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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.

References

  • Allen TJ (1977) Managing the flow of technology: technology transfer and the dissemination of technological information within the R&D organization. MIT Press, Boca Raton

    Google Scholar 

  • Anderson EG, Parker GG (2013) Integration of global knowledge networks. Prod Oper Manage 22(6):1446–1463

    Article  Google Scholar 

  • Baldwin CY, Clark KB (2000) Design rules: volume 1, the power of modularity. MIT Press, Boca Raton

    Book  Google Scholar 

  • Baldwin C, MacCormack A, Rusnak J (2014) Hidden structure: using network methods to map system architecture. Res Policy 43(8):1381–1397

    Article  Google Scholar 

  • Bardhan I, Krishnan VV, Lin S (2013) Team dispersion, information technology, and project performance. Prod Oper Manage 22(6):1478–1493

    Article  Google Scholar 

  • Barkoczi D, Galesic M, Katsikopoulos K, Perrone P, Ay N, Straatman B, Boyd B, Mangalagiu D, Rathje P, Madsen C et al (2015) Social learning strategies reconcile the relationship between network structure and collective problem solving

  • Baumann O, Siggelkow N (2013) Dealing with complexity: integrated vs. chunky search processes. Organ Sci 24(1):116–132

    Article  Google Scholar 

  • Billinger S, Stieglitz N, Schumacher TR (2013) Search on rugged landscapes: an experimental study. Organ Sci 25(1):93–108

    Article  Google Scholar 

  • Blindenbach-Driessen F, Van Dalen J, Van Den Ende J (2010) Subjective performance assessment of innovation projects. J Prod Innov Manag 27(4):572–592

    Article  Google Scholar 

  • Braha D, Bar-Yam Y (2004) Topology of large-scale engineering problem-solving networks. Phys Rev E 69(1):0161131–0161137

    Article  Google Scholar 

  • Burt RS (1992) Structural holes: the social structure of competition. Harvard University Press, MA

    Google Scholar 

  • Burt RS (2004) Structural holes and good ideas1. Am J Sociol 110(2):349–399

    Article  Google Scholar 

  • Burt RS (2005) Brokerage and closure: an introduction to social capital. Oxford University Press, Oxford

    Google Scholar 

  • Buzas J, Dinitz J (2014) An analysis of landscapes: interaction structure, statistical properties, and expected number of local optima. IEEE Trans Evol Comput 18(6):807–818

    Article  Google Scholar 

  • Capaldo A (2007) Network structure and innovation: the leveraging of a dual network as a distinctive relational capability. Strateg Manag J 28(6):585–608

    Article  Google Scholar 

  • Cohen WM, Levinthal DA (1990) Absorptive capacity: a new perspective on learning and innovation. Adm Sci Q 35(1):128–152

    Article  Google Scholar 

  • Coleman J (1990) Foundations of social theory. Harvard University Press, Cambridge

    Google Scholar 

  • Eppinger SD, Whitney DE, Smith RP, Gebala DA (1994) A model-based method for organizing tasks in product development. Res Eng Design 6(1):1–13

    Article  Google Scholar 

  • Ethiraj SK, Levinthal D (2004) Modularity and innovation in complex systems. Manage Sci 59(2):159–173

    Article  Google Scholar 

  • Ethiraj SK, Levinthal D, Roy RR (2008) The dual role of modularity: innovation and imitation. Manage Sci 54(5):939–955

    Article  Google Scholar 

  • Fang C, Lee J, Schilling MA (2010) Balancing exploration and exploitation through structural design: the isolation of subgroups and organizational learning. Organ Sci 21(3):625–642

    Article  Google Scholar 

  • Fisher Colin M, Pillemer Julianna, Amabile Teresa M (2018) Deep help in complex project work: guiding and path-clearing across difficult terrain. Acad Manag J 61(4):1524–1553

    Article  Google Scholar 

  • Fixson SK, Marion TJ (2012) Back-loading: a potential side effect of employing digital design tools in new product development. J Prod Innov Manag 29(S1):140–156

    Article  Google Scholar 

  • Fleming L (2001) Recombinant uncertainty in technological search. Manage Sci 47(1):117–132

    Article  Google Scholar 

  • Fleming L, Marx M (2006) Managing creativity in small worlds. Calif Manage Rev 48(4):6–27

    Article  Google Scholar 

  • Fleming L, Sorenson O (2002) Navigating the technology landscape of innovation. MIT Sloan Manage Rev 44(2):15–24

    Google Scholar 

  • Fleming L, Mingo S, Chen D (2007) Collaborative brokerage, generative creativity, and creative success. Adm Sci Q 52(3):443–475

    Article  Google Scholar 

  • Fujimoto T (1991) Product integrity and the role of designer-asintegrator. Design Manage J (Former Ser) 2(2):29–34

    Article  Google Scholar 

  • Gardner HK, Gino F, Staats BR (2012) Dynamically integrating knowledge in teams: transforming resources into performance. Acad Manag J 55(4):998–1022

    Article  Google Scholar 

  • Giannoccaro I, Nair A (2016) Examining the roles of product complexity and manager behavior on product design decisions: an agent-based study using NK simulation. IEEE Trans Eng Manage 63(2):237–247

    Article  Google Scholar 

  • Gilbert CG (2005) Unbundling the structure of inertia: resource versus routine rigidity. Acad Manag J 48(5):741–763

    Article  Google Scholar 

  • Girotra K, Terwiesch C, Ulrich KT (2010) Idea generation and the quality of the best idea. Manage Sci 56(4):591–605

    Article  Google Scholar 

  • Gokpinar B, Hopp WJ, Iravani SM (2013) In-house globalization: the role of globally distributed design and product architecture on product development performance. Prod Oper Manage 22(6):1509–1523

    Article  Google Scholar 

  • Granovetter MS (1973) The strength of weak ties. Am J Sociol 78:1360–1380

    Article  Google Scholar 

  • Griffin A (1997) The effect of project and process characteristics on product development cycle time. J Mark Res 34(1):24–35

    Article  Google Scholar 

  • Griffith TL, Sawyer JE, Neale MA (2003) Virtualness and knowledge in teams: managing the love triangle of organizations, individuals, and information technology. MIS Q 27(2):265–287

    Article  Google Scholar 

  • Guimera R, Uzzi B, Spiro J, Amaral LAN (2005) Team assembly mechanisms determine collaboration network structure and team performance. Science 308(5722):697–702

    Article  Google Scholar 

  • Harrison JR, Lin Z, Carroll GR, Carley KM (2007) Simulation modeling in organizational and management research. Acad Manag Rev 32(4):1229–1245

    Article  Google Scholar 

  • Katila R, Ahuja G (2002) Something old, something new: a longitudinal study of search behavior and new product introduction. Acad Manag J 45(6):1183–1194

    Google Scholar 

  • Kauffman SA (1993) The origins of order: self organization and selection in evolution. Oxford University Press, New York

    Google Scholar 

  • Kavadias S, Sommer SC (2009) The effects of problem structure and team diversity on brainstorming effectiveness. Manage Sci 55(12):1899–1913

    Article  Google Scholar 

  • Khurana A, Rosenthal SR (1998) Towards holistic “front end” in new product development. J Prod Innov Manag 15(1):57–74

    Article  Google Scholar 

  • Kijkuit B, Van Den Ende J (2007) The organizational life of an idea: integrating social network, creativity and decision-making perspectives. J Manage Stud 44(6):863–882

    Article  Google Scholar 

  • Kilduff M, Brass DJ (2010) Organizational social network research: core ideas and key debates. Acad Manage Ann 4(1):317–357

    Article  Google Scholar 

  • Knudsen T, Levinthal DA (2007) Two faces of search: alternative generation and alternative evaluation. Organ Sci 18(1):39–54

    Article  Google Scholar 

  • Kozlowski SW, Gully SM, Nason ER, Smith EM (1999) Developing adaptive teams: a theory of compilation and performance across levels and time. In: Ilgen DR, Pulakos ED (eds) The changing nature of performance: implications for staffing, motivation, and development. Wiley, New York, pp 240–292

    Google Scholar 

  • Krishnan V, Ulrich KT (2001) Product development decisions: a review of the literature. Manage Sci 47(1):1–21

    Article  Google Scholar 

  • Lazer D, Friedman A (2007) The network structure of exploration and exploitation. Adm Sci Q 52(4):667–694

    Article  Google Scholar 

  • Levinthal DA (1997) Adaptation on rugged landscapes. Manage Sci 43(7):934–950

    Article  Google Scholar 

  • Liang T-P, Jiang J, Klein GS, Liu JY-C (2010) Software quality as influenced by informational diversity, task conflict, and learning in project teams. IEEE Trans Eng Manage 57(3):477–487

    Article  Google Scholar 

  • Lin Y (2014) Dynamics in organizational problem solving and the leveraging of social capital: an agent-based modeling (ABM) perspective. PhD thesis, University of Washington

  • Liu H-B, Bin HU (2017) Modeling and simulation on robust communication network structure of distributed organizations. J Ind Eng 31(3):155–161

    Google Scholar 

  • Luke Rhee, Leonardi Paul M (2018) Which pathway to good ideas? An attention-based view of innovation in social networks. Strateg Manag J 39(4):1188–1215

    Article  Google Scholar 

  • MacCormack A, Rusnak J, Baldwin CY (2006) Exploring the structure of complex software designs: an empirical study of open source and proprietary code. Manage Sci 52(7):1015–1030

    Article  Google Scholar 

  • MacCormack A, Baldwin C, Rusnak J (2012) Exploring the duality between product and organizational architectures: a test of the “mirroring” hypothesis. Res Policy 41(8):1309–1324

    Article  Google Scholar 

  • Manukyan N, Eppstein M, Buzas J (2016) Tunably rugged landscapes with known maximum and minimum. IEEE Trans Evol Comput 20(2):263–274

    Article  Google Scholar 

  • March JG (1991) Exploration and exploitation in organizational learning. Organ Sci 2(1):71–87

    Article  Google Scholar 

  • Mihm J, Loch C, Huchzermeier A (2003) Problem-solving oscillations in complex engineering projects. Manage Sci 46(6):733–750

    Article  Google Scholar 

  • Mihm J, Loch C, Wilkinson D, Huberman BA (2010) Hierarchical structure and search in complex organizations. Manage Sci 56(5):831–848

    Article  Google Scholar 

  • Mishra A, Sinha KK (2016) Work design and integration glitches in globally distributed technology projects. Prod Oper Manage 25(2):347–369

    Article  Google Scholar 

  • Nickerson JA, Zenger TR (2004) A knowledge-based theory of the firm-the problem-solving perspective. Organ Sci 15(6):617–632

    Article  Google Scholar 

  • Obstfeld D (2005) Social networks, the tertius iungens orientation, and involvement in innovation. Adm Sci Q 50(1):100–130

    Article  Google Scholar 

  • Parraguez P, Eppinger SD, Maier AM (2015) Information flow through stages of complex engineering design projects: a dynamic network analysis approach. IEEE Trans Eng Manage 62(4):604–617

    Article  Google Scholar 

  • Perry-Smith JE, Mannucci PV (2017) From creativity to innovation: the social network drivers of the four phases of the idea journey. Acad Manag Rev 42(1):53–79

    Article  Google Scholar 

  • Reagans R, McEvily B (2003) Network structure and knowledge transfer: the effects of cohesion and range. Adm Sci Q 48(2):240–267

    Article  Google Scholar 

  • Rivkin JW, Siggelkow N (2007) Patterned interactions in complex systems: implications for exploration. Manage Sci 53(7):1068–1085

    Article  Google Scholar 

  • Sabbagh K (1996) Twenty first century jet: making and marketing the Boeing 777. Scribner Book Company, New York

    Google Scholar 

  • Schilling MA, Fang C (2014) When hubs forget, lie, and play favorites: interpersonal network structure, information distortion, and organizational learning. Strateg Manag J 35(7):974–994

    Article  Google Scholar 

  • Schultz C, Schreyogg J (2013) The impact of network ties and resource input on research performance: an empirical investigation among surgeons in academic medical centers. IEEE Trans Eng Manage 60(3):457–468

    Article  Google Scholar 

  • Sharman DM, Yassine AA (2004) Characterizing complex product architectures. Syst Eng 7(1):35–60

    Article  Google Scholar 

  • Simon HA (1962) The architecture of complexity. Proc Am Philos Soc 106(6):467–482

    Google Scholar 

  • Smith RP, Eppinger SD (1997) Identifying controlling features of engineering design iteration. Manage Sci 43(3):276–293

    Article  Google Scholar 

  • Sosa ME (2011) Where do creative interactions come from? The role of tie content and social networks. Organ Sci 22:1–21

    Article  Google Scholar 

  • Sosa ME, Marle F (2013) Assembling creative teams in new product development using creative team familiarity. J Mech Des 135(8):0810091–08100913

    Article  Google Scholar 

  • Sosa ME (2014) Realizing the need for rework: from task interdependence to social networks. Prod Oper Manage 23(8):1312–1331

    Article  Google Scholar 

  • Sosa ME, Mihm J, Browning TR (2013) Linking cyclicality and product quality. Manuf Serv Oper Manage 15(3):473–491

    Article  Google Scholar 

  • Strang David, Macy Michael W (2001) In search of excellence: fads, success stories, and adaptive emulation. Am J Sociol 107(1):147–182

    Article  Google Scholar 

  • Tatikonda MV, Montoya-Weiss MM (2001) Integrating operations and marketing perspectives of product innovation: the influence of organizational process factors and capabilities on development performance. Manage Sci 47(1):151–172

    Article  Google Scholar 

  • Tatikonda MV, Rosenthal SR (2000) Technology novelty, project complexity, and product development project execution success: a deeper look at task uncertainty in product innovation. IEEE Trans Eng Manage 47(1):74–87

    Article  Google Scholar 

  • Thomke SH (1997) The role of flexibility in the development of new products: an empirical study. Res Policy 26(1):105–119

    Article  Google Scholar 

  • Thomke S, Fujimoto T (2000) The effect of front-loading problem-solving on product development performance. J Prod Innov Manag 17(2):128–142

    Article  Google Scholar 

  • Thompson LL (2011) Making the team: a guide for managers. Pearson Prentice Hall, Upper Saddle River, NJ

    Google Scholar 

  • Tiwana A (2008) Do bridging ties complement strong ties? an empirical examination of alliance ambidexterity. Strateg Manag J 29(3):251–272

    Article  Google Scholar 

  • Tripsas M, Gavetti G (2000) Capabilities, cognition, and inertia: evidence from digital imaging. Strateg Manag J 21(10–11):1147–1161

    Article  Google Scholar 

  • Tushman ML, Peter MJ (1998) Dominant designs, technology cycles, and organization outcomes. Acad Manage Proc 198:A1–A33

    Article  Google Scholar 

  • Tushman ML, Rosenkopf L (1992) Organizational determinants of technological-change-toward a sociology of technological evolution. Res Org Behav 14:311–347

    Google Scholar 

  • Ulrich K (1995) The role of product architecture in the manufacturing firm. Res Policy 24(3):419–440

    Article  Google Scholar 

  • Uzzi B (1997) Social structure and competition in interfirm networks: the paradox of embeddedness. Adm Sci Q 42(1):35–67

    Article  Google Scholar 

  • Von Hippel E (1990) Task partitioning: an innovation process variable. Res Policy 19(5):407–418

    Article  Google Scholar 

  • Vuori TO, Huy QN (2015) Distributed attention and shared emotions in the innovation process how Nokia lost the smartphone battle. Adm Sci Q. https://doi.org/10.1177/0001839215606951

    Article  Google Scholar 

  • Watts DJ (1999) Networks, dynamics, and the small-world phenomenon 1. Am J Sociol 105(2):493–527

    Article  Google Scholar 

  • Weinberger ED (1991) Local properties of Kauffman’s NK model: a tunably rugged energy landscape. Phys Rev A 44(10):6399

    Article  Google Scholar 

  • Xu D, Xuan LI (2010) Computational experiments for study of complexity of business model innovation. J Manage Sci China 13(11):12–19

    Google Scholar 

  • Young-Hyman T (2016) Cooperating without co-laboring how formal organizational power moderates cross-functional interaction in project teams. Adm Sci Q. https://doi.org/10.1177/0001839216655090

    Article  Google Scholar 

  • Zhou J, Shin SJ, Brass DJ, Choi J (2009) Social networks, personal values, and creativity: evidence for curvilinear and interaction effects. J Appl Psychol 94(6):1544–1552

    Article  Google Scholar 

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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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  1. Mohsen Jafari Songhori
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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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