Internet Research

Qian, Yunyu; Jiang, Hemin

2024 Internet Research

doi: 10.1108/intr-09-2022-0751

Employees' personal Internet usage (PIU) has become increasingly common at work. It is important for both researchers and managers to understand how PIU affects employee creative performance. This study aims to examine what kind of PIU is likely to increase or decrease employee creative performance and why. The authors also examine a potential boundary condition for the effect of PIU on employee creative performance.Design/methodology/approachBased on conservation of resource (COR) theory and broaden and build theory, the authors investigated the impact of two types of PIU, namely within-task PIU and between-task PIU, on the creative performance of knowledge workers. The authors conducted a daily diary study and surveyed 107 knowledge workers in China over 10 consecutive working days (n = 1,070) to test the model.FindingsThe authors find that within-task PIU reduces knowledge workers' creative performance by decreasing the workers' positive emotion, whereas between-task PIU promotes the performance by increasing positive emotion. The above relationships become stronger when knowledge workers perceive a higher level of organisational support.Originality/valueThe authors' study makes theoretical contributions by advancing researchers' understanding of the situations in which PIU may decrease or increase employee creative performance. The findings are also useful for developing organisational policies to take advantage of the positive side of PIU whilst avoiding PIU's negative side.

Chao, Fan; Wang, Xin; Yu, Guang

2024 Internet Research

doi: 10.1108/intr-07-2022-0497

Sharing and disseminating debunking information are critical to correcting rumours and controlling disease when dealing with public health crises. This study investigates the factors that influence social media users' debunking information sharing behaviour from the perspective of persuasion. The authors examined the effects of argument adequacy, emotional polarity, and debunker's identity on debunking information sharing behaviour and investigated the moderating effects of rumour content and target.Design/methodology/approachThe model was tested using 150 COVID-19-related rumours and 2,349 original debunking posts on Sina Weibo.FindingsFirst, debunking information that contains adequate arguments is more likely to be reposted only when the uncertainty of the rumour content is high. Second, using neutral sentiment as a reference, debunking information containing negative sentiment is shared more often regardless of whether the government is the rumour target, and information containing positive sentiment is more likely to be shared only when the rumour target is the government. Finally, debunking information published by government-type accounts is reposted more often and is enhanced when the rumour target is the government.Originality/valueThe study provides a systematic framework for analysing the behaviour of sharing debunking information among social media users. Specifically, it expands the understanding of the factors that influence debunking information sharing behaviour by examining the effects of persuasive cues on debunking information sharing behaviour and the heterogeneity of these effects across various rumour contexts.

Shi, Chencheng; Hu, Ping; Fan, Weiguo; Qiu, Liangfei

2024 Internet Research

doi: 10.1108/intr-07-2022-0510

Users' knowledge contribution behaviors are critical for online Q&A communities to thrive. Well-organized question threads in online Q&A communities enable users to clearly read existing answers and their evaluations before contributing. Based on the social comparison and peer influence literature, the authors examine peer influence on the informativeness of knowledge contributions in competitive settings. The authors also consider three levels of moderating factors concerning individuals' perception of competitiveness: question level, thread level and contributor level.Design/methodology/approachThe authors collected data from one of the largest online Q&A communities in China. The hypotheses were validated using hierarchical linear models with cross-classified random effects. The generalized propensity score weighting method was employed for the robustness check.FindingsThe authors demonstrate the peer influence due to social comparison concerns among knowledge contribution behaviors in the same question thread. If more prior knowledge contributors choose to contribute long answers in the question thread, the subsequent contributions are more informative. This peer influence is stronger for factual questions and questions with higher popularity of answering but weaker in recommendation-type and well-answered questions and for contributors with higher social status.Originality/valueThis research provides a new cue of peer influence on online UGC contributions in competitive settings initiated by social comparison concerns. Additionally, the authors identify three levels of moderating factors (question level, thread level and contributor level) that are specific to online Q&A settings and are related to a contributor's perception of competitiveness, which affect the direct effect of peer influence on knowledge contributions. Rather than focus on motivation and quality evaluation, the authors concentrate on the specific content of online knowledge contributions. Peer influence here is not based on an actual acquaintance or a following relationship but on answering the same question. The authors also illustrate the competitive peer influence in subjective and personalized behaviors in online UGC communities.

Wakefield, Robin; Wakefield, Kirk

2024 Internet Research

doi: 10.1108/intr-07-2022-0552

Social media is replete with malicious and unempathetic rhetoric yet few studies explain why these emotions are publicly dispersed. The purpose of the study is to investigate how the intergroup counter-empathic response called schadenfreude originates and how it prompts media consumption and engagement.Design/methodology/approachThe study consists of two field surveys of 635 in-group members of two professional sports teams and 300 residents of California and Texas with political party affiliations. The analysis uses SEM quantitative methods.FindingsDomain passion and group identification together determine the harmonious/obsessive tendencies of passion for an activity and explain the schadenfreude response toward the rival out-group. Group identification is a stronger driver of obsessive passion compared to harmonious passion. Schadenfreude directly influences the use of traditional media (TV, radio, domain websites), it triggers social media engagement (posting), and it accelerates harmonious passion's effects on social media posting.Research limitations/implicationsThe study is limited by the groups used to evaluate the research model, sports, and politics.Social implicationsThe more highly identified and passionate group members experience greater counter-empathy toward a rival. At extreme levels of group identification, obsessive passion increases at an increasing rate and may characterize extremism. Harboring feelings of schadenfreude toward the out-group prompts those with harmonious passion for an activity to more frequently engage on social media in unempathetic ways.Originality/valueThis study links the unempathetic, yet common emotion of schadenfreude with passion, intergroup dynamics, and media behavior.

Muzatko, Steven; Bansal, Gaurav

2024 Internet Research

doi: 10.1108/intr-12-2021-0939

This research examines the relationship between the timeliness in announcing the discovery of a data breach and consumer trust in an e-commerce company, as well as later trust-rebuilding efforts taken by the company to compensate users impacted by the breach.Design/methodology/approachA survey experiment was used to examine the effect of both trust-reducing events (announced data breaches) and trust-enhancing events (provision of identity theft protection and credit monitoring) on consumer trust. The timeliness of the breach announcement by an e-commerce company was manipulated between two randomly assigned groups of subjects; one group viewed an announcement of the breach immediately upon its discovery, and the other viewed an announcement made two months after the breach was discovered. Consumer trust was measured before the breach, after the breach was announced, and finally, after the announcement of data protection.FindingsThe results suggest that companies that delay a data breach announcement are likely to suffer a larger drop in consumer trust than those that immediately disclose the data breach. The results also suggest that trust can be repaired by providing data protection. However, even after providing identity theft protection and credit monitoring, companies that fail to promptly disclose a breach have lower repaired trust than companies that promptly disclose.Originality/valueThis study contributes to the literature on e-commerce trust by examining how a company's forthrightness in reporting a data breach impacts user trust at the time of the disclosure of the data breach and after subsequent efforts to repair trust.

DSouza, Karen M.; French, Aaron M.

2024 Internet Research

doi: 10.1108/intr-03-2022-0176

Purveyors of fake news perpetuate information that can harm society, including businesses. Social media's reach quickly amplifies distortions of fake news. Research has not yet fully explored the mechanisms of such adversarial behavior or the adversarial techniques of machine learning that might be deployed to detect fake news. Debiasing techniques are also explored to combat against the generation of fake news using adversarial data. The purpose of this paper is to present the challenges and opportunities in fake news detection.Design/methodology/approachFirst, this paper provides an overview of adversarial behaviors and current machine learning techniques. Next, it describes the use of long short-term memory (LSTM) to identify fake news in a corpus of articles. Finally, it presents the novel adversarial behavior approach to protect targeted business datasets from attacks.FindingsThis research highlights the need for a corpus of fake news that can be used to evaluate classification methods. Adversarial debiasing using IBM's Artificial Intelligence Fairness 360 (AIF360) toolkit can improve the disparate impact of unfavorable characteristics of a dataset. Debiasing also demonstrates significant potential to reduce fake news generation based on the inherent bias in the data. These findings provide avenues for further research on adversarial collaboration and robust information systems.Originality/valueAdversarial debiasing of datasets demonstrates that by reducing bias related to protected attributes, such as sex, race and age, businesses can reduce the potential of exploitation to generate fake news through adversarial data.

Ma, Yongchao Martin; Dai, Xin; Deng, Zhongzhun

2024 Internet Research

doi: 10.1108/intr-02-2022-0113

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.Design/methodology/approachUsing four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.FindingsThe authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.Practical implicationsThe authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.Originality/valueThis paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

Zhang, Manyang; Yang, Han; Yan, Zhijun; Jia, Lin

2024 Internet Research

doi: 10.1108/intr-09-2022-0723

Doctor–medical institution collaboration (DMIC) services are an emerging service mode in focal online health communities (OHCs). This new service mode is anticipated to affect user satisfaction and doctors' engagement behaviors. However, whether and how DMIC occurs is still ambiguous because the topic is rarely examined. To bridge this gap, this study explores doctors' participation in DMIC services and its effects on their online performance, as well as its effect on patients' evaluation of them on OHC platforms.Design/methodology/approachThe authors propose hypotheses based on structural holes theory. A unique dataset obtained from one of the most popular OHCs in China is used to test the hypotheses, and difference-in-differences estimation is adopted to test the causality of the relationship.FindingsThe results demonstrate that providing DMIC services improves doctors' online consultation performance and patients' evaluations of them but has no significant effect on doctors' knowledge-sharing performance on OHC platforms. Doctors' knowledge-sharing performance and consultation performance mediate the relationship between participation in DMIC services and patients' evaluation of doctors. Regarding doctors' participation in DMIC services, its impact on doctors' consultation performance and patients' evaluation of them is weaker for doctors with higher professional titles than for doctors with lower professional titles.Originality/valueThe findings clarify the value creation mechanisms of online collaboration between doctors and medical institutions and thereafter facilitate doctors' participation in DMIC services and enhance the sustainable development of OHCs.

Ulqinaku, Aulona; Kadić-Maglajlić, Selma; Sarial-Abi, Gülen

2024 Internet Research

doi: 10.1108/intr-01-2023-0064

Today, individuals use social media to express their opinions and feelings, which offers a living laboratory to researchers in various fields, such as management, innovation, technology development, environment and marketing. It is therefore necessary to understand how the language used in user-generated content and the emotions conveyed by the content affect responses from other social media users.Design/methodology/approachIn this study, almost 700,000 posts from Twitter (as well as Facebook, Instagram and forums in the appendix) are used to test a conceptual model grounded in signaling theory to explain how the language of user-generated content on social media influences how other users respond to that communication.FindingsExtending developments in linguistics, this study shows that users react negatively to content that uses self-inclusive language. This study also shows how emotional content characteristics moderate this relationship. The additional information provided indicates that while most of the findings are replicated, some results differ across social media platforms, which deserves users' attention.Originality/valueThis article extends research on Internet behavior and social media use by providing insights into how the relationship between self-inclusive language and emotions affects user responses to user-generated content. Furthermore, this study provides actionable guidance for researchers interested in capturing phenomena through the social media landscape.

Smith, Kane; Gupta, Manu; Prakash, Puneet; Rangan, Nanda

2024 Internet Research

doi: 10.1108/intr-08-2022-0591

Ethereum-based blockchain technology (EBT) affords members of the Enterprise Ethereum Alliance (EEA) a market advantage in deploying blockchain within their organizations, including cybersecurity and operational benefits, that leads firms to strategically invest in this nascent technology. However, the impact of such strategic investments in EBT has yet to be explored in the context of its relationship to firm value. Therefore, this study explores EBT-specific firm-level characteristics that result in a stock market reaction to announcements of strategic investments.Design/methodology/approachThe authors use the event study methodology, strategic investment literature and signaling theory as contextualizing frameworks for their study. Additionally, the authors explore a new method for examining technology investments as a strategic counter to cybersecurity threats.FindingsFirms that signal to the market their strong commitment to their strategic investment by developing an EBT proof of concept see significantly higher market returns. Firms that have had prior cybersecurity incidents are rewarded by the market for strategically investing in EBT, and when firms with large undistributed free cash flows utilize this cash for strategic EBT investment, the market is more likely to reward these firms, indicating the market views EBT investment positively in these circumstances.Originality/valueThe results of this study provide new evidence of the value impact of EBT for firms that suffered cybersecurity events in the past. The authors provide empirical evidence of firm-level characteristics that investors use to discern whether a strategic investment in EBT will drive organizational value. Likewise, the authors demonstrate how signaling affects investor perceptions of strategic information technology (IT) investments in EBT.