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This paper aims to examine the strength characteristics of a stressful event (i.e. novelty, disruption, and criticality) as factors that drive people’s social media use for seeking different types of supportive resources (i.e. emotional, appraisal, informational, and instrumental support) to facilitate emotion-focused and problem-focused coping. We further assess the impact of different types of social support obtained via social media use on people’s coping effectiveness.

Our study uses an online survey collecting data at two points in time from 291 social media users during the COVID-19 pandemic. Structural equation modeling was used for data analysis.

Empirical results reveal the usefulness and limitations of social media use as a coping mechanism. All three event strength characteristics influence people’s social media use for both emotion-focused and problem-focused coping. Event novelty motivates people’s pursuit of informational support on social media, event disruption drives social media use for seeking all four types of support, and event criticality motivates social media use for seeking emotional and informational support. However, only emotion-focused resources – emotional support and appraisal support – are found to significantly affect people’s coping effectiveness.

Our study contributes to a better understanding of the role played by social media when people cope with a stressful event. Applying the three characteristics of event strength allows us to identify people’s need for different supportive resources depending on how they perceive the event. Our analysis of the main and mediating effects of the four types of social support shows that not all types of social support can significantly enhance users’ coping effectiveness.

Being clear and specific on what moderating and/or mediating variables are included and what effects are observed in academic research helps the reader to better understand the academic research context and results. But in terms of managerial relevance, it is also important to do this in a way that it provides descriptive, goal and operational relevance to decision makers in practice, depending on the type of intended research. This article wants to provide “a question-based step-by-step guide” on how to make the analysis of moderating/mediating variables and their observed effects more managerially relevant.

Based on a critical review of the literature, important criteria of managerial relevance are confronted with important aspects of theory building with respect to mediating and moderating effects, leading to best-practice insights and recommendations. Moreover, exemplary articles are used to illustrate these findings.

The insights and step-by-step recommendations assist the academic researcher in making choices when analyzing moderators and mediators, by not only taking a theoretical perspective, but also a managerial (relevance) perspective. Adding moderators/mediators may for instance challenge the “core logic of managerial practice” (in terms of thinking and decision making), even if it does not change the “core logic of a theory” as such. In the other direction, academics (and their theory) may be challenged by practitioners, in the way they define moderators/mediators and their levels. The steps in this article relate to aspects such as measurability, controllability and role of moderators and mediators in managerial problem and decision contexts. In case of multiple moderating and/or mediating variables, the decision architecture for managers becomes more complex, especially when the effects are countervailing/opposite. Multiple studies in this article illustrate that in that case, making optimal decisions becomes a “balancing” act for managers/decision makers and may even challenge their common beliefs (e.g. linear thinking).

The guidelines on managerial relevance of moderating and/or mediating variables and their effects can be used by academic researchers and editors of academic journals, pursuing not only academic rigor, but also managerial relevance. Besides being a guide for managerially relevant output, it also helps in determining for which questions in the research process, input from practitioners or at least insights from practice (e.g. through sources such as business magazines and portals) may be needed. The guidelines may also be used for teaching purposes, complementing more theoretical articles that mainly focus on methodological/statistical issues of moderating/mediating variables and their effects.

The aim of this study was to measure the level of challenges faced by novice teachers in South Africa. Findings suggest a need for professional development courses to upskill teachers with effective pedagogies that can incorporate the social and emotional components into teaching and learning.

This study applied a descriptive research methodology by administering a questionnaire to 143 novice teachers. The data analysis technique was the Rasch model.

The findings indicated high item and person reliability, with a good item fit and polarity values that are compatible with the Rasch model. The three major challenges identified are uninvolved parents, discipline problems and a lack of guidance and counselling. These challenges can be related to social and emotional learning (SEL) components.

The study used quantitative methods and discovered the challenges that novice teachers face. If the research uses a combination of qualitative methods, it will be possible to better identify the specific causes of the above three challenges of novice teachers.

Due to the complex nature of South African society, many novice teachers are overwhelmed by the challenges they face when entering the profession. These challenges are often embedded in societal risk factors, which complicate the transition from student teacher to novice teacher. The major challenges identified in this study can be related to SEL components, as the challenges are closely linked to the psychological and social backgrounds of the students. Teachers in this study indicated that they found it difficult to deal with these challenges at the beginning of their careers.

By identifying the challenges facing new teachers in South Africa, they will be better prepared for their work in schools. Therefore, they will improve the above situation to continue developing professionally.

The findings indicated high item and person reliability, with a good item fit and polarity values that are compatible with the Rasch model. Teachers in this study indicated that they found it difficult to deal with these challenges in the beginning of their careers. Professional development courses are suggested to help teachers deal with issues such as discipline, uninvolved parents and guidance and counselling effectively. Higher education programmes should also include these topics in their curricula for student teachers. A greater emphasis on training teachers in their pastoral roles is suggested.

The purpose of this article is to illustrate an emerging typology that theoretically links work values with personal values and to provide evidence of construct validity for this typology.

A hypothesis was developed that four types of work values – intrinsic, extrinsic, prestige and affective – underlie the relative importance that individuals place on aspects of work. Evidence of construct validity was provided using triangulation analysis. Data from three different samples in Japan were analysed with three different techniques; exploratory factor analysis (N = 229), hierarchical cluster analysis (N = 244) and smallest space analysis (N = 203).

The results demonstrate acceptable internal consistency and a coherent structure that fits the theoretical model across methods and samples. These findings lend strong support to the use of the intrinsic, extrinsic, affective and prestige typology for studying work values. This will hopefully encourage field scholars to adopt the typology in future values-based explorations in the context of work.

The study adds to the emergent literature in business research that stresses the importance of triangulation analysis to enhance the reliability and validity of findings. In this sense, it is an innovative paradigm of a multiple triangulation approach, which combines both data and within-method triangulation. The methods employed covered – for the first time – all commonly applied techniques for exploring the structure underlying the data and provided inductive, deductive and spatial evidence to corroborate the observed structure of work values.

Artificial intelligence (AI) has received much attention due to its promethean-like powers to transform the management and delivery of public sector services. Due to the proliferation of research articles in this context, research to date is fragmented into research streams based on different types of AI technologies or a specific government function of the public sector (e.g. health, education). The purpose of this study is to synthesize this literature, identify challenges and opportunities, and offer a research agenda that guides future inquiry.

This paper aggregates this fragmented body of knowledge by conducting a systematic literature review of AI research in public sector organisations in the Chartered Association of Business Schools (CABS)-ranked journals between 2012 and 2023.

The search strategy resulted in the retrieval of 2,870 papers, of which 61 were identified as primary papers relevant to this research. These primary papers are mapped to the ten classifications of the functions of government as classified by the Organisation for Economic Co-operation and Development (OECD), and the reported challenges and benefits aggregated.

This study advances knowledge by providing a state-of-the-art of AI research based the OECD classifications of government functions, reporting of claimed benefits and challenges and providing a research agenda for future research.

Given its ability to improve user interaction and labor productivity, ease human workloads and cut maintenance costs, public sectors are using AI-based robotic technology (AI-RT) at an accelerated rate. There is, however, little knowledge about the variables affecting citizens' participation when services backed by AI-RT are offered. In order to better understand the elements that influence AI-RT citizens' involvement and the moderating function of trusts in governmental organizations, this article draws on ideas from the Consumer Value Theory.

Out of 500 survey forms that were distributed to Indonesian people who had experience in using AI-RT devices in public service hall (e.g. airport’s Auto Gate), 367 returned the completed feedbacks. Data analysis used a step-by-step hierarchical moderated regression examination using SPSS 24 version.

Citizens’ involvement is positively correlated with esthetics and customization and adversely correlated with period expended using the AI-RT. Additionally, the findings imply that citizens who have greater levels of faith in governmental institutions are more likely to benefit favorably from the customization and esthetics of AI-RT.

The AI-RT must be capable of customizing the distribution of the appropriate materials to the appropriate individual at the appropriate moment, and public managers should guarantee that it is esthetically pleasing. Additionally, they ought to place a high priority on winning the trust of the populace in order to increase citizens’ involvement.

This paper was among the initial efforts that discover the determinants of citizens’ involvement in the AI-RT and the moderating effect of trusts in governmental organizations on the links between predictors and predicted variable, especially in an emerging country such as Indonesia.

This study investigates the profound impact of artificial intelligence (AI) capabilities on decision-making processes and organizational performance, addressing a crucial gap in the literature by exploring the mediating role of decision-making speed and quality.

Drawing upon resource-based theory and prior research, this study constructs a comprehensive model and hypotheses to illuminate the influence of AI capabilities within organizations on decision-making speed, decision quality, and, ultimately, organizational performance. A dataset comprising 230 responses from diverse organizations forms the basis of the analysis, with the study employing a partial least squares structural equation model (PLS-SEM) for robust data examination.

The results demonstrate the pivotal role of AI capabilities in shaping organizational decision-making processes and performance. AI capability significantly and positively affects decision-making speed, decision quality, and overall organizational performance. Notably, decision-making speed is a critical factor contributing significantly to enhanced organizational performance. The study further uncovered partial mediation effects, suggesting that decision-making processes partially mediate the relationship between AI capabilities and organizational performance through decision-making speed.

This study contributes to the existing body of literature by providing empirical evidence of the multifaceted impact of AI capabilities on organizational decision-making and performance. Elucidating the mediating role of decision-making processes advances our understanding of the complex mechanisms through which AI capabilities drive organizational success.

Residing in a densely populated urban area possesses its allure; nonetheless, it can significantly impact physical and mental well-being owing to the persistent stress and information overload inherent in urban settings. This study aims to introduce a neuro-urbanism framework that can guide urban planners and designers in quantitatively evaluating individuals' responses to virtual simulated environments.

Our study consisted of two phases after randomly selecting six locations representing three types of urban areas in Bratislava, Slovakia: urban spaces, urban streets, and public parks. First, we conducted a Mentimeter live polling (dialogic survey fusion), followed by an experiment involving volunteer participants from the Slovak University of Technology. This experiment employed an electroencephalogram (EEG) with virtual reality headsets to virtually explore participants' responses to the selected locations.

The EEG signal analysis revealed significant differences in relaxation levels across the selected locations in this study. Urban streets with commercial activities promote mental well-being more effectively than public parks, challenging the preconception that restorative environments are exclusively confined to public parks.

The results demonstrate a replicable neuro-urbanism framework comprising three distinct stages: problem-based technology rooted in neuroscience, experimental setup and deliverables, and identification of restorative environments.

The present research aims to explore the drivers of generative artificial intelligence (GEN AI)-based innovation adoption in the hospitality industry in Jordan.

To address the research gap and achieve the research work objectives, the Technology-Organization-Environment (TOE) lens and the structural equation modeling (SEM) approach were employed to analyze the sample data collected (n = 221) from the hospitality industry.

The findings indicate that relative advantage, top management support, organizational readiness, organizational culture, competitive pressures, government regulations support and vendor support significantly influence the GEN-AI-based innovation adoption, while the technological complexity is negatively associated with GEN-AI-based innovation adoption. Furthermore, the results showed there is no significant effect of cost on GEN-AI-based innovation adoption.

The paper analyses the TOE framework in a new technological setting. The paper also provides information about how GEN-AI-based innovation adoption may influence hospitality industry performance. Overall, this article provides new insights into the literature concerning AI technologies and through the TOE lens.

The purpose of this study is to investigate the key characteristics of artificial intelligence (AI) in organizational settings, analyze its capacity to reduce customer service jobs in favor of more advanced roles and analyze its efficacy in candidate screening by emphasizing performance.

A comprehensive analysis of 40 papers is performed using the PRISMA method based on data from Web of Science, Scopus, Emerald and Google Scholar.

The findings show optimized human resource management operations such as recruiting and performance monitoring, resulting in increased precision in hiring and decreased employee turnover. Customer service automation redistributes human labor to more intricate positions that need analytical reasoning and empathetic skills.

The study has two key implications. First, AI can streamline customer service, freeing up human workers for more complex tasks. Second, AI may increase candidate screening accuracy and efficiency, improving recruiting outcomes and organizational performance.

The study adds to the current literature by shedding light on the intricate relationships between AI and organizational performance and providing insights into the processes underpinning trust-building in AI technology.