The competitive benefits of IT investments: a two industry comparison.

ABSTRACT

Researchers have questioned for sometime the financial value that IT investment provides to firms. Primary difficulties have been in the statistical measurements deployed. This paper shows that these difficulties can be explained in part by how the investing firm is classified, i.e. a high-tech or low-tech company. In particular, this study focuses on a low tech industry (the interstate motor carrier industry) and a higher technology industry (the household goods industry) and compares the financial benefits of their respective IT investments. This research finds that IT investments have benefited lower technology firms greater than those of higher technology firms.

1. INTRODUCTION AND DEVELOPMENT OF HYPOTHESES

During the past two decades, information technology (IT) has enabled businesses to go through major transformations. Many new IT initiatives, including Y2K compliance, e-commerce, IT-enabled mega mergers, supply chain integration, euro conversion, and distributed databases, requiring extensive outlays were implemented. To support these initiatives, IT budgets of most companies increased substantially (Hitt, et al., 2002). Past empirical studies exploring the association between profitability and IT resulted in inconclusive and often contradictory findings. To shed additional light on this subject, the importance of the level of technology of the firm as an explanatory factor in understanding the relationship between IT and profitability needs to be examined.

It is essential to know the effectiveness of IT investments in improving business performance and value. The linkage between IT and performance can be better understood by examining the impact of IT on productivity, and profitability separately. Productivity analysis is always difficult. Early evidence from macro-level productivity measures suggested that computerization of businesses did not lead to improved productivity (Brynjolfsson, 1998). This apparent lack of association between IT and aggregate productivity has been studied and the term "productivity paradox" was coined. Possible explanations such as measurement errors in inputs and outputs, aggregation of data, lags in learning and adjustment, redistribution of profits and mismanagement of IT (Brynjolfsson, 1993) have been suggested. However, recent firm-level analyses utilizing larger sample sizes found that information technology had a positive effect on the output of a firm (CSC, 2001; Brynjolfsson and Hitt, 1996; McKinsley and Company, 2002). These studies have led to questioning the validity of the productivity paradox.

Many empirical research studies suggest that association between profitability and IT is either weak, or nonexistent because of the measures used for profitability. Other studies support increased profitability and financial performance (Porter, 1990; Rai, et al., 1997; Hitt and Brynjolfsson, 1996) due to IT. In addition, other financial measures such as the connection between return on investment and IT are either not significant (Porter, 1990; Rai, et al., 1997) or negative (Hickman and Raia, 2002; Brynjolfsson, 1998). These findings support some observations and anecdotal evidence. Still some firms are more successful than others in realizing the value of their IT investments, in some cases, inept IT deployment has led, in part, to poor financial performance, e.g. implementation problems of supply chain management software mainly contributed to third quarter losses of Hershey Foods and bankruptcy of FoxMeyer Health to flawed implementation of SAP software.

However, it is commonly believed that companies investing heavily in IT are able to advantage themselves competitively (Chan, 2002; Kleindl 2000; Watson, et al., 2000). Have recent technological advancements allowed smaller companies to compete with larger companies in service levels? As evidenced by the articles from Chakraborty and Kazarosian (2000); Nagarajan, et al., (1999); and Graham (2001), these and other questions are being asked in the motor carrier industry as companies push to implement IT initiatives. As such, this research provides empirical evidence that establishes a clear relationship between investment in IT and its impact on profitability.

Due to the inconclusive evidence it is plausible that there are company characteristics that influence the financial success of IT implementation. The preceding observations indicate that the value provided by IT investments is non-uniform across firms and industries. Some of the variation may be explained by the level of technology of the offering of the firm or the size of the firm. Therefore, the following hypotheses are derived from studies involving the financial value of IT investments and R&D, and are the basis of this research.

2. HYPOTHESES

H1: Low-Tech firms will financially benefit from investment in IT.

H2: Higher-Tech firms will be less financially benefited than their lower-tech counter-parts by investment in IT.

H3: Investment in IT appears to be traded off with R&D investment for higher-tech firms.

H4: Within an industry, companies that invest in IT are larger than their counterparts.

3. METHODOLOGY AND DATA COLLECTION

Data was collected from secondary sources. The data came from two industries, the interstate motor carrier industry and the household goods industry. These industries were chosen because of their rank differences in levels of technology offerings. Motor carriers are low-tech whereas firms in the household goods industry offer products or services that are higher in level of technology. The definition of high-tech/low-tech is discussed below.

The determination of what constitutes a high-tech or low-tech industry has been the source of much discussion. Luker and Lyons (1997) quoted one analyst as saying, "Everyone knows what [high-tech] is, but no two lists are alike" (p. 13). The Congressional Office of Technology Assessment reported that high-tech firms typically devoted high proportions of expenditures to R&D activities; by extension, low-tech firms spend low proportions of expenditures on R&D activities. Because R&D expenditures as a proportion of sales will normally approximate this definition and because these figures are readily available on an industry-by-industry basis, this latter measure to determine the level of technology of industries was used. While any macro assessment of technology for an industry may find anomalies at the micro level, this criterion provides an objective means for classifying industries into high-tech and low-tech categories. Furthermore, the results of such a classification are generally in agreement with the understanding of what constitutes "high-tech" or "low-tech." For a detailed description of the methodology used to determine the level of technology of offerings, see Kellar and Preis (2003).

Moderately high-tech industries spend a lower percentage of sales on R&D than high-tech industries, nevertheless, their spending on R&D is significant. Similarly, moderately low-tech and low-tech categories are grouped by the similarity of their spending on R&D. Thus, moderately high-tech industries include electrical equipment, appliances, and components, resins, synthetic rubber, fibers, and filament, and machinery; moderately low-tech industries include paper, printing, and support activities, nonmetallic mineral products, and fabricated metal products; and low-tech industries include food, transportation and warehousing, furniture and related products, and wood products. Notice that the motor carrier industry is a low-tech industry while the household goods industry generally consists of higher technology offerings.

A definition allowing product categories to change from high-tech to low-tech needs to be adopted. This will then clarify categories such as candles, light bulbs, refrigerators, steel, and railroads in their areas. While these industries were once considered high-tech, they now can be considered as low-tech because insufficient amounts of R&D funding have been invested in them in recent years. Thus, even such products as 486-based personal computers and DC-3 aircraft, which were once considered high-tech, would now be considered low-tech by comparison to present day offerings. Nevertheless, the product categories of personal computers and aircraft are still considered high-tech because R&D expenditures in these industries have remained high (as a percentage of sales).

The literature suggest motor carriers with revenues smaller than $25 million were mostly privately owned and operated, even though they account for a large number of trucking companies (Nagarajan, et al., 2000). Therefore, this research focuses on companies with over 25 million dollars in net sales. Inbound Logistics 2000 Top 100 list provided the initial list of companies in our study. This list was expanded by searching Standard and Poor's COMPUSTAT database for similar companies. Because the COMPUSTAT database contains only public firms, privately owned and operated companies from the original "Top 100 Company" were excluded and some public trucking companies were added to the list. A representative sample of large motor carriers was obtained. Extensive efforts and processes were utilized to exclude any company whose primary focus might be in peripheral or related businesses such as freight forwarding, logistics services, or transportation consulting rather than trucking. Sources for this determination included: Moody's Transportation Manual (1997-2001), Wall Street Journal Index (1997-2001), Dun and Bradstreet's Million Dollar Directory (1997-2001) and the existing web sites of the companies in the sample.

The screening and validation processes identified the companies that 1) had trucking as a main offering/focus, 2) had annual net sales of over 25 million dollars, and 3) were publicly traded. These were then classified as large or not large companies, using the cut-off of 100 million dollars in annual net sales to distinguish between the two groups.

The trucking companies were divided into two mutually exclusive groups, companies that Invested heavily in IT (ITI) or companies that did Not Invest heavily in IT (NITI). This was operationalized by classifying companies that implemented internet services for their customers as ITI companies whereas those companies that did not implement internet services for their customers were classified as NITI.

The starting point of the data collection for the motor carrier industry was 1997 since Internet services were not widely available before this time. The ending point was the end of 1999. Two reasons prompted the use of this time period. First, at the time of collection, year 1999 was the last period for which the full content of information from S&P's Compustat database was available. Second, according to the survey by Nagarajan et al., (2000), by early 2000, 75% of trucking companies reported having at least minimal Internet activity. These numbers were exceeded in the sample, so it is reasonable to expect the impact of offering Internet services to be demonstrable with these data. Companies were identified as offering Internet services by direct phone calls to the company and visits to their web sites. The data collection process yielded 157 companies with 75 being ITI and 82 being NITI companies.

Data from industries that differed from the motor carrier industry were needed to test the hypotheses. Past studies have found that there are substantial differences among different industries in the level of IT investments (Stiroh, 2001) and the results obtained due to IT (McKinsley and Company, 2002 and Stiroh, 2001). Similarly, literature shows that different industries invest in R&D at very different levels and even use R&D intensity as the definition of industry technology level. To contrast with the extreme low levels of R&D found in the interstate motor carrier industry, extreme low-tech industries were not chosen. The Consumer Goods Industry avoids these extreme low levels of R&D intensity (Porter, 1990 and McKinsley and Company, 2002) and allows for appropriate measurements. Therefore, companies from the Consumer Goods Industry were chosen to contrast with the motor carrier industry for the study.

Information Week's Annual Survey of IT Innovators in the consumer goods industry served as the initial dataset for consumer goods firms in the study. There were 59 different companies listed from 1996 to 2001. A company was considered to be an IT innovator if it appeared on the six annual lists included in the data.

Structural and financial data for similar companies were obtained from Standard and Poor's Compustat. The resulting dataset included only publicly traded companies of which only 36 of the original 59 companies coming from 26 distinct primary SIC codes were included. The total dataset included 651 companies of which 615 companies were not classified as IT innovators. Data concerning each company in the sample included: primary SIC code, annual revenues, assets, R&D expenses, R&D intensity (R&D expenses divided by annual revenues), ROA, ROE, and ROI. These data were collected for the years 1994 to 2001 (from two years preceding the IT innovation listing to the ending date). The dates allowed for testing the companies for differences before their listing and throughout the tested period. This approach allowed for identification of significant changes that may have occurred due to the companies' involvement or lack of involvement in IT initiatives.

The data were grouped by primary SIC code and the above listed fields were averaged for each group. The averages were calculated separately for IT innovators and non-IT innovators.

4. ANALYSES AND RESULTS

The interstate motor carrier industry was chosen to test the financial benefits of IT investments by low-tech firms. Size differences and financial performance from the motor carrier industry were compared across groups (ITI vs. NITI). This was done for the five measures, number of employees, revenues, ROA, ROE and ROI. The data was found to be highly non-normal. Due to sample size limitations, parametric tests were not appropriate. The non-parametric Mann-Whitney test was employed.

Similar tests were performed for the household goods industry. Due to differences in the structure of the industry, some differences existed in statistical tests. In particular, a paired t-test, and non-parametric equivalents were used to determine if significant differences existed between ITI and NITI companies with respect to company size (assets and annual revenues). Also, R&D intensity was compared for the two groups. Finally, financial performances (ROA, ROE, and ROI) were compared across the two groups. All comparisons were performed using t-tests and non-parametric equivalents.

The results from these two industries were compared to determine the role of technology level of offerings on the financial benefits of IT investments.

5. RESULTS

The results of the statistical tests are listed in tables 1 and 2.

The results show that H1 is confirmed for ROA, ROE and ROI. In particular, low-tech firms were advantaged by investments in IT with the least significant p-value being 0.0046 for ROE.

The second hypotheses (H2) suggests that an inverse relationship exists between the level of technology of firms and the derived level of benefits from IT investments. This hypothesis was confirmed. The conclusion was derived from the observation that low-tech firms were greatly advantaged by IT investments whereas the higher-tech firms showed no such relationship (see tables 1 and 2).

Hypothesis three (H3) was confirmed. Consumer goods firms that invested heavily in IT were not as R&D intensive as companies that invested less in IT. This was shown to be true at a p-value of 0.015 (see table 2)

The results also confirm that ITI companies are larger companies than their NITI counterparts. This was confirmed with a p-value of less than 2% (see table 2).

6. STUDY LIMITATIONS, CONCLUSIONS AND MANAGERIAL IMPLICATIONS

6.1 Limitations Of The Study

The findings are a result of a cross-sectional study. Because of this, causality cannot be assumed. Also, the data comes from a limited number of industries thus attributing results across all industries can be questioned. The power of the statistical tests was limited due to the sample size. Larger sample sizes which require primary data acquisition can be used to further validate the results.

Further, the levels of technology and the number of industries can be expanded to add robustness to the results. Future analyses should include data captured across time. Incorporating the above will serve to enhance understanding of the tested relationships.

6.2 Conclusions and Managerial Implications

Smaller companies have received limited value from their IT investments. This may be due to the existence of scale economies playing a role in determining the success of IT investments. As IT costs decrease, smaller companies may eventually reach the necessary scale level to support the required returns. Since this relationship has yet to be proven, anticipated returns from IT investments by smaller firms remain speculative. Figure 1 identifies successful IT practices as confirmed in the results in the study.

The level of technology of a firm's offering is defined as a firm's R&D investment as a percentage of the firm's revenues. Low-tech firms have been shown to be benefited from IT investments while companies with higher-tech offerings have been shown to receive less financial benefit from IT investments. Management of low-technology firms should seriously consider pursuing IT investments, especially large low-tech firms. On the other hand, as a firm's offering increases in level of technology, the investment in technology should be pursued only after careful consideration. This is especially true for smaller companies.

Smaller companies that offer products or services that require a noticeable degree of R&D should continue to focus on their competitive strengths. In particular, investments in R&D were positively correlated with financial performance. However, investments in IT failed to deliver financial benefits to such firms.

Larger companies have high coordination costs, i.e. high complexity, and should look to IT investments as a potential cost reduction and efficiency increasing solution. The findings suggest that they will be financially advantaged.

Additional industry studies involving longitudinal analysis will aid in further answering the question of whether investments in IT significantly provide competitive advantage.

TABLE 1

            HO: Comparison
Dataset     of Means                Conclusions            p-value *

Number of   Mean number of          Mean number of         0.0036
Employees   employees is the same   employees is greater
            for ITI and NITI        for ITI than NITI

Net Sales   Mean net sales is the   Mean net sales is      0.0036
            same for ITI and NITI   greater for ITI than
                                    NITI

ROA         Mean ROA is the same    Mean ROA is greater    0.0115
            for ITI and NITI        for ITI than NITI

ROE         Mean ROE is the same    Mean ROE is greater    0.0046
            for ITI and NITI        for ITI than NITI

ROI         Mean ROI is the same    Mean ROI is greater    0.0031
            for ITI and NITI        for ITI than NITI

* the reported p-value is for the Mann-Whitney U-test.

TABLE 2

            HO: Comparison
Dataset     of Means *                      Conclusions      p-value **

Assets      [[bar]Assets.sub.ITI] =         ITI have more    .009
            [[bar]Assets.sub. NITI]         assets than
                                            NITI

Revenues    [[bar]Revenues.sub.ITI] =       ITI have more    .0129
            [[bar]Revenues.sub.NITI]        revenues than
                                            NITI.

R&D         [[bar]R&D.sub.ITI] =            No statistical   .466
            [[bar]R&D.sub.NITI]             difference

R&D         [[bar]R&Dintensity.sub.ITI] =   NITI are more    .015
intensity   [[bar]R&Dintensity.sub.NITI]    R&D intensive
                                            than ITI.

ROA         [[bar]ROA.sub.ITI] =            No statistical   .420
            [[bar]ROA.sub.NITI]             difference

ROE         [[bar]ROE.sub.ITI] =            No statistical   .424
            [[bar]ROE.sub.NITI]             difference

ROI         [[bar]ROI.sub.ITI] =            No statistical   .333
            [[bar]ROI.sub.NITI]             difference

* Comparison of Means of the items listed across all SIC codes on a
year by year basis

** the reported p-value is the largest value observed for the compared
means across all years and across all tests whenever statistical
significance is shown (sign test, Wilcoxon matched pairs test, and
paired t-test). Others are representative.

FIGURE 1: SUCCESSFUL PAST PRACTICES

Size of Firm

Large          Large Investment in IT      Large Investment in IT

Small          Moderate Investment in IT   Small Investment in IT
                                           (traded off with high R&D
                                           intensity)
               Low-Tech
               Higher-Tech
               Level of Technology

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Gregory M. Kellar, Penn State University, Delaware County

Anthony M. Akel, Long Island University/C.W. Post Campus

Author Profiles

Dr. Gregory M. Kellar earned his Ph.D. at the University of Tennessee at Knoxville in 1996. Currently he is an assistant professor at Penn State University, Delaware County.

Dr. Anthony M. Akel earned his Ph.D. at Northwestern University in 1974. Currently he is a professor of management at Long Island University/C.W. Post Campus, Brookville, NY.