The Partner Selection Modes for Knowledge-Based Innovation Networks: A Multiagent Simulation

Abstract

With the advent of the knowledge economy, technological innovation has become more and more complex. Firms in innovation system need to cooperate with others to upgrade their knowledge level and market competitiveness. Proper partner selection can increase the efficiency of the entire innovation system. Different partner selection mechanism has a significant impact on the efficiency of knowledge transfer for the enterprises and innovation system. In this study, a multiagent simulation is conducted to explore the effects of four different partner selection modes on knowledge transfer of innovation network: random selection mode, mode of selection based on space, mode of selection based on knowledge capital and mode of selection based on complementary knowledge. The results indicate that: in a period time, (1)the spatial selection mode limits the scope of partner selection and reduces the knowledge transfer path;(2)mode of selection based on the knowledge capital enables the large firms to have more cooperation opportunities;(3)complementary knowledge based selection mode improves the knowledge transfer efficiency of the system.

Member Selection for the Collaborative New Product Innovation Teams Integrating Individual and Collaborative Attributions

“…For these reasons, the fuzzy language variables are preferred for evaluation, such as "good knowledge competence," "moderate competence," and "poor competence." In order to deal with the fuzzy and uncertain information in the evaluation of individual knowledge competence, the fuzzy set theory [11,[35][36][37]] is adopted in this paper for the evaluation of candidates' individual knowledge competence. For the purpose of determination of fuzzy evaluation results, the joint evaluation by candidates and experts from enterprises is considered in this paper to measure the individual knowledge competence of candidates under various attributions, with synthesis of evaluation results.…”

“…Comprehensively considering the requirements of knowledge complementarity among members and enough qualified candidates, decision-makers determine the appropriate interval of knowledge complementarity as (θ, θ) � (1.00, 2.50). Accordingly, based on data in Table 9, it is derived that a set of 37 Based on the task cooperation information of candidates, the formal knowledge collaboration competence among candidates can be obtained by formulas (9) and (10). With formulas (11)- (15), the informal knowledge collaboration performance among candidates is acquired.…”

A Multi-Objective Task Reallocating Method in Complex Product Design Process Considering Design Changes

“…What methods are most suitable for dynamic scheduling is an important issue. Advantages and disadvantages of these methods have been discussed by some published works [46], [47]. Genetic algorithms were found not efficient to obtain a near-optimal solution in a reasonable time compared to Tabu search and simulated annealing which operate on a single configuration [48].…”

A Cooperative Partner Selection Study of Military-Civilian Scientific and Technological Collaborative Innovation Based on Interval-Valued Intuitionistic Fuzzy Set

“…China urgently needs to break the barriers of military-civilian technology sharing and crack the problem of military-civilian two-way transfer and transformation. MCSTCI is the most direct form of knowledge and technical cooperation between military and civilian innovation organizations and resource sharing between organizations through the spillover of knowledge and technology [7]. For the organizations in the MCSTCI system, correctly choosing partners is crucial for knowledge and technology cooperation.…”