Internet Research最新文献

Purpose

Self-presentation has emerged as a pivotal marketing strategy for service providers seeking to craft virtual images in the peer-to-peer (P2P) accommodation sector. However, the literature lacks an understanding of conversational self-presentation, which offers more informal and personal communication.

Design/methodology/approach

Drawing upon social interaction theory and uncertainty reduction theory, this study explores the impact of conversational self-presentation and its interplay with space sharing and review ratings on customer purchases.

Findings

Utilizing the fixed effects model on a panel dataset comprising 498,817 observations from Airbnb, we discover a positive association between conversational self-presentation and customer purchases. Furthermore, our analysis reveals that space sharing reinforces this positive effect, while review ratings tend to diminish it. A deeper mechanism analysis indicates that perceived authenticity plays a mediating role in the relationship between conversational self-presentation and customer purchases.

Research limitations/implications

This study highlights the importance of conversational communication in enhancing host–guest relationships under information asymmetry. However, the proposed model can be extended to other accommodation-sharing platforms in various regions, identifying whether cultural differences exist in explaining the importance of conversational self-presentation.

Practical implications

Marketers are suggested to build a harmonious customer–brand relationship and achieve superior performance through effective online communication.

Originality/value

This study adds valuable insights to the literature about self-presentation, social interaction and customer behavior by revealing the significance of conversational self-presentation in driving the growth of P2P accommodation businesses.

Purpose

Sharing co-owned information on social network platforms has become a common and inevitable phenomenon. However, due to the uniqueness of co-owned information, the privacy calculus theory based on a single information owner cannot explain co-owned information disclosure. Therefore, this study tries to investigate the underlying mechanism of users’ co-owned information disclosure from a collective privacy calculus perspective.

Design/methodology/approach

Through a survey of 740 participants, covariance-based structural equation modeling (CB-SEM) was used to verify the proposed model and hypotheses.

Findings

The results show that personal benefit, others’ benefit and relationship benefit promote users’ co-owned information disclosure by positively affecting personal distributive fairness and others’ distributive fairness perception. Meanwhile, personal privacy risk and others’ privacy risk prevent users’ co-owned information disclosure by negatively affecting personal distributive fairness and others’ distributive fairness perception. Besides, others’ information ownership perception enhances the positive effect of others’ distributive fairness perception on co-owned information disclosure intention. Furthermore, others’ information ownership strengthens the mediating role of others’ distributive fairness.

Research limitations/implications

The findings of this study enrich the research scope of information disclosure and privacy calculus theory and help social network platform developers design collective privacy protection functions.

Originality/value

This study develops a collective privacy calculus model to understand users’ co-owned information disclosure on social network platforms, confirming the mediating role of collective distributive fairness and the moderating role of others’ information ownership perception in the process of collective privacy calculus.

Purpose

Online health communities (OHCs) have emerged on the Internet, substantially changing the conventional healthcare delivery model. Despite this emergence, the lack of patient participation and contribution always limits the success and sustainability of OHCs. Previous studies have disclosed that patients’ value co-creation behavior (VCB) helps organizations sustain OHCs. However, how the recent surge in artificial intelligence (AI) tools, such as social support chatbots (SSCs), drives patients’ VCB is still unknown. Therefore, this study examines the complex mechanism behind patients’ VCB to establish sustainable OHCs.

Design/methodology/approach

Using value co-creation and social support theories, the author develops a moderated mediation model and analyzes survey data from 338 respondents using partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) methods.

Findings

Results demonstrate that perceived social support (PSS) from SSCs positively affects VCB directly and indirectly via patient learning (PL). This indirect effect is stronger when patient ability/readiness (PAR) is high. ANN findings highlight the model’s robustness and the significant role of PAR in VCB.

Originality/value

This study’s integrated framework offers unique insights into key drivers of patients’ VCB in OHCs. The findings indicate that PSS from SSCs enhances PL and VCB, with PAR influencing the strength of these relationships. Understanding these dynamics can inform user-centric interventions to promote effective learning and collaboration in OHCs.

Purpose

There are two main objectives in this study. First, we aim to develop a set of constructs for health task management support (HTMS) features to evaluate which health-related tasks are supported by mobile health application (mHealth app) functions. Second, drawing on innovation resistance theory (IRT), we examine the impacts of the newly developed HTMS dimensions on perceived usefulness, alongside other barrier factors contributing to technology anxiety.

Design/methodology/approach

Using a mixed-method research design, this research seeks to develop new measurement scales that reflect how mHealth apps support older adults’ health-related needs based on interviews. Subsequently, data were collected from older adults and exploratory factor analysis was used to confirm the validity of the new scales. Partial least squares structural equation modeling (PLS-SEM) was used to analyze survey data from 602 older adults.

Findings

The PLS-SEM results indicated that medical management task support, dietary task support, and exercise task support were positively associated with perceived usefulness, while perceived complexity and dispositional resistance to change were identified as antecedents of technology anxiety. Perceived usefulness and technology anxiety were found to positively and negatively influence adoption intention, respectively.

Originality/value

This study enriches the information systems literature by developing a multidimensional construct that delineates how older adults’ health-related needs can be supported by features of mHealth apps. Drawing on IRT, we complement the existing literature on resistance to innovation by systematically examining the impact of five types of barriers on technology anxiety.

Purpose

A major portion of our social interaction now occurs online, facilitated by social networking sites (SNSs) that enable people to connect and communicate at will. However, the characteristics of SNS communication can introduce problematic outcomes on otherwise healthy processes, one of which is social comparison. In this work, we investigate whether compulsive SNS use could be driven by two phenomena related to social comparison: the fear of missing out (FoMO) and envy.

Design/methodology/approach

Using the stimulus-organism-response framework, we developed a model that was tested with data from a sample of SNS users (N = 330) based in the United States. The analysis was done through partial least squares structural equation modeling.

Findings

Our findings show FoMO's association with the two forms of dispositional experienced envy, benign and malicious, as well as expected envy of others (expected envy). Interestingly, benign and expected envy were associated with SNS stalking and self-disclosure, but malicious envy had non-significant associations. Finally, both SNS stalking and self-disclosure were linked to compulsive SNS use.

Originality/value

We study the nuanced ways in which the two forms of experienced envy and expected envy can be triggered by FoMO and result in users' engagement with problematic SNS use. Our research provides evidence that, in addition to benign envy being an antecedent of compulsive SNS use, the wish to invoke envious feelings in others can also significantly drive compulsive use.

Purpose

This study examines how users' multidimensional representational fidelity factors affect sociability and cyberself engagement in the Metaverse platform; that is, how they interact with newly defined self-images as their personas in the environments. It investigates how representational fidelity serves platform users to perform social roles and increase their sociability by establishing a new cyberself, thus influencing continuous platform use.

Design/methodology/approach

This study surveyed 314 users of the Metaverse platform Horizon, where users can create a virtual agent avatar, meet people in the same online environment in real time, and interact with a sense of three-dimensional immersion. Data were analyzed using partial least squares regression models.

Findings

User socialization significantly influenced the intention to use the Metaverse platform. Representational fidelity was a crucial variable for sociability, and activity representational fidelity was the most influential aspect among the four other elements. Platforms should consider how to enable users to create and use activities that faithfully represent their personas.

Originality/value

The novelty of this study is that it introduces representational fidelity based on representation theory into the context of virtual persona in the Metaverse platform. This study extended representational fidelity to the socialization perspective by utilizing the integrated model of user satisfaction and the technology acceptance model. Through the results, this study emphasized that users' sociability significantly influences their intention to use the Metaverse platform. Finally, this study provides a feasible guideline on how practitioners could design and strengthen their platforms so that users can represent their cyberselves faithfully.

Purpose

Social networking sites (SNS) have become popular mediums for individuals to interact with others. However, despite the positive impact of SNS on people’s lives, cyberbullying has become prevalent. Due to this prevalence, substantial research has examined cyberbullying from the perspectives of perpetrators, bystanders, and victims, but little is known about SNS users’ confrontations with cyberbullying. The objectives of this study are to examine confrontation as a victim’s coping response, the effect of blockability affordance on victims’ protection motivation, the impact of a victim’s experiences with cyberbullying perpetration, and social desirability (SD) bias in the context of cyberbullying victimization.

Design/methodology/approach

This study examines the effect of blockability affordance on SNS users’ protection motivation. It also investigates the relationships among perceived threat, perceived coping efficacy, and use of confrontation. Furthermore, this investigation analyzes the effect of SNS users’ experiences as perpetrators on their decision to confront cyberbullies. Finally, this study assesses and controls SD bias in SNS users’ confrontation behavior. To test the research model, we used an online vignette study to collect 314 data points.

Findings

Blockability affordance, perceived threat, perceived coping efficacy, and cyberbullying perpetration experiences are essential factors in explaining use of confrontation. This study also finds SD bias in the context of cyberbullying victimization.

Originality/value

This is one of the first studies in information systems research to empirically examine the effect of blockability affordance in the context of cyberbullying.

Purpose

Sports advertisements such as the Super Bowl showcase products and brands that have invested increasingly large sums financially to gain viewers’ attention. However, how audio features in advertisements impact viewers' behavior remains unexplored.

Design/methodology/approach

Using the lens of signaling theory, this research uses advanced data analytics of voice and music audio in Super Bowl advertisements to examine its impacts on viewers.

Findings

Results show that advertisement viewers prefer more voiced frames and have a greater liking behavior of voiced frames with a low intensity (less loud) and a higher F1 frequency, which is typically associated with male vocal tracts. A fast music tempo works better for longer advertisements. The model controls for various types of ad appeals. The research underlines the importance of voice and music in signaling successful brand features that are likely to increase the ad-liking behavior of consumers (positive effect).

Research limitations/implications

The current research implies that brands advertising through sports ads must carefully select voice actors and music in order to provide the most positive signals for a brand to have the most significant effect and, thus, a greater return on the high sums invested in the ads.

Originality/value

First, this research contributes in terms of a new research process for using audio analytics in advertising. The detailed research process outlined can be used for future research examining audio and music from advertisements. Second, our findings provide additional support to the important role of voice features (e.g. intensity and frequency) as signals in inducing responses from consumers (Biswas et al., 2019; Hagtvedt and Brasel, 2016). Third, the study surfaces a new theoretical association: the effect of tempo in moderating the relationship between duration and propensity to like an ad.

Purpose

Although digital transformation (DT) has emerged as an important phenomenon for both research and practices, the influences remain inconclusive and inadequate. The emerging artificial intelligence (AI) technologies further complicate the understanding and practices of DT while understudied yet. To address these concerns, this study takes a process perspective to empirically investigate when and how digital-intelligence transformation can improve firm performance, aiming to enrich the literature on digital-intelligence transformation and strategic information systems (IS) field.

Design/methodology/approach

Drawing on the dynamic capability view and business agility, we took a process perspective to conceptualize and empirically examine the influence of digital-intelligence transformation and the process characteristics. Taking a continuous panel dataset of listed Chinese firms covering 2007 to 2020, we investigated digital-intelligence transformation’s effect on firm performance and the moderating roles of three strategic aspects: pace, scope and rhythm.

Findings

This study found that digital-intelligence transformation positively affects firm performance and is moderated by the characteristics of transformation processes (i.e. pace, scope and rhythm). Specifically, the high-paced and rhythmic transformation processes facilitate the positive relationship, while the large scope undermines the benefits of transformation. These relationships hold across various endogeneity and heterogeneity analyses.

Originality/value

Our findings provide valuable implications for digital-intelligence transformation and strategic IS field. First, this study enriches existing literature on digital-intelligence transformation by empirically investigating the influence from a process perspective. Moreover, this study provides insights into a comprehensive understanding of the complexity of digital-intelligence transformation and the influences of AI. Finally, this study provides practical implications on how to make digital-intelligence transformation to benefit firm performance.

Purpose

The purpose of this study is twofold: first, to identify the categories of artificial intelligence (AI) chatbot service failures in frontline, and second, to examine the effect of the intensity of AI emotion exhibited on the effectiveness of the chatbots’ autonomous service recovery process.

Design/methodology/approach

We adopt a mixed-methods research approach, starting with a qualitative research, the purpose of which is to identify specific categories of AI chatbot service failures. In the second stage, we conduct experiments to investigate the impact of AI chatbot service failures on consumers’ psychological perceptions, with a focus on the moderating influence of chatbot’s emotional expression. This sequential approach enabled us to incorporate both qualitative and quantitative aspects for a comprehensive research perspective.

Findings

The results suggest that, from the analysis of interview data, AI chatbot service failures mainly include four categories: failure to understand, failure to personalize, lack of competence, and lack of assurance. The results also reveal that AI chatbot service failures positively affect dehumanization and increase customers’ perceptions of service failure severity. However, AI chatbots can autonomously remedy service failures through moderate AI emotion. An interesting golden zone of AI’s emotional expression in chatbot service failures was discovered, indicating that extremely weak or strong intensity of AI’s emotional expression can be counterproductive.

Originality/value

This study contributes to the burgeoning AI literature by identifying four types of AI service failure, developing dehumanization theory in the context of smart services, and demonstrating the nonlinear effects of AI emotion. The findings also offer valuable insights for organizations that rely on AI chatbots in terms of designing chatbots that effectively address and remediate service failures.