Seeking and Scaling Model for Designing Technology


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This paper contributes a new model for Design Research that extends existing approaches by taking into account the neglected areas of design seeking and scaling in the underexplored area of workplace informal learning; we place an emphasis on design that is based on a new empirically base. We use PANDORA as an exemplary case study to identify and illustrate the research benefits of the Design Seeking and Scaling model. PANDORA explores, amongst other things, designs for collaborative technologies for processes surrounding a Significant Event Audit (SEA) in UK Health Sector’s General Practices. We claim that the model is useful as a tool for improving collaboration through Personal Learning Networks.

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  • social networking within companies could increase the productivity of “knowledge workers” by 20 to 25 percent: “Two-thirds of this potential value lies in improving collaboration and communication within and across enterprises. The average interaction worker spends an estimated 28 percent of the workweek managing e-mail and nearly 20 percent looking for internal information or tracking down colleagues who can help with specific tasks. But when companies use social media internally, messages become content; a searchable record of knowledge can reduce, by as much as 35 percent, the time employees spend searching for company information. Additional value can be realized through faster, more efficient, more effective collaboration, both within and between enterprises” (McKinsey Global Institute, 2012).
  • Rogers, E. M.: Diffusion of Innovations. Fifth Edition. New York: Free Press (2003).Diffusion of Innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread through cultures. Everett Rogers, a professor of rural sociology, popularized the theory in his book Diffusion of Innovations; the book was first published in 1962, and is now in its fifth edition (2003).[1] The book says that diffusion is the process by which an innovation is communicated through certain channels over time among the members of a social system. The origins of the diffusion of innovations theory are varied and span multiple disciplines. The book espouses the theory that there are four main elements that influence the spread of a new idea: the innovation, communication channels, time, and a social system. This process relies heavily on human capital. The innovation must be widely adopted in order to self-sustain. Within the rate of adoption, there is a point at which an innovation reaches critical mass.The categories of adopters are: innovators, early adopters, early majority, late majority, and laggards (Rogers 1962, p. 150). Diffusion of Innovations manifests itself in different ways in various cultures and fields and is highly subject to the type of adopters and innovation-decision process.
  • The Prior conditions [2, p. 170] phase recognizes the need to look at previous practice, felt needs/problems, innovativeness and the norms of the social system. We extend this notion of prior conditions and also ‘agenda-setting’ [2, p. 421] by making an explicit link to ideas surrounding design creativity and seeking and the question ‘how do design ideas arise’?Clusters are essentially a communication channel in Rogers terms‘Diffusion of innovation’ [2] is a theory that seeks to explain how, why, and at what rate new ideas and technology spread through cultures.
  • Our second phase is called Agreement and is based on Roger’s notion of Persuasion [2, p. 170]; this relates to the perceived characteristics of the innovation as well as the need to keep large heterogeneous research project teams (like Learning Layers) ‘on board’. ‘Redefining’ in Fig. 1 [2, p. 421] is a key notion here, whereby the “innovation is modified and reinvented to fit the organization, and the organizational structures are altered”. Social Semantic Server hook in: The SSS framework will support both, the basic and advanced features ofthe application using its low- and high-level services. The application will enablethe user to enter problem statements into well-structured search queries. Highlevelservices will deliver suitable recommendations (e.g., specialists or notes ofrelated discussions)by exploiting the semantic structures of the knowledge baseformed by low-level services. Thereby the following basic services will be used,as illustrated in the different screens of Figure 1 (above)The first screen shows various ways to enter problem statements (e.g., aska question using voice, video or text) and the upload of documents as attachmentsto the post. The second screen contains the available annotation options(e.g., adding metadata) for classifying the question into a certain category andassigning it a certain importance based on an urgency value. Moreover, the questioncan be shared among different contact circles to start a discussion in thepersonal/trusted or extended/public social networks as demonstrated in screenthree. Furthermore, the fourth screen displays the recommendation filtering indifferent categories and possible privacy levels (e.g., closed or everyone). The recommendationcategories can be users, digital artifacts or metadata (e.g., tags),as stated in section 3.
  • Seeking and Scaling Model for Designing Technology

    1. 1. http://Learning-Layers-euhttp://Learning-Layers-eu 1
    2. 2. http://Learning-Layers-eu • PANDORA as an exemplary case study • Illustrates research benefits of the Design Seeking and Scaling model • PANDORA context – Significant Event Audit (SEA – UK Health Sector’s General Practices • We claim that the model is useful as a tool for – Improving collaboration through Personal/Shared Learning Networks – Scaling 2
    3. 3. http://Learning-Layers-eu 3 • Social networking within companies could increase the productivity of “knowledge workers” by 20 to 25 percent • McKinsey Global Institute, 2012
    4. 4. http://Learning-Layers-eu 4
    5. 5. http://Learning-Layers-eu 5
    6. 6. http://Learning-Layers-eu 6
    7. 7. http://Learning-Layers-eu • Can Networked Scaffolding be achieved by aggregating trust? And can aggregated trust be derived from semantic analysis of the collaborative tagging of people and resources in a Shared Learning Network? • Is trust a phenomenon to be investigated with respect to effective scaffolding of people? Do these questions give the context? – Is there a wider network of people that I could draw on to help me? – Which people have proven to be effective for me and why? – Which meanings or learning context should I select from available resources? – How does my context constrain what I need and could/should use? • How can captured negotiations of meaning (in networked scaffolding) enable me to assess the effectiveness of learning resources and/or people from one context to another context; and how can this fosters cross- organisational learning and learning attitudes? 7
    8. 8. http://Learning-Layers-eu Networking Scaffolding system Aimed at improving interactions among professionals: extra support to help with the sharing, personal network and trust building issues as well as potentially helping with ensuring that question/answers provided are easily accessible to all not buried in one individual’s email folder system. • List of Functional requirements • User stories 8
    9. 9. http://Learning-Layers-eu 9
    10. 10. http://Learning-Layers-eu Back to slide 7 10