Innovation Management Research and Intuition: Mapping the field
University of Sydney Business School, Australia
Faced with an increasing amount of data, managers are required to make rapid judgements about innovation. They must make connections within and between data non-consciously and instantaneously to allow their minds to fill-in the blanks where there is a limit in their rational analysis and understanding.
Intuitive judgements are rapid and non-conscious decisions that appear through holistic associations happening beyond deliberate thinking (Dane & Pratt, 2007; Gigerenzer, 2007; G. Hodgkinson & Sadler-Smith, 2017). Despite the difficulty in explaining the process by which intuitive decision making takes place, it is important to recognize that intuition affects the speed and quality of decisions made in innovation (Dayan & Elbanna, 2011; Eliëns, Eling, Gelper, & Langerak, 2018; Eling, Langerak, & Griffin, 2015; Epstein, 2010). Yet, limited attention has been given to intuitive decision making in innovation management. This may be due to the ambiguous and abstract nature of intuition itself (Epstein, 2008; Lufityanto, Donkin, & Pearson, 2016), but also the fact that innovation management is an emerging research discipline (Dodgson, Gann, & Phillips, 2013). It is nonetheless surprising since intuitive decision making is present in the management of innovation (Proctor, 2014).
Our review reveals that the research in the field is nascent, yet growing, featuring both conceptual and empirical publications. However, a comprehensive and systematic review is (surprisingly) still missing, given the relevance of intuition as a knowledge acquisition and decision making approach for innovation managers.
What has been researched at the junction of intuitive decision making and innovation management; why is it interesting to investigate (why it matters?); and what are the future research avenues to complement our understanding of intuitive decision making in the management of innovation?
In our systematic review, first, we followed a structured search process of publications in the last 20 years, incorporating key Booleans. Second, we reviewed the documents’ full record to evaluate whether each article was relevant for this review. Third, we focused on the article’s content in detail. The main criteria to include an article was that each should clearly define intuition in an innovation context (e.g., NPD, FFE, and/or commercialization). We performed a text-mining analysis, and found different thematic clusters. We also coded the empirical evidence, which resulted in eight evidence-based themes of intuition in the innovation management literature.
Through our systematic review and analysis we reached different results. For example, our text mining analysis allowed to show key concepts in the literature. The coding analysis we performed allowed us to show key evidence-based themes across publications. In combination, through our systematic review, we propose three main research avenues. First, we propose that more research is needed to understand the concepts and themes of intuition in innovation management. Second, we propose that there is more research needed to understand how intuitive decision making works across the stages of innovation management, with focus at the commercialization stage of innovation. Third, we propose that more research is needed to understand intuition in innovation management at the various levels of analysis.
Contribution to Scholarship
This review contributes to the extant research in three ways. First, we discuss why intuition in innovation management matters and how it is conceptualized. Second, we present a systematic review of the extant literature focusing on relevant concepts and key findings. Third, we propose a future research agenda based on the findings of our review. For example, how can we study heuristics and individual bias (Liedtka, 2015) in innovation management research and especially their role in intuitive decision making? This review is unique because our analyses attempt to expose drivers of further theoretical reflection, to determine the directions and limitations of the field, and, more broadly, to advance our comprehension of the cognitive aspects of the discipline of innovation management.
Contribution to Practice
With our review, innovation practitioners can look at intuitive decision making as a product of managerial experience and as a product of managerial uncertainty (Baldacchino et al., 2015; Baron & Ensley, 2006). They can see the process of intuitive decision making as an information processing capability that is unconsciously activated due to changes in their external environment (Ochse, 1990; Raidl & Lubart, 2001). Innovators can look at intuitive decision making as decisions that drive action, without them being fully able to rationalize how these decisions were made (Baron, 1998; Vaghely & Julien, 2010).
This paper aims at contributing to the behavioural innovation discussion. Particularly, the paper deals with the need to understand how innovation managers make decisions in innovation management and the role that intuition has in it. With this, we might be able to contribute to understand different cognitions appearing while innovating.
Baldacchino, L., Ucbasaran, D., Cabantous, L., & Lockett, A. (2015). Entrepreneurship research on intuition: a critical analysis and research agenda. International Journal of Management Reviews, 17(2), 212–231.
Baron, R. A. (1998). Cognitive mechanisms in entrepreneurship: Why and when entrepreneurs think differently than other people. Journal of Business Venturing, 13(4), 275–294. https://doi.org/10.1016/S0883-9026(97)00031-1
Baron, R. A., & Ensley, M. D. (2006). Opportunity recognition as the detection of meaningful patterns: Evidence from comparisons of novice and experienced entrepreneurs. Management Science, 52(9), 1331–1344.
Dane, E., & Pratt, M. G. (2007). Exploring intuition and its role in managerial decision making. Academy of Management Review, 32(1), 33–54.
Dayan, M., & Elbanna, S. (2011). Antecedents of Team Intuition and Its Impact on the Success of New Product Development Projects. Journal of Product Innovation Management, 28(s1), 159–174. https://doi.org/10.1111/j.1540-5885.2011.00868.x
Dodgson, M., Gann, D. M., & Phillips, N. (2013). Perspectives on Innovation Management. In M. Dodgson, D. M. Gann, & N. Phillips (Eds.), The Oxford handbook of innovation management (pp. 3–26). Oxford; UK: Oxford University Press.
Eliëns, R., Eling, K., Gelper, S., & Langerak, F. (2018). Rational Versus Intuitive Gatekeeping: Escalation of Commitment in the Front End of NPD. Journal of Product Innovation Management. https://doi.org/10.1111/jpim.12452
Eling, K., Langerak, F., & Griffin, A. (2015). The Performance effects of combining rationality and intuition in making early new product idea evaluation decisions. Creativity and Innovation Management, 24(3), 464–477.
Epstein, S. (2008). Intuition from the perspective of cognitive-experiential self-theory. Intuition in Judgment and Decision Making, 23, 37.
Epstein, S. (2010). Demystifying intuition: What it is, what it does, and how it does it. Psychological Inquiry, 21(4), 295–312.
Liedtka, J. (2015). Perspective: Linking design thinking with innovation outcomes through cognitive bias reduction. Journal of Product Innovation Management, 32(6), 925–938.
Lufityanto, G., Donkin, C., & Pearson, J. (2016). Measuring intuition: nonconscious emotional information boosts decision accuracy and confidence. Psychological Science, 27(5), 622–634.
Gigerenzer, G. (2007). Gut feelings: The intelligence of the unconscious. Penguin.
Hodgkinson, G., & Sadler-Smith, E. (2017). The dynamics of intuition and analysis in managerial and organizational decision making. Academy of Management Perspectives, (ja).
Ochse, R. (1990). Before the gates of excellence: The determinants of creative genius. New York, NY, US: CUP Archive.
Proctor, T. (2014). Creative problem solving for managers: developing skills for decision making and innovation. Routledge.
Raidl, M.-H., & Lubart, T. I. (2001). An empirical study of intuition and creativity. Imagination, Cognition and Personality, 20(3), 217–230.
Vaghely, I. P., & Julien, P.-A. (2010). Are opportunities recognized or constructed?: An information perspective on entrepreneurial opportunity identification. Journal of Business Venturing, 25(1), 73–86
Using Experience Sampling Method to Measure the Impact of Collective Leadership in Open Innovation
RMIT University, Australia
Utilizing a micro-foundational perspective in Open Innovation (OI) calls for new research methodologies. This paper suggests experience sampling method in research at the intersection of leadership and OI. Applying his approach to an organizational setting is a promising method to track individual’s interaction and its impact on knowledge exchange behaviour.
Suggesting a theoretically rather than statistically grounded case selection and promoting the importance of variety and depth in data sources rather than increasing the number of cases (Piekkari et al, 2009), this paper offers a case-study approach to holistically study the impact of relational leadership dynamics (Uhl-Bien, 2006) on knowledge exchange (Laursen & Salter, 2006), in a real-life setting. In the literature on innovation behaviour, the role of leadership is well recognized as a salient factor (Rangus & Černe, 2017). As knowledge exchange in the context of OI is closely related to collaboration under conditions of uncertainty and risk, it is important to acknowledge that influence can be socially constructed (Jarzabkowski & Searle, 2003) and independent from hierarchical positions (Salampasis et al, 2015). In this sense, leadership from a relational perspective (Uhl-Bien, 2006), as a process of interpersonal exchange (Wood, 2005) can provide suitable conditions for knowledge exchange.
Instead of identifying broad rules for human behaviour, leadership research calls for a deeper understanding of the intersubjective context-specific day to day experience and thus, explorative studies that addresses leadership inductively and identify interaction dynamics as well as practices as they occur in a specific setting (Endres & Weibler, 2017).
1. How does relational leadership emerge over time in a real-life setting?
2. How does social interaction and communication create influence in the moment?
3. How does a relational leadership style impact knowledge exchange behaviour?
Reacting to the call for greater innovation in research practice (Piekkari et al, 2009), this study implements experience sampling method in a case-study approach. A longitudinal case study design in an organizational setting unfolds the development over time through a real-time and retroperspective approach. Going beyond the disciplinary convention of increasing the number of cases, we aim to extend the variety and depth in data sources to capture a case holistically. This being said, we design a mixed-method approach consisting of observations, semi-structured interviews, surveys, experience sampling method (ESM) and email/log analysis.
Since there is substantive real-world data missing which supports the key points of process and relational leadership theories, it is promising to exploit the potential for greater innovation in research practice. Therefore, this research applies a case-study approach and aims to provide a in-depth understanding of how relational leadership dynamics impacts innovative behaviour over time. In particular, how communication, interaction and relationships between team members create influence. We aim to analyse whether and how relational leadership style reduces negative attitudes towards knowledge exchange and in turn, foster knowledge exchange behaviour. Moreover, using experience sampling method in the context of knowledge exchange behaviour provides a novel contribution to the methodological approach in the field of management. Especially since a survey application delivered by a smartphone is used to collect the data ‘in the moment’ and as close to the situation as possible.
Contribution to Scholarship
Survey results of the participants at the beginning and the end of the field-study offers conclusions about the development over time. The researcher’s immersion into the case through attendance of meetings and daily observations of team members offers insights in their daily work behaviour, the relationships between team members, team dynamics, role distribution and influence creation. Follow-up interviews after critical events provide insights on the subjective perceptions of the participants and complement the objective observations. By minimising recall biases, ESM is a way to learn about people in, or close to, ‘real-time’ and ‘real-life’ situations. By using this method from the beginning until the end of the field-study, longitudinal changes can be tracked, such as how relationships between team member change, how influence is created and how this impacts knowledge exchange.
Contribution to Practice
A longitudinal, in-the-moment analysis of one practical case will provide practitioners with a holistic picture and in-deep insights. Thus, this approach increases the understanding of the impact and consequences of the relationship between team members for knowledge exchange. Drawing conclusions from real-life data will increase the identification with the case and the derivation of implementations.
Collecting in-depth real-time data is a promising approach to provide practitioners with advice to open up for innovation.This proposed paper aims to contribute to the emerging field of behavioural innovation and add to the current conversation about the role of attitudes and innovative behaviour.
Jarzabkowski, P. & Searle, R. (2003) Top management team strategic capacity: diversity, collectivity & trustAston Business School Research Institute.
Laursen, K. & Salter, A. (2006) Open for innovation: the role of openness in explaining innovation performance among U.K. manufacturing firms. Strategic Management Journal, 27(2), 131-150.
Piekkari, R., Welch, C. & Paavilainen, E. (2009) The case study as disciplinary convention: Evidence from international business journals. Organizational research methods, 12(3), 567-589.
Rangus, K. & Černe, M. (2017) The impact of leadership influence tactics and employee openness toward others on innovation performance: Leadership influence tactics and employee openness. R&D Management.
Salampasis, D. G., Mention, A. L. & Torkkeli, M. (2015) Trust embeddedness within an open innovation mindset. International Journal of Business and Globalisation, 14(1), 32-57.
Uhl-Bien, M. (2006) Relational Leadership Theory: Exploring the social processes of leadership and organizing. Leadership Quarterly, 17(6), 654-676.
Wood, M. (2005) The fallacy of misplaced leadership. Journal of Management Studies, 42(6), 1101-1121.
Visualizing Cognitive Diversity in Innovation Teams: A Network-based Representation Technique
Politecnico di Milano, Italy
The advent and diffusion of information technology have enabled open access to numerous innovative business solutions, to the point that innovation development turned its’ focus from technology and markets to revision of meaning. Working groups are requested enhanced flexibility to leverage on their diversity, while still pursuing one shared direction.
The relevance of a Shared Vision in innovation teams has largely been explored in learning and R&D literature (Calantone, et al. 2002), as has its’ relevance for an ambidextrous organizational culture (Wang and Rafiq, 2014). On the other side, Garcia-Martinez et al. (2017) investigated the importance of deep-level diversity on innovation performance of R&D teams. This interplay among cognitive team distance and creativity is moderated by transformational leadership (Shin, et al., 2012; Wang, et al., 2016) – highlighting how the human factor must leverage on individual skills to achieve meaningful innovation results. In this context, it is fundamental to provide leaders with the means to understand how to manage a teams’ deep-level diversity to foster innovativeness (Garcia-Martinez, et al., 2016).
To the best of our knowledge, no previous literature has addressed the visual representation of Cognitive Diversity among team members. In a world in which images are adopted in a diffused way to share personal experiences, we believe that through these images it is possible to represent personal meaning.
To represent this diversity, a tool was developed to analyse the differences among visual representations and investigate following two hypotheses:
Hyp.1: Diversity among individuals leads to distances among visual representations.
Hyp.2: The visual differences can be represented as a network of distances among individuals, to identify groups of similar-minded people.
Individuals were at first asked to choose representative images of abstract concepts; these images would then be analysed on commonalities to draw conclusions on Cognitive Diversity. The analysis was performed from a two-fold perspective: Images were labelled using ClarifAI labelling API and analysed on their semantic distances in WordNet (Miller, 1995), while emotions were extracted based on the Color Image Scale by Kobayashi (1990). Individuals were further asked to submit a textual description of their vision. Three weighted network graphs would be generated, with edge-weights representing similarity among visual representations, and a final overall network by aggregating similarity edges.
The developed tool was tested in a masters’ course in the area of management engineering at an Italian university. 108 Students were asked to represent their own interpretation of the concepts of leadership and innovation, in order to define a representative image for the course. In order to fully grasp the vision of each student, data gathered where not limited to images only: In addition to the visual representation, three descriptive keywords were collected from each participant. These enriched the analysis of individual meaning, adding the above-mentioned third dimension to the visual representation.
A Social Network Analysis performed with Gephi allowed to identify relevant clusters and most central individuals. Preliminary results confirm the presence of multiple clusters, which is in line with the hypothesis of groups of cognitively distant individuals. In each of the single-analysis networks based on emotions, keywords and labelled objects respectively, it is possible to identify dominant concepts: The clusters of individuals share one overall direction each.
Contribution to Scholarship
This paper’s main contribution is to team development in innovation processes: acknowledging the role of both Cognitive Diversity and Flexibility to enhance Creativity and a Shared Vision to guide the team (Perry-Smith and Mannucci, 2017), this paper aims at representing diversity from a network perspective. Linking individuals through their visual representation, this paper shows that it is possible to create clusters of like-minded individuals – and potentially pre-select teams based on the desired Cognitive Diversity. This is of particular interest to R&D management, as it expands the possibilities of creating diversified teams to support the innovation process throughout its’ phases.
Contribution to Practice
For practitioners this mapping framework is of interest as it introduces an approach to map Cognitive Diversity among individuals via ex-ante measurement. This paper introduces the possibility to cherry-pick individuals from different clusters, thus avoiding (or forcing) the presence of redundancies depending on the desired approach to innovation projects.
The presence of a Shared Vision in innovation processes is key to maintaining an innovative environment: It is fundamental for co-creating innovation and developing an ambidextrous organizational culture which is both focused on the current and future challenges, addressing societal challenges with a balanced approach.
Calantone, R. J., Cavusgil, S. T., & Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. Industrial Marketing Management, 31(6), 515-524.
Garcia-Martinez, M., Zouaghi, F., & Garcia Marco, T. (2017). Diversity is strategy: The effect of R&D team diversity on innovative performance. R and D Management, 47(2), 311-329.
Kobayashi, S. (1990). Color Image Scale. Kodansha Ltd.
Miller, G. A. (1995). WordNet: A Lexical Database for English. Communications of the ACM, 38(11), p. 39 - 41.
Perry-Smith, J. E., & Mannucci, P. V. (2017). From creativity to innovation: The social network drivers of the four phases of the idea journey. Academy of Management Review, 42(1), 53-79.
Shin, S. J., Kim, T. -., Lee, J. -., & Bian, L. (2012). Cognitive team diversity and individual team member creativity: A cross-level interaction. Academy of Management Journal, 55(1), 197-212.
Wang, X. H., Kim, T. -., & Lee, D. -. (2016). Cognitive Diversity and team creativity: Effects of team intrinsic motivation and transformational leadership. Journal of Business Research, 69(9), 3231-3239.
Wang, C. L., & Rafiq, M. (2014). Ambidextrous organizational culture, contextual ambidexterity and new product innovation: A comparative study of UK and chinese high-tech firms. British Journal of Management, 25(1), 58-76.