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

How social media live streams affect online buyers: A uses and gratifications perspective

2023 | International Journal of Information Management | Citations: 2

Authors: Bawack, Ransome Epie; Bonhoure, Emilie; Kamdjoug, Jean-Robert Kala; Giannakis, Mihalis

Abstract: Social media live streams (SMLSs) are becoming mainstream e-commerce platforms, ... Expand

Abstract: Social media live streams (SMLSs) are becoming mainstream e-commerce platforms, especially for direct selling, offering online retailers (e-tailers) immense consumer interaction and engagement levels. Despite their growing prevalence, e-tailers face difficulties capitalising on this platform to enhance retail operations. This paper ex­ plores how SMLSs affect consumers’ purchase intentions by exploring their motivations for participating in ecommerce SMLSs. To this end, we used the uses and gratifications (U&G) theory to explain how using SMLSs to satisfy consumers’ needs could lead to high or low purchase intentions. It was done by collecting cross-sectional data from 795 consumers who have participated in e-commerce SMLSs. The data were analysed using partial least squares-structural equation modelling (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA). Our findings revealed that people use SMLSs to satisfy their need for value, social identification, and the me­ dium’s attributes. Based on these findings, we show six configurations that lead to high or low purchase in­ tentions and make corresponding theoretical propositions. Thus, our study contributes to the research and practice of using SMLSs for online shopping. Collapse

Topics: electronic commerce price management social media social network information exchange

Methods: survey partial least squares path modeling structural equation modeling partial least squares regression cross sectional research

AI-Enhanced Hybrid Decision Management

2023 | Business & Information Systems Engineering | Citations: 0

Authors: Bork, Dominik; Ali, Syed Juned; Dinev, Georgi Milenov

Abstract: The Decision Model and Notation (DMN) modeling language allows the precise speci ... Expand

Abstract: The Decision Model and Notation (DMN) modeling language allows the precise specification of business decisions and business rules. DMN is readily understandable by business users involved in decision management. However, as the models get complex, the cognitive abilities of humans threaten manual maintainability and comprehensibility. Proper design of the decision logic thus requires comprehensive automated analysis of e.g., all possible cases the decision shall cover; correlations between inputs and outputs; and the importance of inputs for deriving the output. In the paper, the authors explore the mutual benefits of combining human-driven DMN decision modeling with the computational power of Artificial Intelligence for DMN model analysis and improved comprehension. The authors propose a model-driven approach that uses DMN models to generate Machine Learning (ML) training data and show, how the trained ML models can inform human decision modelers by means of superimposing the feature importance within the original DMN models. An evaluation with multiple real DMN models from an insurance company evaluates the feasibility and the utility of the approach. Collapse

Topics: artificial intelligence explainable artificial intelligence perceived usefulness price management extensible markup language

Methods: computational algorithm decision tree classification experiment conceptual modelling simulation

A temporal graph framework for intelligence extraction in social media networks

2023 | Information & Management | Citations: 0

Authors: Chung, Wingyan; Lai, Vincent S.

Abstract: Social media networks (SMNs) are increasingly used in professional management of ... Expand

Abstract: Social media networks (SMNs) are increasingly used in professional management of knowledge workers and related assets. However, the factors affecting behavioral trends and activity levels in these networks are not well understood. Although social and cognitive theories can help to explain human behavior in traditional social networks, their application to SMNs has not been validated. Traditional social network modeling techniques may not accurately predict real-world SMN activities. This research developed a temporal graph framework for in­ telligence extraction in SMNs. Theory-based, data-driven models (Conformity Model (COM), Recency-Primacy Model (REM), Trend Interaction Model (TIM), Periodic Interaction Model (PIM)) were developed based on the framework to capture various aspects of user behavior: conformity effect, recency, primacy, periodicity, and dynamic trend. The models capture the activity history and dynamically combine pricing information to enhance predictive accuracy. Using data of 83,536 GitHub software repositories on cryptocurrency, this article reports the results of experiments that compare the models’ performance in predicting SMN activities over time. Experi­ mental results show that the model (REM) that captures recency/primacy effects of human cognitive processing outperformed other models in 9 (out of 18) measures pertaining to engagement, contribution, influence, and popularity. Primacy plays a dominant role in predicting engagement, contribution, and popularity, whereas recency plays a key role in predicting influence. Short-term trend (modeled with TIM) was found to yield significantly better performance on predicting user contribution. The models also outperformed an integrated machine learning (IML) model by most measures. Overall, the effects modeled by REM and TIM were found to be more significant than the effects modeled by COM, PIM, and IML. The research contributes to enhancing un­ derstanding of SMN behavior, developing new models to simulate and predict SMN activities, and designing new artifacts for information systems practitioners to manage knowledge assets and to extract SMN intelligence. Collapse

Topics: cryptocurrency social network systems development situation awareness user behavior

Methods: experiment machine learning longitudinal research computational algorithm time series analysis

Theories: psychological theory social contagion theory social interactionist theory social impact theory social sciences theory

The double-edged effects of data privacy practices on customer responses

2023 | International Journal of Information Management | Citations: 0

Authors: Chen, Shijiao (Joseph); Tran, Khai Trieu; Xia, Zhenhua (Raymond); Waseem, Donia; Zhang, Jing A.; Potdar, Balkrushna

Abstract: Requesting personal information in frontline service encounters raises privacy c ... Expand

Abstract: Requesting personal information in frontline service encounters raises privacy concerns among customers. The proximity contact tracing that occurred during the COVID-19 pandemic provides an intriguing context of in­ formation requests. Hospitality venues required contact tracing details from customers, and customer coopera­ tion varied with concerns about privacy. Drawing on gossip theory, we investigate the roles of businesses’ data privacy practices and government support in driving customers’ responses to contact tracing. Our findings show that perceived transparency of a business’s privacy practices has a positive effect on customers’ commitment to the business, while perceived control exerts a negative effect on commitment. These effects are mediated by customers’ information falsification rather than disclosure, because the former is a sensitive behavioral indicator of privacy concerns. The results also reveal the moderating roles of government support. This research con­ tributes to the customer data privacy literature by demonstrating the distinct effects of perceived transparency and control on commitment and revealing the underlying mechanism. Moreover, the research extends the conceptual understanding of privacy practices from online contexts to face-to-face contexts of frontline service. The findings offer implications for the management of customer data privacy. Collapse

Topics: privacy information privacy concern pandemic unintended consequence behavioral intention

Methods: qualitative interview survey conceptual modelling statistical hypothesis test structural equation modeling

Accounting for Uncertainty in Deceptive Signaling for Cybersecurity

2023 | HICSS | Citations: 0

Authors: Cranford, E.; Ou, H.; Gonzalez, C.; Tambe, M.; Lebiere, C.

Abstract: Deceptive signaling has proven an effective method that can aid security analys ... Expand

Abstract: Deceptive signaling has proven an effective method that can aid security analysists and deter attacks on unprotected targets by strategically revealing information to an attacker. However, recent research has shown that uncertainty in real-time information processing can have a negative impact on the effectiveness of the defense algorithm. The current research developed a new algorithm, dubbed Confusion Signaling, that aims to account for uncertainty in an abstracted insider attack scenario. The results of cognitive model simulations and a human behavioral experiment reveal interesting and unexpected reactions under uncertainty. We discuss the implications of these findings for signaling algorithms that aim to account for uncertainty using deceptive signaling for cybersecurity. Collapse

Topics: IT security decision making accounting physical security

Methods: computational algorithm experiment simulation machine learning survey

Corporate Users’ Attachment to Social Networking Sites: Examining the Role of Social Capital and Perceived Benefits

2023 | Information Systems Frontiers | Citations: 0

Authors: Dhar, Suparna; Bose, Indranil

Abstract: The debate on the pros and cons of employee attachment to social networking site ... Expand

Abstract: The debate on the pros and cons of employee attachment to social networking sites (SNS) has led to social media policy paralysis in many organizations, and often a prohibition on employee use of SNS. This paper examines corporate users’ attachment to SNS. An analysis of 316 survey responses showed that corporate users’ socialization in large public SNS was steeped in perceived work-related benefits, which in turn nourished their SNS attachment. Social use outperformed informational use in generating perceived work-related benefits from SNS. Weak ties in large heterogeneous networks resulted in strategic and operational benefits, whereas the effects of strong bonding in homogenous networks were limited to operational benefits. The paper contributes to research on SNS use by corporate users and the debate on the effect of SNS use for work. The findings will benefit SNS strategists of organizations and policymakers to exploit the benefit potential of public SNS. Collapse

Topics: social capital Facebook LinkedIn information system use accounting

Methods: survey data transformation principal component analysis path analysis structural equation modeling

Theories: uses and gratifications theory social capital theory attachment theory

Factors that Affect Consumer-to-Consumer Sales: An Inductive Predictive Analytics Approach

2023 | Americas Conference on Information Systems | Citations: 0

Authors: El Arabaty, Ashraf; Okoli, Chitu

Abstract: This study investigates factors affecting consumer-to-consumer (C2C) sales perf ... Expand

Abstract: This study investigates factors affecting consumer-to-consumer (C2C) sales performance, specifically in the context of second-hand luxury goods. Using a large dataset of users from a French C2C luxury fashion website, the research identifies factors influencing C2C sales beyond current knowledge. Findings show that the number of followers a seller has, as well as the number of products a seller wishes for and likes, are associated with increased sales. Additionally, the study uncovers unexpected results: sellers listing many products also buy many products, and the number of users a seller follows is associated with increased sales. These findings contribute to a better understanding of the unique characteristics of C2C e-commerce. The article provides practical implications for sellers and C2C platforms to improve their sales performance, catering to the dual-role nature of users in C2C transactions. Collapse

Topics: electronic commerce website explainable artificial intelligence social capital

Methods: machine learning linear regression analysis literature study random forest classification decision tree classification

A Task-Technology-Identity Fit Model of Smartwatch Utilisation and User Satisfaction: A Hybrid SEM-Neural Network Approach

2023 | Information Systems Frontiers | Citations: 0

Authors: El-Masri, Mazen; Al-Yafi, Karim; Kamal, Muhammad Mustafa

Abstract: Smartwatches are wearable devices intended to be smartphone companions that capt ... Expand

Abstract: Smartwatches are wearable devices intended to be smartphone companions that capture health data and ease access to notifications. They have also become personalisable standing as a fashion statement. This combination resulted in staggering adoption rates recently leading to question whether smartwatch users’ choice and use satisfaction emerge from utility features or from its fashion characteristics. This paper proposes and validates a fit theory to investigate the antecedents of adopters’ satisfaction. Besides evaluating fit with identity, the model assesses both perceived and actual task-technology fit of smartwatches. A questionnaire-based quantitative approach is used to collect data from about 300 smartwatch users in Qatar. To test the proposed model, data is analysed using structural equation modeling (SEM) and artificial neural networks (ANN). Furthermore, ANN sensitivity analysis ranks the importance of the fit factors affecting users’ choice during pre- and post-adoption stages. Both task-technology and technology-identity fit factors are quasi-equally important in explaining 62% of satisfaction variance. ANN analysis revealed that post-adoption satisfaction is primarily attributed to smartwatches’ ability to fit with users’ identity and secondarily to its perceived fit with tasks. Nevertheless, pre-adoption choice of smartwatches is mainly guided by their functionality. This paper is the first to propose and validate an integrated task-technology-identity fit model to explain smartwatch utilization and users’ satisfaction. The originality also lies in assessing actual task-technology fit and as perceived by users. Employing two modes of analysis revealed extra insights too. Collapse

Topics: smart watch mobile system user satisfaction personality smart wearable device

Methods: structural equation modeling data transformation survey factor analysis principal component analysis

Theories: theory of task technology fit unified theory of acceptance and use of technology technology acceptance model

Near-Infrared Spectroscopy for Bladder Monitoring: A Machine Learning Approach

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

Authors: Fechner, Pascal; König, Fabian; Kratsch, Wolfgang; Lockl, Jannik; Röglinger, Maximilian

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of th ... Expand

Abstract: Patients living with neurogenic bladder dysfunction can lose the sensation of their bladder filling. To avoid over-distension of the urinary bladder and prevent long-term damage to the urinary tract, the gold standard treatment is clean intermittent catheterization at predefined time intervals. However, the emptying schedule does not consider actual bladder volume, meaning that catheterization is performed more often than necessary, which can lead to complications such as urinary tract infections. Time-consuming catheterization also interferes with patients' daily routines and, in the case of an empty bladder, uses human and material resources unnecessarily. To enable individually tailored and volume-responsive bladder management, we design a model for the continuous monitoring of bladder volume. During our design science research process, we evaluate the model's applicability and usefulness through interviews with affected patients, prototyping, and application to a real-world in vivo dataset. The developed prototype predicts bladder volume based on relevant sensor data (i.e., near-infrared spectroscopy and acceleration) and the time elapsed since the previous micturition. Our comparison of several supervised state-of-the-art machine and deep learning models reveals that a long short-term memory network architecture achieves a mean absolute error of 116.7 ml that can improve bladder management for patients. Collapse

Topics: computer hardware open source mobile application user interface programming language

Methods: design artifact qualitative interview design process support vector machine computational algorithm