Eight to Late

Sensemaking and Analytics for Organizations

Building project knowledge – a social constructivist view

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Conventional approaches to knowledge management on projects focus on  the cognitive (or thought-related) and mechanical  aspects of knowledge creation and capture. There is alternate view, one which considers knowledge as being created through interactions between people who –  through their interactions –  develop mutually acceptable interpretations of theories and facts in ways that suit their particular needs. That is, project knowledge is socially constructed. If this is true, then project managers need to pay attention to the environmental and social factors that influence knowledge construction.  This is the position taken by Paul Jackson and Jane Klobas in their paper entitled, Building knowledge in projects: A practical application of social constructivism to information systems development, which presents a  knowledge creation / sharing process model based social constructivist theory. This article is a summary and review of the paper.

A social constructivist view of knowledge

Jackson and Klobas begin with the observation that engineering disciplines are founded on the belief that knowledge can be expressed in propositions that correspond to a reality which  is independent of human perception.  However, there is an alternate view that knowledge is not absolute, but relative – i.e.  it depends on the mental models and beliefs used to interpret facts, objects and events. A  relevant example is how a software product is viewed by business users and software developers. The former group may see an application in terms of its utility whereas the latter may see it as an instance of a particular technology. Such perception gaps can also occur within seemingly homogenous groups – such as teams comprised of software developers, for example. This can happen for a variety of reasons such as the differences in the experience and cultural backgrounds of those who make up the group. Social constructivism looks at how such gaps can be bridged.

The authors’ discussion relies on the work of Berger and Luckmann, who described how the gap between perceptions of different individuals can be overcome to create a socially constructed, shared reality. The phrase “socially constructed” implies that reality (as it pertains to a project, for example) is created via a common understanding of issues, followed by mutual agreement between all the players as to what comprises that reality. For me this view strikes a particular chord because of it is akin to the stated aims of dialogue mapping, a technique that I have described in several earlier posts (see this article for an example relevant to projects).

Knowledge in information systems development as a social construct

First up, the authors make the point that information systems development (ISD) projects are:

…intensive exercises in constructing social reality through process and data modeling. These models are informed with the particular world-view of systems designers and their use of particular formal representations. In ISD projects, this operational reality is new and explicitly constructed and becomes understood and accepted through negotiated agreement between participants from the two cultures of business and IT

Essentially, knowledge emerges  through interaction and discussion   as the project proceeds.  However, the methodologies used in design are typically founded on an engineering approach, which takes a positivist view rather than a social one. As the authors suggest,

Perhaps the social constructivist paradigm offers an insight into continuing failure, namely that what is happening in an ISD project is far more complex than the simple translation of a description of an external reality into instructions for a computer. It is the emergence and articulation of multiple, indeterminate, sometimes unconscious, sometimes ineffable realities and the negotiated achievement of a consensus of a new, agreed reality in an explicit form, such as a business or data model, which is amenable to computerization.

With this in mind, the authors aim to develop a model that addresses the shortcomings of the traditional, positivist view of knowledge in ISD projects. They do this by representing Berger and Luckmann’s theory of social constructivism in terms of a knowledge process model. They then identify management principles that map on to these processes. These principles form the basis of a survey which is used as an operational version of the process model. The operational model is then assessed by experts and tested by a project manager in a real-life project.

The knowledge creation/sharing process model

The process model that Jackson and Klobas describe is based on Berger and Luckmann’s work.

Figure 1: Knowledge creation/sharing model

Figure 1: Knowledge creation/sharing model

The model  describes how personal knowledge is created – personal knowledge being what an individual knows. Personal knowledge is built up using mental models of the world – these models are frameworks that individuals use to make sense of the world.

According to the Jackson-Klobas process model, personal knowledge is built up through a number of process including:

Internalisation: The absorption of knowledge by an individual

Knowledge creation: The construction of new knowledge through repetitive performance of tasks (learning skills) or becoming aware of new ideas, ways of thinking or frameworks. The latter corresponds to learning concepts and theories, or even new ways of perceiving the world. These correspond to a change in subjective reality for the individual.

Externalisation: The representation and description of knowledge using speech or symbols so that it can be perceived and internalized by others. Think of this as explaining ideas or procedures to other individuals.

Objectivation: The creation of a shared constructs that represent a group’s understanding of the world. At this point, knowledge is objectified – and is perceived as having an existence independent of individuals.

Legitimation: The authorization of objectified knowledge as being “correct” or “standard.”

Reification: The process by which objective knowledge assumes a status that makes it difficult to change or challenge. A familiar example of reified knowledge is any procedure or process that is “hardened” into a system – “That’s just the way things are done around here,” is a common response when such processes are challenged.

The links depicted in the figure show the relationships between these processes.

Jackson and Klobas suggest that knowledge creation in ISD projects is a social process, which occurs through continual communication between the business and IT. Sure, there are other elements of knowledge creation – design, prototyping, development, learning new skills etc. – but these amount to nought unless they are discussed, argued, agreed on and communicated through social interactions. These interactions occur in the wider context of the organization, so it is reasonable to claim that the resulting knowledge takes on a form that mirrors the social environment of the organization.

Clearly, this model of knowledge creation is very different from the usual interpretation of knowledge having an independent reality, regardless of whether it is known to the group or not.

An operational model

The above is good theory, which makes for interesting, but academic, discussions. What about practice? Can the model be operationalised?  Jackson and Klobas describe an approach to creating to testing the utility (rather than the validity) of the model.  I discuss this in the following sections.

Knowledge sharing heuristics

To begin with, they surveyed the literature on knowledge management to identify knowledge sharing heuristics (i.e. experience-based techniques to enable knowledge sharing).  As an example, some of the heuristics associated with the externalization process were:

  • We have standard documentation and modelling tools which make business requirements easy to understand
  • Stakeholders and IS staff communicate regularly through direct face-to-face contact
  • We use prototypes

The authors identified more than 130 heuristics. Each of these was matched with a process in the model. According to the authors, this matching process was simple: in most cases there was no doubt as to which process a heuristic should be attached to. This suggests that the model provides a natural way to organize the voluminous and complex body of research in knowledge creation and sharing. Why is this important? Well, because it suggests that the conceptual model (as illustrated in Fig. 1) can form the basis for a simple means to assess knowledge creation / sharing capabilities in their work environments, with the assurance that they have all relevant variables covered.

Validating the mapping

The validity of the matching was checked using twenty historical case studies of ISD projects. This worked as follows: explanations for what worked well and what didn’t were mapped against the model process areas (using the heuristics identified in the prior step). The aim was to answer the question:   “is there a relationship between project failure and problems in the respective knowledge processes or, conversely, between project success and the presence of positive indicators?”

One of the case studies the authors use is the well-known (and possibly over-analysed) failure of the automated dispatch system for the London Ambulance Service.  The paper has a succinct summary of the case study, which I reproduce below:

The London Ambulance Service (LAS) is the largest ambulance service in the world and provides accident and emergency and patient transport services to a resident population of nearly seven million people. Their ISD project was intended to produce an automated system for the dispatch of ambulances to emergencies. The existing manual system was poor, cumbersome, inefficient and relatively unreliable. The goal of the new system was to provide an efficient command and control process to overcome these deficiencies. Furthermore, the system was seen by management as an opportunity to resolve perceived issues in poor industrial relations, outmoded work practices and low resource utilization. A tender was let for development of system components including computer aided dispatch, automatic vehicle location, radio interfacing and mobile data terminals to update the status of any call-out. The tender was let to a company inexperienced in large systems delivery. Whilst the project had profound implications for work practices, personnel were hardly involved in the design of the system. Upon implementation, there were many errors in the software and infrastructure, which led to critical operational shortcomings such as the failure of calls to reach ambulances. The system lasted only a week before it was necessary to revert to the manual system.

Jackson and Klobas show how their conceptual model maps to knowledge-related factors that may have played a role in the failure project. For example, under the heading of personal knowledge, one can identify at least two potential factors: lack of involvement of end-users in design and selection of an inexperienced vendor. Further, the disconnect between management and employees suggests a couple of factors relating to reification: mutual negative perceptions and outmoded (but unchallenged) work practices.

From their validation, the authors suggest that the model provides a comprehensive framework that explains why these projects failed. That may be overstating the case – what’s cause and what’s effect is hard to tell, especially after the fact. Nonetheless, the model does seem to be able to capture many, if not all, knowledge-related gaps that could have played a role in these failures. Further, by looking at the heuristics mapped to each process, one might be able to suggest ways in which these deficiencies could have been addressed. For example, if externalization is a problem area one might suggest the use of prototypes or encourage face to face communication between IS and business personnel.

Survey-based tool

Encouraged by the above, the authors created a survey tool which was intended to evaluate knowledge creation/sharing effectiveness in project environments. In the tool, academic terms used in the model were translated into everyday language (for example, the term externalization was translated to knowledge sharing – see Fig 1 for translated terms). The tool asked project managers to evaluate their project environments against each knowledge creation process (or capability) on a scale of 1 to 10.   Based on inputs, it could recommend specific improvement strategies for capabilities that were scored low. The tool was evaluated by four project managers, who used it in their work environment over a period of 4-6 weeks. At the end of the period, they were interviewed and their responses were analysed using content analysis to match their experiences and requirements against the designed intent of the tool.  Unfortunately, the paper does not provide any details about the tool, so it’s difficult to say much more than paraphrase the authors comments.

Based on their evaluation, the authors conclude that the tool provides:

  1. A common framework for project managers to discuss issues pertaining to knowledge creation and sharing.
  2. A means to identify potential problems and what might be done to address them.

Field testing

One of the evaluators of the model tested the tool in the field. The tester was a project manager who wanted to identify knowledge creation/sharing deficiencies in his work environment, and ways in which these could be addressed.  He answered questions based on his own evaluation of knowledge sharing capabilities in his environment and then developed an improvement plan based on strategies suggested by the tool along with some of his own ideas.  The completed survey and plan were returned to the researchers.

Use of the tool revealed the following knowledge creation/sharing deficiencies in the project manager’s environment:

  1. Inadequate personal knowledge.
  2. Ineffective externalization
  3. Inadequate standardization (objectivation)

Strategies suggested by the tool include:

  1. An internet portal to promote knowledge capture and sharing. This included discussion forums, areas to capture and discuss best practices etc.
  2. Role playing workshops to reveal how processes worked in practice (i.e. surface tacit knowledge).

Based on the above, the authors suggest that:

  1. Technology can be used to promote support knowledge sharing and standardization, not just storage.
  2. Interventions that make tacit knowledge explicit can be helpful.
  3. As a side benefit, they note that the survey has raised consciousness about knowledge creation/sharing within the team.

Reflections and Conclusions

In my opinion, the value of the paper lies not in the model or the survey tool, but the conceptual framework that underpins them – namely, the idea knowledge depends on, and is shaped by, the social environment in which it evolves. Perhaps an example might help clarify what this means. Consider an organisation that decides to implement project management “best practices” as described by <fill in any of the popular methodologies here>. The wrong way to do this would be to implement practices wholesale, without regard to organizational culture, norms and pre-existing practices. Such an approach is unlikely to lead to the imposed practices taking root in the organisation. On the other hand, an approach that picks the practices that are useful and tailors these to organizational needs, constraints and culture is likely to meet with more success. The second approach works because it attempts to bridge gap between the “ideal best practice” and social reality in the organisation. It encourages employees to adapt practices in ways that make sense in the context of the organization. This invariably involves modifying practices, sometimes substantially, creating new (socially constructed!) knowledge in the bargain.

Another interesting point the authors make is that several knowledge sharing heuristics (130, I think the number was) could be classified unambiguously under one of the processes in the model. This suggests that the model is a reasonable view of the knowledge creation/sharing process. If one accepts this conclusion, then the model does indeed provide a common framework for discussing issues relating knowledge creation in project environments. Further, the associated heuristics can help identify processes that don’t work well.

I’m unable to judge the usefulness of the survey-based tool developed by the authors because they do not provide much detail about it in the paper. However, that isn’t really an issue;  the field of project management has too many “tools and techniques” anyway.  The key message of the paper, in my opinion, is the that every project has a unique context, and that the techniques used by others have to be interpreted and applied in ways that are meaningful in the context of the particular project. The paper is an excellent counterpoint to the methodology-oriented practice of knowledge management in projects; it should be required reading for methodologists and  project managers who believe that things need to be done by The Book, regardless of social or organizational context.

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