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The purpose of this study was to investigate how to generate innovative work behaviors among Millennial and Generation Z sport employees and its impact on their career satisfaction and psychological well-being.

The authors used structural equation modeling to examine the relationships among predictors of job engagement, innovative work behaviors, career satisfaction and psychological well-being. The model was tested across managerial sport employees of Division I athletics departments (N = 224).

The highlights of the study include job engagement's positive relationship with innovative work behaviors and the positive influence of innovative work behavior on career satisfaction and psychological well-being.

These findings signify the importance of considering job engagement and innovative work behaviors to develop a positive work experience for Millennial and Generation Z sport employees. Doing so is thought to be a critical step in cultivating an organizational competitive advantage via younger generations of sport employees.

The rapid rise of large language models (LLMs) has propelled them to the forefront of applications in natural language processing (NLP). This paper aims to present a comprehensive examination of the research landscape in LLMs, providing an overview of the prevailing themes and topics within this dynamic domain.

Drawing from an extensive corpus of 198 records published between 1996 to 2023 from the relevant academic database encompassing journal articles, books, book chapters, conference papers and selected working papers, this study delves deep into the multifaceted world of LLM research. In this study, the authors employed the BERTopic algorithm, a recent advancement in topic modeling, to conduct a comprehensive analysis of the data after it had been meticulously cleaned and preprocessed. BERTopic leverages the power of transformer-based language models like bidirectional encoder representations from transformers (BERT) to generate more meaningful and coherent topics. This approach facilitates the identification of hidden patterns within the data, enabling authors to uncover valuable insights that might otherwise have remained obscure. The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

The analysis revealed four distinct clusters of topics in LLM research: “language and NLP”, “education and teaching”, “clinical and medical applications” and “speech and recognition techniques”. Each cluster embodies a unique aspect of LLM application and showcases the breadth of possibilities that LLM technology has to offer. In addition to presenting the research findings, this paper identifies key challenges and opportunities in the realm of LLMs. It underscores the necessity for further investigation in specific areas, including the paramount importance of addressing potential biases, transparency and explainability, data privacy and security, and responsible deployment of LLM technology.

This classification offers practical guidance for researchers, developers, educators, and policymakers to focus efforts and resources. The study underscores the importance of addressing challenges in LLMs, including potential biases, transparency, data privacy, and responsible deployment. Policymakers can utilize this information to shape regulations, while developers can tailor technology development based on the diverse applications identified. The findings also emphasize the need for interdisciplinary collaboration and highlight ethical considerations, providing a roadmap for navigating the complex landscape of LLM research and applications.

This study stands out as the first to examine the evolution of LLMs across such a long time frame and across such diversified disciplines. It provides a unique perspective on the key areas of LLM research, highlighting the breadth and depth of LLM’s evolution.

Though alternative building technologies (ABTs) have been encouraged to address accessible and affordable issues in low-cost housing (LCH) provision, their adoption is still overwhelmed with encumbrances. The encumbrances that hinder ABT adoption require an in-depth study, especially in developing countries like Nigeria. However, studies regarding ABT and its role in improving Nigeria's LCH to achieve Sustainable Development Goal (SDG) 11 are scarce. This research investigates encumbrances to ABT adoption in Nigeria's LCH provision and suggests feasible measures to prevent or reduce the encumbrances, thereby improving achieving SDG 11 (sustainable cities and communities).

This research utilised qualitative research and adopted a face-to-face interview as the primary data collection. The interviewees comprised ABT practitioners and end users in Nigeria who were chosen by a convenient sampling technique. The study's data were analysed manually through a thematic approach.

This study shows that stakeholders should embrace ABT in LCH provision to improve achieving SDG 11 in Nigeria. Also, it clustered the perceived 20 encumbrances to ABT adoption in LCH provision into government/policymaker, housing developers/building contractors, ABT users and ABT manufacturers-related issues in Nigeria's context. This study suggested mechanisms to mitigate encumbrances to ABT adoption in LCH provision, thereby improving achieving SDG 11.

This research adds to the limited literature by analysing ABT adoption encumbrances in Nigeria's LCH provision, which could assist policy formulation for the uptake of ABT in LCH provision and improve achieving Goal 11.