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This paper aims to map literature about innovation capabilities (IC) taking into consideration industrial clusters to propose a conceptual framework that synthetizes the main factors and subfactors responsible for ICs; in addition, the paper also proposes a research agenda.

A systematic literature review (SLR) was performed; academic papers were analyzed qualitatively and quantitatively.

The authors provide a descriptive analysis followed by a thematic synthesis, in which we present 05 enablers and 20 critical factors (CF) of IC in clusters. The proposed framework emphasizes what needs to be done or improved to increase IC in cluster-based companies. Based on this systematic review and the framework proposed, the authors identified opportunities for future research.

The enablers and CF identified through SLR were not validated empirically. Therefore, future studies on the current topic are required to validate the framework by investigating which factors are more relevant to cluster-based companies that intend to improve their innovative performance.

The present findings have important implications for the identification of the factors and subfactors that may contribute to the development of IC, which may help managers and decision-makers in recognizing which factors are the most responsible for business innovation.

The paper identifies enablers related to the development of IC in industrial cluster and presents a research agenda. The framework represents a guideline for companies to achieve better innovation performance.

The purpose of this paper is to analyze the trends of global automobile industry toward energy saving and smart application. The automobile industry has its own special supply, safety and reliability system. It combined with the legislations that progressive safety driving leads to increase automobile electronic (AE) facilities and enlarges the proportion of the electronic control for vehicles. Based on matured communication and semiconductor industries in Taiwan, the government assists these hi-tech industries in entering the AE domain by clustering relevant enterprises with assembly supply chains. Flagship IC packaging and LCD panels enterprises are located in the Kaohsiung Industrial Park which possesses advantages for the clustering of AE industry.

In this paper, a systematic investigation on establishing AE industry cluster in Kaohsiung Industry Park (KIP) is presented. First, 42 electrical and electronics enterprises are site visited and interviewed for operation and management preferential demands. Second, problems encountered in enterprises’ operation are identified through expert weight method. Third, the main factors involved in the operational management are then analyzed based on five flagship enterprises in KIP. The integration of industrial–university–government resources and assistance is demonstrated. Finally, the establishment of an innovative talents cultivation model is illustrated.

The developed systematic talent program is proposed to resolve the urgent demands in recruiting talents, operational costs and new products development.

This paper satisfies needs of research enterprise management. The establishment of an innovative talents cultivation model is illustrated.

While entrepreneurship may be driven by personal interests and lifestyle choices, entrepreneurial actions are not only economically driven opportunity-searching processes but also enactments of social transformation that may or may not lead to socioeconomic benefits. We advance that exploring these entrepreneurial processes can inform a theory of the firm that may explain how socioeconomic processes shape the socioeconomic environment of communities while serving individuals. This article discusses several understandings of the firm, as theorized in extant literature. Guided by these different conceptualizations, we present a case study of an artist and artisan cluster in Western Massachusetts to demonstrate various understandings of entrepreneurial processes. By way of conclusion, we develop the idea of the firm as a geographically embedded relational understanding aiding entrepreneurs to achieve personal goals while coconstructing their local environment.

Vehicle fault diagnosis is a key factor in ensuring the safe and efficient operation of the railway system. Due to the numerous vehicle categories and different fault mechanisms, there is an unbalanced fault category problem. Most of the current methods to solve this problem have complex algorithm structures, low efficiency and require prior knowledge. This study aims to propose a new method which has a simple structure and does not require any prior knowledge to achieve a fast diagnosis of unbalanced vehicle faults.

This study proposes a novel K-means with feature learning based on the feature learning K-means-improved cluster-centers selection (FKM-ICS) method, which includes the ICS and the FKM. Specifically, this study defines cluster centers approximation to select the initialized cluster centers in the ICS. This study uses improved term frequency-inverse document frequency to measure and adjust the feature word weights in each cluster, retaining the top τ feature words with the highest weight in each cluster and perform the clustering process again in the FKM. With the FKM-ICS method, clustering performance for unbalanced vehicle fault diagnosis can be significantly enhanced.

This study finds that the FKM-ICS can achieve a fast diagnosis of vehicle faults on the vehicle fault text (VFT) data set from a railway station in the 2017 (VFT) data set. The experimental results on VFT indicate the proposed method in this paper, outperforms several state-of-the-art methods.

This is the first effort to address the vehicle fault diagnostic problem and the proposed method performs effectively and efficiently. The ICS enables the FKM-ICS method to exclude the effect of outliers, solves the disadvantages of the fault text data contained a certain amount of noisy data, which effectively enhanced the method stability. The FKM enhances the distribution of feature words that discriminate between different fault categories and reduces the number of feature words to make the FKM-ICS method faster and better cluster for unbalanced vehicle fault diagnostic.

Intellectual capital (IC) plays a crucial role in today’s volatile business landscape, yet its measurement remains complex. To better navigate these challenges, the authors propose the Integrated Intellectual Capital Measurement (IICM) model, an innovative, robust and comprehensive framework designed to capture IC amid business uncertainty. This study focuses on IC measurement models, typically reliant on secondary data, thus distinguishing it from conventional IC studies.

The authors conducted a systematic literature review (SLR) and bibliometric analysis across Web of Science, Scopus and EBSCO Business Source Ultimate in February 2023. This yielded 2,709 IC measurement studies, from which the authors selected 27 quantitative papers published from 1985 to 2023.

The analysis revealed no single, universally accepted approach for measuring IC, with company attributes such as size, industry and location significantly influencing IC measurement methods. A key finding is human capital’s critical yet underrepresented role in firm competitiveness, which the IICM model aims to elevate.

This is the first SLR focused on IC measurement amid business uncertainty, providing insights for better management and navigating turbulence. The authors envisage future research exploring the interplay between IC components, technology, innovation and network-building strategies for business resilience. Additionally, there is a need to understand better the IC’s impact on specific industries (automotive, transportation and hospitality), Social Development Goals and digital transformation performance.

This paper examines the theoretical issues and research themes of business and management impact. Our empirical setting is the UK Research Excellence Framework 2014 (REF 2014) and the focus is on the nature of research impact. Stakeholders, including Governments, now expect academic outputs to translate to real world benefits beyond the narrow bibliometric type metrics.

Despite decades of academic literature devoted to business and management research impact, current theories cannot explain the apparent disconnect between academic, economic and societal practice. Adopting a UK Business and Management perspective to frame our investigation, we consider the highly contested rhetorical question – What are the current themes and impacts of Business and Management research?

We propose a definition for research impact and consider its measurement. Then, using the 410 Impact Case Studies submitted to REF 2014 #x2013; Unit of Assessment 19, business and management, we examine how high impact unfolds. The implications for business and management research impact from the perspectives of economic, knowledge and responsibility impacts are considered.

The purpose of this study is to shed light on the twin transition in China in the organization of innovation processes in artificial intelligence (AI) and green technology (GT) development and to understand the role of foreign multinationals in Chinese innovation systems.

A qualitative research approach is used by interviewing executives from German multinationals with expertise in AI and GT development and organization of innovation processes in China. In total, 11 semi-structured interviews were conducted with companies, and the data were analysed with a thematic qualitative text analysis.

The findings show that AI applications for GT are primarily developed in cross-company projects that are led by local and regional authorities through the organization of industrial districts and clusters. German multinationals are either being integrated, remaining autonomous or being excluded from these twin transition innovation processes.

This paper aims to fill the gap in the literature by providing one of the first qualitative approach towards twin transition innovation processes in China and exploring the integration of multinational enterprises in cluster organizations. To the best of the author’s knowledge, this is one of the first twin transition studies from this perspective in emerging economies.

At the inception of the Journal of Humanitarian Logistics and Supply Chain Management (JHLSCM), logistics coordination was identified as important, both in practice and research, but few studies on the topic had been published. Ten years later, many, if not most, papers in the journal mention the topic. So the picture has changed, but to what extent? This paper discusses how coordination research has followed humanitarian logistics practice and vice versa.

The point of departure in the present article is the most salient topic from the study’s original papers (Jahre et al., 2009; Jahre and Jensen, 2010). The authors discuss how these topics have developed in research and practice. A recent literature review (Grange et al., 2020) enables us to pick relevant papers from JHLSCM and supplement them with more recent ones. The authors complement this approach with updated data on the cluster system, particularly the logistics cluster, to add insights from the empirical domain.

In practice, the cluster concept has developed from coordination within clusters in response to the inclusion of inter-cluster coordination in preparedness, and more recently a focus on localized preparedness. However, JHLSCM research does not appear to have kept pace, with a few notable exceptions. The majority of its papers still focus on response. To the extent that preparedness is covered, it is primarily done so at the global level.

The authors use a framework to discuss humanitarian logistics coordination research and identify important gaps. Based on developments in practice, the study’s key contribution is a revised model with suggestions for further research.