Fenglian Wang, Qing Su and Zongming Zhang
This study is aimed at making an inspection of the effects of collaborative innovation network characteristics on firm innovation performance, and the intermediary roles of…
Abstract
Purpose
This study is aimed at making an inspection of the effects of collaborative innovation network characteristics on firm innovation performance, and the intermediary roles of knowledge transfer efficiency is taken into account.
Design/methodology/approach
This study used a convenient sampling method to obtain population and samples. Using data obtained by publishing online and paper questionnaires, and using on-site interviews in Anhui Province in the Yangtze River Delta region of China, descriptive analysis, regression analysis and correlation analysis are utilized to study the direct influence of collaborative innovation network characteristics on knowledge transfer efficiency as well as firm innovation performance, and the intermediary roles of knowledge transfer efficiency on firm innovation performance, respectively. In this study, 3,000 questionnaires were distributed to the employees of enterprises engaged in research and development (R&D) activities, of which 2,560 were valid. With the help of SPSS24.0 software, the reliability and validity of the questionnaire was analyzed.
Findings
The results are indicative of that network centrality and relationship strength positively affect knowledge transfer efficiency and firm innovation performance. Nevertheless, network scale has no significant correlation with knowledge transfer efficiency and enterprise innovation performance. In addition, knowledge transfer efficiency is an intermediary between collaborative innovation network characteristics and enterprise innovation performance, and positively affects enterprise innovation performance, which demonstrated that managers should take advantage of collaborative innovation network characteristics to elevate knowledge transfer efficiency because well-realized transferals of knowledge can help accelerate the coordination of resources in knowledge, and finally bring about the advancement of firm's innovation abilities and performance.
Research limitations/implications
There are few previous studies that fully examined the relationships among collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. This paper developed previous researches on the relationships between collaborative innovation network characteristics, knowledge transfer efficiency and firm innovation performance. The mediation of knowledge transfer efficiency on the relationship between collaborative innovation network characteristics and firm innovation performance is analyzed. Further, studies on collaborative innovation network characteristics using data obtained from employees engaged in R&D activities are very limited in the literature. On account of that, the findings in this study may make sense to the innovation ability of innovative enterprise and expand the literature in the field of enterprise strategic management and knowledge management.
Practical implications
This analysis shows that collaborative innovation network characteristics have both positive and negative effects on firm innovation performance. Therefore, business managers should pay attention to their position in the collaborative innovation network and maintain the relationship strength with other innovation subjects. Special consideration should be given to the knowledge transfer of innovative enterprises, so as to improve firm innovation performance practically.
Originality/value
The study may provide additional understandings for researchers, government managers, universities and enterprises with regard to strategic management from the visual angle of innovation ecosystems. It is instrumental in the exploration of the mechanisms enabling firm innovation performance.
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Keywords
Ayesha Nusrat, Zhang Zongming, Jie Li and Farhan Muhammad Muneeb
This study examines the impact of entrepreneurial leadership (EL) on Chinese micro and small family businesses’ (MSFBs) innovativeness. Drawing on the resource-based view, this…
Abstract
Purpose
This study examines the impact of entrepreneurial leadership (EL) on Chinese micro and small family businesses’ (MSFBs) innovativeness. Drawing on the resource-based view, this research study further explores the intermediary roles of proactive personality (PP) and affective commitment (AC) between ELs’ and MSFBs’ innovativeness. Besides this, the present work proposes a novel contingency impact of big data-powered artificial intelligence (BDAI) between EL, PP and AC, which indirectly spurs MSFBs’ innovativeness.
Design/methodology/approach
This study proposed a moderated mediation model using multi-wave, multi-source, time-lagged datasets of 380 employees from 190 Chinese MSFBs. We tested our hypotheses using structural equation modeling through the PLS technique.
Findings
The findings reveal a significant impact of EL on MSFB innovativeness, underscoring the pivotal intermediary roles of EL in driving MSFB innovativeness. Furthermore, BDAI emerges as a critical contingency factor, amplifying the effects of EL on both PP and AC to spur MSFBs’ innovativeness.
Practical implications
Our research offers several practical implications for Chinese MSFBs aiming to enhance innovativeness and competitive advantage. Firstly, understanding the direct impact of EL on MSFBs’ innovativeness provides valuable guidance for MSFB leaders. Secondly, recognizing the mediating roles of PP and AC underscores the importance of human and social capital in driving innovation within Chinese MSFBs. Thirdly, leveraging BDAI as a contingency factor can further augment the effects of EL on both PP and AC, thereby enhancing innovation outcomes. Thus, managers can capitalize on BDAI to gain actionable insights to increase MSFBs’ innovativeness.
Originality/value
This study enlightened how EL can develop MSFBs innovativeness through PP and AC. Our findings reveal that MSFBs can increase their innovation by leveraging PP and AC, leading to higher proactive provision in employees’ behavior. Subsequently, our results synchronized the exploration of BDAI as a novel insight for MSFB innovativeness. This shed light on a highly notable contribution to understanding BDAI to benefit MSFBs, acting as a critical contingency between EL, PP and AC.
Details
Keywords
Wireless mesh networks (WMNs) have evolved quickly during the last several years. They are widely used in a lot of fields. Channel allocation provides basic means to guarantee…
Abstract
Purpose
Wireless mesh networks (WMNs) have evolved quickly during the last several years. They are widely used in a lot of fields. Channel allocation provides basic means to guarantee mesh networks’ good performance such as efficient routing. The purpose of this paper is to study channel allocation in mesh networks.
Design/methodology/approach
First, the papers in channel allocation fields are surveyed, and then the limitations in existing methods noted. Graph theory is used to find a better model to represent the problem and algorithms are proposed based on this model. Simulation proved that algorithms are better than the previous conflict graph‐based approaches.
Findings
The paper analyzes the conflict graph‐based model and finds its limitations, then proposes a bipartite graph‐based model. Algorithms were devised based on this model. Simulation results illustrate that the algorithms can reduce the starvation ratio and improve the bandwidth utilization, compared with previous conflict graph‐based algorithms.
Research limitations/implications
The research of this paper is based on an ideal network environment without interference or noises. It will be better if the noises are considered in future work.
Practical implications
To study the routing strategies of WMNs, it is not sufficient to only consider path length as routing metric since the nodes are heterogeneous. The routing metrics should include the channel bandwidths which are the results of channel allocation.
Originality/value
This paper presents a new bipartite graph‐based model to represent the channel allocation problem in mesh networks. This model is more efficient and includes more information compared with conflict graph model, and it also proposes channel allocation algorithms based on bipartite graph‐based model. The algorithms can reduce starvation ratio and improve the bandwidth utilization.