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Business rescue, as a mechanism to aid financially distressed companies in South Africa, has received considerable academic and practical recognition. However, the business rescue plan is an overlooked and, perhaps, underdeveloped aspect of the regime. For stakeholders, this is the ultimate decision-making document. Creditors are the most influential stakeholders in business rescue proceedings owing to their voting rights. For creditors to make informed decisions and exercise their votes meaningfully, the business rescue plan should be transparent and adequately disclose relevant and reliable information. This study aims to identify creditors’ primary information needs to enhance the sufficiency and decision-usefulness of business rescue plans, not only to entice the vote of creditors but to enforce accountability from practitioners.

Using a qualitative research design, semi-structured interviews were conducted with 14 executives from 10 South African financial institutions.

The findings reveal that comprehensive disclosure of financial, commercial and legal information in business rescue plans was a critical antecedent for stakeholder decision-making. Additionally, leadership and social impact information were influential determinants. This study advances academic knowledge and, for practitioners, adds value to the development of business rescue plans. This can enhance creditors' confidence in supporting the rescue effort and approving the plan.

This study advances academic knowledge and, for practitioners, adds value to the development of business rescue plans. This can enhance creditors' confidence in supporting the rescue effort and approving the plan.

The originality of this article lies in its investigation of how creditors assess the information in BR plans as a precursor to supporting the company’s reorganisation in a creditor-friendly business rescue system such as South Africa. This study provides novel insights into the decision-making process, particularly how creditors assess BR plans, address information asymmetry and vote on the plan.

This paper aims to investigate the impact of retrenchment strategy on firm performance in the context of Pakistani firms while considering the moderating role of chief executive officer (CEO) power. By examining the influence of CEO duality and CEO share ownership on the relationship, this study contributes to strategic management and corporate governance knowledge within the Pakistani business environment.

A quantitative approach was used to analyze the relationship using data from annual financial statements. The sample consisted of 76 companies from the KSE-100 index from the year 2015 to 2020. Random effects regression models were used, along with hierarchical regression to explore the moderating effect of CEO power.

The findings demonstrate that the implementation of a retrenchment strategy positively impacts firm performance in Pakistani firms. The study also reveals that CEO power plays a crucial role in strengthening the relationship between retrenchment strategy and firm performance. Moreover, the study highlights the importance of considering the temporal sequence, size and age of firms when examining the impact of CEO power and retrenchment strategy on firm performance.

The study enhances the understanding of the contingent nature of retrenchment strategies and the influence of CEO power in the Pakistani business context. Practically, the research contributes to strategic management and corporate governance dynamics, facilitating the development of strategies that enhance firm performance and sustainability in Pakistan.

This research provides original insights by specifically focusing on the Pakistani context and analyzing the interplay between retrenchment strategy, CEO power and firm performance. The study adds to the limited literature on the relationship between retrenchment and performance in the Pakistani business environment. Additionally, it highlights the significance of CEO power as a critical factor in determining the success of retrenchment.

With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.

With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.

Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.

The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

This study aims to focus on key approaches to education for sustainability (EfS) leadership development in the context of Malaysian and Japanese universities. The authors identify key indicators of effective EfS leadership development approaches using both descriptive and inferential analyses, identify and compare the preferred leadership learning methods of academics and examine the impact of marital status, country of residence and administrative position on the three EfS leadership development approaches.

The study is quantitative in approach and survey in design. Data were collected from 664 academics and analysed using the efficient partial least squares (PLSe2) methodology. To provide higher education researchers with more analytical insights, the authors re-estimated the models based on the maximum likelihood methodology and compared the results across the two methods.

The inferential results underscored the significance of four EfS leadership learning methods, namely, “Involvement in professional leadership groups or associations, including those concerned with EfS”, “Being involved in a formal mentoring/coaching program”, “Completing formal leadership programs provided by my institution” and “Participating in higher education leadership seminars”. Additionally, the authors noted a significant impact of country of residence on the three approaches to EfS leadership development. Furthermore, although marital status emerged as a predictor for self-managed learning and formal leadership development (with little practical relevance), administrative position did not exhibit any influence on the three approaches.

In addition to the theoretical and methodological implications drawn from the findings, the authors emphasize a number of practical implications, namely, exploring the applicability of the results to other East Asian countries, the adaptation of current higher education leadership development programmes focused on the key challenges faced by successful leaders in similar roles, and the consideration of a range of independent variables including marital status, administrative position and country of residence in the formulation of policies related to EfS leadership development.

This study represents an inaugural international comparative analysis that specifically examines EfS leadership learning methods. The investigation uses the research approach and conceptual framework used in the international Turnaround Leadership for Sustainability in Higher Education initiative and uses the PLSe2 methodology to inferentially pinpoint key learning methods and test the formulated hypotheses.

Utilising componential theory of creativity the study aims to examines the roles of green transformational leadership, organizational culture and green mindfulness as antecedents of green creativity.

A three-wave data collection method was used to collect data from the 304 hotel employees belonging to hotels located in the tourist’s location of India. The study used PROCESS macro to test the hypothesized model.

The results found a significant serial mediating effect of green organizational culture and green mindfulness for strengthening the association between green transformational leadership and green creativity.

The study establishes that a transformational leadership can bring about a much-needed green turnaround and thus makes significant practical contribution. As customers are becoming environmentally conscious, the industry can translate the green practices and motivate their subordinates by exhibiting the environmentally conscious behaviour and exhibit the same in their actions at work.

The current research work expands the body of literature on green transformational leadership and green creativity nexus in tourism and hospitality industry by exploring the boundary condition that increases the strength of this relationship.

The main aim of this study is to identify and prioritize the factors that influence the adoption of big data analytics (BDA) within the supply chain (SC) of the food industry in India.

The study is carried out in two distinct phases. In the first phase, barriers hindering BDA adoption in the Indian food industry are identified. Subsequently, the second phase rates/prioritizes these barriers using multicriteria methodologies such as the “analytical hierarchical process” (AHP) and the “fuzzy analytical hierarchical process” (FAHP). Fifteen barriers have been identified, collectively influencing the BDA adoption in the SC of the Indian food industry.

The findings suggest that the lack of data security, availability of skilled IT professionals, and uncertainty about return on investments (ROI) are the top three apprehensions of the consultants and managers regarding the BDA adoption in the Indian food industry SC.

This research has identified several reasons for the adoption of bigdata analytics in the supply chain management of foods in India. This study has also highlighted that big data analytics applications need specific skillsets, and there is a shortage of critical skills in this industry. Therefore, the technical skills of the employees need to be enhanced by their organizations. Also, utilizing similar services offered by other external agencies could help organizations potentially save time and resources for their in-house teams with a faster turnaround.

The present study will provide vital information to companies regarding roadblocks in BDA adoption in the Indian food industry SC and motivate academicians to explore this area further.

The purpose of this paper is to explore the reintegration process of Wounded, Injured or Sick Employees (WISE) of the Dutch Military Armed Forces.

The research method is an exploratory, qualitative case study. A purposive sampling was drawn, including 10 WISE, and 6 reintegration stakeholders. A total of 16 interviews were conducted to explore the individual, organisational and socio-environmental factors that influence reintegration of WISE.

Findings show the importance of involvement and participation of members of the social environment in the reintegration process. Findings show that the complexity of the plethora of WISEs' injuries and disabilities requires a more person-centric reintegration approach with personalized-customized provisions, rather than a policy-driven approach to the reintegration, in order to enhance the reintegration experience and to arrive at beneficial individual and organisational reintegration outcomes.

This cross-sectional study on a limited sample of WISE and reintegration stakeholders does not allow for making inferences about the long-term effects of the reintegration process on reintegration outcomes of the wider population of WISE. Future longitudinal research, encompassing a larger sample, could examine the long-term career, organisational and societal implications of reintegration of WISE within and outside the Military Armed Forces.

This paper presents a “Wounded Warrior Workplace Reintegration Program”, aimed at deriving beneficial outcomes for all stakeholders involved in the reintegration trajectory.

This paper contributes to the literature by presenting a Model of Occupational Reintegration of WISE that considers the factors at an individual, social-environmental, and institutional level as determinants of successful reintegration.

This study aims to investigate how to identify and address production levelling problems in assembly lines utilising an intensive manual workforce when higher productivity levels are urgently requested to meet market demands.

A mixed-methods approach was used in the research design, integrating case study analysis, interviews and qualitative/quantitative data collection and analysis. The methodology implemented also introduces to the literature on operational performance a novel combination of data analysis methods by introducing the use of the Natural Language Understanding (NLU) methods.

First, the findings unveil the impacts on operational performance that transportation, limited documentation and waiting times play in assembly lines composed of an intensive workforce. Second, the paper unveils the understanding of the role that a limited understanding of how the assembly line functions play in productivity. Finally, the authors provide actionable insights into the levelling problems in manual assembly lines.

This research supports industries operating assembly lines with intensive utilisation of manual workforce to improve operational performance. The paper also proposed a novel conceptual model prescriptively guiding quick and long-term improvements in intensive manual workforce assembly lines. The article assists industrial decision-makers with subsequent turnaround strategies to ensure higher efficiency levels requested by the market.

The paper offers actionable findings relevant to other manual assembly lines utilising an intensive workforce looking to improve operational performance. Some of the methods and strategies examined in this study to improve productivity require minimal capital investments. Lastly, the study contributes to the empirical literature by identifying production levelling problems in a real context.

Democratic countries all over the world are embarking on initiatives to empower citizens through public participation. One of the tools used by countries to promote public participation is the enactment and implementation of freedom of information (FOI) legislation, as it is the case with South Africa and Zimbabwe. Despite having legislation reaffirming the need for people’s right to know, practices in South Africa and Zimbabwe indicate the opposite. The purpose of this study is to explore FOI models in South Africa and Zimbabwe, with a view to recommend ways in which people’s right to know can be promoted.

This qualitative study used interviews to collect the data from 12 FOI experts in South Africa and Zimbabwe, who were selected through the snowball sampling technique. Data collected through interviews were supplemented by the data collected through document analysis.

The study concluded that the key role players need to make efforts to ensure that the right to know, which is associated with FOI, is being realised in both countries. FOI legislation, in both countries, is imprecise and needs to be revised to ensure effective implementation.

The study demonstrates that FOI is a necessary tool for people to be involved in decision-making in government. People’s rights to know can be achieved by successfully implementing FOI legislation.

Chinese firms are winning market share from foreign multinational enterprises in domestic markets. The international business literature suggests that this is happening because these firms are developing non-traditional firm-specific advantages (FSAs). Strategic factor market (SFM) theory provides a good basis for explaining how this is happening. However, it is underdeveloped in terms of analysing unique resources and unique access to those resources by Chinese firms in their domestic markets. This paper aims to develop a framework to understand how Chinese firms have developed non-traditional FSAs.

The case study method is adopted to explore how Chinese firms develop non-traditional FSAs. Specifically, the authors compare paired case studies of a Chinese firm and a foreign multinational in each of two industries.

The authors find that Chinese firms have developed non-traditional FSAs because of more relevant experience, better adapted strategies and privileged relationships. This has enabled Chinese firms to develop non-traditional FSAs.

The authors propose a framework that conceptualises non-traditional FSA development in Chinese firms as a product of superior access to unique and valuable resources in their domestic SFMs.