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The purpose of this paper is to propose the optimal combination forecasting model based on closeness degree and induced ordered weighted harmonic averaging (IOWHA) operator under the uncertain environment in which the raw data are provided by interval numbers.

Starting from maximizing the closeness degree of combination forecasting, which is different from minimizing absolute errors, weighted coefficient vectors of combination forecasting methods are obtained. The new concepts of closeness degree for the center and radius of interval numbers sequences are put forward and the optimal interval combination forecasting model is constructed by maximizing the sum of convex combination with closeness degree of interval center and closeness degree of interval radius. The solution to the model is discussed.

The results show that this model can improve the combination forecasting accuracy efficiently compared with that of each single forecasting method.

The method proposed in the paper can be used to forecast future tendency in a wide ranges of fields, such as engineering, economics and management. In particular, the raw data are provided in the form of interval numbers under the uncertain environment.

The combination forecasting model proposed in this paper is based on closeness degree and IOWHA operator, which is a new kind of combination forecasting model with variant weights.

This study aims to examine the ramifications of corporate environmental, social and governance (ESG) investing in zombie firms and considers how external funding support may moderate this relationship given the sustainable nature of ESG performance, which often incurs costs.

Panel regression analyses used data from China’s A-share listed companies from 2011 to 2019, resulting in a data set comprising 6,054 observations.

Despite firms’ additional financial burdens, corporate ESG investing emerges as a catalyst in resurrecting zombie firms by attracting investor attention. Further analysis underscores the significance of funding support from entities such as the government and banks in alleviating ESG cost pressures and enhancing the efficacy of corporate ESG investing. Notably, the positive impact of corporate ESG investing is most pronounced in non-heavily polluting and non-state-owned firms. The results of classification tests reveal that social (S) and governance (G) investing yield greater efficacy in revitalizing zombie firms compared to environmental (E) investing.

This research enriches the discourse on corporate ESG investing and offers insights for governing zombie firms and shaping government policies.

By extending the domain of ESG research to encompass zombie firms, this paper sheds light on the multifaceted role of corporate ESG investing. Furthermore, this study comprehensively evaluates the influence of external funding support on the positive outcomes of ESG investing, thereby contributing to the resolution of the longstanding debate on the relationship between ESG performance and corporate financial performance, particularly with regard to ESG costs and benefits.

This study aims to grounded in the dynamic capability theory and focuses on the dynamic exchange capability framework, encompassing networkbuilding capability and resource integration capability, to explore the relationship between coopetition and resilience, with ecosystem digitalization serving as the boundary condition.

This study uses a survey study among 382 B2B startups with second-hand data from the city level.

Startups engaged in coopetition activities can build resilience through the mediating effects of network-building capability and resource integration capability. In addition, ecosystem digitalization positively moderates these relationships.

This study advocates for entrepreneurs to leverage coopetition to enhance resilience by activating network-building capability and resource integration capability and to apply ecosystem digitalization throughout this transformation process.

Many studies have discussed how to strengthen the resilience of startups, but the role of the entrepreneurial ecosystem in constructing resilience has received little attention. This study contributes to the understanding of the causal relationship between entrepreneurial ecosystem and entrepreneurship, promotes the development of the dynamic exchange capability framework, and sheds light on the flow of resources across borders within ecosystems.

Manufacturers are facing growing institutional pressures to enhance the manufacturers' sustainability. Establishing appropriate environmental strategy and implementing green supply chain integration (GSCI) are imperative initiatives for them. Nevertheless, prior research has predominantly examined the individual net impacts on sustainable performance. Drawing on the strategy-structure-environment (SSE) framework and configurational perspective, this study investigates the synergistic effects and multiple equivalent combinations of environmental strategy and GSCI under diverse institutional forces.

To empirically validate this relationship, the present study utilizes fuzzy-set qualitative comparative analysis (fsQCA) to analyze two-wave survey data collected from 317 manufacturers in China.

The findings indicate that individual dimension of environmental strategy and GSCI is not independently necessary. However, when combined, this results in seven equifinal configurations that lead to high sustainable performance. Combining all dimensions of environmental strategy and GSCI leads to the simultaneous achievement of high environmental, economic and social performance under perceived social pressure.

This study offers firms the flexibility to select from a range of pathways, allowing the firms to strategically filter and develop diverse combinations of environmental strategy and GSCI. These choices empower firms to enhance the firms' sustainable performance while navigating various institutional forces.

This study contributes to the existing literature by utilizing the SSE framework to investigate the configurational paths that influence sustainable performance. Additionally, this work introduces the fsQCA method to enhance the understanding of sustainable performance in the literature.

This paper aims to explore coopetition within the entrepreneurial ecosystem and answer the following two fundamental questions: How does coopetition affect the entrepreneurial learning and performance of startups? and What learning strategies should startups adopt to promote their growth in the coopetition activities?

Using the structural equation model and instrumental variable, this study used a sample of 371 startups to test the hypotheses. Data comes from startups in Jiangsu, Shanghai and Zhejiang, China.

This study finds that the coopetition-performance relationship of startups is marginally negative. This study also finds that exploitative learning and exploratory learning positively mediate this relationship. Ecosystem’s social capital can enhance the coopetition-exploration relationship, but the coopetition-exploitation relationship is not affected.

Many studies propose that the coopetition-performance relationship is ambiguous, which makes it meaningful to explore startups individually. Based on the resource-based view and the knowledge-based view, this study deepen the works of Bouncken and Fredrich (2016c), that is, how startups can learn and grow through coopetition activities. This study proposes that coopetition is one of the foundations of the ecosystem and explore the coopetition-performance relationship in this special context. Thus, the present paper adds to the budding literature on the effects of the entrepreneurial ecosystem and to the literature on coopetition.

In this paper, we present a new hybrid binary version of bat and enhanced particle swarm optimization algorithm in order to solve feature selection problems. The proposed algorithm is called Hybrid Binary Bat Enhanced Particle Swarm Optimization Algorithm (HBBEPSO). In the proposed HBBEPSO algorithm, we combine the bat algorithm with its capacity for echolocation helping explore the feature space and enhanced version of the particle swarm optimization with its ability to converge to the best global solution in the search space. In order to investigate the general performance of the proposed HBBEPSO algorithm, the proposed algorithm is compared with the original optimizers and other optimizers that have been used for feature selection in the past. A set of assessment indicators are used to evaluate and compare the different optimizers over 20 standard data sets obtained from the UCI repository. Results prove the ability of the proposed HBBEPSO algorithm to search the feature space for optimal feature combinations.