Software managers and practitioners often must make decisions about what technologies to employ on their projects. They might be aware of problems with their current development practices (for example, production bottlenecks or numerous defect reports from customers) and want to resolve them. Or, they might have read about a new technology and want to take advantage of its promised benefits. However, practitioners can have difficulty making informed decisions about whether to adopt a new technology because there's little objective evidence to confirm its suitability, limits, qualities, costs, and inherent risks. This can lead to poor decisions about technology adoption. Software engineers might make incorrect decisions about adopting new techniques it they don't consider scientific evidence about the techniques' efficacy. They should consider using procedures similar to ones developed for evidence-based medicine. Software companies are often under pressure to adopt immature technologies because of market and management pressures. We suggest that practitioners consider evidence-based software engineering as a mechanism to support and improve their technology adoption decisions.

Evidence-based software engineering for practitioners

The need to learn from one project to the next is clearly of vital importance, but is often neglected. Furthermore, there are fundamental issues within projects that inhibit such learning, such as the temporary nature of project organizations and the fundamental complexity of projects. This paper surveys the diverse literature that can help explain these factors and help projects to learn, and describes a large survey of project managers to look at what actual practice is and how successful it is perceived, as well as some empirical work. From this, a number of general conclusions are drawn as to how to create project organizations that are learning organizations.

How Do Organizations Learn Lessons From Projects—And Do They?

/pdf/fish-for-life-interactive-governance-for-fisheries-n0zg6avoso.pdf

Fish for Life: Interactive Governance for Fisheries

Postmortem analysis (PMA) is a practical method for initiating knowledge management by capturing experience and improvement suggestions from completed projects. It requires little effort and quickly provides initial results, making it suitable even for small- and medium-size projects and companies. The authors describe their experiences with applying PMA techniques for collecting and analyzing experience in software organizations.

/pdf/postmortem-never-leave-a-project-without-it-3gc1blxodo.pdf

Postmortem: never leave a project without it

Purpose – Popularity in knowledge management has, unfortunately, not been matched by parallel empirical research on the processes, challenges and benefits of knowledge capture in small and medium enterprises (SMEs) in the construction industry, given the fact that 99 per cent of firms in the UK construction industry can be classified as SMEs. This paper aims to discuss the output of a research study, which is focused on knowledge capture in SMEs in construction industry. The paper also aims to present and discuss a computer‐based awareness tool on knowledge capture underpinned by Kolb's experiential learning theory.Design/methodology/approach – The empirical study involved a total of 51 professionals from 26 SMEs in the construction industry. Grounded theory approach was adopted. Also, a content analysis was considered.Findings – The results show that there is lack of awareness of complex issues associated with an effective knowledge capture process as well as ensuing benefits for SMEs in the construction...

A knowledge capture awareness tool: an empirical study on small and medium enterprises in the construction industry

Preface. Acknowledgements. PART I: OVERVIEW. 1. Setting the Context. 2. What is Knowledge Management? 3. The Holistic Model -- It's More Than the Sum of the Parts. 4. Getting the Environment Right. 5. Getting Started -- Just Do It. PART II: TOOLS AND TECHNIQUES. 6. Connecting Sharers with Learners -- Using Self--Assessment. 7. Learning From Your Peers -- Somebody Has Already Done It. 8. Learning Whilst Doing -- Time to Reflect. 9. Learning After Doing -- When it's All Over. 10. Finding the Right People -- If Only I Knew Who. 11. Networking and Communities of Practice. 12. Leveraging What We Have Learned -- Capturing Knowledge. PART III: TODAY AND TOMORROW. 13. Embedding it in the Organization -- Preparing To Let Go. 14. Review of the Book -- What Did We Set Out To Do? Appendix A. Appendix B. Resources 1. Resources 2: One Page Resources. Index.

Learning to Fly: Practical Knowledge Management from Some of the World's Leading Learning Organizations

What is a Meeting Facilitator? The “facilitator” is a guide or “discussion leader” for the group. The process of facilitation is a way of providing leadership without taking the reigns. A facilitator’s job is to get others to assume responsibility and take the lead. Some common definitions include: “A person who is acceptable to all group members, substantively neutral, and has no decision-making authority who helps a group improve the way it identifies and solves problems and makes decisions.”

The Role of the Facilitator

No More Consultants: We Know More Than We Think

Knowledge management : praktický management znalostí zprostředí předních světových učících se organizací

ion. Thus, the outermost part of the expression in (14.1b) represents the abstraction h hutt-time,ui ! N, . . . , hexploits,u 3 i ! R 2 i .hire(X 1 ,X 2 ,T) j . . . . Roughly speaking, this rst part of the interpretation says that the content of the sentence Smith hired Jones is obtained by nding in context values for the parameters N,X 1 ,X 2 ,R 1 ,R 2 ,T and S, i.e., by applying the simultaneous abstraction in (14.1b) to an assignment whose domain includes the indices hutt-time,ui,hdo,u 1 i, hexploits,u 1 i,hdescr-sit,u 2 i, hrt,u 2 i, hdo,u 3 i, and hexploits,u 3 i. Inside the box representing a simultaneous abstraction is a box of the form ( jj ), which represents a restricted proposition. We are assuming the notion of proposition used in Situation Theory, according to which what can be true or false are statements of the form A holds at situation S. (Such propositions are also called austinian propositions.) A restricted proposition is an expression that denotes a proposition if all the conditions on the right of the vertical double line are satis ed (i.e., if it is true that the object denoted by X 1 is named \Smith"); unde ned otherwise. The grammar currently covers the basic aspects of predication, quanti cation, tense and aspect, and pronominal anaphora, including both quanti ers and inde nites. The grammar generates all possible interpretations of an ambiguous sentence. All possible interpretations of an anaphoric expression are generated by keeping track of the discourse referents introduced

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