Control del pronóstico

In this chapter we analyse data on the population of Max Planck inventions over the 1990 – 2003 period and found that market as well as technological un-certainty influences the likelihood that an invention will be licensed in the pre-licensing phase and abandoned/ terminated in the post pre-licensing phase. We therefore present evidence that inventions have option like characteristics in dif-ferent phases of their lifecycle. The empirical results provide strong support for the view that the uncertainty regime the invention and license is found in is im-portant for understanding the technology lifecycle.

In more detail we found that in the pre-licensing phase market uncertainty has a positive effect on licensing whereas threat of preemption has a negative effect.

Unlike suggested in Hypothesis 2 technological uncertainty does not have a sig-nificant negative relationship to licensing. In the post licensing phase we found that both postulated hypotheses are significant, namely that high market uncer-tainty has a negative impact on licensing termination whereas technological un-certainty is positively influencing it.

Our methodological design resembles the design of Shane (2001b) and assures the validity of the results due to following characteristics. In the analysis of the pre-licensing phase as well as the post-licensing phase our design includes the complete population of patents and licenses issued and therefore avoids sample selection bias within the dataset.

Contributions to two streams of research are addressed, namely the innovation and licensing literature as well as the real option literature. Previous empirical

studies on technology licensing (e.g. Anand and Khanna, 2000) have analyzed licensing events and the license contract structures and are generally extremely rare in nature due to the difficult accessibility of data which makes contributes to the fact that understanding of licensing is significantly further behind empiri-cal understanding of other issues in contracting (Anand and Khanna, 2000). Ex-isting studies are generally able to examine factors that influence the decision on an aggregated level rather than on the invention level. Our study overcomes this limitation by utilizing this specific dataset as well as studying different lifecycles of the innovation.

We are therefore the first to examine empirically different forms of uncertainty in the lifecycle of an invention and its effects on the licensing likelihood and li-censing termination likelihood. Our results show significant but opposing effects of endogenous and exogenous uncertainty when an invention is licensed and when it is terminated/ abandoned. We demonstrate that commercialization and termination of agreements changes with uncertain market conditions as well as with the uncertainty regime the technological opportunity is found in. The dy-namic nature of market uncertainty will affect the amount of licenses contracted out.

The results of this study also suggest that option value can be firm specific e.g.

firms that have a higher licensing experience are more likely to license an inven-tion. These firms are more familiar with the process of licensing and technology transfer and more likely to be better able to evaluate external technologies. This supports findings by Arora and Gambardella (1994) that their internal

knowl-edge base allows firms to be more confident about their decision making in un-certain environments and benefiting from their ‘absorptive capacity’.

Recently real option studies have analyzed and tried to explain a wide range of phenomena especially in the range of strategic decisions (Amram and Kulati-laka, 1999). Still very little empirical work has tested the predictions made by real option theory on the value and optimal decision timing of option contracts Ziedonis (2007). Our objective in this paper was therefore to understand how managers use real options in the different phases of its lifecycle. Empirical justi-fication of real option theory represents an important contribution to the litera-ture as it gives evidence for the applicability of real option reasoning for a wide range of phenomena.

6.5.1 Interaction Effects

The tests for the interaction effects for all dependent variables can be found in chapter 9.4.6 for the licensing model whereas the chapter 9.4.8 contains the analysis for the interaction effects of licensing failure.

The licensing model shows several statistically significant interaction effects which are summarized in Table 6-7.

Table 6-7 Summary of interaction effects for the licensing model

Interactions Coefficient

Market Uncertainty * Patent Quality -0.598 **

Market Uncertainty Square * Patent Quality 1.879 **

Technological Uncertainty * Partnered Research -0.313 * Technological Uncertainty * Invention Experience -1.858 †

Threat of Preemption * Technology Age 0.16 *

Threat of Preemption * Partnered Research 0.656 ***

Threat of Preemption * Invention Experience 0.003 † Threat of Preemption * Licensing Experience 0.004 *

† p < 0.1

* p < 0.05

** p < 0.01

*** p < 0.001

 In more certain markets patent quality attenuates the effect of market uncertainty on licensing an invention whereas in uncertain markets pat-ent quality accpat-entuates the effects of market uncertainty on licensing

 Technologies that are more certain are more likely to be licensed when they were developed in partnered research efforts whereas when technolo-gies are uncertain they more likely to be licensed when they were not de-veloped in partnered research efforts

 Technologies that are more certain are more likely to be licensed when the invention experience is high whereas when technologies are uncertain they more likely to be licensed when invention experience is low

 When threat of preemption is high, inventions whose technology age is high are more likely to be licensed whereas when threat of preemption is low, inventions are more likely to be licensed when their technology age is low

 When threat of preemption is high inventions that were developed in partnered research efforts are more likely to be licensed whereas when

threat of preemption is low inventions are more likely to be licensed when their development was not developed in partnered research efforts

 When threat of preemption is high inventions are more likely to be li-censed that were developed by highly experienced inventors whereas when threat of preemption is low inventions are more likely to be li-censed when they are developed by inventors with less experience

 When threat of preemption is high inventions are more likely to be li-censed to licensees with high licensing experience whereas when threat of preemption is low inventions are more likely to be licensed to licensees with low licensing experience

The interaction effect model for licensing failure only has interaction effects with technology transfer experience and technology age which can be seen in Table 6-8.

Table 6-8 Summary of interaction effects for the licensing failure model

Interactions Coefficient

Market Uncertainty * Patent Quality -0.59 *

Market Uncertainty Square * Patent Quality 1.49 *

Technological Uncertainty * TTO Experience -0.01 **

Technological Uncertainty * Technology Age -0.01 †

† p < 0.1

* p < 0.05

** p < 0.01

*** p < 0.001

 In more certain markets patent quality attenuates the effect of market uncertainty on licensing termination whereas in uncertain markets patent

quality accentuates the effects of market uncertainty on licensing termi-nation

 Technologies that are more certain are more likely to be terminated when the technology transfer officer experience is high whereas when technolo-gies are uncertain they more likely to be terminated when technology transfer experience is low

 Technologies that are more certain are more likely to be terminated when the technology age is high whereas when technologies are uncertain they more likely to be terminated when technology age low