Knowledge Management In a Projects-Based Organisation: Part 1 – Building and transferring knowledge

by Jan 1, 2017

by | Jan 1, 2017

This is the first part of a two-part series of articles on knowledge management in a projects-based organisation.  These articles are based on a recently completed master’s degree in engineering.  The two parts are as follows:

  • Part 1 – Building and transferring knowledge; and

  • Part 2 – Exploiting knowledge capital.

In the first article, I focus on the learning processes used to build knowledge, as well as types of knowledge. I then discuss specific knowledge transfer mechanisms, looking at what mechanisms will improve knowledge transfer from the project to the organisation.

Introduction

I’ve recently completed my master’s degree in engineering, focussing on knowledge management (KM) within a projects-based organisation (PBO). My reason for choosing this topic was quite personal. After reading the literature and observing the employees within my own and other organisational working environments, I realised how easily underutilised and misunderstood KM could be. The full scope of KM should be properly understood and used to sustain the competitiveness of an organisation.

Projects provide the flexibility required within a PBO to respond to the changing market. The organisational capabilities, knowledge and resources are developed and improved through the process of executing projects and sharing the learnings developed through execution. Once shared, these learnings can be used by subsequent projects and thereby improve organisational capabilities (Eskerod and Skriver, 2007; Boh, 2007). Knowledge sharing is not limited to compiling a lessons-learned document and storing it on a common database. In fact, knowledge sharing was defined by Yang (2010) as the degree of communication and distribution of experience, expertise and ideas amongst employees of an organisation. This is conducted throughout the lifetime of the employee as well as the life-cycle of the project and can be achieved in myriad different ways.

A typical life-cycle project execution methodology divides a project life-cycle into phases, each with a set of specific deliverables. These project phases are outlined in Figure 1, below, and broadly consist of initiate, develop (which includes front-end engineering), implement (which includes basic and detail engineering, documentation, procurement and construction), beneficial operation of the facility and final closure of the facility (OTC, 2014; CESA, 2010).

 

Figure 1:  Typical Project Phases

Difficulty in Managing Knowledge Effectively

So why is it difficult to fully utilise the KM lever? Challenges with the management of knowledge is well documented in literature and is evident in the workplace (Prencipe and Tell, 2001; Pemsel and Müller, 2012; Boh, 2007). For example, the organisational structure can inhibit knowledge transfer and effective KM. A functional organisational structure acts as a knowledge silo where knowledge is created, however not necessarily transferred or disseminated throughout the rest of the organisation. Geographical dispersion plays a role, employees within an international organisation might have less contact with one another, creating a geographical divide. The unique and transient nature of the projects themselves also pose several problems. These concerns include misalignment between the short-term objective of the project and the long-term objectives of the organisation as well as the risk that a project resource will leave the organisation with new knowledge still embedded, but not yet transferred (Pemsel and Müller, 2012; Prencipe and Tell, 2001; Eskerod and Skriver, 2007).

If we then consider the general issues outlined above, who is to blame for the predicament? Is it the project team, for a lack of motivation, only focussing on completing the project as compared to also focussing on properly coding and sharing the knowledge gained? Or is it perhaps the inability of the organisation to receive and absorb the new knowledge?

Knowledge-Pull Framework

Specific KM aspects and pitfalls that should be considered when assessing your KM strategy are discussed using the knowledge-pull framework I developed as part of my research. The knowledge-pull framework is illustrated in Figure 2. This highlights best
approaches to optimally capitalise on the knowledge within your PBO.

 

Figure 2:  Knowledge-Pull Framework

In this article, the focus is on the learning processes used to build knowledge, as well as types of knowledge. Specific knowledge transfer mechanisms are discussed, looking at what mechanisms will improve the PBO knowledge transfer from the project to the organisation. The final aspect of the knowledge-pull framework is organisational knowledge; dissemination of organisational knowledge and how the organisation can improve to make better use of its innate knowledge capital. When considering

Figure 2, two important aspects to take note of is the value assurance step within the formal knowledge transfer process flow as well as the process flow of knowledge back to the projects.

The knowledge-pull framework will also form the basis of the discussion in the second article in the series, scheduled for publication in February 2017

Knowledge Gained and Characterised

Learning Processes

The starting point when considering a KM strategy is to consider how each employee will initially gain knowledge. Organisational literature outlines three main learning processes within an organisation. Learning takes place through organisational routine, knowledge articulation and knowledge codification.

Organisational routine is a behavioural learning process, learning through experience, utilised to improve the current organisation procedures (Argote, 2012; Zollo and Winter, 2002). Training might decrease the time required to gain all the essential knowledge from organisational routine.

Knowledge articulation and codification are more evolved learning processes. Understanding of the causation between the action and resulting performance is required. In addition, the codification process requires an understanding of the process within its context, together with a consideration for future actions (Adenfelt and Lagerström, 2006; Zollo and Winter, 2002; Pemsel and Müller, 2012). We will see what this means in practice when discussing aspects of tacit vs explicit knowledge.

Explicit knowledge

Explicit knowledge is codified, tangible knowledge that resides in the organisational database, for example lessons learned documents and engineering standards. Dissemination throughout the organisation can be achieved with relative ease and is less capital intensive. This codified knowledge is then readily available to the next project (Pemsel and Müller, 2012; Yang, 2010; Yang et al., 2012; Reich et al., 2012). However, the pitfall here is the loss of depth of knowledge. You need to also maintain the context of the information, for example keep track of why a specification was changed as well as consider the impact on future projects. You require the context of the information to accurately apply it.

Tacit knowledge

Tacit knowledge can be defined as know-how which is rooted in context, experience and values. It is difficult and expensive to communicate as it resides in the minds of the organisational resources (Antonelli, 2006; Eskerod and Skriver, 2007; Gold et al., 2001; Conger, 2014).

For the effective transfer of tacit knowledge, employees need access to the correct specialist, or the resource with the required knowledge. To improve access to tacit knowledge, resources should build and maintain quality networks within, as well as outside one’s own organisation. Networks must include subject matter experts and be available to all resources. Tacit knowledge transfer also takes place between members of the team and, as such, the composition and synergy that the team creates will benefit the organisation.

The organisational structure, culture and governance process has an impact on the success of KM (Adenfelt and Lagerström, 2006; Berg et al., 2012; Gold et al., 2001). One size never fits all, the governance processes should not restrict the flow of knowledge.

Knowledge Transfer Mechanisms

The knowledge transfer mechanisms considered are based on the framework as proposed by Boh (2007).  Depending on the size and geographical dispersion, the organisation will make use of some or all the mechanisms, namely:

  • Individualised-Personalised mechanism: Knowledge sharing occurs as part of person-to-person interactions between individuals and is informal of nature e.g. social networks;
  • Individualised-Codified mechanism: Knowledge sharing occurs on an individual level, but in documented form in an informal and ad-hoc manner e.g. informal documentation;
  • Institutionalised-Personalised mechanism: Knowledge sharing occurs in a personalised way but is institutionalised within the routines and structures of the organisation e.g. mentoring programmes; and
  • Institutionalised-codified mechanism: Knowledge sharing is institutionalised within the routines and structures of the organisation and is documented in a formal knowledge management system e.g. document repository.

These knowledge transfer mechanisms are used to transfer knowledge between the permanent and temporary organisational structures, and resources. At the one end of the spectrum is the individualised-personalised knowledge sharing mechanism. This mechanism is most commonly used mechanism within a small organisation where the employees have regular contact.

At the other end of the spectrum is the institutionalised-codified mechanism. This transfer mechanism is often used in larger, geographically dispersed, organisations. Here it is required that the organisation put formal process in place and codify its knowledge to ensure the knowledge is available to the entire organisation. These formal organisational processes are used to update the current execution methodology, work flows, procedures, processes and templates. This is when a centralised function becomes important. The idea of a centralised function and value assurance will be introduced in the next article. The centralised function will bridge the gap between the permanent and temporary structures and resources (Lindner and Wald, 2011)

When considering the spectrum of knowledge sharing mechanisms it infers that the bigger and more complex the organisation, the less practical this mechanism becomes. This then infers that the KM strategy for any organisation must change as the organisation grows and expands.

Concluding remarks

Based on the considerations outlined above, the following key recommendations were identified throughout the development of the knowledge-pull framework:

  • Learning takes place through organisational routine, knowledge articulation and codification;
  • Explicit knowledge is easier to store and access and cheaper to disseminate. However, you lose depth of knowledge and require the full context to implement the codified learnings accurately;
  • Tacit knowledge maintains the context of the learnings, however, it is easily lost from the organisation as it resides in the mind of the employee. It is expensive to disseminate. Here it is important to build and maintain the correct networks to gain access to these resources; and
  • The organisational KM strategy should develop together with the specific organisational requirements. Specific considerations include the size and geographical dispersion of the organisation.

The concept of a centralised knowledge management and value assurance function was mentioned.  This concept will be discussed in the next article to indicate how and where it will create benefit.

References

Adenfelt, M. & Lagerström, K., 2006. Enabling knowledge creation and sharing in transnational projects. International Journal of Project Management, 24(3), pp 191-198.

Antonelli, C., 2006. The business governance of localized knowledge: an information economics approach for the economics of knowledge. Industry and Innovation, 13(3), pp 227-261.

Argote, L., 2012. Organizational learning: Creating, retaining and transferring knowledge, Springer.

Berg, S., Lindstrom, A., Nilsson, M., Bosh, P. & Gluch, P., 2012.  Knowledge transfer within and across organizational boundaries – a case study in the construction industry.  28th Annual Conference of the Association of Researchers in Construction Management (ARCOM), Edinburgh, 3-5 September, 2012, p1435-44.

Boh, W.F., 2007. Mechanisms for sharing knowledge in project-based organizations. Information and Organization, 17(1), pp 27-58.

CESA, 2010. Procurement guideline for consulting engineering services. Available: www.cesa.co.za [Accessed 4 April 2015].

Conger, S., 2014. Knowledge Management for Information and Communications Technologies for Development Programs in South Africa. Information Technology for Development, (ahead-of-print), pp 1-22.

Eskerod, P. & Skriver, H.J., 2007. Organisational culture restraining in-house knowledge transfer between project managers-a case study. Project Management Journal, 38(1), pp 110-122.

Gold, A. H., Malhotra, A. & Segars, A.H., 2001. Knowledge management: an organizational capabilities perspective. Journal of Management Information Systems, 18(1), pp 185-214.

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

OTC (Owner Team Consultation), 2014. OTC Stage-gate model: Downstream Projects. Available: www.ownerteamconsult.com [Accessed 2 April 2015].

Pemsel, S. & Müller, R., 2012. The governance of knowledge in project-based organizations. International Journal of Project Management, 30(8), pp 865-876.

Prencipe, A. & Tell, F., 2001. Inter-project learning: processes and outcomes of knowledge codification in project-based firms. Research policy, 30(9), pp 1373-1394.

Reich, B. H., Gemino, A. & Sauer, C., 2012. Knowledge management and project-based knowledge in it projects: A model and preliminary empirical results. International Journal of Project Management, 30(6), pp 663-674.

Yang, J., 2010. The knowledge management strategy and its effect on firm performance: A contingency analysis. International Journal of Production Economics, 125(2), pp 215-223.

Yang, L.R., Chen, J.-H. & Wang, H.-W., 2012. Assessing impacts of information technology on project success through knowledge management practice. Automation in Construction, 22, pp 182-191.

Zollo, M. & Winter, S.G., 2002. Deliberate learning and the evolution of dynamic capabilities. Organization science, 13(3), pp 339-351.

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