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

Equity Crowdfunding and Access to Capital for User Entrepreneurs: Evidence from a Randomized Field Experiment

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Bapna, Sofia; Ganco, Martin

Abstract: We examine whether equity crowdfunding democratizes access to funding for nontr ... Expand

Abstract: We examine whether equity crowdfunding democratizes access to funding for nontraditional user entrepreneurs. User entrepreneurs start by creating a product to serve their own unmet needs with no expectations of monetary profit, then later decide to commercialize the product through entrepreneurship. In contrast, traditional (producer) entrepreneurs take a more profit-driven path to entrepreneurship and start by identifying an opportunity that has commercial potential. Through a randomized field experiment, we randomly reveal to some investors that a firm producing a product used by musicians is founded by a musician and conceal this founder-related information from other investors. Revealing the information suggests that the firm is a user innovator firm and concealing it suggests that the firm is a traditional producer firm. We find that investors are significantly more interested in the traditional producer firm. Through an additional field experiment, we identify that the bias against user entrepreneurs is statistical (based on a response to limited information) rather than taste based (based on an idiosyncratic dislike). Our findings suggest that user entrepreneurs can mitigate investor bias by displaying signals of quality such as those regarding firm growth and broad product appeal. Collapse

Topics: electronic mail crowdfunding crowdsourcing logistics management entrepreneurship

Methods: experiment survey expert panel regression analysis method field experiment

Great Divisions: The Evolution of Polarization During the Man-made Emergency of January 6, 2021

2023 | HICSS | Citations: 0

Authors: Bhatt, P.; Vemprala, N.; Valecha, R.; Hariharan, G.; Rao, H.

Abstract: Polarization, which refers to the formation of opposing groups based on peoples ... Expand

Abstract: Polarization, which refers to the formation of opposing groups based on peoples' beliefs and opinions, is a phenomenon that has existed for eons. However, in the recent past, social media has caused a quantum change in the dynamics of polarization since beliefs may change almost instantaneously on social media because of unfolding events. We investigate social media communication that has resulted in polarized opinions among individuals prior to, during, and after a crisis event; the January 6th US Capitol riots. Analyses of the dominant narratives on Twitter surrounding the incident reveal a high level of polarization throughout the unfolding of the event, with increased polarization possibly attributable to the onset of the crisis. We also observe the evolution of the phenomenon: as the event unfolds, polarization changes. We suggest that understanding the evolution of polarization is important for timely crisis resolution by crisis managers. We utilize three measures, spread, distinctness and regionalization, to examine polarization during a crisis. Based on these measures our findings indicate that social media discussions exhibit an increase in the negative sentiment during the crisis as compared to precrisis, but there is a rebound in terms of positive sentiments emerging in the post crisis period. Also, the results signal reduced diversity in discussion topics during the crisis as compared to pre and post-crisis. Collapse

Topics: social media Twitter terrorist incident artificial intelligence application programming interface

Methods: topic model longitudinal research qualitative content analysis longitudinal survey cluster analysis

AI Algorithms and Time Experience in Social Media: Explaining Discontinued Use

2023 | International Conference on Information Systems | Citations: 0

Authors: Bello Rinaudo, Nadia; Matook, Sabine; Dennis, Alan

Abstract: Social media (SM) relies on AI algorithms to decide which information to displa ... Expand

Abstract: Social media (SM) relies on AI algorithms to decide which information to display and in which order. The algorithms are invisible, fast, and adaptive when creating tailored SM experiences. Yet, each SM user has a different experience of what is real and true in one moment of time. This phenomenon is known in the literature as time experience. It captures how individuals subjectively perceive and live through the passage of time. In this study, we propose a research model to theorize AI capabilities as positively impacting time experience in SM, and discontinued use of SM. Drawing on the AI literature and the theory of time, we detail the effect of three AI algorithm capabilities (i.e., invisibility, velocity, and adaptability) on the subjective experiences of time, captured by the dimensions of time orientation, time flow, and time enjoyment. We conclude with a proposed methodology and outline potential contributions. Collapse

Topics: artificial intelligence marketing management Facebook systems development

Methods: computational algorithm survey design survey structural equation modeling longitudinal research

Exploring Teams' Temporal Factors for Determining Process Tailoring that Promotes the Evolution of Agilebased Software Development

2023 | ACM SIGMIS Database | Citations: 0

Authors: Chen, Chung-Yang; Lee, Jung-Chieh

Abstract: In contemporary software development, software process tailoring (SPT) refers to ... Expand

Abstract: In contemporary software development, software process tailoring (SPT) refers to the continual planning and adjustment of the development process, which evolves to better accommodate dynamics and meet customer needs. SPT is a critical teamwork involving the differences in time-related perceptions among team members when tailoring to adjust and refine the development. This is particularly true in agile-based projects that promote iterative development over shorter cycle times for early release; thus, temporality is critical to efficiently and effectively determine a process solution for the next iteration of time-bound development. In this regard, this study theorizes a temporal model to explore how teams' temporal leadership, temporal reflexivity and shared temporal cognitions (STC) collectively determine SPT performance in agile environments. Based on an empirical examination, we suggest facilitating the role of temporal leadership in agile development, as the results demonstrate that it not only directly increases SPT performance but also stimulates both temporal reflexivity and STC in their collateral influences on effectual development planning. Notably, the results also show that team reflexivity does not exert the same favorable effect in SPT as in the distributed team environment. Theoretical implications are considered, and practical applications of the findings are provided. Collapse

Topics: agile software development virtual teamwork co-located work systems development collaborative work

Methods: survey partial least squares regression design methodology theory development longitudinal research

Trust in Online Ride-Sharing Transactions: Impacts of Heterogeneous Order Features

2023 | Journal of Management Information Systems | Citations: 0

Authors: Cheng, Xusen; Fu, Shixuan; Sun, Jianshan; Zuo, Meiyun; Meng, Xiangsong

Abstract: With the development of the sharing economy, online ride-sharing has become a pr ... Expand

Abstract: With the development of the sharing economy, online ride-sharing has become a primary form of commuting. Using secondary transaction data, this study investigates the associations between the heterogeneous features and mutual trust in sharing economy-driven online ride-sharing transactions. Based on an examination of 12,404 ride-sharing orders in Beijing, we propose a set of trust distribution maps using order location data to reveal heterogeneous spatial patterns of the relationship between online ride-sharing transactions and mutual trust. The results show that the historical order completion rate and order distance are positively associated with mutual trust in ride-sharing transactions, whereas order time and departure density negatively and significantly influence mutual trust. Furthermore, we use machine learning algorithms to predict trust. The implications for theory and practice and future research directions are discussed. Collapse

Topics: ride sharing platform sharing platform logistics management geographic information system online review

Methods: machine learning computational algorithm regression analysis method experiment exploratory data analysis

Theories: signaling theory

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

Who’s Watching TV?

2023 | Information Systems Research | Citations: 0

Authors: Clark, Jessica; Paiement, Jean-François; Provost, Foster

Abstract: This work addresses the problem of “user disambiguation”—estimating the likeliho ... Expand

Abstract: This work addresses the problem of “user disambiguation”—estimating the likelihood of each member of a small group using a shared account or device. The main focus is on television set-top box (STB) viewership data in multiperson households, in which it is impossible to tell with certainty which household members watch what. The first main contribution is formulating user disambiguation as a predictive problem. The second contribution is a solution for estimating the likelihood that each individual in a multiperson household watches each TV segment. Kernel theories from the marketing, economics, and sociology literature inform the design of our method. This method learns priors for viewership in single-person households and then adapts them to the specifics of each multiperson household’s viewership history. Finally, we formalize two ad hoc heuristics that are currently used in industry (and research) for estimating audience composition of STB data and conduct a comparative analysis using simulated data, real large-scale viewership data, and a fully labeled panel-based data source. We find that our method has superior performance and practical value. The proposed solution has implications for advertisers, researchers who seek better understanding of TV viewership, and anyone using data generated by shared devices or accounts. A major TV provider has deployed this new method for use in its TV ad-targeting system. No personally identifiable information was gathered or used in conducting this study. To the extent any data were analyzed, it was anonymous and/or aggregated data, consistent with the carrier’s privacy policy. History: Eric Zheng, Senior Editor. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1204. Collapse

Topics: marketing management logistics management advertising management internet technology

Methods: machine learning experiment simulation longitudinal research descriptive statistic

Theories: social sciences theory economic theory

Understanding Employee Perspectives During an Acquisition – Case of Twitter

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Dwivedi, Rahul

Abstract: Employee reviews offer valuable insights into the organizational culture of the ... Expand

Abstract: Employee reviews offer valuable insights into the organizational culture of the employer. Two important facets of such reviews are the topics addressed by the employees and the underlying sentiments. For this research, one year of employee reviews for Twitter was collected from Glassdoor. The goal is to extract the topics and underlying employee sentiments across three time periods (before the acquisition, after the acquisition announcement, and after the acquisition) in view of the recent privatization. Based on the findings from topic models, variations in topics discussed by the employees were found across different time periods. Statistically significant differences were also found in the sentiments of Twitter employees across the three periods as expressed in the reviews. The study can help business organizations to understand the employee's perspectives towards them during a drastic contextual event such as acquisition, thus helping them to identify areas of improvement. Collapse

Topics: Twitter website mergers and acquisition human resource management pandemic

Methods: product review sentiment analysis longitudinal survey longitudinal research two sample t-test with equal variance

The importance of theory at the Information Systems Journal

2023 | Information Systems Journal | Citations: 0

Authors: Díaz Andrade, Antonio; Tarafdar, Monideepa; Davison, Robert M.; Hardin, Andrew; Techatassanasoontorn, Angsana A.; Lowry, Paul Benjamin; Chatterjee, Sutirtha; Schwabe, Gerhard

Abstract: Theory is a crucial aspect of the information systems (IS) discipline. Authors ... Expand

Abstract: Theory is a crucial aspect of the information systems (IS) discipline. Authors draw from articles on how to develop theory and from the theories themselves to anchor knowledge contributions. Editors and reviewers expect to see novel theoretical insights in conjunction with empirical rigour and sophistication (cf. Hardin et al., 2022). The thinking of PhD students is shaped by discussions on the importance of theory through formal coursework and research seminars, as well as socialisation with peers, supervisors and senior scholars in the field. Journals often solicit submissions to special issues that champion particular kinds of theory or theories on specific topics, for example indigenous theory (Davison, 2021). Advice is given to authors in different ways that they can theorise (Hassan et al., 2022;Hong et al., 2014;Sandberg & Alvesson, 2021;Weick, 1989). The peer review process emphasises the importance of theory and tends to reject research articles that lack substantial theoretical contribution.However, assessing theoretical contributions is often a challenging task. IS scholars research a variety of topics with a pluralistic set of methods and epistemological approaches , which have several implications for our engagement with theory. Traditionally, reference disciplines have informed the diversity of topics IS scholars investigate. The IS field is at a point in its disciplinary evolution where we are seeing an even greater ambit of the application and use of IS, which fosters new topics being investigated from different epistemological and methodological viewpoints as well as new types of contributions . Consequently, IS theories take on different roles for different types of epistemologies and methods, and not understanding or respecting these differences can lead to unreasonable or unbalanced evaluation of papers.In addition to the diversity of theoretical approaches, we also perceive differences in the nature of engagement with theory. For example, papers that analyse large amounts of secondary data (textual and numerical, structured and unstructured) often focus on complex empirical techniques to analyse such datasets, often engaging minimally with theory (Miranda et al., 2022). We believe that sophisticated data analysis does not relieve IS researchers from the obligation to make a theoretical contribution. In this context, we believe, that we should take heed of the advice by Gurbaxani and Mendelson (1994) who warned, almost 30 years ago, about 'the risks of ignoring the guidance of theory' and recommended that IS researchers refrain from tinkering with 'atheoretical "black box" extrapolation techniques ' (p. 180).In an earlier editorial in this journal, Davison and Tarafdar (2018) noted how baselines for what is an acceptable contribution in a discipline shift over time. However, it is our view that a robust theoretical contribution should be (and is) a consistent expectation, even if the nature of the theoretical contribution varies. Journals play a key role in establishing baselines and in that spirit, recent and emerging intellectual trends in IS and other disciplines have implications for how we apply and develop theory in IS and point to an evolving and multi-focused role of theory in IS research. Therefore, in this editorial, we revisit and explicate why theory is important at the Information Systems Journal (ISJ) in these emerging scenarios. Seven of the ISJ's regular senior editors ( Collapse

Topics: crowdsourcing information system use digital platform usability user interface

Methods: case study experiment longitudinal survey longitudinal research literature sample

Theories: theory of affordance information systems theory