Go-No Go Decision-Making Performance in Innovation Projects: An Information Processing Perspective

1. Prof. Dr. Allard van Riel (IMR, Radboud University Nijmegen) a.vanriel@fm.ru.nl Wafa Hammedi MSc (University of Liege) w.hammedi@ulg.ac.be Dr. Zuzana Sasovova (VU University, Amsterdam) zsasovova@feweb.vu.nl Go-No Go Decision-Making Performance in Innovation Projects:An Information Processing Perspective

2. Agenda • Introduction • Research context • Definition of the concepts • Problem Statement • Research questions • Hypotheses • Method • Results • Recommendations • Conclusion

3. Definition of the Issue Go-No Go decisions in Innovation Projects: • Investment decisions • Assessment of project progress/feasibility against criteria that all projects must meet, and then prioritize the projects to identify which will be further developed (Cooper and Edgett, 1996). Allocation of scarce company resources. • Risky and critical

4. STAGE-GATE INNOVATION PROCESS (Cooper, 1990)

5. Definition of the Concepts (2/2) Decision-making Effectiveness: Avoiding two types of potential errors: • Scarce resources are wasted on failures (De Brentani and Droge, 1988) • Projects that might be potentially successful are killed (Baker and Albaum, 1986) Decision-making Efficiency • An efficient process is expected to lead rapidly to a consensus and to generate higher levels of commitment to the decision (Baker and Albaum, 1986, De Brentani, 1986)

6. Research Gap • Research attention so far was focused on the exploration of staticresources. • Decision-making criteria that should be used • Development of methods and screening tools However: • Go-No Go decision-making is ranked high in top managerial issues (Cooper et al. 2009) Few antecedents of go-no go decision-making performance in innovation projects have been explored!

7. Go-No Go Decision-Making Issues Information is scarce = Decision-Making is risky and ambiguous. • Multitude of projects to be evaluated = various sources of information to be considered. • Conceptualizing the decision-making committee as a an information processor is relevant to understand go-no go decision- making performance.

8. Cross-functional • Distributed knowledge • Senior management • Risk of fragmentation • Absence of cooperation • Lack of information exchange and integration • Committees often fail to reach their informational potential. Decision-making committee

9. Research Questions • How can we facilitate the integration of distributed information and knowledge? • How can we stimulate coordination between decision-making committee members?

10. What we would like to see What we see C A A C B D B D Group Space Performance

11. Introducing Transactive Memory Systems: Defined as a set of distributed, individual memory systems that combines the knowledge possessed by memberscoupled with shared awareness of “who knows what ” (Wegner, 1987). TMS: Meta-knowledge of who knows what + knowledge embodied within individuals (what individuals know personally) TMS: cooperative division of labor for learning, remembering and communicating relevant task-knowledge (Hollingshead, 2001)

12. Literature Review • Previous Findings: • TMS increases team performance • TMS increases TMT information gathering • TMS increases NPD performance • Main issues: • Laboratory settings – No field studies • Used either student samples or teams in single organization. • Absence of unique measure of TMS

13. Theoretical Model Transformational Leadership Decision- making effectiveness H1(+) H3(+) TMS H2(+) Organizational Climate H4(+) Decision- making efficiency

14. Hypotheses ( 1/3) • TMS / Go-No Go decision-making effectiveness: • Emphasis on recognition of expertise increases the use of specialized rather than general information during project comparison & assessment. • Accentuates accountability of the team members = increase of individual contributions in terms of quantity & quality • Enhances individual learning = More accurate understanding of external environment • Knowing information locations rather than contents = Reduction of members’ cognitive load. H1: TMS positively affects decision-making effectiveness

15. Hypotheses ( 2/3) • TMS / Screening decision-making efficiency: • Expertise recognition = increased acceptance of diverse and unique information • Decrease of conflicts during collective discussion. • Knowing “Who knows what” provides rapid access to information location = time and effort in information searching and retrieving are saved. H2: TMS positively affects decision making effectiveness

16. Hypotheses ( 3/3) • Organizational Climate • H3: OC positively affects TMS • Transformational leadership H4:TL positively affects TMS

17. Method • Data collection: • Technology-based service industries. • Cross-sectional study • Online survey • Senior managers • 500 invitations sent out. • 136 valid observations. • Measures: • Efficiency: 2 items • Effectiveness: 6 items Dooley et al.(1999) • TMS: specialization, credibility and coordination (Lewis, 2003) • Transformational leadership: 8 items (Den Hartog et al., 1997, Schippers, 2003). • Organizational Climate : fairness, innovativeness and affiliation ( Bock et al. 2005) • Data analysis: • SPSS • Partial Least Squares regression technique ( PLS)

18. Sample

19. Antecedents TMS Decision-making performance Specialization R²=.22 ns .47(5.47) Decision-making efficiency R²=.23 Transfor-mationalLeadership .30(2.49) ns Credibility R²=.38 ns .21(2.27) Fairness .48(5.94) .45(2.75) .28(10.20) ns ns Organizational Climate Decision-making effectiveness R²=.35 Innovativeness .37(3.70) .34(16.08) Coordination R²=.61 .33(2.38) .58(9.63) Affiliation .52(18.47)

20. Results • TMS affects screening efficiency and effectiveness positively • 3 components of TMS are important and highly correlated • Coordination affects the screening decision- making process performance • Transformational leadership plays an important role in • Facilitating “expertise recognition” • Initiating coordination at the committee level • Organizational climate affects • Credibility (trusting the others to be experts) • coordination behaviors at the decision-making committee.

21. Recommendations • Managerial recommendations: • Creation of meta-knowledge of “who knows what”: • Explicit identification of “experts” within the committee. • Foster a an appropriate climate characterized by fairness, innovativeness and cohesiveness. • Strengthen the coordination between decision-making committee members. • Team leader has to show transformational leadership to facilitate TMS and make it work.

22. Conclusion • Suggestions for Further Research • Longitudinal study: to detect long-term benefits of TMS, e.g. learning effects • Exploration of other enablers of TMS: for instance, include other organizational antecedents. • Study of TMS effects regarding innovation types (incremental/ radical) • Etc… Thank you for your attention