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This review provides a comprehensive, step-by-step guide to the application of partial least squares structural equation modeling (PLS-SEM) for novice researchers. This is a conceptual and literature-based review that focuses on best practices and PLS-SEM literature. It highlights the rationale for using PLS-SEM, sample size, software tools, and essential metrics in PLS-SEM analysis. Drawing on best practices and recent literature, the review offers a framework for conducting and reporting PLS-SEM analysis. The review presents essential such as outer loadings, Cronbach’s alpha coefficients, average variance extracted (AVE), composite reliability, cross-loadings, Heterotrait-Monotrait ratio of correlations (HTMT), the Fornell-Larcker criterion, variance inflation factor (VIF), and redundancy analysis. Moreover, for more consistent results, the paper emphasizes on researchers to employ 10,000 bootstrap subsamples and Bias-corrected and accelerated (BCa) bootstrap in assessing the structural model. Insights regarding path coefficients, p-values, R-square (R2), f-square (f2), and Q-square (Q2),  are also presented. Furthermore, the review underscores the trade-off between predictive power and model fit when applying PLS-SEM. The presented practical insights alert novice researchers in avoiding common pitfalls and enhance the methodological rigor of empirical research that utilizes PLS-SEM. This step-by-step guide supports early-career researchers and contributes to the ongoing debates on improving methodological clarity and transparency.

This study aimed to explore how soft skills development is done in higher education institutions (HEIs) by considering the roles of HEIs and students. The study used a mixed research approach comprising qualitative and quantitative methods. Qualitative data was collected from 16 students using semi-structured interviews. In comparison, quantitative data was collected using a standard questionnaire from 342 students pursuing management and administrative undergraduate programmes in one of the universities in Tanzania. Through questionnaires, participants were asked to rank soft skills according to their relevance to their careers. The top five most relevant soft skills identified were communication, leadership, decision-making, teamwork/orientation and problem-solving. In addition, participants self-evaluated their levels of soft skills. Findings demonstrate that students perceived to have more customer orientation, communication, decision-making, problem-solving, and work-ethic skills. Through qualitative interviews, participants narrated the role of HEIs in developing soft skills and their personal initiatives to acquire the skills. It was found that joining clubs and associations, taking leadership roles, reading self-development books, watching videos and attending soft skills training are initiatives that students use for soft skills acquisition.

This study examines the strengths and limitations of 22 published journal articles by conducting a systematic literature review. The review revealed that the qualitative approach was flexible, offered in-depth and detailed information, allowed the use of multiple data collection methods and minimised the chance of having missing data. The approach was found (i) to integrate human touch, was cost-effective and was indeed the only option in some cases of research problems; (ii) to be prone to researchers’ subjectivity, involves complex data analysis, makes anonymity difficult and has limited scope in its generalizability, and (iii) to make replication of findings challenging, and the findings may be influenced by the researcher’s bias. It is concluded, therefore, that researchers should take necessary precautions when using the approach to ensure that the weaknesses of qualitative research do not bar them from achieving research objectives.

Novice and experienced researchers may encounter numerous methodological challenges when it comes to data collection. Data have to be collected from the right sources using appropriate data collection tools to make research studies impactful. There are numerous data collection methods that researchers may use but not all methods are appropriate for all research studies. Choosing inappropriate research methods may result in invalid research findings and lead to erroneous conclusions. This means researchers have to carefully choose data collection methods to make their studies achieve their respective goals. This study intended to identify factors that researchers have to consider in choosing data collection methods. A systematic literature review was used to analyze 28 journal articles published between 2018 and 2022. From the analysis the study identified ten (10) factors that researchers have to consider in choosing data collection methods. These factors are; the research goal, the scope of the study, sample size, type of data, time and user-friendliness to the subjects. Other factors are research approach, safety and security of a researcher, the need for triangulation and theoretical framework. Before choosing methods for data collection, it is important to put into consideration these factors to increase the chances of achieving research objectives and answering research questions of research projects.

This study aims to evaluate the factors that influence data saturation in qualitative studies. The study adopted a systematic literature review of 24 journal articles published between 2018 and 2022. The articles were obtained from a Google scholar database.  The findings point to five factors that affect data saturation namely, pre-determined codes and themes, sample size, relevancy of research subjects (respondents), number of research methods, and length of data collection sessions. It is argued in this paper, therefore, that in order to attain data saturation, researchers should consider multiple factors that may affect data saturation in order to increase the validity of qualitative studies.