How does Strategic Staffing affect Innovation Performance of High-Tech Firms? ([Yonca Deniz Gürol, Haldun Şecaattin Çetinarslan]Cem Ener] [Yeliz Palaz] [Gökçe Sönmez])

2016 ◽  
Keyword(s):  
Innovation Performance ◽  
High Tech ◽  
Strategic Staffing

scholarly journals Accelerating Green Innovation Performance from the Relations of Network Potential, Absorptive Capacity, and Environmental Turbulence

2021 ◽  
Vol 13 (14) ◽  
pp. 7765
Author(s):  
Shuizheng Song ◽  
Md Altab Hossin ◽  
Xiaohua Yin ◽  
Md Sajjad Hosain
Keyword(s):  
Sustainable Development ◽  
Absorptive Capacity ◽  
Bootstrap Method ◽  
Manufacturing Firms ◽  
Innovation Performance ◽  
Mediation Effect ◽  
Environmental Turbulence ◽  
High Tech ◽  
Green Innovation ◽  
The Relationship

The demand for sustainable development and the advantages of industries are expediting over time with the triggering of green innovation performance (GIP). Improving a firm’s GIP, especially in manufacturing industries, can accelerate green development and mitigate the global-concerned environmental issues. Thus, to investigate GIP from its antecedent factors, we delineate the relationship between network potential, absorptive capacity, environmental turbulence, and GIP based on social network theory, organizational learning theory, and contingency theory. We tested our hypotheses based on 233 sets of questionnaire surveys from high-tech manufacturing firms in China through deploying the hierarchical regression and bootstrap method. Our empirical findings reveal that the network potential dimensions, including network position centrality (NPC), network structure richness (NSR), and network relationship closeness (NRC), significantly positively impacted the GIP. The absorptive capacity (AC) partially mediated the relationship between the network potential dimensions and GIP. Environmental turbulence (ET) as an essential mechanism not only positively moderated the relationship between AC and GIP but also enhanced the AC mediation effect. These findings indicate that manufacturing firms should continue to improve network potential and AC and respond rapidly to changes in the external environment to enhance GIP, consequently contributing to the sustainable development of the economy.

scholarly journals Relationships among Government Funding, R&D Model and Innovation Performance: A Study on the Chinese Textile Industry

2020 ◽  
Vol 12 (2) ◽  
pp. 644
Author(s):  
Zhiying Zhang ◽  
Hua Cheng ◽  
Yabin Yu
Keyword(s):  
Textile Industry ◽  
Industrial Development ◽  
Innovation Policy ◽  
Threshold Effect ◽  
Government Funding ◽  
Innovation Performance ◽  
High Tech ◽  
Promoting Effect ◽  
Pillar Industry ◽  
The Relationship

The textile industry is a traditional pillar industry of the national economy in China. The strategic goal of Chinese innovation is to upgrade and transform traditional industries and make them develop in coordination with high-tech industries, so as to realize sustainable industrial development. At the core of industrial sustainable development, the innovation of the textile industry in China has become an important issue worthy of attention. Based on resource-based theory and signal transfer theory, the relationship between government funding, R&D models and the innovation performance of the Chinese textile industry is studied. The results show that government funding has a significant, direct promoting effect on the internal R&D and science-based cooperation of enterprises. Government funding indirectly promotes market-based cooperation through internal R&D. The promoting effect of internal R&D on innovation performance is greater than that of cooperative R&D. Internal R&D and cooperative R&D have more promoting effects on R&D reserve performance than those on market performance. Government funding indirectly promotes innovation performance through the mediation of internal R&D and science-based cooperation. The threshold effect of cooperative R&D indicates that only when the cooperative R&D intensity exceeds the threshold can government funding foster innovation performance more effectively. The conclusions can provide theoretical guidance for the formulation of innovation policy.

The relationship between network capabilities and innovation performance

2019 ◽  
Vol 119 (8) ◽  
pp. 1638-1654 ◽  
Author(s):  
Gang Fang ◽  
Qing Zhou ◽  
Jian Wu ◽  
Xiaoguang Qi
Keyword(s):  
Social Network ◽  
Positive Impact ◽  
Innovation Performance ◽  
High Tech ◽  
Innovation Networks ◽  
Content Type ◽  
Complementary Assets ◽  
Contingent Approach ◽  
Network Capability ◽  
New Evidence

Purpose Innovation networks provide an efficient mechanism for organizations to realize their potential for knowledge learning and innovation improvement. Firms situated within innovation networks require specific abilities to acquire the knowledge and the complementary assets that facilitate their innovation performance. Motivated by recent research studies in the area of social network and RBV, the purpose of this paper is to improve the understanding of the precise manner in which network capability affects a firm’s innovation performance. Design/methodology/approach Based on the data obtained from Chinese high-tech firms, the hypotheses are tested by using hierarchical multiple regressions. Findings This study identifies two types of network capabilities: network structural capability and network relational capability. The findings suggest that network structural capability has a greater positive impact on innovation performance than network relational capability does within an exploration-orientated network. However, network relational capability is more positively associated with innovation performance within an exploitation-orientated network. Practical implications A firm can enhance the value of its ego network by shaping and adjusting network configurations, rather than by passively reaping the benefits from existing relationships or ties with partners. Originality/value This paper contributes to strategic management theory and social network theory by illustrating how a networked firm can enable network value and appropriate this value according to its strategic purposes and by suggesting that a firm can improve its ego network’s value through exerting its network capabilities to shape and adjust network configurations. This paper also advances the contingent approach within social network research by offering a new complementary perspective and new evidence from a Chinese context.

Individual, group and organizational efficacies in predicting performance

2018 ◽  
Vol 47 (2) ◽  
pp. 535-554 ◽  
Author(s):  
Erez Yaakobi ◽  
Jacob Weisberg
Keyword(s):  
Employee Performance ◽  
Performance Measure ◽  
Innovation Performance ◽  
High Tech ◽  
Content Type ◽  
General Performance ◽  
Predicting Performance ◽  
Efficiency Performance ◽  
Explained Variance ◽  
Organizational Levels

Purpose The purpose of this paper is to develop a framework for predicting three facets of employee performance (quality, innovation and efficiency) based on the evaluation of individual (self and occupational), group (collective) and organizational (means) efficacies. Design/methodology/approach A sample of 109 managers employed mainly in high-tech industries evaluated their employees’ quality, innovation and efficiency performance. The employees’ efficacies were also evaluated on three organizational levels. Findings Evaluation of employees’ self-efficacy accounted for most of the explained variance for all performance facets. Evaluation of group efficacy added incremental explained variance to the general performance as well as to the innovation performance and efficiency performance. Evaluation of means efficacy (provided to employees) added incremental explained variance to the general performance as well as to the innovation performance and the efficiency of performance. Male managers differed from female managers in their predictions of employees’ performance. Originality/value This is the first study to examine the concurrent effects of four types of efficacies, based on three organizational levels, in predicting performance. It also examines three facets of performance instead of only a general performance measure. It presents a model of the relative importance of these efficacies in predicting facets of performance.

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