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In Decolonizing Methodologies (1999), Linda Tuhiwai Smith asserted that “the master’s tools of colonization will not work to decolonize what the master built.” Smith challenged Indigenous researchers to fashion “new tools for the purpose of decolonizing and Indigenous tools that can revitalize Indigenous knowledge” (p. 22). A quarter of a century later, this paper reflects on the powerful impact that Smith’s call to action has had upon recent generations of bright, politically active and culturally grounded Native Hawaiian researchers, many of whom are innovatively turning to the Native epistemologies embedded in our traditional cultural practices to craft (k)new research tools and methodologies.

This paper features three Native Hawaiian scholars who are simultaneously hula and mele (traditional Hawaiian dance and song) practitioners and who instinctively turned to their hula training to guide and indigenize their research practice.

Each of these three scholars describes how they creatively applied the Hawaiian epistemologies embedded in their hula and mele training to fashion (k)new, Indigenous methodologies to guide (1) their research conduct, (2) their data analyses or interpretations and (3) the presentation of their research findings, respectively.

These three Hawaiian scholars and hula practitioners represent a larger groundswell of Native Hawaiian researchers who are bravely and creatively drawing upon the traditional wisdom and sensitivities embedded in our cultural practices to craft and wield (k)new research tools to “dismantle the master’s house” (Lorde, 1981) and build an Indigenous hale (house) of our own.

This research paper aims to integrate the employee-related factors that empirical literature considers antecedents of performance (skills, work motivation, personal characteristics) into a multiple linear regression model, and to test such a model in order to measure the level of each individual factor on the performance.

Quantitative, multisource research approach. After testing the validity of the model with a Confirmatory Factor Analysis, this research applies the multiple linear regression model Work performance = a(Skills) + b(Work Motivation) + c(Personal Characteristics) + e(constant) to two different samples of workers: chemical technicians (N = 63) and salespeople (N = 61).

This study confirms the factorial structure of the antecedents of work performance, showing that skills, motivation, and personal characteristics are three general employee-related factors underlying work performance. The statistical analysis highlights a variance in performance between 40 and 65% explained by employee-related factors, hence leaving 35–60% as due to factors outside the model (firm/environment-related and/or job-related factors, or other skills and personal characteristics not considered in the model). The study also highlights that employee-related factors sometimes affect performance differently than job designers' expectations, and sometimes even negatively.

The equation was tested on two case studies, so further explorations are needed. Furthermore, the approach adopted is inductive thus describing performance as it is, not as it should be. Therefore, it explains the best actual performance of workers, not the ideal performance.

The equation tested here represents a simple and valid tool to guide many Human Resource Management practices, such as; selection, training, development, and career orientation.

Findings provide a valid indication for designing and managing human resource management systems more even-handedly, from an organizational and employee point of view. In doing so, it drives organizations towards a better Person/Job fit.

The study represents one of the first attempts to take into consideration multiple factors simultaneously in explaining work performance.

This paper critically evaluates the political economy of Web3 and offers a neo-institutional model to explain qualitative observations of contemporary digital social movements. By starting to develop a sociological model of Web3 rooted in micro-organizational practices, including trust mediation and social coproduction, this paper re-evaluates assumptions of scarcity, economic value, and social belonging. It concludes by introducing a novel research program to study digital polycentric governance that focuses on community self-governance of digital common pool resources (DCPRs) and looks forward to empirical research using on-chain datasets from decentralized autonomous organizations (DAOs).

This study aims to carry out a systematic literature review (SLR) on the integration of Lean, Industry 4.0 and the supply chain or the Lean Supply Chain (LSC) 4.0. The research analyses the current research on the LSC 4.0 concept in an increasingly digitalised world. The authors present the benefits, motivations, critical success factors and challenges of integrating the LSC with Industry 4.0 technologies within this emerging area of research.

An SLR is carried out on how Lean can be integrated with Supply Chain 4.0. Using the search strings of “Lean Supply Chain 4.0,” “Lean Supply Chain Management 4.0” and “Lean Supply Chain Digitalisation,” a review of published literature was carried out via searches on academic databases.

Industry 4.0 has a synergistic effect on the LSC and, depending on the technology and sector applied in, can complement and enhance the LSC. Similarly, the LSC is a precursor for digitalisation. There are considerable implications in the LSC 4.0 for green and sustainable processes.

Organisations can use this study to understand what the LSC 4.0 means to industry, the benefits and motivating factors for implementation, the critical success factors (CSFs) to implementation and the challenges for implementation.

This study adds to state of the art around the LSC 4.0 and future directions in this nascent research area. This study will aid organisations in understanding how Lean, supply chain management and Industry 4.0 can be integrated.

This paper investigates the moderating role of values congruence, an element of person–organisation (P–O) fit, on the relationship between intrinsic work satisfaction and anticipated employee turnover. The model uses data from employees of the Australian Public Service (APS).

The study draws upon data from the APS’s annual Employee Census for 2018. We first use principal component analysis (PCA) to derive measures of collegial values congruence, managerial values congruence, intrinsic satisfaction and extrinsic satisfaction. The study then uses ordinary least squares (OLS) regression analysis to examine the main effects of intrinsic and extrinsic satisfaction, collegial and managerial values congruence as well as their interaction effects on anticipated future employee tenure.

Our results show the significant linear effects of intrinsic satisfaction, extrinsic satisfaction and managerial values congruence on anticipated tenure. Collegial values congruence, however, did not show a significant linear effect. Interaction effects were then tested, and both collegial and managerial values congruence were shown to moderate the relationship between intrinsic satisfaction and anticipated tenure. Higher levels of both forms of values congruence buffered the negative impact of lower intrinsic satisfaction on turnover intention.

The study is limited by its reliance on cross-sectional self-reported data within a specific and atypical organisational context (the Australian Public Service, or APS). Additionally, the cross-sectional nature of the data limits the establishment of causal inferences. Future research could explore longitudinal data and examine other potential moderators of the turnover intention.

Understanding the moderating role of values congruence, and in particular P–O fit, can develop organisational strategies that aim to reduce turnover by emphasizing the alignment between employee values and organisational, collegial and supervisor culture and values.

This study contributes to the relevant literature by showing the importance of values congruence in partially offsetting the negative effects of low intrinsic satisfaction on anticipated turnover. This is particularly relevant in the public sector context. The examination of both collegial and managerial values congruence provides a more nuanced understanding of the mechanisms that drive turnover intention.

This study aims to define, classify and interconnect the wide range of known entrepreneurial abilities with terms such as skills, capabilities and competencies, which have been used inconsistently within the entrepreneurial field.

This investigation is based on a systematic literature review and strengthened by a meta-analysis equipped with a bibliometric study to assist the generation of outcomes with a quantitative investigation.

This study proposes an evolving entrepreneurial ability model which interconnects genetic and acquired skill types, capabilities and competencies and is equipped with an Entrepreneurial Skills Map essential to operate in the 21st century.

The proposed model is specific to the entrepreneurial field.

This study supports universities and government agencies for the development of educational programs to prepare current and future entrepreneurs to match the changes in the new environment that has emerged with the COVID-19 pandemic.

This research contributes to the entrepreneurship research domain by shedding light on the inconsistent use of non-standardised terminologies and providing an entrepreneurial model and updated skills map to guide scholars to frame research in the post-COVID era with more clarity.

Programming education is being rapidly transformed by generative AI tools and educators must determine how best to support students in this context. This study aims to explore the experiences of programming educators and students to inform future education provision.

Twelve students and six members of faculty in a small technology-focused university were interviewed. Thematic analysis of the interview data was combined with data collected from a survey of 44 students at the same university. Self-determination theory was applied as an analytical framework.

Three themes were identified – bespoke learning, affect and support – that significantly impact motivation and learning outcomes in programming education. It was also found that students are already making extensive use of large language models (LLMs). LLMs can significantly improve learner autonomy and sense of competence by improving the options for bespoke learning; fostering emotions that are conducive to engendering and maintaining motivation; and inhibiting the negative affective states that discourage learning. However, current LLMs cannot adequately provide or replace social support, which is still a key factor in learner motivation.

Integrating the use of LLMs into curricula can improve learning motivation and outcomes. It can also free educators from certain tasks, leaving them with more time and capacity to focus their attention on developing social learning opportunities to further enhance learner motivation.

To the best of the authors’ knowledge, this is the first attempt to explore the relationship between motivation and LLM use in programming education.