has renewed the call for caution for anyone intending to develop research on innovation. The collective study coordinated by Godin and Vinck (2017) offers a synthesis of this research: a statement of the various biases faced by researchers who primarily study innovation; a call for enhanced reflexivity; and a desire to identify innovation processes in a comprehensive manner. These critical analyses aim to avoid the reinforcement of a particularly prevalent “ready-made thinking”, whose concrete repercussions go beyond the academic sphere (Sveiby 2017). It is in fact a call for a less enthusiastic, more balanced and nuanced approach. We propose below a synthesis of these approaches, in the form of advice and calls for vigilance, which for the most part echo aspects developed earlier.
Several biases orient the study of innovation in a sometimes very marked direction. In some ways, they also shape managerial practices and political decisions in favor of innovation. The purpose of this section is to identify the factors that prevent us, in a certain way, from considering and analyzing innovation “as it is done”, in as realistic a manner as possible.
These biases are problematic insofar as they contribute to forming excessive confidence in the benefits of innovation, to exaggerating the control exercised over processes and to trivializing disruptive innovations, while strongly orienting towards techno-push proposals. All this is to the detriment of understanding complex, contingent and risky processes that require anticipation and preparation. Derived from or associated with a certain number of myths, they are at the origin of innovation models (Joly 2019). These interpretive frameworks generate shared representations and interpretations of how innovation is produced, then acting performatively, they guide our collective way of seeing innovation. Jasanoff and Kim (2015) speak of socio-technical imaginaries, imbued with values that impact both discourse and practice, more or less consciously.
Table 1.1. Summary of biases in innovation studies
| Type of bias | Explanations and effects on the study of innovations |
| Pro-innovation bias | Taking for granted that innovation is positive, insisting on its virtues and neglecting the study of collateral effects and negative externalities. Understanding it less as a phenomenon to be studied than as a remedy to social and economic problems. |
| Pro-success bias | Drawing optimistic conclusions (feeding the pro-innovation bias) from the analysis of success (studying what worked). Leaving aside the analysis of unsuccessful projects and semi-failures, which are nevertheless in the majority and rich in lessons learned. |
| Pro-disruptive innovation bias | Associating innovation with disruption and exaggerating its “disruptive” character. Most often focusing on radical innovations while supporting and incremental innovations (recycling, maintenance are the majority). |
| Originality bias | Putting creativity and inventiveness on a pedestal. Associating innovation with the pioneering dimension of the precursor. Badmouthing imitation, considered the antithesis of innovation, although it is an integral part of the innovation process. Underestimating the rigidity and caution of most companies which are actually quicker to imitate and draw inspiration from others than to invent. |
| Short-term bias | Compressing the duration of innovation projects (being the first to market, introducing the novelty to the market and benefiting from a return on investment, keeping followers at bay). Accelerating innovation when real processes often take a long time. Overestimating the control that can be exerted on innovation processes. |
| Pro-technological innovation bias | Sanctifying technology as the main provider of solutions (“technological solutionism”), to the detriment of a detailed understanding of the problems to be solved. Approach sold as a technological fix to remedy the difficulties generated by the new solutions, thanks to other technological advances. Focusing on high-tech solutions (new technologies) and adding functionalities to the point of forgetting the low-tech possibilities (simplification, minimalism, removal, use or improvement of already proven technologies, continuity, etc.). Relegating service, organizational, process and social innovations to a secondary role. |
| Pro-business bias | Considering the enterprise, and in particular start-ups, as the natural cradle of innovations. Minimizing or even ignoring innovations coming from the third sector (associations, foundations), public actors or communities of practice (escaping at least temporarily from market-oriented rationale). |
1.5. Lessons learned from resistance to innovation and unsuccessful processes
Often described as irrational, transient and related to the lack of knowledge of positive effects or anxiety about new things (“neophobia”) (Bauer 2017), resistance to innovation can take on many other meanings. There is indeed rationality in the arguments of actors who do not adhere to innovation (Godin and Vinck 2017), which Cañibano et al. (2017) state very simply through the concept of “novation”: some actors formulate non-innovative strategies and succeed in implementing them. They therefore have “good reasons”, entirely rational, for not adhering to, not using, and ultimately not appropriating an innovation. The conception of rationality is here open to the pursuit of goals and to the engagement of plural actions, including those that push to innovate, and those that lead to the absence of innovation, even if this clashes with the contemporary call to innovate permanently and in every respect.
Provided that the choices that led to non-adoption are considered to be sensible and well-founded, resistance can be considered an asset that can contribute to the innovation process (Bauer 2017). Not adopting an innovation is indeed an adaptive strategy that it is important to understand as such (Kimberly 1981) in order to be able to alter the trajectory in question, or to energize other projects. Studies on this subject are few and far between (Thomas et al. 2017) and do not sufficiently reveal the actions of resistance. However, an in-depth analysis of resistance to innovation, also known as “re-innovation”, allows us to make sense of it (ibid.). The rejection of certain technologies, for example, carries messages, identifications and meanings in relation to hegemony or domination-based rationale. It is not only an act of non-consumption, to be approached from a strictly economic angle, but also a question of alignment and coherence compared to a vision of the world and a set of values.
In the same way, the unsuccessful trajectories of innovations (a term that is preferable to that of failure) are rich in teachings. Vinck (2017) approaches them as collective learning processes (learning by failing). In fact, organizations learn more effectively from their failures (and the failures of others) than from their successes (Cyert and March 1992), which are often analyzed in a simplistic and superficial manner. Conversely, when things do not go as planned, there is fertile ground for an in-depth study to identify the causes of the failure. This is not only about transparency, but also about the search for long-term efficiency. Learning in this way implies accepting to testify about one’s professional activity in a way that is a priori devaluing, in a culture marked by the call to succeed, to excel and to promote oneself. Although it is an integral part of professional life, failure is rarely openly discussed (Vinck 2017), which is another effect of the pro-success bias mentioned above.
KEY POINT – Not all research on innovation
