ICT solutions and labor productivity: evidence from firmlevel data Juha-Miikka Nurmilaakso Frends Technology Oy Fax number: +358-9-41300301 E-mail address:
[email protected] Postal address: Taivaltie 5, 01610 Vantaa, Finland
Abstract This paper analyses the impacts of information and communication technology (ICT) solutions on labor productivity, i.e. revenue per employee. Based on cross-sectional data on 1955 European firms in 2005 and a linear regression model derived from the microeconomic theory of production, the impacts of six common ICT solutions in electronic commerce (e-commerce) cannot be ignored. According to the linear regression analysis, Internet access, standardized data exchange with the trading partners, enterprise resource planning (ERP) system, and customer relationship management (CRM) system contribute significant increases in labor productivity, whereas a website on the Internet, or supply chain management (SCM) system do not result in a significant increase. Especially, Internet access has a significant effect on labor productivity, and the website on the Internet has an insignificant effect. Keywords: Electronic commerce (e-commerce), information and communication technology (ICT) solutions, labor productivity
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1. Introduction Information and communication technologies (ICT) have changed the way how firms do business. First, this development was about Electronic Data Interchange (EDI) that is standardized data exchange without human intervention. Then, the development took a turn toward electronic commerce (e-commerce) which is the selling of products to the customers and their buying from the suppliers using ICTs [18]. Now, business-tobusiness (B2B) e-commerce that takes place between firms has been transformed to electronic business (e-business) that covers all kinds of collaborations with the trading partners using ICTs [18]. The productivity paradox was originally stated by Solow [32] who claimed that “you can see the computer age everywhere but in the productivity statistics”. A number of studies have documented the significant impacts of IT on productivity [7] and ICT solutions on financial performance [9]. For example, computer capital and IS labor have contributed to productivity in large US firms during the period 1987-1991 [3]. There are still arguments that the firms are overinvesting in IT [6]. Since improvements in labor productivity, i.e. revenue per employee, are a potential reason to invest in ICT solutions, this paper studies the effects of six common ICT solutions in e-commerce. First, the paper proposes six hypotheses based on the findings presented in the literature. Next, this paper summarizes data, derives a linear regression model from the microeconomic theory of production [13], and tests the hypotheses by estimating the model. Finally, the paper discusses limitations and further research, and presents the implications.
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2. Hypotheses Internet access, website on the Internet, and standardized data exchange with the trading partners as well as the enterprise resource planning (ERP) system, customer relationship management (CRM) system, and supply chain management (SCM) system can be regarded as common ICT solutions in e-commerce [12, 14, 18, 26]. Figure 1 illustrates how the firm can use these ICT solutions to process and to communicate information with its customers and suppliers. Focusing on common ICT solutions in e-commerce, six hypotheses can be proposed. The Internet gives globally available immediate access independent of place and time and lowers the information processing and communication costs [20, 24]. It has been estimated that the Internet has resulted in cost savings of 163.5 billion USD and revenue increases of 522.9 billion USD in the US, UK, German, and French firms during the period 1988-2001 [34]. Sánchez et al. [29] present that Internet usage has contributed positively to labor productivity in Spain. H1. Internet access has a positive effect on labor productivity. According to Teo and Pian [33], firms view differentiation and growth as more salient benefits of the website than cost savings. The website is also linked to higher financial performance [28, 36]. H2. Website on the Internet has a positive effect on labor productivity. Although EDI does not necessarily increase revenue [22, 35], it can reduce costs and improve financial performance [19, 23]. Standardized data exchange improves information accuracy and enables faster communication with the trading partners [2, 30].
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H3. Standardized data exchange with the trading partners has a positive effect on labor productivity. There is evidence that ERP systems have improved labor productivity and financial performance [15, 16, 17, 27]. The ERP system improves the control of information, material, and financial flows [25]. H4. The ERP system has a positive effect on labor productivity. According to some studies, CRM systems do not improve financial performance [15]. Other studies argue that the impacts of the CRM system are related to sales performance improvements by saving time and improving communication with customers [4, 11]. H5. The CRM system has a positive effect on labor productivity. SCM systems are linked to improvements in financial performance [8, 15]. The SCM system reduces lead-time and inventories [5]. H6. The SCM system has a positive effect on labor productivity.
3. Results 3.1. Data The data used in this paper come from the e-Business Survey [10] that was carried out in January and February 2005 by e-Business W@tch. This survey focuses on the use of ICTs but it also provides some basic data on the firms. The survey consists of 5218 observations from ten industries in seven countries. The response rate was 14.9%. The dependent variable LP is the firm’s annual revenue in million EUR divided by the number of employees in the firm. In the Czech Republic, Poland, and UK, non-EUR revenues were converted into EUR using the exchange rates at the end of the year 2004:
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1 EUR = 30.464 CZK = 4.085 PLN = 0.705 GBP. The independent variables Internet, Website, Data, ERP, CRM, and SCM are binary. A firm may have an access to the Internet (Internet = 1), a website on the Internet (Website = 1), or it may exchange standardized data, e.g. orders and invoices, with its trading partners without human intervention (Data = 1). The firm may also have an ERP system (ERP = 1), a CRM system (CRM = 1), or a SCM system (SCM = 1). The control variable Human is the share of the employees with a college or university degree in the firm. For example, Mankiw et al. [21] have used the percentage of the working-age population in secondary school to measure human capital. The control variables Industryi and Countryj are binary. The firm operates in the industry i (Industryi = 1) and in the country j (Countryj = 1). Observations with missing values were removed by listwise deletion. This left a sample of 1955 observations. Table 1 reports descriptive statistics and Table 2 Pearson correlations between the variables. Tables 3 and 4 present the distribution of observations according to the industry and country. 3.2. Model In the model of ICT solutions and labor productivity, this paper follows the microeconomic theory of production. Each firm is represented by a production function that relates the output y that the firm produces to the inputs x1,…, xK that the firm uses or consumes. The Cobb-Douglas production function that is perhaps the most common production function is K
y = eα ∏ x kα k ,
(1)
k =1
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where the coefficient α is a constant and αk is an output elasticity of the input xk. The coefficient αk indicates that an increase in the input xk by 1% changes the output y by
αk% when all other inputs are held invariable. For example, the Cobb-Douglas production function has been used for studying the contribution of labor, physical capital, and human capital to gross domestic product [21]. The Cobb-Douglas production function has also been employed to measure the contribution of computer capital, non-computer capital, IS labor, and non-IS labor and other expenses to sales [3]. As a basis of microeconomics, the chosen ICT solutions, i.e. Internet access xI, website on the Internet xW, standardized data exchange with the trading partners xD, ERP system xE, CRM system xC, and SCM system xS, labor xL, human capital xH such as training and education of employees, physical capital xP such as equipment and plant, and other production factors xO such as raw materials and intermediate goods are related to labor productivity y / xL. The model is
y / x L = px αL L −1 x αHH x αP P xoαO x αI I xWαW x αDD x αE E xCα C x αS S ,
(2)
where p is an output price, y / xL = LP, xH = exp(Human), xI = exp(Internet), xW = exp(Website), xD = exp(Data), xE = exp(ERP), xC = exp(CRM), and xS = exp(SCM). The control variables Countryj are a proxy for the output price p. The control variables Industryi approximate the labor-intensity which measures the ratio of labor xL to physical capital xP and other production factors xO. Since the Cobb-Douglas production function is non-linear, the model (2) must be linearized by taking natural logarithms. The linear regression model is ln (LP ) = α + α I Internet + α W Website + α D Data + α E ERP + α C CRM + α S SCM 9
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+ α H Human + ∑ α i' Industry i + ∑ α "j Country j + ε i =1
j =1
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,
(3)
where α is a constant and ε is an error term. The hypotheses can be tested by analyzing the coefficients αI, αW, αD, αE, αC, and αS. If the coefficient is statistically significant and positive it supports the hypothesis that the related ICT solution has a positive effect on labor productivity. 3.3. Analysis The linear regression analysis is employed to study the effects of ICT solutions on labor productivity. Before this analysis, the linear regression model (3) should be examined for non-normality, multicollinearity, heteroscedasticity, and autocorrelation [13]. Due to non-normality, the t test and F test are not necessarily robust. Since the Shapiro-Wilk test rejects that the distribution of the error term is normal (WShapiro-Wilk = 0.973, p < 0.001), the Wald test is used instead of the t test and F test. Multicollinearity is assessed by the variance inflation factor (VIF). If some independent or control variable has a large VIF (> 4.0), interpretations of the relative importance of the independent and control variables are unreliable. The control variable for the manufacture of food products and beverages has the largest VIF (= 2.3) which does not imply high multicollinearity. Due to heteroscedasticity, ordinary least squares (OLS) estimators are not necessarily efficient. Since the White test rejects that the variance of the error term is homoscedastic (NR2 = 79.959, p < 0.001), the White estimators are used instead of the OLS estimators. Finally, there is no need to examine autocorrelation because the linear regression model is cross-sectional. The value of R2 0.268 implies that the linear regression model in Table 5 has a satisfactory fit. Since the restriction αI = αW = αD = αE = αC = αS = 0 is rejected (WWald = 27.542, p < 0.001), the joint effect of the chosen ICT solutions on labor productivity is significant. Of the chosen ICT solutions, all the independent variables except for Website and SCM
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have statistically significant positive coefficients at the 0.05 level. In addition, the control variable Human has a significant positive coefficient αH = 0.287 (WWald = 10.228, p = 0.001). The restriction α'i = 0 for every industry i is rejected (WWald = 193.87, p < 0.001) as well as the restriction α"j = 0 for every country j (WWald = 302.633, p < 0.001). This is no surprise because output price and labor-intensity vary from one country and industry to another. In all, H1 is strongly accepted (p < 0.01), and H2 is strongly rejected (p ≥ 0.1). H3, H4, and H5 are weakly accepted (0.01 ≤ p < 0.05), and H6 is weakly rejected (0.05 ≤ p < 0.1).
4. Discussion 4.1. Limitations and further research Due to the nature of the available data, four limitations must be considered. Firstly, the inputs are measured in non-monetary terms but the output in monetary terms. Some inputs may place more emphasis on the revenue side than the cost side. Secondly, the data is cross-sectional. The time-lag effects of ICT solutions can be significant. Thirdly, the data do not contain observations from some important industries such as retail and countries such as the US. This may limit the generalizability of the findings. Finally, a small number of continous variables can result in non-normality and heteroscedasticity that may lower the reliability of the findings. For these reasons, the findings must be interpreted with caution. Research on the long-run productivity impacts of different ICT solutions is needed. A linear regression model that is based on the Cobb-Douglas production function is a good starting point. The model should take into account the time-lag effects of ICT solutions
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as well as the costs of labor, human capital, physical capital and other production factors. 4.2. Implications This paper provides four implications. It is exaggerated to say that IT cannot lead to competitive advantage because it is widely available to most firms today [6]. Common ICT solutions in e-commerce seem to improve labor productivity in Europe. Focusing on Internet access and website on the Internet, the differences in the effects are most significant. On the one hand, Internet access has a positive effect on labor productivity, as Sánchez et al. [29] also report. Surprisingly, this effect is strong although Internet access is very common. Having no Internet access can even be a competitive disadvantage. On the other hand, a website on the Internet is also common but it does not affect labor productivity. A fancy website alone does not ensure improved financial performance [36]. The importance of the website on the Internet is not self-evident, especially in B2B e-commerce. Standardized data exchange with the trading partners has a positive influence on labor productivity. The finding can explain why firms have started to pay attention to the needs for integration of business processes with their trading partners [1]. More firms have adopted standardized data exchange due to the importance of B2B e-commerce. In addition, the ERP system has a positive influence on labor productivity, as Hitt et al. [16] report. The finding can clarify why ERP systems have become popular. Firms are increasingly aware of the importance of integration of business processes within the firm because it affects performance [1]. Overall, the importance of the ICT-integrated business processes within and between the firms is evident in e-commerce.
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Finally, the CRM system has a positive influence but the SCM system has no influence on labor productivity. If the CRM systems primarily enhance revenue and the SCM systems primarily reduce costs [31], this paper may overestimate the impact of the CRM system and underestimate the impact of the SCM system.
Acknowledgements This paper was written when the author was at the BIT Research Centre, Helsinki University of Technology. The author thanks e-Business W@tch and the European Commission for the e-business survey data.
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Table 1. Descriptive statistics. Variable Mean Standard deviation LP 0.157 0.317 Internet 0.96 0.188 Website 0.69 0.464 Data 0.335 0.472 ERP 0.24 0.426 CRM 0.14 0.35 SCM 0.09 0.29 Human 0.291 0.317
Min 0.0003 0 0 0 0 0 0 0.0
Max 8.333 1 1 1 1 1 1 1.0
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Table 2. Pearson correlations. Variable LP Internet Internet 0.028 Website 0.035 0.254*** * Data 0.056 0.122*** ** ERP 0.066 0.077*** * CRM 0.05 0.08*** *** SCM 0.093 0.034 Human 0.005 0.07**
Website
Data
ERP
CRM
0.233*** 0.212*** 0.191*** 0.106*** 0.134***
0.294*** 0.271*** 0.226*** 0.132***
0.309*** 0.282*** 0.032
0.299*** 0.114***
All significance levels are two-sided: * p < 0.05, ** p < 0.01, and *** p < 0.001.
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SCM
0.001
Table 3. Observations according to industries. Industry (NACE) Number Industry (NACE) Manufacture of food Manufacture of products and 266 machinery and beverages (15) equipment (29.1-5) Manufacture of Manufacture of textiles (17) 130 motor vehicles and trailers (34.1-3) Manufacture of Manufacture of wearing apparel (18) 100 aircraft and spacecraft (35.3) Publishing and Construction (45) 196 printing (22) Manufacture of Computer and pharmaceuticals and related activities 228 cleaning perpetrates (72) (24.4-5) Total
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Number 248
229
70 263
225 1955
Table 4. Observations according to countries. Country Number Country Czech Republic 231 Poland France 427 Spain German 299 UK Italy 320 Total
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Number 170 178 330 1955
Table 5. Linear regression model. Variable αWWaldcoefficient statistics Internet 0.427 7.591 Website 0.063 1.396 Data 0.122 4.93 ERP 0.134 4.907 CRM 0.169 5.028 SCM 0.199 3.691
pvalue 0.006 0.237 0.026 0.027 0.025 0.055
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Author biography Juha-Miikka Nurmilaakso holds a master’s, licentiate, and doctorate degree in computer science and engineering from the Helsinki University of Technology, and a master’s degree in economics from the University of Helsinki. His research has been published in such journals as Computers in Industry, Computer Standards & Interfaces, International Journal of Production Economics, and Production Planning & Control. He currently works as an integration architect at Frends Technology, an international software company specialized in system integration and business process management solutions.
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