Identifying New Knowledge Areas to Strengthen the Project Management Institute (PMI) Framework

We are yet to come across any study that had a stated objective of proposing a methodology to distinguish the causal factors for delays in execution schedule, which, if addressed, could move a project from average to superior performance. Accordingly, our research objective was set up to propose and test a methodology by collecting empirical evidence to identify factor(s) that drive superior performance at the project execution stage. Such factor(s) could be better distinguished when projects were studied in their natural context. If this could be achieved, it would provide useful insights to researchers and practitioners to focus on these factors (rather than on all factors) to achieve excellence in their projects.

An initial analysis of reported data on 630 infrastructure projects of Indian state-owned enterprises (SOEs), with an outlay of more than INR 1500 million (approx. USD 25 million) for 5 years (years spanning 2009–2013), showed that there were inefficiencies in the execution of large engineering projects. Most of these were design-and-build projects. Considering that schedule delays of large infrastructure projects in India have consistently remained around 50% for the past few years, there was a need to identify the causal factors that distinguished between average and superior project performances at the execution stage.

The research team deliberated on the option of either (a) adopting a conventional structured questionnaire survey that could capture the opinion of associated performers or (b) adopting a detailed case research approach to flexibly build up a grounded inquiry. While an opinion survey-based approach would be amenable to analysis using statistical tools, thereby easing generalisation, it stood the risk of not capturing critical issues and nuances that could be embedded in the natural settings of this case. On the other hand, a case research approach would not isolate the phenomenon from its context (Yin, 2003) and thus allow a better understanding of the contextual causalities. The researchers chose the case study approach as the respondents to a structured survey instrument were likely to respond only to the questions asked and might not have proactively identified any areas requiring further research.

There are two widely accepted steam methane reforming process technology providers in the world, namely Haldor Topsoe, Denmark; and Kellog Brown and Root (KBR), USA. Both these technologies work on the steam methane reforming process. The only superiority of KBR over Haldor Topsoe is the presence of a purifier in the KBR process, which removes impurities (i.e. methane and argon) from synthesis gas by washing it with excess nitrogen while adjusting the hydrogen to nitrogen (H₂/N₂) ratio to 3:1. The Haldor Topsoe process does not use purifiers.

The total project cost was financed using 95:5 debt–equity ratio, in which the debt package was duly backed by Government of India guarantee. The 95% of fund was raised through loan from public sector banks and other state-owned financial institutions. Besides financing, Government of India also allocated NG linkage to the project.

The NFL Project Management group (based out of its headquarters) coordinated the entire project at all the three locations. It had between 15 and 20 members during the course of the project and was accountable to NFL senior management. At the site, NFL had function-specific leaders drawn from respective departments. NFL senior management continuously monitored the project progress and provided guidance whenever necessary. An SOE design engineering and consultancy firm was appointed by NFL as project management consultants to support them from concept to commissioning. The consultants were involved in the design, engineering and construction supervision of almost all the major fertiliser projects in India. They also had prior experience of executing KBR technology projects in Australia. The engineering, procurement and commissioning (EPC) contract was awarded through a bidding process to an international premier company having experience in executing KBR technology projects and other large projects worldwide on a lump sum turnkey (LSTK) basis. The company has a wholly owned subsidiary in India. The contractor was mandated to design, procure, construct and commission the project under licencing from KBR.

Two of the AFCPs (termed Location 1 and Location 2) adopted the Haldor Topsoe technology. A large reputed Indian EPC conglomerate won both these projects through a competitive bidding process. The AFCP in Location 3 adopted KBR technology, and the project was competitively won by another reputed international EPC firm that has Indian operations. At the time of award of contract for Location 3, the AFCP when implemented would become the second installation of KBR technology in India and the first for NFL in terms of technology adoption. Table 1 summarises the approval and completion status of these three AFCPs.

Only the AFCP at Location 3 showed construction time overrun. Hence, this project was selected for detailed study to understand the causes of such time overrun. Following the Eisenhardt (1989) model for theory develop ment, findings from this within-case analysis of AFCP Location 3 was validated through cross-case analysis of the findings obtained from AFCPs at Locations 1 and 2 and, then, compared with literature for generalisation.

In a seminal work, Eisenhardt (1989) had synthesised three previous works on (a) codification procedures for analysing qualitative data, (b) design of case study research and (c) grounded theory (GT) as a qualitative social research technique, to suggest an approach to build theory from case studies. Subsequent GT versions (Strauss and Corbin, 1998) adopted both inductive and deductive thinking for systematised generation of theory from open-coded granular textual data. Since then, researchers have used GT for descriptive and exploratory case studies, though it is still not widely prevalent as a combined research method (Lehmann 2010, Barrett and Sutrisna 2009, Krueger et al. 2014, Coakes and Elliman 2011).

Concurrently, the Project Management Institute (PMI) Knowledge Process framework has also been steadily gaining acceptance across the world. Since 1999, the PMI’s Guide to the Project Management Body of Knowledge has been approved by the American National Standards Institute (ANSI) as an American national standard. From 2007, PMI certification has also been accredited by the International Standards Organisation (ISO). Following these global trends, the PMI framework is increasingly becoming popular among researchers and practitioners in India. As advocated by Goldkuhl and Cronholm (2003) and Iyer and Banerjee (2017), to increase the flexibility of the inquiry, our study combined a typical empirically-driven inductive GT analysis with the pre-existing PMI framework-driven deductive analysis. This helped to effectively integrate existing knowledge frameworks for developing a synthesised theory.

Formalisation of case-oriented analysis needs configurable comparative methods. One such method is qualitative comparative analysis (QCA), which has its roots in the social sciences (Ragin 1987, 1998, 2005, 2006; Skaaning 2011; Rihoux 2006). An advantage of QCA is its support for set theory-based causal analysis of small-sized populations (small-N analysis), which is typically the approach adopted in case-oriented analysis. QCA now has a wider acceptance across disciplines due to its ability to identify the combination of necessary and sufficient conditions that can cause an outcome.

While QCA has been adopted by researchers in other fields, it has only recently started to gain attraction in construction research (Jordan et al., 2011). The authors have recommended QCA as a research technique in the complex environment of construction, where interactions between conditions and outcomes are not well understood and can be used to build theory. Verweij and Gerrits (2013) found the comparative case-based approach to be the most suitable way to study the relationship between context and outcomes in projects. They adopted a QCA-based framework for the evaluation of complex transportation infrastructure projects. Having found it to be a promising approach, they recommended that its viability be studied as a tool for analysing complex infrastructure development projects.

QCA has three variants adopting crisp sets (csQCA), multiple values (mvQCA) and fuzzy sets (fsQCA). In this study, there was a need to capture case uniqueness by scoring the causal variables of supply schedule and erection schedule performances in a non-dichotomous way. At the same time, the other ‘causal’ and ‘observed’ variables needed to be scored in a dichotomous way. Of the three available variants of QCA, such flexibility was offered only by mvQCA, as csQCA had the limitation of using only dichotomous variables and fsQCA used continuous variables. Hence, mvQCA was chosen as the tool for analysis. The QCA-based methodology to identify the distinguishing factor(s) for driving superior schedule performance at the execution stage comprised six sequential steps. They were as follows:

Define variables and set thresholds to enable assigning of scores to the variables.

At the third stage of the methodology, adopting Eisenhardt’s (1989) recommended approach, the within-case analysis was supplemented by a cross-case search for patterns by listing the similarities and differences between pairs of cases. In our study, the AFCPs at Locations 1 and 2 were almost identical and hence were considered as one cohort, while the AFCPs at Location 3 represented a different cohort.

The set-theoretic approach of QCA also becomes a valuable technique at this stage to systematically look for patterns held across a small number of cases. Researchers have found it useful for studying how factors combine into configurations of the necessary and sufficient conditions that underlie outcomes (Bakker et al., 2011). It has allowed them to conduct a comparative case study of 12 cases of knowledge transfer between temporary interorganisational projects and permanent parent organisations.

The first-stage inquiry used the 10 knowledge areas from the PMI Body of Knowledge (Project Management Institute, 2013), along with its four construction extension knowledge areas (Project Management Institute, 2007), totalling 14 knowledge areas. The outcomes from this stage were the shortlisted knowledge areas and GT ‘anchors’ for further study in the second-stage inquiry. The scope of the project was studied under 11 work packages, which exceeded 90% of the project value. The first-stage inquiry revealed that eight out of the 11 identified work packages were delayed. The delay analysis is shown in Table 2.

Four GT ‘anchors’, namely Fund management (abbreviated as Funds), Labour productivity (abbreviated as Labour), Supply sequencing (abbreviated as SupSeq) and Activity sequencing (abbreviated as ActSeq), were short-listed as an outcome of this stage and were taken forward to the second-stage inquiry.

The second-stage inquiry was conducted over a period of 4 months through site visits and by studying progress reports, schedule variance analyses and drawings. The eight delayed packages were analysed to identify causes. Following a GT approach, open coding was undertaken to collect data from these sources, as well as through interviews of key NFL project executives at the site and at the headquarters, project consultants and the EPC contractor. The data thus collected were grouped under the four anchors (outcomes of the first-stage inquiry) and were taken forward for case configuration analysis using the set-theoretic QCA technique.

4.2.5

Identify distinguishing factor through Boolean minimisation

A unique feature of QCA is its ability to explain observations through parsimonious causal combinations. This is achieved through a Boolean minimisation technique, which reduces long, complex Boolean expressions into shorter, more parsimonious ones. If two Boolean expressions differ in only one causal condition yet produce the same outcome, the causal condition that distinguishes the two expressions can be considered irrelevant and can be removed to create a simpler combined expression (Figure 2).

Fig. 2

As QCA is a small-N analysis tool, the number of observed cases is usually smaller in number when compared to all possible combinations of condition variables. Hence, many a time, there may not be any observed outcome for a causal combination. In such situations, the process of Boolean minimisation is achieved by bringing in a set of simplifying causal combinations from the Boolean space where there are no observed variables. These are termed ‘Logical remainders’ (or ‘Simplifying assumptions’), which, in the absence of any other outcome, are assumed to have such outcomes that help in reduction of condition variables.

This study used Tosmana software, version 1.302, for Boolean minimisation, which has been adopted by previous researchers in this field (Cronqvist, 2003). The Boolean minimisation results are given in Table 7.

Tab. 7

Results of Boolean minimisation (using Tosmana software, version 1.302).

Explain observed variable	Terms
0	ActSeq{0,2} +	SupSeq{0,1} +	Techstab{0}
	(Primary reformer, Refrigeration compressor + Electrical substation, Piping network)	(Primary reformer, Refrigeration compressor + Secondary reformer + Process air compressor + Insulation)	(Cryogenic purifier)
1	ActSeq{1}*SupSeq{2}*Techstab{1}
	(HVAC, Gasification plant, Automation E&I)
0 and 1	Funds{0,1}
	(Primary reformer, Refrigeration compressor + Secondary reformer + Process air compressor + Cryogenic purifier + HVAC, Gasification plant, Automation E&I + Electrical substation, Piping network + Insulation)

Note: {value} indicates variable values in the set; + denotes Boolean OR; * denotes Boolean AND

As in AFCP for Location 3, the projects for Locations 1 and 2 were also executed through LSTK contracts. The AFCPs at Locations 1 and 2 used Haldor Topsoe technology and were delivered on time by a common EPC contractor. The EPC contractor for Location 3 and the one for Locations 1and 2 were both large and reputed organisations. A comparison of their strengths on items that were relevant for this study is presented in Table 8.

The project consultant was the same for all three projects. NFL confirmed that the quality of design engineering and construction work by the EPC contractors in all these projects conformed to standards. Hence, it was thought appropriate to conduct a cross-case analysis between AFCP Location 3 with the AFCPs at Locations 1 and 2 to understand any underlying project management causal factors that could explain this variance in schedule performance. For this, data were collected through structured interviews with senior NFL officials who were involved in all the three projects. Two key learning points emerged from the cross-case analysis.

The key findings from this study can be summarised as follows:

Effective financial management was the distinguishing causal factor that could explain schedule success or failure of any work package.

Developing countries, such as India, execute key infrastructure projects through the design-and-build and Build mode projects. Ling and Liu (2004) found that in order to ensure the success of such projects, the following points need to be borne in mind: (a) contractors should have adequate staffing level, a good track record for completion on budget and adequate ability in financial management and quality control; (b) consultants should have a high level of construction sophistication and should have handled such projects in the past; and (c) clients would need to have handled design-and-build projects in the past. In addition, they should decide on the optimal level of design completion when the budget is fixed and tenders are invited. All of these were found to exist in the projects we studied, thereby corroborating the predictable successes of these projects.

An earlier survey-based research of the Indian construction industry (Iyer and Jha, 2006) found that in a number of the projects where the ‘owners’ were technically competent and financially strong and where payments to the contractors were released promptly, the projects had achieved better performance than desired. In our present study, all three EPC contractors carried end-to-end responsibility for engineering, design, procurement, construction and commissioning works on a turnkey basis for their respective projects. It can thus be assumed that they were technically the ‘owners’ for their respective projects. Hence, the EPC contractor organisations’ demonstrated all-round techno-commercial management ‘competence’ on the project could have enhanced their schedule performance. The findings from the cross-case analysis, where all three projects were high performing, show that the incremental all-round competence of the EPC contractor at Locations 1 and 2 relative to that of the EPC contractor at Location 3 was a differentiator to achieve higher schedule performance at Locations 1 and 2.

Our findings support the generally accepted view that schedule performance on construction projects can be improved by appropriate planning and methodical execution. But, as found in our study, once these projects cross a certain threshold of schedule performance, effective management of financial resources becomes the distinguishing factor to achieve schedule performance excellence.