Computing, Information and Control ICIC International c_{°2007 ISSN 1349-4198}

Volume 3, Number 1, February 2007 pp. 31-39

THE DYNAMIC ASSESSMENT OF INFORMATION TECHNOLOGY INVESTMENT

Liping Kao

Department of Accounting

Chihlee Institute of Business and Technology

No.313, Sec.1, Wenhua Rd., Banqiao City, Taipei 220, Taiwan Liping.kao@mail.chihlee.edu.tw

Berlin Wu

Department of Mathematics National Chengchi University

64, Sec.2, Zhi-nan Rd., Wenshan, Taipei 11605, Taiwan berlin@math.nccu.edu.tw

Received May 2006; revised October 2006

Abstract. Evaluating the eﬀectiveness of Information Technology (IT) investment has always been an elusive but important goal of IS researchers. The purpose of this paper is to present new dynamic approaches in the IT benefits evaluation. We use essential financial indicators to measure the impact of sample entities which performed IT in-vestment before December 31, 2004 in China and Taiwan. From the field study we find that both in China and Taiwan, the IT investment make a negative impact. However, from the Taiwan experience, most entITrises will meet the temporary decline situation after implementing the IT investment, but in the long-term, about 4 years, it will make a significant financial progress. While in China after IT investment, it always keeps a negatively related performance.

Keywords: Information technologies, Fuzzy theory, Performance evaluation

1. Introduction. Investments by firms in information technologies (IT) have increased rapidly over the past three decades. IT investment decisions have the potential to either improve a firm’s competitive position or to allow the firm to become more vulnerable to competitive forces. Recent evidence indicates that IT investments have been a very important contributor to productivity [2,5,6]. These literatures suggest that IT investment has a significant impact on firm performance and, therefore, is of value to the firm.

Unfortunately, empirical support for these claims consists almost exclusively of indi-vidual case studies [8-10,20]. As a result, there is some doubt about whether the claimed impacts can be generalized from the individual cases to all firms. A number of recent empirical studies have suggested that IT investment do not benefit firms as much as the case studies might lead one to expect [3,14,15,18].

People have concluded from their own study and studies conducted by others, that IT investment have not resulted in significant productivity gains [2-4]. These studies cast doubt as to the real value of IT investment to firms. Determining whether IT investment

can increase firm value poses many problems that are widely discussed in the information systems (IS) literature [7,13,16,19].

In front with the important business project, there are many companies performing the IT investment without examining its economic gains. One of the reasons is that it is not easy to set up an appropriate IT performance evaluation. Precisely quantifying the performance of IT is still problematic since many other factors such as economics, politics or transportation will aﬀect the result.

However, there are many approaches in detecting or testing procedures for IT invest-ment performance evaluation. Among them the t-test or event study method for evaluat-ing the performance of IT are the mostly frequency used [2,5,6,10,12]. These procedures, although easy to implement, have several disadvantages. The main reason for the decimal cost/benefit comparison comes from that measuring IT investment benefits are not imme-diately evident, but implementation costs can be readily identified ex post [12]. Moreover, the absence of an explicit statistical model for the structure changes makes it diﬃcult to investigate statistical properties of the models and to make forecasts.

In this research we present an integrated testing procedure for IT investment perfor-mance evaluation. It contains a single financial factor eﬀect and a single company’s performance evaluation. Fuzzy rule base about IT impacts of time and impact of com-pany size are suggested for the testing hypothesis of IT investment impacts. Finally an empirical study about IT investment impacts for China and Taiwan are demonstrated.

2. Research Design.

2.1. Previous methods and factors analysis. IT investment is expected to help firms improve their productivity and profitability [12,17]. Much research has supported IT spending and operational improvements, such as lower growth in operating expense [11], improved cost eﬃciency [1], and higher return on assets, sales growth and nonproduction labor productivity [21]. Hence in this paper we summarize the factors and features for each factors follows: 1. Analysis of operation: (1a) Accounts Receivable Turnover (1b) Inventory turnover; 2. Analysis of profitability: (2a) Pretax profit to sales (2b) Gross profit ratio; 3. Analysis of investment return: (3a) Return on total assets (3b) Return on common equity; 4. Analysis of growth rate: (4a) Sales Growth Rate (4b) Gross Profit Growth Rate, as our financial performance evaluation indicators, the relationship of factors and elements. See Table1.

Given the all-encompassing entITrise-wide nature of IT investment, eﬀects should be large enough to reflect in financial statement values. Prior research has indicated that a time lag is necessary for capturing the performance improvements from information technology [4].

2.2. Designs of performance evaluations. By observing the financial index with nt
companies, we measure the IT performance with fuzzy logic system. In this study, the
degree of financial linguistic fluctuation is set to be _{{plunge (very non-eﬃcient) = }
[-1,-0.5], down (non-eﬃcient) = [-0.5, -0.1], unchanged (medium) = [-0.1, 0.1], up (eﬃcient)
= (0.1, 0.5), and soar (very eﬃcient) = (0.5, 1). The following procedure demonstrates
the evaluation decision.

Algorithm for a single factor’s evaluation procedure.

Step 1. Let ntbe the number of companies at time t, xijt : be the ithstandardize finan-cial feature of jthcompany at the t year, i=1,2,. . . ,m, be the numbers of features. j=1,2,. . . ,nt. Calculate ∆xijt= xijt− xij0, and Rit= median

1≤j≤nt |∆xijt|.

Step 2. Calculated l(∆xijt) the ith financial linguistic variable of jth company at the t
year
l(∆xijt) =
⎧
⎪
⎪
⎪
⎪
⎨
⎪
⎪
⎪
⎪
⎩
1, if 1.3Rit< ∆xijt;
0.5, if 1.1Rit< ∆xijt≤ 1.3Rit
0, if _{−1.1R}it≤ ∆xijt≤ 1.1Rit;
−0.5, if −1.3Rit≤ ∆xijt<−1.1Rit;
−1, if ∆xijt<−1.3Rit.
Step 3. Calculate ¯xit= _{n}1_{t}
nt
P
j=1

l(∆xijt), the financial linguistic value for the average of nt company, i=1,2,. . . ,m.

Step 4. Find xt = m P i=1

six¯it, the weighted IT performance of a factor, where si is the weight of the ith feature, Psi = 1.

Step 5. Output the result according to the fuzzy rule base:

If 0.5 < xt, then IT performance of the factor is very eﬃcient up to year t;
If 0.1_{≤ x}t< 0.5, then IT performance of the factor is eﬃcient up to year t;
If _{−0.1 < x}t≤ 0.1, then IT performance of the factor is no change up to year t;
If _{−0.5 ≤ x}t<−0.1, then IT performance of the factor is non-eﬃcient up to year t;
If xt <−0.5, then IT performance of the factor is very non-eﬃcient up to year t.
Algorithm for macro evaluation procedure.

Step 1. Let Xit be the weighted IT performance of the ithfactor, i=1,2,. . . ,n, the number of factors.

Step 2. Find the weighted performance of the macro-IT. Xt =

n P i=1

F WiXit, where F Wi is the weight of the ith financial factor, P

F Wi = 1. Step 3. Output the result according to the fuzzy rule base:

If 0.5 < Xt, then macro-IT performance is very eﬃcient up to year t;
If 0.1_{≤ X}t< 0.5, then macro-IT performance is eﬃcient up to year t;
If -0.1 < Xt ≤ 0.1, then macro-IT performance is no change up to year t;
If _{−0.5 ≤ X}t <−0.1, then macro-IT performance is non- eﬃcient up to year t;
If Xt<−0.5, then macro-IT performance is very non-eﬃcient up to year t.
2.3. Decision of the fuzzy weight. In the evaluation process, people usually treat
each factor with the equal weight. That is, we assume that the factors have the same
contribution to the universe domain. However, in order to get a more accurate evaluation,
we had better use diﬀerent weights, according to their contributions to the object, for
diﬀerent factors. Since then, the macro-performance evaluation will reflect the real world
situation.

To investigate the fuzzy weight of each factor, we may use the fuzzy set theory and sampling survey technique. By fuzzy memberships and multiple values assignment, we

can get an appropriate fuzzy weight for the object. Hence, we give a brief definition about fuzzy weight.

Definition 1. Fuzzy weight (data with multiple values)

Let U be a finite set (a discussion domain), L = _{{L}1, L2,· · · , Lk} be a set of k-linguistic
factors on U , and _{{F S}i = m_{L}i1_{1} +m_{L}i2_{2} + ... + m_{L}ik_{k} , i = 1, 2, ..., n} be a sequence of random
fuzzy sample on U, mij(

k P j=1

mij = 1) is the membership with respect to Lj. Then, the fuzzy

weight was defined as F W =

1 n n P i=1 mi1 L1 + 1 n n P i=1 mi2 Li2 + ... + 1 n n P i=1 mik Lk .

Example 2.1. Let the universe set U= _{{factor 1, factor 2, factor 3, factor 4}. In a}
sampling survey with 7 experts, we get the following fuzzy sample for 4 factors:

Table 1. Fuzzy sample for 4 factors Factor 1 Factor 2 Factor 3 Factor 4

F1 0.4 0.1 0.2 0.3 F2 0.2 0.1 0.2 0.5 F3 0.3 0.2 0.3 0.2 F4 0.3 0.2 0.4 0.1 F5 0.3 0.1 0.2 0.4 F6 0.2 0.2 0.4 0.2 F7 0.5 0.1 0.3 0.1 Total 3.2 1 2 1.8 weight 0.41 0.14 0.19 0.26

The fuzzy weight for factors of the universe set is F W = 0.41_{1} +0.14_{2} +0.19_{3} + 0.26_{4}

3. Empirical Study.

3.1. Sample selection procedure. The samples are selected by identifying listed firms that publicly disclosed their IT investment before December 31, 2004. Because not all firms have relevant data available in the TEJ (Taiwan Economic Journal) database for measuring their characteristics; therefore, the sample size is further reduced. Finally, IT investment announcements leave a usable sample of 87 for Taiwan and 52 for China.

The distribution of IT investment by firms, in implementation year, is presented in Table 2. Of the Taiwan sample firms, 88% (82 of 87) of the firms have finished the implementation for more than 2 years, and 44% (38 of 87) for more than 5 years. As compared to Taiwan samples, the average time needed for implementation by China firms is shorter. Of the China sample firms, 75% (39 of 52) have finished the implementation for more than 2 years and 61% (32 of 52) for more than 3 years. But only 29% (15 of 52) have finished the implementation for more than 5 years. The survey sampling is performed both in Taiwan and China by choosing the companies that have publicly disclosed IT investment before December 31, 2004. The data was assessed from the internet and the literatures data base.

Table 2. Distribution of sample firms in IT investment year

Taiwan China

Number Percent Number Percent

More than 1 year 87 100% 52 100%

More than 2 year 72 83% 39 75%

More than 3 year 62 71% 32 61%

More than 4 year 54 62% 16 31%

More than 5 year 38 44% 15 29%

3.2. Main results.

3.2.1. Analysis of operation. Two financial features of operation are Inventory turnover and Accounts Receivable Turnover. The dynamic evaluation is illustrated at Table 3.

Table 3. Operation after IT investment

Operation performance T = 1 T = 2 T = 3 T = 4 T = 5

Inventory turnover China (¯xit) −0.138 −0.111 −0.171 −0.133

Taiwan (¯xit) −0.089 −0.063 −0.03 0.134 0.207

Accounts Receivable Turnover China (¯xit) −0.431 −0.259 0.333 0.250

Taiwan (¯xit) −0.273 −0.264 −0.33 0.175 0.339

Factor 1:Operation China(xt) −0.285 −0.185 0.081 0.058

Taiwan(xt) −0.181 −0.164 −0.18 0.156 0.273

1: ¯xit=_{n}1_{t}
nt

P

j=1

l(∆xijt), the financial linguistic value for the average of nt companies, i = 1, 2, . . ., m.

2: xt = m

P

i=1

si¯xit, the weighted IT performance of a factor, where si is the weight of the ith feature. It can be seen that in China, the dynamic performance of the feature Inventory turnover ¯

xit is not eﬃcient(¯xi1 = 0.138 ¯xi2 = −0.111 ¯xi3 = 0.171 ¯xi4 = −0.133). While the dynamic performance of Accounts Receivable Turnover is eﬃcient after two years’ non-eﬃciency(¯xi1 = −0.431 ¯xi2 = −0.259). The result of operation performance, IT is not eﬃcient during the first two years. Then the performance moves from negative to positive, while the change is not so steep, the following years its measure is not significant change with the financial performance.

As for the case in Taiwan, the performance of the feature Inventory turnover ¯xit at the three years is no change (¯xi1 = −0.089 ¯xi2 = −0.063 ¯xi3 = −0.03). Then the performance becomes eﬃcient year after year. While the Accounts Receivable Turnover becomes eﬃcient after two years’ of non-eﬃciency. In general, the operation performance is not eﬃcient during the first three years. Then the performance moves from negative to positive and at the fifth year the performance becomes eﬃcient.

3.2.2. Analysis of profitability. Two financial features of profitability are Gross profit ratio and pretax profit to sales. The dynamic evaluation is illustrated at Table 4.

It can be seen that in China, after the first year of IT investment, the performance of the feature Gross profit ratio ¯xitis no change, while it becomes worse after the succeeding

years, the IT performance is non-eﬃcient. While the dynamic performance of Pretax profit to sales is no change after two years’ non-eﬃcient. The result of profitability performance of IT is no change.

As for the case in Taiwan, the performance of the feature Gross profit ratio sales at the first three years is non-eﬃcient. Then the performance turns out eﬃcient (¯xi4 = 0.35 ¯xi5= 0.339) while the Pretax profit to sales is worse year after year. In general, the profitability performance is non-eﬃcient during the first two years. Then the performance moves from negative to positive, the performance remains unchanged.

Table 4. Profitability after IT investment

Profitability performance T = 1 T = 2 T = 3 T = 4 T = 5

Gross profit ratio China (¯xit) −0.069 −0.214 −0.159 −0.133

Taiwan (¯xit) −0.246 −0.173 −0.214 0.35 0.339

Pretax profit to sales China (¯xit) −0.093 −0.135 −0.100 0.000

Taiwan (¯xit) −0.097 −0.132 −0.265 −0.281 −0.276

Factor 2: Profitability China(xt) −0.081 −0.175 −0.130 −0.067

Taiwan(xt) −0.172 −0.153 −0.24 0.035 0.032

1: ¯xit= _{n}1_{t}
nt

P

j=1

l(∆xijt), the financial linguistic value for the average of nt companies, i = 1, 2, . . ., m.

2 : xt= m

P

i=1

six¯it, the weighted IT performance of a factor, where si is the weight of the ith feature.

3.2.3. Analysis of investment return. Two financial features of investment return are Re-turn on common equity and ReRe-turn on total assets. The dynamic evaluation is illustrated at Table 5.

It can be seen that in China, after IT investment, both the performance of the feature Return on common equity and Return on total assets is non-eﬃcient. The result of investment return after the IT investment is non-eﬃcient.

As for the case in Taiwan, after IT investment, both the performance of the features Return on common equity and that of Return on total assets is non-eﬃcient. The result of investment return after the IT investment is non-eﬃcient. In general, the investment return exhibits a negative benefit both in China and Taiwan. And there is no inclination to change the situation.

3.2.4. Analysis of growth rate. Two financial features of growth rate are Sales Growth Rate and Gross Profit Growth Rate. The dynamic evaluation is illustrated at Table 6.

It can be seen that in China, after IT implementation, both of the performance of the feature Return on common equity and Return on total assets is non-eﬃcient. The result of investment return after the IT implementation is non-eﬃcient.

As for the case in Taiwan, after IT implementation, both of the performance of the feature Return on common equity and Return on total assets is non-eﬃcient. The result of investment return after the IT implementation is non-eﬃcient. In general, the investment return exhibits a negative benefit both in China and Taiwan. And there is no inclination to change the situation.

Table 5. Investment return after IT investment

Investment return performance T = 1 T = 2 T = 3 T = 4 T = 5

Return on common equity China (¯xit) −0.367 −0.429 −0.409 −0.267

Taiwan (¯xit) −0.281 −0.281 −0.382 −0.439 −0.379

Return on total assets China (¯xit) −0.333 −0.429 −0.364 −0.367

Taiwan (¯xit) −0.316 −0.316 −0.373 −0.402 −0.397

Factor 3: Investment returnChina(xt) −0.350 −0.429 −0.387 −0.317

Taiwan(xt) −0.299 −0.299 −0.378 −0.421 −0.388

1: ¯xit=_{n}1_{t}
nt

P

j=1

l(∆xijt), the financial linguistic value for the average of nt company, i = 1, 2, . . ., m.

2: xt= m

P

i=1

six¯it, the weighted IT performance of a factor, where si is the weight of the ith feature.
3.2.5. The macro IT performance. In this section we will examine the macro IT
perfor-mance via above four financial factors. According to the method of Section 3.2, we ask
for 7 experts’ opinion, and find the fuzzy weight is F W = 0.41_{1} + 0.14_{2} + 0.19_{3} + 0.26_{4} . The
results of macro-IT performance are illustrated at Table 7.

Table 6. Growth rate after IT investment

Investment return performance T = 1 T = 2 T = 3 T = 4 T = 5

Return on common equity China (¯xit) −0.367 −0.429 −0.409 −0.267

Taiwan (¯xit) −0.281 −0.281 −0.382 −0.439 −0.379

Return on total assets China (¯xit) −0.333 −0.429 −0.364 −0.367

Taiwan (¯xit) −0.316 −0.316 −0.373 −0.402 −0.397

Factor 3: Investment returnChina(xt) −0.350 −0.429 −0.387 −0.317

Taiwan(xt) −0.299 −0.299 −0.378 −0.421 −0.388

1: ¯xit=_{n}1_{t}
nt

P

j=1

l(∆xijt), the financial linguistic value for the average of nt company, i = 1, 2, . . ., m.

2: xt= m

P

i=1

six¯it, the weighted IT performance of a factor, where si is the weight of the ith feature. It is interesting to find that the IT performance in China exhibits an optimistic progress, from non-eﬃcient to the third year no change. Though the IT investment does not meet the expected achievement, we may see from the dynamic trend that as the time goes by it may get the positive benefits.

The same situation can be found in Taiwan, the macro-IT performance is no change. 4. Conclusion. In this research, we present new dynamic approaches in IT investment evaluation for Taiwan and China before December 31, 2004. We use four financial factors with each factor has two features, to evaluate IT performance. We applied the fuzzy rule base decision rule to examine the benefit of IT investment. From the single feature of evaluation of IT, we can see that in China only Accounts Receivable Turnover exhibits positive improvement, the others are getting worse. While in Taiwan, the Inventory turnover, Accounts Receivable Turnover and Cross profit ratios getting better as the year goes. As for the impact of the four factors evaluation of IT: IT performance in China, the

Table 7. Results of macro-IT performance

T=1 T=2 T=3 T=4 T=5

China

Factor 1:Operation 0.29 _{−.185 0.09 0.06}

Factor 2: Profitability 0.09 0.18 0.13 0.07

Factor 3: Investment return 0.35 0.43 0.39 0.32

Factor4: Growth rate 0.24 0.22 0.11 0.05

Macro IT performance 0.16 0.28 0.18 0.12

Taiwan

Factor 1:Operation 0.18 0.16 0.18 0.16 0.27

Factor 2: Profitability 0.17 0.15 0.2 0.04 0.03

Factor 3: Investment return 0.30 0.30 0.38 0.42 0.39

Factor4: Growth rate 0.25 0.29 0.32 0.24 0.24

Macro IT performance 0.13 0.25 0.29 0.23 0.19

Xt= n

P

i=1

F WiXit,where F Wi is the weight of the ith financial factor,

P

F Wi= 1

IT performance of operation, the growth rate and the profitability is no change. But the investment return factor becomes worse than after the IT investment. While in Taiwan, the best performance is operation, it exhibits a positive trend, from negative measurement to positive. The IT performances for other factors are no change.

Suggestions to the China side: many entITrises are public; the western economic ad-ministration system is not well constructed. Hence they may reform their adad-ministration concept, promote the management system before they invest the IT. Suggestions to the Taiwan side: After IT investment, at the first several years it may be non-eﬃcient or no change, but in the long run (about four years) it is improving. As for the other financial factors, though IT investment makes no change during the short run, we believe, in long run it will improve, say after 4 years.

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