An Integrative Model of Information Systems Spending Growth

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<ul><li><p>An Integrative Model of Information Systems Spending GrowthAuthor(s): Vijay Gurbaxani and Haim MendelsonSource: Information Systems Research, Vol. 1, No. 1 (MARCH 1990), pp. 23-46Published by: INFORMSStable URL: .Accessed: 25/06/2014 00:39</p><p>Your use of the JSTOR archive indicates your acceptance of the Terms &amp; Conditions of Use, available at .</p><p> .JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range ofcontent in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new formsof scholarship. For more information about JSTOR, please contact</p><p> .</p><p>INFORMS is collaborating with JSTOR to digitize, preserve and extend access to Information SystemsResearch.</p><p> </p><p>This content downloaded from on Wed, 25 Jun 2014 00:39:07 AMAll use subject to JSTOR Terms and Conditions</p><p></p></li><li><p>An Integrative Model of Information Systems Spending Growth </p><p>Vijay Gurbaxani </p><p>Haim Mendelson </p><p>Graduate School of Management </p><p>University of California Irvine, California 92717 </p><p>Graduate School of Business </p><p>Stanford University </p><p>Stanford, California 94305 </p><p>This paper develops a model of the growth of information systems expendi tures in the United States. The model incorporates two major factors that </p><p>influence the rate and pattern of spending growththe diffusion of techno </p><p>logical innovation and the effect of price on the demand for computing. Traditional studies have focused on the role of innovation while ignoring the effects of price on the growth process. We show that while information </p><p>systems expenses initially grew following an S-curve, more recent growth has converged to an exponential pattern. These patterns are consistent </p><p>with our integrative price-adjusted S-curve growth model. </p><p>Information systems expendituresBudgetDiffusion of innovationDemand for computingComputing costs </p><p>1. Introduction </p><p>The growth rate of information systems (hereafter IS) expenditures in the U.S. </p><p>has been and continues to be extremely rapid. The data processing (DP) </p><p>industry now accounts for approximately 2% of the GNP; the stock of information </p><p>technology capital represents roughly 7% of total U.S. capital stock (BEA 1989); and firms in information intensive sectors of the economy such as banking and </p><p>finance spend over 4% of their revenues on IS. Noting that the industry was </p><p>virtually nonexistent a half century ago, it becomes apparent that the rate of </p><p>adoption and use of information technology by organizations is unparalleled by any other industry. While information technology is undisputedly one of the most </p><p>important innovations of recent times, few theoretical or empircal studies have focused on its diffusion (Swanson 1989) and, in particular, quantified its growth. An IS expenditures reflect the patterns of technology adoption and use, an analysis of the trends governing them provides an understanding of the underlying factors that drive the growth process. </p><p>This paper analyzes the growth of IS expenditures over time. Clearly, these </p><p>expenditures are driven by the demand for information technology. We suggest </p><p>1047-7047/90/0101/0023/S01.25 Copyright 1990, The Institute of Management Sciences </p><p>Information Systems Research 1:1 23 </p><p>This content downloaded from on Wed, 25 Jun 2014 00:39:07 AMAll use subject to JSTOR Terms and Conditions</p><p></p></li><li><p>Gurbaxani Mendelson </p><p>that there are two major factors that influence this demand. The first is the </p><p>diffusion of technological innovation, including the effects of learning, and the second is the effect of price. We hypothesize that in the early years of computing, the diffusion effects dominated the dynamics of spending growth. That is, the </p><p>growth in these years was driven primarily by the development of previously unconsidered applications in firms that had adopted information technology and </p><p>by the entry of new users into the user base. However, even as users gain experience with the technology and the application portfolio matures, expenditures continue to grow as a result of rapidly declining costs of computing. This occurs because the trends in the price-performance of hardware technology have made it cost-effective for organizations to automate a constantly increasing set of tasks. As we shall show, even though the cost of performing any specific task has decreased over time, the development of new applications resulted in continually larger outlays on IS. </p><p>The growth of information processing may be analyzed from the perspective of the diffusion of innovation literature. Studies in this area focus on the effects of behavioral and social influences on the timing of adoption of an innovation, whereas the impact of economic factors such as price is often ignored. While demand functions always express the quantity demanded as a function of price, suggesting the obvious importance of this variable in general, its omission is even more significant in the case of information technology, where the price decline has been so rapid for so long. Our goal here is to develop an integrative model which </p><p>incorporates both social and economic factors as they apply to IS spending growth. The considerable influence of the declining costs of computing on the growth of </p><p>DP expenditures was strongly indicated in our earlier research (Gurbaxani and Mendelson 1987, 1988), where it was shown that most of the recent (1976-1984) </p><p>growth in these expenses could be attributed to the price trend. A formal model was developed wherein a DP manager maximized the net value of information services to the organization by determining the optimal investment in both hard ware and software-development in each period. DP was modeled using a produc tion-function approach with hardware and software-development effort as the </p><p>inputs.1 Since we were studying budget allocation when IS management practice had matured, our primary interest was in examining steady-state behavior. Our results showed that even in the absence of diffusion and learning effects, the </p><p>optimal investment policy corresponding to the current trend of exponentially decreasing costs (Phister 1979, Mendelson 1990) results in the exponential growth of these budgets. Thus, while it is important to focus on the role of innovation in </p><p>studying the growth of computing, incorporating the effects of price and the </p><p>corresponding steady-state behavior is necessary for the development of a compre hensive model of IS spending. </p><p>In this paper, we propose an integrative model which accommodates two </p><p>patterns of growth in different periods: (i) an early transient period, when users </p><p>gain familiarity with information technology and its applications, and (ii) a steady state growth period, when DP expenditures continue to grow steadily as a result of the decreasing prices. As the technology and its users mature, the transient </p><p>'The model is reviewed in 3. </p><p>24 Information Systems Research 1:1 </p><p>This content downloaded from on Wed, 25 Jun 2014 00:39:07 AMAll use subject to JSTOR Terms and Conditions</p><p></p></li><li><p>Integrative Model of Information Systems Spending Growth </p><p>behavior in period (i) converges to the steady-state behavior in period (ii). Our model generates hypotheses which are tested using aggregate IS spending data collected by Phister (1979) and the International Data Corporation (IDC). The </p><p>empirical tests support our integrative model and provide estimates of its parame ters. </p><p>The outline of this paper is as follows. In 2, we discuss the diffusion of innovation from the social and behavioral perspectives and derive its implications for IS spending growth. Our integrative model is presented in 3. The data used in our empirical work are described in 4. 5 analyzes the data and tests the existence of a price effect, and our concluding remarks are in 6. </p><p>2. The Diffusion of Innovation and 5-Curves Information technology, broadly defined, is one of the most important innova </p><p>tions of the last three decades, if not the most important. Thus, it is useful to apply existing theories of the diffusion of innovation to study its growth pattern over time. Researchers of the innovation process such as Rogers (1962, 1983) and </p><p>Rogers and Shoemaker (1971) have demonstrated that most innovations follow well-defined patterns in diffusing through society. These patterns are described by diffusion models which represent the level and spread of the innovation among a </p><p>given set of its prospective adopters. This literature characterizes the adoption process using a bell-shaped curve which depicts the density function of the time taken by different segments of the population to adopt the innovation (see Figure 1). Convenient breaks in the distribution are used to classify the potential adopters into innovators, early adopters, early majority, late majority and laggards, depend ing on the relative time they require to adopt the innovation. Rogers (1962, 1983) and Rogers and Shoemaker (1971) have characterized the major traits of the </p><p>adopters and attributed their behavior to factors such as learning, social pressure and imitation, suggesting that the probability of adoption is an increasing function of the number of existing adopters. </p><p>Turning from the density function of the time to adoption (Figure 1) to the mean number of adopters by a given time t, which we denote by 4&gt;(t), we note that </p><p>Figure 1. Adopter Categorization on the Basis of the Relative Time to Adopt an Innovation. </p><p>March 1990 25 </p><p>This content downloaded from on Wed, 25 Jun 2014 00:39:07 AMAll use subject to JSTOR Terms and Conditions</p><p></p></li><li><p>Gurbaxani Mendelson </p><p>the latter is proportional to the integral of the former up to time t. But if the </p><p>density function is bell-shaped, its integral will be S-shaped. Thus, the expected number of adopters depicts an S-shaped function of time. Indeed, most existing models of new-product growth operationalize these ideas using an S-shaped curve which depicts product sales as a function of time (cf. Mansfield 1961, Bass 1969, Kotler 1987, to name a few; Mahajan and Muller 1979 provide a comprehensive review). The different product-growth models differ in their diffusion model </p><p>assumptions and result in different functional forms of the S-curve The </p><p>general demand patterns implied by them are, however, similar, as can be seen from the following definition. </p><p>Definition. The real function 4&gt;(t), defined for t &gt; 0, is called an S-curve if </p><p>(i) is twice differentiate for all t; (ii) (t) &gt; 0 for all t; (iii) Xt) &gt; 0 for all t; (iv) (t) is bounded; and </p><p>(v) There exists some time T such that &gt; 0 for all t &lt; T, and </p></li><li><p>Integrative Model of Information Systems Spending Growth </p><p>study the diffusion of modern software practices among software-development groups. </p><p>Consistent with these studies, we apply the general results from the diffusion of innovation literature to the case of IS spending growth. These results imply that IS </p><p>expenditures should grow according to an S-curve. To operationalize this notion, we have to specify the functional form of the S-curve. Our analysis of IS spending will employ three functional forms for the S-curve, The first twothe </p><p>Gompertz and the Logistic curvesare widely used in the diffusion and new-prod uct planning literature (cf. Mansfield 1961, Hendry 1972, Mahajan and Muller </p><p>1979). A third form for cf&gt;(t) is the modified exponential form used by Lucas and Sutton (1977) in their analysis of IS budgets. Obviously, these curves satisfy the five conditions in our definition of an S-curve. We briefly outline additional useful </p><p>properties of each of these S-curves below. </p><p>1. The Gompertz Curve: For the Gompertz curve, defined by </p><p>(T) = K AbT, (1) </p><p>the growth increments of the logarithms of decline by a constant percentage b. When both A and b are between 0 and 1, 4&gt;(T) is an increasing S-curve which </p><p>tends to the upper bound K as T -* . The Gompertz curve reaches its maximum </p><p>growth rate when (T) = K/e, i.e.,when (T) reaches about 37% of its upper bound. Hendry (1972) applied the Gompertz curve to study the growth of durable </p><p>products in the U.K., and Lackman (1977) used it to study the growth of a new </p><p>plastic product. 2. The Logistic Curve: For the Logistic curve, defined by </p><p> 0 and 0 &lt; b &lt; 1, the Logistic is an increasing S-curve which tends to the limit l/K as T - . The maximum growth rate is achieved when </p><p>cfr(T) = 1 /(2K), that is, when the S-curve reaches 50% of its limiting value, and the growth rate function is symmetric around this maximum value. This functional </p><p>form was used by Mansfield (1961) and Blackman (1974) to study the diffusion of </p><p>several technological innovations. 3. The Modified-Exponential Curve: The modified-exponential function used by </p><p>Lucas and Sutton (1977) is given by </p><p>4&gt;(T) = ea~b/T. (3) </p><p>This curve depicts an increasing S-curve for b &gt; 0. The growth rate is maximized </p><p>at T = b/2, when 4&gt;(T) is at l/e2 of its limiting value (ea). These three S-curves </p><p>will be used in the sequel to quantify the effects of information technology diffusion on aggregate spending. </p><p>While we focus on the diffusion of innovation at the aggregate economy-wide level, a related body of work (cf. Nolan 1973 as well as the references cited above) </p><p>March 1990 27 </p><p>This content downloaded from on Wed, 25 Jun 2014 00:39:07 AMAll use subject to JSTOR Terms and Conditions</p><p></p></li><li><p>Gurbaxani Mendelson </p><p>suggests that the diffusion of information technology within firms also leads to </p><p>5-shaped budget growth. The relationship between firm-level and economy-wide diffusion process is not surprisingin fact, we show below that the aggregation of S-curves is itself an S-curve. </p><p>Theorem. Let 4&gt;,(t), i = 1,2,..., N be S-curves, and let </p><p>*(0 = E u0 (4) i=i </p><p>be the sum of these S-curves. Then (0 is an S-curve. </p><p>The proof of the above theorem is immediate, since the defining conditions </p><p>(i)-(v) are preserved under linear transformations. Since (?) is obtained from the functions $,-(f) by simple addition, the result follows. This theorem implies that if the pattern of growth of DP budgets in individual organizations is 5-shaped, then the aggregate growth curve should also be 5-shaped. </p><p>3. An Integrative Model of IS Spending Growth The role of price in determining the quantity demanded of a good or service is </p><p>central to economic theory. In fact, by their very definition, demand functions </p><p>express the quantit...</p></li></ul>


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