Skip to main content
SpringerLink
Log in

Constructing Algorithms for Forecasting High (Low) Project Management Performance

  • Chapter
  • First Online:
Accurate Case Outcome Modeling

  • 263 Accesses

Abstract

Applying complexity theory tenets, the study here provides a unique asymmetric modeling perspective for examining causal conditions indicating high (low) project management performance (PMP). Complexity theory tenets include (tenet 1) recognizing that the causal conditions resulting in high PMP frequently have different components (i.e., ingredients) than the causal conditions resulting in low PMP—adopting this perspective supports the usefulness of asymmetric rather than the currently pervasive symmetric approach to theory construction and empirical modeling. A second complexity theory tenet is that the same causal condition can foster, be irrelevant, or inhibit high PMP, depending on how it is configured with other causal conditions—thus, high knowledge management effectiveness (KME) by itself is neither a sufficient nor a necessary causal condition for indicating all cases of high PMP. A third tenet is that the disparate configurations of causal conditions are equifinal in leading to adoption. The study here constructs a general model and specific configurational propositions that include social capital, project management types, processes, and complexity as causal conditions indicating case outcomes of high versus low PMP. The study includes examining the model and propositions empirically using survey data on the causal conditions for completed projects (n = 302, US sample of product and service industrial firms). The findings support the perspective that high (as well as low) PMP depends on combination effects—not the additive or net effects of causal conditions. For project managers, adopting a configurational approach to the study of project outcomes can reveal which combinations of causal conditions consistently lead to high PMP as well as which combinations indicate low PMP and the conditions when high KME associates with low PMP.

The authors are grateful for the insightful comments that colleagues, Rouxelle De Villiers, Auckland University of Technology, and Kurt Hozak, Coastal Carolina University, provided on an earlier draft of this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Armstrong, J. S. (2012). Illusions in regression analysis. International Journal of Forecasting, 28, 689–694.

    Article  Google Scholar 

  • Banihashemi, S., Hosseini, M. R., Golizadeh, H., & Sankaran, S. (2017). Critical success factors (CSFs) for integration of sustainability into construction project management practices in developing countries. International Journal of Project Management, 35, 1103–1119.

    Article  Google Scholar 

  • Barczak, G. (1995). New product strategy, structure, process, and performance in the telecommunications industry. Journal of Product Innovation Management, 12, 224–234.

    Article  Google Scholar 

  • Bartleby. (2018). For richer, not poorer. The Economist, 429, 60, Economist.com/blogs/barleby.

    Google Scholar 

  • Björk, J., & Magnusson, M. (2009). Where do good innovation ideas come from? Exploring the influence of network connectivity on innovation idea quality. Journal Product Innovation Management, 26, 662–670.

    Article  Google Scholar 

  • Campion, M.A., Medsker, G.J. & Higgs, A.C. (1993). Relations between work group characteristics and effectiveness: implications for designing effective work groups. Personnel Psychology, 46, 823–50.

    Google Scholar 

  • Chereshnyuk, O., Panasyuk, V., Sachenko, S., Banasik, A., &, Golyash, I. (2017). Fuzzy-multiple approach in choosing the optimal term for implementing the innovative project. Proceedings, the 9th IEEE international conference on intelligent data acquisition and advanced computing systems: Technology and applications 21–23 September, 2017, Bucharest, Romania, pp. 532–536.

    Google Scholar 

  • Colclough, G., & Sitaraman, B. (2005). Community and social capital: What is the difference? Sociological Inquiry., 75(4), 474–496.

    Article  Google Scholar 

  • Coleman, J. S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94(Supplement), S95–S120.

    Article  Google Scholar 

  • Denison, D. R., Hart, S. L., & Kahn, J. A. (1996). From chimneys to cross-functional teams: Developing and validating a diagnostic model. Academy of Management Journal, 39, 1005–1023.

    Google Scholar 

  • Earl, M. (2001). Knowledge management strategies: Toward a taxonomy. Journal of Management Information Systems, 18, 215–233.

    Article  Google Scholar 

  • Ellison, N. B., Seinfeld, C., & Lampe, C. (2007). The benefits of Facebook “friends”: Social capital and college students’ use of online social network sites. Journal of Computer‐Mediated Communication, 12(4), 1143–1168.

    Article  Google Scholar 

  • Fiss, P. C. (2007). A set-theoretic approach to organizational configurations. Academy of Management Review, 32, 1180–1198.

    Article  Google Scholar 

  • Fiss, P. C. (2011). Building better casual theories: A fuzzy set approach to typologies in organizational research. Academy of Management Journal, 54, 393–420.

    Article  Google Scholar 

  • Garrido, S. (2014). Plenty of trust, not too much cooperation: Social capital and collective action in early twentieth century eastern Spain. European Review of Economic. History, 18, 413–432.

    Article  Google Scholar 

  • Gibbs, A., Campbell, C., Akintola, O., & Colvin, C. (2015). Social contexts and building social capital for collective action: Three case studies of volunteers in the context of HIV and AIDS in South Africa. Journal of Community Applied Social Psychology, 25, 110–122.

    Article  Google Scholar 

  • Gigerenzer, G. (1991). From tools to theories: A heuristic of discovery in cognitive psychology. Psychological Review, 98, 254–267.

    Article  Google Scholar 

  • Gladwin, C. H. (1980). A theory of real-life choice: Applications to agricultural decisions. In P. Bartlett (Ed.), Agricultural decision making: Anthropological contributions to rural development (pp. 45–85). New York: Academic Press.

    Chapter  Google Scholar 

  • Han, J., & Hovav, A. (2013). To bridge or to bond? Diverse social connections in an IS project team. International Journal of Project Management, 31(3), 378–390.

    Article  Google Scholar 

  • Henard, D. H., & Szymanski, D. M. (2001). Why some new products are more successful than others. Journal of Marketing Research, 38, 362–375.

    Article  Google Scholar 

  • Hubbard, R. (2015). Corrupt research: The case for reconceptualizing empirical management andsocial science. Los Angeles: Sage.

    Google Scholar 

  • Justin, A. (2018). Project framework comparisons: Agile vs. Waterfall vs. Hybrid vs. Lean. Downloaded by A. G. Woodside on November 7, 2018, at: https://medium.com/@jdelosangeles/project-framework-comparisons-agile-vs-waterfall-vs-hybrid-vs-lean-dc6801d217e4

  • Kao, Y.-F., Shen, Y.-Z. (2009). The effects of social entrepreneurial personality, entrepreneurial motivation and personal abilities on relationship network—a case study. J. Entrepreneurship Res. 4(4), 29–55.

    Google Scholar 

  • Kumar, V., & Sharma, R. R. K. (2017). An empirical investigation of critical success factors influencing the successful TQM implementation for firms with different strategic orientation. International Journal of Quality & Reliability Management, 34, 1530–1550.

    Article  Google Scholar 

  • Lindner F and Wald A (2011), Success factors of knowledge management in temporary organizations. International Journal of Project Management, 29(7), 877–888.

    Article  Google Scholar 

  • LaPorte, T., & Consolini, P. M. (1991). Working in practice but not in theory: Theoretical challenges of “high-reliability organizations”. Journal of Public Administration Research and Theory: J-PART, 1(1), 19–48.

    Google Scholar 

  • Malach‐Pines, A., Dvir, D., & Sadeh, A. (2009). Project manager‐project (PM‐P) fit and project success. International Journal of Operations & Production Management, 29, 268–291.

    Article  Google Scholar 

  • Marzagão, D. S. L., & Carvalho, M. M. (2016). Critical success factors for Six Sigma projects. International Journal of Project Management, 34, 1505–1518.

    Article  Google Scholar 

  • Obstfeld, D. (2005). Social networks, the tertius iungens orientation, and involvement in innovation. Administrative Science Quarterly, 50, 100–130.

    Article  Google Scholar 

  • Pal, R., Wang, R., & Liang, X. (2017). The critical factors in managing relationships in international engineering, procurement, and construction (IEPC) projects of Chinese organizations. International Journal of Project Management, 35, 1225–1237.

    Article  Google Scholar 

  • Powell, T. C., Lovallo, D., & Fox, C. R. (2011). Behavioral strategy. Strategic Management Journal, 32, 1369–1386.

    Article  Google Scholar 

  • Ragin, C. C. (2000). Fuzzy-set social science. Chicago: University of Chicago Press.

    Google Scholar 

  • Ragin, C. C. (2008a). Redesigning social inquiry: Fuzzy sets and beyond. Chicago: Chicago University Press.

    Book  Google Scholar 

  • Ragin, C. C. (2008b). Measurement versus calibration: A set-theoretic approach. In The Oxford handbook of political methodology (p. 174e198). Oxford, UK: Oxford University Press.

    Google Scholar 

  • Ramesh, B., & Tiwana, A. (1999). Supporting collaborative process knowledge management in new product development teams. Decision Support Systems, 27, 213–235.

    Article  Google Scholar 

  • Rodriquez, A. G., & Williams, T. M. (1998). System dynamics in project management: Assessing the impacts of client behavior on project performance. Journal of Operations Research Society, 49, 2–15.

    Article  MATH  Google Scholar 

  • Seashore, S E (1954). Group cohesiveness in the industrial work group Ann Arbor University of Michigan Press.

    Google Scholar 

  • Simon, H.A. (1978). Rationality as process and as product of thought. American Economic Review, 68:1–16.

    Google Scholar 

  • Song, S., Nerur, S., & Teng, J. (2007). An exploratory study on the roles of network structure and knowledge processing orientation in the work unit knowledge management. Advances in Information Systems, 38(2), 8–26.

    Google Scholar 

  • Song, S., Nerur, S., & Teng, J. T. C. (2008a). An exploratory study on the roles of network structure and knowledge processing orientation in work unit knowledge management. The DATA BASE for Advances in Information Systems, 38, 8–26.

    Article  Google Scholar 

  • Song, S., Nerur, S., & Teng, J. T. C. (2008b). Understanding the influence of network positions and knowledge processing styles. Communications of the ACM, 51, 123–126.

    Article  Google Scholar 

  • Stewart, G. L., & Barrick, M. (2000). Team structure and performance: Assessing the mediating role of intrateam process and the moderating role of task type. Academy of Management Journal, 43, 135–148.

    Google Scholar 

  • Teo, M. M., & Loosemore, M. (2017). Understanding community protest from a project management perspective: A relationship-based approach. International Journal of Project Management, 35, 1444–1458.

    Article  Google Scholar 

  • Tiwana, A. (2008). Do bridging ties complement strong ties? An empirical examination of alliance ambidexterity. Strategic Management Journal, 29, 251–272.

    Article  Google Scholar 

  • Tsai, W. (2001). Knowledge transfer in intra-organizational networks: Effects of network position and absorptive capacity on business unit innovation and performance. Academy Management Journal, 44, 996–1004.

    Google Scholar 

  • Van der Vegt, G. S., Emans, B. J. M.,&Van de Vliert, E. 2000. Affective responses to intragroup interdependence and job complexity. Journal of Management, 26:633–655.

    Google Scholar 

  • Wallace, L., Keil, M., & Rai, A. (2004). Understanding software project risk: A cluster analysis. Information & Management, 42(1), 115–125.

    Article  Google Scholar 

  • Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p‐values: Context, process, and purpose. The American Statistician, 70, 129–133.

    Google Scholar 

  • Wasserstein, R. L., & Lazar, N. A. (2017). The ASA’s statement on p-values: Context, process, and purpose. The American Statistician, 70, 129–133.

    Article  MathSciNet  Google Scholar 

  • Weick, K. (1987). Organizational culture as a source of high reliability. California Management Review, 29, 112–127.

    Article  Google Scholar 

  • Weick, K. (2007). The generative properties of richness. Academy of Management Journal, 50(1), 14–19.

    Article  Google Scholar 

  • Weick, K. E. S., Sutcliffe, K. M., & Obstfeld, D. (2008). Organizing for high reliability: Processes of collective mindfulness. Crisis Management, 3(1), 81–123.

    Google Scholar 

  • Wiig, K. M. (1997). Knowledge management: An introduction and perspective. Journal of Knowledge Management, 1, 6–14.

    Article  Google Scholar 

  • Woodside, A. G. (2013). Moving beyond multiple regression analysis to algorithms: Calling for a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory. Journal of Business Research, 66, 463–472.

    Article  Google Scholar 

  • Woodside, A. G. (2017). Releasing the death‐grip of null hypothesis statistical testing (p< .05): Applying complexity theory and somewhat precise outcome testing (SPOT). Journal of Global Scholars of Marketing Science, 27, 1–15.

    Article  Google Scholar 

  • Woodside, A. G., Nagy, G., & Megehee, C. M. (2018). Applying complexity theory: A primer for identifying and modeling firm anomalies. Journal of Innovation & Knowledge, 3, 9–25.

    Article  Google Scholar 

  • Yang, L.-R., Chen, J.-H., & Huang, C.-F. (2012). Requirements definition and management practice to improve project outcomes. Journal of Civil Engineering and Management, 18(1), 114–124.

    Google Scholar 

  • Young, R., & Poon, S. (2013). Top management support—almost always necessary and sometimes sufficient for success: Findings from a fuzzy set analysis. International Journal of Project Management, 31, 943–957.

    Google Scholar 

  • Zadeh, L. (1965). Fuzzy sets. Information and Control, 8(3), 338–353.

    Article  MathSciNet  MATH  Google Scholar 

  • Zadeh, L. (1996). Fuzzy logic: Computing with words. IEEE Transactions on Fuzzy Systems, 4, 103–111.

    Article  Google Scholar 

  • Zadeh, L. (2010). Lofti Zadeh on CWW. In J. M. Mendel, L. A. Zadeh, E. Trillas, R. Yager, J. Lawry, H. Hagras, & S. Guadarrama (Eds.), What computing with words means to me. IEEE computational intelligence magazine (February, 20–26). https://people.eecs.berkeley.edu/~zadeh/papers/What%20Computing%20with%20Words%20Means%20to%20Me-CIM%202010.pdf. Accessed 8 Oct 2016.

    Google Scholar 

  • Ziliak, S. T., & McCloskey, D. N. (2008). The cult of statistical significance: How the standard error costs us jobs, justice and lives. Ann Arbor: University of Michigan Press.

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Coastal Carolina University, Wall College of Business of Administration, Conway, SC, USA

    Olajumoke A. Awe

  2. Yonsei University, Yonsei Frontier Lab, Seoul, Republic of Korea

    Arch G. Woodside

  3. Department of Information Systems and Operations Management, University of Texas, Arlington, TX, USA

    Sridhar Nerur & Edmund Prater

Authors
  1. Olajumoke A. Awe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Arch G. Woodside
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sridhar Nerur
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Edmund Prater
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Olajumoke A. Awe .

Editor information

Editors and Affiliations

  1. Yonsei Frontier Lab, Yonsei University, Seoul, Korea (Republic of)

    Arch G. Woodside

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Awe, O.A., Woodside, A.G., Nerur, S., Prater, E. (2019). Constructing Algorithms for Forecasting High (Low) Project Management Performance. In: Woodside, A. (eds) Accurate Case Outcome Modeling. Springer, Cham. https://doi.org/10.1007/978-3-030-26818-3_2

Download citation

Publish with us

Policies and ethics

Search

Navigation