Maintaining or Altering the Status Quo in the Nonmarket Arena: Theory and Evidence from Government Contract Disputes

2023 | Organization Science | Citations: 0

Authors: Abdurakhmonov, Mirzokhidjon; Hasija, Dinesh; Ridge, Jason W.; Hill, Aaron D.

Abstract: We develop a theory that considers important differences in competition in the p ... Expand

Abstract: We develop a theory that considers important differences in competition in the political arena based on whether firms are attempting to maintain or alter the status quo. Particularly, we argue that although investing resources and time to build relationships with politicians and government officials fosters trust, solidarity, and reciprocity, likely producing desired outcomes for both firms attempting to maintain the status quo and those trying to alter it, such relationships are more strongly positive for firms maintaining the status quo. As the effectiveness of relational strategies may differ based on firms’ political strategies and firm characteristics, we then theorize that the effects of a more relational approach on ”winning” these desired outcomes for firms seeking to maintain or alter the status quo will be moderated by (1) prior targeting in the political arena where the competition unfolds and (2) a firm’s social reputation. Specifically, we argue that knowledge and relationships from prior targeting in a political domain where competition unfolds, and a firm’s better social reputation will strengthen the effect of a more relational approach on securing desired outcomes for both firms seeking to maintain and alter the status quo. We expect the moderating effect for a firm’s targeting and social reputation will be stronger for firms seeking to maintain the status quo compared with firms seeking to alter the status quo. Our study contributes to corporate political activity literature and notably to the research on political markets by advancing a more nuanced but realistic understanding of interfirm rivalry for governmental outcomes. Collapse

Topics: resource allocation database system competitive advantage legal context anonymity

Methods: theory development statistical hypothesis test chi squared test cluster analysis Compustat

Theories: resource based view of the firm

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

Ambivalence as a Predictor of Online Review Helpfulness

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Akgul, Mehmet; MONTAZEMI, Ali Reza

Abstract: Online platforms, such as Yelp and TripAdvisor, have facilitated ubiquitous and ... Expand

Abstract: Online platforms, such as Yelp and TripAdvisor, have facilitated ubiquitous and convenient access to a large number of online reviews about almost any product/service. However, they impose an information load on the consumers due to their large number of reviews. The review star rating is practiced to ameliorate consumers' information load. A review star rating shows the reviewer's overall evaluation of a product/service. Nonetheless, the extant research findings about the relationship between star rating and review helpfulness are mixed. Star rating, being a unidimensional bipolar measure, is unable to correctly capture the underlying evaluations of the review content. Our contention in this research is that the use of review ambivalence is a better predictor of review helpfulness than the traditional star rating. To that end, we provide the theoretical foundation and pertinent methodology to assess our research question. KeywordsOnline review helpfulness, ambivalence, electronic word of mouth, eWoM Collapse

Topics: online word of mouth word of mouth

Methods: natural language processing sentiment analysis

The Roles of Opinion Leaders in Elevating Online Health Discussion: Evidence from Reddit

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Alazazi, Massara; Wang, Bin

Abstract: Social networking sites and online communities are used by billions of people g ... Expand

Abstract: Social networking sites and online communities are used by billions of people globally. It is important to understand the participation in these collaborative platforms and the roles different participants play. In this study, we examine the roles both local and global opinion leaders play and their influence on the community using content related to Covid mental health from Reddit. We employed social network analysis to identify opinion leaders based on their network position and connections, and we used sentiment analysis and econometric models to examine their impact on online health community participation. Our study reveals that global opinion leaders have more influence on thread participation than local opinion leaders. In addition, negative emotions expressed encourage more participants' involvement in online health community discussion. Collapse

Topics: electronic health Reddit covid 19 social network website

Methods: sentiment analysis social network analysis regression analysis method econometric modeling statistical hypothesis test

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

Introduction to the Minitrack on Data Analytics, Data Mining, and Machine Learning for Social Media

2023 | HICSS | Citations: 0

Authors: Babb, J.; Mentzer, K.; Yates, D.

Abstract: Thank you for your interest in the Data Analytics, Data Mining, and Machine Le ... Expand

Abstract: Thank you for your interest in the Data Analytics, Data Mining, and Machine Learning for Social Media Minitrack. This year's paper contributions vary in scope and focus and thus provide a rich spectral view of developing insights and understanding from social media. According to Monique Thomas of Later, social media is experiencing a significant evolution: "We've said goodbye to simple status updates (we miss you!) and hello to social selling and growing digital communities." However, our digital communities continue to struggle with the challenge of being free and open, allowing anyone to express their thoughts and ideas, while at the same time working to combat misinformation and deep fakes. We are grateful to social media scholars who continue on various paths of sensemaking to better understand social media's impact on our society. This year's papers provide many new insights into this vital area of research. Collapse

Topics: social media misinformation privacy online community Twitter

Methods: structured literature research cluster analysis sentiment analysis case study

The Sound of Progress: Investigating the Representation of Artificial Intelligence in Music

2023 | International Conference on Business Informatics | Citations: 0

Authors: Banh, Leonardo; Strobel, Gero

Abstract: The role of artificial intelligence in daily life is constantly advancing and h ... Expand

Abstract: The role of artificial intelligence in daily life is constantly advancing and has become an important topic of discussion. With music and its lyrics being a vehicle to express topics of society, this paper investigates how artificial intelligence is perceived by musicians and reflected in their songs. By analyzing the lyrics of over 1,200 songs over three decades, this work applies sentiment analysis to extract polarities and emotions. The results provide insights into how musicians view and reflect the impact of artificial intelligence on society and how this is reflected in their song texts. The findings show an increase in songs mentioning artificial intelligence-related terms, with a trend of more songs implying negativity, such as anger. However, minor increases in positive emotions indicate musicians' ambivalent views on hopes and fear of artificial intelligence. Collapse

Topics: artificial intelligence deepfake database system

Methods: sentiment analysis deep learning machine learning language model simple linear regression

Theories: communication theory

Augmenting Social Bot Detection with Crowd-Generated Labels

2023 | Information Systems Research | Citations: 0

Authors: Benjamin, Victor; Raghu, T. S.

Abstract: Social media platforms are facing increasing numbers of cyber-adversaries seekin ... Expand

Abstract: Social media platforms are facing increasing numbers of cyber-adversaries seeking to manipulate online discourse by using social bots (i.e., social media software robots) to help automate and scale their attacks. Likewise, some social media users can identify social bot activity at varying degrees of confidence. In this research, human reactions to social bot messages are used to augment existing social bot detection capabilities. Speech act theory is used to inspire a framework for assessing the credibility of instances where users identify potential bot activity, as not all user responses are of equal credibility for assisting with the bot detection task. The framework is then operationalized through deep learning methodologies to develop a computational system for identifying social bots. Real-world performance and practicality of the developed framework is demonstrated on a live, crowd-sourced data set collected from a real-world social media platform. Results show that consideration of crowd reactions to suspected bots can significantly improve bot detection performance. Furthermore, consideration of speech acts to evaluate crowd reactions can even further augment the system’s performance, although speech acts themselves are not necessary to observe performance boost through crowd intelligence. This study serves as a grounding point for future work that can explore an augmented model for detecting other forms of algorithmically generated content within social media platforms.History: Olivia Sheng, Senior Editor; Jesse Bockstedt, Associate Editor.Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2022.1136. Collapse

Topics: Reddit social media crowdsourcing IT productivity social media analytics

Methods: experiment BERT machine learning support vector machine random forest classification

Theories: speech act theory

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

The Relationship Between Twitter Sentiment and Stock Performance: A Decision Tree Approach

2023 | HICSS | Citations: 0

Authors: Chen, Rongjuan; Dong, Ruoxi

Abstract: Social media has become a communication tool, but also a valuable database for r ... Expand

Abstract: Social media has become a communication tool, but also a valuable database for researchers and practitioners to gather information, share knowledge, as well as express opinions about stock performance. The sentiment embedded in social media content can be analyzed to predict stock performance. Although numerous past studies have attempted to predict stock price movement using social media sentiment, some emerging analytical tools, like existing lexicons, may require further testing and validation in a financial decision making context. In this study, we develop and test predictive models for stock price and trend forecasting. By using a large-scale sample of tweets collected from Twitter, related to four companies, Apple, Google, Microsoft, and Netflix, we propose a novel decision tree approach to stock performance prediction. Based on our findings, we then provide theoretical and practical implications and discuss the directions for future work. Collapse

Topics: Twitter social media decision making digital service Netflix

Methods: sentiment analysis decision tree classification regression analysis method multiple linear regression NLP tool

Theories: behavioral economics