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

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 e
• Yt c0 Xt ? cj xt-j ? dj Yt-j ?
• If some bj is not zero, Y causes X
• If some cj 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
• ProfitabilityROA, 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 R2adj 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 R2adj 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?