Team Tacit Knowledge

Creating a model for team tacit knowledge and how it is measured is a great challenge in the knowledge field. Scholars such as Sternberg, Busch or Ryan and O’Conner have tried to define the different types of team tacit knowledge and have created methods to extract it. Sternberg et al. (2000) have created a psychological approach, referred to as the ‘Yale group approach’ by Busch, defining tacit knowledge as a practical intelligence feature. It is acquired through a minimum of environmental support and aids in peruse of personal goals. Most importantly, it is not about what an

organizational member knows but their ability to utilize knowledge, ‘know- how’. Taking into account that tacit and explicit knowledge are constructed

through the interplay of personal and social interactions (Kelly, 1955/1991), it is essential to compare and contrast group tacit knowledge and individual tacit knowledge.

In projects, teams are assembled to create a well-functioning group of experts in order to complete the task at hand. Mohammed and Dumville (2001) define a team mental model as ‘an organized understanding of relevant knowledge that is shared by team members’. Each team member having a specific skill set, with their tacit knowledge differentiating from one another, allows a more complete exchange of tacit knowledge. Ryan and O’Connor (2008) define team tacit knowledge as the aggregation of

articulable tacit, individual, goal driven expert knowledge to the team-level where different member of the team possess different aspects of tacit knowledge. This assumption sets the basis for their TTKM (Team Tacit Knowledge Measurement) model, and confirms Nonaka and Teece’s (2010) idea of the SECI, where knowledge is created through learning form others. Team tacit knowledge gradually prospers over time by the interplay of knowledge and group cohesion. The more people work together, the more they understand an individual’s skill set and how it can best be utilized within the group. Berman et al. conducted a study on the NBA (National Basketball Association), assessing played minutes in a season with a player’s

experience, revealing that a player’s success is related to the increase of team tacit knowledge, hence experience and the cohesion within the group is directly related to success. Berman et al., as well as the Yale group have founded their studies on proxies’ attempts to address and challenge the unobservable character of tacit knowledge. Ryan and O’Conner have used the theory of proxies in order to create the TTKM.

The TTKM seeks to reveal the further understanding of team tacit knowledge in software development projects. As previously stated Ryan and O’Conner base their work on the Yale group’s proxy approach, which

commences with the differentiation of experts and novices. Experts, unlike novices, possess task performance expertise relative to their domain (Ryan and O’Conner, 2008). Building from this assumption three main assumptions

need to be taken into account in order to build the TTKM in a software development environment. First, team tacit knowledge reflects domain specific practical knowledge, which differentiates experts from novices. Secondly, the TTKM needs to measure the tacit knowledge of the entire team, taking the weight of the member into account. Finally, only tacit knowledge at the articulate level of abstraction can be taken into account.

Having set a basis for analysis, Ryan and O’Conner chose to focus on Kelly’s (1955/1991) Repertory Grid, rather than the Yale group’s situational judgement, in order to reveal personal knowledge and enter private worlds. The gird is classified in two ways, first to illuminate elements, which are a person’s observation of the world and the classification of these elements. The links a person constructs between the elements and their classification also plays a vital role in the comprehension of tacit knowledge within groups. In addition, using these classifications to compare and contrast expert and novice knowledge can show different degrees of tacit knowledge as well as separate the levels of domain specific practical knowledge.

To get started, transactive memory systems (TMSs) were conceived several years ago by Wegner (1987). Ryan and O’Connor (2013) summarize

Wegner’s work by noting that members of “long-tenured” groups tend to rely heavily on one another in order to obtain, process and communicate

information from various distinct knowledge domains. Wegner (1987) enforced the idea that knowledge specialization is actually greater in such groups that feature strong transactive memory systems. In such a group, there is immediate expertise recognition and group members will consult with other group members when they have concerns about acquiring relevant information. They will also, as expected, evaluate that information on the basis of the source involved (Wegner, 1987; Moreland, 1999). In a software development team that is functioning in a healthy manner, the trust amongst members will be implicit. That is to say, each party within the team will

believe in the competency and veracity of the person aside from him or her – or anyone he or she wishes to consult about peculiar questions. The memory of the group is all about people relying upon people and knowing that their respective inputs are valued and appreciated. Once more, this sort of

collective memory draws heavily upon social interaction and upon the use of social interaction to tease as much tacit knowledge out of all parties as possible.

Having delineated the broad contours of Wegner (1987) and Moreland (1999) into their own work, Ryan and O’Connor (2013) present their own synthesis of what an effective transactive memory system should look like in software development projects. Principally, they argue that an effective team will coordinate the differentiated or specialized knowledge that defines each of the group members. Knowing who has the knowledge, and then

coordinating this knowledge, is the essence of maximizing or optimizing group learning or knowledge, particularly in software development (Ryan and O’Connor, 2013). When in the centre of a difficult and demanding project cycle, it may be put forth that great leadership entails identifying individual competencies and adjusting roles and responsibilities in light of this. A well- functioning transactive memory system builds up implicit trust through interpersonal congruence, through getting to know each member and what he or she is comfortable doing and actually capable of doing, creating a flattened decisional hierarchy in which those with capabilities in various areas are allowed to step forward and seize in the initiative on matters that refer to their area of specialty. A controversial view to optimise tacit

knowledge and its transfer is rooted in knowing people.

Organizations can create cultures and routinize best practices. They can even arrange project teams or pods in a manner that is satisfying and effective. But, at the end of the day, an organization can only do so much: those who are actually involved in the software development project at the ground level are the ones who are going to have to facilitate and nurture effective transactive memory and tacit knowledge acquisition. Transactive memory, wherein people rely upon each other in an interdependent manner, is a group-level process, having previously defined it as the ‘meso-level’ process, whereby software development project team members work informally via interpersonal communication. Work teams that interact

regularly tend to perform at a much more productive level than dyads which do not interact constantly (Liang et al., 1995; Moreland and Myaskovsky,

2000). The key is to make time for interpersonal communication and to foster a sense of togetherness when embarking on a project. Absent this sense of togetherness, success can be almost impossible to achieve because so much tacit knowledge is transferred informally and in collegial settings.

Transactive memory systems are absolutely one way of achieving strong collective memory and expertise in a project that demands the aggregation of many different skills and specialties. However, more than a particular system, a software development project must rely on people working together as one. It is stated in the literature that group member familiarity, communication volume and frequency, and “task characteristics of interdependence, cooperative goal interdependence and support for

innovation” were vital to TMS and, by extension, elevated productivity (Ryan and O’Connor, 2013; Lewis and Herndon, 2011). If at all possible, ideally a software development project team should be drawn from a professional group that has complementary skills and pre-existing professional and personal relationships that heighten comfort and faith. Admittedly, that is not always easy to achieve, since finding individuals who possess both

exceptional technical skills and an easy familiarity with one another is a dyad that most organizations struggle to find. The amount of scientific knowledge accumulated in an individual is at best perceived only intuitively by his more experienced peers.

The meetings most people attend generally have characteristics belonging to more than one of these three prototypes according to Ryan (2013) (conference, school, workshops). These can be seen in Figure 9.

1) Team tacit knowledge has been (and is being) created by team members

2) Individuals draw from the team tacit knowledge and create their own tacit knowledge. This is a background process which is dynamic and reciprocal relying on constructivist situated learning

3) This knowledge is re-integrated and becomes individual knowledge 4 &5) As individuals interact, informally and face-to-face, tacit knowledge is

knowledge to be stored and shared, and are therefore both dynamic and static.

Nonetheless, transactive memory systems that cultivate

interdependence and complementarity appear to be a snug fit for many software development projects. Research in recent years by Akgun et al. (2005) stresses that a TMS paradigm has a greater impact on team learning, on speed-to-market, and on new product success when the task complexity was of a greater magnitude. Tacit knowledge may not be easily explicable, but having teammates leaning on one another does appear to allow for sufficient knowledge transfer and clarification to expedite success in challenging group tasks. As Ryan and O’Connor (2013) note, software development teams work on very complex tasks that feature many interacting elements that demand coordination and integration. A TMS

framework could be one way of making what appears incomprehensible a bit more comprehensible. There is certainly nothing to indicate that it will make matters worse. Tacit knowledge acquired and shared through social Enacted into Transactive Memory Team Tacit Knowledge Tacit knowledge acquired by individuals via constructive learning Individual Knowledge Other Human Factors 3 4 5 1 2

Figure 9 - Theoretical Model for the Acquisition and Sharing of Tacit Knowledge in Teams (Ryan, 2013).