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Nursing has for a long time used a variety of technological tools to improve and support patient care. Tool use changes knowledge processes, offering opportunities to explore processes of specialization in this field. The purpose of this paper is to report from a collaborative process to achieve shared meaning potential while adapting a generic learning tool to meet learning needs of specialized nursing. A complex chain of actions, interactions and negotiations during the adaptation process is disentangled. The paper draws from the theoretical construct known as trajectories of participation.

The method employed in data analysis is interaction analysis, allowing detailed studies of the actions represented in the participants' intersecting trajectories.

The analysis shows how project members seek to combine different modes of knowledge when they sort out and establish shared meaning potential. Typically the negotiations start with a concrete problem arising from the current practice's use of tools. The participants clarify and specify a shared object of activity by mobilizing three different modes of knowledge (practical, diagnostic and technical). During this process, the participants' trajectories converge toward consensus. This consensus is a process of constructing and reconstructing tools and practices and an interdependency of tools and practices that is “materialized” in the adapted learning tool.

This analysis shows the importance of taking account of processes in the concrete settings when developing new tools for change in specialist nursing. Different trajectories of participation that intersected in the planning activities give insight into how knowledge is mobilized when tools and practices co-evolve on an interactional level.

This paper aims to provide a methodological road for the next generation of e‐learning environments.

This paper considers a survey of recent publications (1995‐2002), which aim to provide practical and theoretical indications and advice, which are coupled with practical experimentations.

The paper provides road‐mapping elements, indicating the impact on services and systems to be expected by this design approach.

The survey is based on a selection of sources and it is not exhaustive. The methodology experiments that are used for argumentation are based on the authors’ platform.

The paper presents a useful source of knowledge for researchers and advanced students.

This paper identifies a road for advanced e‐learning systems, and can help researchers and those in industry who desire to introduce and understand the design methodological context of advanced e‐learning systems.

This research aims to investigate how a generic web‐based ITS can be created which will adapt the training content in real time, to the needs of the individual trainee across any domain.

After examining the various alternatives SCORM was adopted in this project because it provided an infrastructure that makes it possible to deliver personalised learning dynamically using re‐usable learning objects.

The results show that a system which presents a student with content that is supplementary to an authored course should be accompanied by a tool to help the trainee's navigation. For such a tool, key functionality would be: first, to identify learning objects that would take the student towards the ultimate learning goals; second, to suggest a pathway through the authored course structure and additional learning objects to the student; and finally, to present the student with different choices of pathway, such as fastest, most comprehensive and most popular routes.

This investigation has taken another approach of adapting the course by displaying an adapted set of learning objects to the trainee, instead of using a linear course structure.

When managers set aspirations for their firms, they typically compare their own firms' performance to past aspirations as well as to the performance of social reference groups. The authors explore how firm generic strategy affects managers' adaptation of firm aspirations in response to feedback from three social reference groups that vary in terms of breadth (population average, strategic group, and one direct rival).

The authors propose that firm generic strategy (low-cost or differentiation) functions as an organizational information filter through with managers interpret performance feedback. The authors test for whether generic strategy has a moderating effect on the influence of performance feedback from social reference groups.

Based on a longitudinal sample of US airlines, the study shows that all firms are influenced most strongly by their strategic groups. Low-cost and differentiation generic strategies differ in terms of which social reference group motivates a larger reaction when overperforming: low-cost firms are more influenced by the population average which is contributed to by the entire industry than are differentiating firms, while differentiating firms are more swayed by the narrow focus of their direct rivals than are low-cost firms.

Although firm strategy represents a core decision at the firm level, to the best of the authors’ knowledge, performance feedback research, surprisingly, has not yet integrated generic strategy into its models.

This study provides a comprehensive framework of adaptation in triadic business relationship settings in the service sector. The framework is based on the industrial network approach (see, e.g., Axelsson & Easton, 1992; Håkansson & Snehota, 1995a). The study describes how adaptations initiate, how they progress, and what the outcomes of these adaptations are. Furthermore, the framework takes into account how adaptations spread in triadic relationship settings. The empirical context is corporate travel management, which is a chain of activities where an industrial enterprise, and its preferred travel agency and service supplier partners combine their resources. The scientific philosophy, on which the knowledge creation is based, is realist ontology. Epistemologically, the study relies on constructionist processes and interpretation. Case studies with in-depth interviews are the main source of data.

The purpose of this paper is to present results of a four‐year qualitative research project on the dynamics of skill development strategies in e‐learning workplace environments.

A potential knowledge city, Greater Manchester relies on its human (individual and collective) capitals, put to work in knowledge engines such as its universities. Such context has become a complex and uncharted territory for research. Research analysis within knowledge‐based higher education territory clearly demands knowledge‐based tools. Therefore, the research behind this paper has adopted Carrillo's generic system of capitals, an integrative KM3 taxonomy. Such framework has been instrumental in identifying contextual aspects, drivers and rooted strategies of k‐facilitators' adaptation to emergent learning environments.

The grounded model reported here further conceptualised how Mancunian e‐learning practitioners seemingly follow an embedded process of adaptation. Practitioners actually develop strategies to adapting in emerging learning spaces while they adapt to swiftly changing conditions in their workplace environments. The skill developments facilitators undertake seemingly allow them to link and connect to learning spaces, as well as to the existing university social systems and networks of learning. Those systems and networks are integrated to the city's knowledge capitals, and beyond.

By exploring Greater Manchester (UK) universities' e‐learning strategies, this paper contributes to KM theoretical understanding of how facilitators develop their knowledge‐based skills in emergent higher education learning spaces.

Through harnessing the benefits of the internet, e-learning systems provide flexible learning opportunities that can be delivered at a fixed cost at a time and place to suit the user. As such, e-learning systems can allow students to learn at their own pace while also being suitable for both distance and classroom-based learning activities. Adaptive educational hypermedia systems are e-learning systems that employ artificial intelligence. They deliver personalised online learning interventions that extend electronic learning experiences beyond a mere computerised book through the use of intelligence that adapts the content presented to a user according to a range of factors including individual needs, learning styles and existing knowledge. The purpose of this paper is to describe a novel adaptive e-learning system called dyslexia adaptive e-learning management system (DAELMS). For the purpose of this paper, the term DAELMS will be employed to describe the overall e-learning system that incorporates the required functionality to adapt to students’ learning styles and dyslexia type.

The DAELMS is a complex system that will require a significant amount of time and expertise in knowledge engineering and formatting (i.e. dyslexia type, learning styles, domain knowledge) to develop. One of the most effective methods of approaching this complex task is to formalise the development of a DAELMS that can be applied to different learning styles models and education domains. Four distinct phases of development are proposed for creating the DAELMS. In this paper, we will discuss Phase 3 which is the implementation and some adaption algorithms while in future papers will discuss the other phases.

An experimental study was conducted to validate the proposed generic methodology and the architecture of the DAELMS. The system has been evaluated by group of university students studying a Computer Science related majors. The evaluation results proves that when the system provide the user with learning materials matches their learning style or dyslexia type it enhances their learning outcomes.

The DAELMS correlates each given dyslexia type with its associated preferred learning style and subsequently adapts the learning material presented to the student. The DAELMS represents an adaptive e-learning system that incorporates several personalisation options including navigation, structure of curriculum, presentation, guidance and assistive technologies that are designed to ensure the learning experience is directly aligned with the user's dyslexia type and associated preferred learning style.