an integrative model of information systems spending growth

Download An Integrative Model of Information Systems Spending Growth

Post on 01-Feb-2017




0 download

Embed Size (px)


  • 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

    Your use of the JSTOR archive indicates your acceptance of the Terms & Conditions of Use, available at .

    .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


    INFORMS is collaborating with JSTOR to digitize, preserve and extend access to Information SystemsResearch.

    This content downloaded from on Wed, 25 Jun 2014 00:39:07 AMAll use subject to JSTOR Terms and Conditions

  • An Integrative Model of Information Systems Spending Growth

    Vijay Gurbaxani

    Haim Mendelson

    Graduate School of Management

    University of California Irvine, California 92717

    Graduate School of Business

    Stanford University

    Stanford, California 94305

    This paper develops a model of the growth of information systems expendi tures in the United States. The model incorporates two major factors that

    influence the rate and pattern of spending growththe diffusion of techno

    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

    systems expenses initially grew following an S-curve, more recent growth has converged to an exponential pattern. These patterns are consistent

    with our integrative price-adjusted S-curve growth model.

    Information systems expendituresBudgetDiffusion of innovationDemand for computingComputing costs

    1. Introduction

    The growth rate of information systems (hereafter IS) expenditures in the U.S.

    has been and continues to be extremely rapid. The data processing (DP)

    industry now accounts for approximately 2% of the GNP; the stock of information

    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

    finance spend over 4% of their revenues on IS. Noting that the industry was

    virtually nonexistent a half century ago, it becomes apparent that the rate of

    adoption and use of information technology by organizations is unparalleled by any other industry. While information technology is undisputedly one of the most

    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.

    This paper analyzes the growth of IS expenditures over time. Clearly, these

    expenditures are driven by the demand for information technology. We suggest

    1047-7047/90/0101/0023/S01.25 Copyright 1990, The Institute of Management Sciences

    Information Systems Research 1:1 23

    This content downloaded from on Wed, 25 Jun 2014 00:39:07 AMAll use subject to JSTOR Terms and Conditions

  • Gurbaxani Mendelson

    that there are two major factors that influence this demand. The first is the

    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

    growth in these years was driven primarily by the development of previously unconsidered applications in firms that had adopted information technology and

    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.

    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

    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

    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)

    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

    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

    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

    studying the growth of computing, incorporating the effects of price and the

    corresponding steady-state behavior is necessary for the development of a compre hensive model of IS spending.

    In this paper, we propose an integrative model which accommodates two

    patterns of growth in different periods: (i) an early transient period, when users

    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

    'The model is reviewed in 3.

    24 Information Systems Research 1:1

    This content downloaded from on Wed, 25 Jun 2014 00:39:07 AMAll use subject to JSTOR Terms and Conditions

  • Integrative Model of Information Systems Spending Growth

    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

    empirical tests support our integrative model and provide estimates of its parame ters.

    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.

    2. The Diffusion of Innovation and 5-Curves Information technology, broadly defined, is one of the most important innova

    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

    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

    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

    adopters and attributed their behavior to factors such as learning, soci


View more >