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An Empirical Study of the Causal Relationship Between IT Investment and Firm Performance

An Empirical Study of the Causal Relationship Between IT Investment and Firm Performance. Hu, Q. and Plant, R. IRMJ, 14(3), 2001, pp. 15-26. Outline . Introduction Research background Research model and hypotheses Data and method Results Discussions Conclusions . Introduction .

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An Empirical Study of the Causal Relationship Between IT Investment and Firm Performance

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  1. An Empirical Study of the Causal Relationship Between IT Investment and Firm Performance Hu, Q. and Plant, R. IRMJ, 14(3), 2001, pp. 15-26

  2. Outline • Introduction • Research background • Research model and hypotheses • Data and method • Results • Discussions • Conclusions

  3. Introduction • Productivity paradox • What value does IT add to an organization? • The literature in 80s and 90s contend that IT can: • provide competitive advantages • add value • improves operational performance • reduces costs • increases decision quality, and • enhances service innovation and differentiation.

  4. Introduction (cont.) • The underlying theory • Effective use of IT  improvement in production, revenue, and profit • Several empirical studies support the arguments • Brynjolfsson and Hirt (1996)…

  5. Introduction (cont.) • However, not all studies of industry and firm level financial data have shown positive causal relationship between IT investment and improved firm performance. • Loveman (1994) found that IT investment has a negative output elasticity. • The figure implies that the marginal dollar would have been better spent on other categories of capital investments.

  6. Introduction (cont.) • Closer examinations of these studies revealed a flaw in the methodologies: • The impact of IT on firm performance was tested using the IT capital data and the performance data of the same period. • Under such circumstances, the correlation between IT capital variables and the firm performance variables has no inherent implication of a casual relationship, no matter how this correlation is established. • Why?

  7. Introduction (cont.) • In this study, the authors investigate the impact of IT investment on firm productivity and performance using well accepted casual models based on firm level financial data.

  8. Introduction (cont.) • It is unlikely that using concurrent IT and firm performance data would yield conclusive causal relationship between the two. • Arguments: • IT investment  performance • Performance  IT investment

  9. Research background • MIS literature contend the value of IT. • However, it is difficult to discern the “added value” from business financial data. • The main reason is the inability of organizations to track the return of IT investment when such investment may cross many business processes and activities.

  10. Research background (cont.) • It is difficult for IS managers to convince CEO to invest in IT projects when other capital spending opportunities exist. • We need empirical evidences.

  11. Research background (cont.) • Measuring IT effectiveness is always the top one issue in IM domain. • Management pressure want to scrutinize IT investment. • Are we sure that there is a payback on IT investment ? • The necessity to understand IT investment

  12. Research background (cont.) • Previous studies • Alpar and Kim (1990) • IT investment  financial performance • Subjects: commercial banks • Mixed results • IT investment is negatively correlated with cost • The relationship between the IT expense ratio and the ROE was insignificant in six out of the eight years studied.

  13. Research background (cont.) • Mahmood and Mann (1993): • Use Pearson correlation and Canonical correlations • Test 6 organization performance variables and 6 IT investment variables • Subjects: Computerworld “Premier 100” companies • Mixed results

  14. Research background (cont.) • A summary of the major studies reviewed above is presented in Table 1. • Overall, the literature on the IT impact on firm performance has been overwhelmingly positive. • Some studies asserted the causality. • Some used the correlation method. • Few used explicit casual models.

  15. Research background (cont.) • Correlation  related • Correlation  causality • It is possible • IT investment  firm performance • The assumption of Hirt and Brynjolfsson (1996) • The correlation-based models will not discover the true relationship between IT investment and firm performance.

  16. Research background (cont.) • Another flaw in the previous studies is using the same time periods. • Casual relationships between two factors inferred from concurrent data assume instantaneous causality between the two factors. • The lagged effect of IT investment • Osterman (1986), Brynjolfsson (1993), and Loveman (1994)

  17. Research background (cont.) • Two study objectives: • Determine whether there is a causal relationship between IT investment and firm performance with explicit causal modeling techniques • Determine the direction of the causal relationship

  18. Research model and hypotheses • Correlation does not necessarily imply causation. • If X causes Y, three conditions must hold. • Time precedence • Relationship • Nonspuriousness • For a relationship between X and Y to be nonspuriousness, there must not be a Z that causes both X and Y such that the relationship between X and Y vanishes once Z is controlled.

  19. Research model and hypotheses (cont.) • We can not use concurrent IT data and performance data with correlation analysis.

  20. Research model and hypotheses (cont.) • Porter and Millar (1985) asserted the three most important benefits from IT in a firm: • Reducing costs • Enhancing differentiation • Changing competitive scope • In any of the cases or as a combined result, the net effect of IT investment should be the increased productivity and better financial performance.

  21. Research model and hypotheses (cont.) • IT benefits come not form replacing old computers with new ones, in which the effect of investment can be realized immediately, but from organizational and procedural changes enabled by IT. • The effect of such changes may take years to realize.

  22. Research model and hypotheses (cont.) • Lagged effect • IT projects usually take years to implement. • Organization adaptation • Employees need time to be trained and re-skilled. • Finally, customers and the market are the last of these time-delayed chain reactions to respond which ultimately determines the firm performance.

  23. Research model and hypotheses (cont.) Previous IT investments Annual Sales Growth Operating Cost reduction Profitability improvement Present IT investments Productivity improvement

  24. Research model and hypotheses (cont.) • H1a: The increase in IT investment per employee by a firm in the preceding years may contribute to the reduction of operating cost per employee of the firm in the subsequent year.

  25. Research model and hypotheses (cont.) • Figure 1 shows the research model. • The solid arrow lines (the study) • The dashed arrow lines (previous studies) • It is reasonable to argue that the opposite causal relationships exist between IT investment and firm performance.

  26. Research model and hypotheses (cont.) • H1b: The reduction of operating cost per employee by a firm in the preceding years may contribute to the increase in IT investment per employee of the firm in the subsequent year.

  27. Research model and hypotheses (cont.) • “contribute to “ replaces “cause” • Interfering factors exist • Operational, technological, and economic factors • The authors have no control over these factors.

  28. Data and method • It is important to obtain reliable company IT-related data. • However, it is difficult. • Most companies regard these data as private and competitive information.

  29. Data and method (cont.) • Important sources: • ComputerWorld database • InformationWeek database • Compustat database

  30. Data and method (cont.) • To test the hypotheses, we need data for at least 4 consecutive years. • Preceding years, • Present year, and • Subsequent year(s)

  31. Data and method (cont.) • Constraints: three separate data sets • Figure 2 shows the sample characteristics. • Annual revenue • Industry • Annual IT spending • Size…. • Method • Granger causal model

  32. Data and method (cont.) • Let Xt and Yt be two time series data, the general causal model can be written: • Xt +b0 Yt = ∑ aj xt-j +∑ bj Yt-j + ε • Yt +c0 Xt = ∑ cj xt-j +∑ dj Yt-j + η • If some bj is not zero, Y causes X • If somecj is not zero, X causes Y • If both of these event occurs, there is a feedback relationship between X and Y. • If b0 is not zero, the instantaneous causality is occurring and Yt causes Xt • If c0 is not zero, the instantaneous causality is occurring and Yt causes Xt

  33. Data and method (cont.) • Substituting X and Y in the casual model with firm IT data and performance data, we can derive a set of models for testing the research hypotheses. • To minimizing the impact of firm size, we used per employee metrics.

  34. Data and method (cont.) • IT investments • Equation 2 • Operating costs • Equation 3 • Sale growth • Equation 4 • Productivity • Equation 5 • Profitability—ROA, ROE • Equations 6, 7

  35. Results • Consider the inflation factor • We inflated the financial figures of the preceding years to the real dollar values of the subsequent year (t) based on the annual percentage change of implicit price deflator of the Gross Domestic Product.

  36. Results (cont.) • Because we are using the year-to-year changes as variables, the upper limit (n) for subscript j in all models is two ( j = 2). • Use SAS software • The results are presented in Tables 3 to 7. • These results are summarized in Tables 8 and 9.

  37. Discussion • Table 8 shows: • No convincing evidence that IT investments in the preceding years have made any significant contribution to the subsequent changes in any of the four categories of firm performance measures: operating cost, productivity, sales growth, and profitability. • The only noticeable significant b parameter is the one for the effect of IT investment on the ROA in the 1990-1993 data set.

  38. Discussion (cont.) • However, given the overall non-significant tone of the results, this one case of significance is not enough to be considered as convincing evidence to conclude that IT investment has a positive impact on firm profitability.

  39. Discussion (cont.) • Table 9 shows: • There is clear evidence to support the hypotheses that firms budget their IT investment based on the financial performance of preceding years, especially the sales growth. • The faster the sale growth was achieved, the more money was allocated for IT investment.

  40. Discussion (cont.) • R2 (Tables 3 – 7) • When IT investment is used as the effect and the measures of financial performance as the causes, most F are significant and R2–adj are at decent levels. • When the measures of financial performance are used as the effect and IT investment as the cause, most F are insignificant and R2–adj are very small.

  41. Discussion (cont.) • We can not find the instantaneous causality between IT investment and firm performance. • instantaneous causality : b0 & c0 are significantly different from zero. • We can not find the figures in Tables 3 -7. • We cast serious doubt on the research methodology that uses concurrent data for testing causal relationship between IT investment and firm performance.

  42. Discussion (cont.) • Constraints: • We do not consider the effects of industry differences and IT maturity levels

  43. Conclusions • We have shown the hypothesized positive casual relationship between IT investment and firm performance cannot be established at acceptable statistical significant levels

  44. Conclusions (cont.) • On the other hand, there is clear evidence that firms had budgeted IT investment based on the financial performance of the preceding years, especially the growth rate of annual sales.

  45. Conclusions (cont.) • Implications • IT budget allocation • Overspending in IT by firms may be another complicating factor. • “It has become so easy to spend a lot of money on hardware, software, and maintenance -- and not necessarily see any return” • IT asset management

  46. Conclusions (cont.) • Measure is a big problem. • Economic value of IT • Present measure: ROE, ROA • Barua et al. (1997) advocated the use of intermediate variables to study the impact of IT since they reflect the direct impact of IT investment. • Capacity utilization, inventory turnover

  47. Conclusions (cont.) • Brynjolfsson (1996) suggested: • If “IT investment  producers’ performance” can not be shown, we can use the surplus concept. • Consumer surplus • Debate • Whether it is necessary to measure the value of IT investment • CEO care profitability!

  48. Conclusions (cont.) • It seems that we have raised more questions than provided answers in this study. • How to measure IT value?

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