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

Incorporating Multiple Knowledge Sources for Targeted Aspect-based Financial Sentiment Analysis

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

Authors: Du, Kelvin; Xing, Frank; Cambria, Erik

Abstract: Combining symbolic and subsymbolic methods has become a promising strategy as re ... Expand

Abstract: Combining symbolic and subsymbolic methods has become a promising strategy as research tasks in AI grow increasingly complicated and require higher levels of understanding. Targeted Aspect-based Financial Sentiment Analysis (TABFSA) is an example of such complicated tasks, as it involves processes like information extraction, information specification, and domain adaptation. However, little is known about the design principles of such hybrid models leveraging external lexical knowledge. To fill this gap, we define anterior, parallel, and posterior knowledge integration and propose incorporating multiple lexical knowledge sources strategically into the fine-tuning process of pre-trained transformer models for TABFSA. Experiments on the Financial Opinion mining and Question Answering challenge (FiQA) Task 1 and SemEval 2017 Task 5 datasets show that the knowledge-enabled models systematically improve upon their plain deep learning counterparts, and some outperform state-of-the-art results reported in terms of aspect sentiment analysis error. We discover that parallel knowledge integration is the most effective and domain-specific lexical knowledge is more important according to our ablation analysis. Collapse

Topics: microblog domain-specific language Twitter social media knowledge graph

Methods: sentiment analysis RoBERTa transformer model BERT XLNet

AI-augmented Business Process Management Systems: A Research Manifesto

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

Authors: Dumas, Marlon; Fournier, Fabiana; Limonad, Lior; Marrella, Andrea; Montali, Marco; Rehse, Jana-Rebecca; Accorsi, Rafael; Calvanese, Diego; De Giacomo, Giuseppe; Fahland, Dirk; Gal, Avigdor; La Rosa, Marcello; Völzer, Hagen; Weber, Ingo

Abstract: AI-augmented Business Process Management Systems (ABPMSs) are an emerging class ... Expand

Abstract: AI-augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology. An ABPMS enhances the execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for ABPMSs and discusses research challenges that need to be surmounted to realize this vision. To this end, we define the concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a set of challenges to realize systems with these characteristics. Collapse

Topics: artificial intelligence knowledge graph business process management business process management system

Methods: natural language processing machine learning modeling language business process model and notation business process modeling

The Recursive Theory of Knowledge Augmentation: Integrating human intuition and knowledge in Artificial Intelligence to augment organizational knowledge

2023 | Information Systems Frontiers | Citations: 0

Authors: Harfouche, Antoine; Quinio, Bernard; Saba, Mario; Saba, Peter Bou

Abstract: Artificial intelligence (AI) has increased the ability of organizations to accum ... Expand

Abstract: Artificial intelligence (AI) has increased the ability of organizations to accumulate tacit and explicit knowledge to inform management decision-making. Despite the hype and popularity of AI, there is a noticeable scarcity of research focusing on AI's potential role in enriching and augmenting organizational knowledge. This paper develops a recursive theory of knowledge augmentation in organizations (the KAM model) based on a synthesis of extant literature and a four-year revised canonical action research project. The project aimed to design and implement a human-centric AI (called Project) to solve the lack of integration of tacit and explicit knowledge in a scientific research center (SRC). To explore the patterns of knowledge augmentation in organizations, this study extends Nonaka's SECI (socialization, externalization, combination, and internalization) model by incorporating the human-in-the-loop Informed Artificial Intelligence (IAI) approach. The proposed design offers the possibility to integrate experts' intuition and domain knowledge in AI in an explainable way. The findings show that organizational knowledge can be augmented through a recursive process enabled by the design and implementation of human-in-the-loop IAI. The study has important implications for research and practice. Collapse

Topics: artificial intelligence organizational memory explicit knowledge human in the loop human computer interaction

Methods: computational algorithm action research qualitative interview participant observation statistical hypothesis test

Theories: SECI model of knowledge dimensions organizational knowledge creation

Metaverse Knowledge Graph Construction: An Unsupervised Relation Extraction Approach based on Semantic Mining

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Li, Litao; Xu, Ruiyun; Zhao, J. Leon

Abstract: Metaverse has gained widespread attention in both academia and industry. As a r ... Expand

Abstract: Metaverse has gained widespread attention in both academia and industry. As a result, many new concepts, methodologies, and applications are emerging in recent years. Constructing a Metaverse knowledge graph can help with knowledge management and facilitate various intelligent tasks such as recommendations, information retrieval, and chatbots in this emerging field. However, existing knowledge graph construction approaches are not directly applicable in the Metaverse domain due to a lack of labeled dataset, making existing supervised relation extraction approaches impracticable. In this study, we develop a novel graph construction framework by integrating the deep learning approach (i.e., BERT-BiLSTM-CRF) with an unsupervised relation extraction approach based on semantic mining. The framework we propose in this paper can automatically construct knowledge graphs while significantly reducing labeling efforts. Further, we plan to implement the proposed framework on a Metaverse corpus to construct a Metaverse knowledge graph. To test our approach, we will employ an expert-survey method to evaluate the quality of triple sets. Collapse

Topics: virtual reality knowledge graph mobile application blog artificial intelligence

Methods: BERT survey word2vec deep learning named entity recognition

The Future of Enterprise Information Systems

2023 | Business & Information Systems Engineering | Citations: 0

Authors: Sunyaev, Ali; Dehling, Tobias; Strahringer, Susanne; Da Xu, Li; Heinig, Martin; Perscheid, Michael; Alt, Rainer; Rossi, Matti

Abstract: EIS. The penultimate essay offers an outlook on how future EIS can allow for en ... Expand

Abstract: EIS. The penultimate essay offers an outlook on how future EIS can allow for enough heterogeneity to facilitate business logic diversification while retaining sufficient homogeneity to enable interoperability. The final essay wraps up the discussion with a call for more research articles on EIS implementation and usage in IS journals. 2 Enduring Enterprise Information Systems Decentralization Ali Sunyaev and Tobias Dehling 123 732 A. Sunyaev et al.: The Future of Enterprise Information Systems, Bus Inf Syst Eng 65 (6): 731-751 (2023) Collapse

Topics: enterprise resource planning business process management enterprise information system cloud computing innovation management

Methods: qualitative interview design artifact case study data modeling literature study

SocioLink: Leveraging Relational Information in Knowledge Graphs for Startup Recommendations

2023 | Journal of Management Information Systems | Citations: 0

Authors: Xu, Ruiyun; Chen, Hailiang; Zhao, J. Leon

Abstract: While venture capital firms are increasingly relying on recommendation models in ... Expand

Abstract: While venture capital firms are increasingly relying on recommendation models in investment decisions, existing startup recommendation models fail to consider the uniqueness of venture capital context, including two-sided matching between investing and investee firms and a lack of information disclosure requirements on startups. Following the design science research paradigm and guided by the proximity principle from social psychology, we develop a novel framework called SocioLink by depicting and analyzing various relations in a knowledge graph via machine learning. Our experimental results show that SocioLink significantly outperforms state-of-the-art startup recommendation methods in both accuracy and quality. This improvement is driven by not only the inclusion of social relations but also the superiority of modelling relations via knowledge graph. We also develop a web-based prototype to demonstrate explainable artificial intelligence. This work contributes to the FinTech literature by adding an innovative design artifact—SocioLink—for decision support in the investment context. Collapse

Topics: electronic finance recommender system collaborative filtering database system mergers and acquisition

Methods: experiment design artifact computational algorithm machine learning design science

On Left and Right: Understanding the Discourse of Presidential Election in Social Media Communities

2023 | HICSS | Citations: 0

Authors: Zhou, L.; Tao, J.; Wang, K.

Abstract: As a promising platform for political discourse, social media becomes a battleg ... Expand

Abstract: As a promising platform for political discourse, social media becomes a battleground for presidential candidates as well as their supporters and opponents. Stance detection is one of the key tasks in the understanding of political discourse. However, existing methods are dominated by supervised techniques, which require labeled data. Previous work on stance detection is largely conducted at the post or user level. Despite that some studies have considered online political communities, they either only select a few communities or assume the stance coherence of these communities. Political party extraction has rarely been addressed explicitly. To address the limitations, we developed an unsupervised learning approach to political party extraction and stance detection from social media discourse. We also analyzed and compared (sub)communities with respect to their characteristics of political stances and parties. We further explored (sub)communities' shift in political stance after the 2020 US presidential election. Collapse

Topics: social media Reddit Twitter knowledge graph online community

Methods: metamodel machine learning ensemble learning topic model chi squared test

Towards a Theory-Based Evaluation of Explainable Predictions in Healthcare

2022 | International Conference on Information Systems | Citations: 0

Authors: Ghanvatkar, Suparna; Rajan, Vaibhav

Abstract: Modern Artificial Intelligence (AI) models offer high predictive accuracy but o ... Expand

Abstract: Modern Artificial Intelligence (AI) models offer high predictive accuracy but often lack interpretability with respect to reasons for predictions. Explanations for predictions are usually necessary in making high-stakes clinical decisions. Hence, many Explainable AI (XAI) techniques have been designed to generate explanations for predictions from blackbox models. However, there are no rigorous metrics to evaluate these explanations, especially with respect to their usefulness to clinicians. We develop a principled method to evaluate explanations by drawing on theories from social science and accounting for specific requirements of the clinical context. As a case study, we use our metric to evaluate explanations generated by two popular XAI algorithms in the task of predicting the onset of Alzheimer's disease using genetic data. Our preliminary findings are promising and illustrate the versatility and utility of our metric. Our work contributes to the practical and theoretical development of XAI techniques and Clinical Decision Support Systems. Collapse

Topics: explainable artificial intelligence local interpretable model agnostic explanation knowledge graph application programming interface health information system

Methods: computational algorithm machine learning case study artificial neural network functional neuroimaging

Integrating human knowledge into artificial intelligence for complex and ill-structured problems: Informed artificial intelligence

2022 | International Journal of Information Management | Citations: 0

Authors: Johnson, Marina; Albizri, Abdullah; Harfouche, Antoine; Fosso-Wamba, Samuel

Abstract: Artificial intelligence (AI) has been gaining significant attention in various f ... Expand

Abstract: Artificial intelligence (AI) has been gaining significant attention in various fields to reduce costs, increase rev­ enue, and improve customer satisfaction. AI can be particularly beneficial in enhancing decision-making pro­ cesses for complex and ill-structured problems that lack transparency and have unclear goals. Most AI algorithms require labeled datasets to learn the problem characteristics, draw decision boundaries, and generalize. However, most datasets collected to solve complex and ill-structured problems do not have labels. Additionally, most AI algorithms are opaque and not easily interpretable, making it hard for decision-makers to obtain model insights for developing effective solution strategies. To this end, we examine existing AI paradigms, mainly symbolic AI (SAI) guided by human domain knowledge and data-driven AI (DAI) guided by data. We propose an approach called informed AI (IAI) by integrating human domain knowledge into AI to develop effective and reliable data labeling and model explainability processes. We demonstrate and validate the use of IAI by applying it to a social media dataset comprised of conversations between customers and customer support agents to construct a so­ lution – IAI defect explorer (I-AIDE). I-AIDE is utilized to identify product defects and extract the voice of cus­ tomers to help managers make decisions to improve quality and enhance customer satisfaction. Collapse

Topics: artificial intelligence social media Twitter decision making shapley additive explanation

Methods: computational algorithm support vector machine design science data transformation machine learning