Technical efficiency and use of information and communication technology in Spanish firms

ARTICLE IN PRESS Telecommunications Policy 33 (2009) 348–359

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Technical efficiency and use of information and communication technology in Spanish firms Jose´ Ferna´ndez-Mene´ndez, Jose´ Ignacio Lo´pez-Sa´nchez, Antonio Rodrı´guez-Duarte , Francesco D. Sandulli ´n en Produccio ´n, Tecnologı´as de la Informacio ´n y las Comunicaciones (GIPTIC), Universidad Complutense de Madrid, Spain Grupo de Investigacio

a r t i c l e i n f o

Keywords: Information and communications technology Technical efficiency Data envelopment analysis Supply chain Spanish firms

abstract The literature concerned with the relationship between performance and information and communications technology (ICT) is usually focused on the ICT investments. This paper shows that it is the level of use of ICT within organisations, with preference as regards the expenses of ICT, which is responsible for the effect on performance. A general sample of 2255 Spanish companies has been used. Firms’ performance is measured as technical efficiency, which is determined by a data envelopment analysis (DEA), in which special attention is paid to the problem of the outliers. Finally, the analysis of the level of use of ICT is focused on a key area of the organisations, the supply chain, which affects the technical efficiency of the firms analysed. Results show that there is evidence of a positive effect of the use of ICT on technical efficiency. This effect is especially notable at intensive use levels in activities related to operations/manufacturing, purchasing or sales. & 2009 Elsevier Ltd. All rights reserved.

1. Introduction The naive idea that information and communications technologies (ICTs) directly, immediately and evidently contribute to the improvement of productivity and the efficiency of organisations came to an abrupt end in 1987 when Robert Solow pointed out the fact that massive investments being made in ICT by US firms coexisted with a stagnation of productivity at macroeconomic level. This led to a surge in the studies on the relationship between ICT and business performance, which continues today and has shown the contribution of ICT to the work of organisations. However, unclear, insignificant or negative results have been sufficiently numerous for the debate on dimension of real effect of ICT on productivity and efficiency, the mechanisms through which this impact occurs and the variables on which it depends to continue. Most studies on the relationship of ICT and productivity are based on one of the two theoretical frameworks: the Economic Theory of Production (e.g. Brynjolfsson & Hitt, 1996; Dewan & Min, 1997; Jalava & Pohjola, 2007; Lichtenberg, 1995; and, in the case of Spanish firms, Lo´pez-Sa´nchez, Minguela-Rata, Rodrı´guez-Duarte, & Sandulli, 2006) and the Resource-Based Theory (Bharadwaj, 2000; Powell & Dent-Micallef, 1997; Santhanan & Hartono, 2003). In accordance with the first of these approaches the ICT would be just a productive factor, a particular type of capital and/or work, whose consumption would be explicitly included in the production function of the firms. Once estimated, the coefficients of this production function would make it possible to determine whether the contribution of ICT to output (that is to say, whether the contribution of each monetary unit of ICT consumed in the production process) is greater than

 Corresponding author. Tel.:+34 913942461; fax: +34 913942371.

E-mail address: [email protected] (A. Rodrı´guez-Duarte). 0308-5961/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.telpol.2009.03.003

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the other productive factors or not. The studies based on the Resource-Based Theory have usually attributed the character of a commodity to ICT (Clemons, 1991), a resource available on the factors market and within the reach of anyone disposed to pay for it; thus, a possible direct relationship between ICT and competitive advantage would disappear immediately if all the firms were able to acquire the resource capable of providing them with an advantage with no difficulty. Consequently, this can be sustainably achieved only when ICTs are used in order to lever idiosyncratic capacities of the firm in a way that is difficult for the competitors to replicate. However, independently of the theoretical framework, the usual approach is to analyse the impact of ICT investment on performance. This paper has some features that differentiate it from previous papers that have dealt with the relationship between ICT and productivity. It is based on the Theory of the Diffusion of Technology, specifically of inter-firm diffusion, in order to show that it is the level of use of ICT within organisations, with preference as regards the expenses of ICT, which is responsible for the effect on performance. In the second place, a general sample of Spanish companies has been used, without giving priority to any type of firm with particular characteristics. As concerns the firms in the sample, their technical efficiency as regards the input–output process is determined by a data envelopment analysis (DEA), in which special attention is paid to the problem of the outliers. Finally, an analysis is made of the level of use of ICT within a key area of the organisations, the supply chain, which affects the technical efficiency of the firms analysed. To achieve this, the use of ICT by workers integrated into the supply chain, and its use in the three essential functions of the supply chain: operations/manufacturing, purchasing and logistic/sales, is taken into consideration. In both cases there is evidence of a positive effect of the use of ICT on technical efficiency. This effect is especially notable at intensive use levels in activities related to operations/manufacturing. As regards these types of activities, moderate or low levels of use of ICT do not show appreciable effects on efficiency. However, low levels of use in tasks related to purchasing or sales are associated with improvements in efficiency. It has also been observed how the extension of use of computers by employees in the supply chain also has an appreciable impact on the increased technical efficiency of firms. The rest of the paper is structured in the following way: in Section 2 the theoretical background is established, in Section 3 the hypotheses and methodology is discussed, Section 4 deals with the sources of data, in Section 5 the results are presented and in Section 6 some of the conclusions reached are commented on.

2. Theoretical background The objective of this paper is to determine how the use of ICT within a key area for the firms, the supply chain understood as the group of supply, production/operations and distribution functions affects the performance of firms. The proper management of the supply chain consists of the integration of its activities, encouraging synergies among its stages in order to achieve an optimisation of the chain understood as a whole, and not of each one of its parts independently (Handfield & Nichols, 1999; Narasimhan & Das, 2001; Vakharia, 2002). This integration will permit the improvement of global functioning of the chain (Cachon & Fisher, 2000; Carr & Pearson, 1999; Germain & Droge, 1998). Given the fact that it is a tool for massive processing of information, ICTs constitute the ideal tools for achieving this integration. This is especially true since the development of ICT based on the Internet has made it possible to overcome many of the inconveniences of previous information technologies (Frohlich, 2002; Johnson & Whang, 2002), such as the fact that these were generally costly technologies, based on proprietary standards, difficult to implement and with very complex interfaces (Johnston & Mak, 2000). This is supported by the growing standardisation of ICT. Until recently, the use of these technologies, for example the traditional Electronic Data Interchange (EDI) in transactions, required the creation of infrastructures specific to the relationship (Zaheer & Venkatraman, 1994). However, the coming of the Internet changed this situation, to a certain extent, as this involved a group of technologies and infrastructures very open and accessible, and which do not require substantial investment in specific assets as occurred with the private EDI networks (Rasheed & Geiger, 2001). Thus, the use of ICT reduces the costs of transactions as it facilitates coordination between firms and between different parts of the same firm. Therefore, there are substantial improvements in performance, in the reduction of costs, in delivery speed and in the administration of transactions as a consequence of the use of ICT in integration throughout the supply chain (Boyer & Olson, 2002; Frohlich, 2002). These improvements will not only be a consequence of the use of ICT as regards the management of relations with customers and suppliers, but also internal use, for production and operations as they facilitate coordination with other functions of the supply chain. Moreover, the fact that the incorporation of ICT to production gives rise to technologies such as the Advanced Manufacturing Technology (ATM), or Computer-Integrated Manufacturing (CIM) and other similar technologies makes it possible to obtain flexibility and volume at low cost, that is to say, they permit mass customization (Hart, 1995; Noori, 1989). Studies on ICT and performance usually analyse how the expenses in ICT contribute to productivity. With this approach, it is necessary to carry out complex estimations (see, for example, Berdnt, 1990 or Brynjolfsson & Hitt, 1996, 2003) on the real figure of the expenses, and also to calculate depreciation rates, price deflators, hedonic prices, etc. in order to take account of the (rapid) variations in quality and services of the successive generations of ICT assets. Moreover, the use of expenses in ICT as an explanatory variable of performance has been criticised by referring to the real or presumed nature of commodity (Carr, 2003; Clemons, 1991; Powell & Dent-Micallef, 1997) regarding these types of assets. This commodity-like nature of ICT would prevent the companies from differentiating from their competitors by investing in it. Also, it has been

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pointed out by authors like Strassman (1997) that ICT investments are made essentially by a type of competitive mimesis, with no real need, in most cases, to make them. Thus, it has been proposed (Igrabia, Pavri, & Huff, 1989; Miller, 1993; Smith & Mackeen, 1993; Wang, Gopal, & Zionts, 1997) that the level of use of ICT within firms is a more adequate measurement of computerisation than the mere investment in ICT, in order to explain the gains in performance. It is evident that there must be a strong correlation between investment in and use of ICT. The opposite would suppose accepting levels of economic irrationality by managers of firms, which are not very realistic; however, it is not difficult to find abundant anecdotal evidence of strong investment in underused ICT (for whatever reason, bad planning, deficient implementation, rapid obsolescence, y). This justifies that, at least to some extent, the impact on performance is more closely related to the use of ICT than to mere investment. Also, this is coherent with the findings of the literature on the diffusion of technology (Battisti & Stoneman, 2003), which show how the adoption of new technology does not imply its complete use within the firms, but that the intra-firm diffusion (the process of diffusion and increase of the level of use of technology inside the firms) is a slow, complex, irregular and very backward process as regards inter-firm diffusion. The effects of the adoption of technology will depend on its use within the firms and it will be this level of use that is the authentic driver of productivity. According to this literature, the companies decide their levels of use of new technology after an estimation of the associated risks and yields (Stoneman, 1981). The experience acquired with the initial use makes it possible to re-estimate more precisely the risks and costs linked to technology and, from here, to adjust the level of use initially chosen. This leads to the possibility that firms buy technology, not to use it, but to learn about it, to experiment with it so as to determine its optimum level of use, and so, to acquire what has been termed the ‘‘software’’ aspect of new technology (Geroski, 2000; Rogers, 1995), which is the information required to use it effectively. The information initially obtained regarding the use of the new technology has a much greater impact than the one obtained subsequently (Geroski, 2000). In a context such as that of ICT, where innovations come in quick succession, the firms are forced to make investments in order to learn the new technologies, and there is no certainty that these investments will be transformed into the real use of technology. This means that the expenses of ICT become detached, at least partially, from the use of the ICT. Added to this is the fact that the cost associated with the learning of the use of a technology depends on the absorptive capacity of the firm (Cohen & Levinthal, 1989) and this capacity is accumulative (Cohen & Levinthal, 1990), i.e., what permits the exploitation of new knowledge is the existence of previous knowledge. If a firm stops investing in its absorptive capacity in a field that is rapidly changing, it loses the capacity to assimilate and exploit new information in this field. Thus, it can be expected that a considerable part of the investments in ICT is assigned to the acquisition of knowledge and experience about the new technologies. The real and intensive use of technologies occurs only subsequently, after the learning process, and to the extent that it is justified by the expectations of profitability. This is the point of view adopted by Devaraj and Kohli (2003), who consider that the literature has misestimated the use of ICT, and put forward the proposition that it is the real use of ICT in the organisation that is associated with improvements in performance. Similarly Lehr and Lichtenberg (1999) find that the use given to computers seems to be the key factor to explain productivity, and Ataay (2006) finds a significant relation between productivity and level of use of information technologies. Furthermore, the attempt to clarify the impact of ICT in performance has led to empirical studies with firms belonging to very specific sectors, such as banking (Alpar & Kim, 1990; Wang et al., 1997), insurance (Francalanci & Galal, 1998) and health care (Devaraj & Kohli, 2003), or with very specific characteristics, such as big size or high technological reputation. This is the case of the firms included in the lists of IT leaders drawn up by ComputerWorld, InformationWeek and Fortune 500, widely used in empirical studies (Brynjolfsson & Hitt, 1996; Lichtenberg, 1995). In such situations, the link between ICT and productivity will be more easily detectable, but the results will inevitably be biased upwards, and there are doubts on the degree to which the relation of ICT and business performance can be extended to other types of companies and to other circumstances, not so specifically targeted. This paper endeavours to clarify this question by analysing the impact of the use of ICT on business performance with a large sample of the totality of Spanish companies. Thus, the companies analysed will be Spanish companies in their totality, not a specific sector, nor a group of companies that, due to their size, intensity of use of technology, experience, resources, or having been pre-selected in some way, can be expected to have a special aptitude for converting the use of ICT into improved performance. Another theoretical issue is related to the measurement of firm’s performance. A company can be considered as an open system that interacts with its environment and exchanges a number of inputs and outputs with this environment. The introduction of new technology that affects the performance of the firm must somehow suppose a rebalancing or change in the proportions in which these inputs and outputs are combined (for example, a substitution of work for capital, one type of capital for another, one output for another, etc.). A good way to determine whether an increase of performance really takes place is to evaluate all the inputs and outputs through a common unit of measurement. This aggregation of heterogeneous figures is not simple, but can be somehow achieved by taking the technical efficiency, calculated by a DEA, as a measurement of the performance of firms in their process of conversion of inputs into outputs. The DEA makes it possible to introduce a relationship of order in a space with n dimensions and, therefore, directly compare companies, characterized by sets of n variables (inputs and outputs), by converting the comparison into a simple ordering of real numbers.

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Another advantage of the DEA is that it is based on the estimation of a production frontier, which is in accord with Economic Theory, which considers the production process as an optimising activity. This does not happen when production functions estimated by statistical procedures are used, which show average relationships (Fa¨re, Grosskopf, & Lovell, 1994). Moreover, the DEA is a non-parametric method; therefore, it is not necessary to make assumptions on functional forms of the relationship of inputs and outputs. In addition, efficiency (understood as the distance of each firm from the production frontier), as calculated by the DEA, may be a more indicative figure than productivity (understood as the ratio value of the output/value of the input) of the real impact that the use of ICT can have on the productive process. This is due to the fact that, in certain circumstances (Quan, Hu, & Hart, 2004; Tatcher & Oliver, 2001), the adoption of a technology that increases efficiency (therefore, it makes it possible to generate greater output with the same consumption of resources) by a firm maximising profits may lead to a reduction in productivity (but not in profits, logically). 3. Hypotheses and methodology In accordance with the above, and following a growing stream of the literature in the context of the ICT research (Giokas & Pentzaropoulos, 2000; Pentzaropoulos & Giokas, 2002; Shao & Lin, 2002), this paper uses the DEA in order to obtain a measurement of performance of the firms included in the sample analysed. This measurement will be the technical efficiency in the process of conversion of inputs into outputs. The ultimate objective is to determine how the use of ICT in a key area for the functioning of organisations, the supply chain (understood as the group of functions involving provisioning, production/operations and distribution), affects the efficiency of firms. The use of ICT in the supply chain, and in the firm as a whole, can be considered in a dual dimension: its use by the workers linked to the supply chain, and its use in the activities, tasks and processes that make up this supply chain. Although both dimensions of use are related, they are not identical due to the differences in intensity of use of labour in different tasks; so, they will be considered separately. As regards the use of the ICT by the workers in the supply chain, one adequate indicator of the degree of computerisation might be the number of PCs per employee (Lehr & Lichtenberg, 1999). However, this is a variable that includes the investment in ICT and not so much their use; therefore, the percentage of workers in the supply chain who use computers to carry out their normal tasks was chosen as the indicator of the use of information technologies by these workers. A similar method is used in Black and Lynch (2001). Consequently, the following hypothesis is established: Hypothesis 1. The percentage of workers in the supply chain who use computers to carry out their usual tasks positively influences the technical efficiency of the firms. As concerns the use of ICT in activities that are carried out in the supply chain, the activities involved in production/ operations have been taken into consideration as these constitute the nucleus of tasks of a firm and its reason for existing. In addition, the supply and distribution activities have been also incorporated, because a substantial part of advantages for firms that have been described as derived from the use of ICT are related to them (Feeny, 2001; Frohlich, 2002; Keeny, 1999; Lin, Huang, & Lin, 2002; Power & Sohal, 2002; Shah, Goldstein, & Ward, 2002). Thus the following hypotheses have been formulated: Hypothesis 2. The computerisation of activities involved in production/operations positively influences the technical efficiency of the firms. Hypothesis 3. The computerisation of relations with suppliers (purchases) positively influences the technical efficiency of the firms. Hypothesis 4. The computerisation of relations with customers (sales) positively influences the technical efficiency of the firms. In order to test these hypotheses, a very general sample of Spanish firms is obtained. Their technical efficiency is evaluated by means of a DEA, and then, in a second stage, the efficiency is regressed against a set of explanatory variables that reflect the level of ICT usage within the supply chain in the organisations of the sample. The DEA carries out an analysis of the process of transformation of inputs into outputs. From the point of view of the DEA, each firm, or decision-making unit (DMU), is characterised by the inputs consumed while carrying out the tasks that correspond to it, and by the outputs generated as a consequence of these tasks. The DEA makes it possible to determine whether a DMU is more or less efficient than another and to assign numerical values to the respective efficiencies. This is done by estimating an efficient frontier and comparing the efficiency of each DMU with the efficiency of the DMUs located on the frontier. The efficient frontier is obtained from the frontier of the set of feasible DMUs, which will be a set of DMUs that can be reasonably expected to exist given its similarity with the DMUs that really exist. This makes it necessary to make certain suppositions regarding the nature of the feasible set. If it is admitted that a DMU can modify its size, or the scale at which it operates, by modifying all its inputs and outputs in the same proportion, the constant returns-to-scale models are obtained, normally termed CCR (Charnes, Cooper, &

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Table 1 Variables used in the calculation of technical efficiency (first stage of the analysis). OUTPUT

INPUTS

Operative income

Fixed assets Cost of sales Number of employees

Amortisation Other operative costs Labour costs

Rhodes, 1978). If the previous case is not admissible, resort is made to the variable returns-to-scale models, termed BCC (Banker, Charnes, & Cooper, 1984). The efficiency of a DMU is obtained as its distance from the frontier. Different ways of measuring this distance give rise to different types of efficiency. By convention, a value of 1 is assigned to the efficiency of points located on the efficient frontier (this will be the maximum value of efficiency) and for the rest of the DMUs, the efficiency will adopt a value between 0 and 1 (the greater the proximity to the frontier, the closer to 1 the efficiency). In a DEA analysis all the inputs and outputs that are considered to be relevant to the study of performance of firms, regardless of their number or disparity, can be incorporated into the study. This paper uses a multi-sector sample of Spanish companies. Given their heterogeneity, it will be necessary to use figures of a general nature, available for any type of company, as inputs and outputs, instead of other more specific figures, which might be of interest in the event of analysing a group of homogeneous companies (for example, when analysing fast food restaurants, it might be advisable to incorporate the time it takes to serve a meal as an output; in a heterogeneous sample of companies this does not make sense). Measurements indicating extraordinary or exclusively financial results have been dispensed with, in order to characterise the efficiency of each company as it carries out its ordinary productive tasks. Thus, the operative earnings of the firms analysed, which logically the firms want to be as high as possible, have been taken as output, while a set of aggregated, general figures, which indicate the use of resources by the companies, have been taken as inputs. The number of employees and the annual cost of labour have been taken as indicators of use of the work factor. Although the figures are related, they are not identical, and, on generating its output, a firm will be more efficient than another both if it uses a smaller staff and if its staff supposes less cost. The fixed assets and the annual consumption of these fixed assets measured through amortisation (which are figures essentially linked to the long term) and the cost of sales and other operative expenses (linked to the short term) have been taken as components indicating the use of capital factor. The variables used in the determination of efficiency are shown in Table 1. The previous inputs and outputs are used to calculate the technical efficiency of each firm analysed through an inputoriented DEA model with variable returns to scale. With the notation of Cooper, Seiford, and Tone (2007) it will be a BCC-I model. A performance model with variable returns to scale was chosen due to the substantial heterogeneity as regards the size of the companies included in the sample; otherwise, we would be implicitly assuming that any firm could modify the scale of its operations with no restrictions in order to adopt the one with the maximum economies of scale. This idea is clearly not realistic. Subsequently, in a second stage of the analysis, the technical efficiencies of the firms were introduced as the dependent variable in a regression in which the explanatory variables were those used to measure the use of ICT in the supply chain. The modelling of the efficiency calculated through the DEA in order to analyse its causes or explanatory factors is usually known as the second-stage DEA (Hoff, 2007). There is a certain lack of consensus as regards the most suitable way to carry out this second stage of analysis due to the fact that the range of values of technical efficiency (is limited to between 0 and 1) means that none of the solutions normally proposed in the literature is fully satisfactory. An OLS regression would be valid as an approximation, but its results would have to be considered with caution due to the fact that the limitation of the dependent variable does not make it possible to consider a linear model, such as the one supposed by the ordinary regression, as fully valid. One alternative normally used (for example in Chilingerian, 1995; Ruggiero & Vitaliano, 1999), and which has been chosen here, is to use a Tobit regression model with a censored dependent variable, where the estimation of the model is carried out, not by least squares, but by maximum likelihood. Although, in practice, the differences between the results obtained by both models are small (Hoff, 2007), the Tobit model must be considered to be more rigorous and, therefore, preferable. This approach is not new in this context, and has been used before (Shao & Lin, 2002), but in our work the main difference is that we are using the level of use of ICT, and not the expenses, as the independent variable.

4. The data The population analysed for the study is made up of Spanish companies registered in the Mercantile Register Office and with at least one worker, which excludes natural persons and the companies that declare that they have no employees.

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Two sources of information have been used. On the one hand, the information regarding the use of ICT by the company was obtained by telephone interviews with the questions analysed in this paper made to the person in charge of the Information Systems area in the company or, if there was no such person, the manager or owner of the company. On the other hand, the financial-accounting information used to determine the technical efficiency of each of the firms analysed was extracted from the Sistema de Ana´lisis de Balances Ibe´ricos [System for the Analysis of Iberian Balances] (SABI) database. This is a database distributed by the Bureau van Dijk Electronic Publishing and contains accounting information obtained from the annual accounts of more than 900 000 Spanish and Portuguese companies. The telephone survey was carried out in the year 2004. A first random selection of 3363 firms was made, oriented by quotas depending on the activity sector, size of the total staff and regions. Due to the asymmetric distribution of the Spanish firms in terms of number of employees, with a considerably greater proportion of small and medium enterprises, the sample distribution was semi-proportional by size strata. This was due to the fact that, if a strictly proportional criterion by size strata had been followed, it would have been difficult to ensure a minimally self-sufficient analytical base for the strata of larger sized companies. As regards the other variables, the sample distribution has maintained strict proportionality. Through this survey, information on the percentage of employees who use computers in their normal tasks was obtained as well as on the degrees of computerisation of production, purchases and sales.1 One problem that affects the DEA is the possible presence of outliers. This problem is potentially more dangerous in a method that is based on the establishment of a frontier, which, in principle, will be determined by the extreme cases, than a method that is based on the determination of an average, as occurs with usual statistical techniques. Thus, a single, atypical observation may suppose an enormous displacement of the estimated production frontier, completely altering the results obtained. This potential problem is not considered in previous works. In order to deal with this problem, it was decided to use a technique for the detection of outliers, previous to the application of the DEA, as this makes it possible to identify cases that are notably atypical as regards the variables used in the calculation of efficiency, and subsequently eliminate these observations from the sample. The method used is the minimum volume ellipsoid (Rousseeuw, 1984; Rousseeuw & Leroy, 1987; Venables & Ripley, 2002). This method is based on the classical idea of determining the atypical condition of a multi-variant observation in accordance with its Mahalanobis distance from the centre of distribution. Although this is especially oriented towards multi-variant analysis, it can be applied to the detection of atypical observations from the point of view of the DEA due to the fact that, on using a BCC model, it is admitted that the set of feasible DMUs is, essentially, the convex envelope of the set of DMUs observed, an envelope that is a polyhedron of the n-dimensional space. The atypical observations will be those that seriously distort the form or volume of the polyhedron and will be the same as those that are very distant, according to the Mahalanobis distance, from the centre of the ‘‘typical’’ observations. Using this method, 33 very atypical firms are eliminated. This is a very small subset of firms that distort the calculus of the frontier, and therefore of the efficiency, leading to biased results. These firms are, roughly speaking, some of the smallest and, particularly, of the biggest in the sample (for instance, a firm with 32 000 employees, the biggest in the sample, which can be considered as ‘‘enormous’’, and absolutely non-representative in the Spanish economy, is discarded). By eliminating these firms, more representative results are obtained. The technical efficiency is taken as a dependent variable that, in the second stage, will be explained by a group of variables indicating the use of ICT by the firms analysed, which are the percentage of the employees of the supply chain who use computers in their normal tasks, the degree of computerisation of purchases, sales and operations. These variables were obtained from the telephone interviews. The elimination of atypical data and nonrespondent firms (observations with missing values, incoherent or clearly erroneous in the variables obtained in the telephone survey) means that the final sample used to carry out the regression is made up of 2255 companies. However, this procedure could eventually cause a bias in the sample, if the firms eliminated follow a significant pattern. Probit analysis (available from the authors on request) of the characteristics of nonrespondents and atypical data indicates that there was no significant pattern in the likelihood of being included in the final sample, which means that the 2255 data can be still considered a random sample. Only largest firms, and firms of the services sector, have slightly more probability of not to be included in the sample, as a result of the procedure of elimination of the outliers described above. Table 2 includes information on the distribution of the sample, with the number of firms in each stratum (sector and number of employees) and the sampling error.2 Since sampling errors are below 5% in almost all the strata (except in the stratum of firms with 200 employees or more, where it is slightly greater, again due to the procedure of elimination of the outliers), the sample can be considered as representative of the whole population of the Spanish firms, at least in terms of sector and number of employees.

1

A description of the use of ICT by Spanish companies that use data from this same telephone survey is included in Lo´pez-Sa´nchez (2004). Sampling error or estimation error is the error caused by observing a sample instead of the whole population. The likely size of the sampling error can generally be controlled by taking a large enough random sample from the population. If the observations are collected from a random sample, statistical theory provides probabilistic estimates of the likely size of the sampling error for a particular statistic or estimator. These are often expressed in terms of its standard error. In our case, the sampling error is estimated by the standard error of the proportion, assuming P ¼ Q. 2

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Table 2 Distribution of the sample. Number of firms

Sampling errora

Sector Manufacturing Construction Services

661 371 1223

1.94 2.60 1.43

Total

2255

1.05

Size (number of employees) Less than 10 10–49 50–199 200 or more

1226 758 191 80

1.43 1.81 3.61 5.57

Total

2255

1.05

a

Standard error of the proportion, P ¼ Q, in %.

Table 3 Variables used in the second-stage regressions. Independent and control variables

 Degree of computerisation of the operations (DIT_O): categorical variable with 5 levels, which represent a growing level of computerisation, from ‘‘nothing’’ to ‘‘totally’’

 Degree of computerisation of the purchases (DIT_P): categorical variable with 5 levels, which represent a growing level of computerisation, from ‘‘nothing’’ to ‘‘totally’’

 Degree of computerisation of sales (DIT_S): categorical variable with 5 levels, which represent a growing level of computerisation, from ‘‘nothing’’ to ‘‘totally’’

 Percentage of employees who use computers in their normal tasks (PEUT): continuous variable with values between 0 and 100

 Sector (SECTOR): control variable; categorical variable: industry at 2-digit NIC level  Size of firm (NE): control variable; continuous variable: the logarithm of the number of employees Dependent variable

 Technical efficiency: continuous variable with values between 0 and 1

5. Results Once calculated, the technical efficiency is used as a dependent variable in a Tobit regression in which this efficiency is regressed against a set of explanatory variables. These variables account for the ICT usage in the supply chain. According to the previous hypotheses, the explanatory variables are the percentage of employees in the supply chain who use computers in their usual tasks (PEUT, continuous variable that takes on values between 0 and 100), and the degree of use of the ICT in tasks involving operations, purchases and sales (variables DIT_O, DIT_P and DIT_S, respectively, which are categorical variables with 5 levels, from 0, no computerisation, to 4, complete computerisation). These four variables were obtained through the telephone survey. A number of control variables have also been added: a categorical one (SECTOR), which contains the industry in which the firm operates, (codified at 2-digit NIC level), and a continuous one (NE), the size of the firm measured through their number of employees. The variable SECTOR is included in order to deal with possible industry-level factors that could contribute to the efficiency, as competitive pressure or regulatory or legal dispositions for which there is no detailed information available in the databases used in this work. The variable NE tries to absorb the effects of firm size on efficiency. Table 3 contains the variables used in the second-stage regression. Table 4 shows the non-parametric correlations (Kendall’s tau and Spearman’s rho) between the variables analysed (DIT_O, DIT_P, DIT_S, PEUT). Although a priori a high level of correlation would be expected, as these are all different measurements of the degree of computerisation, the correlations observed show a range of values in the 0.3–0.5 interval; so these can be considered to be moderate. Nevertheless, as the absence of high correlations between pairs of variables is not always an indicator of the absence of collinearity in the group of variables (Rawlings, Pantula, & Dickey, 1998), eigenvalues of the correlations matrix have been calculated in order to obtain the condition number (the square root of the proportion from the greatest to the least eigenvalue of the correlation matrix), which is normally used as a measurement of the degree of global correlation between the variables and, therefore, of the existence of possible collinearity problems (Fox, 1997). The value of the condition number is 1.813, which is sufficiently small for important problems of collinearity to be discarded (Fox, 1997 indicates that these problems may be relevant for values of the condition number above 10).

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Table 4 Non-parametric correlations between the independent variables. Kendall’s tau/Spearman’s rho DIT_O DIT_P DIT_S PEUT

DIT_O

DIT_P

DIT_S

PEUT

1.000 – – –

0.322/0.369

0.302/0.348 0.447/0.504

0.145/0.182 0.099/0.125 0.173/0.220 1.000

1.000 – –

1.000 –

 po 0.01.

Table 5 Regression results.

Intercept DIT_O_1 DIT_O_2 DIT_O_3 DIT_O_4 DIT_P_1 DIT_P_2 DIT_P_3 DIT_P_4 DIT_S_1 DIT_S_2 DIT_S_3 DIT_S_4 PEUT log (NE) 2-digits sector dummies Scale Log likelihood Adj. R2

Tobita

OLSb

Stochastic frontierc

0.57119002  (0.02530155) 0.00249003 (0.01490976) 0.01019758 (0.01343447) 0.03499084  (0.01286282) 0.05913161  (0.01030881) 0.02002823 (0.01456357) 0.02711412  (0.01416649) 0.03900467  (0.01388617) 0.03221079  (0.01211363) 0.01312771 (0.01452029) 0.02681798  (0.01513629) 0.02437836  (0.01440682) 0.03785661  (0.01386237) 0.00050276  (0.00011526) 0.00726786  (0.00389663) Significant 0.174 522.6

0.55977034  (0.02315182) 0.00371998 (0.01424305) 0.01121088 (0.01281766) 0.03554552  (0.01227134) 0.05707201  (0.00967745) 0.01983501 (0.01380834) 0.02570341  (0.01334019) 0.03671595  (0.01297211) 0.03153149  (0.01141701) 0.01279181 (0.01383333) 0.02568228  (0.01434537) 0.02382357  (0.01368035) 0.03733652  (0.01312259) 0.00046339  (0.00010835) 0.00489771 (0.00350262) Significant

1.5400  (0.1759) 0.0033232 (0.031700) 0.0048121  (0.016855) 0.030745  (0.015337) 0.082354  (0.011191) 0.025111  (0.014598) 0.027469 (0.021194) 0.036654 (0.025397) 0.063645 (0.036603) 0.008492 (0.020993) 0.091409  (0.016858) 0.10174  (0.021615) 0.14584  (0.014304) 0.000031203 (0.0001268) 0.085239  (0.0096722) Significant 585.6556

0.1246

Standard errors in parentheses.  po0.01.  po0.05.  po0.1. a QML (Huber/White) standard errors and covariance. b White heteroskedasticity standard errors and covariance. c Estimated using Frontier 4.1 (Coelli, 1996).

The results obtained in the regression are shown in Table 5. In order to avoid biases in the estimation of the standard errors due to the possible heteroskedasticity present in the data motivated for the heterogeneity in terms of sector and size of the firms in the sample, Table 5 reports standard errors robust to heteroskedasticity (Hubert–White method). As a robustness check, two additional regressions have been added to the table. The first one is similar to the Tobit regression, but performed by OLS, so leaving behind the censoring of the dependent variable. In the second one, the efficiency has been evaluated with a parametric model, specifically a Stochastic Frontier one, in which the second-stage explanatory variables have been included in the first stage. This alleviates some problems of the two-stage methodology

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when applied to a Stochastic Frontier Model (Battese & Coelli, 1995). The calculus has been performed with the statistical software R (R Development Core Team, 2008) and the R packages ‘‘DEA’’ (Dı´az-Martinez & Ferna´ndez-Mene´ndez, 2008) and ‘‘frontier’’ (Coelli & Henningsen, 2008). In the light of the results of the Tobit regression, the four variables relevant for the hypothesis formulated (DIT_O, DIT_P, DIT_S and PEUT) are globally significant and the sign of the coefficients is what was expected in accordance with the hypotheses; the technical efficiency increases with the degree of use of information technologies in the supply chain. The PEUT variable (percentage of employees in the supply chain who use computers in their usual tasks) has a positive, very significant coefficient (with a p-value that is virtually zero); so it positively influences technical efficiency. As this variable ranges from 0 to 100, the value of the coefficient in the regression indicates that computer usage by the workers in the supply chain could increase the efficiency by about 5% points. This is 7.4% of the average efficiency in the sample (0.68). This confirms Hypothesis 1. As concerns the other variables analysed, it can be seen how medium and high (but not low) levels of computerisation of the production/operations increase significantly the technical efficiency. With the computerisation of purchases and sales the situation is similar (increasing level of use of ICT in these tasks supposes an increase in the efficiency), instead of the fact that the influence of degree of computerisation is also significant at low levels. These results confirm Hypotheses 2–4. Furthermore, the similarity of the coefficients suggests that high computerisations of purchases and of sales have similar effects on efficiency. This can be evaluated by means of a Wald test with the null hypothesis of equality of the coefficients of DIT_P_4 (high level of computerisation of purchases) and DIT_S_4 (high level of computerisation of sales). The high p-value (0.805) of the test clearly shows that there are no reasons to reject the null hypothesis, which confirms the appreciation that the effects on efficiency of high levels of computerisation of purchases and of sales are approximately the same. Additionally, two Wald tests can be carried out (as unilateral contrasts) in order to evaluate whether the effect on efficiency of high computerisation of operations is greater than the effect of computerisation of purchases and sales, as the results seem to indicate. The p-values for the null hypothesis on the equality of the coefficient of DIT_O_4 (high level of computerisation of operations) with the coefficients of DIT_P_4 and DIT_S_4 are, respectively, 0.085 and 0.077, which makes it possible to reject (at a 90% confidence level) the null hypothesis in favour of the alternative hypothesis that the coefficient of DIT_O_4 is greater than the other two coefficients. In any case, it seems evident that the effect of high computerisation of production is greater than that of computerisation of purchases and sales, but the variability of the results is sufficiently high for this superiority of the effect not to be assumed unconditionally. As regards the impact on efficiency of medium levels of computerisation, the evidence seems to indicate just the opposite as that for high levels—a greater effect of computerisation of purchases and sales than that of production/ operations. In fact, the coefficients of DIT_O_1 and DIT_O_2 have values that, in practice, are insignificant (not significantly different from zero), which would indicate a virtually null impact on the efficiency of low or moderate levels of computerisation. This result is somewhat surprising. What follows from the data is that it would be possible to improve the efficiency of firms through the computerisation of operations only if the level of computerisation is high. Doing so, the efficiency would improve significantly more than by computerising the functions of purchases and sales. However, moderate computerisation of operations would have an irrelevant effect on efficiency (which could be interpreted in the sense of improvements in some aspects being compensated with a worsening of other aspects, which leads to a null improvement). Thus, there seems to be a ‘‘threshold’’ effect for the computerisation of operations; efficiency improves only when a determined threshold is passed, but once this is passed, the improvement is considerable. So, while the coefficients of DIT_P_2 (medium computerisation of purchases) and DIT_S_2 (medium computerisation of sales) indicate a ‘‘moderate’’ improvement in the efficiency and they are significant to 10% (p-values of 0.055 and 0.076, respectively), the coefficient of DIT_O_2 is not significantly different from zero. Probably this difficulty to achieve an improvement in the efficiency through a low or moderate level of computerisation of operations is a consequence of the fact that this computerisation is supported by technologies (computers, robotics, automatics, etc.) that have been available for several decades before the technologies required to carry out the computerisation of communications (and, therefore, of the purchase and sales functions) efficiently. Thus, the companies have had more time to use ICT in production. This has lead to a situation in which possibilities to increase the efficiency and differentiate from other firms through moderate computerisation of operations are used up. However, there is still a margin to increase the efficiency by having resort to intense computerisation as the technologies required for this, such as the Advanced Manufacturing Technology (ATM), or the Computer-Integrated Manufacturing and other similar technologies, are difficult to implement properly (Jaikumar, 1987; Upton, 1995), and involve very high fixed costs (Smunt & Meredith, 1999), which has probably slowed up their incorporation into companies, with the consequence that there are still substantial possibilities for improvements of efficiency through their use. However, this would require a strong commitment to computerise the operations, and so, only high levels of such computerisation would lead to a significant increase of the efficiency. On the contrary, the computerisation of purchase and sale functions has required the appearance of the Internet and the technologies associated with it, as the previous technologies, such as the EDI, came up against a number of problems, which hindered its adoption (Johnston & Mak, 2000). In general, these are costly technologies based on proprietary standards, difficult to implement, with complex management interfaces, and important trade-offs as regards cost, wealth and scope. The Internet, much cheaper and more flexible than the traditional EDI, has made it possible to overcome many

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of these inconveniencies (Frohlich, 2002; Johnson & Whang, 2002). Also, as this is a more recent technology, it is still far from its full adoption; so it (still) offers many opportunities to differentiate from competitors, and get significant improvements in efficiency, with only a moderate level of usage. As regards the control variables, the results of the Tobit regression indicate that both are significant; there is a slight decrease in efficiency as firm size (measured by the number of employees) increases, and, also, the industry in which a firm operates significantly affects its efficiency. The two other regressions in Table 5 (OLS and Stochastic Frontier) are included as a robustness check. The results of the OLS regression are virtually identical to that of the Tobit one. On the other hand, the Stochastic Frontier is rather similar, but not identical, to the Tobit model. However, the differences between the two models fall within the expected range. Contrary to the DEA, the Stochastic Frontier is a parametric model in which the shape of the frontier is imposed a priori. Also, the efficiency in a Stochastic Frontier model is estimated, not calculated as in the DEA; so there is a certain uncertainty margin in its value. As a consequence, one can conclude that the Tobit regression is a fairly robust model with regard to changes in model specification. Furthermore, the results of the Tobit model are broadly comparable to that of previous Spanish studies, with similar ˜ ez objectives, but different methodologies and more restrictive samples, as is the case of the works of Hernando and Nu´n (2004) and Quiro´s and Rodriguez (2008). Globally, and as a conclusion, it can be seen that ICT use in supply chain can improve the efficiency of firms by about 3–5% points, depending on the stage of the supply chain in which ICTs are used. Perhaps this is not a big figure in relative terms, but not so small taking into account that Litan and Rivlin (2001), who made an approximate estimation of the savings in costs that can be produced for the total US economy due to the use of ICT for the interconnection of computers, find that these savings are approximately 1–2%. Furthermore, the estimated improvements of efficiency include all the possible expenses in which firms must incur to reach them; so they are net improvements. A potential problem in this paper derives from the use of subjective measurements of use of ICT, measurements that have been criticised (Straub, Limayem, & Karahanna-Evaristo, 1995). However, considering the heterogeneity of the sample and the heterogeneity of possible uses of ICT, it is virtually impossible to find objective measurements of use that cover all this heterogeneity. Moreover, one of the main problems of the use of subjective measurements is the common variance bias that would arise as the origin of independent and dependent variables is in the same source (Malhotra, Kim, & Patil, 2006). However, this kind of bias does not exist in this paper as both types of variables have different origins (SABI and a telephone survey).

6. Conclusions The study of the relationship between ICT and productivity (or another performance measurement) has tended to be focused on the ICT expenses as an explanatory variable of productivity. However, it is reasonable to expect that it is the use of ICT, and not simply the expenses, which is the real driver of productivity. Although, undoubtedly, both measurements (use and expense) will be correlated, and the expenses can be considered to be a reasonable proxy of use, it is the use that is actually responsible for the impact of ICT on the performance of firms. If the ICT affects productivity, it will have to be the use of ICT that really has an effect. Thus, it will be advisable to take the use of ICT as an explanatory variable of performance with preference over mere expenses or investment involved in ICT. This paper has tried to determine the degree to which variations of efficiency of firms can be explained considering the use of ICT by the employees for tasks related to a key component of organisations—the supply chain. The results obtained clearly show how, as the use of computers by workers in the supply chain increases, the efficiency of firms also increases. Moreover, as the computerisation of activities in the supply chain increases, technical efficiency also increases. However, in this last case, the increase shows different characteristics depending on the type of task computerised. Thus, while in the tasks related to purchases and sales, the improvement in efficiency is more or less proportional to the increase in information and is similar for both types of tasks, the computerisation of activities related to production/operations demonstrates a threshold effect—low or medium level of computerisation do not correspond to appreciable improvements in efficiency (nor a worsening); however, high levels entail an increase in efficiency above that achieved with the computerisation of purchases and sales. This suggests that the computerisation of operations constitutes a channel for the improvement of efficiency already widely explored and used by some of the companies; therefore, no margin would be left for subsequent improvements, except by resorting to higher levels of computerisation, which, in general, is difficult to achieve. The same would not occur with the computerisation of purchases and sales, which are presented (still?) as valid tools for the improvement of efficiency with no need to resort to very high levels of computerisation. In addition, in the studies on ICT and productivity it is normal to use relatively restricted samples of firms, or those with particular characteristics that convert these into specially favourable for the detection of a possible link between ICT and performance. On the other hand, this work has used a multi-sector sample of the totality of Spanish economy. Consequently, it is expected that the results obtained have a high level of generality and applicability. Although the improvements in efficiency obtained as a consequence of the use of ICT in the supply chain may be relatively modest (a few percentage points), the values obtained fall within the expected a priori and, in any case, they are not restricted to a

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particular type of company nor are they conditioned by compliance with any type of requisite other than the use of ICT in the supply chain. Achieving a determined level of use may be more or less expensive, complex and difficult, and these costs or difficulties are not included in this work; however, it can be stated that, regardless of these, profits will be greater and, globally, technical efficiency will increase. These results are especially relevant in an economy like the Spanish, given the poor levels of productivity and competitiveness of the Spanish firms—following the World Economic Forum (2008), Spain ranks in position 96 over 134 countries in the labour market efficiency, position 39 in the degree of innovation, position 84 in productivity and position 57 in firm-level technology absorption. These data indicate the necessity to improve competitiveness in the Spanish economy, and a way to achieve this goal can be improving firm efficiency via high levels of use of ICT. 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