The Role of Cognition and Emotions in Explaining Innovation Process Performance

Posted: 13 Jan 2017 Last revised: 26 Mar 2018

See all articles by Carlos A. Osorio

Carlos A. Osorio

Yuken Impact Research Lab; Deusto Business School

Maria Renard

Yuken Impact Research Lab

Date Written: January 11, 2017

Abstract

Innovation process performance has been largely understood as resulting from the interaction among strategic, contextual and procedural factors, and a team’s capabilities for decision-making and performing technical tasks under uncertainty. As result, failures in innovation are often explained by the risky and uncertain nature of discovery-driven development processes. This paper summarizes our exploration about the role of individual and team’s cognition and emotions on innovation learning performance. Based on an integrative review of the literature and action research, we examine the mobilization of fifteen competencies among twenty-eight stages of an innovation process with 1,176 participants of 134 innovation teams between 2013 and 2017. This analysis was performed based on knowledge about the effect 46 critical decisions, 118 types of cognitive bias, and 49 common technical mistakes along week-long experimental programs. Our results show that, regardless of industry, personal traits, gender or technical background, some specific tasks generate higher levels of frustration and types of mistakes in development teams. We identified how various cognitive and emotional limitations affect technical performance, and explored the effectiveness of preemptive actions for “troubleshooting” cognitive and emotional response under high uncertainty. We argue that project failure results from the accumulative effect of mistakes in decision-making under uncertainty, which in turn result from failures to learn and the effects of biased sense-making and inadequate response to challenges. We propose understanding innovation as a learning process under risk and uncertainty; where complex, conflicting, inaccurate and incomplete information becomes available incrementally through mechanisms of selective search and sense-making (individual and collective). As result, cognitive biases, inertia and overload affect substantive and procedural rationality limiting a team’s ability to learn and achieve superior technical results. Our results support the theoretical role and dynamics among individual and collective cognitive and emotional powers, self-esteem, personal disposition, and cognitive load across different milestones and stages of an innovation processes. We propose that, under highly uncertain and fast-pacing environments, technical tasks relying on synthesis capabilities tend to generate higher levels of frustration. Increased frustration lead to cognitive overload, thus increasing the frequency of mistakes and diminishing an individual’s performance, and affecting a team’s social cognition. We propose that ex-ante team configuration by explicit psychological variables and on-purpose generation of a noisy, risky and ambiguous climate reinforces the dynamics between cognition and emotions deepening significant innovation learning, positively affecting how a team represents the problem at hand, devise and manages the appropriate courses of action (process and methods) for solving it.

Keywords: Cognition, emotions, learning, innovation, cognitive load, bias

JEL Classification: D8, D81, O3, O31, D83

Suggested Citation

Osorio-Urzua, Carlos Alberto and Renard, Maria, The Role of Cognition and Emotions in Explaining Innovation Process Performance (January 11, 2017). Available at SSRN: https://ssrn.com/abstract=2897544

Carlos Alberto Osorio-Urzua (Contact Author)

Yuken Impact Research Lab ( email )

Monsenor Sotero Sanz 161
Santiago, RM 7500007
Chile

HOME PAGE: http://www.yuken.cl/

Deusto Business School ( email )

Hermanos Aguirre Kalea, 2
Bilbao, Euskadi 20012
Spain

Maria Renard

Yuken Impact Research Lab ( email )

Monsenor Sotero Sanz 161
Santiago, RM 7500007
Chile

Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
757
PlumX Metrics