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The imperative to conserve resources and minimize operational expenses has spurred a notable increase in the adoption of lean manufacturing within the context of the circular economy across diverse industries in recent years. However, a notable gap exists in the research landscape, particularly concerning the implementation of lean practices within the pharmaceutical industry to enhance circular economy performance. Addressing this void, this study endeavors to identify and prioritize the pivotal drivers influencing lean manufacturing within the pharmaceutical sector.

The outcome of this rigorous examination highlights that “Continuous Monitoring Process for Sustainable Lean Implementation,” “Management Involvement for Sustainable Implementation” and “Training and Education” emerge as the most consequential drivers. These factors are deemed crucial for augmenting circular economy performance, underscoring the significance of management engagement, training initiatives and a continuous monitoring process in fostering a closed-loop practice within the pharmaceutical industry.

The findings contribute valuable insights for decision-makers aiming to adopt lean practices within a circular economy framework. Specifically, by streamlining the process of developing a robust action plan tailored to the unique needs of the pharmaceutical sector, our study provides actionable guidance for enhancing overall sustainability in the manufacturing processes.

This study represents one of the initial efforts to systematically identify and assess the drivers to LM implementation within the pharmaceutical industry, contributing to the emerging body of knowledge in this area.

The main objective of this study is to evaluate the driving forces behind the adoption of indoor environmental quality (IEQ) principles in building designs from the perspectives of Nigerian quantity surveying firms.

A quantitative approach was used which involved administering a well-structured questionnaire to a sample of 114 quantity surveyors. The collected data were analyzed using various statistical methods, including frequencies, percentages, mean item scores, Kruskal–Wallis test and exploratory factor analysis.

The top five ranked drivers were climate change mitigation, conservation of natural resources, reduction of waste and pollution, use of sustainable building materials and development of new materials and building systems. Based on the factor analysis, the study identified five clusters of drivers: (1) health and well-being drivers (2) economic drivers (3) environmental drivers (4) innovation and technology drivers and (5) regulatory drivers.

The findings from this study suggest that to effectively integrate IEQ principles, quantity surveying firms should consider developing comprehensive guidelines and checklists that align with the identified drivers and clustered categories. These resources can serve as practical tools for project teams, facilitating a structured and holistic approach to the incorporation of IEQ factors throughout the project lifecycle.

The study’s identification of the top drivers and the subsequent clustering of these drivers into five distinct categories contributes to the existing body of knowledge on IEQ. This approach provides a structured framework for comprehensively understanding the factors influencing IEQ adoption, offering a valuable tool for researchers, policymakers and industry practitioners.

This study investigates the drivers of CSR practices among construction companies in Lagos, Nigeria.

This research adopted a questionnaire survey to achieve the aim. A purposefully chosen group of managing directors, directors, other senior construction experts or management personnel working for small, medium and large-sized construction enterprises in Nigeria were given questionnaires to complete. 196 questionnaires were issued, out of which 103 were properly completed and returned, with a return percentage of 61% which served as the basis for this study. Utilizing both descriptive and inferential statistics, the collected data were examined.

The results from the study revealed that “vision of the founder”, “creating public attention”, “competitive advantage” and “business strategy” were the top most four drivers of CSR initiatives among construction companies in Lagos, Nigeria. The results also showed that, there is a statistically significant degree of agreement between various categories of construction companies in Lagos, Nigeria regarding the drivers of CSR initiatives.

The study contributes more effectively to CSR studies by highlighting the drivers of CSR initiatives in construction companies. The finding would facilitate the adoption and implementation of CSR initiatives in construction organizations. The adoption of best CSR practices fosters strong relationship among construction companies, project stakeholders and communities.

Lack of appropriate student support and drawbacks in academic progression signify the importance of enhancing assessment and feedback in higher education (HE). Although assessment and feedback are significant in HE, minimal empirical research holistically explores the best practices. This study aims to address the niche and develop a decisive guideline for enhancing assessment setting and feedback provision within HE curricula.

A systematic approach was taken to obtain data for the study: a literature review underpinning the thematic content analysis of study documents, followed by semi-structured interviews. Document analysis contained mid-module reviews/student feedback; rubrics used in assessment; and formative/summative feedback provided for the graded work. Documental analysis informed the key attributes of the semi-structured interview. Interpretive structural modelling (ISM) analysis identified the influence and reliance of each driver.

This study revealed 15 drivers – 4 fundamental, 6 significant and 5 important – for enhancing assessment and feedback. The level partitioning from the ISM analysis established that all assessment and feedback needs to be underpinned by the university policy and fed into the assessment regime and marking scheme. This study identified that National Student Survey results were significantly improved due to implementing said drivers compared with the national and sector benchmarks.

The developed drivers enable the best practices in assessment setting and feedback provision. The level partition diagram can be used as a decisive guideline or a provisional framework in assessment and feedback provision for quality assurance in HE.

This study is one of, if not the only, to develop a guideline for signposting drivers and their influence and reliance to enhance assessment and feedback in a holistic HE setting. The developed drivers and the level partition diagram bring novelty and add to the current body of knowledge.

Algorithms are widely used to manage various activities in the gig economy. Online car-hailing platforms, such as Uber and Lyft, are exemplary embodiments of such algorithmic management, where drivers are managed by algorithms for task allocation, work monitoring and performance evaluation. Despite employing substantially, the platforms face the challenge of maintaining and fostering drivers' work engagement. Thus, this study aims to examine how the algorithmic management of online car-hailing platforms affects drivers' work engagement.

Drawing on the transactional theory of stress, the authors examined the effects of algorithmic monitoring and fairness on online car-hailing drivers' work engagement and revealed the mediation effects of challenge-hindrance appraisals. Based on survey data collected from 364 drivers, the authors' hypotheses were examined using partial least squares structural equation modeling (PLS-SEM). The authors also applied path comparison analyses to further compare the effects of algorithmic monitoring and fairness on the two types of appraisals.

This study finds that online car-hailing drivers' challenge-hindrance appraisals mediate the relationship between algorithmic management characteristics and work engagement. Algorithmic monitoring positively affects both challenge and hindrance appraisals in online car-hailing drivers. However, algorithmic fairness promotes challenge appraisal and reduces hindrance appraisal. Consequently, challenge and hindrance appraisals lead to higher and lower work engagement, respectively. Further, the additional path comparison analysis showed that the hindering effect of algorithmic monitoring exceeds its challenging effect, and the challenge-promoting effect of algorithmic fairness is greater than the algorithm's hindrance-reducing effect.

This paper reveals the underlying mechanisms concerning how algorithmic monitoring and fairness affect online car-hailing drivers' work engagement and fills the gap in the research on algorithmic management in the context of online car-hailing platforms. The authors' findings also provide practical guidance for online car-hailing platforms on how to improve the platforms' algorithmic management systems.

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.

The purpose of this study is to identify the drivers and future scenarios of Iran’s Social Security Organization.

The research is applied in terms of orientation and mixed in terms of methodology. In this research, the methods of theme analysis, root definitions, fuzzy Delphi and Cocoso were used. The theoretical population is the managers and senior experts of the social security organization, and the sampling method was done in a judgmental way. The tools of data collection were interviews and questionnaires. The interview tool was used to extract the main and subdrivers of the research and develop the scenarios.

Through theme analysis, 35 subdrivers were extracted in the form of economic, sociocultural, financial and investment, policy, marketing, environmental and legal themes. Due to the large number of subdrivers, these factors were screened with fuzzy Delphi. Eleven drivers had defuzzied coefficient higher than 0.7 and were selected for final prioritization. The final drivers were prioritized with the CoCoSo technique, and the two drivers of social security holdings governance and state of government revenues had the highest priority. Based on these two drivers, four scenarios of prosperity, resilient social security, unstable development and collapse have been developed.

Some of the suggestions of the research are: using the capacity of FinTechs and financial startups to invest the government revenues of the organization, using digital technologies such as business intelligence for more efficient decisions and developing corporate governance in the organization.

The automotive industry appears to overcome much of its obstacles, despite the constant struggle facing COVID-19. The pandemic has resulted in significant improvements in the habits and conduct of consumers. There is an increased preference for personal mobility. In this dynamic environment with unexpected changes and high market rivalry, automotive supply chains focus more on executing responsive strategies with minimum costs. This paper aims to identify and model the drivers to the responsiveness of automotive supply chain.

Seventeen drivers for supply chain responsiveness have been identified from the extensive literature, expert interview. An integrated methodology of fuzzy decision-making trial and evaluation laboratory–interpretive structural modelling (DEMATEL–ISM) is developed to establish the interrelationship between the drivers. The cause–effect relationship between the drivers was obtained through fuzzy DEMATEL technique, and a hierarchical structure of the drivers was developed using the ISM technique.

The result of the integrated methodology revealed that strategic decision-making of management, accurate forecasting of demand, advanced manufacturing system in the organisation and data integration tools are the critical drivers.

This study has conceptual and analytical limitations. In this study, a limited number of drivers are examined for supply chain responsiveness. Further research may examine the role of other key performance indicators in the broad field of responsiveness in the automotive supply chain or other industry sectors. Future study can uncover the interrelationships and relative relevance of indicators using advanced multi-criteria decision-making methodologies.

The authors proposed an integrated methodology that will be benefitted to the supply chain practitioners and automotive manufacturers to develop management strategies to improve responsiveness. This study further helps to compare the responsiveness of the supply chain between various automotive manufacturers.

In keeping with global developments rendering online shopping as an emerging trend among consumers, the present study extends the unified theory of use and acceptance of technology (UTAUT2) comprising the digital payment mode (DPM) as a new driver of online shopping and with the mediation of attitudes toward technology (ATTs) to gauge a better and deeper understanding of behavioral intention (BI).

This study used a survey instrument with snowball sampling from 600 consumers in northern India. Partial least squares structural equation modeling was used to find the association between drivers using UTUAT2, along with DPM and ATTs. The data were divided into a test group (20%) and validated through a training group (80%).

DPM was shown to be directly associated with BI. The mediation of ATTs was also validated through the model. The predictability of the model was 67.5% for the test group (20%) and 69.6% for the training group (80%). The results also indicated that facilitating conditions is a critical driver of BI.

This study enhances the understanding of the roles that DPM and ATTs play in BI during online shopping, suggesting that Indian managers need to adopt DPM as a support service to make online shopping a worthwhile experience.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.