Determining an appropriate sample size is vital in drawing realistic conclusions from research findings. Although there are several widely adopted rules of thumb to calculate sample size, researchers remain unclear about which one to consider when determining sample size in their respective studies. ‘How large should the sample be?’ is one the most frequently asked questions in survey research. The objective of this editorial is three-fold. First, we discuss the factors that influence sample size decisions. Second, we review existing rules of thumb related to the calculation of sample size. Third, we present the guidelines to perform power analysis using the G*Power programme. There is, however, a caveat: we urge researchers not to blindly follow these rules. Such rules or guidelines should be understood in their specific contexts and under the conditions in which they were prescribed. We hope that this editorial does not only provide researchers a fundamental understanding of sample size and its associated issues, but also facilitates their consideration of sample size determination in their own studies.

Partial least squares structural equation modeling (PLS-SEM) is one of the most widely used methods of multivariate data analysis. Although previous research has discussed different aspects of PLS-SEM, little has been done to explain the attributes of the various PLS-SEM statistical applications. The objective of this editorial is to discuss the multiple PLS-SEM applications, including SmartPLS, WarpPLS, and ADANCO. It is written based on information received from the developers via emails as well as our ongoing understanding and experience of using these applications. We hope this editorial will serve as a manual for users to understand the unique characteristics of each PLS-SEM application and make informed decisions on the most appropriate application for their research.

Purposive sampling has been extensively utilized across research disciplines, particularly in business and management studies. While existing literature has primarily focused on its application in qualitative research, the rationale for using it in quantitative contexts remains underexplored, leaving researchers with limited practical guidance. Moreover, much of the available literature is found within academic texts, specifically books, which may not always provide sufficiently actionable guidance on successfully implementing and reporting purposive sampling in quantitative research. This editorial addresses these gaps by presenting structured guidelines specifically tailored for applying purposive sampling in quantitative research. It provides practical recommendations for systematically implementing and transparently reporting purposive sampling. The editorial discusses different types of purposive sampling, including criterion sampling, maximum variation sampling, and theoretical sampling, and illustrates their applications in quantitative contexts. It also discusses how purposive sampling can be combined with other techniques, such as snowball sampling and quota sampling, to enhance data quality and relevance. Detailed step-by-step guidelines are presented for employing purposive sampling and reporting its use in quantitative studies in a robust manner. Additionally, we put forward a salient checklist for authors and reviewers to ensure rigor, transparency, and consistency in reporting. This work contributes to the growing discourse about sampling by presenting one of the first comprehensive frameworks for employing purposive sampling in quantitative research. By guiding researchers to adopt purposive sampling with greater rigor and transparency, we hope it will be useful to establish the representativeness of data and provide more credible estimations to explain or predict the phenomena under investigations.

Purpose: This study aims to examine the impact of employees' satisfaction with human resource management (HRM) practices (i.e. training satisfaction, performance appraisal satisfaction and pay satisfaction) on work engagement and subsequently employee turnover intentions. The mediating role of work engagement between employee satisfaction with HRM practices and turnover intentions is also assessed.Design/methodology/approach: Data were collected from Malaysian oil and gas (O&G) professionals. A total of 442 useable questionnaires were obtained for the final data analysis. Partial least squares structural equation modeling (PLS-SEM) was performed to test the hypothesised relationships.Findings: The findings indicate that training satisfaction and performance appraisal satisfaction are the key drivers of employee engagement at work. Work engagement in turn has a negative impact on employee turnover intentions. Furthermore, work engagement mediates the relationship between employees' satisfaction with HRM practices (i.e. training satisfaction and performance appraisal satisfaction) and turnover intentions. Nevertheless, it did not have any mediating effect on pay satisfaction and turnover intention.Practical implications: Training plans should be designed to make the relevant jobs more attractive and fulfilling, thus increasing employees' level of work engagement. Besides, ensuring that the appraisal system is fair is pivotal to work engagement. Work engagement will cultivate a strong sense of emotional attachment between employees and employers, thus reducing the turnover intention of Malaysian O&G professionals.Originality/value: To date, little has been done on employees' satisfaction with HRM practices with respect to their attitudinal and behavioural outcomes. The present study enhances our understanding of the importance of employees' satisfaction with HRM practices and its relation to employees' work engagement and turnover intentions.

Purpose: The purpose of this paper is to examine the casual relationship between training satisfaction, work engagement (WE) and turnover intention and the mediating role of WE between training satisfaction and turnover intention.Design/methodology/approach: Data were collected from 409 oil and gas professionals using an email survey questionnaire. Structural equation modelling, using Analysis of Moment Structures (IBM AMOS) 22.0, was performed to test the hypothesized model.Findings: The results suggest that training satisfaction is significantly positively related to employees’ level of WE and is negatively related to turnover intention. The results also reveal that WE mediates the relationship between training satisfaction and turnover intention.Practical implications: Training has long been thought to play an important role in achieving positive attitudinal and behaviours outcomes among employees. This study reconfirms these ideas and highlights the importance of training satisfaction as being key to achieving greater WE and reducing voluntary turnover. Therefore, the finding of this study have a number of implications for research and human resource development practitioners.Originality/value: This study makes a significant theoretical contribution to the literature as this is the first study to demonstrate the significance of training satisfaction and the mediating effects of WE in reducing the turnover intention of employees.