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This study aims to explore the strategies corporations use in engaging stakeholders to sustain healthy corporate partnerships and create value for the corporate entity and the society in which they operate and their influence on the corporate manager’s cognitive abilities and decision-making.

The authors used an interpretive research approach leveraging the strengths of qualitative method of content analysis and comparative and critical analyses to report the results. Interpretive methods incorporate social theories and standpoints that view reality as the social construction of understandable events in the context of organizational communication.

The findings of this study suggest that corporations are assumed to follow and execute the principles of engaging stakeholders to achieve corporate social responsibility (CSR) claiming to manage a sustainable and responsible business practices that recognize local cultures, human rights and protect the environment. However, little attention has been paid to the cognitive reasoning of the individuals responsible for CSR and corporate sustainability (CS) as opposed to the growing concerns about strategies corporations use in engaging stakeholders to sustain healthy corporate partnerships and create value – especially the processes that take place during engagement and decision-making including cognitive offloading.

Stakeholder engagement requires practical approaches that enable corporations and individuals charged with decision-making responsibilities to understand, respond and fulfill their CSRs. To achieve CSRs, corporations and managers responsible for relevant decision-making would need to involve stakeholders in social performance planning, as social reporting/auditing has long been advocating for preventing managerial biasness, groupthink and increased information dissemination via detailed reporting practices toward more collaborative stakeholder relationships. Thus, it is crucial for corporations to implement enhanced stakeholder and managerial decision-making strategies such as integrative approaches to achieve balance in the trio elements of sustainability as well as the growing use of paradox perspective to understand the nature of the tensions being sought to balance and, in the process, provide opportunity for a better evaluation of complex sustainability issues for innovative approach to resolving them. While cognitive decision-making is at play, in practice, managers tasked with making decisions must ensure the most effective stakeholder engagement strategies that are transparent and inclusive are used.

The main contribution of this study is its argument regarding the tools corporations use in engaging key stakeholders and the cognitive reasoning of the individuals responsible for CSR and CS. The study further contributes to interpreting the integrative approach to achieving balance in the trio elements of sustainability as well as the growing use of paradox perspective to understand the nature of the tensions being sought to balance and, in the process, provide an opportunity for a better evaluation of complex sustainability issues for an innovative approach to resolving them.

Public debt forecasts represent a key policy issue. Many methodologies have been employed to predict debt sustainability, including dynamic stochastic general equilibrium models, the stock flow consistent method, the structural vector autoregressive model and, more recently, the neuro-fuzzy method. Despite their widespread application in the empirical literature, all of these approaches exhibit shortcomings that limit their utility. The present research adopts a different approach to public debt forecasts, that is, the random forest, an ensemble of machine learning.

Using quarterly observations over the period 2000–2021, the present research tests the reliability of the random forest technique for forecasting the Italian public debt.

The results show the large predictive power of this method to forecast debt-to-GDP fluctuations, with no need to model the underlying structure of the economy.

Compared to other methodologies, the random forest method has a predictive capacity that is granted by the algorithm itself. The use of repeated learning, training and validation stages provides well-defined parameters that are not conditional to strong theoretical restrictions This allows to overcome the shortcomings arising from the traditional techniques which are generally adopted in the empirical literature to forecast public debt.

Diversity and inclusion is a key focus of the profession. This paper investigates the ecological inherited niche of Indonesia and which employers accounting students choose and whether this will result in a diverse and inclusive profession. The authors conceptualise diversity as the demand-from the profession encompassing professional accounting firms, and inclusion as the supply of individuals wishing to enter the profession.

The 1377 responses to a questionnaire survey of students deciding on their career paths were analysed using a multinomial logistic regression and path model.

The findings show that a lack of diversity in the profession is caused by the ecological background, constructing a local niche, that prevents diversity. This is manifest in ethnicity, gender and education, whereby the local niche consists of Chinese males recruited from B-rated private universities. To bring diversity and inclusivity into the workplace, the profession needs to entice people from multi-faceted groups and match ecological niche underpinnings to expectations of the professional landscape. Non-Chinese females are needed to become role models and trail blazers to establish a diverse profession. The public interest will then be better served.

This study uses niche construction as the theoretical framing and demonstrates that the profession needs to take action to become truly diverse and inclusive.

This article provides an overview of lessons we can learn from human resource (HR) policies and practices related to neurodiversity.

We conducted a practice-based review using information obtained from organizations’ websites, summarized the information and reflected on how scholars can continue to advance this area of research based on what is happening in practice.

The review provided a selective overview of programs and practices per HR cluster: selection and recruitment; onboarding, integration and retention; job design; flexible work options and working remotely; training; employee resource groups (ESGs) and support. The review provides a description of practices and policies implemented within organizations that focus on neurodiversity among employees.

Our review showed that organizations have a multitude of HR practices and policies in place to include neurodivergent individuals in their workforces, though many of these have not been empirically investigated. Sharing this knowledge is important so that research insights and practice can reciprocally influence one another.

This paper aims to shed new light on the contextual embeddedness of intersectional entrepreneurs, i.e. entrepreneurs situated at the intersection of multiple marginalized diversity attributes, beyond simply business strategies and decisions. Taking an emic perspective on everyday practices as intersectional entrepreneurs, it uncovers neglected dimensions of the contextuality of intersectional discriminations.

The study presents qualitative data analysis results of in-depth narrative interviews with six intersectional entrepreneurs, all LGBTIQA* entrepreneurs with further diversity dimensions. It provides an emic view of intersectional entrepreneurs, in their everyday lives and the contexts, in which they develop their businesses.

Intersectional entrepreneurs face different burdens induced by social structures along the entrepreneurial process. While access to the niche market is more difficult and they lack community support, their realization of intersectional discrimination is crucial for the development of business strategies. Simultaneously, intersectional entrepreneurs use their specific diversity attributes to develop their business, yet this proximity of their identity and the business contents has severe consequences for their mental state. Intersectional entrepreneurs adjust to the balance of opportunity and vulnerability.

As intersectional entrepreneurs are barred from conventional institutional support and also have limited access to resources from their respective social networks, there is an urgency to provide specific support for such entrepreneurs.

Owing to highly volatile and chaotic external events, predicting future movements of cryptocurrencies is a challenging task. This paper advances a granular hybrid predictive modeling framework for predicting the future figures of Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Stellar (XLM) and Tether (USDT) during normal and pandemic regimes.

Initially, the major temporal characteristics of the price series are examined. In the second stage, ensemble empirical mode decomposition (EEMD) and maximal overlap discrete wavelet transformation (MODWT) are used to decompose the original time series into two distinct sets of granular subseries. In the third stage, long- and short-term memory network (LSTM) and extreme gradient boosting (XGB) are applied to the decomposed subseries to estimate the initial forecasts. Lastly, sequential quadratic programming (SQP) is used to fetch the forecast by combining the initial forecasts.

Rigorous performance assessment and the outcome of the Diebold-Mariano’s pairwise statistical test demonstrate the efficacy of the suggested predictive framework. The framework yields commendable predictive performance during the COVID-19 pandemic timeline explicitly as well. Future trends of BTC and ETH are found to be relatively easier to predict, while USDT is relatively difficult to predict.

The robustness of the proposed framework can be leveraged for practical trading and managing investment in crypto market. Empirical properties of the temporal dynamics of chosen cryptocurrencies provide deeper insights.

This study aims to explore the information needs of Chinese parents of children with Autism Spectrum Disorder (ASD) and how these needs evolve as their children develop.

This study collated 17,122 questions regarding raising children with ASD via the Yi Lin website until November 2021.

The information needs of parents of children with ASD were classified into two categories: 1) Cognition-motivation: related to children with ASD; and 2) Affection-motivation: related to their parents. Child development causes the adaptation of information needs of these parents. Within the first three years, nine different topics of these parents' information needs were identified. Major information needs at this stage are as follows: intervention content, intervention methods and pre-diagnosis questions. During the ages of three to six years, there were 13 topics of information needs for parents, focusing on three areas: intervention content, intervention methods and diagnosis and examination. There are eight topics of information needs post six years. Parents are more concerned with the three topics of intervention content, life planning and intervention methods.

This novel study indicates the complex and changing information needs of parents of children with ASD in China. It may enhance the understanding of the information needs of these parents at theoretical and practical levels, provide support for them to understand their own information needs and provide a reference for relevant government and social organisations to provide targeted information services for them.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2022-0247

Water pipes degrade over time for a variety of pipe-related, soil-related, operational, and environmental factors. Hence, municipalities are necessitated to implement effective maintenance and rehabilitation strategies for water pipes based on reliable deterioration models and cost-effective inspection programs. In the light of foregoing, the paramount objective of this research study is to develop condition assessment and deterioration prediction models for saltwater pipes in Hong Kong.

As a perquisite to the development of condition assessment models, spherical fuzzy analytic hierarchy process (SFAHP) is harnessed to analyze the relative importance weights of deterioration factors. Afterward, the relative importance weights of deterioration factors coupled with their effective values are leveraged using the measurement of alternatives and ranking according to the compromise solution (MARCOS) algorithm to analyze the performance condition of water pipes. A condition rating system is then designed counting on the generalized entropy-based probabilistic fuzzy C means (GEPFCM) algorithm. A set of fourth order multiple regression functions are constructed to capture the degradation trends in condition of pipelines overtime covering their disparate characteristics.

Analytical results demonstrated that the top five influential deterioration factors comprise age, material, traffic, soil corrosivity and material. In addition, it was derived that developed deterioration models accomplished correlation coefficient, mean absolute error and root mean squared error of 0.8, 1.33 and 1.39, respectively.

It can be argued that generated deterioration models can assist municipalities in formulating accurate and cost-effective maintenance, repair and rehabilitation programs.

This study aims to explore and ranks the factors that might determine attitudes and intentions toward using autonomous vehicles (AVs).

The “technology acceptance model” (TAM) was extended by assessing the moderating influences of personal-related factors. Data were collected from 378 Vietnamese and analysed using a combination of “partial least squares” and the “adaptive neuro-fuzzy inference system” (ANFIS) technique.

The findings demonstrated the power of TAM in explaining the attitude and intention to use AVs. ANFIS enables ranking the importance of determinants and predicting the outcomes. Perceived ease of use and attitude were the most crucial drivers of attitude and intention to use AVs, respectively. Personal innovativeness negatively moderates the influence of perceived ease of use on attitude. Data privacy concerns moderate positively the impact of perceived usefulness on attitude. The moderating effect of price sensitivity was not supported.

These findings provide insights for policymakers and automobile companies' managers, designers and marketers on driving factors in making decisions to adopt AVs.

The study extends the AVs literature by illustrating the importance of personal-related factors, ranking the determinants of attitude and intention, illustrating the inter-relationships among AVs adoption factors and predicting individuals' attitudes and behaviours towards using AVs.

This paper aims to present the development of a holistic campus facility management (CFM) performance assessment framework that incorporates a fuzzy logic approach and integrates a comprehensive set of key factors for successful management of campus facilities. The devised framework aims to cater to the needs of campus facilities management firms and departments for the purpose of gauging and assessing their performance across different management domains. Through this approach, facility management organizations can detect potential areas of enhancement and adopt preemptive steps to evade issues, foster progress and ensure success.

After a comprehensive analysis of the literature, conducting in-depth interviews with industry experts and employing the Delphi technique in two rounds, a total of 45 indicators critical to CFM success were identified and subsequently sorted into seven distinct groups. Through an online questionnaire, 402 subject-matter experts proficiently assessed the significance of the critical success indicators and their groups. A fuzzy logic framework was developed to evaluate and quantify a firm's compliance with the critical success indicators and groups of indicators. The framework was subsequently weighted using computations of the relative importance index (RII) based on the responses received from the questionnaire participants. The initial section of the framework involved a comprehensive analysis of the firm's performance vis-à-vis the indicators, while the latter part sought to evaluate the impact of the indicators groups on the overall firm's performance.

The utilization of fuzzy logic has uncovered the significant effects each effective CFM key indicator on indicators groups, as well as the distinct effects of each CFM indicators group on the overall performance of CFM. The results reveal that financial management, communications management, sustainability and environment management and workforce management are the most impactful indicators groups on the CFM performance. This suggests that it is imperative for management to allocate increased attention to these specific areas.

This study contributes to the advancement of current knowledge by revealing vital indicators of effective CFM and utilizing them to construct a thorough fuzzy logic framework that can assist in evaluating the effectiveness of CFM firms worldwide. This has the potential to provide crucial assistance to facility management organizations, facility managers and policymakers in their quest for informed decision-making.