An investigation of risk perception and risk propensity on the decision to continue a software development project
Introduction
While there are undoubtedly success stories in the information systems (IS) area, the sad fact remains that many software development projects end in failure Lyytinen and Hirschheim, 1987, Jones, 1995. Descriptions of such failures appear regularly in business press reports Rifkin and Betts, 1988, Kindel, 1992, Cringely, 1994, Gibbs, 1994, Tomsho, 1994. As a result, both researchers and practitioners have expressed concerns about how to manage IS project risk Keider, 1974, Ginzberg, 1981, Boehm, 1991, Barki et al., 1993, Lyytinen et al., 1993, Jones, 1994.
Many researchers have suggested that inadequate assessment of project risk may be a major source of problems in IS development Alter and Ginzberg, 1978, Ginzberg, 1981, McFarlan, 1981, Charette, 1989, Boehm, 1991, Barki et al., 1993. One approach for avoiding failure lies in the concept of software development risk management. Advocates of this approach claim that by identifying and analyzing threats to success, action can be taken to reduce the chance of failure.
The software risk management advocates would argue that managers may not accurately perceive risks, causing them to pursue IS projects that ultimately result in failure. If managers have faulty perceptions of risk, then their management efforts are likely to be misdirected (Slovic et al., 1981). Furthermore, when managers do not formally assess risks they may underestimate them and unknowingly make risky decisions. Within the IS literature, several checklists and instruments have emerged to help managers assess and manage software development risk. McFarlan (1981), for example, has developed a series of questions designed to gauge the riskiness of an IS project. Boehm and Ross (1989) provide a “top-ten” list of risk factors that can be used as a checklist for identifying risks that can adversely affect IS projects. More recently, Barki et al. (1993) have published a risk assessment instrument designed to measure the risk associated with an IS development project.
Risk assessment devices, such as the ones mentioned above, will be useful to organizations if they allow managers to more effectively assess and manage the risk associated with IS development Barki et al., 1993, Lyytinen et al., 1993, Ropponen and Lyytinen, 1993. Implicit in this line of research, however, is the assumption that risk assessment devices will provide managers with more accurate perceptions of the risk associated with a project, thereby allowing them to make better informed decisions and ensuring more successful outcomes McFarlan, 1981, McComb and Smith, 1991. In order for this to be true, however, the following relationships must hold: (1) risk assessment devices must have the intended effect on risk perception (i.e., a heightened awareness and sensitivity to the risks involved), and (2) changes in risk perception must translate to changes in decision-making. To date, neither of these relationships have been explicitly studied in an IS project context and therefore it would be premature to conclude that risk assessment instruments will be effective tools for influencing managerial decision-making.
The purpose of the research reported here was: (1) to examine the relative contribution of two factors that are believed to shape risk perception: probability that a loss will occur and the magnitude of the potential loss, and (2) to explore the relative influence of risk perception and risk propensity on decision-making within an IS project context. By carefully examining the nature of risk perception and its influence on decision-making, this study represents an important first step toward understanding whether risk assessment instruments will actually help managers to avoid or minimize IS project failure.
The rest of the paper is organized into five sections. The first two sections provide a brief review of the relevant constructs and introduces the research model, questions, and hypotheses. This is followed by a description of the research method used and the results obtained from the study. The paper concludes with a discussion on the implications of the study along with directions for future research.
Section snippets
Background
In decision theory a risk may lead to either positive or negative consequences (Arrow, 1970). Although Charette (1989) defines software risk along decision-theoretic lines, most of the software risk management literature has focused only on the negative consequences associated with a course of action (Barki et al., 1993). Consistent with this focus on negative outcomes, we define risk as the non-zero probability that one or more undesirable outcomes will occur; in other words, there is some
Research model, questions, and hypotheses
Based on the above review of the literature, we elected to focus on two key areas that require further investigation: (1) the relative impact of probability of loss vs. magnitude of loss in shaping risk perception, and (2) the exact nature of the relationship between risk perception, risk propensity, and decision-making. The underlying research model is shown in Fig. 1.
Understanding the factors that influence risk perception and the nature of the relationship between risk perception, risk
Research method
A laboratory experiment was selected as the most appropriate methodology for addressing our research questions. This approach allowed for a high degree of control and enabled us to test the four hypotheses of interest.
Results
As a manipulation check, subjects were asked to respond to a series of four Likert-type questions designed to measure their perceptions of the probability of failure and the magnitude of potential loss. Based on a reliability analysis, two of these items were combined to form an aggregate measure of perceived probability of failure and the other two items were combined to form an aggregate measure of perceived magnitude of potential loss. Analysis of variance (ANOVA) revealed that subjects were
Discussion and conclusions
Before discussing the key findings of this study, it is appropriate to consider the study’s limitations. As with all laboratory experiments, there are limitations concerning generalizability. While we believe that students are appropriate subjects for experiments involving human decision-making under uncertainty, it should be noted that student subjects may have different criteria for judging the risk associated with business opportunities than those that would be exhibited by practicing
Mark Keil is an Associate Professor in the Department of Computer Information Systems at Georgia State University. He holds a D.B.A. from Harvard Business School, an S.M. from MIT’s Sloan School of Management, and a B.S.E. from Princeton University. His research focuses on software project management, with particular emphasis on understanding and preventing software project escalation. His research is also aimed at providing better tools for assessing software project risk and removing barriers
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Mark Keil is an Associate Professor in the Department of Computer Information Systems at Georgia State University. He holds a D.B.A. from Harvard Business School, an S.M. from MIT’s Sloan School of Management, and a B.S.E. from Princeton University. His research focuses on software project management, with particular emphasis on understanding and preventing software project escalation. His research is also aimed at providing better tools for assessing software project risk and removing barriers to software use. Keil’s research has been published in MIS Quarterly, Sloan Management Review, Communications of the ACM, Journal of Management Information Systems, IEEE Transactions on Engineering Management, Decision Support Systems, Information & Management, and other journals. He currently serves as an Associate Editor for the MIS Quarterly and as Co-Editor of The DATA BASE for Advances in Information Systems.
Linda Wallace is an Assistant Professor of Accounting and Information systems at Virginia Tech, Blacksburg, VA, USA. She holds a B.B.A. in Accounting and a B.S. in Math/Computer Science from Oglethorpe University in Atlanta, Georgia. She also holds a Ph.D. from the Department of Computer Information Systems at Georgia State University. Her primary research interests are in the areas of software project risk, risk management methodologies and mitigation strategies, and project management tools and techniques.
Dan Turk is an Assistant Professor in the Computer Information Systems Department at Colorado State University. He holds a B.A. in Psychology from Southern College of Seventh-day Adventists and an M.S. in Computer Science from Andrews University. He is currently completing his Ph.D. thesis in the Department of Computer Information Systems at Georgia State University. His primary research interests are in software engineering, software development processes, and software project management.
Urban Nulden is a lecturer in Informatics at Goteborg University where he teaches information technology management. His field of research is information technology and education. He is currently completing his dissertation on computer support for problem-based learning.
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Present address: Department of Accounting and Information systems, Virginia Tech, Blacksburg, VA, USA.