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)
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)