Transcript An Empirical Study of the Casual Relationship Between IT

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
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Introduction
Research background
Research model and hypotheses
Data and method
Results
Discussions
Conclusions
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Introduction
• Productivity paradox
– What value does IT add to an organization?
– The literature in 80s and 90s contend that IT
can:
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provide competitive advantages
add value
improves operational performance
reduces costs
increases decision quality, and
enhances service innovation and differentiation.
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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)…
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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.
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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?
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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.
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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
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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.
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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.
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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
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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.
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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
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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.
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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.
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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)
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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
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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.
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Research model and
hypotheses (cont.)
• We can not use concurrent IT data
and performance data with
correlation analysis.
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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.
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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.
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Research model and
hypotheses (cont.)
• Lagged effect
– IT projects usually take years to implement.
– Organization adaptation
– Employees need time to be trained and reskilled.
• Finally, customers and the market are the
last of these time-delayed chain reactions
to respond which ultimately determines
the firm performance.
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Research model and
hypotheses (cont.)
Previous IT
investments
Annual Sales
Growth
Present IT
investments
Operating Cost
reduction
Profitability
improvement
Productivity
improvement
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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.
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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.
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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.
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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.
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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.
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Data and method (cont.)
• Important sources:
– ComputerWorld database
– InformationWeek database
– Compustat database
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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)
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Data and method (cont.)
• Constraints: three separate data sets
• Figure 2 shows the sample characteristics.
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Annual revenue
Industry
Annual IT spending
Size….
• Method
– Granger causal model
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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 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
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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.
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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
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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.
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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.
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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.
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Discussion (cont.)
• However, given the overall nonsignificant 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.
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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.
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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.
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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.
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Discussion (cont.)
• Constraints:
• We do not consider the effects of
industry differences and IT maturity
levels
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Conclusions
• We have shown the hypothesized
positive casual relationship between
IT investment and firm performance
cannot be established at acceptable
statistical significant levels
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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.
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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
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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
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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!
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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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