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Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.

We investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.

Different interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.

Findings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.

The applications of Artificial Intelligence (AI) in various areas of professional and knowledge work are growing. Emotions play an important role in how users incorporate a technology into their work practices. The current study draws on work in the areas of AI-powered technologies adaptation, emotions, and the future of work, to investigate how knowledge workers feel about adopting AI in their work.

We gathered 107,111 tweets about the new AI programmer, GitHub Copilot, launched by GitHub and analysed the data in three stages. First, after cleaning and filtering the data, we applied the topic modelling method to analyse 16,130 tweets posted by 10,301 software programmers to identify the emotions they expressed. Then, we analysed the outcome topics qualitatively to understand the stimulus characteristics driving those emotions. Finally, we analysed a sample of tweets to explore how emotional responses changed over time.

We found six categories of emotions among software programmers: challenge, achievement, loss, deterrence, scepticism, and apathy. In addition, we found these emotions were driven by four stimulus characteristics: AI development, AI functionality, identity work, and AI engagement. We also examined the change in emotions over time. The results indicate that negative emotions changed to more positive emotions once software programmers redirected their attention to the AI programmer's capabilities and functionalities, and related that to their identity work.

Overall, as organisations start adopting AI-powered technologies in their software development practices, our research offers practical guidance to managers by identifying factors that can change negative emotions to positive emotions.

Our study makes a timely contribution to the discussions on AI and the future of work through the lens of emotions. In contrast to nascent discussions on the role of AI in high-skilled jobs that show knowledge workers' general ambivalence towards AI, we find knowledge workers show more positive emotions over time and as they engage more with AI. In addition, this study unveils the role of professional identity in leading to more positive emotions towards AI, as knowledge workers view such technology as a means of expanding their identity rather than as a threat to it.

The objective of this paper is to further the understanding of netnography as a research method for supply chain academics. Netnography is a method for gathering and gaining insight from industry-specific online communities. We prescribe that viewing netnography through the lens of the supply chain will permit researchers to explore, discover, understand, describe or report concepts or phenomena that have previously been studied via survey research or quantitative modeling.

To introduce netnography to supply chain research, we propose a framework to guide how netnography can be adopted and used. Definitions and directions are provided, highlighting some of the practices within netnographic research.

Netnography provides the researcher with another avenue to pursue answers to research questions, either alone or in conjunction with the dominant methods of survey research and quantitative modeling. It provides another tool in the researchers’ toolbox to engage practitioners in the field.

The development of netnography as a research method is associated with Robert Kozinets. He developed the method to study online communities in consumer behavior. We justify why this method can be applied to supply chain research, how to collect data and provide research examples of its use. This technique has room to grow as a supply chain research method.

This study addressed a relatively understudied process of school leadership: the principal-teacher conversation about instructional change. Two distinct conversation structures were examined: controlling conversation and transformative leadership conversation (TLC). Self-determination theory (SDT) was used to make the case that TLC is a better fit for instructional change than controlling conversation. Hypotheses were developed on the relationship between principal-teacher conversation and teacher trust in the principal, teacher autonomy and teacher vitality. These mental states were identified for empirical testing because of their influence on change processes.

The empirical study used a correlational research design with survey data. The data came from a random sample of 2,500 teachers from the population of certified teachers in a southwestern state in the USA. Useable survey responses were obtained from 1,615 teachers, for a response rate of 65 percent. Teachers in the sample averaged 15 years of teaching experience, with 7 years in their current school. Around 81% of teachers identified as female and 18 percent as male and 79% of teachers listed a racial identification as white. Hypotheses were tested in a path model using AMOS 28.0 with robust maximum likelihood (MLR).

As hypothesized, TLC had moderate to strong positive relationships with teacher trust in the principal, teacher autonomy and teacher vitality. Controlling conversation had small, negative relationships with teacher trust in the principal and teacher autonomy. Controlling conversation was not related to teacher vitality in the path analysis. Compared with controlling conversation, TLC had stronger relationships with teacher mental states.

The results of this study begin to reveal useful evidence on the inherent social-psychological mechanisms active in principal-teacher conversations. With results indicating that conversation structure has consequences for positive teacher mental states, the study directs attention to a ubiquitous yet understudied leadership process.

This paper aims to develop a comprehensive model on intra- and interpersonal emotion regulation (ER) in hospitality and tourism (H&T) service encounters.

A critical review and reflection of ER research from multiple disciplines was conducted. Methodologies appropriate for investigating ER were also reviewed.

A comprehensive framework was proposed to outline key influential factors, processes and consequences of intra- and interpersonal ER in service encounters in the H&T industry. Methodologies integrating advanced tools were suggested to measure complex and dynamic emotion generation and regulation processes in social interactions from a multimodal perspective.

The researchers developed a comprehensive conceptual model on both intra- and interpersonal ER based on a critical review of the most recent psychological research on ER. Various theoretical and methodological considerations are discussed, offering H&T scholars a solid starting point to explore dynamic emotion generation and regulation processes in complex social settings. Moreover, the model provides future directions for the expansion of ER theories, which have been mostly developed and tested based on laboratory research.

The proposed model addresses two critical issues identified in emotion research in the H&T field: the lack of a dynamic perspective and the neglect of the social nature of emotions. Moreover, the model provides a roadmap for future research.

As Australians experience more fierce and frequent natural disasters, there are urgent calls for businesses to meaningfully respond to climate change. Australian financial and professional services employees occupy an ambiguous space as climate mitigation measures have different economic implications for their clients. The purpose of this paper is to investigate how Australian professionals experience climate change and respond to the issue within their workplace.

This mixed methods study applies a systems thinking framework to investigate: how do professionals’ experiences of the issue of climate change and the workplace influence their cognitions, emotions and behaviour? And in particular, what psychosocial antecedents precede voicing climate concern?

Firstly, a survey of professionals (N = 206) found social norms, perceived behavioural control and biospheric values, but not attitudes, significantly predicted prohibitive green voice. Middle managers were significantly likely to voice climate concern, whereas senior managers were significantly likely to express climate scepticism. Ten professionals were then interviewed to gain a contextualised understanding of these trends. Interpretive phenomenological analysis identified five interrelated themes: (1) active identity management, (2) understanding climate change is escalating, (3) workplace shapes climate change response, (4) frustration and alienation and (5) belief that corporations prioritise profit.

Findings are discussed in relation to how employees may both embody and adapt their organisations. These results have implications for understandings of workplace meaningfulness and organisational risk governance.

This conceptual paper explores the complex, and neglected, area of mentor development in initial teacher education (ITE) in Australia. It focuses on the emotionality of mentoring, drawing on concepts of emotional labour and emotional intelligence to develop a framework of effective mentoring that helps explain the essence of a mentor’s role in supporting preservice teachers.

This conceptual paper draws together mentor-support practice wisdom and research literature from several relevant areas. It draws on constructive developmental theories and complex stage theory to reaffirm the intricate nature of mentor learning and development. This paper critiques the current utilitarian emphasis on mentoring as a way to improve student outcomes without first having clarity on how to improve mentoring itself.

We introduce the mentoring as emotional labour framework as a way to better understand the nature of mentoring within ITE and as a tool for developing more effective mentor supports. We present “exemplar cases”, which are amalgamations of field observations to illustrate aspects of the framework – however, we do not claim they provide evidence of the utility or accuracy of the framework.

Previous research and policy have tended to gloss over the skills required for effective mentoring, whereas this paper places the emotional labour of mentoring front and centre, explicitly conceptualising and describing the personal and interpersonal skills required in a way that aims to support and empower mentors to recognise existing strengths and areas of potential growth.

The spread of technology usage and the increasing popularity of network technology have formed a basis for alternative working systems as virtual teams and teleworking in the working environment. However, social relations have weakened in these working systems and loneliness has become a common emotion in the workplace. Hence, the concept of loneliness has attracted significant interest in recent years. Yet, despite this interest, research examining loneliness in the workplace is limited in the literature. This study aims to investigate the role of workplace loneliness on intrinsic motivation and the effect of intrinsic motivation on employee well-being through the moderating role of organizational nostalgia.

Data were collected from 380 respondents working in different sectors. The hypotheses were tested by using structural equation modeling and PROCESS macro for SPSS.

Results empirically showed that workplace loneliness negatively relates to intrinsic motivation, intrinsic motivation positively relates to employee well-being, intrinsic motivation mediates the link between workplace loneliness and employee well-being and organizational nostalgia moderates the link between workplace loneliness and intrinsic motivation. Furthermore, organizational nostalgia also moderates the indirect effect of workplace loneliness on employee well-being through intrinsic motivation.

To the best of the authors’ knowledge, this study contributes to the existing literature on workplace loneliness by investigating underlying mechanisms and boundary conditions for loneliness in the workplace.

People are more likely to participate in work-related events that might cause positive and negative affective reactions. Prior research linked coworker friendship with incivility; however, few studies investigated negative workplace gossip. Simultaneously, linking coworker friendship with incivility through positive/negative affective responses is lacking. As such, this paper aims to examine this relationship via the dual mediation effect of positive and negative workplace gossip.

A total of 398 subordinates at family restaurants in Greater Cairo were surveyed, and data was analyzed using SmartPLS4.

Coworker friendship significantly influences coworker incivility via positive and negative workplace gossip and other underlying mechanisms.

Managers should take the initiative to decrease gossip by sharing information promptly and thoroughly and establishing effective channels for information exchange. In the case of an informal plan, restaurant managers may seek to create a welcoming and motivating corporate atmosphere and cultivate social ties among subordinates to prevent the creation of negative gossip. Restaurant managers should give victims of negative gossip timely psychological counseling.

This paper contributes to the restaurant literature on affective emotional responses to coworkers’ judgment-driven behavior from new perspectives.

This study aimed to evaluate the potential of artificial intelligence (AI) as a tool for knowledge synthesis, the production of written content and the delivery of coaching conversations.

The research employed the use of experts to evaluate the outputs from ChatGPT's AI tool in blind tests to review the accuracy and value of outcomes for written content and for coaching conversations.

The results from these tasks indicate that there is a significant gap between comparative search tools such as Google Scholar, specialist online discovery tools (EBSCO and PsycNet) and GPT-4's performance. GPT-4 lacks the accuracy and detail which can be found through other tools, although the material produced has strong face validity. It argues organisations, academic institutions and training providers should put in place policies regarding the use of such tools, and professional bodies should amend ethical codes of practice to reduce the risks of false claims being used in published work.

This is the first research paper to evaluate the current potential of generative AI tools for research, knowledge curation and coaching conversations.