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This study explores the influence of textual social cues on virtual influencers' perceived attractiveness, homophily and credibility, and their impact on consumers' purchase intentions. The moderating role of perceived anthropomorphism is also assessed.

A randomized between-subjects experiment with 265 participants (134 low social cue/131 high social cue) was conducted. Participants viewed a fictional virtual influencer’s social media profile and post, then completed a survey. Partial least squares structural equation modeling (PLS-SEM) analysis was used to examine the effects of textual social cues on attractiveness, attitude homophily, credibility and purchase intention as well as the moderating role of perceived anthropomorphism.

The study found that textual social cues directly influence attractiveness and attitude homophily, which significantly impact virtual influencer credibility. Credibility, in turn, strongly predicted purchase intention.

Incorporating textual social cues into a virtual influencer’s profile to create a likable persona can help overcome the novelty effect and build lasting relationships with followers. Marketers should use textual cues, like emojis and self-disclosure, to enhance marketing effectiveness and select virtual influencers aligned with their target audience.

This study is among the first to explore the role of textual social cues in virtual influencers, extending the source credibility model and social information processing theory to the influencer marketing context.

This study aims to understand the reasons for individuals switching from traditional banks to Internet-only banks and examine how switching intentions differ between Generation X and Generation Z. Notably, Generation Z, being digital natives, exhibits distinct characteristics compared to Generation X, who often referred to as digital immigrants. Given the technology-driven nature of Internet-only banks, a multi-group analysis between these two generations was conducted.

This study utilizes Bansal’s push–pull–mooring model as a framework to analyze switching intention. The study collected survey data from 383 Korean participants, consisting of 198 participants from Generation Z and 185 participants from Generation X.

The findings indicate that low satisfaction and discomfort are factors that push people to leave traditional banks. Specifically, Generation Z shows a significantly higher inclination to leave traditional banks due to discomfort. On the other hand, relative advantage, compatibility, observability and trialability are factors that pull people to switch to Internet-only banks. Generation X is more likely to consider adopting Internet-only banks when compatibility is high and complexity is low.

This study is the first to explore unique motivators for Generation Z, such as their discomfort with interpersonal interactions in the retail banking sector. These findings challenge earlier research emphasizing human interaction’s importance in technology adoption, offering insights into their future adoption of contactless services.

The purpose of this paper is to examine the resistance behavior of smart factories in small and medium-sized enterprises (SMEs). Drawing upon dual factor perspective, this study examines how two types of quality and perceived usefulness impact user resistance as enabling factors and how switching cost, skepticism, habit and inertia contribute to user resistance as inhibiting factors. Additionally, multi-group analysis is employed to compare small and medium enterprises.

Purposive sampling technique was employed to collect 460 Korean SMEs employees, consisting of 235 small enterprises and 225 medium enterprises. Partial least squares structural equation modeling was used for data analysis.

The results reveal that all three inhibiting factors, switching cost, skepticism and habit, are key antecedents of inertia. In small enterprises, skepticism has a greater impact on inertia, which in turn strongly affects resistance. Additionally, system quality is more crucial for small enterprises, whereas information quality holds more importance for medium enterprises in mitigating resistance. Moreover, when the implementation level of a smart factory is high, the effect of perceived usefulness on user resistance diminishes.

This study has revealed the importance of considering both enabling and inhibiting factors for the adoption of smart factory systems in the context of SMEs. Additionally, it has provided evidence that as the level of the smart factory system increases, the effect of perceived usefulness on user resistance decreases, thus making the transition to smart factory systems more challenging.

Digital stickers are being used frequently due to a rapid increase in the usage of mobile messenger services. Moreover, characters featured on digital stickers are being used as spokes-characters for brand marketing and advertisements. These characters create positive consumer attitude toward the brand and the brand's product or service.

Based on 307 surveys collected in Korea, this paper examines the relationship between the dimensions of the characters and the usage intention of an Internet-only banking service.

Results indicate that unlike conventional spokes-characters, the expertise of sticker characters does not have an influence on usage intention. However, all three dimensions of characters are effective in forming a positive brand equity, and this has a mediating role in enhancing usage intention.

Current research has brought academic attention to characters featured on digital stickers and have verified their significant role. Moreover, in an Internet-only banking context, branding strategy is an effective way to encourage customers to use services. Theoretical implications are addressed, as are implications for managers who are looking for a character that will execute effective marketing campaigns.

The purpose of this paper is to derive and empirically test a theoretically grounded model of motivations (socializing, entertainment, self-expression, and information seeking) that lead to the use of Facebook Places. Store atmosphere factors (ambient, design, and social factors) were also investigated as moderators of the relationship between the motivation and use of Facebook Places.

Based on a survey of 504 South Korean Facebook users, this research applies a structural equation modeling approach to test the research hypotheses.

The analysis revealed that motivations such as socializing, entertainment, and self-expression influence the use of Facebook Places, and design factors moderate the relationship between the motivations and the use of Facebook Places.

This study provide insights that can help Facebook and store owners develop effective marketing strategies that could increase revenue. When users check-in at a location it creates free word of mouth advertising. Thus, identifying the factors that motivate users to check-in at a particular location will be an important way to advertise.

While uses and gratification theory has mainly been used to study mass media, this study applied the theory to Facebook Places, a type of social networking sites-based location-based services. Additionally, this study added store atmosphere elements as moderators, which is an appropriate concept in the context of the Facebook check-in service.

Many scholars and practitioners claim that a supportive organizational culture can enable the successful implementation of knowledge management (KM) technology initiatives. However, little empirical research has been conducted to define the key organizational culture attributes that support more effective application of KM technologies. In this study, the 44 cultural attributes of the organizational culture profile (OCP) and the knowledge management technology profile (KMTP) instruments were used to identify and rank the most critical organizational culture attributes that promote knowledge sharing and KM technology implementation success. Data were collected from 26 US organizations that were implementing KM. The results of the data analysis revealed sufficient evidence to establish a correlation between specific cultural attributes and the successful implementation of knowledge management technology and knowledge sharing.

This study investigated information needs on COVID-19 by identifying topics discussed on social questions and answers (Q&A) about daily routines, problems, and health issues for survival. A layered model of contexts for everyday life information seeking (ELIS) was adapted for interpreting topics to better understand the contexts in which users could relate information needs.

Questions and answers posted on Naver Knowledge-iN were collected and analyzed during the first nine months following the outbreak. Time distribution, topic modeling, and association rule mining were applied to examine the topics on COVID-19 and their temporal variation.

Numerous topics related to the cognitive context (symptoms and masks) and situational contexts (international affairs, financial support, study, and work) were discovered. Topics related to social context were discussed moderately, but the number of questions on this topic increased with time. Strong associations were observed between terms related to symptoms, indicating their importance as a COVID-19 topic in health.

This study investigated topics of information needs using social Q&A data in which not only information inquiry but also information sharing coexist. The findings can help bridge the theory of ELIS to topic modeling in practice. The insights gained from this study can be used by information service providers for developing guidance and programs about how to survive during a pandemic.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2021-0547.

The authors derive a risk lower bound in estimating the threshold parameter without knowing whether the threshold regression model is continuous or not. The bound goes to zero as the sample size n grows only at the cube-root rate. Motivated by this finding, the authors develop a continuity test for the threshold regression model and a bootstrap to compute its p-values. The validity of the bootstrap is established, and its finite-sample property is explored through Monte Carlo simulations.