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In order to reveal why there are favorable growing environments in industrial clusters (IC), the author examines the effects of the HR environment on talent growth from five aspects: industrial clusters economy (ICE), industrial clusters HR policy (ICHRP), industrial clusters living setting (ICLS), industrial clusters culture (ICC) and industrial clusters HR management (ICHRM).

The sample for this study came from four IC in China. Exploratory factor analysis and SEM analyses were conducted to analyze data.

It proved that: the economy of ICE has a positive direct impact on the talent growth in the clusters; ICHRP has a positive direct impact on talent growth in the clusters; ICC does not have a direct impact on talent growth in the clusters; ICLS has a positive direct impact on talent growth in the clusters; the HRM of companies in IC has a positive direct impact on talent growth in the clusters; the culture and ICHRP has a positive direct impact on the HRM of companies in the clusters.

The paper helps to reveal why talent grows better in IC than in other regions. It offers theoretic support for government to set HR policy.

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.

This paper aims to propose an assessment framework to evaluate companies' innovation capability in the context of industrial clusters.

The assessment framework was built based on the Graph-Theoretic Approach (GTA) to measure the influence of the factors and sub-factors of innovation capabilities. To quantify the level of interdependence between factors and sub-factors of innovation capability Delphi method was adopted. The authors developed five case studies in firms from an Information and Communications Technology and Creative Economy cluster in Northeastern Brazil to test the framework's applicability.

The results showed that identifying and evaluating the factors of innovation capability allows a larger understanding of what affects these capabilities to a greater or lesser extent and contributes to strategic decision-making.

The framework evaluates the innovation capability of each firm, not providing an index for the whole industrial cluster. Besides, the framework does not consider the innovations developed by the companies through the innovation's capabilities. As the Delphi technique was adopted to analyze the levels of influence or interdependence between factors and sub-factors of innovation capability, different experts may lead to different results.

Among the managerial implications, the authors can highlight the innovation capability index as a practical performance measure to stimulate improvement initiatives regarding innovations in industrial clusters. Besides, as the proposed framework is generic, research organizations, public institutions and regional governments can adopt it to analyze innovation capabilities in cluster-based companies.

Previous industrial cluster studies have concentrated on knowledge transfer as the main attribute influencing innovation capabilities. The literature also presents assessment frameworks focusing on qualitative analyses or innovation capabilities outcomes (patents and products). Differently, the authors proposed a quantitative assessment framework considering specific factors (and sub-factors) of innovation capabilities in industrial clusters.

This paper aims to optimize the interactions of businesses located within industrial clusters (ICs) by using a supply-demand hub in ICs (SDHIC) as a conjoint provider of logistics and depository facilities for small- and medium-sized enterprises (SMEs) as producers, where all of these interactions are under supervision of a third-party logistics provider (3PL).

To evaluate the values of SDHIC, three mathematical models are proposed, optimally solved via GAMS and then compared. Also, a “linear relaxation-based heuristic” procedure is proposed to yield a feasible initial solution within a significant shorter computational time. To illustrate the values of SDHIC, comprehensive calculations over a case study and generated sets of instances are conducted, including several sensitivity analysis.

The experimental results demonstrate the efficiency of SDHIC for SMEs via combining batches and integrating the holding space of inventories, while the outcomes of the case study are aligned with those obtained from random sample examples, which confirms the trueness of used parameters and reveals the applicability of using SDHIC in real world. Finally, several interesting managerial implications for practitioners are extracted and presented.

Some of the managerial and practical implications are optimizing interactions of businesses involved in a supply chain of an IC containing some customers, suppliers and manufacturers and rectifying the present noteworthy gaps pertaining to the previously published research via using real assumptions and merging upstream and downstream of the supply chain through centralizing on storage of raw materials (supply echelon) and finished products (demand echelon) at the same place simultaneously to challenge a classic concept in which supply and demand echelons were being separately planned regarding their inventory management and logistics activities and showing the positive consequences of such challenge, showing the performance improvement of the proposed model compared to the classic model, by increasing the storing cost of raw materials and finished products, considering some disadvantages of using SDHIC and showing the usefulness of SDHIC in total, presenting some applied findings according to the obtained results of sensitivity analysis.

The key contributions of this paper to the literature are suggesting a new applied mathematical methodology to the supply chain (SC) of ICs by means of a conjoint provider of warehousing activities called SDHIC, comparing the new proposed model with the two classic ones and showing the proposed model’s dominancy, showing the helpful outcomes of collaborating 3PL with the SMEs in a cluster, proposing a “linear relaxation-based heuristic” procedure to yield a feasible initial solution within a significant shorter computational time and minimizing total supply chain costs of such IC by optimum application of facilities, lands and labor.

In this paper, a multi-objective multi-echelon supply-distribution model is proposed to optimize interactions of entities located within an Industrial Cluster (IC) including small- and medium-sized enterprises (SMEs), using a third-party logistics provider (3PL)-managed supply-demand hub in industrial cluster (SDHIC) as a specific public provider of warehousing and logistics services.

The three considered objectives are minimizing the total logistics costs, maximizing the rate of demand satisfaction and maximizing the quality of delivery. Because some parameters such as “demand of customers” are naturally fuzzy because of incompleteness and/or inaccessibility of the needed information, the triangular fuzzy number is applied for all fuzzy parameters to handle this difficulty. The proposed model is primarily changed into a correspondent supplementary crisp model. To solve such a model, a revised multi-choice goal programming (RMCGP) approach is then used with the purpose of finding a compromise solution.

Experimental results demonstrate that all enterprises involved in such a supply chain benefit with several advantages using SDHIC by consolidating shipments and merging the storage space of inventories. The applicability of the presented model is shown by conducting these experiments over an applied industrial case study.

The main contributions of this research are proposing a practical mathematical approach to the supply chain of ICs using a specific public warehouse managed by a 3PL, called SDHIC, bridging the existing gaps with respect to the already published researches in this area by applying real-world assumptions such as uncertainty; optimizing the interactions of involved entities in the supply chain of an IC, comprising suppliers, SMEs as manufacturers and customers; minimizing the total incurred logistics costs to such a system through optimum usage of lands, facilities, labors, etc. and boosting the satisfaction of customers through maximizing the service level criteria, illustrating the positive consequences of cooperation of 3PL with the SMEs/manufacturers in an IC, showing the applicability of the adopted approach by implementing it on an applied industrial instance.

The purpose of this paper is to focus on the science park (SP) “physical” location and the innovation cluster (IC) “virtual” location, and aims at investigating: the motivations driving firms to settle in these two agglomerations; the main problems firms, belonging to the two structures, face in their growth process; similarities and differences between a “physical” and a “virtual” location; which forms of proximity (geographical, relational, social, cognitive, organizational, and institutional) play a role within the SP and the IC.

A literature review on proximity is followed by an investigation of the Bioindustry Park and the IC BioPmed in Piedmont region in Italy, through a structured questionnaire, sent between February and March 2002, to firms co-located in the park and/or member of the cluster.

From the analysis did emerge that the physical location in the park and the virtual location in the cluster might be complements rather than substitutes.

Shortcomings like the limited number of companies interviewed, and the absence of a sample of companies exclusively co-located in the park, are observable. Additional research might corroborate the results, which are specifically valid for the two case studies.

The idea of understanding differences and similarities between the SP and the IC, and of investigating which proximity typologies play a role in a “physical” and in a “virtual” location, may be useful to design future policy strategies.

The originality of this paper is given by the analysis of a new phenomenon: physical and virtual agglomeration typologies, characterized by several forms of proximity enhancing knowledge diffusion.

The purpose of this paper is to explore the relationship between intellectual capital (IC) and performance of public universities in emerging countries in order to identify patterns and provide recommendations that may turn the universities’ IC into development opportunities, in terms of research, innovation, and education.

The analysis targeted the whole population of the public universities in the Republic of Colombia. A cluster analysis, based on five performance variables, has been conducted. Then, the IC of the universities pertaining to the three resulting clusters has been compared. Subsequently, for each performance variable, the IC of above-average and below-average universities has been benchmarked.

The results of this study show how different aspects of IC are associated with University performance. Among the many, the authors found that universities should achieve a critical mass to obtain outstanding research and innovation results. The findings also identify the particular importance of both students and scholars’ international mobility programs for most of the performance variables.

This study provides a baseline for the assessment of the impact on society of the IC available in the universities of emerging countries. The application may serve as a guide in the choice of public policies, dedicated to the strengthening of the universities’ IC in order to improve their performance.

This paper proposes an innovative model to analyze the relationship between IC and university performance in emerging countries. The model identifies the association between the IC accrued in the universities and their capability of transferring it to the society under the form of science, innovation, and education.

The purpose of this paper is to: first, divide external knowledge into different levels and understand how these different levels of external knowledge create different knowledge networks. Second, explore the relation among different levels of external knowledge, different types of knowledge and their influences on innovation. Different types of knowledge may act as mediators between different levels of external knowledge and innovation performance are also discussed. Third, further explicate the possible reasons behind the phenomena observed in the first and second objectives.

The quantitative and qualitative research methods were adopted in this study. In terms of the quantitative research method, data were collected from 157 information and communications technology (ICT) companies in Zhangjiang. There were 38 interviews carried out using the qualitative research method. Interviewees included 26 directors from the Zhangjiang ICT chip companies and 12 directors from China’s domestic mobile phone manufacturing sector.

This study finds out that the source of external knowledge in the Zhangjiang Hi-Tech Park is mostly derived from cross-regional and cross-national connections. Through the connection with global companies, the firms in Zhangjiang acquire technical knowledge. Also, through the interaction with cross-regional companies, the firms in Zhangjiang absorb market knowledge. The results of this study indicate that the cross-regional connections are spurred by the increasing demand of the Chinese domestic market and the government’s support for cross-regional interactions. Cross-national connections are encouraged and led by the Chinese Government after China’s 3G communication standard is adopted.

This study is built upon a case study in Zhangjiang of China. The findings of this study may not be applicable to other countries or regions in China. Also, this study only focusses on the ICT industry in Zhangjiang. Knowledge from different industries is not included in this study. In addition, the formal and informal knowledge flow is neglected in this study as well.

The findings of this study provide business executives and policymakers with a new way of thinking about the development of industrial clusters and local firms. Firms may be able to find new ways to raise innovation performance using different external knowledge.

The major contribution of this study is an initial attempt to provide a comprehensive analysis of external knowledge of industrial clusters, different types of knowledge and their influences on innovation performance. Moreover, the mediating effects of different types of knowledge are also discussed.

The purpose of this paper is to provide a strategic framework and tool to measure and value intellectual capital (IC) in regional clusters.

A theoretical cluster strategic framework is presented and cluster fundamentals are discussed for proper model development. Design methodology was used to construct a model which achieves the aforementioned purpose.

The paper provides a comprehensive model to describe, map, measure and value IC on clusters and systematically control the IC evolution.

The system provided is not an exhaustive use of all the available measures. A more comprehensive practical application on several clusters would be necessary to validate and readapt the model.

A very useful tool of information and practical assessment for IC is provided to cluster agents and policymakers to establish proper strategic initiatives. New ideas about IC measurement in clusters are provided to academia.

So far, no IC cluster model has been designed. This paper fulfils an IC measurement model to help individuals involved in clusters, such as mangers, policymakers, etc.