Process Models as a Knowledge Creation Arena
Steinar Carlsen†, Håvard D. Jørgensen #‡, John Krogstie #‡, Arne Sølvberg‡ †Computas
[email protected] # SINTEF Telecom and Informatics {jok,hdj}@informatics.sintef.no, ‡Norwegian University of Science and Technology,
[email protected]
Abstract Active process support utilising workflow technology is promising for building information systems that are flexible regarding both business and IT infrastructure transformation. To achieve such agile systems, process-models have to be aligned with interests of several stakeholder. In particular, user access and update to process models presuppose comprehensible in addition to expressive models. We propose a framework for evaluating quality of process models from this point of view. Active process models may be seen as a representation of knowledge and an arena for the joint creation of knowledge during process enactment. The proposed process model quality framework in particular incorporates Nonaka and Takeuchi's theory on knowledge creation.
1.
Introduction
Flexible process support should be coupled to organisational learning, which can be seen both in a wide organisational and a narrower group perspective. Argyris and Schön’s Theory of Action perspective seems to apply best at the organisational level [2], while at the group level organisational learning and the concept of “social construction of reality” are linked and relevant for how we view the creation, update and enactment of process fragments. Organisations are socially constructed through joint action by involved social actors. Theories on social reality construction [11] are linked to theories on knowledge creation in organisations [12,13,14], which also encompass theories on organisational learning. By giving end-users control over process definitions, we empower them to externalise knowledge, thus creating organisational knowledge and enabling organisational learning. We propose a framework for evaluation of the capacity of creating and sustaining process models of high quality. The proposed framework is an extension of a general framework for model quality [8,9,10]. The remainder of this paper is structured as follows: in section 2 we review the theory of knowledge creation, in section 3 we interpret this theory in a process support setting, in section 4 we highlight the consequences of this theory as related to our quality evaluation framework, and finally we present the updated framework in section 5.
2.
Theory of Knowledge Creation
The terms data, information and knowledge often are misinterpreted, and in the literature they are defined in various ways. From the standpoint of social constructivism, some current definitions suit our own view:
From the FRISCO 1-report [6] we have the following definitions (where italicised terms are other terms defined in the same report): Knowledge is a relatively stable and sufficiently consistent set of conceptions possessed by single human actors. Data denotes any set of representations of knowledge, expressed in a language. Information is the knowledge increment brought about by receiving action in a message Nonaka and Takeuchi’s theory on organisational knowledge creation [13,14] use the following definitions: “knowledge is justified true belief” and “information is a flow of messages, while knowledge is created and organised by the very flow of information, anchored on the commitment and beliefs of its holder”. We notice that these definitions are quite close to the FRISCO definitions above; with the exception that the latter talk of personal and not organisational knowledge. Nonaka and Takeuchi tightly link knowledge to human activity. Central to their theory is that organisational knowledge is created through a continuous dialog between tacit and explicit knowledge performed by organisational “communities of interaction” that contribute to the amplification and development of new knowledge. Thus their theory of knowledge creation is based on two dimensions:
1) The epistemological dimension that embraces the continued dialog between explicit and tacit knowledge 2) The ontological dimension which is associated with the extent of social interaction between individuals developing and sharing knowledge. The distinction between explicit and tacit knowledge follows from Polanyi [15]: Explicit or codified knowledge is transmittable in formal systematical language, while tacit knowledge has a personal quality which makes it hard to formalise and communicate. Nonaka and Takeuchi identify four patterns of interaction between tacit and explicit knowledge commonly called modes of knowledge conversion as depicted in Figure 1 below: Tacit knowledge Tacit knowledge From Explicit knowledge
Dialogue
To Explicit knowledge
Socialization creating tacit knowledge through shared experience
Externalization conversion from tacit to explicit knowledge
Internalization conversion of explicit knowledge to tacit knowledge
Combination creation of new explicit knowledge from explicit knowledge
Socialization
Externalization
Linking Explicit Knowledge
Field Building
Internalization
Combination
Learning by doing
Figure 1: Modes of Knowledge Creation [13]
Figure 2: Knowledge spiral [14]
The internalisation mode of knowledge creation is closely related to “learning by doing”, hence action is deeply related to the internalisation process. Nonaka and Takeuchi criticise traditional theories on organisational learning, like [1,2,16], for not addressing the critical notion of externalisation and having paid little attention to the importance of socialisation. The authors also argue that a double-loop learning ability implicitly is built into the knowledge creation model, since organisations continuously make new knowledge by reconstructing existing
1
FRramework of Information Systems Concepts.
perspectives, frameworks or premises on a day-to-day basis. It is this very dynamic view of knowledge as something continuously being created, refined and reformed based on available information that makes Nonaka and Takeuchi’s theory unique. When tacit and explicit knowledge interacts, innovation emerges. Nonaka and Takeuchi propose that the interaction is shaped by shifts between modes of knowledge conversion, induced by several triggers as depicted in Figure 2. From Figure 2, we have socialisation mode starting with building a field of interaction facilitating the sharing of experience and mental models. This triggers externalisation mode by meaningful dialogue and collective reflection where the use of metaphor or analogy helps articulate tacit knowledge hard to communicate. Combination mode is triggered by networking newly created knowledge with existing organisational knowledge, and finally learning by doing triggers internalisation. These contents of knowledge interact with each other in the spiral of Figure 2. When we in addition to this epistemological dimension consider Nonaka and Takeuchi’s ontological dimension of knowledge creation, we end up with the spiral of organisational knowledge creation depicted in Figure 3, which shows how the organisation mobilises tacit knowledge created and accumulated at the individual level, organisationally amplified through the four modes of knowledge conversion and crystallised at higher ontological levels. Thus the authors propose that the interaction between tacit and explicit knowledge becomes larger in scale as the knowledge creation process proceeds up their ontological levels. The spiral process of knowledge creation starts at the individual level and moves upwards through expanding interaction communities crossing sectional, departmental, divisional and possibly organisational boundaries. Epistemological dimension
Externalization Combination
Explicit knowledge
Socialization
Tacit knowledge
Internalization Ontological dimension Individual
Group
Organization
Inter-organization
Knowledge level
Figure 3: Spiral of organisational knowledge creation [14] In [14] five enabling conditions of knowledge creation are discussed according to how organisations may provide proper context to promote the knowledge spiral:
•
Intention; i.e. the knowledge spiral is driven by organisational intention.
•
Autonomy; ranging from the individual level through team levels to sections and departments.
•
Fluctuation and creative chaos; where fluctuation is viewed as an “order whose patterns are hard to predict at the beginning” (order without recursiveness) which fosters continuous self-assessment and the creative chaos may be intentionally introduced to increase organisational tension and focus attention on problem definition and solving.
•
Redundancy; i.e. the existence of information that goes beyond the immediate operational requirements of the organisational actors.
•
Requisite variety; i.e. that an organisation's internal diversity matches the variety and complexity of the environment with which it interacts.
3.
Re-interpretation of knowledge creation in a process support context
Nonaka and Takeuchi’s theory on knowledge creation has implications relevant for process modelling, enactment, and evolution. In this section we re-interpret their theory in a flexible process support context. A process model may be viewed as externalised knowledge guiding or supporting organisational actions. Hence the Externalisation and the Combination knowledge creation modes, that both result in explicit knowledge, are particularly relevant for process modelling. The Socialisation mode deals with communities of practice utilising flexible process technology and the technical skills necessary for expressing work plans and procedures as process models. The Internalisation mode corresponds to doing, i.e. actually using flexible process support technology as a natural way of working both for articulating work in the form of process models as plans and have work supported through process model enactment. Externalisation corresponds to the ability of actually creating process models (or process model fragments) intended for work process support by a team or an individual. On the other hand, well-known dangers regarding articulation work and situated actions [18] imply that for process modelling it should be possible to renounce on (explicit) knowledge completeness; allowing for details or parts of the model to remain tacit; possibly to be expanded (corresponding to situation specific knowledge creation) at the discretion of performing actors during performance and when it seems natural, and to enable users also to change the existing model if deemed right. Combination corresponds to the ability of linking newly created fragments with past fragments; thus combination is vital for the reuse of process model fragments. The property of constructivity is important in order to support (process modelling) knowledge combination. The constructivity property is that we may compose larger models from interconnected model parts and derive the composed model’s properties from the properties of the parts and their interconnection at the process modelling language level.
4.
Consequences for process model quality evaluation framework
Knowledge according to Nonaka and Takeuchi is a highly dynamic phenomenon, continuously being created and reshaped. Furthermore, knowledge creation is a collective endeavour, hence process modelling as knowledge creation also should be viewed as a dynamic and collective endeavour. The implication of this is that we are in search of process modelling languages and corresponding tools that score high for knowledge articulation appropriateness where this property may be subdivided:
•
Knowledge externalisability appropriateness, i.e. how easy is it to express relevant knowledge in a process model using the modelling language. This can be further subdivided into how easy the original creation of the model is and how easy it is to modify or enhance the model.
•
Knowledge combinability appropriateness, i.e. how easy is it to create a new process model involving other process model fragments as parts (development with reuse) and how easy is it to reuse this particular model (or fragments of it) in another future model (development for reuse).
•
Knowledge harvesting appropriateness, i.e. how knowledge externalised in past model fragments, that were created or modified during process enactment, may be re-combined, repackaged and mobilised to form repositories of future reusable process model assets.
On the tool side, the implication of knowledge creation as a collective endeavour is that collaborative process modelling and enactment should be supported. The term collaborative is here used both in the sense of collaborative planning - where collaboration proceeds through a divide and conquer approach with individuals taking responsibility of “their own” process model fragments - but also extended into multi-user tools providing a shared interface to the shared task of process modelling; i.e. groupware tools for process modelling. Such tools could be asynchronous (i.e. allowing posting comments and performing discussions related to model elements) or synchronous (i.e. allowing joint real-time editing of the same model by several users) At the epistemological level, our framework needs to take into account the distinction between tacit and explicit knowledge, the fact that any modelling effort should be viewed as a knowledge creation effort, and the enabling conditions of organisation knowledge creation.
5.
The process model quality framework
Our framework, depicted in Figure 4, is based on the following concepts: A Business Process is represented in a Business Process Model that is expressed in a Process Modelling Language (PML). The model is subject to Interpretation from various social and technical actors (i.e. tools). Some of the stakeholders contribute to the modelling, and are called modellers. They reflect their Knowledge of the Business Process in the model. Added to this is a temporal dimension covering that the Business Process, the Business Process Model, the Process Modelling Language (the case of an extendible language), the Actor Interpretation and the Actor Knowledge all may - and will - change as modelling proceeds. Social Actor Knowledge at any point in time includes participant’s tacit knowledge (of the kind that might be made explicit), externalised knowledge available in other forms than a process model, and externalised knowledge in the form of the process model under development
Knowledge articulation appropriateness Modeller language knowledge appropriateness
Social actor knowledge Ks
Knowledge quality
Social actor interpretation I
Modeller knowledge Km
Social pragmatic quality
Physical quality
Business process (domain D)
Semantic quality
Perceived semantic quality
Business process model externalization M Empirical quality
Syntactic quality
Social quality Comprehensibility appropriateness
Process Modelling Language extension L
Technical pragmatic quality
Technical actor interpretation T
Technical actor interpretation appropriateness
Domain appropriateness
Figure 4 Model quality framework
The framework separates quality goals from quality means; model quality goals were formalised in [9] based on viewing the model, the domain, the interpretation and the participant knowledge all as sets of statements. For this formalisation the business process corresponds to all possible statements that would be correct and relevant. Similarly, the model consists of all statements actually made, including deduced statements, and the audience interpretation consists of all the statements the audience thinks the model contains. Quality is defined on different levels, relating the defined set of statements. Physical quality has two goals: externalisation and internalisability. Externalisation means a model is available as a physical artefact, representing externalised knowledge of some social actor using statements of the PML. The physical quality goal of externalisation is closely linked to language quality as means, which we cover separately below. Internalisability means the externalised model is available and persistent enabling the model audience to interpret it. For process support technology this is linked to who the model is made available for; is the model made available to users, and is extendible at runtime, or is the process model primarily “canned" to regulate and control users work practices. In the first case, we have so-called active process models. The modellers include not only software professionals, but also normal end users interacting with the system and its active models.
Hence, stronger requirements on physical quality are likely, both because end users lack
experience with conceptual modelling, and one will want to update the models more frequently due to learning.
The possibility to rapidly update the model (and thus the system) is one of the main advantages with this approach. Also, users are likely to have more in-depth knowledge K of their domain D than software developers who have seldom taken part in the practice of the domain. Consequently, the potential for high semantic quality is greater. Hence, simplicity, adaptability and user-orientedness of the modelling language are even more crucial for active models than for their passive counterparts. Empirical quality deals with error frequencies when a model is read or written by different users, coding, and ergonomics of computer-human interaction. For computer-output based on the process models specifically, many of the principles and tools used for improved human-computer interface are relevant at this level. For graphical models in particular, layout modifications are found to improve the comprehensibility of models. Syntactic quality has only the goal of syntactic correctness, i.e. that all statements in the model are according to the syntax and vocabulary of the PML. As a result of providing user oriented process models, flexible workflow approaches will have to enhance formal syntax means by providing more functionality in the area of error detection, prevention and recovery; especially regarding allowing the continued enactment of completed, errorfree parts. Semantic quality has the quality goals of feasible validity and feasible completeness. Validity here means all statements in the model are correct and relevant to the problem, while completeness means the model contains all statements that would be correct. Since we normally cannot proceed with modelling endlessly, we have introduced the notion of feasibility in these quality goals; i.e. we relax upon validity and completeness by letting the modelling process end when the model has reached a state where further modelling is regarded less beneficial than accepting the model in its current state. Consistency checking here is regarded as means to achieve semantic quality goals of feasible validity and feasible comprehension. Other semantic quality means are formal semantics, model reuse, and modifiability of the models. Actions often involves changing the domain D, and should thus be reflected in the model M. If an action is supported by a CIS with active models, it can be automatically captured, increasing the semantic quality of the active model without extra work for the users. The gap between real and modelled processes has been highlighted as a major inhibiting factor of process support systems and organisational learning [1] alike. Thus active models has a great potential for flexibly supporting knowledge management and process improvement. Perceived semantic quality covers the correspondence between actors’ interpretation of the model and their current knowledge of the domain and has the goals of perceived validity and perceived completeness. Important means to achieve high perceived semantic quality are the same as those for achieving semantic quality, with the addition of participant training and requisite variety, i.e. to maintain several, possibly conflicting, model views since the domain (business process) may not be intersubjectively agreed upon. Pragmatic quality is the correspondence between the process model and the audience’s interpretation of the model and has one goal, feasible comprehension meaning that the model has been understood. Means to increase pragmatic quality include executability, animation and simulation, but also advanced techniques like model transformations, model filtering to present model abstractions from several viewpoints, model translation (to a more user-oriented modelling language) and explanation generation explaining both the model, the meta-model and model execution through the generation of natural language explanation. The core of active models is how models are activated. Activation implies interpretation of the model and corresponding action by either the social or the technical actors [5]. Hence pragmatic quality is paramount. Technical pragmatic quality demands complete models with an operational semantics, while the social pragmatic quality of the models and the
cognitive economy of externalisation (K→M) often demands more flexible, informal approaches. The interaction framework has been proposed to address this challenge [20]. In emergent workflow [7], interactive enactment has enabled simple and flexible models where users need not resolve incompleteness until the time when the flow of work reaches the incompletely specified parts. Social quality has the goal of feasible agreement, where agreement covers agreement in knowledge, agreement in interpretation and both relative and absolute agreement. Feasible agreement does not have to imply consensus, it only implies resolving inconsistencies by choosing alternatives where benefits of choosing exceed the costs of working out an agreement. Means to achieve high social quality include inconsistency handling, model integration and conflict resolution; but also the so far seemingly untapped potential of linking process model tools with argumentation support tools. In systems development, agreement among participants about the requirements is crucial since they form the basis for a lot of detailed technical work that cannot easily be redone. Active models have a more immediate connection to the system and the environment it represents, so users have access also to the domain when negotiating a shared understanding. Social quality is thus perhaps not as important when assumptions readily can be tested immediately in the real world. If an active model is to be reused in another setting, agreement on semantics is more important. Social quality of active models influences the processes of negotiating meaning and domesticating reusable model fragments into the local situation and work practice [19]. In these processes, the ability to represent conflicting interpretations and make local modifications, is just as important as the ability to represent agreement (the end result) in an unambiguous way. Also, since people learn through their work and use of the models, agreement is likely to be partial and temporary. Knowledge quality has the goals of feasible knowledge validity, feasible knowledge completeness and feasible knowledge creation enabling conditions. Knowledge quality means include participant selection, participant training and Nonaka and Takeuchi’s enabling conditions as described in section 2. Language quality is viewed as means to achieve model quality, and may be described from several perspectives in our framework: •
Domain appropriateness denotes the ability of the PML to capture the domain of business processes, i.e. what can be expressed about the domain.
•
Comprehensibility appropriateness denotes how easily the PML can be used and understood. It covers aspects like language ontology, composability, grouping of statements, external representation, expressive economy etc. [9,17].
•
Technical actor interpretation appropriateness covers to what extent the PML is formalised, a necessary requirement for process enactment but also a necessity for simulations and formal (tool-supported) analysis of the process model.
•
Knowledge articulation appropriateness covers how relevant process knowledge may be articulated in the PML. This is linked to concepts like “articulation work” and “situated action” and the debate in CSCW whether actual work can be captured in a model, or whether such a model always will be a post-hoc rationalisation [3,18].
•
Modeller language knowledge appropriateness, i.e. to what extent the participants know, or are able to learn the language.
6.
Conclusions and future work
Updated perspectives and views on knowledge result in updated views on conceptual models and process models. A conceptual model traditionally has been claimed to be “a model of the real world”, or (taking social construction into account) “a model of someone’s perception of the real world”. Our updated point of view is that the process model (or executable conceptual model) is a representation of knowledge and indeed even may be considered as an arena for knowledge creation. We have proposed a framework for the understanding of quality related to process models in a flexible process support context. Being more than just a list of partially overlapping desirable properties, our systematic framework separates model quality and language quality and establishes goals and means to achieve models of high quality. A previous version of the proposed framework was used for the evaluation of workflow products in [4] by using the framework for evaluating the various products potential for creating and maintaining high quality models. For this evaluation, the distinction between static and dynamic process technology was crucial. Static products maintain a strict separation between build-time - when the process model is built, and run-time - when the model is enacted. Dynamic products support dynamic modifications to process models at runtime and may also support enactment of incomplete models. If we accept the recent theories on knowledge creation, it is not likely that static workflow products are able to sustain process models of high quality since newly created knowledge cannot be added as the process is enacted. The dynamic products may be utilised to sustain models of high quality through increased modifiability and ability of capturing new relevant knowledge, but as a consequence will be even more demanding regarding pragmatic quality, variety in the form of fragments usable as model templates and tool supported error detection and correction. Future work on the proposed framework includes deriving useful metrics from its formalisation in order to further operationalise its use.
7.
References
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