Search results

This study aims to propose a combined effect framework to explore the relationship between research and development (R&D) team networks, knowledge diversity and breakthrough technological innovation. In contrast to conventional linear net effects, the article explores three possible types of team configuration within enterprises and their breakthrough innovation-driving mechanisms based on machine learning methods.

Based on the patent application data of 2,337 Chinese companies in the biopharmaceutical manufacturing industry to construct the R&D team network, the study uses the K-Means method to explore the configuration types of R&D teams with the principle of greatest intergroup differences. Further, a decision tree model (DT) is utilized to excavate the conditional combined relationships between diverse team network configuration factors, knowledge diversity and breakthrough innovation. The network driving mechanism of corporate breakthrough innovation is analyzed from the perspective of team configurations.

It has been discerned that in the biopharmaceutical manufacturing industry, there exist three main types of enterprise R&D team configurations: tight collaboration, knowledge expansion and scale orientation, which reflect the three resource investment preferences of enterprises in technological innovation, network relationships, knowledge resources and human capital. The results highlight both the crowding-out effects and complementary effects between knowledge diversity and team network characteristics in tight collaborative teams. Low knowledge diversity and high team structure holes (SHs) are found to be the optimal team configuration conditions for breakthrough innovation in knowledge-expanding and scale-oriented teams.

Previous studies have mainly focused on the relationship between the external collaboration network and corporate innovation. Moreover, traditional regression methods mainly describe the linear net effects between variables, neglecting that technological breakthroughs are a comprehensive concept that requires the combined action of multiple factors. To address the gap, this article proposes a combination effect framework between R&D teams and enterprise breakthrough innovation, further improving social network theory and expanding the applicability of data mining methods in the field of innovation management.

This paper aims to examine the impact of social capital and self-efficacy in the performance of producer organizations. It also tests the mediating influence of self-efficacy in the relationship of social capital and performance of producer organizations.

The study used data from a survey of 226 members of farmer producer organizations (FPO) in India. The model was tested through structural equation modeling wherein all hypotheses were tested using “R” studio.

The findings reveal that social capital and self-efficacy play a significant role in predicting the performance of FPO. It was found that in the process of social capital influencing the performance of FPO, self-efficacy plays a significant role as a partial mediator with a mediating effect of approximately 69.28%.

The study considered only one antecedent while identifying the reasons for perceived performance of FPOs. Hence, further studies of the various other constructs such as attitude, subjective norms, etc., may be considered.

No previous work has examined the mediating role of self-efficacy in the relationship between social capital and perceived performance of FPO. This study is possibly the only one that joins two streams of thought – social capital and self-efficacy – to examine the performance of FPO.

The purpose of this study was to establish the cause-effect relationship between determinants of trust in the buyer–supplier integration and the procurement performance of large manufacturing firms in Tanzania.

The study surveyed 52 firms from Temeke Municipality, Tanzania using questionnaire subjected to one procurement manager and one stores manager tallying a sample size of 104 respondents. Explanatory design was employed due to the presence of cause–effect relationship and the null hypotheses were tested using binary logistic regression technique at p values < 0.05 and ExpB > 1.

Mutual goals, geographical vicinity among partners, and supplier reliability are significant for the procurement performance of the manufacturing firms in Tanzania, whereas interpersonal and inter-organizational trusts and perceived buyers’ confidence are of no significant impact.

Buyer–supplier integration is a recently embraced and paramount practice for the manufacturing firms in Tanzania. Therefore, longitudinal study would further add value. The presence of the causality from the tested hypothesis appeals for the necessity of progress tracking.

Causality has been established, and a framework has been developed for the performance of large manufacturing firms using trust of buyer–supplier integration.

There shall be creation of more employment opportunities and timely availability of materials from large manufacturing firms in Tanzania.

Anchored on transaction cost economics and resource dependency theories, the study disclosed the root cause of procurement performance in the context of manufacturing firms in Tanzania whilst considering trust as a resource advantage of buyer–supplier integration.