I just read an article by Vallet-Bellmunt et al. (2011): Supply Chain Management: A Multidisciplinary Content Analysis of Vertical Relations between Companies, 1997–2006. It gives a good overview of SCM research published in journals related to marketing, logistics, management, and marketing channels. The authors find that the work type of most research is empirical in nature, primary information is used more often than secondary information, the information type is mostly quantitative in nature, studies are mainly explanatory-predictive, the period of time in which the research is carried out is mostly cross-sectional, the geographical area is mainly national, and manufacturing samples are used most often. Particularly, the article confirms my suspicion that there is “shortage of studies conducted on the supply chain as a network of enterprises“. Instead, most research turns out to focus on a single enterprise or on the relationships of a single enterprise with its suppliers or customers.
Previous studies about SCM in China have highlighted that uncertainty in the supply chain can be reduced by process flexibility, information sharing, and process integration. However, a new study by Roland Berger Strategy Consultants, titled The end of the China cycle?, warns that “the value proposition for many firms in China is disappearing as the competitive cost advantage is beginning to erode relative to other countries” and that “government policy and social issues are further compounding the complexity of doing business in China”. The authors argue that some industries in China have already passed the tipping point. As China is in transition towards high-value add manufacturing, firms need to rethink the strategy of their manufacturing footprints. Supply chain managers will continue to face volatility and uncertainty, also in China, but the new study demonstrates that managers can seize the opportunity right now. However, the Chinese window is closing quickly.
In their interesting article, A tale of three perspectives: Examining post hoc statistical techniques for detection and correction of common method variance, Richardson et al. (2009) define common-method variance (CMV) as “systematic error variance shared among variables measured with and introduced as a function of the same method and/or source”. Post-hoc techniques promise help, if the research design does not allow the independent and dependent variables to use data from different methods and sources. Richardson et al. evaluate (1) the correlational marker, (2) the confirmatory factor analysis (CFA) marker, and (3) the unmeasured latent method construct (ULMC) techniques. Interestingly, they find that only the CFA marker technique appears to have some practical value, but “recommend the CFA marker technique be used only as a means for providing evidence about the presence of CMV and only when researchers can be reasonably confident they have used an ideal marker”. A good description of the CFA marker technique can be found in an article by Williams et al. (2010).