This study examines how interviewer type (AI vs. human), task type (factual vs. evaluative), and interview outcome (success vs. failure) jointly shape applicants' perceptions of procedural justice, source credibility, and negative word-of-mouth (WOM) intentions. Using a 2 × 2 × 2 between-subjects experimental design with 199 Korean adults, the study tested (a) the sequential mediating roles of procedural justice and source credibility (trustworthiness and expertise) in the relationship between interviewer type and negative WOM intentions, and (b) the moderating roles of task type and interview outcome on these indirect pathways. Results showed that AI interviewers, compared to human interviewers, generated lower procedural justice perceptions, which in turn reduced source trustworthiness and ultimately increased negative WOM intentions. A significant three-way interaction among interviewer type, task type, and interview outcome further emerged for source trustworthiness. In the success condition, AI interviewers were perceived as more trustworthy on factual than evaluative tasks. In the failure condition, however, this pattern reversed: AI interviewers were perceived as substantially less trustworthy than human interviewers specifically when the task was factual. Moderated mediation analyses further showed that the sequential indirect effect on negative WOM was significant only when applicants failed a factual task. These findings highlight that both task type and interview outcome are critical boundary conditions shaping applicants' responses to AI-mediated evaluation.
Leadership is a communicative process, yet little is known about how communication media shape leader effectiveness. Drawing on media richness theory, we investigated how communication media (written, audio, video) interact with leader communication techniques (cognitive, affective, behavioral) to influence follower reactions. In a preregistered vignette experiment with military recruits ( N = 400), participants received identical leader messages delivered using cognitive, affective, or behavioral communication techniques via written text, audio, or video. We assessed follower reactions, including task understanding, task motivation, and intention to act. Affective techniques positively influenced followers’ reactions across all media. Cognitive techniques elicited stronger perceived task relevance and task understanding when communicated in writing, suggesting that the effectiveness of communication media depends on the communication technique employed. Overall, written communication appeared most effective for cognitive leader communication techniques. These findings highlight the importance of jointly considering communication media and leader communication techniques when examining leader communication effectiveness.
Generative artificial intelligence (GenAI) is frequently presented as a linguistic equalizer in workplaces where English mediates recruitment, collaboration, documentation, and customer interaction. Productivity research partly supports this claim, particularly for less-experienced workers. This conceptual piece argues that such gains do not constitute linguistic equality and that, for business communication scholarship, the key issue is how GenAI redistributes communicative authority, adaptation, and credibility within organizations. I call this process Algorithmically Mediated Unequal Englishes (AMUE). It operates through four connected mechanisms: normative compression into AI-polished English, unequal linguistic and affective labor, automated misrecognition and screening, and a credibility trap surrounding authorship and disclosure. These mechanisms offer resources while preserving institutional ‘standards’ and listener expectations. Linguistically equitable AI governance must therefore move beyond providing access to tools. It must redefine effective communication through task relevance, intelligibility, accuracy, reciprocal accommodation, worker control over voice, and institutional responsibility for technological risk.
Communicating appreciation to employees is a simple yet powerful practice. When appreciation is expressed consistently by multiple organizational members, it can foster an organizational appreciation climate that benefits both individuals and organizations. An instrument measuring the organizational appreciation climate enables communication professionals to assess the extent to which appreciation is embedded in their organization and to implement related communication practices accordingly. To assess the organizational appreciation climate, we developed and validated the Organizational Appreciation Climate Scale (OACS). The unidimensional six-item scale demonstrated validity and reliability across five studies in its German, English and Danish versions (total N = 1,350). This paper demonstrates that an appreciation-oriented organizational climate is a measurable and distinct construct, shaped by communication among supervisors and colleagues. The multiple language versions of the OACS enables cross-cultural comparisons. Communication professionals can use the OACS to continuously assess and refine organizational communication, thereby fostering a supportive environment in organizations.
Given the rapid technological advances, organizations are increasingly adopting technologies to reach their audiences. Virtual influencers-computer-generated characters that mimic real human beings-have emerged as AI-driven communication tools on social media. Grounded in social presence theory, this study explores how their visual and mental human-likeness influence audience responses to corporate social responsibility (CSR) messages on environmental issues. An online survey of 502 U.S. social media users revealed that virtual influencers' lifelike appearance and cognition enhance perceived expertise and issue-influencer congruence. These perceptions, in turn, increase CSR message acceptance. The study offers theoretical and practical implications for adopting virtual influencers.
