تناسب برون سپاری با اولویت های رقابتی : تاثیر بر زنجیره تامین و عملکرد شرکت
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|598||2010||20 صفحه PDF||سفارش دهید|
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Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Operations Management, Volume 28, Issue 2, March 2010, Pages 124–143
The growth of outsourcing has led outsourcing strategies to become an increasingly important component of firm success (Gottfredson et al., 2005). While the purported goal of outsourcing in supply chains is to derive a competitive advantage, it is not clear whether the outsourcing decisions of firms are always strategically aligned with their overall competitive strategy. In this paper we evaluate the degree of congruence (fit or alignment) between a firm's outsourcing drivers and its competitive priorities and assess the impact of congruence on both supply chain performance and business performance, using empirical data collected from manufacturing business units operating in the United States. We find outsourcing congruence across all five competitive priorities to be positively and significantly related to supply chain performance. We also find the level of supply chain performance in a firm to be positively and significantly associated with the firm's business performance.
It is abundantly evident that outsourcing is a key business trend that has become increasingly important in recent years. While in the past, outsourcing was primarily relegated to the procurement of non-core components and services, today the outsourcing trend has expanded to include virtually every activity of a firm, including core and non-core components, business processes, information technology processes, manufacturing and distribution activities, and customer support activities (Chamberland, 2003, Gottfredson et al., 2005 and Venkatraman, 2004). Today's hyper-competitive environment, characterized by constant change, market unpredictability, and the pressure to reduce costs and cycle times, coupled with the globalization trend, has provided further impetus to the growth of outsourcing (D’Aveni et al., 1995). Outsourcing, which refers to the allocation of business activities from a source internal to an organization to a source outside of the organization, has become a key component of supply chain management strategies (Chase et al., 2004 and Lankford and Parsa, 1999). Increasingly, manufacturing firms are now outsourcing functions and processes related to the supply chain, including research and design, product development activities, product component manufacturing, product final assembly and distribution and logistics functions (Heikkila and Cordon, 2002 and Kirk, 2001). The growth of outsourcing has led outsourcing strategies to become an increasingly important component of firm success (Kakabadse and Kakabadse, 2000 and Talluri and Narasimhan, 2004). While the purported goal of outsourcing supply chain functions is to derive a competitive advantage, it is not clear whether the outsourcing decisions of these firms are always strategically aligned with its overall competitive strategy (Garaventa and Tellefsen, 2001).
نتیجه گیری انگلیسی
The full structural equation model for the relationship between outsourcing congruence and performance is shown in Fig. 3. A multi-step process was used to evaluate the structural equation model (Kline, 1998). All analyses were conducted using Version 6.1 of Multivariate Software's EQS program. Fit statistics for the model evaluation are shown in the table in Fig. 3. The chi-square statistics are presented for inspection, however their importance in evaluating the model fit is limited as the chi-square tends to be almost always significant for sample sizes approaching 200 or greater (Hatcher, 1994). The pure measurement model (in which all the latent factors are allowed to covary with each other) was tested first to determine if the overall model structure is appropriate before evaluating our hypotheses (Mulaik, 1997). The fit indices showed that the model fits the data very well (NFI, NNFI, CFI, and IFI > 0.95) which permitted hypothesis testing using the structural equation model. The full structural equation model was tested using both ML estimation and the Sattora and Bentler (1988) robust estimation methods. When using SEM, the sample size must be large enough to achieve a level of model power high enough to support hypothesis testing (MacCallum et al., 1996). Our sample of 196 responses exceeds the minimum sample size of 178 recommended by MacCallum et al. (1996) to achieve a model power of at least 0.80 (for α = 0.05). The goodness of fit statistics indicates an acceptable level of fit between the data and the model; three of the ML fit statistics (NFI, CFI, and IFI) exceed the recommended value of 0.90, while the fourth (NNFI = 0.89) is only slightly below the recommended level (Hu and Bentler, 1999). The robustness of the SEM analysis, despite the inclusion of the non-normal interaction terms in the model, is supported as the standardized residuals exhibits a near normal distribution, and in addition, the robust fit statistics are very similar to the ML fit statistics (Fig. 3).