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The study aims to identify the possible risk factors for electricity grids operational disruptions and to determine the most critical and influential risk indicators.

A multi-criteria decision-making best-worst method (BWM) is employed to quantitatively identify the most critical risk factors. The grey causal modeling (GCM) technique is employed to identify the causal and consequence factors and to effectively quantify them. The data used in this study consisted of two types – quantitative periodical data of critical factors taken from their respective government departments (e.g. Indian Meteorological Department, The Central Water Commission etc.) and the expert responses collected from professionals working in the Indian electric power sector.

The results of analysis for a case application in the Indian context shows that temperature dominates as the critical risk factor for electrical power grids, followed by humidity and crop production.

The study helps to understand the contribution of factors in electricity grids operational disruptions. Considering the cause consequences from the GCM causal analysis, rainfall, temperature and dam water levels are identified as the causal factors, while the crop production, stock prices, commodity prices are classified as the consequence factors. In practice, these causal factors can be controlled to reduce the overall effects.

From the results of the analysis, managers can use these outputs and compare the risk factors in electrical power grids for prioritization and subsequent considerations. It can assist the managers in efficient allocation of funds and manpower for building safeguards and creating risk management protocols based on the severity of the critical factor.

The research comprehensively analyses the risk factors of electrical power grids in India. Moreover, the study apprehends the cause-consequence pair of factors, which are having the maximum effect. Previous studies have been focused on identification of risk factors and preliminary analysis of their criticality using autoregression. This research paper takes it forward by using decision-making methods and causal analysis of the risk factors with blend of quantitative and expert response based data analysis to focus on the determination of the criticality of the risk factors for the Indian electric power grid.

Tanzania is rich in small hydropower (SHP) potentials. However, many of these potentials have yet to be fully used, and more than two-thirds of its rural population lacks access to electricity. The purpose of this paper is to explore the role of SHP stations in improving rural welfare in the southern highlands of Tanzania. It further explores the history, cost-effective analysis and threats to the sustainability of SHP as one of the renewable energy sources.

The study uses a qualitative research design to explore respondents’ views on the role of SHP stations in facilitating rural electrification and welfare improvement. Primary data were gathered using semi-structured interviews with the 27 key informants and beneficiaries of SHP stations from the Southern Highlands of Tanzania. In addition, the study used documentary research to complement the information from the field survey.

The findings found that SHP stations enhance rural electrification and welfare by providing electricity in remote areas with sparse populations. They operate as standalone off-grids, often by church communities and individuals. However, the sustainability of SHP stations is hampered by challenges such as climate change impacts, high capital investment costs, heavy siltation of small reservoirs, skilled manpower shortages, limited local manufacturing capabilities and infrastructural issues.

The study contributes to the ongoing debate on renewable energy supply and uses, focusing on how SHP stations could contribute to sustainable rural electrification and achieve the 2030 United Nations agenda for sustainable development, which, among other things, aims to safeguard access to sustainable and modern energy and alleviate energy poverty.

This study aims to investigate the impact of the 2013 privatization of Nigeria’s energy sector on the technical performance of the Benin Electricity Distribution Company (BEDC) and its workforce.

This study used a questionnaire-based approach, and 196 participants were randomly selected. Analytical tools included standard deviation, Spearman rank correlation and regression analysis.

Before privatization, the energy sector, managed by the power holding company of Nigeria, suffered from inefficiencies in fault detection, response and billing. However, privatization improved resource utilization, replaced outdated transformers and increased operational efficiency. However, in spite of these improvements, BEDC faces challenges, including unstable voltage generation and inadequate staff welfare. This study also highlighted a lack of experience among the trained workforce in emerging electricity technologies such as the smart grid.

This study’s focus on BEDC may limit its generalizability to other energy companies. It does not delve into energy sector privatization’s broader economic and policy implications.

The positive outcomes of privatization, such as improved resource utilization and infrastructure investment, emphasize the potential benefits of private ownership and management. However, voltage generation stability and staff welfare challenges call for targeted interventions. Recommendations include investing in voltage generation enhancement, smart grid infrastructure and implementing measures to enhance employee well-being through benefit plans.

Energy sector enhancements hold positive social implications, uplifting living standards and bolstering electricity access for households and businesses.

This study contributes unique insights into privatization’s effects on BEDC, offering perspectives on preprivatization challenges and advancements. Practical recommendations aid BEDC and policymakers in boosting electricity distribution firms’ performance within the privatization context.

The purpose of this research is to study how compliance evaluation becomes performed in practice. Compliance evaluation is a common practice among organizations that need to evaluate their posture against a set of criteria (e.g. a standard, legislative framework and “best practices”). The results of these evaluations have significant importance for organizations, especially in the context of information security and continuity. The author argues that how these evaluations become performed is not merely a “social” activity but shaped by the materiality of the evaluation criteria

The authors adopt a sociomaterial practice-based view to study the compliance evaluation through in situ participant observations from compliance evaluation workshops to evaluate organizational compliance against a information security and business continuity criteria. The empirical material was analyzed to construct vignettes that serve to illustrate the practice of compliance evaluation.

The research analysis shows how the information security and business continuity criteria themselves partake in the compliance evaluations by operating through (ventriloqually) the evaluators on three strata: the material, the textual and the structural. The author also provides a conceptualization of a hybrid agency.

This research contributes to lack of studies on the organizational-level compliance. Further, the research is an original contribution to information security and business continuity management by focusing on the practices of compliance evaluation. Further, the research has theoretical novelty by adopting the ventriloqual agency as a hybrid agency to study the sociomateriality of a phenomenon.

This study aims to investigate the intricate relationships between a community energy system, water resources and biodiversity conservation, with a specific focus on augmenting community energy resilience in Bondo. The primary objective is to gain an in-depth understanding of how community members perceive and experience the challenges related to balancing the often-conflicting demands of energy, water and biodiversity conservation within this context.

The research uses a qualitative approach to unravel the multifaceted dynamics of community energy systems, water resources and biodiversity conservation in Bondo. Data were collected through focus groups and direct observations, enabling a nuanced exploration of community perspectives and lived experiences. The subsequent analysis of this qualitative data follows established thematic analysis procedures.

The study's findings shed light on the formidable barriers that impede rural communities in Malawi from accessing electricity effectively. Even in communities fortunate enough to have electricity connections, the lack of knowledge regarding productive electricity use results in community energy systems operating at significantly reduced load factors. Furthermore, the intricate challenge of managing a biodiversity hotspot persists, exacerbated by the densely populated peripheral communities' continued reliance on forest, land and water resources. These activities, in turn, contribute to ecosystem degradation.

In a context where government-led management of forest reserves and game reserves has not yielded the expected results due to a multitude of factors, there arises a compelling need for innovative approaches. One such innovation involves fostering partnerships between the government and experienced trusts as lead organisations, providing a fresh perspective on addressing the complex interplay between community energy systems, water resources and biodiversity conservation. This novel approach opens doors to explore alternative pathways for achieving the delicate balance between human energy needs and the preservation of vital ecosystems.

The ongoing paradigm shift in the energy sector holds paramount implications for the realization of the sustainable development goals, encompassing critical domains such as resource optimization, environmental stewardship and workforce opportunities. Concurrently, this transformative trajectory within the power sector possesses a dual-edged nature; it may ameliorate certain challenges while accentuating others. In light of the burgeoning research stream on open innovation, this study aims to examine the intricate dynamics of knowledge-based industry-university-research networking, with an overarching objective to elucidate and calibrate the equilibrium of ambidextrous innovation within power systems.

The authors scrutinize the role of different innovation organizations in three innovation models: ambidextrous, exploitative and exploratory, and use a multiobjective decision analysis method-entropy weight TOPSIS. The research was conducted within the sphere of the power industry, and the authors mined data from the widely used PatSnap database.

Results show that the breadth of knowledge search and the strength of an organization’s direct relationships are crucial for ambidextrous innovation, with research institutions having the highest impact. In contrast, for exploitative innovation, depth of knowledge search, the number of R&D patents and the number of innovative products are paramount, with universities playing the most significant role. For exploratory innovation, the depth of knowledge search and the quality of two-mode network relations are vital, with research institutions yielding the best effect. Regional analysis reveals Beijing as the primary hub for ambidextrous and exploratory innovation organizations, while Jiangsu leads for exploitative innovation.

The study offers valuable implications to cope with the dynamic state of ambidextrous innovation performance of the entire power system. In light of the findings, the dynamic state of ambidextrous innovation performance within the power system can be adeptly managed. By emphasizing a balance between exploratory and exploitative strategies, stakeholders are better positioned to respond to evolving challenges and opportunities. Thus, the study offers pivotal guidance to ensure sustained adaptability and growth in the power sector’s innovation landscape.

The primary originality is to extend and refine the theoretical understanding of ambidextrous innovation within power systems. By integrating several theoretical frameworks, including social network theory, knowledge-based theory and resource-based theory, the authors enrich the theoretical landscape of power system ambidextrous innovation. Also, this inclusive examination of two-mode network structures, including the interplay between knowledge and cooperation networks, unveils the intricate interdependencies between these networks and the ambidextrous innovation of power systems. This approach significantly widens the theoretical parameters of innovation network research.

The purpose of this study is to investigate the connections between the regulatory governance, human capital, stakeholder orientation, management control systems (MCSs) and sustainable performance (SP) of power companies. The authors especially looked at how much regulatory governance, human capital, stakeholder orientation and MCSs affect the SP across power companies in Uganda.

This is a cross-sectional and correlational study. Data were collected from 105 power companies using a questionnaire and analysed using SPSS.

Stakeholder orientation, MCSs, human capital and regulatory governance significantly predict variances in the SP of power providers in Uganda. Stakeholder orientation is the most important predictor of SP of power companies.

The absence of validation from important stakeholders and the major reliance on company-provided data in existing research on SP raises the possibility of self-desirability bias. To evaluate and verify the information supplied by firms with external stakeholders, further studies might consider using an explanatory mixed methods technique, in which quantitative data are initially gathered from the managers of power companies and analysed and then validated by interviews with important stakeholders.

Using stakeholder, legitimacy and resource-based theories has provided a better explanation for SP which is a multi-dimensional notion. Moreover, the study adds to the body of perception-based research that offers direct management incentives for SP. The perspectives of managers have been gathered through the use of self-administered questionnaires to gather impressions of managers of businesses, which has helped to tap into all aspects of SP. The study’s results offer, probably for the first time to the best of the authors’ knowledge, evidence of the contextual elements that affect SP in African nations like Uganda particularly in the power sector.

This study aims to examine the impact of socioeconomic factors on electricity usage and assess the feasibility of implementing a mini-grid system in Kasangazi, Malawi. The primary aim is to understand the community’s current and potential utilisation of electrical equipment.

A mixed-methods approach was used to collect quantitative and qualitative data. Information was gathered through structured questionnaires, and energy audits were conducted among 87 randomly selected households from 28 Kasangazi communities. Data analysis relied on descriptive statistics using IBM SPSS version 28.

The study indicates that every household in Kasangazi uses non-renewable energy sources: 60 households use disposable batteries for lighting, 20 for radios and all use firewood, freely sourced from local forests, for cooking and heating water. The study shows that firewood is the community’s preferred energy source, illustrating the challenges faced in the fight against deforestation. Most household income comes from farming, with smaller contributions from businesses, employment and family remittances. Access to higher education is scarce, with only one out of 349 family members receiving tertiary education. Despite the constraints of low education levels and income, there is a demand for larger electrical appliances such as stoves and refrigerators. This underscores the need for mini-grid solutions, even in less technologically advanced, agriculture-dependent communities.

This study underscores that in Sub-Saharan Africa, factors like household size, income and education levels do not significantly influence the electricity demand but should be taken as part of the fundamental human rights. Rural populations express a desire for electricity due to the convenience it offers, particularly for appliances like refrigerators and stoves. Mini-grids emerge as a viable alternative in regions where grid electricity provision is challenging. It is concluded from this paper that the issue of using renewable energy should not only be taken for environmental preservation but also to promote energy access, augmenting efforts in supplying electricity to the remotest parts of the country.

The purpose of this paper is to investigate the most recurrent variables characterizing the collaborative relationships of industrial symbiosis (IS) (hereinafter also referred to as “anatomic” variables) established in the attempt to adopt circular economy (CE) by collecting evidence from a rich empirical set of implementation cases in Italy.

The current literature on IS was reviewed, and a content analysis was performed to identify and define the “anatomic” variables affecting its adoption in the circular economy. We followed a multiple-case study methodology investigating 50 cases of IS in Italy and performed a content analysis of the “anatomic” variables characterizing each case.

This research proposes the “anatomic” variables (i.e. industrial sectors involved, public actors involvement, governmental support, facilitator involvement and geographical proximity) explaining the cases of IS in the circular economy. Each “anatomic” variable is discussed at length based on the empirical evidence collected, with a particular reference to the impact on the different development strategies (i.e. “bottom-up” and “top-down”) in the cases observed.

Current literature on IS focuses on a sub-set of variables characterizing collaboration in IS. This research builds on extant literature to define a new framework of five purposeful “anatomic” variables defining IS in the circular economy. Moreover, we also collect and discuss a broad variety of empirical evidence in what is a still under-investigated context (i.e. Italy).

This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.