Evaluating Mergers and Acquisitions: A Belief Function Approach

  • Rajendra P. Srivastava
  • Deepak K. Datta
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 88)


Studies indicate that mergers and acquisitions are characterized by a high failure rate, often attributed to an inability on the part of the acquiring firm management to effectively evaluate potential acquisition candidates. This is not surprising given the considerable uncertainties surrounding both the relationships among factors in the evaluation process and also in the assessment of evidence. This paper develops a conceptual framework for acquisition and merger decisions using evidential reasoning approach under the belief function framework. It seeks to illustrate how expert knowledge of relevant factors can be mapped and how an evidential network can be used by decision makers to incorporate uncertainties in the evidence. Also highlighted is the fact that the nature and extent of evidence that needs to be collected depends on the rules assumed to govern relationships among input variables. Implications for theory and practice are discussed.


Candidate Attractiveness Belief Function Target Firm Potential Synergy Competitive Strength 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Lajoux, A.R. and J. F. Weston (1998). Do deals deliver on postmerger performance? Mergers and Acquisitions, 33 (2), pp. 34 - 37.Google Scholar
  2. [2]
    Ravenscraft, D.J. and F.M. Scherer (1989). The profitability of mergers, International Journal of Industrial Organization, 7, pp. 101-116..Google Scholar
  3. [3]
    Datta, D.K., V.K. Narayanan and G.E. Pinches (1992). Factors Influencing Wealth Creation in Mergers and Acquisitions: A Meta-analysis’, Strategic Management Journal, 13 (1), pp. 67 - 84.CrossRefGoogle Scholar
  4. [4]
    Datta, D.K., and G. Puia (1995). Cross-border acquisitions: An examination of the influence of strategic and cultural fit on shareholder value creation in U.S. acquiring firms’. Management International Review, 35 (4), pp. 325 - 336.Google Scholar
  5. [5]
    Achtmeyer, W.F., and M.H. Daniell (1988). How advanced planning widens acquisition rewards Mergers and Acquisitions, 23, pp. 37 - 42.Google Scholar
  6. [6]
    Rappaport, A. (1979). Strategic analysis for more profitable acquisitions. Harvard Business Review, 57, pp. 99 - 110.Google Scholar
  7. [7]
    Reilly, R.F. (1982). Planning for an Acquisition Strategy. Managerial Planning, 30 (5), pp. 36 - 42.Google Scholar
  8. [8]
    Curley, S. P., and J. I. Golden (1994). Using belief functions to represent degrees of belief. Organizational Behavior and Human Decision Processes 58 (2), pp. 271 - 303.CrossRefGoogle Scholar
  9. [9]
    Harrison, K. (1999). Evaluation and Aggregation of Audit Evidence under Uncertainty: An Empirical Study of Belief Functions. Ph.D. Dissertation, School of Business, University of Kansas.Google Scholar
  10. [10]
    Cohen, P. R. (1987). The control reasoning under uncertainty: A discussion of some programs, The Knowledge Engineering Review, Cambridge University Press.Google Scholar
  11. [11]
    Duda, R. O., P. E. Hart, and N. J. Nilsson (1976). Subjective Bayesian Methods For Rule-Based Inference Systems, Proceedings National Computer Conference (AFIPS), 15.Google Scholar
  12. [12]
    Lauritzen, S. L., and Spiegelhalter, D. J. (1988). Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems (with discussion), Journal of the Royal Statistical Society, series B, 50 (2), pp. 157 - 224.Google Scholar
  13. [13]
    Pearl, J. (1988). Bayesian and Belief-Functions Formalisms for Evidential Reasoning: A Conceptual Analysis, Proceedings of the 5th Israeli Symposium on Artificial Intelligence, Tele-Aviv.Google Scholar
  14. [14]
    Shenoy, P. P. and G. Shafer (1990). Axioms for Probability and Belief- Function Propagation, in Uncertainty in Artificial Intelligence, Elsevier Science Publishers.Google Scholar
  15. [15]
    Andersen, S. K., K. G. Olsen, F. V. Jensen, and F. Jensen (1989). HUGIN A Shell for Building Bayesian Belief Universes for Expert Systems, Proceedings of the Tenth International Joint Conference on Artificial Intelligence.Google Scholar
  16. [16]
    Saffiotti, A, and E. Umkehrer (1991). Pulcinella: A General Tool for Propagating Uncertainty in Valuation Networks, Proceedings of the Seventh National Conference on Artificial Intelligence, University of California, Los Angeles, pp. 323 - 331.Google Scholar
  17. [17]
    Shafer, G., P. P. Shenoy, and R. P. Srivastava (1988). AUDITOR’S ASSISTANT: A Knowledge Engineering Tool For Audit Decisions, Proceedings of the 1988 Touche Ross University of Kansas Symposium on Auditing Problems.Google Scholar
  18. [18]
    Zarley, D., Y.-T. Hsia, and G. Shafer (1988). Evidential Reasoning using DELIEF, Proceedings of the National Conference of Artificial Intelligence.Google Scholar
  19. [19]
    Srivastava, R.P., S.K. Dutta, and R. Johns (1996). An Expert System Approach to Audit Planning and Evaluation in the Belief-Function Framework, International Journal of Intelligent Systems in Accounting, Finance and Management.Google Scholar
  20. [20]
    Rappaport, A. (1988) Calculating the value-creation potential of a deal. Mergers and Acquisitions, 33 (1), pp. 33 - 44.Google Scholar
  21. [21]
    Roll, R. (1986). The Hubris Hypothesis of Corporate Takeovers. Journal of Business, 59, pp. 197 - 216.CrossRefGoogle Scholar
  22. [22]
    Barney, J.B. (1995). Gaining and Sustaining Competitive Advantage, Reading, MA: Addison-Wesley.Google Scholar
  23. [23]
    Markides, C. and P.J. Williamson. (1994). Related Diversification, Core Competencies and Corporate Performance, Strategic Management Journal, 15, pp. 149 - 165.CrossRefGoogle Scholar
  24. [24]
    Porter, M. (1980). Competitive Strategy. New York: Free Press.Google Scholar
  25. [25]
    Scherer, F.M. (1980). Industrial Market Structure and Economic performance, Chicago: Rand McNally.Google Scholar
  26. [26]
    Lisle, C. and J. Bartlam (1999). Can the target pass the competitive intelligence test? Mergers & Acquisitions, 33 (4), pp. 27 - 32Google Scholar
  27. [27]
    Wernerfelt, B. and C.A. Montgomery (1986). What is an attractive industry? Management Science, 32 (10).Google Scholar
  28. [28]
    Hitt, M.A., R. Hoskisson and D. Ireland (1990). Mergers and Acquisitions and Managerial Commitment to Innovation in M-Form Firms, Strategic Management Journal, 11, pp. 29 - 47Google Scholar
  29. [29]
    Porter, M.E. (1985). Competitive Advantage: Creating and sustaining superior performance, New York: Free Press.Google Scholar
  30. [30]
    Porter, M.E. (1987). From competitive advantage to corporate strategy. Harvard Business Review, 65 (3), pp. 43 - 59.Google Scholar
  31. [31]
    Bielinski, D.W. (1992). Putting a realistic dollar value on acquisition synergies, Mergers and Acquisitions, November-December, pp. 9 - 12.Google Scholar
  32. [32]
    Datta, D.K. (1986). Assimilation of Acquired Organizations: An Empirical Assessment of the Impact of Selected Organizational and Behavioral Factors on Acquisition Performance, Unpublished Doctoral Dissertation, University of Pittsburgh.Google Scholar
  33. [33]
    Jensen, M.C. and R.S. Ruback (1983). The Market for Corporate Control: The Scientific Evidence, Journal of Financial Economics, 11, pp. 5 - 50.CrossRefGoogle Scholar
  34. [34]
    Salter, M.S., and W.A. Weinhold (1979). Diversification through acquisitions: Strategies for creating economic value, New York: The Free Press.Google Scholar
  35. [35]
    Singh, H. and C. Montgomery (1987). Corporate acquisition strategies and economic performance, Strategic Management Journal, 8, pp. 377-386.CrossRefGoogle Scholar
  36. [36]
    Sirower, M.L. (1997). The Synergy Trap: How Companies Lose in the Acquisition game. New York; The Free Press.Google Scholar
  37. [37]
    Datta, D.K. (1991). Organizational Fit and Acquisition Performance: Effects of Post-Acquisition Integration, Strategic Management Journal, 12 (4), pp. 281 - 298.CrossRefGoogle Scholar
  38. [38]
    Datta, D.K. and J.H. Grant (1990). The relationship between type of acquisition, the autonomy given to the acquired firm and acquisitions success: An empirical analysis, Journal of Management, 16 (1), pp. 2944.CrossRefGoogle Scholar
  39. [39]
    Jemison, D.B. and S.B. Sitkin (1986). Corporate Acquisitions: A Process perspective, Academy of Management Review, 11 (1), pp. 145 - 163.Google Scholar
  40. [40]
    Slowinski, G. (1992). The human touch in successful strategic alliances, Mergers and Acquisitions, 27 (1), pp. 44 - 47.Google Scholar
  41. [41]
    Marks, M.L. (1999). Adding cultural fit to your diligence checklist, Mergers and Acquisitions, 34 (3), pp. 14 - 20.Google Scholar
  42. [42]
    Buono, A.F., Bowditch, J.L., and J.W. Lewis (1985). When cultures collide: The anatomy of a merger, Human Relations, 38 (5), pp. 477 - 500.CrossRefGoogle Scholar
  43. [43]
    Srivastava, R. P., and K. O. Cogger (1995). Algorithm for Converting Beliefs on Individual Variables from a Single Source of Evidence to m-values on the Joint Space, Working Paper, School of Business, The University of Kansas.Google Scholar
  44. [44]
    Smets, P. (2001). Decision making in a Context where Uncertainty is Represented by Belief Functions. Belief Functions in Business Decisions. Edited by R. Srivastava and T. Mock. Springer-Verlag, Inc (forthcoming).Google Scholar
  45. [45]
    Smets, P. (1990a). The Combination of Evidence in the Transferable Belief Model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 5 (May).Google Scholar
  46. [46]
    Smets, P. (1990b). Constructing the Pignistic Probability Function in a Context of Uncertainty. Uncertainty in Artificial Intelligence 5. ed. by Henrion, M., Shachter, R.D., Kanal, L.N., and Lemmer, J.F. North-Holland: Elsevier Science Publishers B. V.Google Scholar
  47. [47]
    Smets, P. (1998). The Transferable Belief Model For Quantified Belief Representation. Quantified Representation for Uncertainty and Imprecision, Vol. 1. Edited by P. Smets. Kluwer Academic PublishersGoogle Scholar
  48. [48]
    Shafer, G. (1976). AMathematical Theory of Evidence, Princeton University Press.Google Scholar
  49. [49]
    Yager, R. R, J. Kacprzyk, and M. Fedrizzi. (1994). Advances in the Dempster-Shafer Theory of Evidence, John Wiley & Sons, New York, NYGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Rajendra P. Srivastava
    • 1
  • Deepak K. Datta
    • 1
  1. 1.School of BusinessUniversity of KansasLawrenceUSA

Personalised recommendations