Search results

This paper aims to conduct a tertiary review to analyse the state of the art of literature reviews on knowledge management (KM) published in academic journals and provide an overview of their evolution. From 2000 to 2022, about 500 reviews have been published in the KM field, with most systematic studies compared to bibliometric or meta-analytic studies, and an absence of previous tertiary studies. Therefore, given the lack of previous tertiary research, this paper provides a complete picture of the evolution of review topics in the past and presents implications for both researchers and practitioners.

A classification scheme was defined to cluster and evaluate the literature reviews, both in terms of methodological approach and content. Regarding the content, the various secondary papers were classified according to the purpose of the research (state of the art, taxonomy, research agenda and research framework), the unit of analysis (small and medium enterprise, large company, start-up and university), the KM models adopted and the thematic areas addressed. Furthermore, a tertiary review methodology was identified integrating two main approaches: a bibliometric approach for cluster identification and a systematic approach for the discussion.

Two categories of contributions emerge from the results: those concerning research topics that have found a continuous interest over time and those that have not yet found a constant research interest. This latter aspect is relevant to help researchers conduct future literature analysis in KM research to bridge existing research gaps.

This paper provides a unique compendium of search directions to offer a comprehensive overview of the scientific debate about KM. This overview can also be used as a managerial panacea to identify best KM practice guidelines from existing reviews.

This is a unique attempt to conduct a tertiary study on KM for more than two decades by providing insights into the structural body of knowledge through academic progress in the subject of KM. Thus, this study expands the field of KM and provides original approaches for research in the field.

This paper aims to assess the cumulative evidence on the determinants of sustainability assurance (SA) reports and the choice of assurance provider quality. It addresses the contradictory and inconsistent findings of past studies conducted over the past two decades.

The authors undertake a meta-regression analysis that enables systematic, comparative assessment of the variables associated with the choice of SA and the type of assurance provider. The authors undertake a chronological analysis with the aim of identifying systematic differences in the empirical evidence across distinct time periods.

The results indicate that there is very little evidence to support many of the expected associations between commonly studied predictor variables (namely, measures based on agency and corporate governance conceptions) and the choice of SA and the assurance provider type. As a result, research on this topic does not make as effective a contribution as might be expected. There is, however, a time period difference. The authors find results from studies using company data prior to 2010 are significantly different from those using post-2010 data. The results indicate the decision to publish SA to be significantly associated with companies in the oil industry and utilities, and larger organisations where agency costs tend to be higher. Obtaining assurance from a higher-quality provider is found to be associated with companies in environmentally sensitive industries and in stakeholder-oriented countries.

The study shows that as yet there is not sufficient evidence to support expected results. Users of the research should be aware of this, and researchers should know that more work is needed. The authors suggest researchers take greater care in the choice and comparability of variable measurement and expand the conceptual base when selecting predictor variables.

Companies need to be more transparent and accountable to critical stakeholders such as report users and regulators, and the latter should be more aware that the organisational practice of SA and choice of service provider have changed over time and are increasingly open to agency and other cultural biases.

To the best of the authors’ knowledge, this is the first study to apply meta-regression techniques for understanding the body of literature on SA and provider choice.

Reacting to the calls in the contemporary literature to further examine the relationship between board attributes and firms’ decisions to obtain corporate social responsibility assurance (CSRA) through the use of pioneering techniques, this study aims to analyse the influence of such attributes together with the existence of a corporate social responsibility (CSR) committee on the adoption of CSRA using fuzzy set qualitative comparative analysis (Fs-QCA).

Fs-QCA was performed on a sample of nonfinancial European companies listed on the STOXX Europe 600 index over the period 2016–2018.

The study findings indicate that the decision to obtain a CSRA report depends on a complex combination of the influence of the CSR committee and certain board attributes, such as size, experience, independence, meeting frequency, gender and CEO separation. These attributes play essential contributing roles and, if suitably combined, stimulate the adoption of CSRA.

The study findings are important for policymakers, professionals, organisations and regulators in forming and modifying the rules and guidelines related to CSR committees and board composition.

To the best of the authors’ knowledge, this study represents the first examination of the impact of board attributes and CSR committees on the adoption of CSRA using Fs-QCA method. It also offers a novel methodological contribution to the board-CSRA literature by combining traditional statistical (logistic regression) and Fs-QCA methods. This study emphasises the benefits of Fs-QCA as an alternative to logistic regression analysis. Through the use of these methods, the research illustrates that Fs-QCA offers more detailed and informative results when compared to those obtained through logistic regression analysis. This finding highlights the potential of Fs-QCA to enhance our understanding of complex phenomena in academic research.