practice  users of e commerce. The basic idea of DEA is multi input and multi output oriented 
efficiency evaluation without any further assumptions about the structure (e.g., normal 
distribution) or side conditions. Unlike parametric methods, DEA can use all kinds of input and 
output data to analyze the production behavior. The DEA model used was non input  or output 
oriented because neither an input minimizing (input oriented) nor an output maximizing (output 
oriented) analysis was necessary to evaluate the observed, actual input/output relation identified 
in the survey. Moreover, the model assumes returns of scale for each DMU depending on the 
size and a concave function of decreasing returns. The software used for the data analysis 
together with a detailed description is available from Scheel (2000). 
DEA was chosen due to the unique alternative way of analyzing a set of data in comparison to 
the best performing data sets. A regression analysis, for example, only describes the deviation of 
best performing data sets from the average. Unlike parametric approaches, DEA optimizes on 
each individual observation independent of any distribution assumptions (Charnes et al., 1994, p. 
5; Cooper, Seifert & Tone 2003, p. 13). Different kinds of DEA models have been used in a large 
number of ways to measure the impacts of IT, e.g., in the banking industry (Barr et al. 2002), the 
manufacturing industry (Beck, Wigand & Konig, 2004), or in the distribution industry (Beck, 
Konig & Wigand, 2003). 
In this paper, the DEA was used as follows: As input variables for the DEA model, the results of 
seven questions are used (Table 16), measuring the number of e commerce technologies in place 
as binary variables. The variables are coded as 0 when an establishment uses the e commerce 
technology and 1 if it does not use it. The coding is equivalent to more costs of input when e 
commerce is not available or the other way around, i.e., firms using e commerce gain benefits by 
reducing their processing costs. 
Input variables (Internet usage indicator) = u (online advertising, online sales, online procurement,  , same formal 
business processes along supply chain)   
 s.t. 
v  
i
{ }
1
;
0
The ten output variables of the model are measured by a five point scale (Table 24) with 1 (no 
impact at all) to 5 (a great deal). The DEA model uses a linear program to analyze the ratio 
between low costs of input (using e commerce) and the resulting satisfaction output for each 
establishment. As a result, the DEA identifies the best practice cases or the most efficient 
establishments within the sample. Firms on this so called  efficient frontier line  are relatively 
more efficient users than other firms below the frontier line. For a better explanation of the 
results, the average of  efficient  and  inefficient  establishments was calculated. The seven 
input variables are aggregated to an Internet usage indicator, while the ten output variables are 
aggregated as an average e commerce satisfaction index. The input variables are used 
unweighted so that each e commerce technology has the same explanation weight or loading for 
the efficiency of DMUs. 
Output variables (E commerce satisfaction index) = v (internal process more efficient, staff productivity increased, 
 , competitive position improved)   
 s.t. 
u  
j
{
;
1
;
3
;
2
}
5
;
4
8






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