Search results

The purpose of this study is to investigate the impact of leaders’ use of motivational language on psychological relatedness and its effect on employee well-being in flexible and remote working conditions.

A survey among 375 full-time working professionals in the US was conducted with varying frequencies of remote work arrangements. For the analysis, we used a series of PROCESS analyses to examine the moderating effect of leaders’ motivational language use on the relationship between participants’ remote work status and relatedness, with employee well-being as the dependent variable.

The findings revealed a significant moderating effect of leaders’ perlocutionary (direction-giving) language use on the relationship between employees’ remote work status and relatedness. Specifically, the relationship between remote work status and relatedness was stronger when the use of perlocutionary (direction-giving) language gradually increased. Such enhanced relatedness, in turn, generated higher satisfaction and psychological well-being. The study shows the strategic advantage of direction-giving language in enhancing relatedness, thereby contributing to higher levels of employee satisfaction and psychological well-being in remote work environments.

The originality of this article lies in its integration of motivational language theory and self-determination theory to explore the well-being of employees within flexible and remote work status. Furthermore, we conceptualize remote work as a continuous variable with different degrees of flexibility, ranging from occasional telecommuting to fully remote work, allowing for a nuanced understanding of how leaders’ use of motivational language interacts with varying levels of remote work arrangements to influence employee well-being.

This study empirically investigates the long-run and interactive effect of Chinese foreign direct investment (CFDI) on Africa's industrialization process.

The authors employed industry and manufacturing value-added (% GDP) as the dependent variables and applied the two-step GMM and panel-corrected standard errors' (PCSE) techniques involving a panel of 49 African countries from 2003 to 2020.

The industry value-added (% GDP) results show that the presence of CFDI propels industrial productivity by contributing to value-addition in the short and long run. Moreover, the study shows that the magnitude of the CFDI effect on industrialization is pronounced in the short-run when it is associated with labor and natural resources. This result reveals efficiency-seeking behavior of CFDI and the CFDI-Africa industrialization nexus is not primarily resource-driven. More importantly, the authors found human capital, electricity and political stability, as primary factors that magnify CFDI's effect on industrialization in the short and long run.

This study is the first to use macro-level data to empirically investigate and find the significant effect of CFDI on Africa's industrialization in the long run. More importantly, the authors investigated channels through which CFDI magnifies industrialization in Africa in the short and long run.

The study aims to investigate how artificial intelligence (AI)-powered virtual streamers can supercharge brands in live-streaming virtual commerce (v-commerce). Built upon social identity theory (SIT) and experiential value theory, we developed a framework to investigate the impact of AI-powered virtual streamers’ personalization and human-like personalities and live-streaming v-commerce’s system quality and content quality on brand image, mediated by parasocial interaction and experiential value.

A survey was designed and distributed to the target respondents via social media channels. SmartPLS version 4.0.9.4 was used to analyze a total of 354 responses after the data were obtained via purposive sampling.

The results show that personalization, human-like personality, system quality and content quality are positively associated with parasocial interaction and experiential value, which subsequently impact brand image.

This study addresses the gap of relatively sparse academic literature on the implications of AI-powered virtual streamers in live-streaming v-commerce on brand image.

The overarching goal of the study was to formulate an integrated research model to empirically demonstrate the complex interplay between heuristics, project characteristics, information system usage quality, empathy, and mindfulness in predicting users'/donors' donation behaviour and well-being in the context of donation-based crowdfunding (DBC) mobile apps.

The data were collected from 786 respondents and analysed using the multi-stage SEM-ANN-NCA (Structural equation modelling-artificial neural network-necessary condition analysis) method.

Increased perceived aesthetics, narrative structure, self-referencing, project popularity, project content quality, and initiator reputation would foster empathy. Empathy and mindfulness lead to donation behaviour, and, ultimately emotional well-being.

This study offers a clear framework by ranking the key contextual predictors and assessing the model’s necessity logic to facilitate crowdfunders' donation behaviour and well-being on DBC platforms. This research provides practical insights for bank marketers and further aids financial service providers in formulating an optimal DBC mobile app strategy.

The wine market has witnessed intensified competition across all aspects. However, there is a limited systematic assessment of wine marketing literature. This study employs bibliometric and thematic content evaluation methodologies to present a comprehensive summary and trend of the existing research in wine marketing.

This integrates bibliometric and thematic content analytical techniques to trace the evolutionary arc of wine marketing research, underpinned by a framework-based evaluation - the 4 Ws (what, where, why, and how) to give a thorough overview of wine marketing literature. We draw on the thematic structure of wine marketing research by conducting keyword co-occurrence, thematic, social network, and cluster-based content analyses. A total of 291 publications from peer-reviewed journals, spanning the years 2001–2023, were examined in the present study.

The bibliometric analysis is used to determine the most notable journals, authors, nations, articles, and themes, thereby offering a comprehensive understanding of the publication trends within the sphere of wine marketing. Based on the findings, this study advocates a research agenda to extend further contributions to the field of wine marketing.

The study performs a comprehensive analysis of the publication patterns, influential authors, and emerging trends within the wine marketing research field. It provides a unique insight into the evolution and development of wine marketing as a scholarly discipline, facilitating future research directions and knowledge advancement.

Through the theoretical lenses of media richness, perceived realism and customer engagement, this study aims to investigate the mechanisms that promote customer engagement in metaverse-mediated environments in the meetings, incentives, conferences and exhibitions (MICE) context, as well as the impact of customer engagement on customers’ metaverse usage intensity and future visit intention.

A survey of customers who have experience with metaverse-mediated MICE activities was conducted. Data from 267 respondents were analysed using partial least squares-structural equation modelling and fuzzy-set qualitative comparative analysis (fsQCA) to test our research framework.

Media richness dimensions, including multiple cues, immediate feedback and personal focus, were found to enhance perceived metaverse realism, which in turn affects the dimensions of customer engagement, leading to customers’ metaverse usage intensity and future visit intention. The fsQCA analysis identifies three configurations that lead to high event visit intention.

This research helps developers and marketers better understand how rich media contents create realistic experiences in the metaverse, aiding them to devise strategies for customer engagement and improve resource allocation.

Despite its potentially revolutionary impacts, empirical studies on the mechanisms driving customer engagement in the metaverse and its effects are scarce. This study contributes by revealing the multiple-phase mechanism of the customer engagement journey in the metaverse-mediated MICE context. By expanding the media richness theory into this area, our study provides new insights by illustrating how media richness dimensions create multisensory experiences and real-time interactions, enhancing perceived metaverse realism and customer engagement. It also addresses the debate on whether metaverse-mediated events substitute or complement real-life events.

This study integrates the theories of perceived value and human-robot interaction to develop a framework for examining the influence of artificial intelligence-based service chatbot’s (AI-BOT) attributes on user stickiness (US) in the context of human-centric services. The study specifically examines the mediating role of perceived competence (PC) in the association between AI-BOT attributes and the US. It also examines how perceived empathy (PE) moderates the correlation between PC and US.

The empirical data was collected through a self-administered questionnaire from 470 respondents with prior experience of interacting with AI-BOTs. The data was analysed using SMART-PLS by performing structural equation modelling.

The study found a positive and significant impact of selected AI-BOT attributes on US. Among all selected attributes, personalization had the strongest impact on PC and recommendations had the strongest impact on US. Responsiveness did not emerge as a contributory factor for generating PC. This paper confirmed the mediating role of PC among relationships between selected attributes and US but such mediation was minor. PE moderated the relationship between PC and US negatively.

This study provides novel insights by identifying that PE dampens the relationship between PC and US. Additionally, it provides a framework to stimulate the US for AI-BOTs by combining technical aspects (human-computer interaction theory) with value aspects (theory of perceived value) and by positioning constructs specific to human-centric services. All in all, the study offers a dual-layered perspective regarding value-in-use resulting in a comprehensive understanding of human-technology interactions during human-centric service encounters.

While operational performance is important for a company's competitiveness and profitability, this study claims that procurement may be required to drive operational performance. This study aims to focus on how procurement practices is related to operational performance among selected manufacturing firms.

The model was tested on a sample of 329 manufacturing firms in clothing and textiles, chemicals and plastics, food and beverages processing, wood processing, metal processing and pharmaceutical manufacturing industries in a developing economy.

The results indicate that firms with higher and optimal level of procurement practices achieve higher levels of operational performance. Regardless of the H1 rationale, this study suggests that increasing procurement techniques has a limit in terms of improving operational performance. This study establishes that extreme level of procurement practices is associated with decreasing operational performance.

This study calls attention to how managers can guide organizations in refining their procurement strategies and practices; there is the need to strive to strike a balance between diversification and efficiency in procurement.

Manufacturing firms are often integral parts of local communities. The decisions they make regarding procurement practices can impact the economic health of these communities. Striking a balance between diversification and efficiency can contribute to sustainable business practices that benefit both the company and the local community.

As the main contribution, this study brings the additional perspective that extreme level of procurement practices is associated with decreasing operational performance.

This study aims to analyze government hotline text data and generating forecasts could enable the effective detection of public demands and help government departments explore, mitigate and resolve social problems.

In this study, social problems were determined and analyzed by using the time attributes of government hotline data. Social public events with periodicity were quantitatively analyzed via the Prophet model. The Prophet model is decided after running a comparison study with other widely applied time series models. The validation of modeling and forecast was conducted for social events such as travel and educational services, human resources and public health.

The results show that the Prophet algorithm could generate relatively the best performance. Besides, the four types of social events showed obvious trends with periodicities and holidays and have strong interpretable results.

The research could help government departments pay attention to time dependency and periodicity features of the hotline data and be aware of early warnings of social events following periodicity and holidays, enabling them to rationally allocate resources to handle upcoming social events and problems and better promoting the role of the big data structure of government hotline data sets in urban governance innovations.