The Theory of PMT 2.0
PPGE has expanded PM extant theory domain beyond the iron triangle viz. scope, cost, and schedule (Artto et al., 2015; Godenhjelm et al., 2014; Rijke et al., 2014). The expansion has also gone beyond the temporality and project lifecycle contextualization. This limited vista makes PM extant theory ineffective in predicting events that may drive organizations to succeed or fail. Garel (2012) indicated PM has no theory but an orchestrated and tested customary practices, standards, and tools. Parker et al. (2015) demonstrated that managing project uncertainty and predictability was a critical factor in achieving sustainable success.
A pharmaceutical firm, for instance, that is project and program based may find it conducive and propitious to use its internal and standard operating procedure rather than follow a PM prescriptive and predetermined template or process. Another essential drawback about PM current theory is the absence of interconnected relationships between its constructs considering the PPGE effects (Wilkinson et al., 2015). The lack of interconnected and independent relations among PPGE constructs makes it harder, if not impossible, to predict the relationship between PM extant theory and constructs of complex projects.
The reality is that the effects of PPGE have expanded the PM base integrating the PM Body of Knowledge processes and the core function of program manager’s strategic responsibilities including improving performance, fostering sustainable growth, maximizing ROI, and competitiveness (Huarng & Mas-Tur, 2016). The integration of project and program, for instance, constitutes a challenge to PM’s notion of the temporality and infinite nature of projects; program-oriented projects are now said to operate semi-permanently or permanently.
One of the viable components of programification is resource management accomplished via project portfolio management application (PPM) to manage resource scarcity. In the absence of relationships between constructs, it inevitably becomes impossible to precisely predict projects both in the short and long terms (Parker et al., 2015). Rigby et al. (2014) indicated that the problem with PM was not whether there was an absence of a theory. The problem was the missing of a coherent description, explanation and, therefore, prediction of phenomena. PMT 2.0 explicitly defines and describes the limits of the PM domain and creates relationships between or among the constructs. It comprises the capacity to predict events about complex, uncertain, and chaotic phenomena.
PMT 2.0 provides the answers to the who, what, when, where, how, why, could, should, and would question about the complexity of what the PM has become due to the escalation of projectification, programification, and globalization. The theory’s goodness and virtue fit were analyzed and explained using PM domain as a case in point (Artto et al., 2015; Godenhjelm et al., 2014; Rijke et al., 2014). The study also evaluated PMT 2.0 against the criteria and virtues of good theory (See Appendix L). These virtues and the good of theory are as follows: uniqueness, parsimony, conservatism, generalizability, fecundity, internal consistency, empirical riskiness, and abstraction (Wacker, 1998, p. 364). These evaluation processes also contribute to either verifying an old assumption or testing a new one about PM phenomena, thus contributing to the building of a new theory.
The project management field is being expanded rapidly due to successes in projectification, programification, and globalization efforts. Any research that intends to contribute in a meaningful or significant way to theory in this domain must consider the linear (deterministic and inductive) as well as the nonlinear (holistic and deductive) dimensions that result from these expansions. If deductive, the researcher may start with a hypothesis intended to test cause and effect relationships between variables such as traditional and modern project management methodologies. In the case of inductive, the researcher may start with observation and questions and build the theory from the data (Ellis & Levy, 2008).
Harlow (2009) and Stam (1998, 2000, 2006, 2010) indicated that there is a symbiotic relationship between research data and theory and that expanding our understanding of data leads to contributing to theory as well. Understanding the complex nature of PM helps the researcher to cogently define and explain the phenomena and accurately predict events based on conceptual foundations.
Research contributes to theory through methods specific to three categories as follows: (a) analytical, conceptual research (b) case study and (c) empirical statistical research. The application of multiple-case study method is propitious to build theory (Wacker, 1998). Stam (1996, 2000, 2006, 2010) and Harlow (2009) acknowledged the significance of case multiple-case study method in contributing to theory development. They explained that a case study could disprove the viability of existing theory and therefore can potentially help theory building through empirical evidence and testing. Harlow (2010) averred that theory development and testing are interconnected.
Through retroduction, a researcher can retest a theory when a new set of data is discovered that can determine whether the discovery is significant enough to replace the existing theory. Researchers can develop a theory through the interpretation of new realities or facts gathered through interviews, surveys, literature reviews, and archival records. Harlow (2009, 2010) stated that the process of theory development is recursive or cyclical: the researcher can gather data through observations, surveys, interviews or case studies to test an existing theory. If the data fail to validate the theory, the researcher can reject the research or place a moratorium on it and start an entirely new project. However, if the data provide sufficient empirical evidence to invalidate the current theory, a new theory is espoused.
The analytical, conceptual research method encourages creativity, innovation, and organization of concepts or constructs. It creates internally consistent logical relationships among complex and abstract ideas. Like an empirical case study, this method gathers data through case study as evidence to validate what the researcher assumes about these conceptualizations. The researcher can draw information or ideas from experience, conceptual modeling, and hermeneutics, i.e., interpretation or deduction of facts from observation to develop theory. Experimental design examines and tests performance differences among the old and new theory including the cause and effect relationships among variables or constructs, some of which can be direct or indirect, endogenous or exogenous (Solaiman et al., 2016). By putting in place some control mechanism during the research, the research investigators can test various hypothetical scenarios including, for instance, whether projectification, programification, and globalization efforts have created a significant mismatch or epistemological gap between current project management theory and the complex and nonlinear realities of project management domain.
