نوآوری، یادگیری سازمانی، و عملکرد
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|4050||2011||10 صفحه PDF||سفارش دهید|
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|شرح||تعرفه ترجمه||زمان تحویل||جمع هزینه|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Business Research, Volume 64, Issue 4, April 2011, Pages 408–417
Literature examines the relationship between innovation and performance and asserts a positive relationship between organizational learning and both performance and innovation. However, few empirical studies analyze these relationships together. This article explores those relationships using SEM with data from 451 Spanish firms. The findings show that both variables — organizational learning and innovation — contribute positively to business performance, and that organizational learning affects innovation. Another finding of this study is that size and age of the firm, industry and environmental turbulence moderate these relations.
The literature on management emphasizes the key role that both innovation (Baker and Sinkula, 2002, Balkin et al., 2000, Darroch and McNaugton, 2002, Lyon and Ferrier, 2002, Utterback, 1994, Vrakking, 1990 and Wolfe, 1994), and organizational learning (Brockmand and Morgan, 2003, Dodgson, 1993, Fiol and Lyles, 1985, Garvin, 1993, Gnyawali et al., 1997, Nevis et al., 1995 and Stata, 1989) play in enhancing a firm's competitive advantage. Some studies suggest that organizational learning and its output, organizational knowledge, are antecedents of innovation (Baker and Sinkula, 1999, Cohen and Levinthal, 1990, Coombs and Hull, 1998, Darroch and McNaugton, 2002, Hage, 1999, Kogut and Zander, 1992, Leonard-Barton, 1999, Nonaka and Takeuchi, 1995, Nooteboom, 1999, Sørensen and Stuart, 2000 and Stata, 1989). The basic assumption is that learning plays a key role in enabling companies to achieve speed and flexibility within the innovation process (Brown and Eisenhard, 1995, Miles and Snow, 1978 and Weerd-Nederhof et al., 2002). Organizational learning, innovation and performance relate positively to each other. However, research that studies the interrelationships between the three concepts simultaneously is still scarce. Previous studies usually focus on the innovativeness of the firm, which is to say, on the degree to which the organizational culture promotes and supports innovation (Keskin, 2006 and Lee and Tsai, 2005) or analyzes only one type of innovation, mainly product innovation (Salavou and Lioukas, 2003). Thus, previous research provides a partial explanation only of the phenomenon of innovation. Similarly, most studies of organizational learning adopt a cultural perspective for measuring this concept. Very few studies (Darroch and McNaugton, 2003 and Tippins and Sohi, 2003) analyzes the process of organizational learning. Since culture values are more difficult to change than specific actions, focusing on the process may be more helpful for practitioners. This study attempts to address the weaknesses of the preceding literature and analyzes the relationships between organizational learning, innovation and performance together in a single model. This study focuses on the organizational learning process and uses a complete measure of innovation. In addition, this paper analyzes the likely moderating effect of firm size and age, industry and environmental turbulence on the relationships between organizational learning, innovation and performance. The article starts with a review of the literature on these topics and a description of the model proposals. Then, the article presents the design of the study to test the model and the findings of this study. In the last section, the article discusses the managerial and academic implications of the study, its limitations and recommendations for future research.
نتیجه گیری انگلیسی
The study uses structural equation modeling (SEM) to test the hypotheses. Figure 1 shows the proposed structural model. The analysis includes conventional maximum likelihood estimation techniques to test the model (Jöreskog and Sörbom, 1996). The fit of the model is satisfactory (χ2 = 780.80, df = 392; GFI = 0.90; RMSEA = 0.046; CFI = 0.94; TLI = 0.94; IFI = 0.94), suggesting that the nomological network of relationships fits the data — another indicator of support for the validity of the measurement scales (Churchill, 1979)To provide greater confidence in the model configuration, Table 6 shows the findings of testing the theoretical model (MT) against an alternative model (MA) that treats innovation as an intermediate variable between organizational learning and performance, and omits the direct relationship between organizational learning and performance. Anderson and Gerbing (1988) recommend this procedure. They suggest the use of the chi-square difference test (CDT) to test the following null hypothesis,: MT − MA = 0. Compared with a less parsimonious MA, a non-significant CDT would lead to acceptance of the more parsimonious MT. Table 6 reports a significant change in chi-square between the proposed model and MA. The CDT has a probability of p < 0.01, which allows us to conclude that the alternative model's fit is significantly worse.In terms of the hypotheses (Table 7), the findings for H1 (Innovation → performance; β95 = 0.57, p < 0.01) suggest that innovation has a positive and significant effect on performance, supporting the widespread idea that innovation is a key driver of company success.The findings also provide support for H2 (Organizational learning → performance; γ91 = 0.26, p < 0.01) and H3 (Organizational learning → innovation; γ51 = 0.66, p < 0.01), showing that the organizational learning has a positive effect on both performance and innovation. In addition, organizational learning effect on innovation is higher than its effect on performance. Taking into account the fact that innovation also improves performance, these results seem to reflect that innovation partially mediates the relationship between organizational learning and performance. Finally, H4 states that firm size, age, industry and environmental turbulence moderate the relationships between organizational learning, innovation and performance. To test these moderating effects this research uses the two-group comparison of structural equation modeling. This study splits the sample into two groups along the median of the levels of each variable (with the exception of industry, since this variable is dichotomous). One group contains firms with the higher levels of each moderator and the other group contains firms with lower levels. Then, the analysis includes a two-group comparison to examine the existence or not of differences in structural parameters between high and low values of these variables. The first step constrains the parameter from hypothesized relationships (β95, γ91 or γ51) to be equal. In the second step, they do not constrain the parameter. If the difference between the two tests is significant (chi-square difference), that means that the variable used for splitting the sample moderates the relationship studied. The analysis repeats this method to study the possible moderating effect of the four variables in the three relationships included in this study model (see Table 8).Table 8 shows that, although the relationships between organizational learning, innovation and performance are significant and positive for all the groups, the four variables studied influence how intense these relationships are. Thus, they moderate the relationships between the three main constructs of the model, confirming H4. According to the findings, the relationship between innovation and performance is positive for all the groups but this relationship is stronger when firms are bigger, older and belong to manufacturing industry, which is consistent with previous research. However, the relationship is less strong when the firm operates in turbulent environments. In contrast with this, the findings concerning the relationship between organizational learning and performance indicate that this relation is always positive but stronger for smaller and younger firms, services and in high turbulent environments. These results are only partially consistent with the previous literature. Finally, the findings confirm that size, age, industry and environment moderate the relationship between organizational learning and innovation but their moderating effects are not always as expected. In particular, they show that the positive relationship between organizational learning and innovation is more intense in the group of firms that are smaller, older, operating in environments that are more turbulent and in the service sector.