Search results

The purpose of this study is to explain gender imbalance by theorising how guilt arises as an externally imposed negative emotion that subsequently impairs women's performance in the accounting academia.

The method involves an analysis of gender distributions at junior and senior levels in New Zealand universities in 2019 and 2024 and relevant case studies of junior academics using unstructured interviews.

This paper unpacks the nuances of gender imbalance in a “gender-neutral” subject and provides empirical evidence that many women academics may internalise a sense of externally imposed guilt for various reasons. Such feelings of guilt, where they are imposed by workplace expectations and social constructions, may make women more concessionary with regard to a greater teaching workload substituting for research expectations. The more prolonged-term effect on career prospects of such substitutions as practiced in New Zealand may account for the imbalance that exists and seemingly will continue to exist.

This paper sets out first to discover the gender balance in accounting in universities in New Zealand. It contributes to the literature on gender and accounting education in understanding how negative emotions are externally imposed and become career-negating obstacles for women in the accounting academic.

This chapter first examines how the confluence of the three forces discussed in the previous chapter is affecting demand and supply dynamics and giving rise to new business models that could form the core of the emerging digital financial landscape. This chapter then examines the challenges that arise from these new business models as well as from digitalization of financial services in general. The next chapter will review how these challenges might affect monetary and financial stability and the strategy that central banks might use to address them.

In this research, we seek to understand the effects of artificial intelligence (AI) and knowledge management (KM) processes in enhancing proactive green innovation (PGI) within oil and gas organizations. It also aims to investigate the moderator role of trust and sustainability in these relationships.

This paper employs a quantitative analysis. Surveys have been gathered from the middle-line managers of twenty-four oil and gas government organizations to evaluate the perceptions of the managers towards AI, KM processes, trust, sustainability measures and proactive measures toward green innovation. Analytical and statistical tools that were employed in this study, including structural equation modeling with SmartPLSv3.9, have been used to analyze the data and to examine the measurement and structural models of this study.

The study results reveal a significant and positive impact of AI utilization, KM processes and PGI within oil and gas organizations. Furthermore, trust and sustainability turn out to be viable moderators affecting, and influencing the strength and direction of AI, KM and PGI relationships. In particular, higher levels of trust and more substantial sustainability commitments enhance the positive impact of AI and KM on green innovation outcomes.

Understanding the impact of AI, KM, trust and sustainability offers valuable insights for organizational leaders and policymakers seeking to promote proactive green innovation within the oil and gas industry. Thus, organizations can increase the efficiency of sustainable product development, process improvement and environmental management by using robust AI technologies and effective KM systems. Furthermore, fostering trust among stakeholders and embedding sustainability principles into organizational culture can amplify the effectiveness of AI and KM initiatives in driving green innovation outcomes.

This study extends the current knowledge by assessing the effect of AI and KM on proactive green innovation while accounting for trust and sustainability as moderators. Utilizing quantitative methods offers a nuanced understanding of the complex interactions between these variables, thereby advancing theoretical knowledge in the fields of innovation management, sustainability and organizational behavior. Additionally, the identification of specific mechanisms and contextual factors enriches practical insights for organizational practitioners striving for a practical understanding of the dynamics of the complexities of sustainable innovation in an AI-driven era.

The literature concerning higher- and degree-level apprenticeships (HDAs) in the post-levy era (2017–) is sparse and fragmented. Therefore, the purpose of this paper is to contribute to the international landscape of higher education work-based learning by identifying barriers to effective HDA programmes and outlining a future research agenda to address these.

This study employs a systematic literature review methodology to explore barriers in HDAs. Adhering to PRISMA 2020 guidelines, the review follows a three-stage process to develop the sample. A meticulously designed search strategy uses targeted queries on the SCOPUS database, focusing on terms related to HDAs and their challenges. Inclusion criteria is set to papers from 2017 to 2024, covering book chapters and peer-reviewed journals in the English language. Articles were cross-checked with the Chartered Association of Business Schools (CABS) database, which refines the total to 68 papers. Data from these sources is then extracted and tabulated for qualitative content analysis.

The systematic literature review of 68 manuscripts finds challenges are faced by apprentices, employers and training providers alike. This includes, but is not limited to, ethnically correlated performance, stigma, careers advice, job design, mentoring practices and the incorporation of technology. Two additional themes of pedagogy/andragogy and policy emerge.

Based on the analysis, the paper suggests an extensive portfolio of future research focused on collaborative practices. If addressed, this yields the power to influence future legislation and subsequent implementation.