5. Practical section
5.2. Performance of the Cost Objective
5.2.5. Statistical Tests and Results
A total of three regressions were done for the years 2010, 2012, and 2014. The purpose was to determine the relationship between adoption of ERP and improvement in performance ratio Labour Productivity. These analyses will serve as a support to enhance the evidence that was gathered to answer the research question. By using dummy coding to distinguish between adopters and non-adopters, it was possible to estimate the impact that ERP = 1 (an increase of one unit on the independent variable X), has on the dependent variable. As compared with ERP = 0 for non-adopters. Furthermore, the regression was done for three different years with the purpose of investigating how, during the four years observed, the performance of the companies changes.
Table 8 presents a summary of the results from the regression analysis for the variable Labour Productivity in year 2010.
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Coefficients β1 Standard Error P-value
Intercept β0 5.22310 0.21899 0.00000
ERP adoption 0.17105 0.07563 0.02435
Firm size -0.00247 0.00041 0.00000
Profit/loss before tax -0.00003 0.00001 0.00002
Added value 0.00005 0.00001 0.00000
GERMANY 0.41700 0.22005 0.05894
FRANCE 0.02524 0.20789 0.90344
SPAIN -0.26946 0.19867 0.17589
HUNGARY -0.83425 0.22622 0.00026
ITALY 0.26544 0.20283 0.19154
High Tech -0.23419 0.08474 0.00603
Specialized -0.31584 0.10267 0.00227
Economies of Scale -0.62953 0.15462 0.00006
Skilled Blue Collars -0.00421 0.00185 0.02385
Unskilled Blue Collars 0.00215 0.00189 0.25578
Table 11. Results for regression. DV: ln (Labour Productivity) for the year 2010.
Since the dependent variable was transformed using the logarithm function, to interpret the results it is necessary to multiply 100 * β1 obtained for each of the independent variables, to get the percentage of change in the dependent variable (The Wharton School of the University of Pennsylvania, 2001). Hence:
In the year 2010, firms that adopted ERP achieved an estimated increase of 17% in their Labour Productivity if the other independent variables are held constant. For this regression model, an adjusted R2 of 0.441 was achieved. This means that the regression constructed is able to estimate approximately 44.1% of the variation of the values from the dependent variable Labour Productivity.
As can be observed from the descriptive statistics presented in table 7, the firms in the sample made on average 195.24 thousands of euros per employee in 2010. Therefore, if the companies which adopted ERP presented an estimate of 17% increase in their labour productivity figure, this can be translated as the following: Compared to non-adopters, during 2010, ERP-adopting firms made an average of 33.4 thousands of euros more per employee.
In a similar manner, the results obtained for the year 2012 are summarized in the table below.
Coefficients β1 Standard Error P-value
Intercept β0 5.36235 0.21777 0.00000
ERP adoption 0.15652 0.07482 0.03719
Firm size -0.00203 0.00041 0.00000
Profit/loss before tax -0.00003 0.00001 0.00006
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Added value 0.00004 0.00001 0.00000
GERMANY 0.36581 0.21836 0.09482
FRANCE 0.02007 0.20685 0.92276
SPAIN -0.38349 0.19871 0.05447
HUNGARY -1.02815 0.22874 0.00001
ITALY 0.18734 0.20200 0.35438
High Tech -0.23553 0.08355 0.00510
Specialized -0.33537 0.10154 0.00106
Economies of Scale -0.58986 0.15394 0.00015
Skilled Blue Collars -0.00382 0.00184 0.03851
Unskilled Blue Collars 0.00210 0.00187 0.26421
Table 12. Results for regression. DV: ln (Labour Productivity) for the year 2012.
It can be seen that the difference between year 2010 and year 2012, in terms on performance of the labour productivity ratio, is not large. In the year 2012, ERP-adopting firms experienced, on average, and increase of 15.7 percent in their sales per employee ratio, compared to non-adopting companies. In the same manner as with the figures of 2010, the descriptive statistics table can be used to estimate the change in thousands of euros per employee. 15.7 % of 203.88, equals 32 thousand euros more per employee during 2012, for companies who invested in ERP. Comparing with 2010, the values are very similar.
Furthermore, the regression for the year 2012 yielded a result of 0.442 in the adjusted R2. Henceforth, the regression constructed is able to explain 44.2% of the variation of the values of the dependent variable Labour Productivity. This result is almost exactly the same as 2010’s adjusted R2 (0.441). Considering that a very similar sample and the same statistical model (with the same control variables) were used for the analysis, this is expected.
Lastly, the results for the regression analysis for the year 2014 are presented in the following table.
Coefficients β1 Standard Error P-value
Intercept β0 5.46167 0.22826 0.00000
ERP adoption 0.15015 0.07862 0.05700
Firm size -0.00185 0.00042 0.00001
Profit/loss before tax -0.00003 0.00001 0.00562
Added value 0.00004 0.00001 0.00000
GERMANY 0.32730 0.23035 0.15628
FRANCE -0.02538 0.21977 0.90814
SPAIN -0.36751 0.20981 0.08075
HUNGARY -1.09346 0.24093 0.00001
ITALY 0.11134 0.21314 0.60174
High Tech -0.26087 0.08814 0.00330
Specialized -0.33041 0.10792 0.00238
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Economies of Scale -0.55829 0.16196 0.00064
Skilled Blue Collars -0.00442 0.00194 0.02317
Unskilled Blue Collars 0.00141 0.00199 0.47920
Table 13. Results for regression. DV: ln (Labour Productivity) for the year 2014.
The results for the regression analysis performed for the year 2014 do not differ a lot from the results obtained from the years 2010 and 2012. On average, for the year 2014, the firms that adopted ERP saw an increase of 15.015 percent in their sales per employee, compared to non-adopting firms. In thousands of euros per employees, this represents a figure of approximately 31.9. It can be observed that the three years in the analysis display similar results.
Furthermore, this last regression is able to explain 41.7% of the variation in the values of Labour Productivity.
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Table 14. Results summary in percentage and in thousands of euros per year
As it can be observed, ERP had a significant impact on labour productivity. These suggest that the implementation of ERP systems can help improve performance of firms in terms of sales per employee. Similar results were found by the study conducted by Hitt et al.
(2002).
Hitt et al. (2002) interprets from the results found in their analysis that the firms are generating more revenues per unit of input. The results in this thesis confirm the findings stated by the authors. Moreover, it is believed that the improvement seen in this study, for ERP-adopting firms in the dependent variable labour productivity, is caused by a better performance of the cost objective of operations management. As previously explained, the five objectives are interrelated, and improving the performance of each one of them, is usually followed by a better cost performance.
On the other hand, while these findings confirm the conclusions found by some researchers, it contradicts the results presented by Buleje (2014). In his paper, the author argues that ERP does not caused any significant impact on the firms that adopted the system.
However, some other points expressed in his publication can be reiterated by these results.
As Buleje (2014) stated, it is expected that, after the implementation of ERP takes place, productivity will decrease. Nevertheless, in the long term, the sales are anticipated to increase, and companies tend to decrease the number of employees. The later believe is also supported by Velcu (2007).
Table 15 shows the change in the average number of employees as compared to the previous year. It can be observed that non adopting firms experienced an increase in employee numbers, the difference between 2012 and 2010 being more significant. However, as anticipated, for ERP adopters, on average the number of employees was reduced. Again, the difference between year 2012 and 2010 being larger that for the years 2014 and 2012. This evidence partially explains the increase in labour productivity. This performance ratio is the division of sales between employees. Therefore, it is expected to increase if the average