It Investment & Productivity
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  • 1. ARTICLE IN PRESS International Journal of Information Management 25 (2005) 39–53 www.elsevier.com/locate/ijinfomgt Evaluating the impact of IT investments on productivity: a causal analysis at industry level Qing Hua,Ã, Jing ‘‘Jim’’ Quanb a Department of Information Technology & Operations Management, College of Business, Florida Atlantic University, 777 Glades Road, Boca Raton, FL 33431, USA b Department of Information and Decision Sciences, Perdue School of Business, Salisbury University, Salisbury, MD 21801, USA Abstract While many studies have shown positive and significant relationships between IT investments and firm productivity or performance, the question of causality remains: do higher IT investments contribute to better performance or does better performance lead to higher IT investments? In this study, we examine the issue of IT investment impact on productivity using Granger causality model with industry level data over a 30-yr period. Our results suggest that a causal relationship exists between IT investments and productivity at the industry level. IT investments contribute to productivity growth in most of the industries in our sample. In addition, we find a feedback relationship from productivity to IT investments, as suggested by some studies. We postulate that the impact of IT investments on productivity is moderated by the interaction of product information intensity and value-chain information intensity. r 2004 Elsevier Ltd. All rights reserved. Keywords: IT investments; IT impact; Productivity; Causality; Information intensity 1. Introduction Despite numerous attempts to quantify the contribution of IT investments to firm performance in both theoretical and empirical studies over the last two decades, a division persists between ÃCorresponding author. Tel.: +1 561 297 3675. E-mail addresses: qhu@fau.edu (Q. Hu), jxquan@salisbury.edu (J.J. Quan). 0268-4012/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijinfomgt.2004.10.006
  • 2. ARTICLE IN PRESS Q. Hu, J.J. Quan / International Journal of Information Management 25 (2005) 39–53 40 academics and practitioners. While significant returns on IT investments have been reported in various academic studies at firm level (Brynjolfsson & Hitt, 1996; Hitt & Brynjolfsson, 1996; Barua & Lee, 1997; Rai, Patnayakuni, & Patnayakuni, 1997), at industry-specific sectors (Siegel, 1997; Devaraj & Kohli, 2000; Menon, Lee, & Eldenburg, 2000), and at country level (Kraemer & Dedrick, 1993; Dewan & Kraemer, 1998; Tam, 1998), practitioners seem to be less certain about the contributions (Roach, 1987; Strassmann, 1996, 1997; Violino, 1997, 1998; McKinsey Global Institute, 2001; Olazabal, 2002; Carr, 2003). The debate over the well-known IT productivity paradox (Brynjolfsson, 1993; Brynjolfsson & Hitt, 1996; Triplett, 1999; Gordon, 2002) only highlights the complexity of this critical issue. This study is motivated by the lack of explicit causality inquiries into the relationship between IT investments and productivity or other performance measures in the literature. We believe that such a missing link may have contributed directly to the conflicting findings in IT impact studies and the on-going IT productivity paradox debate. Instead of the ‘‘impact’’ of IT investments, it is ‘‘correlation’’ that is often being investigated. We are also intrigued by the idea that the impact of IT is moderated by the information intensities of products and value chain of a firm as described in Porter and Millar (1985). The main contribution of this research can be summarized in the following two areas. First, we use an explicit causality model for analyzing the relationship between IT investments and productivity. This enables us to state unequivocally that in many industries IT investments indeed lead to increased productivity while in other industries higher productivity also leads to increased IT investments. Second, we show why the contribution of IT investments is not uniform across industries using the framework of information intensity. That is, that the impact of IT investments on productivity is moderated by the information intensities of products and value chains of the firms in individual industries. 2. Theoretical foundations 2.1. The relationship between IT and firm performance The value of IT to firms is one of the most critical research topics in the MIS literature. A review of the extant literature shows that at least four prevailing thoughts exist on where, how, and why IT creates value to firms exist. The microeconomics-based view believes that IT investments create excess return over other types of capital investments in production processes of firms (Brynjolfsson & Hitt, 1996; Hitt & Brynjolfsson, 1996; Dewan & Min, 1997; Siegel, 1997; Lehr & Lichtenberg, 1999). The process-based view believes that IT investments create competitive advantages by improving operational efficiency of intermediary business processes which in turn, under the appropriate conditions, lead to better firm-level performance (Barua, Krieble, & Mukhopadhyay, 1995; Soh & Markus, 1995; Mooney, Gurbaxani, & Kraemer, 1996). The resource-based view believes that IT investments improve firm performance by creating sustainable competitive advantage via unique, immobile, and path-dependent strategic resources and capabilities (Clemons & Row, 1991; Mata, Fuerst, & Barney, 1995; Powell & Dent-Micallef, 1997; Bharadwaj, 2000; Sambamurthy, Bharadwaj, & Grover, 2003). The digital option view argues that IT investments create value by creating options and flexibility for firms in the
  • 3. ARTICLE IN PRESS Q. Hu, J.J. Quan / International Journal of Information Management 25 (2005) 39–53 41 increasingly competitive and uncertain market environments (Dos Santos, 1991; Benaroch & Kauffman, 2000; Taudes, Feurstein, & Mild, 2000; Sambamurthy et al., 2003). Since this study focuses on the economic impact of IT investments at the industry level, the economic and process views provide a more salient theoretical foundation. Thus, we provide a brief review of the literature on the economic and process views of IT value, based on which we develop our research hypotheses. The fundamental argument of the economic view of IT value is that IT can be treated as an input in the production function of a firm and there is a substituting effect between IT and other production factors (Dewan & Min, 1997). Thus, IT creates value for a firm when IT capital or IT labor produces higher return than ordinary capital and labor (Brynjolfsson & Hitt, 1996; Hitt & Brynjolfsson, 1996). The impact of IT investments can be estimated using an economic production function. Using this approach, Brynjolfsson and Hitt (1996) estimated that the marginal product of IT capital is about 81%, with an estimated net marginal product of 48%, and that each dollar increase in IT staff is associated with a marginal increase of $2.62 in output, with a net marginal output of $1.62. They are significantly higher than the marginal products of non-IT capital and non-IT staff. Significant returns on IT investments have been reported in many similar studies at firm level (Barua & Lee, 1997; Rai et al., 1997; Lehr & Lichtenberg, 1999; Kudyba & Diwan, 2002), at industry-specific sectors (Siegel, 1997; Devaraj & Kohli, 2000; Menon et al., 2000), and at country level (Kraemer & Dedrick, 1993; Dewan & Kraemer, 1998). With a few exceptions (Loveman, 1994; Berndt & Morrison, 1995), overall published empirical studies using the economic production function approach have found significant impact of IT investments on productivity and performance (Kohli & Devaraj, 2003). The economic view of IT value, essentially a variance theory (Markus & Robey, 1988), however, fails to explain where and why such impact occurs (Soh & Markus, 1995; Mooney et al., 1996). In light of this, the process view of IT value considers IT investments as a necessary but not sufficient condition for superior firm performance. It postulates that the impact of IT investments on firm performance is the result of interactions among three processes: the IT conversion process in which IT investments become IT assets, the IT use process in which IT assets create impacts, and the competitive process in which IT impacts are converted into firm performance (Soh & Markus, 1995). Each of these processes is influenced by a multitude of technological, organizational, industry, and competitive environmental factors. There is plenty of empirical evidence that supports the process view. For example, Barua et al. (1995) suggested that IT has first-order effects on operational level variables such as capacity utilization and inventory turnover for manufacturing firms and these intermediary variables in turn affect higher level variables such as productivity and profitability. This conjecture is supported by the analyses of data collected from 60 business units in 20 large firms. Mitra and Chaya (1996) analyzed the relationship between IT investments and operational costs at the firm level and found that higher IT investment levels are usually associated with lower production cost and lower total operating cost of firms. Although most of the studies agree that IT has a positive impact on firm performance and productivity, a few have conjectured that performance and productivity may also impact the level of IT investments. Weill (1992) formally described a circular causal relationship between IT investments and firm performance. That is, investments in IT lead to better performance, and better performance in turn leads to higher IT investments. While some later studies have also
  • 4. ARTICLE IN PRESS Q. Hu, J.J. Quan / International Journal of Information Management 25 (2005) 39–53 42 noted the possibility of this reverse causality between IT investments and firm performance measures (Kraemer & Dedrick, 1993; Brynjolfsson & Hitt, 1996; Hitt & Brynjolfsson, 1996; Siegel, 1997; Sircar, Turnbow, & Bordoloi, 2000; Hu & Plant, 2001; Shin, 2001; Hitt, Wu, & Zhou, 2002), only a few have explicitly tested the existence of this relationship. Weill (1992), for instance, tested the relationship with the data collected from the US valve manufacturing industry. His results suggest that the transactional IT investments in prior years are significantly correlated to the performance measures of the firms and vice versa. Hitt and Brynjolfsson (1996) and Shin (2001) both noticed the possibility of the reverse causality in their production function- based models and used two-staged least-square (2SLS) regression and 1-yr time lag in order to address this issue. However, no significant changes were found that affect the general conclusions of the main models in both cases. Hu and Plant (2001) explicitly tested the reverse causal relationship and their analyses of firm-level IT spending data substantiate the conjecture that it is more likely that better financial performance leads to higher IT spending than the other way around. 2.2. Research model and hypotheses We develop our research model based on the economic and process views of IT value and the feedback relationship between IT investments and firm performance as discussed above. We postulate that IT investments create positive impact on the performance and productivity of a firm through improving the efficiency and effectiveness of business processes and that the impact on productivity at the firm level eventually shows up in the statistics at the industry level. Moreover, we suggest that the concept of information intensity of value chain and products proposed by Porter and Millar (1985) can be used to complement the process view of IT value for a better understanding of IT impact on firm performance. The value-chain theory posits that the degree of influence of IT on the value-chain activities is moderated by the interaction between the information intensity of the value chain and the information intensity of the products. Firms with high value-chain information intensity and high product information intensity would benefit most from IT investments while firms with low value-chain information intensity and low product information intensity would benefit least from such investments (Porter & Millar, 1985). In addition, firm performance and productivity could also have a positive impact on the level of IT investments: better performing and highly productive firms tend to invest more in IT while firms experiencing financial difficulties tend to cut IT budgets in order to reduce cost and redirect resources to more essential areas. These hypothesized relationships are summarized in the research model shown in Fig. 1. In this study, we focus on the issue of IT impact on productivity at the industry level. Productivity measures have many benefits over financially oriented performance measures, such as profitability, often found in empirical studies. Productivity is inherently an internal measure that reflects the operational efficiency of firms or industries and not necessarily a zero sum game at the aggregated level. Financial measures, on the other hand, are dependent as much on the external competitive and economic conditions as internal operational efficiency. Difficulties often arise when the firms are aggregated for analysis of financial performances due to the redistribution effect. As a result, IT investments are often found to have positive impact on productivity but not on profitability at firm level when aggregated data sets are used (Hitt & Brynjolfsson, 1996;
  • 5. ARTICLE IN PRESS Q. Hu, J.J. Quan / International Journal of Information Management 25 (2005) 39–53 43 Products Info. Intensity Industry IT Firm Business Investments Productivity Productivity Processes Value Chain Info. Intensity Fig. 1. Research model. Low High Digital goods & Digital goods & Product Information Intensity service service High Low IT/Total High IT/Total Investment Ratio Investment Ratio Physical goods & Physical goods & service Low service High IT/Total Low IT/Total Investment Ratio Investment Ratio Value Chain Information Intensity Fig. 2. Classification of industries based on information intensity. Rai et al., 1997). In a recent meta analysis of firm-level IT payoff studies, Kohli and Devaraj (2003) also suggested that productivity-based measures are more suitable for capturing IT investment payoff than profitability-based measures because productivity measures are less likely to be confounded by external factors. Another key construct in the research model is information intensity of both product and value chain. Although the term ‘‘information intensity’’ was first used by Porter and Millar (1985) and later appeared in a number of studies (Glazer, 1991; Palmer & Griffith, 1998; Bhatt, 2000; Andal- Ancion, Cartwright, & Yip, 2003), its definitions vary greatly and are rarely operationalized. In this study, we adopt the definition of information intensity as the degree to which a firm’s products and operations are based on the information collected and processed as part of the exchanges along the value chain (Glazer, 1991). We assume that the information intensity of an industry reflects the collective information intensity of all firms in that industry. In this research, we operationalize the product information intensity using the dichotomy of digital goods and services vs. physical goods and services, and the value-chain information intensity based on the ratio of IT investments to total capital investments firms make each year in an industry. This classification scheme is shown in Fig. 2.
  • 6. ARTICLE IN PRESS Q. Hu, J.J. Quan / International Journal of Information Management 25 (2005) 39–53 44 Finally, we extend the theories of IT value, most of which are based on the firm-level analysis, to an industry-level analysis. We argue that the collective behavior of all firms in an industry should reflect in general the behavior of the majority of the individual firms. It is reasonable to assume that at the economy level, each individual industry has its own value-chain activities and products and services, just like individual firms, that represent the collective behavior and characteristics of the firms in the industry. In this regard, the use of productivity rather than financial performance measures becomes essential when the analysis is anchored at the industry level. We postulate that industries with high product information intensity, such as banking, financial, and insurance services, should benefit more from IT investments than industries with low product information intensity, such as energy, mining, and construction. This is due to the fact that if the products moving through the primary value-chain activities are primarily information, extensive use of IT can result in significant improvement of operational efficiency. On the other hand, if the products are physical goods, the impact of IT on the operational efficiency of the value-chain activities may not be as profound as in the former case. Consider the case of a stock brokerage firm vs. a coal mine. By switching to an on-line stock trading and account management system, the brokerage firm can greatly enhance its productivity; while the productivity gain in the coal mine as a result of switching to a computer-based production and inventory management system may not be as great. This line of thought leads to our first hypothesis. H1. Investments in IT have positive and significant impact on the productivity of the industries that are product information intensive, Ceteris paribus. Similarly, industries with high value-chain information intensity, such as retail, manufacturing, and transportation, should benefit more from investments in IT than those with low value-chain information intensity. The main characteristic of the industries with high value-chain information intensity is that the efficiency of the entire value chain heavily relies on the efficiency of information processing due to the complexity of inbound and outbound logistics and internal material flow and control. Use of computer applications such as ERP and supply chain management systems, can significantly increase the efficiency of the value chain and greatly reduce errors, slacks, cycle time, etc. So, our next hypothesis is H2. Investments in IT have positive and significant impact on the productivity of the industries that are value-chain information intensive, Ceteris paribus. The dynamics between product information intensity and value-chain information intensity must also be considered. For industries with both high product information intensity and high value-chain information intensity, such as manufacturing and transportation, IT impact on productivity could be further enhanced by the multiplier factor of product and value-chain information intensities. This leads to H3. Investments in IT have positive and the most significant impact on the productivity of industries that are both value-chain information intensive and product information intensive, Ceteris paribus.
  • 7. ARTICLE IN PRESS Q. Hu, J.J. Quan / International Journal of Information Management 25 (2005) 39–53 45 For industries with low product information intensity and low value-chain information intensity, the impact of IT investments on productivity is limited. Consider the case of the construction and building materials industries where productivity is mainly dependent on the production technology. IT investments can improve the efficiency of certain aspects of the supportive value-chain activities—such as human resource management, procurement, and technology development, but their contributions to productivity are relatively minor. This leads to H4. Investments in IT should have positive but insignificant impact on the productivity of the industries that are neither value-chain information intensive nor product information intensive, Ceteris paribus. Finally, we address the circular feedback relationship between IT investments and productivity identified by prior studies. That is, firms invest in IT assets to enhance their strategic and operational capabilities whenever conditions are favorable, such as when they experience or anticipate better performance or higher productivity. The conjecture calls for H5. There is a positive feedback relationship between IT investments and productivity: higher IT investments lead to higher productivity, which in turn stimulates higher IT investments. 3. Data and methods 3.1. Characteristics of the data sets Three separated data sets on IT investments and productivity at industry level were gathered from the US government databases available online. The first data set, obtained from the Bureau of Economic Research (BEA) web site, contains real-cost, non-residential capital investments (in 1996 dollars) of more than 60 categories of equipment and structural assets in different industry groups from 1970 to 1999. Out of these asset categories, 15 are identified as IT assets and 46 as non-IT assets. The second data set, also from the BEA web site, contains the GDP and GDP price deflators of nine industries. The industry groups in the investment data set are regrouped according to the nine industry groups of the GDP data set based on the SIC code, and all statistics are aggregated accordingly. The annual GDP data for each industry are converted to the 1996 real dollar value using the GDP price deflator. The third data set, obtained from the online database of Bureau of Labor Statistics (BLS), contains employment statistics for all of these industries except agriculture. Henceforth, the number of industries in our sample is reduced to eight (mining, construction, transportation, manufacturing, retail, wholesale, finance, and services). The final data set used in the statistical analyses thus contains eight industries over a 30 yr period, with a total of 240 data points. We define productivity in this study as GDP per employee for each industry, essentially labor productivity. Its advantages are that it scales the outputs of all industries to the same comparable scale and that it is considered more sensitive to the changes of IT capital than the more general multifactor productivity (Triplett, 1999; Gordon, 2002). Similar definitions of productivity have been used in other IT investment studies (Kraemer & Dedrick, 1993; Dewan & Kraemer, 1998).
  • 8. ARTICLE IN PRESS Q. Hu, J.J. Quan / International Journal of Information Management 25 (2005) 39–53 46 Table 1 IT investments as a percentage of total capital investment1 Manuf. Transport Wholesale Finance Service Year Mining Constr Retail Average 4.04 13.75 8.28 11.90 13.88 1970 0.29 0.92 2.04 6.89 9.99 14.98 15.29 16.65 14.23 1975 0.35 0.79 5.60 9.73 13.04 22.36 25.13 16.14 18.16 1980 4.13 1.45 7.63 13.51 18.22 24.10 31.95 15.67 25.57 1985 4.98 1.28 11.85 16.70 22.17 27.80 38.65 21.01 34.16 1990 6.07 5.23 14.09 21.15 25.08 33.26 50.62 35.33 35.97 1995 11.23 12.71 19.09 27.91 42.49 39.87 69.58 50.50 56.28 1999 14.73 20.80 37.94 41.52 1 Industries in italic are those with above average IT/TOT ratio and thus considered as value-chain information intensive in this study. The overall characteristics of IT investments in these industries during the time period are shown in Table 1. The percentages of IT investments in the total capital investments in these industries have increased significantly over the last 30 years and even more so since 1985. For instance, in finance and insurance industry, IT investments were about 12–15% of total capital investments from 1970 to 1985, but jumped to about 50% from 1985 to 1999. Wholesale, service, and manufacturing all show dramatic increases in IT investments as a percentage of the total capital investment. 3.2. Analytical methods The extant literature on IT impact is ambiguous about the causal relationship between IT investments and productivity or other performance measures, especially given the fact that ‘‘impact’’ implies causality. The use of economic production functions and the related log-linear regression models as the base research models for estimating the contribution of IT investments to productivity leaves the question of causality largely unanswered (Brynjolfsson & Hitt, 2000). One of the main objectives of this study is to verify explicitly this causal relationship. To this end, we adopt the Granger (1969) causality model, a well-established causal technique, to determine explicitly the exact nature of the relationship between productivity and IT investments. 3.2.1. The Granger causality model The main strength of the Granger causality (1969) model is that it can simultaneously test all possible causal relationships between two variables or vectors of variables without any predetermined causal assumptions. Let xt and yt be two time series data; the general Granger causal model with the consideration of possible instantaneous causality can be written as P P n n xt ¼ b0 yt þ aj xtÀj þ bj ytÀj þ t ; j¼1 j¼1 (1) P P n n yt ¼ c0 xt þ cj xtÀj þ d j ytÀj þ Zt ; j¼1 j¼1
  • 9. ARTICLE IN PRESS Q. Hu, J.J. Quan / International Journal of Information Management 25 (2005) 39–53 47 where t and Zt are two uncorrelated white noise error terms with zero means, a, b, c, d are coefficients of the time series variables. This definition of causality implies that yt causes xt only if some of the bjs are not zero, and xt causes yt only if some of the cjs are not zero. A feedback relationship between xt and yt exists if both of these events occur. If b0 is not zero, then the instantaneous causality is occurring and the knowledge of yt will improve the ‘‘prediction’’ or goodness of fit of the first equation for xt, and vice versa is true if c0 is not zero. Specifically, in terms of our research question, we define xt to be IT investments and yt to be productivity. If some of the coefficients of yt in the first equation of (1) are non-zero, we can conclude that the better productivity of an industry had in prior years, the more it will invest in IT this year. On the other hand, if some of the coefficients of xt in the second equation of (1) are non-zero, we can conclude that the IT investments made in prior years cause better productivity in this year. 4. Results and analyses Before we can use the model as specified in Eq. (1), two issues with the data series have to be addressed. The first is the stationarity issue of the time series. Granger model requires that both time series xt and yt be I(0) stationary and purely non-deterministic. However, later studies have shown that these conditions can be relaxed without affecting the validity of the inference so long as only linear relations are of interest (Geweke, 1984). We tested the stationarity for the time series xt and yt, assuming a linear relation. The results show that all data series are I(0) stationary except the retail productivity series, which is a non-stationary I(1). The second issue is the multicollinearity between xt and x(tÀj) and between yt and y(tÀj). Since the condition indices are well above the threshold of 20, three modifications are made to the Granger model (1). First, the instantaneous causality terms are dropped. This change should not affect the model’s ability to detect causal relationships between IT investments and productivity because of the lagging effect of IT investments due to learning and process adaptation (Weill, 1992; Brynjolfsson, 1993). Second, only one time lag is used. And third, the ratio of IT investments to total capital investments (IT/TOT) is used as a proxy for IT investments. Using the ratio has an additional benefit: the ratio scales all industries to the comparable level. Similar ratio as measure of IT investments has been used in other studies (Weill, 1992). With these changes, the working model for causality between IT investments and productivity is modified as xti ¼ a1 xðtÀ1Þi þ b1 yðtÀ1Þj þ t ; (2) yti ¼ c1 xðtÀ1Þi þ d 1 yðtÀ1Þi þ Zt ; where xti ¼ ðIT=TOTÞti ; GDPti yti ¼ ; ð3Þ EMPti
  • 10. ARTICLE IN PRESS Q. Hu, J.J. Quan / International Journal of Information Management 25 (2005) 39–53 48 and i ¼ 1; 2, y, 8, representing the industry under consideration and t ¼ 1; 2, y, 30, representing the time period from 1970 to 1999. EMPti is the total number of employees of industry i at year t. Eq. (2) results in eight models for each hypothesized causal relationship. Considering the fact that these eight industries are not completely independent of each other and are constrained by the same set of macroeconomic conditions of the economy, we choose the seemingly unrelated regression (SUR) procedure to jointly estimate the parameters for all eight industries. The results are presented in Tables 2 and 3. Table 2 shows that out of the eight industries, all but two (construction and finance) have statistically significant c1’s, establishing not only the correlation but also the temporal sequence, a strong indication of causal relationship. The overall model fits well with the data, with a system weighted R2 ¼ 0:970; and a system weighted MSE=0.974. The results largely support hypotheses H1–H3 that IT investments contribute positively to the productivity of the industries where at least one of the information intensities is deemed as high. For example, in the manufacturing and the transportation industries, which are value-chain information intensive (H2), the estimated coefficients of c1 are very strong in both statistical significance levels and magnitudes. In the service industry, which is value-chain intensive (H2), the Table 2 Testing for causality: GDP/EMP as dependent variable t-Value c1 t-Value d1 t-Value Industry Intercept Mining 0.344*** 3.26 1.278* 1.73 0.658*** 6.23 Construction 0.047 1.43 0.046 1.16 0.879*** 9.91 Manufacturing 0.041* 1.99 0.136** 2.36 0.859*** 11.62 Transportation 0.097*** 3.74 0.177** 2.38 0.797*** 12.54 Wholesale 0.026 0.81 0.039* 1.72 0.936*** 12.43 Retail 0.023 1.29 0.043*** 3.35 0.875*** 10.18 Finance 1.151*** 19.73 À0.114** À2.53 À0.160 À1.48 Service 0.082*** 5.29 0.106*** 5.95 0.625*** 8.98 System weighted R2=0.970, and system weighted MSE=0.974 with 208 degrees of freedom. ***po0:01; **po0:05; *po0:1: Table 3 Testing for causality: IT/TOT as dependent variable t-Value a1 t-Value b1 t-Value Industry Intercept Mining 0.017* 2.01 1.081*** 18.26 À0.014 À1.68 Construction 1.039*** 32.55 0.213*** 3.22 À0.076*** À3.10 Manufacturing 0.922*** 8.13 0.208 1.44 À0.057 À1.40 Transportation 0.909*** 10.12 0.094 1.23 À0.029 À0.94 Wholesale 0.959*** 28.65 0.295** 2.67 À0.111** À2.37 Retail 1.102*** 30.33 0.555** 2.76 À0.118*** À2.84 Finance 0.919*** 10.32 0.098** 2.14 À0.080** À2.32 Service 0.106 1.19 1.237*** 11.93 À0.538 À1.34 System weighted R2 ¼ 0:962; and system weighted MSE=0.958 with 208 degrees of freedom. ***po0:01; **po0:05; *po0:1:
  • 11. ARTICLE IN PRESS Q. Hu, J.J. Quan / International Journal of Information Management 25 (2005) 39–53 49 estimated coefficient of c1 is significant but its magnitude is not as large as in the manufacturing and the transportation industries. We should note that mining industry seems to be an outlier, and the results for retail may be unreliable because of non-cointegration. The insignificance of the construction industry is consistent with hypothesis H4 that IT investments have positive but insignificant impact on the productivity of industries that are low in both product information intensity and value-chain information intensity. The insignificance of the finance industry seems to contradict hypothesis H1 and thus deserves some discussion. Our hypothesis predicts that IT investments should have a strong and significant impact on the productivity of the firms in the finance industry. So why is the test insignificant? We believe it could be attributed to the phenomenon of a diminishing rate of return on IT investments. The very nature of high product information intensity and high value-chain information intensity of the finance industry makes it the ideal candidate for using IT to improve its productivity. As a matter of fact, firms in the finance industry were among the first that embraced IT for operational and strategic applications and are the heaviest users of IT in comparison to firms in all other industries. As a result, realization of any significant productivity improvement from further investment in IT would be difficult simply because the potential contributions of IT assets may be close to or already at their limits. Results in Table 3 show that hypothesis H5 is partially supported. Out of the four industries that have significant estimates of b1, only the wholesale and retail1 industries are also significant in the previous causal model, suggesting that a circular feedback exists between IT investments and productivity in these two industries. Interestingly, the construction and finance industries show an unequivocal feedback relationship: higher productivity in the previous year leads to higher IT investments in the subsequent year, but not the other way around. In both of the causal test results, a1 and d1 represent auto-correlation in the time series of GDP/ EMP and IT/TOT, respectively. The dominant magnitude of the estimates and the strong statistical significance (po0:01) clearly indicate that the primary influencing source on the current levels of productivity and IT investments are their previous levels, an indication of the existence of a momentous trend in both series. In practical terms, the results suggest that productive industries tend to continue to be productive and that the current level of IT investments is largely based on the previous levels. Since our data span over a period of 30 years, we conjecture that there might be changes in terms of the degree of IT penetration and how IT assets were used by different industries when the IT infrastructure and applications shifted from mainframe-based centralized architecture to client/server-based networked architecture. Since this shift occurred after the proliferation of PCs in the corporate computing environment starting in the mid-1980s, we decided to test the causality models in two time periods, 1970–1984 and 1985–1999. The results are shown in Table 4. The results suggest that differences exist between these two time periods. The trend suggests that the impact of IT investments became significant in more industries in the later time period, which might explain why earlier studies on IT impact tend to find negative or insignificant results while later studies tend to find positive and significant results. It also shows that the contribution of IT investments to productivity in the transportation industry had disappeared in the later 1 Even though b1 is significant for the retail industry in the causal model, caution is suggested because of the non- stationary data.
  • 12. ARTICLE IN PRESS Q. Hu, J.J. Quan / International Journal of Information Management 25 (2005) 39–53 50 Table 4 Causality over different timesa Industry GDP/EMP as dependent variable IT/TOT as dependent variable 1970–1984 1985–1999 Overall 1970–1984 1985–1999 Overall c1 c1 c1 d1 d1 d1 Mining 4.273** 3.319*** 1.278* Finance 0.362*** 0.098** Service 0.102*** 0.081*** 0.106*** Retail 0.143*** 0.043*** 1.253*** 0.555*** À0.585* Wholesale 0.438*** 0.039* 0.349* 0.926*** 0.295*** Transportation 0.344*** 0.177** 1.009*** Construction 0.417** 0.213*** Manufacturing 0.340*** 0.194*** 0.136** ***po0:01; **po0:05; *po0:1: a Only significant c1 and d1 values are shown. period, perhaps because of IT capital saturation and a diminishing rate of return on IT investments. The results in Table 4 also indicate that there are more industries in which IT investments are influenced by productivity in the later time period than in the earlier time period. We believe this is largely because of the ‘‘IT as a strategic necessity’’ phenomenon (Clemons & Row, 1991; Powell & Dent-Micallef, 1997) that occurred in the early 1990s when many firms, especially those in the high information intensity industries, simply had to invest heavily in IT just to stay in business. Under such a competitive environment, firms tend to allocate more capital spending when they are in good financial standing and less when they are under financial pressure. 5. Conclusions In this study, we use the industry-level IT investments and productivity data from 1970 to 1999 and Granger causality model to test the exact nature of the ‘‘strong correlation’’ between IT investments and productivity. Our results suggest that there is a causal relationship between IT investments and productivity in six out of the eight industries in our sample. Therefore, IT investments have contributed positively to the productivity growth in these industries. We also confirm that a feedback causal relationship between IT investments and productivity exists in some industries. We explain our results using the information characteristics of the business value chain and the products of the firms. We highlight the issue of causality in the studies of IT investment impact on productivity. We believe that the theoretical framework and the analytical methodology used in this study can be applied to the area of research on IT impact on firm performance in general. Future causal studies at process or firm level should be interesting from the perspectives of better understanding of IT value and impact and better management practices. The causal modeling techniques could also be expanded beyond the Granger causality. Finally, we believe that the phenomenon of a
  • 13. ARTICLE IN PRESS Q. Hu, J.J. Quan / International Journal of Information Management 25 (2005) 39–53 51 diminishing rate of return on IT investments observed in this study warrants further theoretical and empirical investigation. This study suffers certain limitations. First, our industry-level data sets are not detailed enough to differentiate the diverse characteristics of firms. Second, the IT investment data are not quality- adjusted. But since non-quality-adjusted data are more likely to cause underestimation rather than overestimation of the impact of IT investments, quality adjustments would only strengthen our conclusions. Third, the effect of the two moderators, product and value-chain information intensities, are not tested directly to quantify their impact on the causal relationships between IT investments and productivity. Finally, the operationalization of the two moderators is intuitive and categorical. Future studies could be designed to address the moderator effect explicitly with more advanced information intensity measures and modeling techniques. Acknowledgments This paper has benefitted greatly from the comments and suggestions provided by the anonymous reviewers, the North America Regional Editor, and our colleagues. The first author would like to acknowledge the financial support from the InternetCoast Institute Scott Adams Professorship at Florida Atlantic University. References Andal-Ancion, A., Cartwright, P. A., & Yip, G. S. (2003). The digital transformation of traditional businesses. MIT Sloan Management Review, 44(4), 34–41. Barua, A., Krieble, C., & Mukhopadhyay, T. (1995). Information technologies and business value: an analytical and empirical investigation. Information Systems Research, 6(1), 3–23. Barua, A., & Lee, B. (1997). The information productivity paradox revisited: a theoretical and empirical investigation in the manufacturing sector. Industrial Journal of Flexible Manufacturing Systems, 9(2), 145–166. Benaroch, M., & Kauffman, R. J. (2000). Justifying electronic banking network expansion using real options analysis. MIS Quarterly, 24(2), 197–225. Berndt, E. R., & Morrison, C. J. (1995). High-tech capital formation and economic performance in US manufacturing industries: an exploratory analysis. Journal of Econometrics, 65(1), 9–43. Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: an empirical investigation. MIS Quarterly, 24(1), 169–196. Bhatt, G. D. (2000). An empirical examination of the effects of information systems integration on business process improvement. International Journal of Operations & Production Management, 20(11), 1331–1348. Brynjolfsson, E. (1993). The productivity paradox of information technology. Communications of the ACM, 36(12), 67–77. Brynjolfsson, E., & Hitt, L. M. (1996). Paradox lost? Firm-level evidence on the returns to information systems spending. Management Science, 42(4), 541–558. Brynjolfsson, E., & Hitt, L. M. (2000). Beyond computation: information technology, organizational transformation and business performance. Journal of Economic Perspectives, 14(4), 23–48. Carr, N. G. (2003). It doesn’t matter. Harvard Business Review, 81(5), 41–49. Clemons, E. K., & Row, M. C. (1991). Sustaining IT advantage: the role of structural differences. MIS Quarterly, 15(3), 275–292.
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