Industrial Cluster Development and Innovation in Singapore

Wong, Poh Kam; Ho, Yuen‐Ping; Singh, Annette

doi:10.1057/9780230251014_3

Cited by 20 publications

(23 citation statements)

References 5 publications

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“…Clusters located in a certain region can integrate along the value chain, which creates synergies and fosters innovation and competitive advantages. Many empirical evidences from statistical and econometric studies confirm that the role of talent agglomeration is important in high-tech clusters for firms’ productivities (Barrios et al , 2006; Miranda-Martel et al , 2017; Huang, et al , 2011; Lai et al , 2014; Zhang and Li, 2010; De Vos and Dries, 2013; Festing et al , 2013; Renzulli, 2012; Chou et al , 2011; Wong et al , 2010; Esmaeilpoorarabi et al , 2016; Vivas-López, 2014; Munyon et al , 2011). An industrial cluster is not only based on the specific technology knowledge but also relied on the extensive advanced mechanisms facilitating effective application of industrial knowledge, as they enable the diffusion, accumulation and absorption.…”

Section: Resultsmentioning

confidence: 99%

Incentive factors of talent agglomeration: a case of high-tech innovation in China

Shi

Lai

2019

IJIS

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Purpose Over recent decades, talent agglomeration has emerged as a critical topic for scholars, businesses and government officers. Innovative ability is a core competition for high-tech talents. In China, low innovation is the bottleneck, as the high-tech industry usually cannot provide sufficient support for the continuous needs of innovative talents. To enhance the continuous support of talents, it is important to obtain the mechanisms of talent evaluation and flow in high-tech industry. Exploring the incentive factors influencing the scientific and technological personnel, adjust the layout of talents and promote the rational agglomeration. It’s significant to realize the regional economic development. Design/methodology/approach This study proposes an assessment model using the multi-criteria decision-making method of analytical hierarchy process (AHP) to determine the weights of incentive factors and a nonlinear programing model, from micro, meso and macro perspectives of individual, organizational and social incentives by adopting Maslow’s hierarchy of needs theory, Kurt Lewin’s field theory and Lee’s push-pull theory. After the literature review and interviews with 14 experts, this study produced a research framework and a pairwise comparison questionnaire. In addition, the relative quantitative weights of 3 main categories and 15 indicators are identified and ranked based on the AHP method. Findings The results demonstrate that the most important dimension is the individual, and the top three highest weighted factors are job satisfaction, sense of working accomplishment and interpersonal relationships. The discussion in this study showed that the proposed model is rational and acceptable to motivate high-tech innovation talent (HTIT) agglomeration for high-tech enterprises, universities, government and start-ups. Research limitations/implications The pairwise comparison using the AHP method is limited to expert opinions, which are considered comparatively subjective. The number of incentive factors should be increased, as some indicators may have been omitted from the AHP model. Practical implications According to the results, some suggestions can be recommended to corporate executives, HR managers and government officers to attract and retain high-tech talents and further to improve industrial clusters and economic development. Originality/value This paper derives a relative ranking of importance based on the opinions of experienced HR specialists, high-tech talent, scholars and government official, and assesses the consistency of results. The ordering represents the importance of indicators and sub-indicators of two levels from respondents’ perspectives in an industry cluster background. The study, focusing on the high-tech industry in China (which is a developing country), offers a unique view, as earlier studies mainly collect data from developed countries.

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Section: Resultsmentioning

confidence: 99%

Incentive factors of talent agglomeration: a case of high-tech innovation in China

Shi

Lai

2019

IJIS

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“…An important component of Singapore's innovation policy is the development of knowledge-based industrial clustering [17]. The timing of government intervention depends on the maturity and specificity of a sector.…”

Section: Singaporementioning

confidence: 99%

The Efficiency of R&D Expenditures in ASEAN Countries

Dobrzański

Bobowski

2020

Sustainability

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The aim of this study is to determine whether funds spent on research and development are used efficiently in Association of Southeast Asian Nations (ASEAN) countries. Fifteen countries in the 2000-2016 period have been examined. Measuring the efficiency of research and development spending was performed using the non-parametric Data Envelopment Analysis (DEA) methodology, which allows for the assessment of input–output efficiency. The research includes the following input and output variables: annual public and private spending on innovation, high-technology exports as a percentage of manufactured exports, patent applications to the World Intellectual Property Organisation (WIPO) by priority year for million inhabitants, trademark applications (TA) for million inhabitants and information and communications technology (ICT) exports as a percentage of manufactured exports. Hong Kong and the Philippines are perhaps the most efficient with respect to research and development (R&D) when analysed using the constant return to scale (CRS) approach. However, according to the variable return to scale (VRS) approach, the most efficient ASEAN countries are Hong Kong, Indonesia, Singapore and the Philippines. The study also confirms that increased spending on innovation is resulting in non-proportional effects.

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“…The lingering mechanisms of developmentalism, including socio‐political elitism in policymaking, endure even amidst Singapore's transition to a knowledge‐based economy. For example, the government has asserted its strategic vision through the establishment of science research agencies such as ASTAR and SPRING, and the formation of industry‐specific creative clusters (Gwee, ; Lee & Tee, ; Wong, Ho, & Singh, ). Aside from the adaptation of developmental‐style policy strategies such as industrial favouritism through R&D (Lee & Tee, ), Singapore's priority shift towards innovation and creative clustering has also enabled the emergence of new policy subsystems.…”

Section: Cases: Singapore and Seoulmentioning

confidence: 99%

Urban innovation policy in the postdevelopmental era: Lessons from Singapore and Seoul

Hartley

Woo

Chung

2018

Asia & Pacific Policy Stud

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| INTRODUCTIONThe legacy of industrial planning has been embedded in Asia's government institutions and state-backed corporate conglomerates since the post-World War II era, when countriesThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Industrial Cluster Development and Innovation in Singapore

Cited by 20 publications

References 5 publications

Incentive factors of talent agglomeration: a case of high-tech innovation in China

Incentive factors of talent agglomeration: a case of high-tech innovation in China

The Efficiency of R&D Expenditures in ASEAN Countries

Urban innovation policy in the postdevelopmental era: Lessons from Singapore and Seoul

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