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Meta-synthesis of the research evidence adds value to the process of literature reviewing, providing useful knowledge for researchers, practitioners and policymakers. The aim of the chapter is to explain what meta-synthesis involves and how it illuminates our understanding of concepts. Previous papers on meta-synthesis (by the author) have reviewed research strategies in information behaviour research and methods for meta-synthesis, discussed application of meta-synthesis to research on information behaviour of women, and proposed methods suitable for integrating information literacy research. Meta-synthesis methods have been applied to many areas of social science research. The literature review examines how to reduce the risks involved in suitable for integrating qualitative research or qualitative and quantitative research; outlines the main approaches used in meta-synthesis before explaining the processes used in a meta-synthesis of research on information behaviour of women, reflects on the meta-synthesis methods used, and which might have been used, and shows what meta-synthesis achieves. Meta-synthesis should be used more in information behaviour research, but it is a rigorous process, requiring time and effort to get useful results. On the other hand, meta-synthesis provides more new knowledge, and a deeper understanding of our ideas, than a conventional literature review.

In recent years, there has been an extraordinary accumulation of qualitative research in special education. However, as yet, there has been little accumulation of the understandings gained from these studies. This omission has important implications for knowledge development, the utilization of findings in practice, and providing implications for policy. In this chapter, we review and discuss perspectives and procedures from other fields with respect to aggregation of qualitative data. Additionally, we propose a specific method for the meta-synthesis of qualitative research in the area of special education. This synthesis would not be a numerical compilation of outcomes, as in traditional meta-analysis, but would treat individual research reports as “informants,” and employ procedures, such as analytic induction and the constant comparative method to develop higher understandings across individual cases. Such efforts are thought to be essential to reaching higher analytic goals and also to enhancing the generalizability of qualitative research. It is argued that meta-synthesis efforts could do much to promote the impact of the shared understandings gained from individual qualitative research efforts.

The study endeavours to bring out a critical synthesis of the effect of quality of financial reporting in the BRICS (Brazil, Russia, India, China, and South Africa) countries pertaining to the International Financial Reporting Standards (IFRS) adoption. BRICS is the group composed by the five major emerging countries, which together represent about 42% of the population, 23% of gross domestic product (GDP), 30% of the territory, and 18% of the global trade. The study synthesised 57 quantitative, qualitative, and theoretical studies between the period 2005 and 2020. The findings reflect that the BRICS countries are far way behind with the qualitative and quantitative outcomes on IFRS adoption, which may be on a voluntary basis or mandatory basis. However, there are mixed revelation based on the implications of the domestic convergence of standards with IFRS, which demonstrate that 15 papers’ results revealed a negative impact.

Ever since our cognitive make-up allowed it, human beings have used their information behaviour abilities to help them survive. Information behaviour evolved in response to the need by early humans to benefit from information that could not be immediately accessible in the nearby environment or obtained through communication. Humans developed an information behaviour ability, including processes of information sense making, foraging, seeking, organising and using. Information behaviour brought several benefits to early humans, including greater influence and control over their environment, and the degree in which they could use the environment for their own gain and survival. Information behaviour thus brought several advantages for the survival of early humans, and consequently emerged as a genetically favoured trait (Spink, 2010).

Education policies in the United States reinforce social stratification by prioritizing and normalizing middle-class whiteness in schools (Leonardo, 2007; Picower, 2009). The teacher labor market has also become more feminized, making white middle-class women paragons of exemplary educators (Rury, 1989; Tolley & Beadie, 2006). These sociopolitical and historical factors continue to play out in the current U.S. education workforce where 80% teachers are white and 76% of teachers are female (Hussar et al., 2020). Meanwhile, student demographics are shifting with students of color comprising over 50% of the public student population (de Brey et al., 2019). Diversifying the educator pipeline is a well-documented strategy to improve educational outcomes for all students, specifically students of color, and to achieve greater equity and inclusion in public education. However, the retention and promotion of educators of color remains a critical and complex issue.

Thus, looking at the intersection of race and gender in the education workplace, the purpose of this chapter is to highlight the experiences and expertise of women K-12 educators of color to identify best practices for career development. Applying Psychology of Working Theory (PWT) and utilizing modified meta-synthesis methodology, the chapter highlights the experiences of Black, Latinx, Asian American, and Indigenous/Native American women K-12 principals and superintendents to (1) thematize and conceptualize how women of color define their work in education spaces through a PWT lens and (2) understand how PWT themes can illuminate ways to build more diverse and inclusive career pathways for women of color leaders.

This chapter employed a systemic meta-synthesis literature review to reflect on the transactional variables of the theory of transactional distance (TTD) in addressing barriers to student engagement in the open and distance learning (ODL). Literature sources were obtained from unlimited databases around the globe; however, articles published before 2015 were not included in this review. Through the literature review, the authors identified barriers to student engagement in the ODL through the lens of TTD. The identified barriers to student engagement are presented according to three transactional variables of the TTD and later classified concerning student engagement dimensions. Findings suggest that key instructional dialogue barriers emanate from the teacher and student personality. For program structure, the authors found the poorly designed courses while for learner autonomy there are situational, institutional, and dispositional barriers. The identified barriers to student engagement in ODL revealed the interrelatedness of the transactional variables and the strong link with the student engagement dimensions. By integrating the transactional variables of TTD and student engagement dimensions, this chapter identified possible strategies to address barriers to student engagement in the ODL.

The COVID-19 pandemic ushered in multiple challenges for employees, which led to employee turnover, disengagement at work, employees’ mental health issues, etc. The study tries to elucidate how artificial intelligence (AI) herald great promise in human resource management in decreasing cost, attrition level and enhancing productivity. Considering the dearth of studies on recent trends in human resource management (HRM) in the context of AI, the study elucidates the role of AI in facilitating seamless onboarding, diversity and inclusion (D&I), work engagement, emotional intelligence and employees’ mental health. Thus, a conceptual model of recent trends in HRM in the context of AI and its organisational outcomes is proposed. A systematic review and meta-synthesis method are undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes. The findings of the study suggest that using natural language processing (NLP) and robots has eased the onboarding process. D&I is promoted using data analytics, big data, machine learning, predictive analysis and NLP. Furthermore, NLP and data analytics have proved to be highly effective in engaging employees. Emotional Intelligence is applied through AI simulation and intelligent robots. On the other hand, chatbots, employee pulse surveys, wearable technology, and intelligent robots have paved way for employees’ mental health. The study also reveals that using AI in HRM leads to enhanced organisational performance, reduced cost and decreased intention to quit the organisation. Thus, AI in HRM provides a competitive edge to organisations by enhancing the performance of the employees.

Introduction: The Covid-19 pandemic wreaked havoc on the organisations in the form of increased job demands which manifested through increased workload, time pressure, etc. Similarly, stress and burnout engulfed the employees. Remote work became the new normal post-pandemic. Remote workers require more engagement. This has brought Artificial Intelligence (AI) to the forefront for engaging employees in the new normal.

Purpose: With limited studies on AI-enabled employee engagement in the new normal, this study investigates and proposes a conceptual framework of employee engagement in the context of AI and its impact on organisations.

Methodology: A systematic review and meta-synthesis method is undertaken. A systematic literature review assisted in critically analysing, synthesising, and mapping the extant literature by identifying the broad themes.

Findings: Since many organisations are turning to remote work post-pandemic and remote work requires more engagement, organisations are investing in AI to boost employee engagement in the new normal. Several antecedents of employee engagement such as quality of work life, diversity and inclusion, and communication are facilitated by AI. AI helps enhance the quality of work life by playing a major role in providing fair compensation, safe and healthy working conditions, immediate opportunity to use and develop human capacities, continued growth and security, work and total life space, and social relevance of work life. This has led to positive organisational outcomes like increased productivity, employee well-being, and decreased attrition rate. Furthermore, AI helps in measuring employee engagement. The various tools of AI, such as wearable technology, digital biomarker, neural network, data mining, data analytics, machine learning (ML), natural language processing (NLP), etc., have gone a long way in engaging employees in the new normal.