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This paper examines the role played by business cluster ecosystems and intellectual capital (IC) in achieving high-growth firm (HGF) status.

We draw our insights from the knowledge-based perspective and economic geography as a theoretical lens, which combined offer a more unifying understanding of how business cluster ecosystems and IC foster high growth entrepreneurship.

Drawing on a sample of 11,360 German incorporated firms across 80 clusters, we find that cluster ecosystems play a significant role in supporting firms to become HGFs. More specifically, being located in business clusters increases the likelihood of becoming HGFs by 2.2% to 4.49%. We also find that clusters with more productive firms in the ecosystems provide favorable conditions for member firms to achieve HGF status, while the impact of other cluster-specific conditions (high-tech cluster membership and multinational enterprise share in clusters) is less clear. Additional insights suggest that firm IC (investments in intangible assets) enables firms to achieve high growth status.

The findings of this paper hold theoretical and managerial relevance and shed more light on the impact of cluster-specific factors in the ecosystems and firm IC in achieving high growth entrepreneurship.

This paper is among the first of its kind to bring together three distinct literatures (HGFs, business clusters and IC) and utilize insights from each to derive a conceptual framework that links them in explaining high-growth entrepreneurship.

This study analyzes the performance implications of adopting blockchain to support supply chain business processes. The technology holds as many promises as implementation challenges, so interest in its impact on operational performance has grown steadily over the last few years.

Drawing on transaction cost economics and the contingency theory, we built a set of hypotheses. These were tested through a long-term event study and an ordinary least squares regression involving 130 adopters listed in North America.

Compared with the control sample, adopters displayed significant abnormal performance in terms of labor productivity, operating cycle and profitability, whereas sales appeared unaffected. Firms in regulated settings and closer to the end customer showed more positive effects. Neither industry-level competition nor the early involvement of a project partner emerged as relevant contextual factors.

This research presents the first extensive analysis of operational performance based on objective measures. In contrast to previous studies and theoretical predictions, the results indicate that blockchain adoption is not associated with sales improvement. This can be explained considering that secure data storage and sharing do not guarantee the factual credibility of recorded data, which needs to be proved to customers in alternative ways. Conversely, improvements in other operational performance dimensions confirm that blockchain can support inter-organizational transactions more efficiently. The results are relevant in times when, following hype, there are signs of disengagement with the technology.

This paper aims to reduce the impact of noise on the prediction accuracy of remaining useful life (RUL) for supercapacitor. First, Savitzky–Golay (SG) smoothing filter method (Savitzky and Golay, 1964) is used to eliminate the local small fluctuation and high-frequency noises that are generated by the capacity drop and rebound during the charging and discharging process of supercapacitor. Then, the variational mode decomposition (VMD) method is used to eliminate large fluctuation noises that are caused by internal temperature change of supercapacitor and chemical reaction of the supercapacitor. Its parameters are optimized by using marine predators algorithm (MPA), and the capacity sequence after denoising is reconstructed. Finally, long short term memory neural networks (LSTM) is used to predict the performance degradation law (PDL) and remaining useful life (RUL) of supercapacitor for the reconstructed sequence, then the comparative analysis is conducted with other methods, which results show this method improves the prediction accuracy effectively, and provides theoretical support for timely and accurately understanding the PDL and RUL of supercapacitor backup power supply.

First, SG smoothing filter method is used to eliminate the local small fluctuation and high-frequency noises that are generated by the capacity drop and rebound during the charging and discharging process of supercapacitor. Then, the VMD method is used to eliminate large fluctuation noises that are caused by internal temperature change of supercapacitor and chemical reaction of the supercapacitor. Its parameters are optimized by using MPA, and the capacity sequence after denoising is reconstructed. Finally, LSTM is used to predict the PDL and RUL of supercapacitor for the reconstructed sequence, then the comparative analysis is conducted with other methods, the results show that this method improves the prediction accuracy effectively, and provides theoretical support for timely and accurate understanding the PDL and RUL of supercapacitor backup power supply.

These factors will bring different types of noise during the service process of supercapacitor backup power supply, such as capacity regeneration, differences of charging and discharging rate, internal temperature change of supercapacitor, chemical reaction and external electromagnetic interference. Therefore, the paper proposes an LSTM prediction method of supercapacitor’s PDL and RUL based on composite denoising, which is divided into three stages: smoothing, noise reduction and prediction. First, SG smoothing filter method is used to eliminate the local small fluctuation and high-frequency noises, and MPA-VMD method is used to eliminate the nonlinear and nonstationary noises. Then, the capacity sequence after denoising is reconstructed, LSTM is used to predict PDL and RUL of supercapacitor. Finally, the comparative analysis with other methods is carried out. The results show that SG-VMD-LSTM method has higher prediction accuracy, which can accurately predict PDL and RUL of supercapacitor backup power supply, and improve the safety and reliability of wind turbine operation under the severe wind conditions.

The comparative analysis with other methods is carried out. The results show that SG-VMD-LSTM method has higher prediction accuracy, which can accurately predict PDL and RUL of supercapacitor backup power supply, and improve the safety and reliability of wind turbine operation under the severe wind conditions.

This chapter explores the multifaceted relationship between quantum computing (QC) and sustainability, with a focus on the Quadratic Unconstrained Binary Optimisation (QUBO) framework. The manuscript delves into the theoretical underpinnings of QUBO and its formulation as a quantum annealing problem, identifying the quantum principles that facilitate the resolution of such optimisation challenges. It offers a critical analysis of the suitability of QUBO for unconstrained problems and its efficacy in consistently locating the global minimum – a pivotal concern in optimisation tasks. Further, this study provides a nuanced discussion on the intersection of QC and sustainability. It delineates the types of optimisation problems within sustainability initiatives that are amenable to formulation as QUBO problems, while also highlighting sustainability challenges that elude the QUBO framework. It argues for the integration of quantum solutions into business operations, highlighting the potential for QC to play a transformative role in achieving sustainability objectives. The critique of the current hype surrounding QC provides a balanced viewpoint, ensuring a grounded approach to the adoption of quantum technologies in tackling pressing global issues.

Climate risks are crucial for non-life insurers due to their significant exposure to both transition and physical risks. The aim of this study is to develop a multi-period model that represents climate risks in non-life insurance, encompassing the impacts of both physical and transition risks as well as their reinforcing dependence. Literature suggests that as physical climate risks increase, the urgency for climate policies intensifies, leading to higher climate transition risks.

Our model includes a stochastic transition process affecting assets based on their exposure in climate policy-relevant sectors (green and brown investments) and a dependence structure between this process and liabilities, where the physical risks manifest as an increase in claims.

Our simulation indicates that the choice of the transition process, as well as the consideration of dependencies, has a significant influence on the insurers’ profit, but even more on the probability of ruin. The impact of green versus brown investment strategies varies considerably based on whether dependencies are taken into account or not.

The results of this study are intended to deepen the understanding of the effects of climate risks on non-life insurers and provide a quantitative analysis of the impact of green and brown investing within this framework.

This chapter discusses a survey of literature on efficiency, productivity and other issues related to pharmaceutical industry in India and across the globe. Econometric theoretical literature on technical efficiency (TE) as well as econometric theoretical literature on total factor productivity growth (TFPG) are discussed in separate sections. Individual sections discuss studies on the performance of the pharmaceutical industries in the Indian context, studies on the performance of the pharmaceutical industries in the international context, studies on the efficiency of Indian pharmaceutical industry (IPI), studies on the TFPG of the IPI, other studies on IPI and also studies on the performance of the pharmaceutical industries in the international context (other than India). This chapter discusses the existing study highlighting the gaps and presents the connection of the present study with the existing literature in the conclusion section.

The purpose of this study was to address waste management in the food supply chain (FSC) through the integration of inspection processes in production and distribution centers under uncertain conditions, aiming to enhance sustainability across environmental, economic and social dimensions. The study introduces a sustainable forward and reverse FSC network using a closed-loop supply chain network approach to prevent the transfer of spoiled products, ultimately providing competitive advantages to stakeholders.

A robust multi-objective mathematical programming model is proposed, incorporating inspection processes to manage perishable products effectively. The model is solved using the Augmented Epsilon Constraint technique implemented in GAMS software, providing Pareto-optimal solutions tailored to decision-makers’ preferences. Furthermore, the methodology is applied in a real-world case study and solved with the Benders Decomposition algorithm to validate its practicality and effectiveness.

The proposed methodology effectively minimizes waste and enhances sustainability in the FSC by optimizing decision-making processes under uncertainty. The illustrative examples and real case study demonstrate the efficiency of the model and solution approach, highlighting the significant role of inspection in improving all three dimensions of sustainability.

The study offers valuable insights into and tools for food industry managers to make informed strategic and tactical decisions. By addressing waste management through advanced supply chain modeling, the research helps organizations reduce costs, improve sustainability and gain a competitive edge in the market.

This research is novel in its focus on integrating inspection into the FSC network and addressing uncertainty through robust mathematical modeling. It contributes to the existing literature by demonstrating the impact of inspection on sustainability in FSCs and providing practical solutions for real-world implementation.

The purpose of this paper is to examine the relationship between Chinese female executives and corporate risk-taking the contingencies that affect this relationship.

A integrated theoretical framework was established, on the basis of which theoretical hypotheses were developed and tested using 20,315 firm-year observations collected from China’s publicly listed companies during the period 2005–2020. Data were collected from China's Shanghai and Shenzhen A-share Stock Exchanges and analyzed using a moderated regression analysis, PSM, 2SLS-IV and PSM-DID model.

The empirical results indicate a negative effect of the ratio of female executives in top management team on corporate risk-taking, and this negative effect can be weakened by the social capital of board directors and the regional marketization.

The paper contributes to research on the relationship between female executives and risk-taking by considering the effect of eastern culture on female executives’ business decision-making and examining the moderating factors inside and outside the firm.

The paper illustrates the active steps that corporations can take to enhance female executives' willingness and capacity to take firm-related risks so as to improve the firm value in the long run.

The paper explores how Chinese culture and Chinese traditional value affect female executives’ decision-making on risky projects or uncertain investments. In addition, our study for the first time examines the moderating effect of board social capital as an internal factor and marketization as an external one on the relationship between Chinese female executives and corporate risk taking. The research examines the gender inequality in the work and competitive environment facing female executives in the areas of different marketization level, which would affect female executives’ cognition and motivation in corporate risk taking.

Despite the popularity of leveraging cause-related marketing (CRM) to make societal contributions and bolster business profits, sellers face a profound dilemma when conducting CRM due to consumers’ ambivalent understanding of sellers’ motivation for the initiative. Therefore, it is imperative to unravel consumers’ ambivalent understanding of CRM and determine how sellers can effectively employ CRM to elicit positive evaluations from consumers.

This study gathered survey data from 217 participants and applied a polynomial regression model and response surface analysis for disentangling ambivalent perception of CRM by investigating the influence of (in)congruence between perceived egoistic and altruistic motivation.

The incongruence between perceived egoistic and altruistic motivation can positively influence consumers’ evaluations of sellers. Moreover, when perceived egoistic and altruistic motivations are congruent, increasing their absolute level also enhances consumers’ evaluation of sellers. Moreover, sellers’ platform function usage behavior can amplify the positive effect of incongruence but has no salient moderating role on the congruence effect.

Differing from prior literature that predominantly focused on either the positive or negative interpretation of CRM, this study reveals the coexistence of both positive and negative viewpoints and disentangles the congruence and incongruence effect between the two motivational understandings.