The effects of trust on the effectiveness of project risk management for engineering and construction projects

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Abstract

The main objective of this research project is to explore the potential correlation between the level of trust between project managers and clients and the effectiveness of their Project Risk Management. The research project is executed in collaboration with Tebodin, a Dutch company that offers engineering and consultancy services worldwide; therefore, one of the project’s main targets is to professionalize Tebodin’s PM practices through the implementation of managerial recommendations for the PM department. Trust is a complex psychological state (Rosseau, Sitkin Burt & Camerer1998) that has plethora of different definitions that are often derived from discipline-driven studies subsequently causing ambiguity and controversy (Brewer & Strahorn 2012). Nevertheless, the definitions presented in this documented highlight the importance of vulnerability as a key component of trust between two co-existing individuals; subsequently, vulnerability brings up the concept of uncertainty which is an important concept when dealing with Project Risk Management. The idea behind the conceptual model that is tested in this research project is that trust serves as a driver for projects managers and clients to engage in productive communication when using Project Risk Management techniques that include human interaction; “If trust is present, people can spontaneously engage in constructive interaction without pondering what hidden motives exchange partners might have, who is formally responsible for problems, or the risks of disclosing information” (Kadefors 2004, pg. 176). The scope of the research follows a 6-phase process starting by performing a literature review on types and dynamics of trust as well as models and techniques of project risk management. The objective of the literature review is to be able to find a method to measure the level of trust and the performance of the Project Risk Management executed in the ten selected projects for analysis. For this study, the dimensions described by Hartman (1999) serve as building blocks to define the basis of trust between project managers and clients’ project managers. Competence trust, Integrity trust and Intuitive trust are the notions that better match the scope of the project as being focused on a project manager-client relationship. Project Risk Management is operationalized according to present practices at Tebodin which are based on existing literature (PMBOK, Gray & Larson 2008, Hillson & Simon 2012) Among such practices certain tools are used in which trust is likely to influence performance (Raz & Michael 2001). Then, two surveys are formulated to measure the variables of the conceptual model: the level of trust (LOT) and the Effectiveness of Project Risk Management (EPRM). The analysis consists of comparing these two in order to verify the existence of a correlation. Moreover, the approach of the research project is to analyze how trust develops in two different scenarios: on the one hand there are projects that were conducted on a partnering basis and on the other hand there are projects that were performed in an operational environment. Therefore, projects were selected with certain criteria including the type of working environment. After analyzing quantitatively and qualitatively the data obtained from ten different construction and engineering projects at Tebodin, there is evidence to claim that there is a significant correlation between the level of trust from clients towards project managers and the effectiveness of the risk management techniques that involve human interaction. Hartman’s three trust dimensions were tested separately against the variable EPRM to verify their individual correlation and the only dimension that showed a significant level of correlation was integrity trust. The data showed that projects belonging to the type-partnering environment ranked in average higher on both variables, LOT and EPRM, than the projects under the type-operational environment conditions. Furthermore, the qualitative analysis performed supports the correlations explained before. Interviews with project managers and Customer satisfaction reports filled by the clients describe specific incidents that support the correlations between studied variables. Important aspects such as cultural differences, client proximity, technical affinity, technological complexity and project manager-client professional history were identified and supported with existing literature on Project Management. These aspects gave insight into the data provided by project managers and their clients which created the correlation lines between the studied variables. The last section of the document includes as part of the conclusions of the research a list of recommendations for future research that include: 1. analyze the importance of Meyerson’s concept “swift trust” in projects executed in operational environments or as she defines it: temporary organizational structures (Meyerson, Weick & Kramer 1996); 2. Characterization of the trust between members of the project organization in terms of its resiliency and fragility and how could project managers deal with the two different types; 3. Further develop the effects of the level of trust in the control mechanisms used in Project Risk Management procedures. Under the same section a list of managerial recommendations for Tebodin project managers is given including: 1. Start a program on trust management among all project managers to emphasize the importance of trust on the EPRM techniques used at Tebodin; 2. Emphasize the importance of partnering environments for the execution of projects; 3. Include the concept of integrity trust in future Customer Satisfaction reports; 4. Continue the research on trust dimensions and their effects on EPRM by enlarging the data sample. Finally, the most important limitation of this research project was the reduced data sample of projects; this had undesirable repercussions on the quantitative analysis because the quantitative methods to analyze that type of data were limited to nonparametric techniques. The statistical analysis that was performed was non-parametric because the assumption of normality due to the small sample size could not be made. The consequences this had on the results is that the conclusions about the hypotheses are rather signaling a certain behavior but do not entirely confirm the conceptual model.