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This paper aims to explore how firms enter or exit B Corp certification faced with the tension between local and B Corp institutions, providing a better understanding of the unique impact of institutional complexity on B Corps' decision-making.

This paper applies multi-case analysis to 20 Chinese firms in various stages of B Corp certification, including eight certified B Corps, six decertified firms and six candidates. The qualitative data was used to code separately for two research questions.

The study findings reveal that: (1) Participants who can obtain expected social and economic benefits by innovating their operational mode to efficiently deal with this tension attempt to continuously pursue B Corp certification. A self-renewal model was developed to show how firms hybridize the two institutional logics; (2) Participants who find it hard to mitigate this tension tend to compromise with the local institution and conform less with the B Corp institution due to high opportunity and accounting costs, low short-term benefits and collective culture.

By highlighting the different responses of firms to institutional complexity, this study contributes to B Corp research, social identity theory and institutional complexity, providing practical implications for B Lab strategies in China.

Considering the growing emphasis on sustainability, companies are developing green innovation strategies for creating new products and processes that reduce environmental effects. The impact of green innovation on firm performance is well established in the literature; however, the relationship between a firm’s adoption of green innovation and its social behaviour has not yet been explored. This study aimed to fill this gap by analysing the impact of green innovation on companies’ social behaviour, at both the overall and sub-dimensions levels.

This study was conducted on a sample of 191 companies worldwide between 2016 and 2019. Company data were extracted from the Joint Research Centre database established by the European Commission and the Organisation for Economic Cooperation and Development. In contrast, data on corporate social behaviour was taken from the LSEG Workspace database. We applied a panel regression using a fixed effects model to test the research hypotheses.

The results support the positive impact of green innovations on corporate social behaviour in the immediate and subsequent periods. However, the empirical results do not provide significant evidence for some dimensions of corporate social behaviour, such as respect for human rights and product responsibility.

The study’s novelty lies in its emphasis on how green innovation shapes corporate social behaviour and enhances stakeholder relationships. Green innovation is introduced as a strategic instrument for meeting social duties and increasing trust, loyalty and ethical engagement with important stakeholders.

Green technology, characterized by its environmentally friendly attributes and sustainable practices, has emerged as a crucial tool in harmonizing the economic and ecological benefits. However, the challenge lies in selecting the most effective strategies for acquiring green technology. This paper aims to explore how chemical enterprises choose green technology acquisition strategies across diverse scenarios.

Considering the influence of competition effects, spillover effects and their interactions on selecting green technology acquisition strategies, this paper develops three decision models (independent R&D, cooperative R&D and technology introduction). Drawing on the duopoly game theory as its theoretical framework, this paper delves into the examination of the economic and environmental benefits within distinct scenarios.

Cooperative R&D excels in promoting green technology R&D when spillover effects are strong, while independent R&D demonstrates superiority when spillover effects are weak. The threshold for the strength of spillover effects is related to competition effects. Additionally, cooperative R&D typically yields greater financial advantages than independent R&D and technology introduction. Moreover, the economic and environmental benefits may not be optimized simultaneously. Only enterprises that satisfy low competition and spillover effects as well as high competition and spillover effects, can achieve win-win economic and environmental benefits.

Although green technology R&D and introduction are alternative strategies, they have typically been considered separately in prior literature. This study attempts to incorporate green technology R&D and introduction into a strategic system to investigate the selection of green technology acquisition strategies, taking into account competition effects, spillover effects and their interactions.

With the diversification of professional values, an increasing number of individuals voluntarily choose positions that demand less than their personal knowledge, skills and educational background, a phenomenon known as voluntary overqualification. This study aims to explore the reasons for discerning the motivations driving voluntary overqualification, define its conceptual content and develop the measurement scale for voluntary overqualification.

Through five phases, and using both qualitative and quantitative approaches, the authors constructed a scale comprising three dimensions: earnings-oriented, life-oriented and meaning-oriented to measure voluntary overqualification. Following the steps of scale development, the conceptual connotation and structural dimensions of voluntary overqualification were systematically coded and mined based on grounded theory. The scale’s reliability and validity were measured through exploratory and validation factor analyses. Finally, the validity of the voluntary overqualification scale was verified through the selection of professional identity and subjective well-being.

This study defined connotations and structural dimensions of voluntary overqualification based on grounded theory, resulting in a measurement scale with three dimensions and 13 items. These dimensions include earnings-, life- and meaning-oriented voluntary overqualification. Empirical testing of predictive validity used professional identity and subjective well-being as outcome variables.

This study provides a theoretical foundation and an effective measurement tool for subsequent research in voluntary overqualification by focusing on a new type of voluntary overqualification, defining its connotations and developing a complete set of scales.

Firepower conflicts usually decay the firepower plan's enforceability, thus incurring high survival risks. Previous studies have shown little attention to avoiding firepower conflicts during the weapon target assignment process. This research proposes a new constrained optimization model named Firepower Conflict Free WTA (FCFWTA) and designs a Survival Evolution (SE) strategy for Artificial Fish Swarm Algorithm (AFSA) to solve the complex constrained WTA problem. In this way, commanders can get more reliable firepower assignment decision support.

A new constrained optimization model named Firepower Conflict Free WTA (FCFWTA) is constructed. FCFWTA unifies firepower decision variables for different kinds of weapons and takes the firing time point as a clue for firepower conflict checking. The objective function of FCFWTA is the weighted sum of the minimum threat value rest rate (RRTV), maximum hit efficiency (HE) and minimum latest interception time percentage (PLT). Since previous algorithms do not check and resolve intermediate results during optimization, an adapted strategy named Survival Evolution is designed. It enables making full use of the limited firepower without adjusting the coordination scenario in execution.

The proposed method offers significant advantages in two aspects. Firstly, it effectively enhances the optimization results of WTA in the absence of firepower conflicts. Evidence from Figure. 6 confirms that without the proposed method, there is a high likelihood of generating invalid outcomes. After implementing firepower conflict check and resolution, there is a substantial degradation in the objective function value. Secondly, the method excels at equitably distributing firepower among multiple targets while also enhancing the overall interception probability, irrespective of the varying complexities presented by different scenarios. This ability to maintain balance and efficiency is crucial for tackling defense-related issues.

Specifically, SE is tailored for MWMT problem under time and space constraints. This approach diverges significantly from conventional MWMT research, which typically focuses solely on ammunition quantity or firing range. Consequently, the primary objective was to verify the efficacy of this method. Test results indicated that SE does not exhibit uniform performance across different algorithms; while it significantly enhances the efficacy with PSO and AFSA, its influence is considerably diminished when applied to GA. It might be attributed to the inherent randomness associated with crossover and mutation, which can increase the likelihood of firepower conflicts, coupled with SE's reorganization of the chromosome.

The work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.

Financial market sustainability is gaining attention as investors and stakeholders become more aware of environmental, social and governance issues, pushing demand for responsible and ethical investment practices. Therefore, this study aims to investigate the impact of carbon (CO2) emissions from three sources, oil, gas and coal, on the stock market sustainability via effective government policies.

The eight countries belong to two different regions of world: Asian economies such as Pakistan, India, Malaysia and China, and OECD economies such as Germany, France, the UK and the USA are selected as a sample of the study. The 22-year data from 2000 to 2022 are collected from the DataStream and the World Bank data portal for the specified countries. The generalized methods of movement (GMM) and wavelet are used as the econometric tool for the analysis.

Our findings show that the CO2 emission from coal and gas significantly negatively impacts stock market sustainability, but CO2 emission from oil positively impacts stock market sustainability. Moreover, all the emerging Asian economies’ CO2 emissions from coal and gas have a much greater significant negative impact on the stock market sustainability than the OECD countries due to the critical situation. However, the government’s effective policies have a positive significant moderating impact between them, reducing the effect of CO2 emission on the stock market.

This study advocated strong implications for policymakers, governments and investors.

Effective government policies can protect the environment and make business operations suitable, leading to market financial stability. This study advocated strong implications for policymakers, governments and investors.

This study provides fresh evidence of the government’s effective role to control the carbon environment that provide the sustainability to the organizations with respect to OECD and emerging economy.

Currently, China’s manufacturing industry chain still faces the danger of chain breakage due to the persistent “lack of technology” issue. The definition and detection of key nodes in the industry chain are significant to the enhancement of the stability of the industry chain. Therefore, detecting the key nodes in the manufacturing industry chain is necessary.

A complex network based on the links amongst listed manufacturing enterprises is built, and the authors analyse the network’s basic characteristics and vulnerability, taking into account the impact of scientific and technological innovation on the stability of the industry chain.

It is found that the high structural characteristic of midstream nodes in the naval architecture and marine engineering equipment industry chain determines their importance to stability, and the key status of upstream nodes is reflected in the weakness of technological innovation. The upstream nodes should focus on improving their independent innovation and R&D capability, whilst the midstream nodes should maintain a close supply–demand cooperation relationship.

The key node detection model for industry chain stability is constructed by considering various factors from the perspective of network and technological innovation. Empirical study is conducted to verify effectiveness of proposed method.

The United Nations has demonstrated a commitment to preserving the ecosystem through its 2030 sustainable development goals agenda. One crucial objective of these goals is to promote a healthy ecosystem and discourage practices that harm it. Building materials production significantly contributes to the emissions of greenhouse gases. This poses a threat to the ecosystem and prompts a growing demand for sustainable building materials (SBMs). The purpose of this study is to investigate SBMs to determine their utilization in construction operations and the potential impact their application could have on construction productivity.

A systematic review of the existing literature in the field of SBMs was conducted for the study. The search strings used were “sustainable” AND (“building” OR “construction”) AND “materials” AND “productivity”. A total of 146 articles were obtained from the Scopus database and reviewed.

Bio-based, cementitious and phase change materials were the main categories of SBMs. Materials in these categories have the potential to substantially contribute to sustainability in the construction sector. However, challenges such as availability, cost, expertise, awareness, social acceptance and resistance to innovation must be addressed to promote the increased utilization of SBMs and enhance construction productivity.

Many studies have explored SBMs, but there is a dearth of studies that address productivity in the context of SBMs, which leaves a gap in understanding. This study addresses this gap by drawing on existing studies to determine the potential implications that using SBMs could have on construction productivity.

Various methods are used for cancer detection such as genetic tests, scanning, MRI, mammography, etc. These methods help collect data on patients, which can be utilized for comparing a new patient’s information with the aggregated data to detect cancer. The main step in this process is data classification. There are several cancer detection methods with their own disadvantages in flexibility, non-linear complexity and sensitive in imbalance data. In this paper, a new fuzzy bio-inspired based classification method is designed to classify the imbalance medical data.

In this paper, a new fuzzy bio-inspired-based classification method is designed to classify the imbalance of medical data. The method consists of a new fuzzy draft of the Cuckoo Optimization Algorithm (COA) and separating hyper-planes based on assigning binary codes to separated regions that are called Hyper-Planes Classifier (HPC). Based on the technical review is done in the paper, the HPC has a better structural superiority than the other classification algorithms. The Fuzzy Cuckoo Optimization Algorithm (FCOA), which fills up its challenge in proper tuning parameters, is proposed to optimize the weights of the separating hyper-planes with linear complexity time.

The experimental results were presented in five steps. Step1, the details of the average and the best results of the proposed methods were reported and compared. Step2, the quality of the detection methods with different numbers of hyper-planes were compared. The obtained weights of different numbers of hyper-planes were reported in Step3. Step4, the convergence process of the FCOA and the COA were shown. Step5, the best obtained results were compared with the best reported one in previous literature. The experimental results and the presented comparisons show that the proposed hybrid detection method is comparable to other methods and operates better than them in most cases.

A technical review has been done based on classifying the applied classification methods to cancer detection and analyzing advantages (+) and disadvantages (−) of the methods and their optimizer algorithms. A new fuzzy draft of COA has been designed to dynamically tuning the Egg Laying Radius based on a fuzzy inference system with four fuzzy rules. A novel hybridization of the hyper-planes classification method and the designed FCOA has been proposed to optimize the hyper-planes' weights. The effectiveness of the proposed hybridization has been examined in famous UCI cancer datasets based on one, two, three and four hyper-planes and compared with more than 30 previous researches.

This paper specifically aims to examine how (via which activities, methods and capabilities) organizations’ management deploy Artificial Intelligence (AI) systems to address underperformance. Five mitigation strategies/recommendations are introduced to manage the challenges and facilitate greater efficacies in changing organizations.

This paper conceptually synthesizes 47 articles, thematically reports and critically analyzes the AI–HRM–managerial decision-making relationship in changing organizations and discusses the impacts.

The results highlight three significant challenges and opportunities for changing organizations: (1) job performance challenges, (2) organizational performance challenges and HR and (3) collaborative intelligence opportunities.

The paper’s originality lies in addressing the current lack of a theoretical framework guiding HRM and AI experts on the managerial and strategic capabilities needed to address underperformance and their impacts in facilitating collective efficacies in human–AI collaboration in changing organizations. By further capturing an innovative HR Framework’s (1) human, (2) AI, (3) employees’ well-being, (4) jobs and (5) organizational performance, and its five key managerial recommendations/strategies, this paper develops two concepts: “technological servitization” and “re-ontological in-securitization” to advance theory in Managerial Psychology regarding the unintended/paradoxical consequences of managements’ AI-driven organizational performance interventions, including meaninglessness in organizations.