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

Benchmarking the Robustness of Phishing Email Detection Systems

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Ampel, Benjamin; Gao, Yang; Hu, James; Samtani, Sagar; Chen, Hsinchun

Abstract: Social engineering attacks are currently the most cited cybersecurity threat to ... Expand

Abstract: Social engineering attacks are currently the most cited cybersecurity threat to organizations. Phishing emails are the most salient form of social engineering attacks. Organizations are increasingly implementing AI-enabled systems to detect phishing emails. However, AI-enabled systems are often susceptible to textual perturbations, where an adversary makes a small change to cause a misclassification. In this study, we sought to identify the performance of prevailing phishing email detection systems (PEDS) against character, word, sentence, and multi-level adversarial text perturbations. Through a principled benchmarking framework, we quantitatively demonstrated the lack of robustness prevailing PEDS have to specific types of text-based adversarial perturbations (e.g., character, word, sentence, multi-level). The results of this study provide new insights into the robustness of AI-based PEDS and highlight the need for organizations to adopt a multi-layered approach to phishing protection. Additionally, organizations can implement our benchmark framework to test their PEDS against adversarial perturbations. Collapse

Topics: artificial intelligence electronic mail email phishing phishing IT security

Methods: word2vec random forest classification naïve bayes literature study pre-trained language model

Give me 3W1H: A Bibliometric View on Accountable AI

2023 | International Conference on Information Systems | Citations: 0

Authors: Bartsch, Sebastian; Schmidt, Jan-Hendrik

Abstract: Accountability is crucial to make stakeholders of Artificial Intelligence (AI)- ... Expand

Abstract: Accountability is crucial to make stakeholders of Artificial Intelligence (AI)-based systems justify their actions, thereby explaining the harm such systems cause to AI users. Due to the importance of accountability in the context of AI, accountability was introduced into IS research through literature reviews. Therefore, while IS research's understanding of accountability covers the necessary depth, it comes at the expense of its essential breadth. Using a bibliometric analysis with 19,978 English-language papers, we shed light on the essential breadth posing three W-and one H-questions (When, What, Whereof, and How). Therefore, we contribute to IS research by highlighting the urgent need to revise existing definitions of accountability in the context of AI and establish them in IS research. We argue that a missing revision leads to non-transferrable findings within IS research. Accordingly, this study serves as a starting point for adapting definitions and creating a shared understanding in IS research. Collapse

Topics: artificial intelligence explainable artificial intelligence legal context database system IT security

Methods: word embedding word2vec bibliometric analysis machine learning supervised learning

How Tourist Group Books Hotels Meeting the Majority Affective Expectations: A Group Selection Frame with Kansei Text Mining and Consensus Coordinating

2023 | Group Decision and Negotiation | Citations: 0

Authors: Chang, Jia-Li; Li, Hui; Wu, Jian

Abstract: The decision on which hotel to book is a high-cost process for individuals and e ... Expand

Abstract: The decision on which hotel to book is a high-cost process for individuals and even more for groups due to the information explosion on online travel agent platforms. To assist group selecting the optimal hotel that meets the majority affective expectations, this study proposes a group hotel selection frame for booking with Kansei text mining and consensus coordinating. Firstly, to quantify the tourists’ affective needs for hotels, a Kansei-related dictionary towards hotel domain is constructed by Kansei text mining. Secondly, the bilattice-based Kansei score of hotels is defined and measured to represent hotel affective information scientifically. Then, a group consensus model with minimum adjustment cost is introduced to coordinate consistency of individual preferences in a tourist group. Moreover, a group hotel ranking based on group affective preference is interactively generated to meet the group personalized demands. Finally, a case study on Trip.com is conducted to demonstrate the proposed method. Collapse

Topics: knowledge representation online review personality website

Methods: natural language processing word2vec computational algorithm sentiment analysis case study

Understanding Emojis for Financial Sentiment Analysis

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Siyi; Xing, Frank

Abstract: ... Expand

Abstract: Collapse

Topics: application programming interface social media website cryptocurrency Python

Methods: sentiment analysis machine learning BERT deep learning experiment

How Does (In)consistent Performance Feedback Affect Business Model Digitalization？

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Chen, Yao; Chen, Meng; Liu, Hefu; Tang, Xinlin

Abstract: Despite the merits of business model (BM) digitalization to sustain organizatio ... Expand

Abstract: Despite the merits of business model (BM) digitalization to sustain organizational competitive advantages, many firms are cautious to digitalize their BMs due to the efforts needed and the high risks accompanying them. To date, the existing understanding of the antecedents of BM digitalization is still underdeveloped. We draw on the behavioral theory of the firm to develop a comprehensive understanding of what motivates firms to promote BM digitalization. In so doing, this study explores how (in)consistency between historical and social performance feedback affects BM digitalization. Moreover, we theorize that board experience with BM digitalization from interlock firms is an important contingency that influences these relationships. Based on panel data from 2,373 Chinese-listed firms from 2008 to 2019, the results provide general support for our arguments. This study contributes to the BM digitalization literature by disentangling the critical roles of (in)consistent performance feedback and board experience. Collapse

Topics: organizational productivity research and development corporate governance accounting accounting standard

Methods: theory development machine learning descriptive statistic statistical hypothesis test word2vec

Theories: behavioral theory of the firm

Stand for Something or Fall for Everything: Predict Misinformation Spread with Stance-Aware Graph Neural Networks

2023 | International Conference on Information Systems | Citations: 0

Authors: Chen, Zihan; Sun, Jingyi; Liu, Rong Emily; Mai, Feng

Abstract: Although pervasive spread of misinformation on social media platforms has becom ... Expand

Abstract: Although pervasive spread of misinformation on social media platforms has become a pressing challenge, existing platform interventions have shown limited success in curbing its dissemination. In this study, we propose a stance-aware graph neural network (stance-aware GNN) that leverages users' stances to proactively predict misinformation spread. As different user stances can form unique echo chambers, we customize four information passing paths in stance-aware GNN, while the trainable attention weights provide explainability by highlighting each structure's importance. Evaluated on a realworld dataset, stance-aware GNN outperforms benchmarks by 32.65% and exceeds advanced GNNs without user stance by over 4.69%. Furthermore, the attention weights indicate that users' opposition stances have a higher impact on their neighbors' behaviors than supportive ones, which function as social correction to halt misinformation propagation. Overall, our study provides an effective predictive model for platforms to combat misinformation, and highlights the impact of user stances in the misinformation propagation. Collapse

Topics: misinformation Twitter filter bubble social media social influence

Methods: machine learning RoBERTa experiment deep learning graph neural network

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

Causal Matching for Startups: Methods for Controlling Confounding

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Fernandes Marinho Ferreira, Vitor; Kuruzovich, Jason

Abstract: Causal inference is a critical methodology that allows researchers to empirical ... Expand

Abstract: Causal inference is a critical methodology that allows researchers to empirically test hypotheses and build theories. Ideally, the design method for establishing this causality would be through randomized controlled trials. However, studying the impact of different practices (hereinafter treatments) on initial outcomes when treatments are not applied exogenously is a challenge. Matching methods are an appropriate way to provide these study groups. The objective of this work is to provide an algorithmbased solution to match startups in a way that can mimic a human match. To do this, we use the human matching effort by Yu (2020) as a ground truth to evaluate various natural language processes and compare the performance of each with human raters. By comparing automated approaches with matching, we provide guidance for researchers interested in the causal analysis of startups using textual data and other covariates. Collapse

Topics: entrepreneurship business model strategic management

Methods: causality analysis natural language processing computational algorithm term frequency–inverse document frequency GloVe

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