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Towards an Information Systems Framework for Dynamic Organisational Memory Burstein, F., Linger, H., Zaslavsky, A. Faculty of Computing and Information Technology, Monash University, 900 Dandenong Road, Caulfield East, Victoria 3145, AUSTRALIA { F.Burstein, H.Linger, A.Zaslavsky}@Monash.edu.au

Abstract Organisational memory information systems (OMIS) implement important aspects of organisational memory with the aim of enhancing organisational effectiveness. We propose a three layered framework for a dynamic OMIS. The framework consists of a pragmatic layer to support the actual activity; a conceptual layer to store the concepts (meta knowledge) inherent in that activity; and a process layer to store the experience of performing that activity. Both the conceptual and process layers are representing organisational memory repositories in the form of respective models. The implementation of OMIS assumes that any activity is the instantiation of relevant conceptual and process models and includes the reuse of knowledge stored in association with those models. In this way organisational memory is always mapped to current activity, its adequacy constantly evaluated. This provides the potential for the models to evolve as a direct result of the current activity. A partial implementation of this framework to support epidemiological research is illustrated.

1.

Introduction

The dynamic environment in which organisations find themselves has necessitated that they develop an ability to respond flexibly to external changes [1]. An aspect of this flexibility is the shift in focus from the individual worker to groupwork and the adoption of a communicative perspective. In this context, workers are engaged in activities more closely related to the work of executives; they analyse, reflect and innovate changes to their work activity [1, 2]. This shift is emphasised by computer systems that act as support tools rather than as technological products. Knowledge work requires organisational structures, processes and a culture that encourages and sustains

Crofts, N. Epidemiology and Social Research Unit, Macfarlane Burnet Centre for Medical Research, Melbourne, AUSTRALIA [email protected]

workers in their explorations and needs to be adequately supported with information technology for its potential to be realised. This is exemplified in the literature on DSS (see for example, [3]). Exploration and learning requires the ability to record, recall and manipulate all aspects of the work activity. This represents organisational memory and implies that organisational memory information systems (OMIS) support knowledge work [4]. However, OMIS must extend beyond the facilities offered by database systems. It must integrate diverse information and knowledge that is not conventionally stored in databases including contexts, concepts, processes, behaviours, stories, etc. Furthermore, it needs to have innovative ways to elicit and record such knowledge as well as facilities to bring this store of knowledge to bear on current activities by effectively searching and retrieving relevant knowledge. Our research is directed to the development of a three layered framework for OMIS. In particular, our framework focuses on capturing meta knowledge, in the form of conceptual and process models, while providing an integrated structure in which these models interact with the activity that they support. This explicit modeling of meta knowledge is generally absent from organisational memory repositories. The importance of these models is that they provide the mechanism that supports knowledge acquisition, learning, exploration and knowledge reuse. Direct interaction with the models is the learning mechanism, allowing workers to examine and explore the meta knowledge underlying their activity. This interaction also allows workers to explore new hypotheses. The innovation we have introduced is that practical activity is conducted in terms of the concept and process models with the models defining the data utilised by the activity. Moreover, building the OMIS based on this framework allows organisational memory to be used in a communicative activity. The communicative perspective

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of memory is important if organisational memory is considered in a computer supported collaborative work (CSCW) context [5]. We are conducting an exploratory research study to apply this framework to a research organisation. Scientific research organisations are attractive candidates for OMIS development because their stated mission and core activity is knowledge work [6]. Epidemiological research organisations are usually small, well focussed and their discipline is still young enough to encourage flexibility and innovation in the subject of their investigation and the methods employed. Such an organisation is well suited for our study as the need for a memory system is acknowledged and there are practical imperatives for knowledge reuse. In this paper we explore the possibility of supporting epidemiological research with an information system that provides greater organisational learning, knowledge reuse and reflection within the established practice of conducting survey-based research. The three layered architecture of the framework is used to implement an organisational memory to allow epidemiologists to perform their tasks and record and explore their experiences. We illustrate a partial implementation of this framework with the focus on supporting the process of the design and construction of surveys .

2.

The Role of Organisational Memory in Epidemiological Research

Epidemiology is the study of the distribution and determinants of disease and injuries in various populations. It is an example of population medicine, a study of health and disease on a population level rather than on an individual or clinical basis. It is concerned with the frequencies and types of illnesses and injuries afflicting people to gain insight into the factors that influence their distribution. This implies that aspects of health are not randomly distributed throughout the general population, but rather that subgroups differ in the frequency of different diseases. The central goal of epidemiology is to identify these subgroups by gathering and analysing information about members of these subgroups. The quality and value of the epidemiological information is dependent on the analysis of data that is collected. The quality of the data however is directly affected by the method of its collection and storage. As a relatively young discipline, the quantitative research (QR) methods used by epidemiology are not calibrated instruments in the same sense as, for example, established psychology tests [7]. Epidemiological studies deal with concepts that are usually impossible to measure directly. As a consequence

concepts are expressed as the combination of a number of factors that can be formulated as questions that elicit unambiguous responses. Yet the subject of investigation is often personal and requires considerable tact on the part of the researchers. Questions needed to elicit the required data need to be focused, to avoid redundancy and vagueness, yet must be sufficiently subtle to avoid offending the interviewee. Researchers construct concepts by establishing relationships between questions. Complex concepts are constructed as the aggregation of more basic concepts. Considerable time is devoted to assessing the validity of the concepts in terms of the questions used in their construction. and is the basis of a major problem in epidemiology: the difficulty of direct comparison between studies. For example, when constructing the concept of ethnicity, one possibility would be to define it depending on parental country of birth. Choosing one or other parent would diminish the validity of comparison with studies that chose the other parent. Moreover the choice of marker signifies a political, social or ideological position of the survey further undermining comparison between studies. The sensitivity of the subject matter often dealt with requires the study design to be innovative in the way that it is conducted [8]. The methodology of the type of study is the overarching framework for the conduct of the study but the practical details of the administration of the survey is subject to variation. Epidemiology units develop expertise in the conduct of studies in their chosen area. Thus, for example, a study relating to drug taking might innovate by employing drug users to administer the survey on the premise that other drug users would be more forthright with such interviewers. Review of this innovation would lead to establishing protocols for conducting other such studies. But importantly, the review would also inform the design of the survey in terms of the type of questions that could be possible with such an approach. The significance of epidemiological research as a case study for organisational memory are as follows: • epidemiological organisations are usually small, self contained, vertical units operating within larger biomedical/ health organisations but only sharing results with the host; • as a young discipline, epidemiology is self conscious ensuring that its past, in terms of practice, data, results, etc. is kept visible by ongoing reference to it; • epidemiology is poorly supported by technology. Only its primary activities of writing survey questions, and storing and analysing data are supported with conventional computerised tools; • the discipline needs to be innovative and consequently organisational processes exist to

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continuously review practices, concepts and subjects it deals with. These processes are currently not supported with information technology; • there is a recognition of the limitations that current practice imposes on the discipline particularly in the area of exploiting existing data and comparison between studies; • the characteristics of epidemiological units predispose them to situations where organisational memory resides with the head of the unit or project leader; • there is systematic forgetting in that evolution of elements, such as concepts and practice, is not explicitly documented. Past surveys provide a snapshot of the state of these elements but without adequate explanation of the reasoning by which these elements arrived at that state. Many of these features of epidemiology provide the basis for selectively applying the meta requirements and meta designs of an OMIS identified by Stein and Zwass [9]. Their OMIS framework is based on an organisational perspective premised on “...effectiveness [as] one of the chief concerns of organisations ...” ([9: 95) and expressed in terms of integrative, adaptive, goal attainment and pattern maintenance functions. The focus of our case study relates to the integrative function with the epidemiological unit being treated as a vertical, innovative start-up organisation. Its adaptation to its environment, in terms of the disciplinary debates, are addressed as changes that are introduced into surveys and can be treated within the integrative function. Similarly, aspects of the functions of goal attainment and pattern maintenance are also implicit in survey questions and are incorporated in the integrative function. The memory processes, the mnemonic functions, identified by Stein [4] are explicitly supported albeit in an innovative manner. For epidemiology, the meta requirements of the integrative function relate predominantly to the temporal integration of information allowing reuse of past surveys and data as well as recording the evolution of concepts. The meta design extends to both spatial and temporal integration on the basis of common storage structures which facilitates reuse of surveys and data and temporal indexing and annotations to record the history of concepts. We approach the construction of the OMIS, based on our framework, as the development of computational tools to support existing organisational processes. In the first instance, the design of the tools are based on the need to document the subject of those processes as well as providing the means to use that documentation in those processes. This allows the focus of the case study to be on the OMIS itself rather than the process of adopting OMIS

in the organisation which would involve considerable change management. Importantly, epidemiology exhibits features that allow our case study to by-pass a number of the organisational contingencies identified as impediments to OMIS construction, implementation and usage [9]. The potential value of viewing surveys and data in terms of organisational memory has been explicitly recognised and the implementation of the case study is actively encouraged by the head of the unit. Since the case study deals with tasks performed by the unit, and the processes surrounding those tasks, the expectations are that the OMIS will be used, particularly as its development is also actively supported by the head of the unit. The focus of our research is whether the design of the OMIS fits the organisation and that if the OMIS is used properly that it achieves its potential.

2.1.

The Case Study: VICCS Cohort Study

The case study used to implement our framework is the VICCS project, a large longitudinal cohort study of intravenous drug users as a major risk group for HIV infection conducted by the Epidemiological Research Unit of Macfarlane Burnet Centre for Medical Research in Melbourne, Australia. The emphasis of the cohort study is on subject behaviour, especially drug taking and sexual practices. Because of the illicit nature of some of these activities, situational factors are also important. Another consideration, from both a research and ethical perspective, are details of the subjects’ contacts particularly in cases were infection exists. The development of the case study needs to be viewed from the context of the history of the cohort study. The case study was initiated as a result of the need to refocus the cohort study, a common occurrence in epidemiological studies. Analysis of initial data indicated a totally unexpected result. The prevalence of Hepatitis C in the cohort was the most significant health problem rather than HIV. This meant that the questionnaire needed to be changed, leading to temporal incompatibility in the data and difficulties in the longitudinal analysis of the data. These changes also significantly increased the cost of extracting data to support the new direction of investigation as the data needed to be analysed differently to the way the survey was designed. The cost of conducting analysis focussed attention on the need to review the data storage strategy. The case study was established to design a better “mouse trap”. It was initiated to design and implement a flexible data structure that would allow data to be stored independent of the questionnaire so as to facilitate its use. An implicit aim was to investigate the possibility of a uniform data

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structure across all surveys, within the organisation, so as to simultaneously facilitate comparisons between studies. The design for data storage emerged from a year long process of modelling that involved epidemiologists and computing experts [10]. Fig 1 shows the result of this process as an abstract conceptual model of data for the epidemiology of blood borne diseases. Individual

Relationship

Situation

the necessary key, link and navigational attributes). The first two are effectively classifications while the third is the value of the data. For example a question asking the type of drug used intravenously would translate into a “Transaction” record with the following attribute values: ”Aspect” would be “Drug Use”, “Type” would be “Intravenous” and “Value” would be “Heroin”. The conceptual hierarchy with their generalised attributes achieve the desired aim of flexibility and compatibility of the stored data. The result of the design process was a model of the meta knowledge that epidemiologists use to think about the data. Storing data in accordance with this meta model facilitates its current use for analysis as well as a historic record for comparison. What emerged was “memory in the small” [11]. This was a memory repository based on an organisational task that addressed both the technical and social issues of storing and using data in the short term. By basing the memory on a specific task the repository is readily understandable to the unit and can be used by the epidemiologists in their everyday work.

3.

Transaction

OMIS for Epidemiology: Extension of the Case Study

Fig. 1. Data Storage Conceptual Model

Subject

Significant other

Relationship

Situation

Transaction

Fig. 2. The Epidemiological View of Data Storage Model

The model was modified to make it more understandable to epidemiologists as shown in Fig. 2. However this model folds into the generic conceptual model of Fig. 1 as the entity “Significant Other” is a synonym for the entity “Subject” and direct link between these entities and the entities “Situation” and “Transaction” are equivalent to links through the hierarchy of Fig 1. The other important feature of the conceptual model is that each entity can be fully defined in terms of only three attributes: “Aspect”, “Type” and “Value” (excluding

The significant result of the data storage model was that the focus shifted from building better computerised support tools for epidemiology to designing an organisational memory system. This shift in emphasis resulted in the OMIS framework shown in Fig 3. The importance of the framework is that it provides the context for the definition of the information technology tools that need to be developed to support both epidemiological practice, based around surveys, and the organisational processes that review that practice. Moreover the survey conceptual model emerged as the key element of the OMIS. The static representation of the framework given in Fig. 3 is misleading as it conveys an impression of a hierarchy of layers with each layer being informed (controlled) by the one above. A more accurate view would be a dynamic reshuffling of the layers depending on the particular activity being performed within a particular layer [12]. Such a dynamic view is important for the design of an integrated set of support tools within the context of the OMIS. These tools allow epidemiologists to work flexibly with repositories, or directly on surveys, with seamless transitions between layers as well as tasks. The utility of the data storage model, with its facility to support data reuse, pointed to the need for other conceptual models of the generic tasks involved in survey work. The generic tasks in survey work can be categorised

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as “Survey Design”, “Data Storage” and “Data Analysis”. There are other activities that translate data between these tasks. These are termed “Data Entry” and “Data Extraction”. Together all these tasks constitute the “Pragmatic” Layer of the framework. Data analysis is well

supported with a range of sophisticated software products so there is currently little interest in investigating this aspect of the framework. The case study thus focussed on the design of the survey conceptual model.

PROCESS LAYER CONCEPTUAL LAYER PRAGMATIC LAYER PHASE II

PHASE I

Survey Dessign & Administration

Data Entry

Data Storage

PHASE III Data Extraction

Analysis

Analysis Conceptual Model

Survey Conceptual Model

Data Conceptual Model

Meta-level Rules & Procedures

Meta-level Rules & Procedures

Meta-level Rules & Procedures

Study Design Model

Data Analysis Model

Data Collection Model

Fig. 3. An OMIS Framework for Dynamic Organisational Memory

The difficulty in performing the survey design conceptual model was the absence of any data model. The questionnaire was treated as text and reuse involved cut and paste operations using publishing/writing software. Our initial work modelled the question/response pairs and their organisation (sequence and navigation) as an entity hierarchy. This did not adequately deal with the central issues of memory and reuse. The difficulty revolved around the representation and manipulation of the survey. This impasse was overcome by focussing on the epidemiological concepts which the survey questions, in various combinations, represent. From this perspective we conceived the survey as a hierarchical tree of concepts. This view readily lends itself to a graphical representation and an economical storage structure. The significance of this representation is that epidemiological issues are explicitly expressed as concepts, and the constituents of those concepts. Moreover many activities, such as comparisons between surveys, can be performed graphically.

These two models, together with the (as yet undefined) analysis concept model, form the “Conceptual” layer of the framework. This layer deals with the meta knowledge of the subject matter of activities that constitute epidemiological research. From this perspective the conceptual models represent memory repositories. They allow data to be stored in a manner that is abstract, but meaningful, and consistent temporally. From the perspective of organisational memory, the data is directly accessible, it can be recalled and reused. The conceptual models are heterogenous in terms of their representation. This emerged from the modelling process that focussed on a particular task with the model expressed in terms of that task. These task dependent models proved an important component in the design of the OMIS as they provided the basis for epidemiologists to work explicitly across phases. The different models require data in each phase to be explicitly defined in terms of the other phases thus providing links between the phases, models and layers. For example the

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question/response pairs from the survey model need to be translated to the storage meta model. This facilitates data entry but is not an additional burden rather it provides the epidemiologists with a facility to explicitly record, to some extent, their intention in asking the survey question. Similarly, this intention is the basis for defining the analysis to be performed on this data. Thus each conceptual model not only defines the structure of the meta data of its activity, the organisational memory, but also has a clear role in actively influencing all other tasks. This integration is embedded in the design of the OMIS. In an analogous way, the “Process” layer represents a meta level of the performance of the activities of each phase. As a repository of experience in performing activities, the models can be conceptualised as networks offering different paths to arrive at a given point. What emerges is that the “Process” layer needs to interacts with both the “Concept” layer and the “Pragmatic” layer so as to influence whatever work is being done. An important aspect that needs to be included in the OMIS is support for historical review. The (meta) knowledge that the unit uses evolve through, task performance, internal processes as well as external influences. The problem is that such evolution is not documented leading to organisational forgetting [12]. To address this temporal aspect of organisational memory entails storing sufficient details of the evolution of the knowledge in the OMIS so that a proper understanding of its current state can be reached. The features would include date stamping and annotations in the manner analogous to design rationale [13]. Such facilities would not have a direct bearing on survey design but could be invoked in support of any review process. The last element of the framework is the facility to introduce various technologies that can intelligently support epidemiological work. On the basis of the accumulated data and meta knowledge, various learning techniques can be introduced to derive meta rules and procedures. These rules and procedures can act as guidelines to support epidemiologists both in terms of survey work (design and analysis) and extending the memory repositories. For example, active database techniques can be applied to data collected by survey pilot studies to identify ambiguities in the text of questions [14, 15]. Such techniques could also be applied to ensuring concepts are assembled appropriately [16]. Benfer, Brent and Furbee [16] highlight the role of advanced technologies (eg, expert and knowledge based systems) in social research. We consider the inclusion of this element, while currently not implemented, as an important component of the framework. It has the potential to explicitly use the memory repositories in the “Concept” and “Process” layers to inform the review process, to support the learning function implicit in the OMIS and to

intelligently support the activities addressed by the OMIS [17, 18].

3.1.

Designing Memory Repositories for Reuse: Memory as Concept Trees

From the perspective of organisational memory, the case study is significant in terms of its representation of memory. Both conceptual models address the issue of reuse but the concept tree representation of the survey design conceptual model lends itself to the evolution of the model through knowledge acquisition, organisational learning and exploration. As stated above, survey questions represent markers of issues of interest to epidemiologists so a questionnaire represents, indirectly, the concepts that are being investigated. These concepts are never explicitly documented other than when data is correlated in analysis. What emerged in the case study was that survey design is directed by concepts based on organisational memory and individual memories of epidemiologists. The ability to record concepts was recognised as an important aspect of an OMIS for epidemiology or similar applications [19]. A tree graphically represents a generalisation hierarchy with each node representing a concept which is the aggregation of subordinate concepts. Nodes (concepts) and branches can be readily documented and manipulated. Nodes can be documented with diverse information, including the text of question/response pairs, annotations by epidemiologists, or other pertinent references (see for example [20, 21, 22]). Such documentation can be extended to include the definition of the concept in terms of the storage structure using the storage meta model and the definition of the data analysis necessary to construct the concept. Common tree manipulation facilities, such as pruning, grafting and growing provide opportunities to edit these trees through a graphical interface. The documentation and manipulation facilities together provide the necessary tools to design surveys, in terms of structure, concepts and questions, through a graphical interface. An existing tree, representing a previous survey, can be reused by pruning, grafting and growing new branches while utilising past question/response pairs associated with the concepts represented by the nodes. Existing questions are already defined in terms of the data storage model and the concepts in terms of data analysis. The OMIS stores past surveys as specific instances of concept trees and a database of question/responses, references and commentaries. All of this data in the repositories is available for reuse. The utility of such a memory is enhanced with the construction of a concept tree that represents the

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consensual knowledge of the epidemiological unit. We term them Primary Trees. Primary trees are conceptual models of the area of interest of the epidemiological unit and result from the evolution of the concepts deriving from surveys conducted by the unit and from the memory of, typically, the head of the unit. As such they may not be a general representation of the epidemiology of that area but the particular view of that area held by the unit. Such a tree represents the General Domain Description (GDD) of that unit and thus, can be treated as reusable component of the domain knowledge [17]. The primary trees are the result of double loop organisational learning by the unit and represents how the unit describes its area of interest [23, 24]. The knowledge described by primary trees is used for inducting new staff as well as for dealing with external organisations, which range from the host organisation to consumers of the survey results. Primary trees can also be used to conduct the discourse on the definition of concepts within the discipline. The consensual knowledge represented by the primary trees can be used directly by epidemiologists to design and construct new surveys. A copy of the primary tree is manipulated to express the concepts necessary for the survey. What is constructed is a concept tree for a particular survey and represents a Specific Domain Description (SDD) of the unit [17]. The OMIS repository contains both the primary trees (the GDD) as well as many trees that represent individual surveys (SDDs). The combination of GDD and SDD in the OMIS facilitates knowledge reuse as epidemiologists can construct new surveys using both the general concepts represented by the primary trees and specific concepts used in previous surveys. Reuse is further enhanced as epidemiologists have access to the full documentation of both types of trees, although they are not restricted to using this material exclusively .

4.

A Communicative View of Memory: Reflecting on Concepts

The innovative aspect of our approach is that epidemiologists have facilities to directly access concept trees without reference to any activity associated with survey construction. This explicitly supports reflection on the adequacy of the concepts and their use as the trees represent the consensual knowledge of the unit, its history, as well as the accumulated data and documentation on which this is based. Manipulation of the primary trees allows new or different hypotheses about the concepts to be modelled to assess their impact prior to their adoption [20].

This extends Ackerman and Mandel’s [11] notion of task oriented memory systems to incorporate tasks related to organisational learning. Such tasks are an important extension of activities supported by an OMIS in that they explicitly address organisational processes that formalise learning, review and exploration of the consensual knowledge of the epidemiological unit. What is clear from the case study to date is that adoption of these processes is enhanced by the existence of the OMIS, particularly the ability to access and manipulate the memory repositories: the models in the “Concept” and “Process” layers of the framework. These exploratory processes will evolve under the influence of the OMIS but the evolution of the OMIS will itself be influenced by the organisational processes it supports. In such an OMIS, concepts are constantly under review and are evolving both as a result of task performance (the design and construction of surveys) and reflection (the review processes). Together these activities ensure that the organisational memory represented by such a repository is dynamically maintained and used. The proposed framework is important in that it combines the usual repository view of organisational memory with Tuomi’s communicative perspective [25]. In the discussion above, the models in the “Conceptual” and “Process” layers have been referred to as memory repositories. They are not repositories in the sense that they are not a store of past data but are explicit models that represent the meta knowledge of that data. However, the models can be seen to perform the repository function when used in the construction of new surveys. In this role the use of organisational memory, as represented by the models, changes the task it supports. In the case study, surveys are expressed in terms of concepts before the question/response text is written or selected from the database. In this mode the OMIS explicitly expresses organisational processes that now occur implicitly. It can be argued that operating with meta knowledge, the OMIS formalises, and thus sustains, organisational patterns. This is consistent with Tuomi’s communicative view of memory. The communicative view is even clearer when the OMIS is used to review meta knowledge. Here the framework provides the means to support organisational processes that are an intrinsic part of epidemiological work. This is consistent with Tuomi’s view of “... computer-mediated communication systems as a medium that supports, modifies and eventually becomes intertwined with essential organisational structures.” [25:148]. In the case study the review functions exist as informal, unsupported activities that find numerous expression, for example as question/response text in surveys or as part of internal or external discourse. What is difficult to predict is how these activities will be

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transformed and incorporated into the fabric of the organisation when they are formalised and supported by computer. The communicative perspective is important because it provides the conceptual context in which to discuss organisational memory and inform the design of information systems to actualise these memories. Memory and recall are positioned within an activity which can be temporally displaced necessitating contextualisation of that activity [26]. Such interpretation of organisational memory would argue for an overarching framework in which to locate memory systems. CSCW is a candidate for this role as it is also explicitly concerned with sharing, communicating, learning and task performance. Such an approach is discussed in the literature with, for example, Morrison and Weiser [5] argue for the adoption of the CSCW framework to explore organisational memory. De Michalis [27] on the other hand argues, from the CSCW perspective, a need to reorient the direction of CSCW towards addressing organisational effectiveness. He argues that this requires a unified environment able to support workers across the full range of activities they are engaged in.

5.

Concluding Remarks

In this paper we have presented a framework for an organisational memory information system in which the memory is dynamically maintained as it is incorporated into the underlying task. This framework was developed as a result of work done in a case study relating to supporting the scientific work of an epidemiological unit. The case study involved functional prototypes of various aspects of the proposed framework within the context of a task oriented OMIS and memory in the small. The important aspects of our framework relate to the explicit modelling of the epidemiologists’ meta knowledge as the basis for the organisational memory. These models are used directly in support of organisational learning and explicitly to support task performance, in this case design of surveys. The other aspects of the framework are that it supports the evolutionary development of the OMIS and the use of heterogenous technologies in its implementation including intelligent techniques. Significantly, our approach to the case study adopts a communicative perspective of organisational memory and OMIS that lends itself to incorporating CSCW in the conceptualisation of the support systems being developed. Our future work will be the development of computer tools in this context. The tools will support the processes involved with the “Conceptual” and “Process” layers of the framework as well as facilities for intelligent support of tasks in all layers.

The current case study offers an exemplary environment in which to investigate particular aspects of organisational memory systems. The insights we have gained to date encourage us to apply this framework to different domains with different contexts and diverse requirements. To this end we have invited a commercial consulting organisation, with international operations, to participate in our project.

6.

References

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