The articulation rate in read speech and in spontaneous speech was examined in seven variants of French: Results showed that Swiss speakers articulate at a lower syllable rate than French speakers especially Parisian speakers and Belgian speakers, independently of the speaking style reading or conversation. This finding confirms that articulation rate varies regionally. Moreover, results revealed that extra-linguistic and linguistic factors, such as the speaker's age and gender, the speaking style, the utterance length and the articulation rate of the adjacent inter-pause chunk, also affect articulation rate.
Swiss speakers of French articulate slower than Standard French speakers. Belgian speakers of French articulate as fast as Standard French speakers. Belgian speakers of French articulate faster than Swiss speakers of French. Males articulate faster than females. Articulation rate decreases with age. Les pauses silencieuses dans la parole de Fipsvox: English 20 German 9 French 8. Project Page Feedback Known Problems. In multiagent organizations, a role specifies the necessary capabilities for an agent to collaborate successfully with other agents.
In addition, the channels of communication denote the possibilities and requirements for knowledge transfer between the agents. In order to successfully work together, agents must be able to reason about the capabilities of their counterparts and to integrate them into an appropriate organizational structure for the tasks at hand [39,28].
This leads to the challenge of identifying the required meta-knowledge and providing it to the agents to facilitate their coordination. Researchers of both disciplines face the question of how knowledge should be shared or divided among team members to allow for optimal collaboration. In that context, psychologists can utilize methods from business informatics e. This will open up new research possibilities for analyzing teamwork at an organizational level, at which collaboration not only occurs within teams but teams also interact with each other in a multiteam system.
Such large-scale settings are diflicult to cover in classic laboratory experiments or field studies [47]. This problem can be overcome by combining these methods with computer simulation studies [5]. April in Karlsruhe, Deutschland limits the latter's usefulness for explaining human behavior [38]. A Job-Shop-Scheduling approach Before simulating team cognitions, teams need to be formalized, which includes a formal representation of team members and their knowledge. Therefore, this paper introduces a model based on a job-shop-scheduling approach. In order to map the requirements of modeling team cognitions, the job-shop-scheduling is defined as follows [1,4]: The key requirement for this task allocation is knowing which jobs need to be clone and which machines can process them.
In fact, different machine configurations can be used to present different knowledge structures in teams. Therefore, job-shop-scheduling is an interesting setting to analyze different team configurations in an agent-based model as depicted in Fig. Each machine represents a team member and all machines together describe a whole team. A machine is able to process a predefined set of tasks, which is Seite 31 von WM - 9te Konferenz Professionelles Wissensmanagement 5. Task n Task 1 Task April in Karlsruhe, Deutschland models external effects is created.
A machine is deactivated by an event until it becomes activated by an event again. In the time of deactivation, a machine cannot perform any tasks. Therefore, all jobs which are currently assigned to this machine will be reassigned by the manager. If there is no other machine with the needed qualifications, the job cannot be finished. Following this, the experiment setup with essential parameters and their configuration is shown in Section 4.
The results of the simulation are illustrated in Section 4. Data model of the implemented multiagent-based model The key components of this implementation are the machines and the jobs. Each job consists of a specific number of tasks, which need to be processed in the exact order. The tasks are processed by machines.
Each machine receives tasks via the manager.
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The manager has a list of jobs and is responsible for their scheduling. The scheduling itself is a matching of the next tasks of those jobs and the capabilities of the machines. If a machine finishes a task and the job has unfinished tasks left, the job is transferred to the manager again and reassigned.
A deactivated machine finishes its last task and sends the unfinished ones to the manager. Moreover, this machine signals the manager that it has been deactivated to avoid new task assignments. In the case of activation, the machine notifies the manager of its activation.
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April in Karlsruhe, Deutschland 4. In order to ensure the comparability of simulation results a test case is defined. The specific parameter configuration which was used for the test case is described for each parameter. This parameter quantifies the jobs existent in the simulation, which will be created at simulation start. The test case contains jobs. The task class defines the amount of different tasks available.
Each task has to be processed by minimum one machine. The different task classes a machine can handle characterize the knowledge of this machine. In the test case, ten different task classes were implemented and indexed. The task quantity defines the number of tasks a single job can contain. A job must have at least one task. In this simulation a job contains ten different tasks.
The order in which a job has to be processed is determined by this variable. The order needs to be defined for every job. The order was chosen randomly in this test case.
The process duration of a single task is defined by its task class. In this test case, the task duration is defined by the index of the task class. A task of the first task class has the shortest process duration 5 milliseconds and a task of the last task class has the longest process duration 50 milliseconds. The durations of the tasks in between are linearly increasing. The quantity is set to ten machines, in the test case. The knowledge of a machine is defined by their task process capabilities and needs to be set for every machine. Moreover, the difference between a generalist and a specialist knowledge structure is of interest.
The specific knowledge allocation for every machine is shown in the next Section. This parameter describes how many tasks a machine can store. This ensures that all machines are equally working on capacity. In this case, a machine can store 10 tasks. The retooling time defines how long a machine needs to reconfigure in order to work on a task from a different class as the previous one. The retooling time was set to the arithmetic mean of the task durations. The values for job quantity, task classes, task amount and machine quantity were chosen based on their dependencies and a manageable simulation duration.
The test case configuration is defined based on their necessary complexity to show effects of different knowledge structures as presented in the next Section. The machine knowledge is set to six different configurations and the simulation result is measured as Seite 34 von WM - 9te Konferenz Professionelles Wissensmanagement 5. April in Karlsruhe, Deutschland the overall makespan of jobs. The knowledge configurations can be divided in specialist and generalist structures. The generalist knowledge structures are either comprehensive or partial.
The partial configurations contain every task class two, three or four times.
The remaining knowledge configuration contains each task class twice as well as a deactivation event for a single machine. Makespan as the result for simulating different knowledge structures over time The longest makespan can be observed for the generalist knowledge structure in which every rnachine is able to perform every task class. The retooling time is decisive for the long duration. The shortest processing time is generated by the specialist knowledge structure because of less overhead.
Raising the level of redundancy, the overall makespan is increasing, due to the fact that a higher level of redundancy enhances the probability of different task classes. Furthermore, there are minor variations of results, which implicates a low variance of the simulation. This shows that the knowledge structure has a much higher impact on the makespan than the randomness of the task assignment.
April in Karlsruhe, Deutschland Compared to human team structures, the computed results are plausible. The agents process tasks slower in case of varying assignments which can be observed in human teams as well [18,41]. The results ofthe simulation show that divided knowledge structures are more efficient than shared ones, which coincide with psychological research [9]. Hence, the main conclusion of this simulation is the dependency of knowledge structure and makespan, which results in an optimization problem in finding a suitable team structure [27].
The model can be used to test different scenarios for knowledge configurations and simulate their effects. On the basis of these effects, assumptions about teamwork can be evaluated. These should consider the dichotomy of efficiency and resilience of different knowledge structures as well as risk assessments of possible knowledge loss.
The next Section describes possible extensions which improve the model to simulate real life scenarios. These include its application to real-world scenarios, a more detailed representation of knowledge and capabilities, communication and adaptation processes within a team, as well as a team's embedding in an organizational context.
To enhance the realism ofthe formal model, it is necessary to include real-life scenarios which requires the analysis of team work in practice. By conducting of field studies and laboratory experiments, the psychological insights will be gathered. Moreover, employees learn new skills, which need to be managed and the learning process has to be structured. Summarizing, a description language to formalize a suitable team cognition model is needed which is a foundation for modeling real-life team work. The representation of real-life scenarios requires an agent-model, which is similar to humans.
This knowledge has to enable reasoning about a team member's environment and perceptions. In order to achieve this, a formal ontology, which is commonly used in artificial agents, should be used as a knowledge base. An ontology enables sharing more easily because of a mutual understanding. April in Karlsruhe, Deutschland an ontology which is able to distinguish between assertional and terminological knowledge [2]. In addition, similar to humans, agents need a learning capability, which allows the acquisition of new skills.
In the previously described model, the communication between agents is limited to task assignments by the manager. However, communication between different team members is essential. In order to standardize this communication an ontology for agent communication need to be developed. In addition to architectural agent requirements, a team needs to adapt their work and communication in case of inefficiency or organizational changes. These measurements should lead to an improved team simulation and can be used by the agents themselves to enhance teamwork. Afterwards, the simulation of multiple teams and whole organizations.
This is useful to identify complicated team configurations, where, for example, team members retire and their knowledge becomes unaccessible. In order to achieve this, an interdisciplinary perspective of knowledge management has been applied, which integrates the theory of team cognitions from psychology with formalization, modeling and simulation methods from business informatics. The simulation results show the appropriateness of job-shop-scheduling as a model to analyze different knowledge structures and to optimize team cognitions.
In the test case for the developed simulation, the specialist team processes the given tasks almost two times faster than the generalist team. Moreover, the evaluation has shown that the knowledge structure of teams is decisive for their performance. Hence, the model is a promising first step towards the vision of simulation as a method for designing and evaluating work processes in dynamic environments. However, to achieve that goal, the classic job-shop-scheduling scenario must be extended with a more elaborate model of team cognitions, agent capacities, and adaptivity to dynamically changing environments.
Such an extension has to allow for a more adequate representation of real-world teamwork processes Seite 37 von WM - 9te Konferenz Professionelles Wissensmanagement 5. April in Karlsruhe, Deutschland among humans in order to facilitate the design and evaluation of knowledge management strategies. Consequently, the next step for further developments is analyze teamwork in practice and to derive capabilities, team structures, and simulation scenarios in various domains like administration, logistics, or health care.
Together with a deeper integration of psychological theory, this will lead to the formulation of requirements for extending the mental model for the agents' decision-making. The validated agent-based simulation can then be used to analyze and optimize flexible knowledge practices in human teamwork. ORSA Journal on computing 3 2 , 2. Springer, Berlin 3. The disjunctive graph machine representation of the job shop scheduling problem. European Journal of Operational Research 2 , 5.
Complex systems methods for studying multiteam systems. Agent based social simulation: A computer science view. Journal Artificial Societies and Social Simulation 5 1 7. The cognitive underpinnings of effective teamwork: Journal of Applied Psychology 95 1 , 8. Distributed problem solving and planning. MIT Press, Cambridge 9. Information and Communication Overload in the Digital Age. IGI Global in press Team mental models of expertise location: Validation of a field survey measure. Managing information overload in virtual teams: Effects of a structured online team adaptation on cognition and performance.
European Journal of Work and Organizational Psychology 24 5 , The concept of information overload: The Information Society 20, Kqml - a language and protocol for knowledge and information exchange. Holonic multiagent systems - theory and applications. Progress in Artificial Intelligence, pp. Springer, Berlin Concurrent and distributed shortest path searches in multiagent-based transport systems.
The emerging conceptualization of groups as information processors. Psychological bulletin 1 , Communication, learning, and retrieval in transactive memory systems. Journal of Experimental Social Psychology 34, Past, present and future. Theories of team cognition: Routledge, New York A survey of multi-agent organizational paradigms. The Knowledge Engineering Review 19 04 , On computer simulation as a component in information systems research.
Global Perspectives on Design Science Research, pp. Final report of the Industrie 4. Flexibility of multiagent systems. Theory and Applications in Enterprises, chap. Enhancing the effectiveness of work groups and teams. Psychological Science in the Public Interest 7 3 , Teamwork in multi-person systems: Ergonomics 43 8 , Artificial lntelligence , Organizational learning and forgetting: The effects of turnover and structure. From theory to practice. The seasalt architecture and its realization within the docquery project.
Annual Conference on Artificial lntelligence. The contingent effects of transactive memory: W hen is it more beneficial to know what others know? Management Science 52 5 , Multiagent Coordination Enabling Autonomous Logistics. A new approach for theory building in social psychology. Personality and social psychology review 11 1 , Adapting communication vocabularies using shared ontologies.
Cognitive social simulation incorporating cognitive architectures. Emerging capabilities in intelligent agents for flexible production control. Advanced Engineering Informatics Journal. Agent-mediated knowledge management, pp. Theories of group behavior, pp. Springer, New York Direct and indirect effects of team learning on team outcomes: A multiple mediator analysis. Theory, Research, and Practice 17 4 , Reasoning about Rational Agents. An Introduction to MultiAgent Systems. Context-awareness is a common feature of contemporary knowledge management systems KMS. The intended effect of context-awareness is to tailor appearance, functionality and behavior of KMS to the needs and individual preferences of knowledge workers and to contribute to flexibility in organizational KMS use by supporting the integration of knowledge use into work processes.
The variability of organizational and individual tasks is essential for understanding context in organizations. Context modeling methods have been proposed which analyze this variability and identify context elements. This paper investigates whether there are recurring context elements across organizations and how these recurring elements could be used to improve context modeling methods and KMS configuration processes.
Context modeling, knowledge management systems, variability modeling 1 Introduction Context-awareness has emerged from a special feature of niche applications to a characteristic of most knowledge management systems. The intended effect of context-awareness is to tailor appearance, functionality and behavior of knowledge management systems KMS to the needs of knowledge workers and to contribute to flexibility in organizational KMS [2] use by easing the implementation of knowledgeintensive work processes.
The importance of context-awareness in KM is largely undisputed. Context-awareness basically aims at understanding all relevant information required for knowledge use of an organization or an actor the context and applying this understanding in organizational and technical KM solutions and systems cf.
Such recurring elements could indicate potential for improving both, functionality of KMS by extending and emphasizing functionality and content relevant for the recurring contexts, and context modeling approaches by adding aids or guidelines to early identify recurring context parts and thus making the modeling process more efficient. The work builds upon an earlier developed method for context modelling and on experiences from knowledge management projects in different kinds of organizations.
April in Karlsruhe, Deutschland our perspective, the variability of organizational and individual tasks is essential for understanding context. We argue that making context elements explicit by capturing them in a context model eases the configuration and adaptation of generic knowledge management platforms to solutions for specific organizations more efficient. Furthermore, the paper aims at motivating and demonstrating the modeling and analysis of context in knowledge management.
The paper is structured as follows: Section 3 summarizes the research approach used. Section 4 presents cases of organizational knowledge management which were subject to context modeling and identification of recurring context elements. Section 5 presents selected context models and recurring context elements which were the result of context model analysis. Section 6 summarizes the work and draws conclusions. However, design and development of context-awareness still require substantial engineering work, i. One reason for this probably is the variety of interpretations of the term context in the area of engineering [3, 5].
An essential part of developing context based systems is to analyze and conceptualize the elements of the specific context required for the application under development, including their dependencies and mechanism of use. The context is also required during runtime of a context-aware system, i. At least two different perspectives on KMS have to be differentiated: KMS from an organizational perspective: These systems describe how to establish systematic KM in an organization in terms of activities and organizational structures required.
KMS from a technology perspective, i. In this area, the architecture proposal for such systems of [4] and the differentiation between various knowledge services as components of this architecture is often applied. In both perspectives, it is acknowledged that context plays an important role, either for the process of individual knowledge generation from information, the process of understanding [6] or the creation of new knowledge [8]. Kapitsaki and Venieris [12] describe how to develop context-aware web applications using MDD techniques in six steps.
Other related work has a focus on context-based systems and describes requirements and ways of developing context models for specific application without offering a general view on the method part e. This works primarily focus on the conceptualisation of context, i.
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Also the field of requirement engineering RE has made valuable contributions. Lapouchnian and Mylopoulos [16] propose a process to explore contextual variability and analyze effects on goal models. April in Karlsruhe, Deutschland ating in various contexts. Both proposals are helpful for identifying and formalising the contextual factors.
On previous work addresses context modeling in capability management and proposes a component-oriented method for this purpose [18]. Furthermore, there is work on identifying context elements for applications in e-learning [17]. None of the proposed methods or approaches has a specific focus on knowledge management or KMS. All analysis and modelling followed the same methodology see section 5. The results of context modelling in the cases was analysed for recurring elements or partial models see section 5.
Furthermore, the context modelling showed the usefulness of explicitly capturing and modelling context. Since our objective is to investigate recurring context elements in knowledge management our focus has to be on cases with data sources containing very detailed activity reports and rich case descriptions. As this type of report is quite sparse in scientific literature on context modelling methods see section 2.
For these projects, the original project documentation and the personnel involved in the project are available. Analysis of the projects included study of the documentation, discussions with the researchers involved and analysis of models or specifications, if available. Table 1 shows the list of projects analysed. These projects were chosen because of the availability of documentation and accessibility of the researchers involved.
The cases form the basis for the context modelling and context model analysis performed in section 5. When selecting these cases for presentation, the objective was to achieve a certain heterogeneity regarding the type of organization distributed organization vs. The seat comfort products mainly include seat heater, seat ventilation, climate control, lumber support and head restraint. In this application case, analysis of requirements for knowledge management and collaborative engineering support, knowledge modelling and development of a knowledge management infrastructure and application of this infrastructure in everyday work was performed.
The focus was on the advanced engineering unit, where product development tasks are concentrating on pre-development of new concepts and new materials. Development of products included elicitation of system requirements based on customer requirements, development of functional, design of logical and technical architecture, co-design of material, electrical and mechanical components, integration testing and production planning including production logistics, floor planning and product line planning.
The process was geographically distributed involving engineers and specialists at several locations of the automotive supplier and sub-supplier for specific tasks. A large percentage of seat comfort components can be considered as product families, Seite 45 von WM - 9te Konferenz Professionelles Wissensmanagement 5. April in Karlsruhe, Deutschland i. In this context, flexible product development in networks with changing partners on customer and subsupplier side were of importance.
Modelling product development knowledge in the industrial case was performed according to the active knowledge modelling [20]. The work included two cycles. The first cycle focused on capturing organizational knowledge and best practices for networked manufacturing enterprises with task patterns. The second cycle focused on integration of product knowledge. Figure 1 shows an example for a knowledge model developed in the project. Following the active knowledge modelling philosophy, the developed knowledge models were also made executable in the KM infrastructure see, e. Above the process flow, objectives and documents which are input are included.
The arrows indicate relationships between processes, roles, systems and documents or objectives. On the lower part, the roles involved in the process are included grouped at the left hand side and the IT systems and tools are shown. The major challenge experienced by the municipality and motivating the knowledge management project were budget cuts and reduction in staff size. The municipality did not have a general knowledge management strategy but encouraged the different departments to address challenges in this field individually. April in Karlsruhe, Deutschland consideration experiences that citizens demand better and faster services from public administration and at the same time the number of civil servants in the department is continuously reduced no replacement in case of retirements.
The department has no established structures or processes for KM, but shared work procedures among the civil servants. For publishing and searching these work procedure, a MS-sharepoint infrastructure is provided. As a central element towards introducing a knowledge management solution in the department, it was decided to develop knowledge maps following the approach proposed by Eppler [19]. Figure 1 shows the knowledge application map as an example illustrating the results of the project. Knowledge application map developed in municipality case The aims of the knowledge management project was to make competences explicit which are required for the work procedures, share expertise between different civil servants for the different services offered to citizens.
From an individual perspective of the civil servants, the knowledge maps are not completely developed as some individuals are hesitant to disclose their competence.
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The civil servants of the department can be divided into supporters of the approach the majority and opponents of the approach. April in Karlsruhe, Deutschland 5 Context Modeling and Analysis As indicated in section 3, we performed an ex-post context modeling and analysis of knowledge management cases. This section briefly presents the method for context modelling applied 5. The results are presented in two parts: The actual context model development was performed only for the two cases introduced in section 4 and is presented in 5.
The purpose of the first step is to identify user groups and how their ways of using the KM system differ from each other. The scenarios are captured with the processes supported by the system, the information input and output, possible connections to other systems and processes, and the integration of resources. The second step is probably the most important one.
A context model has to include in what situations and on what inputs or events what kind of adaptations in the context-aware system should be made. In order to determine cause and kind of variation, the variation aspects the cause of variation and the variation points where the variation occurs are investigated. The context model represents the variations aspects and variation points together with the required adaptations in a system-readable model that can be used to configure knowledge management systems.
Here, the context model would be used to extend what Maier refers to as knowledge structure and enterprise-specific vocabulary. This knowledge structure is represented and maintained in the integration layer, often represented as semantic net or ontology, for semantic integration of enterprise knowledge sources. Furthermore, it forms the basis for the knowledge services search and publication, e. With the elements of the context model integrated into the knowledge structure, these elements also become available as parameters on the personalization layer. With respect to scenario modeling, some cases already included the scenarios as part of the project work.
An example is the automotive supplier case cf. Figure 1 can be considered as an example for a scenario model. April in Karlsruhe, Deutschland The most interesting aspect of the first two method steps for our research question is the identification of variation aspects and variation point, as these two aspects lead to the context elements. The identified variation aspects and variation points are shown in table 2. Variation points proved to be too case specific and not suitable for an analysis of recurrence. In order to ease identification of recurring variation aspects, we sorted them into variation related to the individual knowledge worker, to the organizational context and to inter-organizational aspects if appropriate for the case.
The result of this classification step is summarized in table 3. The table also shows how many times each variation aspect and point occurred in the cases which is indicated by the numbers in brackets. The organizational and inter-organizational variation aspects, which are of high interest for organizational knowledge management, can also be sorted into product, process, organization structure and resource-related aspects: April in Karlsruhe, Deutschland actual work processes are required. But some elements are more unexpected, for example the importance of location or time-related adaptations.
The purpose of this step was to investigate whether not only context elements recur but also structures among these context elements recur. Such recurring structures could be an indication that the development of context model patterns could be possible. Furthermore, the context models illustrate what kind of machine-readable models are the result of the context modeling method. The context models were developed with a modelling tool which takes care of a computable representation following a defined meta-model. CDT also allows for visual representation of variation points in process models.
Figure 3 shows the context model for the automotive supplier case developed based on the analysis of the scenarios. The individual and organizational perspectives are represented as different context sets see right hand side of the figure. In the context set, the context elements are represented with their ranges relevant for the set. The context elements are made explicit in the center of the figure. Their attributes are shown in the property box at the lower part of the tool. Each context element is on its left-hand side linked to the variation aspect.
With the elements of the context model integrated into the knowledge structure, these elements also become available as parameters on the personalization layer, i. The comparison of the two models showed structural patterns with recurring elements linked to the variation aspects. An example is the variation aspect regulation which in both modeled cases is connected to local regulation and case classification.
For this purpose we performed an ex-post context modeling and analysis exercise on a number of KM cases from previous work. Variations aspects and variation points were elicited for all cases and context models were developed for two selected cases. The work confirmed that it is important to understand the variability of organizational and individual tasks for understanding context.
The context modeling method used proved — although developed for the area of portal configuration in e-learning — applicable and useful for knowledge management. The analysis of the cases showed that recurring elements of context models can be identified see section 5. These recurring elements concern variation aspects, such as process, product structures and locations. These findings have implications for the context modeling methods and for KMS. Context modeling methods should be complemented with aids or best practices that inform the modeler about typical context elements recurring in a certain field of analysis, for example provided as a guideline.
Such guidelines could help to reduce the time for context modeling. April in Karlsruhe, Deutschland The biggest limitation of our work is the small number of cases investigated. Future work has to address this shortcoming by using the context modeling perspective on more knowledge management cases in order to confirm the findings of this paper. Conceptual Modeling - ER Today, in more and more enterprises digital services are offered to employees.
An inherent characteristic of digital service is the self-service concept. Based on findings from a case study and academic literature, it is argued for the importance of employee knowledge when designing digital services. It is revealed that existing service blueprinting approach are not capable of adequately reflecting knowledge gaps. To fill this research gap, the paper proposes an agenda for future research. Digital service, e-service, service blueprinting, knowledge, skills 1 Introduction Rapid advances in information technology IT have paved the way for internal service organizations e.
Such a digital service can be defined as a process of providing any service through technology-mediated delivery channels, including the internet, intranet and mobile devices [3]. Technology-mediation as the defining characteristic of digital services generates the inherent characteristic of digital service as self-service contributing to the digital service experience [4]. Hence, service designers need to be aware of knowledge gaps: While knowledge gaps can cause unnecessary costs in internal self-service contexts, when offering external self-service they can lead to a loss of income.
April in Karlsruhe, Deutschland So far, a wide range of approaches for designing and analyzing digital services has been introduced by academic literature [11]. However, as discussed in a later section of this paper, these approaches fall short in highlighting potential knowledge gaps required knowledge and skills not possessed by involved actors causing process failures and employee intervention.
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Drawing upon case study evidence and literature findings, it is argued that this information is fundamental when designing digital services and deciding to whether transferring task-performance to internal customers. The present position paper argues for a research agenda that aims to stimulate future research on this topic to fill this research gap.
The digital service offer was designed in the form of a self-service portal, which enables the internal customers to deploy Infrastruce-as-a-Services e. However, regarding the former anticipated benefit, the digital service offer failed to succeed. Virtual machines deployed by the internal customers were often excessively customized regarding CPU, storage and memory and misused for undesired purposes and use cases. In consequence, decision makers of the IT department had to intervene and perform recovery tasks, such as the reconfiguration of oversized virtual machines and migration of misused virtual machines into separate cloud environments, differing from traditional service delivery tasks.
These interventions were necessary to prevent a rapid decrease of limited IT resources and violations of agreed software license terms. Evidence from the case study. April in Karlsruhe, Deutschland plans to redesign the digital service offer and to consider task requirements and characteristics of the internal customers, in terms of establishing control mechanisms for the service process and providing adequate training and support for employees of other departments.
Evidence from academic literature. Academic literature also highlights the importance of customer knowledge and skills for the success of digital service offers see Table 2. Based on Media Richness Theory and Channel Expansion Theory, in [9] it is argued that customers create most value from self-service offers when they are used for simple and recurrent tasks. The authors of [9] further claim that customers are also able to derive value from complex tasks, in case they are confident in their own knowledge and skills.
Overall, service organizations need to be aware of what knowledge and skills their customers require and whether they already acquired it [8]. Table 3 depicts the resulting list of relevant approaches. Examined approaches and level of customer knowledge consideration. Though the approaches described in [13, 14, 19] do allow for the specification and assignment of individual actor roles, it is not possible to describe these roles in terms of their possessed knowledge and skills.
Also, the complexity of the tasks comprised by a modeled service process cannot be described when making use of these tools. The approach in [15] introduces the concept of competence propositions presented by service providers and evaluated by service customers in a given market. The matching of two competence propositions results in the happening of a service episode. Although the concepts of competence propositions and matches are promising, they do not hold for individual service processes, but only for service systems and their interactions. In [16] a Resource Mapping Framework is introduced, which aims to support service designers in identifying changes in resource requirements for the service provider and customer when shifting the service boundary towards either self-service or super-service.
However, the framework is very abstract and general in the sense that it does not provide concrete methods or tools to describe such required knowledge and skills as well as changes in these. The Structured Analysis and Design Technique allows for the assignment of actor roles to specific process tasks. Actor roles that are assigned to process tasks can be incorporated into job descriptions, depicted through separate models.
As such, the focus of these models is not on the knowledge and skills possessed by the employees of the service organization, but on tasks performed by individual roles jointly or separately. Adopting Public Value Process Mapping [18], decision makers are able to derive key actions relevant for the realization of public value. Such associated actions can involve the establishment of specific knowledge and skills for the involved actors. Nevertheless, resulting models rather reflect means-end chains instead of service processes. April in Karlsruhe, Deutschland Identification and clustering of knowledge and skills relevant for digital service delivery.
Future research should aim to identify specific knowledge and skills that are of importance for the production and delivery of digital services. It should be investigated whether this knowledge and skills could be clustered along different service categories, types of process tasks and involved employee roles. Further, research is required to determine how employees can acquire such required knowledge and skills and service providers can ensure the adequate performance of service process tasks.
Scholars are encouraged to identify themes of knowledge and skills relevant to digital service delivery. Design of meta-models allowing for the representation of co-production task and employee characteristics. Therefore, meta-models should be designed describing the necessary constructs for knowledge and skill representation and their interrelations. The meta-models of the approaches in [16, 18] could serve as a basis for the design of appropriate meta-models.
For instance, they could be combined with the meta-model of the well-accepted Service Blueprinting approach. Development and evaluation of service blueprinting approaches supporting the identification of knowledge gaps. Based on designed meta-models, new service blueprinting approaches should be developed and evaluated. Such approaches should enable decision makers to design digital service offers, identify potential knowledge gaps of involved employees and derive training and support programs to ensure task-performance and effectiveness.
Evaluations in the form of case studies or illustrative scenarios should be performed to demonstrate the theoretical and managerial contributions of the approaches. In this paper, the importance of employee internal customer knowledge for the success and design of digital service offers is presented. Evidence were collected from a case study and self-service literature. It is further presented that current service blueprinting approaches do not adequately allow for the identification of knowledge gaps.
To tackle these shortfalls, we present a research agenda. Yun Kyung Cho, Menor, L. Engaging Customers in Coproduction Processes: Employee Self-Service Technology Acceptance: The Value of Self-Service: Empirical Evidence from a Call Center. Springer International Publishing Evolving to a New Dominant Logic for Marketing. The Science of Service Systems. From self-service to super-service: How to describe your service: An invitation to the structured analysis and design technique.
Mapping public value processes. An intentional agent perspective. Recent corporate surveys confirm the significance of experience, but also imply a reluctance in engaging in systematic support. By not delivering on their promises, KM investments are - as consequence - not getting the necessary acceptance from the work force and its leaders. However, such promising scenarios have not materialized yet. Current KM market configurations, however, exhibit a range of potent barriers which prevent Knowledge Workers as well as Knowledge Societies from accessing the full potential of digital opportunities.
The paper approaches the notion of Experience Management from an alternative angle by introducing a novel Personal Knowledge Management Concept and System which incorporates a range of renowned methodologies: Although the primary goal is to strengthen individual sovereignty and personal applications, it is not meant to be at the expense of Organizational KM, but rather as the means to foster a fruitful co-evolution [5,6].
They continually strive to understand the world about them and modify their work practices and behaviors to better meet their personal and organizational objectives. Memes were originally described by Dawkins [16] as units of cultural transmission or imitation e. They are cognitive informationstructures that evolve over time through a Darwinian process of variation, selection and transmission with their longevity being determined by their environment.
April in Karlsruhe, Deutschland Figure 1: Dynamic Meme Modifications 8Rs and Memeplexes, based on [2, 20] At the roots level, the PKMS relies on the digital re-use of captured unique basic information units ideas, memes, or business genes [19] by embedding them in digital documents via structural references [21]. The three basic forward modifications together with linkages between memes, sources, and hosts allow for creating ever more complex information units from memes over memeplexes to knowledge assets.
The trajectories established enable the backward tracing from a meme to their prior version or to other ideas absorbed usually shown as references , to additional augmented details e. This paper will use these three basic modification types as the point of departure to briefly introduce some of the relevant key features and philosophies of the PKM approach, before focusing on its implications on experience management.
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April in Karlsruhe, Deutschland 1. As Simon already noted over four decades ago: This redundancy originates from the rising share of content which has been replicated in a multitude of digital documents instead of digitally embedding and reusing content via structural references. Due to the captured relations, any meme can similarly be tracked and traced by creating as-built genealogies either back in history to locate prior usage or an original author or forward into the future to follow-up on subsequent uses and citations [23].
Compared to industrial inventories, however, a meme stored in PKMS repositories as an atomic information-structure is not consumed when utilized or transferred. As a virtual copy, it can be employed infinitely for integration in any type of authoring and sharing activity independent of time, distance, disciplines, and purposes. These memes comprise not only technical artefacts such as texts, code, charts, audio and video messages , but also classifications as well as the descriptors of people, policies, decisions, relations, and even less tangible things like goals and concepts [23].
Since anything can be expressed in a standardized memetic format and combined, linked distinctive memes of diverse disciplines are able to materialize as a single unified knowledge repository. If these user-defined content and relationships are shared, an extensive mesh of associative multidisciplinary trails of alternative pathways emerges [33].
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Its purpose is to maintain traceable as-authored genealogies of memes and knowledge assets in support of the cross-cultural and cross-disciplinary PKM concept. The methodologies applied are rooted in the paradigms of Design Science Research6 DSR , a claim validated in a dedicated article against parameters of relevance, utility, rigor, and utility.
By reducing redundancy and entropy and widening access and participation, attention management can be strengthened, opportunity divides narrowed, and unsustainable developments defused [1]. Since , over thirty publications have been presented and received feedback from a wide range of multi-disciplinary conferences and journals and have shown that the novel trans-disciplinary approach and scope of anticipated outcomes offers appealing opportunities for stakeholders engaged in the context of curation, education, research, development, business, and entrepreneurship.
A study among KM experts worldwide, hence, stresses the growing importance of enabling interactive KM technologies with priorities set as: A differing reason cited for failing KM systems is their pre-dominant premise of knowledge-as-a-resource which exists prior to practice or is capturable and storable independent of practice to be transferable between people without variances. However, it also revealed that little is done in terms of systematic support if it implies additional effort or by IT possibly due to the articulated wide-spread disenchantment with modern KM methods [48].
But, by not delivering on their promises, KM investments are also, of course, not getting the necessary acceptance from the work force and its leaders. Professionals will be increasingly eager to carry - while moving from one project or responsibility to the next - their particular Personal KMS version with them, presenting them with the sovereignty to develop their personal expertise systematically and sustainably and to voluntarily share it with associates and institutions close to them. Current KM market configurations, however, exhibit a range of potent barriers [51] which prevent Knowledge Workers as well as Knowledge Societies from accessing the full potential of digital opportunities [5,52].
Since the knowledge-as-a-resource view has been briefly covered in sections 1. The relevant elements are organized into a plan of action proceduralized context to carry out related practices in a systematic manner in order to arrive at a satisfactory solution. Failures might occur due to misjudging situational complexities, dynamic events and interventions, or conflicts among collaborative agents. A similar problem or assignment in the future is now able to trigger the utilization of the procedure which, however, needs to be adapted or tailored contextualized to be integrated in the appropriate practices proceduralized context in order to fit the specific work context as described in the second paragraph of this section.
April in Karlsruhe, Deutschland Figure 2: Experience, Context, and Procedures [based on 54,55]. To guide this endeavor, the user might look for an appropriate methodology to follow, capture or select if already available in the PKMS repository the methodological steps and link them to the stored 1A-memes. April in Karlsruhe, Deutschland subsequently followed by self-evaluations and reflections, depicted in figure 3 as 0U codified, abstract, concrete, semi-embedded memes.
People are observed or questioned via field research. The transcribed notes are handled in the same manner as 4I above. Own ideas, insights, and interpretations are formed, captured, or revised according to the eight Rs stipulated figure 1. Apart from authorship, these activities also incorporate project scheduling and management, including 1 searching, identifying, finding, contacting, observing, screening, filtering, evaluating and documenting of sources, 2 reassessing memes and relationships already captured in light of potential new hypotheses, assumptions, and information needs and gaps, 3 maintaining to-do-lists and progress reports concerning the quantity and quality of work outstanding with deadlines and responsibilities for completion at any aggregation level of memes or relationships.
Paper drafts emerge and evolve, further facilitating a progressing learning curve leading potentially to the revision of initial plans and intentions, including the realization that facts, theories, or methods needed are not available or too costly to obtain and have to be developed by oneself or with expert assistance. Responses from peers or evidence provided can further strengthen the draft paper or report. To support self-reflection, retention as well as the creative conversations taking place in a PKMS user community, keywords, topics, scripts, roles, and meme types as used by Rs 2, 4, 6, 8 in figure 1 provide opportunities to classify and cluster any meme and content9.
This tacit internalized know-why has to be made explicit externalized for better retention later or to be more useful to others by placing the items into context, formatting, interpreting, or summarizing them. It means creating links to the PKMS classification systems also by adding own keywords or by adding notes to the contents and references.
This process of integrating the memes into relevant frames of reference topics and scripts or documents knowledge assets and containers further advances memeplexes and creates knowledge. April in Karlsruhe, Deutschland Figure 3: Further work suggestions for the road ahead might be included in the paper or to be captured in the repository for following up in the future.
Additionally, the user might like to amend or newly establish a procedure for future reference bearing in mind the needs for decontextualization alluded to. They cover knowledge management activities as well as other areas, for example, criteria for accreditations or performance management appraisals for assessing institutions, programs, and staff. In these conversations with self, the knowledge under review is biographically self-determined and presents itself as a former state of personal extelligence captured […] in external extensions of the individual knower's mental storage capacity.
As noted by Mintzberg [61], the continuing fixation on the outdated book-age paradigm still compels us to provide linear accounts of a nonlinear world. The novel PKM concept and system offer an alternative. Every shared knowledge item becomes available in its unique meme-representation ready to be utilized for learning, curation, and authorship. While any document or booklike publication can be displayed in its linear structure based on its wholly stored memes and relationships, accessing its virtual copy stored in the PKM repository provides access to the information-rich, multi-dimensional, and transdisciplinary neighborhood of its individual memes based on the relationships of its original author10 and the subsequent additions of the PKMS community.
Figure 1 has focused on a meme linked to its directly related meme neighborhood labelled as a memeplex. Since any meme captured or its closer or wider neighboring memes are to be utilized further in the future according to the eight reuse methods figure 1 , the nets of causative references are constantly connecting and evolving in number, scope, and quality11, but without the currently experienced mounting redundancy, fragmentation, inconsistency, untraceability, corruption, and decay of web-based content [1]. After completing the test phase of the prototype with its currently 40, records, its transformation into a viable PKMS device application and a cloud-based WHOMER server based on a rapid development platform and a noSQL-database is estimated to take 12 months.
The novel PKM concept and system offers an alternative. On the one hand, the availability of these features provides the means to tackle the widening opportunity divides by affording individual knowledge workers with continuous life-long support from trainee, student, novice, or mentee towards professional, expert, mentor, or leader, but might also elicit a profound disruptive market impact12 [62,63].
On the other hand, the novel PKMS approach adds transparency and momentum to the creative digital asset production and value creation and, with it, to the evolution of knowledge at the personal, institutional, and societal level. In a co-evolutionary PKMS-OKMS context, it is also bound to strengthen the absorptive capacity, ambidexterity, and resulting dynamic capability of organizations considerably, not at the expense of disinterested employees but as a means to motivate them by serving their very self-interests [6,43].