To terminate or not an ongoing R&D project: a managerial dilemma

This empirical study attempts to develop a framework to assist managers in deciding whether to abandon an ongoing research and development (R&D)/innovation project at various stages of R&D. The monitoring framework is based upon the models developed through multiple logistic regression analysis on a data set of 60 successful and unsuccessful projects. The technique determines those factors which discriminate between success and failure of a project. The major advantage of the proposed framework is that it provides a single indicator which can be used to monitor the success of an ongoing project at various stages over its life span. The indicator incorporates the combined effect of all the factors and avoids the problem of setting thresholds for individual factors or success indicators.

[1]  R. Balachandra,et al.  When To Kill That R&D Project , 1984 .

[2]  T Dolgoff,et al.  Why innovations fail. , 1982, The Psychiatric hospital.

[3]  Robert G. Cooper,et al.  The new product process: an empirically‐based classification scheme , 1983 .

[4]  J. Pinto,et al.  Variations in Critical Success Factors Over the Stages in the Project Life Cycle , 1988 .

[5]  Robert G. Cooper,et al.  The Dimensions of Industrial New Product Success and Failure , 1979 .

[6]  A. S. Bean,et al.  Why R&D Projects Succeed or Fail , 1986 .

[7]  Lloyd D. Fisher,et al.  A comparison of the maximum likelihood and discriminant function estimators of the coefficients of the logistic regression model for mixed continuous and discrete variables , 1983 .

[8]  Jeffrey K. Pinto,et al.  The causes of project failure , 1990 .

[9]  R. Balachandra,et al.  How to Decide When to Abandon a Project : R&D Management , 1980 .

[10]  T. J. Allen,et al.  The process of innovation in five industries in Europe and Japan , 1976, IEEE Transactions on Engineering Management.

[11]  Roy Rothwell,et al.  SAPPHO updated - project SAPPHO phase II , 1993 .

[12]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[13]  Robert G. Cooper,et al.  New Product Performance and Product Innovation Strategies , 1986 .

[14]  M. Halperin,et al.  Estimation of the multivariate logistic risk function: a comparison of the discriminant function and maximum likelihood approaches. , 1971, Journal of chronic diseases.

[15]  Peter F. Drucker Principles of Successful Innovation , 1985 .

[16]  B. Zirger,et al.  A study of success and failure in product innovation: The case of the U.S. electronics industry , 1984, IEEE Transactions on Engineering Management.

[17]  Richard T. Holzmann To Stop or Not-The Big Research Decision , 1974, IEEE Engineering Management Review.

[18]  Robert G. Cooper,et al.  New product success factors: A comparison of ‘kills’ versus successes and failures , 1990 .

[19]  Robert G. Cooper,et al.  Modular risk management: an applied example , 1979 .

[20]  James M. Utterback,et al.  Innovation in Industry and the Diffusion of Technology , 1974, Science.

[21]  C. K. Buell,et al.  When to Terminate a Research and Development Project , 1967 .

[22]  J. Raelin,et al.  R&D project termination in high-tech industries , 1985, IEEE Transactions on Engineering Management.

[23]  R. Cooper New Products: What Distinguishes the Winners? , 1990 .

[24]  John E. Ettlie,et al.  Innovation among suppliers to automobile manufacturers: an exploratory study of barriers and facilitators1 , 1979 .

[25]  Roy Rothwell,et al.  The `Hungarian sappho': some comments and comparisons , 1974 .

[26]  A. Rubenstein,et al.  Factors Influencing Innovation Success at the Project Level , 1976 .

[27]  D. Mowery,et al.  Inside the black box: The influence of market demand upon innovation: a critical review of some recent empirical studies , 1993 .

[28]  Robert J. Bedell,et al.  Terminating R&D Projects Prematurely , 1983 .

[29]  R. Rothwell Factors for Success in Industrial Innovation , 1974 .

[30]  A. Gerstenfeld,et al.  A study of successful projects, unsuccessful projects, and projects in process in West Germany , 1976, IEEE Transactions on Engineering Management.

[31]  J. Pinto,et al.  Critical Success Factors in R&D Projects , 1989 .

[32]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[33]  Morris Teubal,et al.  analysis of r & d failure , 1993 .

[34]  Mike Saren,et al.  A classification and review of models of the intra-firm innovation process , 1984 .