4. RESULTADOS
4.4. Capítulo 4:
This section estimates different quantitative models to analyze which firm characteristics are related to the individual perception of ICT impact on a set of firm performance indicators. One of the database characteristics is that most of performance indicators have an ordinal nature. This fact conditions both structure and estimation methods and public policy implications. In effect, the database information relies on a report on how different managers qualify technology impact in different types of economic and operational results. The questions include subjects regarding impact on costs (operation, production, advertising, promotion and customer service, among others) and market introduction time of new products, number of new products, market share and incomes, among others. All things considered, we observe what individuals infer with respect to ICT effect on those variables.
The answers are qualitative and ordered according to the impact considered. For example, when evaluating the impact of technology investment on personnel cost, the managers answer among options such as: “it reduced significantly”, “it reduced”,
“it remained stable”, “it increased” or “it increased significantly”. This type of answer presents an important methodological challenge in order to evaluate the variables that may determine better (or worse) results, for each category, of this type of investment at firm level. In this perspective, the most common approach is to use models with ordered answers, which come from models with binary dependent variables. One of these models is the “Ordered Probit Model”, where potential results are not cardinal, but only ordinal.
In other words, we have different types of answers which are mutually excluding and that are only related in terms of order.
8 Self-elaboration based on the refined base BIT-CEPAL.
The basic structure an Ordered Probit model is as follows:
Where xi is a vector of observable characteristics of firm i and ui is the error term. Nevertheless, yi* cannot be observed directly because it is assumed to be a continuous variable. Therefore, it is necessary to define a new variable —denoted by yi — which explains the discreet structure associated to managers’ answers, as previously discussed. A way of doing this, and due to the ordinal structure of answers, is through the following relationship:
Where, each value of di represents a threshold which orders different types of answers, showing an ordinal-natured ordering only. Empirically, these thresholds are already determined by the answers themselves. According to this specification, it is possible to estimate the probability of observing each scenario based on the following structure:
Where F(·) corresponds to a type of probability distribution function to be defined which characterizes the error term, usually modeled by logistic and normal distributions. The model is estimated for a set of outcome variables. In particular, for ICT impact on sales, profits, margins and production costs. Once more, it is important to highlight that what is estimated is the covariance of ICT impact perceptions on outcome variables, controlling by firm characteristics. This is in no way an estimation of quantitative ICT effects on firm performance variables.
Therefore, using an Ordered Probit model, we describe managers’ answers according to several control and “ICT effort” variables. The “ICT effort” variables are basically two:
percentage of ICT employees and ICT budget as percentage of sales. Each type of effort includes a scale variable and a proportional scale variable. Table VII.4 shows descriptive
statistics of estimation variables. The control variables correspond to firm economic sector classification (dummy variable for each sector category). However, these dummy variables are indicative, but not excluding. This means that a firm may report to belong both to manufacturing and service sectors.
Table VII.4
Descriptive statistics of estimation variables
Variable Obs. Mean Std.Dev. Min Max
Total employees 169 125.9 477.2 2.0 6 000.0
ICT employees (%) 169 18.8% 0.3 0.0% 100.0%
Log (sales) 169 20.3 2.0 13.2 28.2
Log (ICT budget) 169 16.5 1.9 11.2 23.1
ICT budget (%) 169 3.6% 0.0 0.0% 33.3%
Production 169 46.2% 0.5 0.0% 100.0%
Services 169 75.7% 0.4 0.0% 100.0%
Source: Authors’ own elaboration based on BIT-Chile 2007.
Thus, in the framework of an Ordered Probit model, we estimate the following equation:
y* = β1TotalEmployeesi + β2ICTEmployeesi + β3LogSalesi + β4LogICTBudgeti + β5ICTBudgeti + β6Manufacturingi + β7Servicesi + εi
The estimation results in Table VII.5 show that, in most cases, neither control nor ICT effort variables are relevant for the perception concerning ICT impacts. Nevertheless, there are also some significant coefficients. With regard to production costs, we find that the probability that an individual attributes a production cost increase because of ICT, increases with number of employees and with percentage of ICT employees and percentage ICT budget over sales, and it decreases with absolute ICT budget and if the firm belongs to service sector.
These results show that larger firms tend to attribute production cost increases to ICT.
Likewise, more ICT employees are associated to attributing production cost increases to ICT. On the other hand, firms with higher ICT budget tend to associate production cost reductions with ICT, which could be associated to managers’ conviction regarding ICT benefits in making processes more efficient. However, when measured as a percentage of sales, ICT budget is associated with production cost increases. Finally, service sector ascribes production cost reductions to ICT incorporation. These results show that managers in the firm end up associating accounting cost with production costs, rather than processes’ improvements. That is, ICT employment is seen more as an expense than a complementary effort to ICT investments aiming at improving efficiency. The same applies to ICT budget as a percentage of total sales. ICT are associated to improvement of productive processes only when it is measured at budget level.
In addition, there are no statistically relevant variables in sales, profits and margins estimations. This is partly due to the small variance in dependent variables. In fact, ICT effect on production costs is the dependent variable with highest variance and, therefore, explanatory variables are more relevant in explaining different answer categories.
However, as we stated before, the case of costs is also different from the other outcome variables. Indeed, this indicates that ICT effects are related to costs in a very direct way.
By contrast, ICT effects on sales, profits and margins are much more noise and difficult to perceive by managers.
Table VII.5
Estimation results: ordered probit model
Variables Production costs Sales Profits Margins
Total employees 0.0004878 0.0004011 0.0003952 0.0015462
(2.92)** (-0.75) (-0.96) (-1.91)
ICT employees (%) 1.0376117 0.4071807 0.291435 0.4656008
(2.78)** (-1.12) (-0.84) (-1.38)
Log (sales) 0.136591 0.0318005 0.0452136 -0.0334052
(-1.34) (-0.34) (-0.48) (-0.39)
Log (ICT budget) -0.2079326 0.041063 -0.0229262 0.0523931
(2.30)* (-0.42) (-0.24) (-0.6)
ICT budget (%) 8.2761751 0.4262619 0.3356526 -1.6565388
(2.62)** (-0.15) (-0.12) (-0.54)
Manufacturing 0.0388378 0.2623126 0.1424386 0.2982099
(-0.19) (-1.16) (-0.64) (-1.35)
Services -0.5383545 0.3692817 0.2041689 0.2514351
(2.22)* (-1.56) (-0.85) (-1.02)
Obs. 169 169 169 169
Source: Author’s own elaboration.
Note: z-statistics in absolute value with robust standard errors in parenthesis; * Significant at 5%; ** Significant at 1%.
6. Concluding remarks
This study presents a first effort to determine the impact of information and communication technologies on a group of Chilean firms. Unfortunately, there is not much early evidence on these effects and therefore this type of empirical exercises is noteworthy. Indeed, the empirical analysis shows the relevance of relying on microdata at firm level, also as a relevant tool in order to design appropriate public policies. Indeed, economic policies aiming at fostering ICT incorporation in large firms are not the same than for small firms, and they do not have the same effect in a firm in the shoe sector than in a firm in the electronic sector.
Nevertheless, the number of firms considered in this study is rather reduced, which generates econometric estimations with few significant relationships. It should be highlighted that this survey measures impact perceptions and not the quantitative ICT effect. From the qualitative and quantitative analysis, it may be observed that many firms ascribe certain ICT benefits in the productive systems. Particularly, firms with higher ICT budget tend to associate production cost reductions to these technologies, although ICT employment is considered more an expense than an ICT effort to improve efficiency.
Unfortunately, restrictions on the firm sample and on the quantitative variables constitute a serious limitation in the correct assessment of ICT impacts on Chilean firms.
Finally, it should be mentioned that this is an exploratory research and therefore it is impossible to generalize its results. As an extension, we suggest to interview a greater number of firms, and to consider the urgent need for the survey form design to collect firm quantitative data such as sales, employment, investments, innovating behavior and exports. This would facilitate the analysis of the relationship between ICT and firm performance. From the econometric exercise, we may also conclude that, if the purpose is to find ICT effects on outcome variables, the use of qualitative surveys is not the best option, but it offers valuable data when it comes to characterizing how firms perceive or rationalize ICT. A study that seeks to determine ICT effects on outcome variables should estimate production functions or apply methodologies related to program evaluation analysis.
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