How Review Quality and Source Credibility Interacts to Affect Review Usefulness: An Expansion of the Elaboration Likelihood Model

2023 | Information Systems Frontiers | Citations: 0

Authors: Aghakhani, Navid; Oh, Onook; Gregg, Dawn; Jain, Hemant

Abstract: This study extends our understanding of what makes an online review useful by ex ... Expand

Abstract: This study extends our understanding of what makes an online review useful by examining the effects of review quality (i.e., as a composite variable of review comprehensiveness and review topic consistency) on review usefulness, and the moderating effects of source credibility on the relationship between review quality and review usefulness. The Elaboration Likelihood Model, convergence theory, and cueing effect literature are used to define the variables of review comprehensiveness and review topic consistency. Analyses of 27,517 restaurant reviews from Yelp show that review topic consistency has a positive effect on review usefulness, but, contrary to our hypothesis, review comprehensiveness has a negative effect on review usefulness. We also found source credibility positively moderates the effect of review comprehensiveness on review usefulness, but negatively moderates the effect of review topic consistency on review usefulness. Theoretical and practical implications are discussed. Collapse

Topics: online review user-generated content Python website data breach

Methods: topic model term frequency–inverse document frequency NLTK statistical hypothesis test robustness check

Theories: elaboration likelihood model

Extraction of Forward-looking Financial Information for Stock Price Prediction from Annual Reports Using NLP Techniques

2023 | HICSS | Citations: 0

Authors: Glodd, Alexander; Hristova, Diana

Abstract: Annual reports are one of the most important sources of information for financia ... Expand

Abstract: Annual reports are one of the most important sources of information for financial decisions. They contain forward-looking statements (FLS), which describe future trends and expectations. Thus, several studies deal with the automated identification of FLS, where the latest ones involve a combination of a rule-based approach and machine learning classification. In this paper, we extend this research with state-of-the-art NLP methods. We use DistilBERT for FLS identification and determine their sentiment with FinBERT. The result is processed by a Random Forest model for stock price growth prediction of different periods. Our evaluation shows that DestilBERT achieves higher accuracies on FLS identification than existing methods. For short-term stock price rate prediction, the extracted FLS information together with historical stock data outperforms the sole use of historical stock data. For mid-term prediction, using FLS alone with DestilBERT shows the best result. Finally, in the long-term, FLS provide no benefit. Collapse

Topics: Python decision making

Methods: BERT support vector machine natural language processing DistilBERT literature study

Automated Knowledge Extraction from IS Research Articles Combining Sentence Classification and Ontological Annotation

2023 | International Conference on Business Informatics | Citations: 0

Authors: Huettemann, Sebastian

Abstract: Manually analyzing large collections of research articles is a time-and resourc ... Expand

Abstract: Manually analyzing large collections of research articles is a time-and resource-intensive activity, making it difficult to stay on top of the latest research findings. Limitations of automated solutions lie in limited domain knowledge and not being able to attribute extracted key terms to a focal article, related work, or background information. We aim to address this challenge by (1) developing a framework for classifying sentences in scientific publications, (2) performing several experiments comparing state-of-the-art sentence transformer algorithms with a novel few-shot learning technique and (3) automatically analyzing a corpus of articles and evaluating automated knowledge extraction capabilities. We tested our approach for combining sentence classification with ontological annotations on a manually created dataset of 1,000 sentences from Information Systems (IS) articles. The results indicate a high degree of accuracy underlining the potential for novel approaches in analyzing scientific publications. Collapse

Topics: knowledge representation open access search engine database system open source

Methods: machine learning ontological modelling experiment transformer model domain ontology

AI-Generated Voice in Short Videos: A Digital Consumer Engagement Perspective

2023 | International Conference on Information Systems | Citations: 0

Authors: Luo, Jihao; Zheng, Chenxu; Yin, Jiamin; Teo, Hock-Hai

Abstract: The AI-generated voice (AIGV) has been widely applied in the short video indust ... Expand

Abstract: The AI-generated voice (AIGV) has been widely applied in the short video industry to facilitate video creation. However, the impact of AIGV on digital consumer engagement (DCE) remains unclear. In light of this, the study investigates the effect of AIGV on DCE by analyzing a panel dataset with observations of 21,541 videos for 3,647 content creators on TikTok. Preliminary results of a series of fixed-effect panel regressions reveal that using AIGV has a significantly negative effect on DCE (5.4% reduction in the number of likes, 5.2% reduction in the number of comments, and 7.4% reduction in the number of shares). Our further analyses show that this negative effect is particularly significant at the rising action stage of short videos. With these findings, this study is expected to have theoretical contributions to the literature on short videos and practical implications about the appropriate usage of AIGV. Collapse

Topics: TikTok artificial intelligence deepfake application programming interface YouTube

Methods: computational algorithm machine learning propensity score matching experimental group theoretical contribution

Theories: dual-system theory

Alternative Dispute Resolution Research Landscape from 1981 to 2022

2023 | Group Decision and Negotiation | Citations: 0

Authors: Mota, Fabio Batista; Braga, Luiza Amara Maciel; Cabral, Bernardo Pereira

Abstract: Alternative dispute resolution (ADR) is an important means of resolving disputes ... Expand

Abstract: Alternative dispute resolution (ADR) is an important means of resolving disputes outside of traditional legal frameworks. It is usually adopted because of its flexibility, cost-effectiveness, and ability to preserve relationships that a contentious court battle might damage. This study aims to evaluate the scientific publication related to ADR. To do so, we used metadata of ADR-related articles from 1981 to 2022 collected in the Web of Science Core Collection and carried out a bibliometric, network, and latent Dirichlet allocation analysis. Our results indicate that ADR research is concentrated in North America, with research organizations from the United States accounting for most publications. At the same time, recent years have seen a shift to a more diverse group of countries, with the Chinese City University of Hong Kong and the Australian Victoria University leading in the last five years. The five main topics in ADR research include online dispute resolution for consumer protection, mediation for family law, arbitration in medical malpractice cases, ADR in construction projects, and ADR in employment law. The most frequent research areas assigned to ADR publications are Government & Law and Business & Economics. Network results show keywords ADR, Mediation, and Arbitration as central nodes, while the Chinese and North American organizations established the most frequent collaborations, addressing ADR applications in various sectors. The findings underscore the interdisciplinary nature of ADR research, its adaptability across industries, and the importance of cross-cultural research partnerships. Collapse

Topics: Python information system use accounting

Methods: action design research network analysis literature sample bibliometric analysis latent dirichlet allocation

A Modular Social Sensing System for Personalized Orienteering in the COVID-19 Era

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

Authors: Pilato, Giovanni; Persia, Fabio; Ge, Mouzhi; Chondrogiannis, Theodoros; D’Auria, Daniela

Abstract: Orienteering or itinerary planning algorithms in tourism are used to optimize tr ... Expand

Abstract: Orienteering or itinerary planning algorithms in tourism are used to optimize travel routes by considering user preference and other constraints, such as time budget or traffic conditions. For these algorithms, it is essential to explore the user preference to predict potential points of interest (POIs) or tourist routes. However, nowadays, user preference has been significantly affected by COVID-19, since health concern plays a key tradeoff role. For example, people may try to avoid crowdedness, even if there is a strong desire for social interaction. Thus, the orienteering or itinerary planning algorithms should optimize routes beyond user preference. Therefore, this article proposes a social sensing system that considers the tradeoff between user preference and various factors, such as crowdedness, personality, knowledge of COVID-19, POI features, and desire for socialization. The experiments are conducted on profiling user interests with a properly trained fastText neural network and a set of specialized Naïve Bayesian Classifiers based on the “Yelp!” dataset. Also, we demonstrate how to approach and integrate COVID-related factors via conversational agents. Furthermore, the proposed system is in a modular design and evaluated in a user study; thus, it can be efficiently adapted to different algorithms for COVID-19-aware itinerary planning. Collapse

Topics: user satisfaction social network personality conversational agent Twitter

Methods: computational algorithm fastText machine learning experiment descriptive statistic

Does Narrative Impact Funding? Analyzing the Relationship Between Project Description and Pledged Amounts for Reward-based Crowdfunding Projects

2023 | International Conference on Information Systems | Citations: 0

Authors: Pu, Shaolin; Srinivasan, Karthik; Sherwood, Ben; Tripathi, Arvind

Abstract: In reward-based crowdfunding (RBC) campaigns, project description text plays a ... Expand

Abstract: In reward-based crowdfunding (RBC) campaigns, project description text plays a critical role in driving market demand, by simplifying complex project information and providing clear backing signals. Indeed, well-crafted textual descriptions could persuade potential backers to better fund the respective projects. Through the lenses of theories on framing and resonance, we examine three attributes, that are key to forming a compelling narrative: innovation disclosure, linguistic specificity, and shared phrase utilization. We posit that better communication regarding product innovation (i.e., innovation disclosure), employing phrases commonly found in comparable projects (i.e., shared phrase utilization), and incorporating concrete and precise language (i.e., linguistic specificity) are associated with higher funding. Using data from technology and product design project categories of a prominent RBC platform, our hypotheses are tested and largely supported. Our study contributes to information systems (IS) research by exploring creators' resonance strategies and the role of the project description narratives in funding outcomes. Collapse

Topics: innovation management crowdfunding crowdsourcing Python website

Methods: descriptive statistic regression analysis method experimental group propensity score method statistical hypothesis test

Theories: lemon market theory

Mapping the Emotional Journey of STD patients in Online Forums

2023 | Americas Conference on Information Systems | Citations: 0

Authors: THIMMANAYAKANAPALYA, Sagarika Suresh; Sharman, Raj; Mulgund, Pavankumar; Parthiban, Lakshmi Prithika; Samant, Harshada

Abstract: People inflicted with diseases such as STDs experience a tremendous number of v ... Expand

Abstract: People inflicted with diseases such as STDs experience a tremendous number of varying emotions from the onset of symptoms to being cured/living with it. Leveraging the power of bidirectional encoder representation transformers our study builds a custom-named entity recognition model which identifies uniquely associated emotions at various stages of the disease. Our study uses over 1 million posts derived from online communities regarding STDs. We further provide analytical insights into varying emotions at different stages based on contextual reasonings. Finally, we utilize the resulting insights to produce prescriptive measures for future online recommendation systems on online communities, and for stakeholders hoping to build devices that address problems about stigmatized disorders such as STDs. Collapse

Topics: online community recommender system misinformation website Reddit

Methods: BERT computational algorithm named entity recognition machine learning language model

MediCoSpace: Visual Decision-Support for Doctor-Patient Consultations using Medical Concept Spaces from EHRs

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

Authors: van der Linden, Sanne; Sevastjanova, Rita; Funk, Mathias; El-Assady, Mennatallah

Abstract: Healthcare systems are under pressure from an aging population, rising costs, an ... Expand

Abstract: Healthcare systems are under pressure from an aging population, rising costs, and increasingly complex conditions and treatments. Although data are determined to play a bigger role in how doctors diagnose and prescribe treatments, they struggle due to a lack of time and an abundance of structured and unstructured information. To address this challenge, we introduce MediCoSpace, a visual decision-support tool for more efficient doctor-patient consultations. The tool links patient reports to past and present diagnoses, diseases, drugs, and treatments, both for the current patient and other patients in comparable situations. MediCoSpace uses textual medical data, deep-learning supported text analysis and concept spaces to facilitate a visual discovery process. The tool is evaluated by five medical doctors. The results show that MediCoSpace facilitates a promising, yet complex way to discover unlikely relations and thus suggests a path toward the development of interactive visual tools to provide physicians with more holistic diagnoses and personalized, dynamic treatments for patients. Collapse

Topics: decision support knowledge base visual analytics knowledge representation healthcare data

Methods: qualitative interview qualitative content analysis exploratory data analysis ontological modelling usability testing

Know Thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes

2022 | Information Systems Research | Citations: 1

Authors: Bauman, Konstantin; Tuzhilin, Alexander

Abstract: In this paper, we study an important problem of parsing contextual information f ... Expand

Abstract: In this paper, we study an important problem of parsing contextual information from user reviews for recommendation purposes. First, we study the ways contextual information is expressed in user reviews and obtain novel insights about it. Among other things, we demonstrate that such type of information tends to appear at the beginning of the review, in longer sentences, in the sentences written in the past tense or using gerund form, and in the sentences referring to some points in time. Second, we propose a novel context parsing method for systematically extracting contextual information from user-generated reviews that relies on the insights obtained in our study. We apply the proposed method to three different Yelp applications (restaurants, hotels, and beauty & spas) and demonstrate that it works well and leads to better recommendation performance than the baseline approaches. Our method systematically extracts more comprehensive sets of relevant contextual variables and corresponding phrases than the baselines. Our analysis also shows the importance of the newly discovered contextual information for recommendation purposes. The obtained results and the proposed method can help to get more comprehensive knowledge about contextual variables in a given application that leads to better recommendations. Collapse

Topics: online review recommender system logistics management user experience user-generated content

Methods: word2vec hierarchical clustering machine learning product review experiment