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In recent years, data science has become a high-demand profession, thereby attracting transmuters (individuals who want to change their profession due to industry trends) to this field. The primary purpose of this paper is to guide transmuters in becoming data scientists.

An exploratory study was conducted to uncover the challenges faced by data scientists according to their educational backgrounds. An extensive set of responses from 31 countries was received.

The results reveal that skill requirements and tool usage vary significantly with educational background. However, regardless of differences in academic background, the data scientists surveyed spend more time analyzing data than operationalizing insight.

The collected data are available to support replication in various scenarios, for example, for use as a roadmap for those with an educational background in art-related disciplines. Additional empirical studies can also be conducted specific to geographical location.

The current work has categorized data scientists by their fields of study making it easier for universities and online academies to suggest required knowledge (courses) according to prospective students' educational background.

The conducted study suggests the required knowledge and skills for transmuters to acquire, based on their educational background, and reports a set of motivational factors attracting them to adopt the data science field.

This study investigates the impact of board characteristics on the stock liquidity of Pakistani listed non-financial firms for the period 2007–2016.

The study uses fixed-effects regression model on a sample of 170 non-financial firms listed on the Pakistan Stock Exchange for regressing the impact of board attributes on stock liquidity while for addressing the endogeneity two-stage least-square (2SLS) and lagged structure models are used.

The study finds that board meetings (BM), directors' attendance (DAT) at BM, board gender diversity, the number of board subcommittees (NBC) and board foreign diversity (BFD) positively affect stock liquidity. Checking the robustness through 2SLS and lagged structure models, it is suggested that the findings are robust to the problem of endogeneity.

Outcomes of the study signify the role of novel board attributes in improving the stock liquidity which has implications for investors, the board of directors and policymakers.

The authors are the first to investigate the impact of novel board attributes–BFD, directors' remuneration (DR), DAT and the number of board sub-committees on stock liquidity. Up to the best of researchers' knowledge, these board attributes have never been examined before in relation to stock liquidity.

While existing research has explored the association between supervisor support and turnover intention among construction workers, there is a notable gap in the literature concerning the potential mediating role of work engagement in elucidating this relationship, warranting further investigation. The paper, hence, aims to examine the mediating role of work engagement in the relationship between supervisor support and turnover intention among construction workers.

Based on the quantitative research method, the hypothesis was tested. The data were collected from 144 construction professionals using a structured questionnaire. Observed variables were tested using confirmatory factor analysis, and the mediating role relationship was validated using hierarchical regression.

The outcome of this study shows a significant positive impact of work engagement and supervisor support on employee turnover intention. The study further showed that work engagement plays a mediating role in the connection between supervisory support and the intention to turnover and improve project and business performance. Turnover intention, on the other hand, negatively affects project and organizational performance.

By enhancing employee work engagement and perceptions of supervisor support, the findings of this study may aid construction organizations in making better judgments regarding the likelihood of employee turnover. The effectiveness of the project and the organization will likely be greatly impacted.

The results of this study provide supporting evidence and advance efforts at reducing employee turnover intention through work engagement and supervisor support in improving project and organizational performance.

The volume of data being generated by various sectors in recent years has increased exponentially. Consequently, professionals struggle to process essential data in the current competitive world. The purpose of the study is to explore and provide insights into the Big Data Analytics (BDA) studies in different sectors.

This study performs a systematic literature review (SLR) with bibliometric analysis of BDA adoption (BDAA) in the supply chain and its applications in various sectors from 2014 to 2018. This paper focuses on BDAA studies have been carried out across different countries and sectors. Also, the paper explores different tools and techniques used in BDAA studies.

The benefits of adopting BDA, coupled with a lack of adequate research in the field, have motivated this study. This literature review categorizes paper into seven main areas and found that most of the studies were carried out in manufacturing and service.

This research insight and observations can provide practitioners and academia with guidance on implementing BDA in different sustainable supply chain sectors. The article indicates a few remarkable gaps in the future direction and trends regarding the integration of BDA and sustainable supply chain development.

The study derives a new categorization of BDA, which investigates how data is generated, organized, captured, interpreted and evaluated to give valuable insights to manage the sustainable supply chain.