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The behavioral theory of the firm suggests that managers respond to the

subjective environment that they perceive rather than the objective environment that they ―really‖ face (Simon, 1982). Therefore, much as managers‘ behaviors are influenced by their subjective perception of the environment, the investment decisions of organizations are impacted by managers‘ perception of the value of the investment. As a result, if managers‘ perception of the value of the investment is subject to cognitive biases and heuristics, the managers will make biased investment decisions.

Sociocognitive literature has observed that managers have difficulty in changing beliefs. Once beliefs are developed, subsequent information processing tends to be biased in the direction of the preexisting belief (Crocker, Fiske, & Taylor, 1984). People often selectively filter information and interpret new information so as to maintain their beliefs (Fiske, 1991).

A common example of belief based bias is anchoring. ―Anchoring‖ refers to the phenomenon that different initial values yield different estimates and that the final

Sunk cost Likelihood of

abandonment

estimates are biased toward the starting point, so there is ―insufficient adjustment.‖ Tversky and Kahneman (1974) suggested that judgment under uncertainty exhibits anchoring and insufficient adjustment. People in many situations make estimates of likely outcome by starting from an initial value and adjusting this value to yield the final answer. The initial value, which acts as a starting point, may be given or it may be the result of some incomplete computation made by the people who make the estimates. In any case, people typically make insufficient adjustments based on the initial value.

Another cognitive heuristic, overconfidence, can make the anchoring effect even larger. Psychological literature shows that many people are often overly confident about their own relative abilities and are unreasonably optimistic about their futures (e.g., Kahneman and Tversky, 1979; Weinsten, 1980; Taylor and Brown, 1988). Such an optimistic bias is referred as overconfidence. Camerer and Lovallo (1999), for example, found that overconfidence leads to excessive business entry. They found that even when people accurately forecast competition and negative industry profits, they may decide to enter anyway because they believe their firm will succeed while most others will fail. The authors suggested that this can be one of the explanations for the high rate of business failure. While overconfidence may lead to excessive new business initiation, it also makes it hard for managers to terminate their existing investment projects. Because the decision makers may believe that, despite the unfavorable signals, they are still able to generate considerable returns, they may become more reluctant to adjust their initial expectation of the project.

Firms initiate innovation investments because they expect that the investments will produce positive returns. As time passes, some projects turn out to be less promising

than expected. Consequently, the managers ideally should revise their investment plan accordingly, abandoning those projects for which the economic value is no longer justified. Anchoring, however, may prevent managers from abandoning those projects in a timely manner. Holding feedback constant, the higher the initial expected future value of an innovation, the greater the adjustment that is needed for the managers to identify the real value.

In the light of the above, anchoring and insufficient adjustment tend to lead firms to stick to their prior expectation even when the signals are unfavorable and thus fail to terminate projects that are no longer justified. This applies to firms‘ innovation strategies; the managers‘ initial expectation of the usefulness of an innovation will impact their decisions between termination and persistence. The higher the initial expectation of an innovation, the more likely the managers will tend to keep it.

Hypothesis 3:

The higher the initial expectation of an innovation is, the less likely the innovation will be abandoned.

Figure 4

The remaining hypotheses are mostly based on the real option reasoning, which suggests that the higher the variance of the future returns on an innovation, the more valuable the growth opportunities embedded in the innovation. This is analogous to stock option pricing. When the downside loss is fixed, firms‘ investments increase in value

Initial expectation Likelihood of

abandonment

with increase in variance of returns, which means that the firms can access a greater range of potential upside outcomes. As Dixit (1992) pointed out, the upside potential to produce future earnings is actually the primary force that governs abandonment decisions. Therefore, innovations that have high variance in future returns should be more valued from a real options theory perspective, while such innovations are less valued using conventional approaches.

There are various types of factors from different sources impacting a technology‘s value, including the adoption and diffusion of new technologies, market and customer acceptance, and competitors‘ strategic actions (Rosenberg, 1996). In this study I identify and study four factors that influence the value of a firm‘s innovations: explorativeness of innovation, scope of application, firm‘s knowledge depth and knowledge

complementarity. The first two factors are technology specific, and the latter two describe a firm‘s knowledge portfolio effect.