THE RELATIONSHIP BETWEEN IT INVESTMENT AND EMPLOYMENT: ARE THEY SUBSTITUTES OR COMPLEMENTS?

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1 Association for Information Systems AIS Electronic Library (AISeL) PACIS 2016 Proceedings Pacific Asia Conference on Information Systems (PACIS) Spring THE RELATIONSHIP BETWEEN IT INVESTMENT AND EMPLOYMENT: ARE THEY SUBSTITUTES OR COMPLEMENTS? Woo-Jin Jung Yonsei University, Sang-Yong Tom Lee Hanyang University, Hee-Woong Kim Yonsei University, Follow this and additional works at: Recommended Citation Jung, Woo-Jin; Lee, Sang-Yong Tom; and Kim, Hee-Woong, "THE RELATIONSHIP BETWEEN IT INVESTMENT AND EMPLOYMENT: ARE THEY SUBSTITUTES OR COMPLEMENTS?" (2016). PACIS 2016 Proceedings This material is brought to you by the Pacific Asia Conference on Information Systems (PACIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in PACIS 2016 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact

2 THE RELATIONSHIP BETWEEN IT INVESTMENT AND EMPLOYMENT: ARE THEY SUBSTITUTES OR COMPLEMENTS? Woo-Jin Jung, Graduate School of Information, Yonsei University, Seoul, Korea, Sang-Yong Tom Lee, Business Department, Hanyang University, Seoul, Korea, Hee-Woong Kim, Graduate School of Information, Yonsei University, Seoul, Korea, Abstract Given the increased digitization of work and automation, the effect of information technology (IT) investment on labor input and employment has been a crucial issue for management. IT investment can a substitute or a complement for labor input in increasing firms performance. It therefore needs to examine the interaction effect between IT investment and labor input. As the labor input increases, it is more likely that the employment will increase accordingly. This study thus examines the relationship between IT investment and labor input and then identifies whether IT and employment are substitutes or complements in increasing firms performance, i.e., whether firms employ less people as they invest more in IT (substitutes) or whether firms employ more people by creating more jobs and tasks as they invest more in IT (complements). This study employs a firm-level panel dataset from Korea Institute for Advancement of Technology (KIAT) that includes 76 firms and 380 data points drawn from the years (5 years). We use utilizes ordinary least squares (OLS) with Huber- White robust standard errors for the testing. The results indicate that the IT investment effect can be reversed on the interaction effect between IT investment and labor input. Based on the test result that hardware investment had a complementary effect with the labor input in increasing the performance of firms, firms can consider employing more people when they invest in hardware. Contrarily, software investment had a substitution effect with the labor input, which implies that firms can consider reducing the number of employees when they invest in software. We also found that IT staff investment had a substitution effect with the labor input, which indicates that firms can consider reducing the number of total employees when they hire more skilled IT staffs. Finally, we additionally found that IT investment effect with firm-level employment may be more salient in smaller firms and in non-it industries. This research provides a useful framework for analyzing the strategic significance of the IT investment and employment. Keywords: IT investment, labor input, employment, complementary effect, substitution effect

3 1 INTORODUCTION Today, no companies can survive without Information Technology (IT). All firms must understand the broad implications and effects of the new technology and how it can create substantial and sustainable competitive advantages. IT capital investment grew from 32% to 52% between 1980 and The new IT investment of firms can redesign jobs and work routines, create new incentive systems, develop other new Software (SW), and retrain employees in how to use IT-enabled systems (Bresnahan & Greenstein 1997). Especially, IT investment of a firm may comprise a labor-related expenditures 2, and the intangible assets such as internally developed SW produced by IT staff may be more than 5 times larger than the stock of hardware (HW) (Saunders 2010). Concerns about the technology effects on labor date back to industrialization still continue todays with digitization 3. Such concerns are supported by the substation effect view that if technology accomplishes more work through automation, there would be fewer jobs left for people (see Brynjolfsson & McAfee 2012). Whereas, there are many studies that IT investment facilitates job creation and growth in productivity of a firm (see Lichtenberg 1995; Brynjolfsson & Hitt 1996, 2003; Mittal & Nault 2009), and the literature has suggested by the complementary effect view that the higher productivity due to IT may increase labor input in the firm. Like this, the empirical evidence of the IT effect 4 on the labor input in a firm is mixed (see Brynjolfsson et al. 1994, Hitt 1999, Ray et al. 2013). Thus, the relationship between IT and labor in a firm is still an open research question 5. If anyone understands this relationship between IT and labor, it can do this relationship between IT investment and employment. This study aims to identify this relationship between IT investment and employment, specifically whether firms employ more or less employees in investing IT. In detail, it reviews the evidence on how the relationship between IT investment and labor input is linked to higher productivity with an emphasis on studies conducted at the firm level. Also, since the type of IT investment differs in their purpose and scale, and can have different effects on economic outcomes (Weill 1992), it examine how the relationship between a various type of IT investment and labor input is linked to higher productivity. Furthermore, it examine how the interaction effect between IT investment and labor input is shaped by the company size and industries. In the literature, company size may be an important factor in explaining variations in the magnitude and timing of IT effects on economic outcomes (see Brynjolfsson & Hitt 2003, Tambe & Hitt 2012), and there is a positive relationship between company size and IT investment (see Harris & Katz 1991). Especially, the labor economics literature shows that the productivity and job creation capabilities of firms differ by their size (see Hall 1987; Acs & Audretsch 1988; Idson & Oi 1999). Also, it examine whether the relationship between IT investment and labor input differs by industry sector (IT industry firms vs. non-it industry firms) because hightech industry firms like IT industry firms can have higher productivity increases associated with IT investment, and they have different skill requirements than low-tech industry firms. Beyond the findings of previous research, this study demonstrates that IT investment has the complementary effect with labor input, as well as that IT investment effect with labor input has the difference between company size, between industries, and among IT investment types. It can, therefore, find asking next two questions: Which IT investment has substation effect or complementary effect with labor input? Does the factors of the company size and industries affect substation effect or 1 US, Department of Commerce, Bureau of Economic Analysis, National income and Product Accounts, InformationWeek Economist, The There is extensive literature on the IT effect on productivity, performance, and profitability (see Dedrick et al. 2003). 5 MIT Technology Review 2015

4 complementary effect with labor input on IT investment? Answering these two questions, after all, we can identify this relationship between IT investment and employment. 2 LITERATURE REVIEW 2.1 Features of IT investment and IT effect in Firm-level Today, IT must be conceived of broadly to encompass the information that businesses create and use as well as a wide spectrum of increasingly convergent and linked technologies that process the information. It is very hard to detect and measure IT investments effect so that IT investment usually affects enterprise business processes, not a specific function of a firm (Porter & Millar 1985). An important concept that highlights IT is value chain. IT is spreading throughout the value chain and is performing optimization and control functions as well as more judgmental executive functions. The value activities of a firm employ purchased inputs, human resources and a combination of technologies. Within each of these generic categories, a firm will perform a number of discrete activities, depending on the particular business. The value chain of a firm is a system of interdependent activities, which are connected by linkages. Linkages exist when the way in which one activity is performed affects the cost or effectiveness of other activities, often create trade-offs in performing different activities that should be optimized. It also requires activities to be coordinated. IT is permeating the value chain at every point, transforming the way value activities are performed and the nature of the linkages among them. These IT effects explain why IT has acquired strategic significance and is different from the many other technology businesses (Porter & Millar 1985). IT can be defined as computers as well as related digital communication technology (Brynjolfsson & Hitt 2000). Having the broad power to reduce the costs of coordination, communications, and information processing, it is not surprising that the enormous reduction in computing and communications costs has engendered a substantial restructuring of the economy. Also, the business value of IT is limited less by computational capability and more by the ability of managers to invent new processes, procedures and organizational structures that leverage this capability. As a complementary effect of IT continue to develop, IT investment will expand well beyond computation for the foreseeable future (Brynjolfsson & Hitt 2000). From the knowledge view, all 3 major types (HW, SW, and internal spending) of IT are assumed to possess different types and levels of knowledge (Armour 2001). Generally HW must contains skill and knowledge about operating SW and controlling the computing environment (Armour 2000). SW developed through intensive knowledge work contains skill and knowledge about a variety of business processes and organization routines (Nicholson et al. 2004). Internal spending represents people which is one of the most important IT resources (Mata et al. 1995). Internal spending allows such people to learn more about HW and SW, and to modify HW and SW to fit the business purpose (Kim et al. 2008). From the resource-based view, sustainable competitive advantage of a firm grows out of valuable and specific resources or capabilities that are heterogeneous and immobile (Peteraf 1993; Rouse et al. 1999). So each type of IT investments can be separated based on its heterogeneity and immobility (Mata et al. 1995). Although it is difficult to regard IT investment itself as a source of competitive advantage, the complementarity of IT may generate more value and create competitive advantage (Powell et al. 1997). Studies of IT investment effect show that there is considerable variation in the firms. This suggests that investing in IT does not by itself guarantee good performance. The concept to account for this variation among firms is complementary asset (Laudon & Laudon 2016). It is those assets required to derive value from a primary investment (Teece 1988). For instance, to realize value from automobiles requires substantial complementary investments in roads, gasoline stations, repair facilities, and a legal regulatory structure to set standards and control drivers. Likewise, HW can

5 produce more value in the presence of SW and employee, while the value of HW without SW and employee is meaningless. This means that there is a complementary relationship among the three types of IT and related to the investment effect of a firm (Kim et al. 2008). There has been considerable progress in the literature linking IT investment to company performance (Lichtenberg 1995; Brynjolfsson & Hitt 1996, 2003; Dewan & Min 1997; Bresnahan et al. 2002). The traditional techniques focus on the observable aspects of investment, such as the price and quantity of computer HW in the economy, and neglect the much larger intangible investment in developing complementary new products, services, markets, business processes, and worker skills (Porter & Millar 1985). However, these studies show that firms with certain complementary organizational practices realize greater effect from their IT investments (see Melville et. al. 2004; Stiroh 2004; Brynjolfsson & Saunders 2010). They also found that IT investments coexist with complementary resources in the firm (Mittal & Nault 2009). 2.2 Relationship between IT investment and Labor input Since the Industrial Revolution, firms have achieved competitive advantage by substituting machines for employees. However, information processing at that time was mostly the result of human effort. That is, the new technology substitutes machines for human effort in the information processing. Therefore, labor-saving investments were associated with an increase in labor (Berndt & Morrison 1995; Morrison 1996). Firm-level performance growth can lead to the expansion of a firm and an increase in the labor. Whereas, if the production level of a firm remains the same, labor can decrease because fewer employees would be needed to produce the same outputs (Atasoy et al. 2016). Therefore, IT investment effect with firm-level employment can be positive, negative, or even zero, depending on whether the substitution or complementary effects of IT investment dominate or cancel each other out within a given firm. There are many studies about the substitution or complementary effects of IT investment. In the complementary or substitute relationship between new technology and a separate type of labor, the increase in the relative input for labor could be attributed to new technology developments (Atasoy et al. 2016). For example, IT substitutes for unskilled labor and routine labor tasks, but complements skilled labor and nonroutine tasks (see Acemoglu 1998, 2002; Bresnahan et al. 2002; Autor et al. 2003; Autor & Dorn 2013). Harrison et al. (2014) studied the impact of process and product innovations introduced by firms on their employment growth. They found that while product innovation is associated with employment growth in the manufacturing industries, process innovation tends to displace employment in the manufacturing industries. As the studies of complementary effect, Bresnahan et al. (2002) found that greater levels of IT are associated with increased the delegation of authority to individuals and teams, greater levels of skill and education in the workforce, and greater emphasis on pre-employment screening for education and training. In addition, they find that these work practices are correlated with each other, suggesting that they are part of a complementary work system. IT can change labor requirements by changing the business processes and needs of various labor skills. Brouwer et al. (1993) analyzed the influence of innovation on growth rates of employment in 859 Dutch manufacturing firms. They found firms which focused their R&D on the IT did have a more favorable development of employment than the sample average. Harrison et al. (2014) studies the impact of process and product innovations introduced by firms on their employment. His results show that the net outcome of product innovation expands employment with a gross unit elasticity with respect to innovative sales. Osterman (1986) investigates how the increased use of computers affects clerical and managerial employment. He found that the net effect of computers in was to depress the employment of clerks and managers substantially, but the pattern over time (a larger displacement effect in the first few years, followed by increased clerical and managerial employment) shows that this depression offset at least in part by complementary effects.

6 Some studies show a strong relationship between high-tech investment like IT and the skilled workers (Berndt & Morrison 1995; Berman et al. 1994; Autor et al. 1998). Berndt & Morrison (1995) investigated relationships between investment in high-tech like IT capital and the distribution of employment in the US manufacturing industries. They found that the increases in the high-tech composition of capital are positively related to growth in the white collar. Bresnahan et al. (2002) show how changes in IT costs and capabilities lead to a cluster of changes in work organization and firm strategy that increase the input for skilled workers. Relationship Between IT and Employment Labor Complementary Effect on the IT investment Labor Substitution Effect on the IT investment Table 1. Study Osterman (1986) Brouwer et al.(1993) Berndt & Morrison (1995) Autor et al. (1998) Bresnahan et al. (2002) Harrison et al.(2014) Brynjolfsson et al. (1994) Dewan et al. (1997) Harrison et al.(2014) Parsons & Gotledb (1993) Research output He investigates how the increased use of computers affects clerical and managerial employment. He found that the net effect of computers in was to depress the employment of clerks and managers substantially, but the pattern over time (a larger displacement effect in the first few years, followed by increased clerical and managerial employment) shows that this depression offset at least in part by complementary effects. They analyzed the influence of innovation on growth rates of employment in 859 Dutch manufacturing firms. They found firms which focused their R&D on the IT did have a more favorable development of employment than the sample average. They investigated relationships between investment in high-tech like IT capital and the distribution of employment in the US manufacturing industries. They found that the increases in the high-tech composition of capital are positively related to growth in the white collar. They examines the effect of skill-biased technological change as measured by computerization on the recent widening of U. S. educational wage differentials. Rapid skill upgrading within detailed industries accountes for most of the growth in the relative demand for college workers, particularly since They found that greater levels of IT are associated with increased the delegation of authority to individuals and teams, greater levels of skill and education in the workforce, and greater emphasis on pre-employment screening for education and training. He studies the impact of process and product innovations introduced by firms on their employment. His results show that the net outcome of product innovation expands employment with a gross unit elasticity with respect to innovative sales. They argued that the substitution effect with labor could be the simplest explanation of why firm-level employment may be linked to IT, despite coordination costs. They studied the relationship between IT capital and ordinary capital and labor. They found that IT capital is a net substitute for both ordinary capital and labor in all sectors of the economy. They studied the impact of process and product innovations introduced by firms on their employment growth. They found that process innovation tends to displace employment in the manufacturing industries. They investigate the impact of these investments on bank performance. They found that information capital is a substitute for every other factor. Especially, it substitutes best for labor. The literature between IT investment and employment

7 On the other hand, Brynjolfsson et al. (1994) argued that the substitution effect with labor could be the simplest explanation of why firm-level employment may be linked to IT, despite coordination costs. Dewan et al. (1997) studied the relationship between IT capital and ordinary capital and labor. They found that IT capital is a net substitute for both ordinary capital and labor in all sectors of the economy. This result suggests that the factor share of IT in production will grow to more significant levels over time. Finally, Parsons & Gotledb (1993) investigated the impact of these investments on bank performance. They found that information capital is a substitute for every other factor. Especially, it substitutes best for labor. 3 DATA AND METHODOLOGY We collected data from the firms of different sizes and industries based on the collaboration with the Korea Institute for Advancement of Technology (KIAT). Our research employs a firm-level panel dataset that includes 76 firms and 380 data points drawn from the years (5year). The information is collected in a survey of financial managers at small and medium sized firms. A feature of the data is the information on specific IT investment. The IT investment survey consists of several indicator variables that denote whether a certain IT investment type is an inactive investment in the firm. We focus on three types of IT investment that were measured over the 5 year panel: HW, SW, IT staff. The monetary values in Korean currency (KRW) are converted to U$ by applying 1,196 rates. The contribution of IT has most commonly been determined using methods from production economics, which allow IT researchers to estimate the relationship between various production inputs, such as capital and labor, and firm output. This literature relies on the concept of a production function, an econometric model of how firms convert inputs to outputs. These studies assumed a standard form (Cobb-Douglas) for the production function, and measured the variables in logarithms (see Brynjolfsson & Hitt 2003; Lee et al. 2005; Gholami et al. 2009; Tambe et al. 2012) Cobb-Douglas production function model: Y = αl β K 1 β Economic theory places some constraints on the functional form used to relate these inputs to outputs, but a number of different functional forms are widely used depending on the firm s economic circumstances. Perhaps the most widely used in these forms is the Cobb-Douglas production function model. Aside from being among the simplest functional form, this model has the added advantage that it has been the most commonly used models in research relating inputs such as IT to output growth at a variety of levels of aggregation and even forms the basis for productivity measurement of the economy as a whole. Furthermore, since the Cobb-Douglas production function model can be considered a first order approximation of an arbitrary production function, it is well suited to estimating the contribution of inputs to outputs, which are typically quoted at the sample mean, the region where a first order approximation is especially accurate. First, we assume that firms produce output, performance or productivity (Y), via a Cobb-Douglas production function model whose inputs are capital (K) and labor (L) Model 1: lny t = lnα t + β 1t lnl t + β 2t lnk t + β 3t lnit t + ε t where Y t is the sales amount, L t is the number of laborers, K t is the capital amount, and IT t is IT investment amount. The coefficient estimate on the IT t input (β 3t ) is the output elasticity of IT t, the percentage increase in output generated by a one percent increase in the IT t input. Model 2: lny t = lnα t + β 1t lnl t + β 2t lnk t + β 3t lnit t + β 4t lnit t lnl t + ε t

8 Next, we suggest a new model which can be incorporated into the production function framework described above to estimate the contributions of interaction effect between IT investment and labor input to sale amount. Overall, Model 2 adds the moderated effect between IT investment and labor input to Model 1. After all, substation effect or complementary effect with labor input on IT investment is among the mechanisms through the moderated relationship between IT investment and labor input. To resolve our research goals described above, we subdivide IT investment into HW investment, SW investment, and IT staff investment, and then suggest the Model 3 and Model 4 for the main empirical specification: Model 3: lny t = lnα t + β 1t lnl t + β 2t lnk t + β 3t lnsw t + β 4t lnhw t + β 5t lnitl t + ε t Model 4: lny t = lnα t + β 1t lnl t + β 2t lnk t + β 3t lnsw t + β 4t lnhw t + β 5t lnitl t +β 6t lnsw t lnl t + β 7t lnhw t lnl t + β 8t lnitl t lnl t + ε t where HW t is HW investment amount, SW t is SW investment amount, and ITL t is IT staff investment amount. We suggest Model 4 which can be incorporated into the production function framework described above to estimate the contributions of interaction effect between each IT investment types and labor input to sale amount like Model 2. Overall, Model 4 add the moderated effect between IT investment and labor input to Model 3. All analyses utilize ordinary least squares (OLS) with Huber-White robust standard errors to account for repeated observations of the same firm over time and any conditional hetero-skedasticity in the model. Generally, Huber-White standard error estimates are used throughout many papers whenever OLS estimators are used unless otherwise noted. Additionally, to estimate whether the relationship between IT investment and labor varies by firm size, we divide the sample into the 16 million dollar of assets to create size groups (Smaller firms vs. Larger firms). Then we examine whether the relationship between IT investment and firm-level employment differs by industry (IT industry firms vs. non-it industry firms). 4 RESULTS Table 2 presents the summary statistics of the main variables in the sample. First, the mean of sale amount as output of firms is US$52 million, and the mean of capital amount is 24 million dollars. Instead, the mean of IT investment amount is US$48 thousand somewhat less than that of sales and capital of firms. Separately, the mean of HW investment amount is US$14 thousand, the mean of SW investment amount is US$12 thousand, and the mean of IT staff investment amount is US$9 thousand. Therefore, the averages of the individual IT investment represent little gap. The firm size and industry index used in our analyses are binary (0 and 1), representing the type of firms. The mean of the size and industry index are and 0.362, respectively. Table 3 presents the summary statistics of the panel OLS Results for the production function models. Regression estimates, shown in the result of Model 1 as the base model, indicate that the coefficient estimate on IT investment is (t=-2.2). The coefficient estimate on the labor (L) is higher at (t=12.07). The coefficient estimate on the capital (K) is also higher at (t=17.78). As a result, the capital and labor of samples are positive while IT investment is negative. This result explains that IT investment affects negatively the sale amount of firms.

9 (Variable) used data (unit) Mean Std. Dev. (P) Sales amount (US$) 5.22e e+7 (K) Capital amount (US$) 2.43e e+7 (L) Labor amount (x) (IT) Information Technology investment amount (US$) 4.83e e+6 (HW) Hardware investment amount (US$) 1.43e e+5 (SW) Software investment amount (US$) 1.08e e+5 (ITL) IT Staff investment amount (US$) 8.76e e+5 Company Size (binary Variable 0/1) : Smaller firms - 1: larger firms IT-orientation Industry (binary Variable 0/1) - 0: non-it industry firms - 1: IT industry firms Table 2. Means and Standard Deviations for Sample Model 2 shows the results where the IT investment is interacted with the labor (L). That is, Model 2 adds the moderated effect between IT investment and labor (L) to Model 1. The coefficient estimate on capital of Model 2 is (t=17.28), and the coefficient estimate on labor is (t= 1.69). The coefficient estimate on IT investment is at (t=-2.30). The coefficient estimate on the moderated relationship between IT investment and labor (L) is (t=1.80). Surprisingly, however, the results of Model 2 show that the IT investment effect is reversed on the interaction effect between IT investment and labor (L). This result explains that although IT investment affects negatively the sale amount of firms, there is a positive interaction between IT investment and labor input on the sale amount of firms. Variable Model 1 Model 2 Model 3 Model 4 _cons (0.55)*** (1.01)*** (0.536)*** (0.661)*** Capital (K) (0.032)*** (0.032)*** (0.031)*** (0.031)*** Labor (L) (0.045)*** (0.160)* (0.044)*** (0.087)*** IT investment (IT) (0.010)** (0.042)** HW investment (HW) (0.005)*** (0.027)*** SW investment (SW) (0.005)* (0.029)** IT staff investment (ITL) (0.004)*** (0.019)*** (L) * (IT) (0.008)* (L) * (HW) (0.006)*** (L) * (SW) (0.006)** (L) * (ITL) (0.004)** R squared Adj R-squared Number of Obs Table 3. Panel OLS Results for the production function models Regression estimates, shown in the result of Model 3, containing HW investment, SW investment and IT staff investment separated from IT investment, indicate the coefficient estimate on HW investment of (t=-5.16), SW investment of (t=1.7), and IT staff investment of (t=3.14). The coefficient estimate on the labor (L) is higher at (t=12.04). The coefficient estimate on the capital (K) is also higher at (t=18.18). As a result, the capital and labor of samples in Model 3 are similar to the results of Model 1 and Model 2. However, the coefficient estimate from the IT investment differ by each IT investment types. Especially, the coefficient estimate from HW investment is negative, but the coefficient estimates from SW and IT staff investment are positive.

10 This result shows that HW investment affects negatively the sale amount of firms, while SW investment and IT staff investment affect positively. Model 4 like Model 2 also shows the results where each IT investment types (HW investment, SW investment and IT staff investment) are interacted with the labor (L). That is, Model 4 adds the moderated effect between each IT investment types and labor (L) to Model 3. The coefficient estimate on capital of Model 4 is (t=18.60), and the coefficient estimate on labor is (t=6.88). Especially, the estimate on labor of Model 4 is double more than that of Model 2. The coefficient estimate on HW investment is at (t=-3.96). The coefficient estimate on SW investment is at (t=2.46). The coefficient estimate on IT staff investment is at (t=2.75). The coefficient estimate on the moderated relationship between HW investment and labor (L) is (t=3.02). One of SW investment is (t=-2.19). One of IT staff investment is (t=-2.18). The results of Model 4 like Model 2 show that the IT investment effect is reversed on the interaction effect between IT investment and labor (L). This result shows that although HW investment affects negatively the sale amount of firms, there is a positive interaction effect between HW investment and labor input on the sale amount of firms. However, although SW investment and IT staff investment affect positively the sale amount of firms, there is a negative interaction effect between HW investment and labor input on the sale amount of firms. In summary, there is a complementary relationship between HW investment and labor input, but substitutive relationships between IT staff investment and labor input and between SW investment and labor input. In summary of Table 3, although IT investment effect is negative, IT investment effect with labor input is positive. In detail, although HW investment effect is negative, HW investment effect with labor input is positive. On the other hand, although SW investment or IT staff investment effect is positive, SW investment or IT staff investment has negative interaction effects with labor input. That is, the interaction effect between IT investment and labor input provides the difference by each IT investment types. We can confirm an important property of this measure. The sale amount of SW investment or IT staff investment is positive and significant, while that of HW is negative and significant. However, this result leads to an opposite result of the interaction effect between IT investment and labor input. That is, because firms can consider reducing employment when they invest in SW or IT staff, SW investment and IT staff investment have the substitution effect with labor input. Whereas, because firms can consider increasing employment when they invest in HW, HW investment has the complementary effect with labor input. Having done this, we performed additional comparisons of company type such as size and industry. Table 4 presents panel OLS Results for the production function models by size and industry. As for the testing by company size, we found that the coefficient of HW investment for labor input is significant in larger firms, whereas the coefficient of HW and SW investment for labor input is significant in smaller firms. In other word, we could not find a significant difference between larger and smaller firms in HW investment and IT staff investment. However, SW investment is associated with smaller firms for labor input. As a result, the substitution effect with labor input on SW investment appear peculiarly in smaller firms. This result shows that SW investment has the substitution effect with labor input not in larger firms but in smaller firms As for the testing by industry type, we found that IT investment increases the interaction effect with labor input only in non-it industries. In detail, although HW investment affects negatively the sale amount of non-it industry firms, there is a positive interaction effect between HW investment and labor input on the sale amount. However, although SW investment and IT staff investment affect positively the sale amount of non-it industry firms, there is a negative interaction effect between HW investment and labor input on the sale amount. As a result, this study distinguishes between IT industry firms and non-it industry firms. All IT investment types are associated only with non-it industry firms for labor input. In other words, HW investment has a complementary effect with labor input and SW investment and IT staff investment have a substitution effect with labor input. This result appears clearly in non-it industry firms. On the other hand, we could not find a significant relationship between each IT investment types and labor input in the IT industry firms.

11 Variable Company size IT-orientation Industry Larger Smaller IT Non-IT _cons (1.184)*** (1.223)*** (1.056)*** (0.971)*** Capital (K) (0.043)*** (0.056)*** (0.051)*** (0.042)*** Labor (L) (0.150)*** (0.271)*** (0.137)** (0.117)*** IT investment (IT) HW investment (HW) (0.041)** (0.119)*** (0.057) (0.039)*** SW investment (SW) (0.050) (0.122)*** (0.056) (0.048)*** IT staff investment (ITL) (0.037) (0.035) (0.035) (0.025)*** (L) * (IT) (L) * (HW) (0.008)** (0.032)*** (0.011) (0.009)** (L) * (SW) (0.010) (0.033)*** (0.011) (0.011)*** (L) * (ITL) (0.007) (0.009) (0.008) (0.005)** R squared Adj R-squared Number of Obs Table 4. Panel OLS Results for the production function models by size and industry Overall, our main results indicate that although IT investment negatively affects the sale amount of firms, there is a positive interaction between IT investment and labor input on the sale amount of firms. Each of IT investment types is also correlated with labor input. We found a relationship between IT investment and labor input, and the relationship differs by the type of IT investment. These results show that IT investment may affect firm-level employment. This would raise concerns of firms, implying that each IT investment types lead to the changes in firm-level employment. Also, we found that IT investment effect with firm-level employment may be more pronounced in smaller firms and in non-it industries. 5 DISCUSION AND CONCLUSION 5.1 Discussion of Findings This study has three key findings. First, although IT investment has a negative effect, IT investment may have a positive effect on the sale amount of firms through the interaction with labor input. It found that because firms can consider increasing employment when they invest in IT, IT investment has the complementary effect for labor input (see Osterman 1986; Berndt & Morrison 1995; Brouwer et al. 1993; Autor et al. 1998; Bresnahan et al. 2002; Harrison et al. 2014). Separately, although HW investment has a negative effect, HW investment has a positive effect on the sale amount of firms through the interaction with labor input. This study found that because firms can consider increasing employment when they invest in HW, HW investment has the complementary effect with labor input. It is consistent with the idea that increasing use of HW like computers is associated with a greater input for human capital (Osterman 1986; Brynjolfsson & Hitt 2000). On the other hand, although SW investment and IT staff investment have a positive effect, SW investment or IT staff investment has a negative interaction effects with labor input. It found that because firms can consider reducing employment when they invest in SW or IT staff, SW investment and IT staff investment have the substitution effect with labor input. Some researchers insist that IT investment has the substitution effect with labor input (Brynjolfsson et al. 1994; Dewan et al. 1997; Parsons & Gotledb 1993). However, their argument is not limited to SW investment or IT staff investment. Beyond the findings of previous research, this study demonstrates that IT investment has the complementary effect with labor input, as well as that IT investment effect with labor input has the

12 difference in IT investment types. This explains that firms must consider reducing employment when they invest in SW or IT staff, but HW investment and employment must be made at the same time. Second, this study found that HW investment effect with labor input is significant in larger firms, but not significant in smaller firms (Table 4). As a result, it could not find the difference between larger and smaller firms in HW and IT staff investment effect with labor input. However, SW investment with labor input is associated only with the sale amount of smaller firms. As a result, the substitution effect with labor input on SW investment appear peculiarly in smaller firms. Some researchers insist that IT investment has the substitution effect with labor (Brynjolfsson et al. 1994; Dewan et al. 1997; Parsons & Gotledb 1993). However, their argument is also not limited to SW investment in smaller firms. When firms invest in IT, the employees in smaller firms are less likely to receive training than those that in larger firms. The smaller firms are not aware of the benefits of training and consequently provide less for their employees. The smaller firms also anticipate that the costs associated with training may exceed the benefits to be derived from (Westhead & Storey 1997). After all, larger firms usually allocate greater financial, technological and personnel resources to IT training (Soto-Acosta, 2008), but smaller firms do not. Beyond the findings of previous research, this study demonstrates concretely that the SW investment of IT investment types has the substitution effect with labor input in smaller firms. This explains that smaller firms must consider reducing employment when they invest in SW. Next, this study found that IT investment effect with labor input is significant only in non-it industry firms, but not significant in IT industry firms (Table 4). As a result, because firms can consider increasing employment when they invest in HW, HW investment has the complementary effect with labor input in non-it industry. On the other hand, because firms can consider reducing employment when they invest in SW or IT staff, SW investment and IT staff investment have the substitution effect with labor input in non-it industry. Some researchers insist that IT investment has the complementary effect with labor input (Osterman 1986; Berndt & Morrison 1995; Brouwer et al. 1993; Autor et al. 1998; Bresnahan et al. 2002; Harrison et al. 2014), the others insist that IT investment has the substitution effect with labor (Brynjolfsson et al. 1994; Dewan et al. 1997; Parsons & Gotledb 1993). However, the argument of IS researchers is not limited to IT investment in non-it industry. Beyond the findings of previous research, this study demonstrates that IT investment effect with labor input has the difference in IT investment types only in non-it industry. This explains that non-it industry firms must consider reducing employment when they invest in SW or IT staff, but HW investment and employment must be made at the same time. Like this, the gap between IT industry and non-it industry can be explained that the tasks in common industries unlike knowledge-based IT industries conducting higher skilled tasks are increasingly codified in computer software and performed by machines like HW (Autor et al. 2002; Levey & Murnane 2004; Bartel et al. 2007). The higher skilled workers have also comparative advantage in the set of non-routine tasks in occupations of higher skill levels (Autor et al. 1998). After all, non-it industries is that tangible asset is more important, while knowledge-based IT industry is that the person of ability is more important. 5.2 Limitations and Future Research Direction There are certain limitations to this research that create interesting opportunities for future research. First, it address some important limitations imposed by sample restrictions. It has relatively small cross-sections (76 firms/year) and, there is limited year-to year consistency in firm responses making them unsuitable for panel data methods. Especially, it used five-year panel data, and this is still a relatively short time frame to analyze long-term effects. This is likely to be a valuable resource in the future, but the current panel has a limited time dimension and consequently cannot be compared to the best available data from other sources that cover prior periods. Future research could explore the change in composition of skills and tasks within firms and question which occupations benefit or suffer as IT investment increases.

13 Second, Most of the IS literature similar to this research has relied on the data from the United States (Brynjolfsson et al. 1994, Hitt 1999, Forman & McElheran 2013, Ray et al. 2013). This research data set peculiarly comes from Korea, and additionally represents firms of different sizes and industries. It explicitly focuses on the relationship between IT investment and firm-level employment, so this data cannot directly infer about the broader effects of IT investment on aggregate employment levels in regional or national labor markets. Nonetheless, the IT investment effect with firm-level employment remains an important question to fully understand the interplay among growth, IT, and employment. 5.3 Implications for Research and Practice This study has several implications for research, especially because it is the first to address three aspects of IS infusion. These are 1) establishing the strategic investment framework between IT and employment as a driver of IS infusion based on the application of economic productivity theory; 2) understanding the salient antecedents of IT investment effect with employment at the firm level; and 3) taking a comprehensive approach to the study of IS infusion. This research discusses the reasons why IT investment has acquired strategic significance and how it is affecting employment. It provides a useful framework for analyzing the strategic significance of the IT investment and employment. By showing how IT investment is altering the relationships between the IT investment types and employment, it identifies that it can create new jobs. A main practical and policy implication at the firm-level is that IT investment is not necessarily replacing all types of jobs in a firm, and can create a positive or negative impact on the total number of employees by IT investment type. In this study, HW investment has an effect in increasing the labor input. Therefore, firms must consider additional employment in investing HW. SW investment has an effect in decreasing the labor input. Therefore, firms must retain or lay off more the exiting employee in investing SW. IT staff investment also has an effect in decreasing the labor input. The effect on IT staff investment is not to hire many IT staff, but to reduce more employee in hiring skilled IT staff or to retrain existing employees in how to use IT-enabled systems. It is consistent with the idea that IT lead to a cluster of changes in work organization and firm strategy that increase the input for skilled workers (Berndt & Morrison 1995; Berman et al. 1994; Autor et al. 1998). This study helps mangers respond to the challenges of IT investment or employment. The emphasis of this study is that firm-level analysis has significant measurement advantages for examining intangible organizational investments and technology innovation associated with IT. This study is a general step toward fully understanding IT investment effect with employment at the firm level by offering micro level evidence on the role of IT investment in firm-level employment. Namely, while the recent macroeconomic evidence about IT contributions is encouraged (see Forman et al. 2012, Atasoy 2013), our views are more strongly influenced by the microeconomic data. IT investment effect with employment has been an important practical and the public policy question because there are concerns that IT investment can have potential jobless growth effects. The jobless growth effect has been a concern since industrialization, and it remains an important question for IS research today given the rapid digitization of the economy. The relationship between IT investment and aggregate employment includes different components, such as workers, firms, and local and national labor markets. Several studies analyzed IT investment effect with regional employment, and empirical evidence indicates that greater IT infrastructure and IT investment in local areas are associated with higher regional employment (see Forman et al. 2012, Atasoy 2013). One of the key questions in this literature is whether the positive relationship between IT investment and regional employment is due to increasing the number of firms in the region, or caused by each firm in the region hiring more employees. This study could be cautiously generalized to imply that IT investment may contribute to the national employment growth, and policies aiming to expand IT investment can be important tools for stimulating employment growth and job creation beyond the firm level.

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