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Product development team formation: effects of organizational- and product-related factors

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Abstract

Although the performance of new product development (PD) is dependent on the structure and formation of design teams, effective configuration of the PD teams remains largely unexplored. According to social network research, teams are often organized in either closely connected or sparse structure. We conceptualize PD projects as collective problem-solving endeavors and develop a computational model of these projects where a number of designers conduct search over an NK(C) performance landscape. We group the designers in teams with closely connected or sparse structure. We also consider various organizational integration capabilities (i.e., coordinated operations, and common principles) as well as interaction networks among the teams (i.e., acyclical, cyclical, and modular). We use simulation and compare the design performance of teams with different configurations. Our results indicate that the extent by which organizations can effectively integrate design solutions determines the team structure and is likely to result in higher development performance. In addition, the design performance of strategies that employ both closely connected and sparse teams is contrasted with the strategies that use either of these structures. Regardless of the integration capabilities of the PD projects, strategies that simultaneously utilize both closely connected and sparse teams are likely to achieve higher development performance than strategies that only use teams with one particular structure.

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Notes

  1. The number of required contributions for an element is \(2^{{K + DE_{i} \times C}}\).

  2. It has been shown that the properties of the fitness landscape are not sensitive to the distribution applied to generate the landscape (Weinberger 1991).

  3. In a very abstract view, the first quality measure represents the capability of a PD system to analyze all design solutions, and select the best ones found for each subsystem. Whereas, the second quality scale indicates less capable systems that use lower amount of resources, and converge toward the average fitness of all the designers’ solutions.

  4. The patterns for \(n_{b} = 7\) teams are exactly the same pattern as those shown in Fig. 1. Note that modular pattern is perfectly modular with every two teams having interactions only with themselves, and do not interact with any other team.

  5. In all Figs. 3, 4, and 5, the following features are common: (1) each point is the average performance of 100 simulation runs, and (2), subsystems interacting in modular (blue), cyclical (green), and acyclical (red); and also \(n_{b} = n_{q} = 5;n_{e} = 4\).

  6. The complete set of results are reported in the Online Appendix.

  7. In other words, the first set of paired t tests, compare \(PQ^{t}\) in Eq. 4 of diverse teams with that of communicative teams. Therefore, positive and negative t values indicate communicative teams are doing, respectively, better and worse than diverse teams.

  8. For both Figs. 6 and 7, the following setting arrangements have been used. Subsystems interact in modular (blue), cyclical (green), and acyclical (red) patterns. Each point is the average performance of 100 simulation runs, and \(n_{b} = n_{q} = 5;n_{e} = 4\).

  9. In other words, the first set of paired t tests, compare \(PQ^{t}\) in Eq. 4 of heterogonous core teams with that of diverse teams. Therefore, positive and negative t values indicate heterogonous core teams are doing, respectively, better and worse than diverse teams.

  10. In the results provided in the Online Appendix, we observe somewhat different patterns for \(n_{b} = 7\) designers in each team, and we speculate that this happens as higher number of agents are conducting searches over the landscape, and intuitively, that may shift the overall performance improvement rate of heterogeneous teams in comparison to that of the uniformly formed teams. It is worth noting, our overall insights are independent from these PD setting-related changes in results.

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Acknowledgements

The authors would like to thank the anonymous reviewers and the editor for their insightful comments and suggestions. Dr. Madjid Tavana is grateful for the partial support he received from the Czech Science Foundation (GAČR19-13946S) for this research.

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Authors and Affiliations

  1. Faculty of Behavioural, Management and Social Sciences, University of Twente, Enschede, The Netherlands

    Mohsen Jafari Songhori

  2. Business Systems and Analytics Department, La Salle University, Philadelphia, PA, 19141, USA

    Madjid Tavana

  3. Business Information Systems Department, Faculty of Business Administration and Economics, University of Paderborn, 33098, Paderborn, Germany

    Madjid Tavana

  4. Department of Computer Science, Tokyo Institute of Technology, Kanagawa, Japan

    Takao Terano

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Jafari Songhori, M., Tavana, M. & Terano, T. Product development team formation: effects of organizational- and product-related factors. Comput Math Organ Theory 26, 88–122 (2020). https://doi.org/10.1007/s10588-019-09302-8

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