Editorial

This issue of Knowledge Management Research & Practice (KMRP) once again illustrates the sheer breadth of our area of focus, knowledge management (KM), in two senses of the term. First, the wide range of topics falling within the scope of KM, from organisational issues such as the management and behaviour of teams to technical issues such as the construction and maintenance of ontologies. Second, its geographical spread: the authors of the eight papers are all based in different countries, across four continents. Theoretical models and tools span qualitative and quantitative, ranging from complexity theory and soft systems through reverse brainstorming and CommonKADS to structural equation modelling.

We lead off with two papers on KM in teams. The first is ‘Performance implications of matching adaption and innovation cognitive style with explicit and tacit knowledge resources’ by James Bloodgood and Michael Chilton. Bloodgood and Chilton focus on knowledge use – the vital but often neglected final link in the KM chain. Using theories of cognitive style based on the Kirton Adaption-Innovation Inventory (Kirton, 1976), and assuming that cognitive style affects knowledge use, they go on to consider how different styles within a team might affect knowledge use and therefore organisational performance. This leads to three propositions: the first two are that teams of individuals with an adaptor cognitive style will perform better when explicit knowledge is available and teams of individuals with an innovator cognitive style will perform better when tacit knowledge is available. The third is potentially the most wide ranging: when both types of knowledge are available, Bloodgood and Chilton propose that a team with a non-uniform mix of styles that has also been trained to use the different types of knowledge resource available will produce the most effective performance. This is consistent with studies on other types of variation in teams which indicate that diverse teams perform better, such as Brodbeck et al (2011).

The second paper is ‘Research teams as complex systems: implications for knowledge management’ by Eleftheria Vasileiadou. Vasileiadou looks specifically at research teams, synthesising findings from previous studies on teams in two projects funded under the European Union's Framework 5 Programme. Her approach is based on the theory of complex adaptive systems. Unusually, rather than assuming that the teams are complex adaptive systems, the prior studies are used to demonstrate that they are, using the time series analysis techniques of AutoRegressive Integrated Moving Average and cross-correlation. The crucial difference stems from whether teams are high or low complexity. Vasileiadou's analysis leads to three insights. First, changes in the environment have a more pronounced impact in teams of high complexity, than in teams of low complexity. This is not necessarily a bad thing, however, as it means that high complexity teams are more adaptable. Second, geographically distributed research teams (typical of projects funded by the European Union) operate at three different levels (local, global and contextual), and conflicts are caused by mismatch of governance mechanisms at these three levels. Third, in teams of high complexity, the use of ICT can provide an advantage facilitating productivity, more so than in low complexity teams. Her work also confirms previous findings that face-to-face meetings have a positive effect on productivity in distributed teams.

Taking the conclusions of these two papers together, we see that both the structure and the specific membership of teams have implications for KM. This is a valuable contribution to understanding why it is so important for KM initiatives to be tailored to the organisation concerned.

The third paper, ‘Beyond cross-functional teams: knowledge integration during organizational projects and the role of social capital’ by Caroline Sargis Roussel and Francois Deltour, concentrates on what Vasileadou would term high complexity teams. Sargis Roussel and Deltour examine complex cross-functional projects: the kind that require the contribution of people with many different knowledge specialisms. The interaction between team members is clearly central to the integration of their knowledge and indeed the overall success of the project. Sargis Roussel and Deltour examine the issue using the concept of social capital, based on a longitudinal qualitative case study of an information technology project in a French SME. This included interviews, observation of meetings and document analysis. Knowledge integration is studied as a cyclical three-phase process, consisting of collection, interpretation and assimilation phases. Sargis Roussel and Deltour find that the roles of internal and external social capital differ between the three phases. They also find that not only does social capital support knowledge integration, but knowledge integration also promotes social capital – thus leading (potentially) to a virtuous cycle.

The fourth paper, ‘Intelligence systems methodology (ISM): a systemic approach to the organizational intelligence function’ by Christian Ganzert and Dante Martinelli, also addresses complexity, but from the perspective of soft systems. Ganzert and Martinelli focus on organisational intelligence – intelligence here meaning knowledge of what is going on in the environment rather than intellectual capacity. They use soft systems approaches, in particular Beer's Viable Systems model (VSM) (Beer, 1985) and Checkland's Soft Systems Methodology (SSM) (Checkland, 1999), to provide the foundations for their proposed ISM. ISM is composed of two simultaneous sets of activities: Active Environmental Mapping and the Stimulated Action Cycle, which have an external and internal focus, respectively. VSM contributes the relationship between the parts of the organisation and the environment, while SSM provides the basis of the processes by which the two parts of ISM are carried out. As with SSM, ISM is intended as a guideline rather than a strict template or set of rules.

Next, come two questionnaire-based studies. The first is ‘Creating patient e-knowledge for patients through telemedicine technologies’ by Antonio Luís Gamo Sánchez and Juan Gabriel Cegarra Navarro. Gamo Sánchez and Cegarra Navarro consider the use of ICT in the work of a hospital, specifically what they term ‘telemedicine technologies’ – ways to exchange healthcare information about patients, and indeed possibly with them. Naturally, the Internet has opened new possibilities for their use, especially for patients who are not physically within the hospital. Gamo Sánchez and Cegarra Navarro study the topic by carrying out an empirical investigation of 252 patients of a Hospital-in-the-Home Unit (HHU) within a Spanish regional hospital. An HHU delivers acute services to patients in their own home. Potentially, this entails significant organisational learning about how the needs of these external patients differ from those of the hospital's internal patients. Gamo Sánchez and Cegarra Navarro take a quantitative approach, building a structural equation model, validated by factor analysis. They test three hypotheses, about the association of knowledge exploration and knowledge exploitation with telemedicine technology and that of telemedicine technology with what they call ‘patient e-knowledge’ – enabling the patients to remotely access knowledge themselves. All three hypotheses are supported, thus suggesting that the relationship between organisational learning and patient e-knowledge can be mediated by telemedicine technologies. From both research and practice perspectives, the emphasis this places on the relevance of prior knowledge is a novel finding.

This is followed by ‘Learning and knowledge transfer performance among public sector accountants: an empirical survey’ by Siong Choy Chong, Kalsom Salleh, Syed Noh Syed Ahmad and Syed Omar Sharifuddin Syed Ikhsan. Chong et al examine knowledge transfer performance through a case study of accountants in the Accountant-General's Department of the Ministry of Finance in Malaysia, which is in the process of implementing KM initiatives. They point out that both Malaysian organisations and public sector accounting organisations are relatively under-researched contexts for KM, though another paper in the previous edition of KMRP, published after this paper had been accepted, also addresses the former (Erwee et al, 2012). Chong et al carry out a quantitative study based on a survey questionnaire, analysed using factor analysis and multiple regression analysis. They find significant positive effects on knowledge transfer performance resulting from training and learning opportunities; performance evaluation and incentives; and ICT skills and know-how; but no significant effect from job rotation. Clearly, these findings would need confirmation beyond the context of one accounting organisation in one country, although it is a large one.

The seventh paper is ‘Destroying collaboration and knowledge sharing in the workplace: a reverse brainstorming approach’ by Nina Evans. The paper by Evans takes as its starting point the (reasonable) assumption that collaboration and knowledge sharing are a good idea, and the problem for organisations is how to do it. As many readers of KMRP will appreciate, it is often most important, but also most difficult, that this collaboration and sharing takes place across the boundaries of the organisational silos that are only too common. Evans then takes a less well-trodden path by using reverse brainstorming. In reverse brainstorming, standing more usual approaches on their head, participants try to think of different ways in which to cause a particular problem, instead of ways of solving it. Thus, in Evans's study, the participants were asked to consider how to destroy collaboration and knowledge sharing. The 306 suggestions from this process are then easily translated into lists of barriers to collaboration and knowledge sharing. Evans groups these into individual, organisational and technical factors. The process then goes on to think of antidotes to these ‘destroyers’: these come in 11 clusters, which are also grouped under the same three headings.

Finally, we have ‘A framework for ontology-based temporal modelling of business intelligence’ by Alexander Mikroyannidis and Babis Theodoulidis. Mikroyannidis and Theodoulidis are concerned with ontologies, and how they might change over time. Ontologies provide a foundation for the interpretation of unstructured content (natural language texts), which supports business intelligence and information management. As they put it, ‘ontologies need to be properly modelled and evolved so that they are constantly aligned with changes that occur in the real world’. They review previous attempts to include temporal aspects of ontologies, and then present a framework, called Heraclitus II, for modelling the temporal aspects of a semantic knowledge base. Proof of concept for Heraclitus II is provided using a case study of Biovista, a supplier of business intelligence products for the life sciences sector based in the U.S.A. and Greece. The impact assessment of Heraclitus II is carried out using the CommonKADS framework (Schreiber et al, 1994) from knowledge-based systems.

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