eliminated simply by increasing the sample size. Although the statistics literature has developed approaches to handling missing values and formulated assumptions regarding when these approaches generate valid statistical inferences, these prescriptions have yet to be broadly accepted by many social science disciplines, including the information systems (IS) discipline. By reviewing recently published empirical research in information systems, we find that missing values are indeed an important and pervasive problem. We believe that a review of missing value theory is necessary and timely for the IS community to understand the nature of missing values and to promote more rigorous research practice when missing values are often unavoidable. In addition, the missing not at random (MNAR) mechanism brings about challenges in parameter estimation. We contribute to research practice by proposing and demonstrating the superior performance of a Monte Carlo likelihood approach in correcting bias in parameter estimation. We conclude by suggesting that research validity can be enhanced through a reasoned adoption of the missing value handling method and structured missing value reporting practices.History: Giri Kumar Tayi, Senior Editor; Pei-Yu Chen, Associate Editor.Funding: This work was supported by the Singapore Ministry of Education [Grants R-253-000-136-114, R-253-000-149-490, and R-253-000-158-114] the National Natural Science Foundation of China [Approval No. 72201288], and the Program for Innovation Research in Central University of Finance and Economics. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of the Singapore Ministry of Education, the Singapore Government or the Central University of Finance and Economics.Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1104. Collapse

Topics: missing data research and development electronic commerce logistics management mindfulness

Methods: simulation regression analysis method robustness check literature sample data imputation

Virtual integration in SMEs: The digitalization circuitry of dynamic strategic planning for SMEs

2023 | International Journal of Information Management | Citations: 0

Authors: Ojha, Divesh; Patel, Pankaj C.; Parida, Vinit

Abstract: Though digital technology adoption among SMEs is widely studied, a manifestation ... Expand

Abstract: Though digital technology adoption among SMEs is widely studied, a manifestation of digitalization – virtual integration, particularly its strategic benefits for SMEs – remains largely understudied. Virtual integration con­ nects and coalesces the interfaces of digitalization in SMEs. Though the need for externally oriented digitalization in SMEs is widely desired and espoused, virtual integration and the ensuing effect on Dynamic Strategic Planning (DSP) could be key factors in realizing higher performance. Using the information theory lens and knowledgebased view and a two-study empirical setup based on data from manufacturing and services firms, we draw on the Competence–Capability–Performance (CCP) framework. Specifically, we test a model of digitalization circuitry operational through the mediating effects of virtual integration and dynamic strategic planning (DSP). The ensuing interweaving effects of digitalization from virtual integration are manifest in DSP through fluid partnering, business continuity planning, and market acuity. We find that digitalization, manifested in the form of virtual integration, cascades through DSP to improve financial performance. Collapse

Topics: business continuity planning financial performance strategic management virtualization technology supply chain management

Methods: SEM tool theory development structural equation modeling survey statistical hypothesis test

Theories: dynamic capabilities theory knowledge based theory of the firm information theory