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Despite the huge financial resources associated with oil, Nigeria has consistently recorded poor growth performance. Therefore, this study aims to examine how corruption and oil rent influence Nigeria’s economic performance during the 1996–2021 period.

Various estimation techniques were used. These include the bootstrap autoregressive distributed lag (ARDL) bounds-testing, dynamic ordinary least squares (DOLS), the fully modified OLS (FMOLS) and the canonical cointegration regression (CCR) estimators and the Toda–Yamamoto causality.

The bounds testing results provide evidence of a cointegrating relationship between the variables. In addition, the results of the ARDL, DOLS, CCR and FMOLS estimators demonstrate that oil rent and corruption have a significant positive impact on growth. Further, the results indicate that human capital and financial development enhance economic growth, whereas domestic investment and unemployment rates slow down long-term growth. Additionally, the causality test results illustrate the presence of a one-way causality from oil rent to economic growth and a bi-directional causal relationship between corruption and economic growth.

Existing studies focused on the effects of either oil rent or corruption on growth in Nigeria. Little attention has been paid to the exploration of how the rent from oil and the pervasiveness of corruption contribute to the performance of the Nigerian economy. Based on the outcome of this study, strategies and policies geared towards reducing oil dependence and the pervasiveness of corruption, enhancing human capital and financial development and reducing unemployment are recommended.

The purpose of this study is to investigate the influence of design for lean six sigma (DFLSS) on operational functions of the innovation management model by appraising an innovation management assessment framework.

An empirical approach for evaluating causal relationships among various constructs in the model phases that identify optimum pathways in achieving commercial success was adopted. A quantitative analysis of survey data were collected from large, medium and small organiations, including incubators in ANZ (Australia, New Zealand) and TMSV (Thailand, Malaysia, Sri Lanka and Vietnam).

The structural equation modelling recursive path analysis results of the model provide empirical evidence and pathways through the various constructs considered in the model. All these pathways lead to delivering optimum commercialization success (CS). Furthermore, DFLSS is confirmed as an enabler and has direct one-to-one and indirect influence on all the operational function constructs of the model including commercial success.

This study had a relatively small sample size of completed responses obtained from the population and a constrained ability to compare commercialization success (CS) between the two regions in the dataset. Future studies could be conducted on a global scale to increase responses.

The research findings enabled the development of important and practical guidelines for managers and innovation practitioners engaged in planning and management of innovation.

This research offers a holistic approach for integrating DFLSS with stage gate phases of innovation management assessment framework, supported by empirical evidence, to aid organizations in effectively managing the innovation process and achieving greater success in commercialization.

Digital servitization research has focused on how manufacturing firms use digital technologies to change business models and offer smart services; less attention has been devoted to the degree to which external actors in the existing ecosystem accept these smart services. Therefore, the authors pose the following research question: How does a manufacturing firm introduce and gain acceptance of new smart services within an established ecosystem?

Building on servitization, ecosystem and legitimacy theories, this paper addresses the research question through an in-depth case study of a world-leading original equipment manufacturer that is currently developing and introducing new smart services in its existing ecosystem.

The findings suggest that external actors emphasize different types of legitimacy in deciding whether to accept a new smart service. The findings also show that the type of legitimacy required to gain acceptance changes throughout the development of the smart service, from the definition of the value proposition to the design and delivery of the service.

This study can assist smart service providers in identifying which type of legitimacy is important for each ecosystem actor and strengthening these types of legitimacy to gain acceptance from the ecosystem.

This study develops a framework to help describe the thresholds for acceptance of a smart service through the development phases, as well as to indicate the types of legitimacy that smart service providers must relate to when seeking to gain acceptance for their new offering.

The research enterprise within higher education is becoming more competitive as funding agencies require more collaborative research projects, higher-level of accountability and competition for limited resources. As a result, research analytics has emerged as a field, like many other areas within higher education to act as a data-informed unit to better understand how research institutions can effectively grow their research strategy. This is a new and emerging field within higher education.

As businesses and other industries are embracing recent advances in data technologies such as cloud computing and big data analytic tools to inform decision making, research administration in higher education is seeing a potential in incorporating advanced data analytics to improve day-to-day operations and strategic advancement in institutional research. This paper documents the development of a survey measuring research administrators’ perspectives on how higher education and other research institutions perceive the use of data and analytics within the research administration functions. The survey development process started with composing a literature review on recent developments in data analytics within the research administration in the higher education domain, from which major components of data analytics in research administration were conceptualized and identified. This was followed by an item matrix mapping the evidence from literature with corresponding, newly drafted survey items. After revising the initial survey based on suggestions from a panel of subject matter experts to review, a pilot study was conducted using the revised survey instrument and validated by employing the Rasch measurement analysis.

After revising the survey based on suggestions from the subject matter experts, a pilot study was conducted using the revised survey instrument. The resultant survey instrument consists of six dimensions and 36 survey items with an establishment of reasonable item fit, item separation and reliability. This survey protocol is useful for higher educational institutions to gauge research administrators’ perceptions of the culture of data analytics use in the workplace. Suggestions for future revisions and potential use of the survey were made.

Very limited scholarly work has been published on this topic. The use of data-informed and data-driven approaches with in research strategy within higher education is an emerging field of study and practice.

This study aims to supplement the international knowledge on factors determining retention in the teaching profession with findings from the Czech Republic. The study aims to answer the question of what factors on the part of schools and teachers are related to teachers’ decision to leave the teaching profession, either temporarily or permanently. It also examines the differences between teachers at the beginning and end of their professional careers.

This study presents a secondary analysis of the data collected in a survey of 1,230 Czech secondary school teachers implemented in 2021 in the same schools as the TALIS survey in 2018. The longitudinal design makes it possible to observe the impact of school variables on teachers’ willingness to remain in the profession. Data are analysed through logistic regression.

The analysis shows the importance of sufficient financial evaluation, the composition of the student body, instructional leadership, and school innovativeness for the retention of teachers in the profession. The analysis further shows that the factors causing teachers to leave the profession differ for those at the beginning and at the end of their teaching careers.

The limitations of the study lie in the fact that this is a secondary analysis. The questionnaires were not designed in accordance with the aim of our study, and so the variables were constructed from existing data to best fit our chosen framework.

The results of our analysis indicate that the Czech education policy should focus not only on general increases in teachers’ salaries, but also on the adjustment of increases over the course of a teacher’s career. The starting salary may be important for retaining young teachers in the profession, while at later stages in their careers their decision to stay may be more influenced by other factors. Our research also showed the importance of leadership. In schools where the principal supports his or her teachers and takes care of their professional development, teachers have a greater tendency to stay in the profession than in schools where the management does not perform this function.

The study enriched international knowledge about factors affecting teachers' retention in the profession with findings from the Czech educational system characterized by high school autonomy and low teacher salaries.

This study aims to examine the impact of the COVID-19 pandemic on knowledge-sharing drivers in small- and medium-sized family firms within the restaurant and fast-food industry. The pandemic has led to significant changes in business culture and consumer behaviour, accelerating digital transformation, disruptions in global supply chains and emerging new business opportunities. These changes have also influenced knowledge sharing (KS) and its underlying drivers.

To address the research objectives, a two-phase study was conducted. In the first phase, an exploratory analysis using the Delphi method was used to identify the essential drivers and factors of KS in family businesses (FBs). This phase aimed to establish a conceptual model for the study. In the second phase, confirmatory factor analysis was conducted to analyse the impact of the COVID-19 pandemic on the identified knowledge-sharing drivers. The study examined both the pre-pandemic and post-pandemic periods to capture the shifts in attitudes towards KS.

The findings indicate a significant shift in attitudes towards knowledge-sharing drivers. Before the pandemic, organisational drivers played a central role in KS. However, after the emergence of the pandemic, technological drivers became more prominent. This shift highlights the impact of the COVID-19 pandemic on KS within FB.

The research contributes to understanding knowledge-sharing in the context of FBs and sheds light on the specific effects of the COVID-19 pandemic on knowledge-sharing drivers. The insights gained from this study can inform strategies and practices aimed at enhancing KS in similar organisational settings.

Studies showed that stakeholders want the construction sector’s organisations to be more accountable and transparent regarding social and environmental issues through corporate social responsibility (CSR). There is a paucity of literature regarding CSR implementation in the construction sector, especially in developing countries like South Africa. Hence, the study evaluated CSR’s merits and hindrances and suggested solutions to enhance its implementation in the South African construction sector of Mpumalanga Province.

The researchers employed a questionnaire survey method to collect data from 68 useable respondents in the South African construction sector of Mpumalanga Province. The main section of the questionnaire was divided into three parts, each addressing an objective mean item score ranking technique.

Findings show management lacks willingness, absence of recognition for implementing CSR at tender adjudication, professionals regard CSR as a “soft issue,” inadequate ability to carry out CSR initiatives and lax CSR knowledge emerged as the key issues hindering construction stakeholders, especially construction companies, from participating in CSR in South Africa. The research suggests initiatives to enhance CSR in the construction industry.

The study shows that the findings can be used to improve the implementation of CSR engagement and possibly enhance a policy to stimulate friendly CSR in the South African construction sector.

The purpose of this study is to develop a scale for measuring housing quality in the context of Pakistan.

The inductive and deductive approaches for item generation have been combined, and items have been purified using multistage expert review. Data was collected from a sizeable purposive sample of 445 respondents, and exploratory and confirmatory factor approaches used for assessing psychometric properties of the scale.

The result is a 21-item scale covering five dimensions, namely, Design and Construction Quality, Neighborhood Quality, Adequacy of Space, Quality of Institutional Services and Proximity of Basic Amenities.

This study contributes to housing quality literature by deepening our understanding of the concept of housing quality in the context of Pakistan, the world’s fifth most populous country. The findings of the study have important implications for both theory and practice.