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Purpose: This study constructs an efficiency evaluation framework for assessing the human, structural and relational capital in the semiconductor industry of Taiwan. Furthermore, we analyze whether there are significant differences in efficiency across different levels concerning the industry supply chain (upstream, midstream and downstream), employee service tenure, capital scale and company establishment years. Design/methodology/approach: This study focuses on Taiwanese semiconductor companies, utilizing data sourced from the Taiwan Economic Journal (TEJ) Database for the period spanning 2017 to 2021, encompassing a total of five years. Due to the nondisclosure of intangible asset values by all companies, an effort was made to ensure a comparable baseline by excluding companies with incomplete or missing data. Finally, empirical analysis was conducted on a sample of 64 companies using the dynamic network data envelopment analysis method. Findings: (1) Overall efficiency demonstrates structural capital as the most prominent, followed by relational capital, while human capital shows relatively poorer efficiency. (2) To enhance the efficiency of intellectual capital, priority should be given to improving the efficiency of outputs related to intellectual property rights such as patents. (3) The midstream segment exhibits the best efficiency in both structural and relational capital. (4) Companies with longer employee service tenures exhibit superior efficiency in human capital in the long run. (5) Companies with extended establishment years and larger capital scales demonstrate superior efficiency in both human and structural capital. Originality/value: Reflecting on past literature, scholars have primarily focused on the relationship between intellectual capital and firm efficiency, often emphasizing the overall efficiency of intellectual capital. However, within organizations, human capital, structural capital, and relational capital are interrelated. This study, for the first time, assesses the efficiency of these three components within an organization. The research addresses the challenges in analyzing the efficiency of intellectual capital and introduces a highly contemporary approach – dynamic network data envelopment analysis (DNDEA). Using the semiconductor industry in Taiwan as a case study, this paper conducts empirical analysis in a captivating and worthy industry. Therefore, the ideas presented in this paper are original. [ABSTRACT FROM AUTHOR]

Purpose: Drawing on the theoretical model of ability–motivation–opportunity (AMO), the author conceptualizes joint learning as the ability, relational capital as motivation and co-production as an opportunity. The purpose of the study is to explore whether joint learning, relational capital and co-production, representing the constituents of the AMO, can enhance customer responsiveness. Design/methodology/approach: The authors explore three possible configurations of the AMO framework, the additive model (primary effect), the combination model (two-way interactions) and the multiplicative model (a three-way interaction). The model is empirically tested by collecting primary data from 149 manufacturers in the information technology industry from Taiwan. In addition, hierarchical regression analysis was performed to test hypotheses. Findings: The findings indicate strong support for the additive model, suggesting that joint learning, relational capital and co-production can enhance customer responsiveness, respectively. Also, the results of this study show strong support for the multiplicative model, indicating that the relationship between joint learning and customer responsiveness is positively significant only when both relational capital and co-production are high. Practical implications: Suppliers can use the findings from this study to develop their joint learning and understand how joint learning in a buyer–supplier relationship enhances customer responsiveness. Specifically, this study guides firms that seek to understand relational capital and co-production seem to support the effectiveness of joint learning. Originality/value: This study suggests that although joint learning enhances the ability to engage in customer responsiveness, the suppliers need adequate motivation and opportunity to exploit this ability entirely. [ABSTRACT FROM AUTHOR]

Abstract: Although the effects of corporate reputation on competitive advantage are well documented, the mechanisms that explain those effects remain unclear. We develop and test a theoretical model to investigate how relational capital mediates the impact of corporate reputation on competitive advantage. The hypotheses were tested using a sample of Taiwanese high-tech firms during the period of 2002–2011. Results from hierarchical regression analyses support the contention that corporate reputation positively affects relational capital, which then positively affects competitive advantage. But surprisingly, we do not find support for the direct effect of corporate reputation on competitive advantage, and we instead find that relational capital is a complete mediator. [Copyright &y& Elsevier]

The Taiwanese venture capital industry has played a critical role in the development of the Taiwan IT industry. The purpose of this study is to construct and prioritize the intellectual capital measures of venture capital in Taiwan and to formulate a strategy map based on these measures. A thorough interview was used to collect the data, while the content analysis method and the analytic hierarchy process were used to analyze the data. Intellectual capital can be categorized into three dimensions: Human Capital, Relational Capital and Structural Capital. The research also developed twelve indicators to assess business intellectual capital, as well as a strategy map for the venture capital industry. Measuring intellectual capital can help to formulate business strategies and allocate business resources. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between relationship learning and dyadic knowledge creation. Structural equation modelling is used to test the model based on data from a survey of 118 Taiwanese supplier-international buyer relationships in information technology industries. As predicted, this study finds relationship memory facilitates the creation of dyadic knowledge. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the influence of intellectual capital of Taiwanese manufacturing companies upon their new product development performance. This study showed that three types of intellectual capital – human capital, structural capital, and relational capital – had a significantly positive relationship with new product development performance. Moreover, the results also indicated that the higher the growth rate of an industry, the stronger were the positive relationships between three types of intellectual capital and new product development performance. Besides, relational capital was the greatest among these three types of intellectual capital in Taiwanese manufacturing companies, human capital was the next, and structural capital was the least. This shows two points; first, Taiwanese manufacturing companies emphasized the interactive ‘relations’ with their suppliers, clients, and partners; second, it was imperative for Taiwanese manufacturing enterprises to develop their structural capital to increase their new product development performance. In addition, comparing three types of intellectual capital of Taiwan's large enterprises with those of Taiwan's small and medium enterprises (SMEs), the results showed that human capital and structural capital of Taiwan's SMEs was obviously less than those of large enterprises. Therefore, it was imperative for SMEs of Taiwan to create and develop their human capital and structural capital. [ABSTRACT FROM AUTHOR]