Table 2 demonstrated basic information on each variable or factor and correlations among them. Positive correlations happen almost among all distribution channel innovations and distribution efficiency leading to firm performance. Most of all the significances are consistent with the literature. The positive highest correlation occurs between information sharing & efficiency while the lowest one between order handling innovation and efficiency. Interaction among the variables was mostly positive.
While the relationship between distribution channel innovation and distribution channel efficiency is shown by the regression results in table 3, innovations in assortment (в = 0.093, p < 0.01), was found to be significantly related to distribution channel efficiency. Hence, H1 was supported. The next relationship between distribution channel innovations and distribution efficiency is shown by the multiple regression results in table 4. In this table, indicated innovations in information sharing (в = 0.064), p < 0.01), and transportation coordination (в = 0.047, p < 0.05) & innovation in warehousing (в = 0.069), p < 0.01 ) were found to be significantly related to distribution channel efficiency while the others were found not significant. Hence, the hypotheses testing can be concluded that H3, H6, & H7 were supported while H2,H4,H5, H6, H8, & H9 were not supported.
Controlling for firm size, firm age, industry and competitive environment hostility, table 5-model 6 demonstrates there is no significant relationship : firm size, firm age, industry and competitive environment hostility with SME’s performance. Based on Baron and Kenny’s approach, in terms of efficiency, as seen in the table 5, when all independent variables with distribution efficiency in the estimation-model 6 were included, it can be seen that the significance of efficiency did eliminate the significance of the innovations particularly information sharing and transportation coordination for predicting SME’s performance-model 5. How to include the innovations in the model was step by step as addressed in the conceptual framework. Therefore, distribution performance in terms of efficiency mediates the relationship between innovation in distribution channel and firm performance economic indicators. Hence, the hypotheses testing can be concluded that H10 was supported. The findings of this study supported the concept that distribution channel efficiency mediated the relationship between distribution channel innovation and SME’s performance. This indicates that innovation in information sharing and transportation coordination can enhance distribution channel efficiency in terms of cost efficiency, which would positively affect SME’s performance. The concept and practice of distribution channel is not new as it can be traced back to the ancient Egyptian; the only new is the way it is done. In consistent with Geroski & Machin and Wolff & Pett, innovation in distribution channel is found to impact positively on firm performance. Innovative information sharing among channel members, such as raw-material suppliers, manufacturers (including SMEs), distributors, and retailers is the key for achieving the flexibility need that enables firms to improve logistic processes in response to the rapid changes in the market, which in turn significantly improve distribution channel efficiency and firm performance.
As the role of transportation improves physical distribution efficiency and it is well appreciated in the literature, this study provides new evidence to the conviction. Innovative transportation coordination was found to improve distribution channel efficiency, which directly influenced the SME performance. This finding is supportive as about one- to two-thirds of the enterprise expenses on logistic costs are spent on transportation. It is also consistent with Stefansson’s argument that the use of technology in transportation would result in more effective transportation coordination, for instance, in selecting goods, vehicles and infrastructure, which brings about positive impact on distribution channel and firm performances.
Table 2: Correlations among variables
|Age of firm||2||.221*|
Table 3. Simple regression
|Regression||Dependent variables||R-Square||Adj R-Square||P||t p-value|
Table 4. Multiple regression
|Product and distribution scheduling||.019||.595|
|Transportation and coordination||.069||.049*|
|Warehousing and product handling||.093||.016*|
Table 5. Multiple regression-Baron & Kenney’s approach (1986)
|Distribution efficiency||419 ***||.312**|