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This study aims to investigate the quality of disclosure of a cutting-edge reporting tool – integrated reporting (<IR>) – in terms of its effectiveness to report on COVID-19 pandemic information, its ability to provide forward-looking information and risk impact implications, and its quality determinants in challenging times.

Thanks to a content analysis of 247 <IR> for FY20, an integrated reporting disclosure score was developed to assess the disclosure quality provided by the sampled companies. Three research questions were tested through logistic regressions.

Non-financial disclosure activities struggle to provide adequate information in terms of potential future scenarios, risk assessment and forward-looking analyses. However, companies incorporated in “Anglo-Saxon” territories drafted integrated reports of higher quality. More recently, incorporated companies have made a greater effort to measure and report COVID-19 pandemic impacts on environmental, social and governance and business activities, also increasing their risk assessment and mitigation efforts. Concerning the determinants of disclosure quality, leverage, corporate governance structures, country of incorporation and belonging to “high impact” industries all lead to a higher quality of <IR> disclosure.

Examining in detail corporate social responsibility activities and corporate governance integrity is pivotal to orienting strategy towards sustainable trajectories: to do so, corporate reporting and disclosure practices are essential tools. In this context, corporate governance systems that emphasize board diversity are proven, even in disruptive circumstances, to play a crucial role in providing corporate reports of higher quality. High disclosure quality that goes beyond mere financial results is considered to be necessary to remain competitive strategically, socially and environmentally.

This study aims to examine whether, and which type of, busy audit committee (AC) directors affect the quality and quantity of forward-looking disclosure (FLD).

The authors use content analysis to measure the quality and quantity of FLD. The authors use a sample of Omani financial institutions listed on the Muscat Securities Market for the period 2014–2018.

The authors find that overlapped AC chairs and total overlapped AC directors negatively (positively) affect disclosure quantity (quality). The authors also find that overlapped AC directors with financial expertise and those with multiple directorships positively affect disclosure quantity and quality.

This study offers new insights to policymakers (and managers) as it informs them about the benefits of overlapping AC directorship. It suggests that corporate governance codes should not limit overlapped AC direcotorship.

Climate change has a direct impact on companies. Therefore, the scenario analysis is used to provide companies and stakeholders in this specific sector with forward-looking measures and narratives of the world's future state. This work aims to provide an independent, wide and rigorous literature review on the topics of scenario analysis and climate change, analyzing a large set of referred papers included in economic journals on the Web of Science Clarivate Analytics data source. This review, by means of a mixed approach, can help address new policy strategies and business models.

The work employs 416 abstracts and relative titles in the field of economics, employing data mining for qualitative variables and performing descriptive statistics and lexicometric measures, similarity analysis and clustering with Reinert's hierarchical method in order to extract knowledge. Furthermore, qualitative content analysis allows for the return of a comprehensive and complete universe of meaning, as well as the analysis of co-occurences.

Content analysis reveals three main classification clusters and four unknown patterns: model area, risks, emissions and energy and carbon pricing, indicating research directions and limitations through an overview with an extensive reference bibliography. In the research, the prevalent use of quantitative instruments and their limitations emerge, while qualitative instruments are residual for climate change assessment; they also highlight the centrality of transition risk over adaptation measures and the combination of different types of instruments with reference to carbon pricing.

Scenario analysis is a relatively new topic in economics and finance research, and it is under-investigated by the academy. The analysis combines quantitative and qualitative research using text analytics.

This study sought to develop an aggregated assessment of the literature on the resource-based view (RBV). The main aim was to map the RBV field based on a systematic literature review (SLR) of 226 academic articles published in refereed journals from 1994 to 2022.

Two bibliometric analysis methods were used: bibliographic coupling and co-citation. These measures are complementary because bibliographic coupling is retrospective in nature and co-citation is forward-looking.

The analysis identified the most influential studies, top-cited articles and journals and six major thematic clusters: RBV, customer orientation and alliance portfolio, resource-based theory, firm performance, entrepreneurial orientation (EO) and dynamic capabilities.

This research was based on a combination of bibliographic coupling and co-citation analysis. The results provide a better understanding of the RBV field’s intellectual structure, which reveals potential new lines of future research.

Future is rarely explicitly addressed or problematized in business network research. This study aims to examine the possibilities of developing a business actor’s future orientation to network studies and imports ideas and concepts from futures research to support the development.

The study is conceptual and interdisciplinary. The authors critically analyze how extant studies grounded in the sensemaking view and process research approach integrate future time and how theoretical myopia hinders the adoption of a future orientation.

The prevailing future perspective is restricted to managers’ perceptions and actions at present, ignoring the anticipation and exploration of alternative longer-term futures. Future time is generally conceived as embedded in managers’ cognitive processes or is seen as part of the ongoing interaction, where the time horizon to the future is not noticed or is at best short.

To enable a forward-looking perspective, researchers should move the focus from expectation building in business interaction to purposeful preparation of alternative future(s) and from the view of seeing future as enacted in the present to envisioning of both near-term and more distant futures.

This study addresses the growing need of business actors to anticipate future developments in the rapidly changing market conditions and to innovate and change business practices to save the planet for future generations.

This study elaborates on actors’ future orientation to business markets and networks, proposes the integration of network research concepts with concepts from futures studies and poses new types of research questions for future research.

Primary agricultural cooperative credit societies (PACSs) in India are considered to be the backbone of the last mile delivery of rural credit for landless and marginal farmers and the poorest sections of the society. Contribution of PACS in social welfare is well documented. However, in spite of being such important form of organization at the grass root level, the authors find a typical baffling phenomenon which is fluctuating growth of PACS. Given that PACS have not been able to establish a consistent growth rate, thus hindering its trajectory to become a strong and consolidated organizational form, it is natural to investigate and identify those success factors that are critical for a newly formed PACS to survive and sustain. Hence it is necessary to undertake a strategic assessment of the function of PACS and hence identify those critical success factors (CSFs). This paper aims to address this issue.

The authors review the existing literature and conduct interview with experts and other stakeholders with the purpose to gain a view of the strategic situation of PACS in India, especially with respect to their birth and death processes and identify a set of factors that are critical to the survival and sustenance of PACS. Based on the inputs from the experts, we modelled the interrelationships amongst these CSFs using interpretive structural modelling (ISM). Further, fuzzy Matriced’ Impacts Croise's Multiplication Appliquée a UN Classement (MICMAC) analysis is performed to cluster these CSFs into four quadrants based on their influence and dependence in the system.

The findings point out that a hierarchical interconnected structure exists amongst the CSFs that influence the survival and sustenance of the PACSs. “Quality and Motivation of Management” was found to be driving all other factors so far the survival of any PACS is considered. Also the authors have found that “Loan Recovery” is the most sensitive factor that needs to be taken care of for the sustenance of PACS. These findings are in the expected line and commensurate with the conventional wisdom yet to be documented till date.

It is a forward-looking analysis that tries to find out the CSFs for the survival of PACS in order to face the changing economic scenario of India which is still agrarian in nature. Both in terms of research question as well as methodology, to the best of the authors’ knowledge, this is first of its kind so far PACS are concerned being one of the most important but neglected entities in India; so far the last mile rural agricultural credit delivery is concerned, this study will help the policy makers to develop a holistic viewpoint in relation to long term sustenance of PACS.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2022-0331

This study aims to take a step toward integrating research regarding the image, role and identity of management accountants by understanding how employers’ perceptions of the ideal management accountant image differ from operational managers’ perceived role expectations, how management accountants perceive their identity and how those factors shape management accountants’ understanding of who they are and want to be.

A qualitative design draws upon the case company’s 100 job advertisements and 31 semi-structured interviews with management accountants and operational managers. Those data are entwined with role theory and its core concepts of expectations and identities and also early recruitment-related theoretical aspects such as image and employer branding.

The findings reveal how employers’ perceptions of the ideal image and operational managers’ role expectations shape and influence the identity of management accountants. However, management accountants distance themselves from a brand image and role expectations. They experience identity conflict between their current and desired identity, the perception of not being able to perform the currently desired role. Although this study presents some possible reasons and explanations, such as employer branding for the misalignment and discrepancy between perceptions of employer (image), expectations of operational managers (role) and management accountants’ self-conception of the role (identity), this study argues that the identity of a management accountant results from organizational aspects of image and role and individual aspects of identity.

Image and external role expectations can challenge identity construction and also serve as a source of conflict and frustration; thus, a more comprehensive approach to studying the identity of management accountants is necessary to understand what contributes to the fragility of their identity.

The results provide an understanding of the dynamics of the image, role and identity to support management accountants and employers and to further address the suggested dissonance and ambiguities.

This study contributes by showing how the dynamics and connections between the image, role and identity influence the identity construction of management accountants. Moreover, this study shows how overpromising as a part of employer branding might not reflect the reality experienced by management accountants but may cause frustration and threaten the management accountants’ identity.

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.

The global economy has been affected by the COVID-19 pandemic, which has placed greater responsibility on companies to fulfill their obligations to Corporate Social Responsibility (CSR) amid the crisis. This chapter investigates the role of a Chief Executive Officer (CEO) attributes in improving a firm's CSR in the emerging economy of Jordan and how the COVID-19 pandemic modifies this relationship. Using a Jordanian sample of 655 firm-year observations during the 2014–2021 period, the research results show that older CEOs, well-educated CEOs, CEOs' remuneration, and CEOs' ownership positively correlate with CSR reporting. However, long-tenured CEOs are associated with lower CSR initiatives. The subsample analysis findings also validate the significance of CEO attributes in improving CSR practice during the COVID-19 pandemic compared to the prepandemic period. These findings are beneficial for the regulatory setters to understand better whether CEO attributes are linked to engagement in CSR-related information. This research is among the limited number of studies that have explored how CEO attributes impact CSR reporting for the stakeholder's welfare. Moreover, it uniquely concentrated on contrasting the findings before and during the COVID-19 pandemic.

This study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.

A data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.

Among five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.

Addressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.