Academic journal article Fuzzy Economic Review

Fuzzy Grouping Variables in Economic Analysis. a Pilot Study of a Verification of a Normative Model for R&d Alliances

Academic journal article Fuzzy Economic Review

Fuzzy Grouping Variables in Economic Analysis. a Pilot Study of a Verification of a Normative Model for R&d Alliances

Article excerpt

(ProQuest: ... denotes formulae omitted.)

1.INTRODUCTION

Many investments decisions in presence of uncertainty can be characterized as real options problems [9, 19]. Consequently, in the last decade, the development of normative techniques to evaluate real options investments, summarized by the seminal book by Dixit and Pyndyck [11], has significantly shaped the research on sequential investments and created a fruitful paradigm for its treatment [17].

More recently real options analysis (ROA) has been used also to evaluate R&D alliances established between firms [8,15,16,17,25]. These agreements not only generate stochastic benefits (which suggest the existence of uncertainty) but also bring sunk costs (which imply irreversibility). In addition, a key element in these agreements is flexibility: firms have the opportunity, but not the obligation to sign an alliance, or sometimes the right to renew an existing one. In other words, they can postpone their decisions to form an alliance when more information is available. Therefore, the study of R&D alliances in a real options framework per sé includes these important three aspects [23]. Despite the normative work on real option valuation, only recently some researches have started analysing behavioral aspects [19]. These studies highlight that individuals exhibit systematic deviations from the predictions derived using normative models [22].

This research attempts to extend the boundaries of real options analysis to environments where people have biases in their decision-making, especially in the context of R&D alliances and merger and acquisitions as well as to provide tools for appropriate validation of the relevant models. Consistent with neoclassical rational theory, prior studies show that mergers are driven by rational expectations of growth options, synergies or reallocation of assets in a response to industry shocks. The rational view also includes real options models, where decisions such as acquisitions waves are driven by growth opportunities [30]. However, contrary to rational theory, decision makers may act irrationally when making acquisition choices under uncertainty [18,28]. Individuals exhibit systematic deviations from the predictions derived using normative models - i.e. models assuming that individuals are risk neutral expected value maximizing agents [22]. Investors' exuberance, positive sentiments of boards as well as cognitive biases - such as overconfidence - influence companies' acquisition behavior under uncertainty. To use Smit and Lovallo's [28] words, "acquisition strategy is vulnerable to the way managers perceive risk and losses, judgment biases in their strategy, the bidding behavior of rivals and mispricing in financial markets". Note, that these assumptions need to be reflected also in the design of the validation instruments and in the collection of validation data. Such data need to be gathered in a way that allows for the identification of specific subgroups in the validation sample that meet the assumptions of the models. Standard grouping variables may not be applied in the analysis of the sample, since the definition of the subgroups may not be crisp (e.g. rationality, risk-aversion, etc. can manifest themselves in a particular respondent to a certain degree, not necessarily fully). The requirement of full satisfaction of certain assumptions (full rationality, full information, etc.) may render the applicable part of the validation sample too small to be still relevant. On the other hand the use of the full sample without any knowledge concerning their fulfilment of the assumptions of the model that is being validated is methodologically incorrect. This calls for two things - first, the tools for the assessment of the degree of fulfilment of the assumptions of the model in the participants (and the data they provide) need to be available (or at least built-in the data gathering procedure), and second, data analysis tools capable of dealing with fuzzy subsets of the validation sample need to be used. …

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