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Breakthrough technological innovation is of vital significance for firms to acquire and maintain sustainable competitive advantages. The construction of an innovation ecosystem and the interaction with heterogeneous participants have emerged as a new dominant model for driving sustained breakthrough technological innovation in firms. This study aims to explore the effects of collaborative modes within the innovation ecosystem on firms’ breakthrough technological innovation and the ecological legitimacy mechanisms involved.

The research employs data from 212 innovative firms and conducts empirical research using a two-stage structural equation modeling (SEM) and artificial neural network (ANN) analysis.

The results indicate that firm-firm collaboration (FF), firm-user collaboration (FU), firm-government collaboration (FG), firm-university-institute collaboration (FUI) and firm-intermediary collaboration (FI) all have significant positive effects on breakthrough technological innovation (BTI), with FU being particularly crucial. Furthermore, the results confirm the positive moderating effects of ecological legitimacy (EL) on the relationships between FF and BTI, as well as between FU and BTI. Conversely, EL has a negative moderating effect on the relationship between FUI and BTI, as well as between FI and breakthrough technological innovation. Additionally, EL does not have a significant influence on the relationship between FG and BTI.

Through resource dependence theory (RDT), this study unveils the black box of how collaboration modes within innovation ecosystems impact breakthrough technological innovation. By introducing ecological legitimacy as a contextual factor, a new research perspective is provided for collaboration innovation within innovation ecosystems. The study employs a combination of SEM and ANN for modeling, complementing nonlinear relationships and obtaining robust results in complex mechanisms.

Given the importance of interdisciplinary research in addressing wicked problems, this paper aims to explore the development of and prospects for interdisciplinary research through evidence gained from academic accountants in Australia.

Extant literature is complemented with interviews of accounting academics in Australia to reveal the challenges and opportunities facing interdisciplinary researchers and reimagine prospects for the future.

Evidence indicates that accounting academics hold diverse views toward interdisciplinarity. There is also confusion between multidisciplinarity and interdisciplinarity in the journals in which academic accountants publish. Further, there is mixed messaging among Deans, disciplinary leaders and emerging scholars about the importance of interdisciplinary research to, on the one hand, publish track records and, on the other, secure grants from government and industry. Finally, there are differing perceptions about the disciplines to be encouraged or accepted in the cross-fertilisation of ideas.

This paper is novel in gathering first-hand data about the opportunities, challenges and tensions accounting academics face in collaborating with others in interdisciplinary research. It confirms a discouraging pressure for emerging scholars between the academic research outputs required to publish in journals, prepare reports for industry and secure research funding, with little guidance for how these tensions might be managed.

This study examined differences related to gender and racial/ethnic identity among academic researchers participating in the National Science Foundation’s “Innovation-Corps” (NSF I-Corps) entrepreneurship training program. Drawing from prior research in the fields of technology entrepreneurship and science, technology, engineering and mathematics (STEM) education, this study addresses the goal of broadening participation in academic entrepreneurship.

Using ANOVA and MANOVA analyses, we tested for differences by gender and minoritized racial/ethnic identity for four variables considered pertinent to successful program outcomes: (1) prior entrepreneurial experience, (2) perceptions of instructional climate, (3) quality of project team interactions and (4) future entrepreneurial intention. The sample includes faculty (n = 434) and graduate students (n = 406) who completed pre- and post-course surveys related to a seven-week nationwide training program.

The findings show that group differences based on minoritized racial/ethnic identity compared with majority group identity were largely not evident. Previous research findings were replicated for only one variable, indicating that women report lower amounts of total prior entrepreneurial experience than men, but no gender differences were found for other study variables.

Our analyses respond to repeated calls for research in the fields of entrepreneurship and STEM education to simultaneously examine intersecting minoritized and/or under-represented social identities to inform recruitment and retention efforts. The unique and large I-Corps national dataset offered the statistical power to quantitatively test for differences between identity groups. We discuss the implications of the inconsistencies in our analyses with prior findings, such as the need to consider selection bias.

The purpose of this study is to explore the increased uptake of Artificial Intelligence (AI) technology by Facility Management (FM) organizations for enhanced operational efficiency and competitive advantage. While AI adoption in FM has been widely reported, limited attempts have been made to assess its impact on the social well-being of FM employees. To contribute towards addressing this gap, this study established the essential employee social well-being factors mostly impacted by the adoption of AI in South African FM organizations.

A four-stage design comprising a comprehensive review of literature, expert interviews, questionnaire census and focus group discussion sessions was used to elicit data from a sample of participants drawn from 22 South African FM organizations. The data was analyzed using a combination of content analysis, relative importance index and interpretative structural modeling for various data sets toward achieving the study’s objectives.

Sixteen employee social well-being factors, classified under job satisfaction, social relationship and knowledge development categories, respectively, were identified as being impacted by AI adoption in FM organizations. Furthermore, it was established that job security, job autonomy and professional status, which belong to the job satisfaction social well-being factor category, were deemed by FM employees as being mostly impacted by AI adoption.

The enhanced understanding of the impact of AI adoption on FM employees’ social well-being factors will contribute to the development of a collaborative intelligence framework for managing AI adoption in FM organizations toward engendering optimal AI–FM employee relationships for improved productivity.

Besides being one of the foremost studies to investigate the impact of AI adoption on FM employees’ social well-being, this study introduces a hierarchical framework of understanding employee social well-being factors based on multi-stakeholder perspectives.

Self-disgust appears to be a prominent feature in anorexia nervosa (AN), which might help explain why AN is often such a persistent disorder. Little is known about how this emotion can impact on recovering from this disorder. This study aims to develop our understanding of how people experience the emotion of self-disgust after physical recovery from AN.

Twelve female participants who reported previously having had a clinical diagnosis of AN but had physically recovered according to their EDE-Q scores took part in a semi-structured interview to explore their experiences of recovery and the role self-disgust played within this. Interpretative phenomenological analysis was used to explore the data.

Three themes were identified within the data to explain the experiences of self-disgust in those with AN: continued self-disgust following physical “Recovery”, multiple manifestations of self-disgust in recovery and increasing self-disgust in recovery as a driver for relapse.

Self-disgust was something each participant appeared to experience often, despite being physically recovered from AN. Disgust-based reactions to the self are enduring and highly resistant to change even whilst other aspects of the disorder become less potent. Self-disgust is multi-faceted and may trigger relapse as the signs of improvement and behaviours inherent in recovering were generally viewed as disgusting to the individuals.

Self-disgust is an emotion that continues to affect people with AN despite physical recovery. The recovery process itself is not linear and self-disgust is enduring and may cause those affected to relapse. Considering this emotion within therapeutic intervention may encourage those with AN to accept their recovered self.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

This study aims to address existing gaps in the literature and theories by investigating the influence of responsible leadership traits on employees’ sustainable performance (E-SuPer) in the Malaysian service sector. Specifically, the authors focus on three key responsible leadership traits: relationship building, relational governance and sharing orientation. Additionally, the authors explore how these traits interact with leader-member exchange (LMX) and whether gender plays a role in this relationship.

A total of 235 usable responses were analysed using partial least squares structural equation modelling. Multi-group analysis (MGA) was employed to examine the moderating impact of gender.

The results showed that both relationship building and relational governance significantly affect E-SuPer among organisations in the service industry. LMX was found to be a significant moderating condition influencing the association between responsible leaders’ sharing orientation and E-SuPer. Interestingly, the MGA results suggest that the effect on male employees was greater than on female employees across the relationships examined. The findings suggest that responsible leadership traits are essential for sustainable employee performance, but there is room for improvement in how these traits are perceived by female employees.

The present study contributes to gender equality agenda, supports the sustainable development goals, adds to the growing body of knowledge on the relationship between responsible leadership traits and E-SuPer within one of the most important economic sectors in Malaysia and sheds lights on the moderating effect of LMX.

This study investigates how responsible leadership traits affect E-SuPer in the service industry, particularly among male and female employees. Moreover, this study is one of the early investigations into the significance of responsible leadership within Malaysian service sector and offers valuable information for industry actors to improve their management approaches.

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.