Conversation Analytics: Can Machines Read between the Lines in Real-Time Strategic Conversations?

2023 | HICSS | Citations: 0

Authors: Chen, Y.; Rui, H.; Whinston, A.

Abstract: Strategic conversations involve one party with an informational advantage and t ... Expand

Abstract: Strategic conversations involve one party with an informational advantage and the other with an interest in the information. This paper proposes machinelearning based measures to quantify the degrees of evasiveness and incoherence of the informed party during real-time strategic conversations. The specific empirical context is the questions and answers (Q&A) part of earnings conference calls during which managers endure high pressure as they face analysts' scrutinizing questions. Being reluctant to disclose adverse information, managers may resort to evasive answers and sometimes respond less coherently due to increased cognitive load. Using data from the earnings calls of the S&P 500 companies from 2006 to 2018, we show that the proposed measures predict worse nextquarter earnings. Moreover, the stock market perceives incoherence as a negative signal. This paper contributes methodologically by developing two novel machinepowered measures to automatically evaluate behavioral cues during real-time strategic conversations. The proposed analytical tools are particularly beneficial to resource-constrained and informationally disadvantaged parties such as retail investors who may not be able to effectively trade on signals buried deep in unstructured conversational data. Collapse

Topics: database system artificial intelligence electronic finance missing data accounting

Methods: computational algorithm BERT deep learning machine learning next sentence prediction

Theories: theory of economic growth

Utilizing the omnipresent: Incorporating digital documents into predictive process monitoring using deep neural networks

2023 | Decision Support Systems | Citations: 0

Authors: Levich, Sergej; Lutz, Bernhard; Neumann, Dirk

Abstract: Predictive process monitoring (PPM) allows companies to improve the efficiency o ... Expand

Abstract: Predictive process monitoring (PPM) allows companies to improve the efficiency of their business processes by predicting aspects such as the process outcome, the next event, or the time until the next event. So far, existing studies have mainly focused on developing novel predictive models while using features solely from event logs. In this study, we aim to go beyond log data and increase the focus of PPM research towards external context information. To this end, we consider digital documents as they are omnipresent in many business processes and their inclusion can often be justified by a business rationale. However, incorporating digital documents into PPM models poses considerable challenges as they present unstructured data that can contain visual and textual cues of future process behavior, while manual feature extraction is generally not feasible. Therefore, we propose an approach that processes digital documents based on automated visual and textual feature extraction methods. Furthermore, we design a tailored integration module which transforms the extracted features from multiple document pages into a fixed-size representation that subsequently serves as input for the predictive models. Our evaluation, based on a real-world dataset of insurance claims from a mid-sized German insurance company, featuring 5131 process instances with 32,058 events and 39,242 document pages, shows that incorporating digital documents improves the performance by significant margins in predicting the damage type, the next event, and the time until the next event. Finally, we analyze how digital documents contribute to the model’s predictions in terms of Shapley additive explanations. Collapse

Topics: shapley additive explanation business process management artificial intelligence explainable artificial intelligence

Methods: BERT machine learning descriptive statistic artificial neural network business process modeling

Fake Review Detection - The Value of Domain-Specificity

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Theuerkauf, René; Peters, Ralf

Abstract: The issue of fake reviews, a perennial challenge faced by e-commerce companies, ... Expand

Abstract: The issue of fake reviews, a perennial challenge faced by e-commerce companies, has been worsened in recent years. In this paper, we investigate the trade-off between the application of general-purpose and domain-specific model by selecting training data using transformer-based models to identify fake reviews. Therefore, we compare two scenarios using different set ups of data selection. First, a general-purpose model was identified and applied on specific domains. Afterwards, domain-specific models were trained and tested. Then, the results from these scenarios were compared, yielding the conclusion that models trained on data from a similar product category outperform general-purpose models in classification performance up to 21% (on average by 4%). Our findings send an important message to e-commerce companies to rethink their strategy on training general-purpose models to identify fake reviews to create several, more domainspecific models on that task according to present data domains. Collapse

Topics: fake review online review Facebook electronic commerce recommender system

Methods: RoBERTa DeBERTa BERT transformer model Cross-Industry Standard Process for Data Mining

Applying and Understanding an Advanced, Novel Deep Learning Approach: A Covid 19, Text Based, Emotions Analysis Study

2021 | Information Systems Frontiers | Citations: 5

Authors: Choudrie, Jyoti; Patil, Shruti; Kotecha, Ketan; Matta, Nikhil; Pappas, Ilias

Abstract: The pandemic COVID 19 has altered individuals’ daily lives across the globe. It ... Expand

Abstract: The pandemic COVID 19 has altered individuals’ daily lives across the globe. It has led to preventive measures such as physical distancing to be imposed on individuals and led to terms such as ‘lockdown,’ ‘emergency,’ or curfew’ to emerge in various countries. It has affected society, not only physically and financially, but in terms of emotional wellbeing as well. This distress in the human emotional quotient results from multiple factors such as financial implications, family member’s behavior and support, country-specific lockdown protocols, media influence, or fear of the pandemic. For efficient pandemic management, there is a need to understand the emotional variations among individuals, as this will provide insights into public sentiment towards various government pandemic management policies. From our investigations, it was found that individuals have increasingly used different microblogging platforms such as Twitter to remain connected and express their feelings and concerns during the pandemic. However, research in the area of expressed emotional wellbeing during COVID 19 is still growing, which motivated this team to form the aim: To identify, explore and understand globally the emotions expressed during the earlier months of the pandemic COVID 19 by utilizing Deep Learning and Natural language Processing (NLP). For the data collection, over 2 million tweets during February–June 2020 were collected and analyzed using an advanced deep learning technique of Transfer Learning and Robustly Optimized BERT Pretraining Approach (RoBERTa). A Reddit-based standard Emotion Dataset by Crowdflower was utilized for transfer learning. Using RoBERTa and the collated Twitter dataset, a multi-class emotion classifier system was formed. With the implemented methodology, a tweet classification accuracy of 80.33% and an average MCC score of 0.78 was achieved, improving the existing AI-based emotion classification methods. This study explains the novel application of the Roberta model during the pandemic that provided insights into changing emotional wellbeing over time of various citizens worldwide. It also offers novelty for data mining and analytics during this challenging, pandemic era. These insights can be beneficial for formulating effective pandemic management strategies and devising a novel, predictive strategy for the emotional well-being of an entire country’s citizens when facing future unexpected exogenous shocks. Collapse

Topics: Twitter pandemic covid 19 artificial intelligence Facebook

Methods: RoBERTa BERT machine learning deep learning natural language processing