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

An Investigation of Domain-based Social Influence on ChatGPT-Related Twitter Data

2023 | International Conference on Information Systems | Citations: 0

Authors: Ahangama, Supunmali

Abstract: ... Expand

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Topics: Twitter social influence social media social identity robotic

Methods: chatGPT sentiment analysis multiple linear regression term frequency–inverse document frequency NLTK

Design of a Novel Information System for Semi-automated Management of Cybersecurity in Industrial Control Systems

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

Authors: Ameri, Kimia; Hempel, Michael; Sharif, Hamid; Lopez, Juan; Perumalla, Kalyan

Abstract: There is an urgent need in many critical infrastructure sectors, including the e ... Expand

Abstract: There is an urgent need in many critical infrastructure sectors, including the energy sector, for attaining detailed insights into cybersecurity features and compliance with cybersecurity requirements related to their Operational Technology (OT) deployments. Frequent feature changes of OT devices interfere with this need, posing a great risk to customers. One effective way to address this challenge is via a semi-automated cyber-physical security assurance approach, which enables verification and validation of the OT device cybersecurity claims against actual capabilities, both pre- and post-deployment. To realize this approach, this article presents new methodology and algorithms to automatically identify cybersecurity-related claims expressed in natural language form in ICS device documents. We developed an identification process that employs natural language processing (NLP) techniques with the goal of semi-automated vetting of detected claims against their device implementation. We also present our novel NLP components for verifying feature claims against relevant cybersecurity requirements. The verification pipeline includes components such as automated vendor identification, device document curation, feature claim identification utilizing sentiment analysis for conflict resolution, and reporting of features that are claimed to be supported or indicated as unsupported. Our novel matching engine represents the first automated information system available in the cybersecurity domain that directly aids the generation of ICS compliance reports. Collapse

Topics: IT security database system website Python electronic authentication

Methods: computational algorithm sentiment analysis natural language processing BERT language model

Low-Coders, No-Coders, and Citizen Developers in Demand: Examining Knowledge, Skills, and Abilities Through a Job Market Analysis

2023 | International Conference on Business Informatics | Citations: 0

Authors: Binzer, Björn; Winkler, Till J.

Abstract: The emergence of low-code/no-code (LCNC) platform technologies and the resultin ... Expand

Abstract: The emergence of low-code/no-code (LCNC) platform technologies and the resulting increase in citizen development programs are facilitating the democratization of the design, development, and deployment of digital solutions. Citizen developers, non-technical employees who leverage LCNC platforms, are at the heart of this trend. While many firms perceive LCNC and citizen development as a crucial component of their digital transformation strategy, little is known about the evolving roles in this field or the necessary knowledge, skills, and abilities (KSA). To address this knowledge gap, we processed 113,106 job postings published on Indeed.com. Our topic modeling methodology identified 34 KSA topics and classified them into the three domains platform, business, and technology. We contribute to research by empirically demonstrating which competencies are required to successfully work in the LCNC field. Our findings can guide individual professionals and organizations alike. Collapse

Topics: online labor market Indeed.com low code development platform Python project management

Methods: topic model natural language processing computational algorithm latent dirichlet allocation NLTK

Influence of Twitter on Hydroxychloroquine Medication Prescriptions for COVID-19 Patients

2023 | International Conference on Information Systems | Citations: 0

Authors: Gorthi, Sai Soundarya; Ganju, Kartik; Pinsonneault, Alain

Abstract: Social media is proposed to play a crucial role in healthcare providers' care d ... Expand

Abstract: Social media is proposed to play a crucial role in healthcare providers' care decisions. However, social media information may not always be reliable as false information is prone to virality, and emotional cues may drive information sharing. Hence, in our study, we strive to understand the influence of social media on healthcare providers' care decisions by empirically examining the influence of Twitter discourse regarding Hydroxychloroquine (HCQ) on the actual prescribing rates of the drug in the USA for treating Collapse

Topics: Twitter social media application programming interface Python missing data

Methods: longitudinal research machine learning qualitative coding autocorrelation analysis mixed method

Theories: elaboration likelihood model

ChatGPT and GPTZero in Research and Social Media: A Sentiment- and Topic-based Analysis

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Heumann, Maximilian; Kraschewski, Tobias; Breitner, Michael H.

Abstract: Conversational AI agents like ChatGPT have gained considerable attention in rec ... Expand

Abstract: Conversational AI agents like ChatGPT have gained considerable attention in recent years, with potential implications across various domains. However, there is a gap in understanding the prevailing opinions, topics, and concerns of the public and research communities regarding these technologies. This study conducts a semi-automated analysis of over 1,006,000 tweets and 279 scholarly articles to explore perspectives on ChatGPT and its potential misuse for plagiarism, as well as the comparatively lower interest in GPTZero, a technology designed to counter AI-driven plagiarism. Employing advanced natural language processing techniques, such as sentiment analysis and topic modeling, we identify challenges and opportunities related to ChatGPT and GPTZero. Our findings uncover potential areas for improvement and provide guidance for future research and development in the field of conversational AI. Collapse

Topics: Twitter artificial intelligence human computer interaction usability Python

Methods: chatGPT topic model chatbot sentiment analysis natural language processing

The Influence of User Feedback on Complementary Innovation in Platform Ecosystems: NLP Evidence on the Value of Multihoming

2023 | HICSS | Citations: 0

Authors: Hoffmann, Philipp; Shmagina, Veronika; Halckenhaeusser, André; Lueker, Nele

Abstract: We study how user feedback affects innovation of multihomed applications within ... Expand

Abstract: We study how user feedback affects innovation of multihomed applications within and across platform ecosystems. Therefore, we conduct a quantitative NLP based case study. Our sample consists of 10 multihomed applications with more than 325,000 user reviews on Apple’s iOS and Google’s Android platform between January and March 2021. We analyze how user reviews translate into functional feature releases of the selected applications within and across platforms. We report three findings. First, we find that about 61% of the functional feature improvements on both platforms were previously demanded by users in the form of user feedback. Second, we show that user feedback of iOS users is more likely to be incorporated compared to Android users’ feedback. Finally, we observe that about 10% of feature releases are inspired by cross-platform feedback, providing initial evidence that user feedback from multihoming applications might stimulate cross-platform innovation and enhance the applications’ quality and innovativeness. Collapse

Topics: innovation management Android iOS mobile application mobile application market

Methods: natural language processing case study word2vec NLTK theoretical contribution

A Collective Opinion Mechanism for Crowdfunding Recommendation

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Jin, Bih-Huang; Li, Yung-Ming; Kamolporn, Sanamlao

Abstract: ... Expand

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Topics: crowdsourcing crowdfunding Twitter social network Python

Methods: sentiment analysis experiment topic model logistic regression parametric test

Interpretable machine learning-based approach for customer segmentation for new product development from online product reviews

2023 | International Journal of Information Management | Citations: 0

Authors: Joung, Junegak; Kim, Harrison

Abstract: For new product development, previous segmentation methods based on demographic, ... Expand

Abstract: For new product development, previous segmentation methods based on demographic, psychographic, and purchase behavior information cannot identify a group of customers with unsatisfied needs. Moreover, seg­ mentation is limited to sales promotions in marketing. Although needs-based segmentation considering customer sentiments on product features can be conducted to develop a new product concept, it cannot identify com­ monalities among customers owing to their diverse preferences. Therefore, this paper proposes an interpretable machine learning-based approach for customer segmentation for new product development based on the importance of product features from online product reviews. The technical challenges of determining the importance of product features in each review are identifying and interpreting the nonlinear relations between satisfaction with product features and overall customer satisfaction. In this study, interpretable machine learning is used to identify these nonlinear relations with high performance and transparency. A case study on a wearable device is conducted to validate the proposed approach. Customer segmentation using the proposed approach based on importance is compared with that employing a previous approach based on sentiments. The results show that the proposed approach presents a higher clustering performance than the previous approach and offers opportunities to identify new product concepts. Collapse

Topics: online review shapley additive explanation customer satisfaction new product development Python

Methods: cluster analysis machine learning sentiment analysis case study word2vec

Theories: game theory

Design for Social Sharing: The Case of Mobile Apps

2023 | Information Systems Research | Citations: 1

Authors: Karanam, Subrahmanyam Aditya; Agarwal, Ashish; Barua, Anitesh

Abstract: With a large number of mobile apps on platforms such as iOS and Android, app dev ... Expand

Abstract: With a large number of mobile apps on platforms such as iOS and Android, app developers face a significant challenge in generating market demand. Apps can incorporate social features to share information and create awareness. We focus on the impact of these features and their interplay with intrinsic features in the head, body, and tail of the demand distribution. Using a panel of version release notes from the iOS platform, we develop a novel hierarchical deep learning model to extract intrinsic and social features. Our results suggest that social features help increase the demand for tail apps and are also useful for head apps in informing users about new intrinsic features. To explore possible mechanisms, we analyze how different types of social features (personal and platform), intrinsic features (common versus differentiating), and app quality influence demand. We find that social features that allow sharing on platforms with large audiences are more effective at increasing demand than those on personal messaging systems. Furthermore, within platforms, social features perform better on those with stronger ties, such as Facebook as opposed to Twitter. Our analysis of different types of intrinsic features reveals that social features can help increase the demand for all apps when introduced with differentiating or less common intrinsic features. Furthermore, we find that there is a negative effect on the demand of tail apps when differentiating intrinsic features are combined with platform-based social features that have weaker ties. However, this negative effect is limited to low-quality tail apps only. Our results underscore the differences in the effect of different types of social and intrinsic features in various parts of the demand distribution. Our study provides managerial guidance to app developers in enabling social sharing through design choices and generating higher demand.History: Kai Lung Hui, Senior Editor; Beibei Li, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1151. Collapse

Topics: mobile application Facebook Twitter iOS accounting

Methods: deep learning robustness check computational algorithm word embedding natural language processing