انتخاب مناسب سیستم های چند کاناله برای به حداقل رساندن هزینه های معاملاتی

This paper proposes that firms can use a transaction cost approach to make multiple channel system design related decisions. The author differentiates between two types of multiple channel systems and hypothesizes that transaction asset specificity, behavioral uncertainty and environmental uncertainty lead manufacturers to adopt either a dual channel system or a multiple independent channel system. Furthermore, the author proposes that when all three transaction cost variables match with the type of multiple channel system used, firms can minimize their transaction costs and eventually increase their channel system performance in terms of contribution to firm profitability. The author tests the hypotheses with survey data collected from 229 firms. The results support that the fit between the type of multiple channel mix and the three transaction-cost theory variables results in lower transaction costs and higher contribution to profit. The author presents theoretical and managerial implications.

As predicted by various researchers (e.g. Frazier, 1999 and Moriarty and Moran, 1990), multiple channel systems1 have become a dominant channel design in numerous industries. Today many firms distribute their products and reach their customers simultaneously through integrated and independent channels (e.g. Dutta, Bergen, Heide, & John, 1995). In many industries, adopting such multiple forms has become a necessity rather than a choice. Firms use multiple channels to minimize their costs, reach different customer segments and remain competitive (Moriarty & Moran, 1990). Also, a well-crafted multiple channel systems could give companies a competitive advantage in the form of increased entry barriers against new entrants (Porter, 1979). Such multiple channel systems may take many forms. For instance, a vertically integrated channel and an independent channel may be used simultaneously or different types of independent channels are adopted to reach customers (John and Weitz, 1988 and Moriarty and Moran, 1990).

The results of the logistic regression analysis are provided in Table 3. The regression model shows significant explanatory power with the chi-square of 111.86. In addition, the Cox and Snell R² and Negelkerke R² measures also demonstrate the good fit of the model, with a high level of explanatory power. The model correctly classifies 80.8 percent of dual channels and 82.9 percent of multiple independent channels. The overall correct classification rate is 81.9 percent. This figure compares favorably with the classification ratios