مقاله انگلیسی رایگان در مورد نوآوری آزاد و مزیت رقابتی پایدار – الزویر 2023

Abstract

     With intensifying competition and increasing dependence on external partners, open innovation has been becoming an inevitable and prevalent strategy to achieve long-term competitive advantage. Considerable research has investigated the benefit of open innovation, the mechanism through which open innovation translates into sustainable competitive advantage have yet been well examined. Drawing on the strategy-competence-competitive advantage framework and the knowledge-based view, this study examines the role of ambidextrous organizational learning in mediating the relationship between open innovation and sustainable competitive advantage as well as the moderating role of knowledge management capability. Based on data collected from 269 Chinese high-tech enterprises in 2021, we found that open innovation contributes to sustainable competitive advantage through enhancing organizational learning including both exploratory and exploitative learning as well as their balance. Moreover, knowledge management capability positively moderates the relationships of open innovation to exploration and exploitation as well as their combination. Research and practical implications are discussed.

Introduction

     As a result of globalization, technological complexity, increased competition and resource scarcity, organizations are changing and adopting a more open, cooperative approach to building their competitive advantage (Barrett et al., 2021; Lewis et al., 2010). Hence, open innovation has been becoming an important concept in both academic research and industrial practice; it refers to “the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively” (Chesbrough, 2006). Compared with closed innovation, open innovation permits firms to explore outside knowledge and to externally exploit existing internal resources to gain a competitive edge (Drechsler and Natter, 2012; Popa et al., 2017). The more a business interacts with other organizations, the higher its chances of acquiring external ideas, capabilities, knowledge, technology and other intangibles, and the greater its chances of successful innovation (Greco et al., 2016). Moreover, the complementarity and collaboration of internal and external innovation resources will gradually improve enterprises’ innate innovation capability, helping them to ultimately form their own core competitiveness (Lichtenthaler, 2011).

Results

Hypothesis testing

     Table 4 shows the descriptive statistics and correlation analysis on all variables. Before the regression analysis, we mean-centered all variables to minimize multicollinearity. After centering, the results revealed that the VIF values of each model ranged from 1.029 to 3.579, and the tolerance was >0.100, implying no serious multicollinearity problem (Kock, 2015). We also analyzed the sample proportion; the sample proportion of balanced learning was 47.584 %, and the sample proportions of “high exploration-low exploitation” and “low exploration-high exploitation” of imbalanced learning were 26.022 % and 26.394 %, respectively, which met the requirements of polynomial regression (Shanock et al., 2010). The empirical regression results of this paper were depicted in Tables 5 and 6. Table 5 showed that open innovation had a significant, positive effect on sustainable competitive advantage (Model 9, β = 0.49, p < 0.001), supporting H1. In addition, open innovation had a significant, positive effect on exploratory learning (Model 1, β = 0.34, p < 0.001), exploitative learning (Model 3, β = 0.47, p < 0.001), OAB (Model 5, β = 0.25, p < 0.001), and OAC (Model 6, β = 0.17, p < 0.05) respectively. It can be seen from Table 6 that exploratory learning (Model 10, β = 0.51, p < 0.001), exploitative learning (Model 11, β = 0.64, p < 0.001), OAB (Model 14, β = 0.22, p < 0.001), and OAC (Model 13, β = 0.10, p < 0.05) had significant, positive effects on sustainable competitive advantage. Thus, we further tested the mediating effect via the SPSS Process V3.3 plugin performing Bootstrap 5000 repeated sampling to test whether the confidence interval at the 95 % level contains 0 (Hayes, 2017). The mediating effect value of exploitative learning was 0.25, and the confidence interval was [0.17, 0.32], thereby supporting H2a. The mediating effect value of exploratory learning was 0.13, and the confidence interval was [0.07, 0.19], thus supporting H2b. The mediating effect value of OAC was 0.01, and the confidence interval was [− 0.01, 0.05], which did not support H3a. The mediating effect value of OAB was 0.02, and the confidence interval was [0.01, 0.05], supporting H3b.