دانلود مقاله نوآوری باز و مزیت رقابتی پایدار
چکیده
1. مقدمه
2. پیشینه نظری
3. فرضیه ها
4. روش ها
5. تجزیه و تحلیل
6. نتایج
7. بحث
نامگذاری کلمات اختصاری
بیانیه مشارکت نویسنده CRediT
منابع مالی
ضمیمه I. پرسشنامه نظرسنجی
در دسترس بودن داده ها
منابع
Abstract
1. Introduction
2. Theoretical background
3. Hypotheses
4. Methods
5. Analysis
6. Results
7. Discussion
The nomenclature of acronyms
CRediT authorship contribution statement
Funding
Appendix I. Survey questionnaire
Data availability
References
چکیده
با تشدید رقابت و افزایش وابستگی به شرکای خارجی، نوآوری باز به یک استراتژی اجتناب ناپذیر و رایج برای دستیابی به مزیت رقابتی بلند مدت تبدیل شده است. تحقیقات قابل توجهی مزیت نوآوری باز را بررسی کرده است، مکانیسمی که از طریق آن نوآوری باز به مزیت رقابتی پایدار تبدیل می شود، هنوز به خوبی بررسی شده است. این مطالعه با تکیه بر چارچوب استراتژی- شایستگی- مزیت رقابتی و دیدگاه مبتنی بر دانش، نقش یادگیری سازمانی دوسویه را در میانجی‌گری رابطه بین نوآوری باز و مزیت رقابتی پایدار و همچنین نقش تعدیل‌کننده قابلیت مدیریت دانش بررسی می‌کند. بر اساس داده‌های جمع‌آوری‌شده از ۲۶۹ شرکت چینی با فناوری پیشرفته در سال ۲۰۲۱، دریافتیم که نوآوری باز از طریق افزایش یادگیری سازمانی از جمله یادگیری اکتشافی و بهره‌کشی و همچنین تعادل آنها به مزیت رقابتی پایدار کمک می‌کند. علاوه بر این، قابلیت مدیریت دانش به طور مثبت روابط نوآوری باز با اکتشاف و بهره برداری و همچنین ترکیب آنها را تعدیل می کند. تحقیقات و مفاهیم عملی مورد بحث قرار می گیرد.
توجه! این متن ترجمه ماشینی بوده و توسط مترجمین ای ترجمه، ترجمه نشده است.
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.