Metacognition: An Overview
A meta was one of the conical columns set in the ground at each end of the Circus in Rome to mark the turning point in the race. Similarly the concept of meta-cognition can be seen as a turning point in our understanding of the mind. The prefix meta has come to refer to something that transcends the subject it is related to. What does it mean then to transcend cognition? The termmetacognition was introduced by Flavell in 1976 to refer to 'the individual's own awareness and consideration of his or her cognitive processes and strategies' (Flavell 1979). It refers to that uniquely human capacity of people to be self-reflexive, not just to think and know but to think about their own thinking and knowing.
"Metacognition" is one of the latest buzz words in educational psychology, but what exactly is metacognition? The length and abstract nature of the word makes it sound intimidating, yet its not as daunting a concept as it might seem. We engage in metacognitive activities everyday. Metacognition enables us to be successful learners, and has been associated with intelligence (e.g., Borkowski, Carr, & Pressley, 1987; Sternberg, 1984, 1986a, 1986b). Metacognition refers to higher order thinking which involves active control over the cognitive processes engaged in learning. Activities such as planning how to approach a given learning task, monitoring comprehension, and evaluating progress toward the completion of a task are metacognitive in nature. Because metacognition plays a critical role in successful learning, it is important to study metacognitive activity and development to determine how students can be taught to better apply their cognitive resources through metacognitive control."Metacognition" is often simply defined as "thinking about thinking." In actuality, defining metacognition is not that simple. Although the term has been part of the vocabulary of educational psychologists for the last couple of decades, and the concept for as long as humans have been able to reflect on their cognitive experiences, there is much debate over exactly what metacognition is. One reason for this confusion is the fact that there are several terms currently used to describe the same basic phenomenon (e.g., self-regulation, executive control), or an aspect of that phenomenon (e.g., meta-memory), and these terms are often used interchangeably in the literature. While there are some distinctions between definitions (see Van Zile-Tamsen, 1994, 1996 for a full discussion), all emphasize the role of executive processes in the overseeing and regulation of cognitive processes.The term "metacognition" is most often associated with John Flavell, (1979). According to Flavell (1979, 1987), metacognition consists of both metacognitive knowledge and metacognitive experiences or regulation. Metacognitive knowledge refers to acquired knowledge about cognitive processes, knowledge that can be used to control cognitive processes. Flavell further divides metacognitive knowledge into three categories: knowledge of person variables, task variables and strategy variables.
Metacognitive Knowledge
Stated very briefly, knowledge of person variables refers to general knowledge about how human beings learn and process information, as well as individual knowledge of one's own learning processes. For example, you may be aware that your study session will be more productive if you work in the quiet library rather than at home where there are many distractions. Knowledge of task variables include knowledge about the nature of the task as well as the type of processing demands that it will place upon the individual. For example, you may be aware that it will take more time for you to read and comprehend a science text than it would for you to read and comprehend a novel.Finally, knowledge about strategy variables include knowledge about both cognitive and metacognitive strategies, as well as conditional knowledge about when and where it is appropriate to use such strategies.
Metacognitive Regulation
Metacognitive experiences involve the use of metacognitive strategies or metacognitive regulation (Brown, 1987). Metacognitive strategies are sequential processes that one uses to control cognitive activities, and to ensure that a cognitive goal (e.g., understanding a text) has been met. These processes help to regulate and oversee learning, and consist of planning and monitoring cognitive activities, as well as checking the outcomes of those activities.For example, after reading a paragraph in a text a learner may question herself about the concepts discussed in the paragraph. Her cognitive goal is to understand the text. Self-questioning is a common metacognitive comprehension monitoring strategy. If she finds that she cannot answer her own questions, or that she does not understand the material discussed, she must then determine what needs to be done to ensure that she meets the cognitive goal of understanding the text. She may decide to go back and re-read the paragraph with the goal of being able to answer the questions she had generated. If, after re-reading through the text she can now answer the questions, she may determine that she understands the material. Thus, the metacognitive strategy of self-questioning is used to ensure that the cognitive goal of comprehension is met.
Cognitive vs. Metacognitive Strategies
Most definitions of metacognition include both knowledge and strategy components; however, there are a number of problems associated with using such definitions. One major issue involves separating what is cognitive from what is metacognitive. What is the difference between a cognitive and a metacognitive strategy?Can declarative knowledge be metacognitive in nature? For example, is the knowledge that you have difficulty understanding principles from bio-chemistry cognitive or metacognitive knowledge? Flavell himself acknowledges that metacognitive knowledge may not be different from cognitive knowledge (Flavell, 1979). The distinction lies in how the information is used.Recall that metacognition is referred to as "thinking about thinking" and involves overseeing whether a cognitive goal has been met. This should be the defining criterion for determining what is metacognitive. Cognitive strategies are used to help an individual achieve a particular goal (e.g., understanding a text) while metacognitive strategies are used to ensure that the goal has been reached (e.g., quizzing oneself to evaluate one's understanding of that text). Metacognitive experiences usually precede or follow a cognitive activity. They often occur when cognitions fail, such as the recognition that one did not understand what one just read. Such an impasse is believed to activate metacognitive processes as the learner attempts to rectify the situation (Roberts & Erdos, 1993).Metacognitive and cognitive strategies may overlap in that the same strategy, such as questioning, could be regarded as either a cognitive or a metacognitive strategy depending on what the purpose for using that strategy may be. For example, you may use a self-questioning strategy while reading as a means of obtaining knowledge (cognitive), or as a way of monitoring what you have read (metacognitive). Because cognitive and metacognitive strategies are closely intertwined and dependent upon each other, any attempt to examine one without acknowledging the other would not provide an adequate picture.Knowledge is considered to be metacognitive if it is actively used in a strategic manner to ensure that a goal is met. For example, a student may use knowledge in planning how to approach a math exam: "I know that I (person variable) have difficulty with word problems (task variable), so I will answer the computational problems first and save the word problems for last (strategy variable)." Simply possessing knowledge about one's cognitive strengths or weaknesses and the nature of the task without actively utilizing this information to oversee learning is not metacognitive.
Metacognition and Intelligence
Metacognition, or the ability to control one's cognitive processes (self-regulation) has been linked to intelligence (Borkowski et al., 1987; Brown, 1987; Sternberg, 1984, 1986a, 1986b). Sternberg refers to these executive processes as "metacomponents" in his triarchic theory of intelligence (Sternberg, 1984, 1986a, 1986b). Metacomponents are executive processes that control other cognitive components as well as receive feedback from these components. According to Sternberg, metacomponents are responsible for "figuring out how to do a particular task or set of tasks, and then making sure that the task or set of tasks are done correctly" (Sternberg, 1986b, p. 24). These executive processes involve planning, evaluating and monitoring problem-solving activities. Sternberg maintains that the ability to appropriately allocate cognitive resources, such as deciding how and when a given task should be accomplished, is central to intelligence.
Metacognition and Cognitive Strategy Instruction
Although most individuals of normal intelligence engage in metacognitive regulation when confronted with an effortful cognitive task, some are more metacognitive than others. Those with greater metacognitive abilities tend to be more successful in their cognitive endeavors. The good news is that individuals can learn how to better regulate their cognitive activities. Most often, metacognitive instruction occurs within Cognitive Strategy Instruction programs.Cognitive Strategy Instruction (CSI) is an instructional approach which emphasizes the development of thinking skills and processes as a means to enhance learning. The objective of CSI is to enable all students to become more strategic, self-reliant, flexible, and productive in their learning endeavors (Scheid, 1993). CSI is based on the assumption that there are identifiable cognitive strategies, previously believed to be utilized by only the best and the brightest students, which can be taught to most students (Halpern, 1996). Use of these strategies have been associated with successful learning (Borkowski, Carr, & Pressley, 1987; Garner, 1990).Metacognition enables students to benefit from instruction (Carr, Kurtz, Schneider, Turner & Borkowski, 1989; Van Zile-Tamsen, 1996) and influences the use and maintenance of cognitive strategies. While there are several approaches to metacognitive instruction, the most effective involve providing the learner with both knowledge of cognitive processes and strategies (to be used as metacognitive knowledge), and experience or practice in using both cognitive and metacognitive strategies and evaluating the outcomes of their efforts (develops metacognitive regulation). Simply providing knowledge without experience or vice versa does not seem to be sufficient for the development of metacognitive control (Livingston, 1996).The study of metacognition has provided educational psychologists with insight about the cognitive processes involved in learning and what differentiates successful students from their less successful peers. It also holds several implications for instructional interventions, such as teaching students how to be more aware of their learning processes and products as well as how to regulate those processes for more effective learning.
Metacognition - Thinking about thinking - Learning to learn
Metacognition refers to higher order thinking that involves active control over the thinking processes involved in learning. Activities such as planning how to approach a given learning task, monitoring comprehension, and evaluating progress toward the completion of a task are metacognitive in nature. Because metacognition plays a critical role in successful learning it is important for both students and teachers. Metacognition has been linked with intelligence and it has been shown that those with greater metacognitive abilities tend to be more successful thinkers.
Most definitions of metacognition include both knowledge and strategy components. Knowledge is considered to be metacognitive if it is actively used in a strategic manner to ensure that a goal is met. Metacognition is often referred to as "thinking about thinking" and can be used to help students “learn how to learn.” Cognitive strategies are used to help achieve a particular goal while metacognitive strategies are used to ensure that the goal has been reached.
Metacognitive knowledge involves executive monitoring processes directed at the acquisition of information about thinking processes. They involve decisions that help
to identify the task on which one is currently working,
to check on current progress of that work,
to evaluate that progress, and
to predict what the outcome of that progress will be.
Metacognitive strategies involve executive regulation processes directed at the regulation of the course of thinking. They involve decisions that help
to allocate resources to the current task,
to determine the order of steps to be taken to complete the task, and
to set the intensity or the speed at which one should work the task.
METACOGNITION: Study Strategies, Monitoring, and Motivation
I. Introduction
In general, metacognition is thinking about thinking. More specifically, Taylor (1999) defines metacognition as “an appreciation of what one already knows, together with a correct apprehension of the learning task and what knowledge and skills it requires, combined with the ability to make correct inferences about how to apply one’s strategic knowledge to a particular situation, and to do so efficiently and reliably.”
The more students are aware of their thinking processes as they learn, the more they can control such matters as goals, dispositions, and attention. Self-awareness promotes self-regulation. If students are aware of how committed (or uncommitted) they are to reaching goals, of how strong (or weak) is their disposition to persist, and of how focused (or wandering) is their attention to a thinking or writing task, they can regulate their commitment, disposition, and attention (Marzano et al., 1988). For example, if students were aware of a lack of commitment to writing a long research assignment, noticed that they were procrastinating, and were aware that they were distracted by more appealing ways to spend their time, they could then take action to get started on the assignment. But until they are aware of their procrastination and take control by making a plan for doing the assignment, they will blissfully continue to neglect the assignment.
II. Metacognition and Three Types of Knowledge
To increase their metacognitive abilities, students need to possess and be aware of three kinds of content knowledge: declarative, procedural, and conditional. Declarative knowledge is the factual information that one knows; it can be declared—spoken or written. An example is knowing the formula for calculating momentum in a physics class (momentum = mass times velocity). Procedural knowledge is knowledge of how to do something, of how to perform the steps in a process; for example, knowing the mass of an object and its rate of speed and how to do the calculation. Conditional knowledge is knowledge about when to use a procedure, skill, or strategy and when not to use it; why a procedure works and under what conditions; and why one procedure is better than another. For example, students need to recognize that an exam word problem requires the calculation of momentum as part of its solution.
This notion of three kinds of knowledge applies to learning strategies as well as course content. When they study, students need the declarative knowledge that (1) all reading assignments are not alike; for example, that a history textbook chapter with factual information differs from a primary historical document, which is different from an article interpreting or analyzing that document. They need to know that stories and novels differ from arguments. Furthermore they need to know that there are different kinds of note taking strategies useful for annotating these different types of texts. And (2) students need to know how to actually write different kinds of notes (procedural knowledge), and (3) they need to know when to apply these kinds of notes when they study (conditional knowledge). Knowledge of study strategies is among the kinds of metacognitive knowledge, and it too requires awareness of all three kinds of knowledge.
III. Metacognition and Study Strategies
Research shows that explicitly teaching study strategies in content courses improves learning. (Commander & Valeri-Gold, 2001; Ramp & Guffey, 1999; Chiang, 1998; El-Hindi, 1997; McKeachie, 1988). Research also shows that few instructors explicitly teach study strategies; they seem to assume that students have already learned them in high school—but they haven’t. (McKeachie, 1988). Rote memorization is the usual learning strategy—and often the only strategy—employed by high school students when they go to college (Nist, 1993).
Study strategies are diverse and don’t work in every context. For example, reading for information acquisition won’t work in a literature course and won’t work if students are supposed to critically evaluate an article. But students who have learned only the strategy of reading to pass a quiz on the information will not go beyond this strategy. Study strategies don’t necessarily transfer into other domains. Students need to know they have choices about which strategies to employ in different contexts. And students who learn study skills in one course need to apply study strategies in other contexts than where they first learned it.
Students need to monitor their application of study strategies. Metacognitive awareness of their learning processes is as important as their monitoring of their learning of the course content. Metacognition includes goal setting, monitoring, self-assessing, and regulating during thinking and writing processes; that is, when they’re studying and doing homework. An essential component of metacognition is employing study strategies to reach a goal, self-assessing one’s effectiveness in reaching that goal, and then self-regulating in response to the self-assessment.
IV. Monitoring Problems with Learning
When students monitor their learning, they can become aware of potential problems. Nickerson, Perkins, and Smith (1985) in The Teaching of Thinking have categorized several types of problems with learning.
A. Problems with Process; Making errors in encoding, operations, and goals:
1.Errors in Encoding
Missing important data or not separating relevant from irrelevant data. For example, some literature students will base their interpretation of a poem on just the first stanza.
2.Errors in Operations
Failing to select the right subskills to apply. For example, when proofreading, some students will just read to see if it sounds right, rather than making separatepasses that check for fragments, subject-verb misagreement, and other errors they have learned from experience they are likely to make.
Failing to divide a task into subparts. For example, some math students will jump right to what they think is the final calculation to get the desired answer.
3. Errors in Goal Seeking
Misrepresenting the task. For example, students in a speech communication class instead of doing the assigned task of analyzing and classifying group communication strategies used in their group discussions will just write a narrative of who said what. Not understanding the criteria to apply. For example, when asked to evaluate the support provided for the major claim of an article, students will explain why they liked the article rather than apply appropriate evaluative criteria.
B. Problems with Cognitive Load
Too many subskills necessary to do a task. For example, some students might have not yet learned how to carry out all the steps in a complex nursing procedure.
Not enough automatic, internalized subskills. For example, students in an argument and persuasion class might have to check their notes on how to analyze persuasive strategies because they have not internalized the procedure.
C. Problems with Abilities
Lacking the level of needed mental abilities. For example, students are asked to think abstractly about general concepts and issues, but they can only think concretely about specific situations.
A good way to discover what kind of errors students are making in their thinking processes is to get them to unpack their thinking, to tell you step by step how they are going about the task. By listening to how they are doing the cognitive task, an instructor can detect where the student is going wrong. Asking students to describe their thinking processes also develops their metacognitive abilities—a very necessary skill to improve thinking.
V. Metacognition and Motivation
Metacognition affects motivation because it affects attribution and self-efficacy. When students get results on tests and grades on assignments (especially unexpected results such as failures), they perform a mental causal search to explain to themselves why the results happened. When they achieve good results, students tend to attribute the result to two internal factors: their own ability and effort. When they fail, they might attribute the cause to these same internal factors or they might, in a self-protective rationalization, distance themselves from a sense of personal failure by blaming external causes, such as an overly difficult task, an instructor’s perverse testing habits, or bad luck. This tendency to attribute success to ability and effort promotes future success because it develops confidence in one’s ability to solve future unfamiliar and challenging tasks. The converse is also true. Attributing failure to a lack of ability reduces self-confidence and reduces the student’s summoning of intellectual and emotional abilities to the next challenging tasks; attribution theory also explains why such students will be unwilling to seek help from tutors and other support services: they believe it would not be worth their effort. In addition to blaming failure on external causes, underachievers often “self-handicap” themselves by deliberately putting little effort into an academic task; they thereby protect themselves from attributing their failure to a painful lack of ability by attributing their failure to lack of effort (Stage et al, 1998) (Click here for a review and summary of Creating Learning Centered Classrooms by Stage et al.)
VI. Metacognition and At-Risk Students
The last two decades have seen a great deal of research directed towards improving the academic success of at-risk students. As McKeachie (1988) explains, the problems are
Students “enter the higher levels of education with . . . strategies that handicap them in achieving success.” (p. 5)
“[N]either home backgrounds nor schools have helped young adults become aware of alternative ways of approaching learning situations, and of options other than increasing or decreasing one’s effort as one approaches different learning situations” (p. 5)
Teachers give plenty of feedback about the correctness of learning outcomes but not about how to achieve these outcomes.
The use of learning strategies is linked to motivation. When students fail, they tend to assign the cause to something stable and unchangeable—low innate ability—rather than to something they have the ability to change—employing different, more effective, learning strategies.
VII. Five Generalizations from a Review of the Literature of Study Strategies
Simpson and Nist (2000) have conducted a review of the literature on strategic learning in the last 20 years and summarize it in five generalizations:
1. Understanding the task is of great importance
The tasks that students need to perform vary not only among disciplines but among instructors in the same discipline. An effective strategy for preparing for a multiple choice test in biology is different from what is needed to prepare for a history exam with an essay that asks students to synthesize information from several chapters. Yet students often employ the same strategy—and sometimes the least effective strategy—for studying for very different kinds of tests. Furthermore, many students who perform badly misinterpret the tasks; for example, by misunderstanding what clearly written essay instructions asked them to do. Students need to understand the task accurately in order to use the most effective strategies.
2. What students believe about learning affects their selection of study strategies “What students believe about learning and studying has an influence on how they interpret the task, how they interact with text, and, ultimately, the strategies they select.”3. Instructors need to provide good instruction in how to use study strategiesSimpson’s and Nist’s first point in this section is that it takes time to teach explicit use of strategies. In one experiment students were explicitly taught the “metacognitive strategies of planning and evaluating,” but “distinct and significant improvement did not emerge until 4 weeks after the initial instruction.” Second, students should not only be taught what the features of a strategy (declarative knowledge of the strategy) but also procedural and conditional knowledge: the steps to use and when to employ them. Students need to practice on authentic texts from the courseand the texts should be challenging enough so that students will not employ simplistic approaches. Third, practice with strategies should occur within a specific course; isolated study skills courses have limited success. Fourth, instruction in study strategies “should be explicit and direct” and include five features: “(a) strategy descriptions; (b) discussions of why the strategy should be learned and its importance; (c) think-alouds, models, and examples of how the strategy is used, including the processes involved and when and where it is appropriate to apply the strategy; (d) explanations as to when and where it is appropriate to apply the strategy; and (e) suggestions for monitoring and evaluating whether the strategy is working and what to do if it is not.” Instructors should design guided practice where students use the strategies on authentic course tasks and provide feedback.
4. Instructors should teach a variety of strategies that research has shown to be effective.
Researchers have found that four reading and studying strategies are effective:
Generate questions and answer them. Students need to be taught how to create higher-level questions and how to answer them; sometimes this is done in small groups or pairs. The strategy improves students’ comprehension of the text.
Write summaries. Students need to use their own words and be taught the rules of summarizing (which is difficult). “Writer-based summaries not only improve students’ comprehension, but also help them monitor their understanding.”
Write elaborations. Ask students to create examples, make analogies, explain relationships between concepts. [The Cornell note-taking method and double-entry notebook are examples of elaborations.]
Use organizing strategies. Concept maps, network representations, and other graphic organizers can be effective.
5. Emphasize the cognitive and metacognitive processes that underlie a study strategy.
The value of a strategy lies more in the cognitive and metacognitive processes used than the steps in the strategy itself. The key steps are “elaborating, planning, monitoring, and evaluating.”
VIII. What Instructors Can Do To Help Students.
A. Some Sample Metacognitive Strategies
Learning portfolio. Commander and Valeri-Gold (2001) describe a learning portfolio as a collection of student papers applying learning strategies to their course work. Among the benefits for instructors evaluating student work are that learning portfolios “(a) capture the intellectual substance and learning situation in ways that other methods of evaluation cannot; (b) encourage students to take a role in the documentation, observation, and review of learning; are a powerful tool for improvement; and (d) create a culture of professionalism about learning” (p.6). The chief benefits for students are their actually performing effective learning strategies and the opportunity for self-assessment.
Individual learning plan (ILP) as a contract with the instructor. Linda H. Chiang (1998) describes the process as “setting ILP goals, developing an ILP, monitoring the learning process, writing a reflective journal, conducting one-on-one conferences, and making summative evaluations” (p. 5).
Test Debriefing. Maryellen Weimer (2002) in Learner-Centered Teaching describes how she uses metacognition as she debriefs students after returning an exam in order to give them a sense of control over their learning. She asks students to write down the numbers of questions they missed and then has perform three analyses:
Students first go through their notes on the missed questions and determine whether any of these were on days they missed class and had to rely on someone else’s notes.
Dr. Weimer then identifies which questions came from the assigned reading and which from her lectures and asks students to identify whether more missed questions came from reading notes or class notes.
She then has students look through their exam, check for answers that they changed, and determine how many any of their changes resulted in correct answers. If there is a pattern, it is useful self-knowledge.
Then students write a reflective note to themselves about what they learned from preparing for and taking this exam that will help them prepare for the next one and to describe what steps they will take between now and the next exam. (Click here for a review and summary of Maryellen Weimer’s Learner-Centered Teaching.)
B. Strategies for Instructors to Use in Teaching Textbook Reading
1. Preview the assigned reading
Have students write down what they already know about the subject of the chapter; briefly discuss
Present an oral summary of the chapter in the previous class
Ask interesting questions that will be answered in the reading assignment
Take a poll on some of the issues addressed in the reading assignment
Emphasize the interest, usefulness, and fit in the course sequence of the chapter
2. Do not repeat the reading in a lecture
Do not make listening to your lecture become the students' reading strategy. It is tempting when students do not or can not read the textbook chapters to make sure the course content is "covered" by telling the students what they should have learned if they had read the textbook. Among the reasons for not lecturing on assigned reading are
Your students will not learn to read for comprehension--a needed skill.
As passive learners listening and taking notes, students will not use class time on higher order thinking tasks, such as applying, analyzing, synthesizing, comparing, evaluating.
3. Teach explicitly those study strategies that will be effective in your course
Demonstrate how to do the assigned writing tasks
Provide models
Provide feedback
Make the students’ reading goals clear: read for general or detailed comprehension, read critically, or read for insight.
4. As homework have students write in response to the assigned textbook reading Write your daily instructions for students in the daily course syllabus
5. Monitor compliance
Develop ways to ensure that students do their daily written homework without burdening yourself with daily feedback or recordkeeping.
6. Use the written homework in whole-group or small-group discussions and activities
C. Strategies for Students to Use for Textbook Reading
Answer instructor-provided questions
Ask and answer student-generated questions
Produce an outline or concept map
Write summaries of each section in the chapter
Use the SQ4R method: Survey the text, formulate questions, read, record notes, recite, reflect
Write notes that elaborate on the textbook:
a. Cornell method: one column for key words and concepts, a second column for comments, summaries. Useful for comprehension and later recall.
b. Double-entry method: one column/page for copied passage, adjacent column/page for personal reflections on the passage. Developed by Berthoff (1987); useful for engaging with the text.
c. Simpson and Nist (1990): seven textbook annotation processes
· Write brief summaries in the text margins
· List ideas (causes, effects, characteristics, etc.)
· Identify examples in the margin (write “EX”)
· Write key information on graphs and charts
· Predict potential test questions
· Call attention to confusion with a ? in the margin
· Underline key words
7. Connect the reading to a past lecture or to prior knowledge8. Compare/contrast with another reading9. Critique/evaluate the reading10. Apply the chapter content to a scenario or case11. Write self-assessments of your understanding of the reading. See D. below in next list of topics.
D. Sample Reflective Topics for Self-Monitoring and Self-Assessment
Reading for Comprehension
“What do you notice about your reading when you are understanding what you read? What is it that causes you difficulties when you read? In what areas of reading and remembering do you feel most at ease?” (Soldner, 1997)“Did any parts of the passage confuse me? What did I do to clarify the confusion?” (Gourgey, 1997)
Associative and Affective Personal Response
“How does this poem make you feel? What in your own life might have influenced how you responded to the poem?” (Newton, 1991)
At the Start of an Online Course
· What concerns do you have about the course? How do you plan to deal with your concerns?
· What are your chief strengths as a learner?How will they help you in an online course?
· Read the section "Plan How to Succeed in a Web-Based Course" (in the Syllabus, in "Course Introduction"). How do you plan to manage your time to do well in this course?
· Considering past courses you have taken, what will you need to improve or to continue doing orin order to do well in this course? (Peirce, business writing course)
Sample Topics Connecting Class Activity, Textbook, and Personal Experience
Reflect on what you learned about the group writing process from your experience with the Module One group task on reporting on web sites. What appropriate advice does chapter 2 (in the section on working in teams and small groups) have that applies to your experience? What went well? What went badly? What would you do differently next time? What helps and hinders your own involvement in group writing projects?
Reflect on what you learned from the Module Two (Employment Messages) reading and writing tasks, even if you had already prepared your résumé before starting this course. Did you learn anything new? What prior knowledge was reenforced? Did you improve your approach to writing tasks? What was easy/hard? (Peirce, business writing course)
· course? (Peirce, business writing course)
Wednesday, September 21, 2011
Tuesday, August 30, 2011
Thursday, November 19, 2009
Knowledge management
Knowledge management
From Wikipedia, the free encyclopedia
Jump to: navigation, search
Knowledge management (KM) comprises a range of practices used in an organisation to identify, create, represent, distribute and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organisational processes or practice.
An established discipline since 1991 (see Nonaka 1991), KM includes courses taught in the fields of business administration, information systems, management, and library and information sciences (Alavi & Leidner 1999). More recently, other fields have started contributing to KM research; these include information and media, computer science, public health, and public policy.
Many large companies and non-profit organisations have resources dedicated to internal KM efforts, often as a part of their 'business strategy', 'information technology', or 'human resource management' departments (Addicott, McGivern & Ferlie 2006). Several consulting companies also exist that provide strategy and advice regarding KM to these organisations.
KM efforts typically focus on organisational objectives such as improved performance, competitive advantage, innovation, the sharing of lessons learned, and continuous improvement of the organisation. KM efforts overlap with organisational learning, and may be distinguished from that by a greater focus on the management of knowledge as a strategic asset and a focus on encouraging the sharing of knowledge. KM efforts can help individuals and groups to share valuable organisational insights, to reduce redundant work, to avoid reinventing the wheel per se, to reduce training time for new employees, to retain intellectual capital as employees turnover in an organisation, and to adapt to changing environments and markets (McAdam & McCreedy 2000)(Thompson & Walsham 2004).
Contents[hide]
1 History
2 Knowledge management as an academic discipline
3 Research
3.1 Dimensions
3.2 Strategies
3.3 Motivations
3.4 Technologies
4 See also
5 References
5.1 Notes
6 External links
//
[edit] History
KM efforts have a long history, to include on-the-job discussions, formal apprenticeship, discussion forums, corporate libraries, professional training and mentoring programs. More recently, with increased use of computers in the second half of the 20th century, specific adaptations of technologies such as knowledge bases, expert systems, knowledge repositories, group decision support systems, intranets and computer supported cooperative work have been introduced to further enhance such efforts[1].
In 1999, the term personal knowledge management was introduced which refers to the management of knowledge at the individual level (Wright 2005).
In terms of the enterprise, early collections of case studies recognized the importance of knowledge management dimensions of strategy, process, and measurement (Morey, Maybury & Thuraisingham 2002). Key lessons learned included: people, and the cultures that influence their behaviors, are the single most critical resource for successful knowledge creation, dissemination, and application; cognitive, social, and organizational learning processes are essential to the success of a knowledge management strategy; and measurement, benchmarking, and incentives are essential to accelerate the learning process and to drive cultural change. In short, knowledge management programs can yield impressive benefits to individuals and organizations if they are purposeful, concrete, and action-oriented.
More recently with the advent of the Web 2.0, the concept of knowledge management has evolved towards a vision more based on people participation and emergence. This line of evolution is termed Enterprise 2.0 (McAfee 2006). However, there is an ongoing debate and discussions (Lakhani & McAfee 2007) as to whether Enterprise 2.0 is just a fad that does not bring anything new or useful or whether it is, indeed, the future of knowledge management (Davenport 2008).
[edit] Knowledge management as an academic discipline
KM emerged as a scientific discipline in the earlier 1990s. It was initially supported by only practitioners, when Scandia hired Leif Edvinsson of Sweden as the world’s first Chief Knowledge Officer (CKO). Hubert Saint-Onge (formerly of CIBC, Canada), started investigating various sides of KM long before that. The objective of CKOs is to manage and maximize the intangible assets of their organizations. Gradually, CKOs became interested in not only practical but also theoretical aspects of KM, and the new research field was formed. The KM ideas were quickly endorsed by several highly regarded academics, such as Ikujiro Nonaka (Hitotsubashi University), Hirotaka Takeuchi (Hitotsubashi University), Thomas H. Davenport (Babson College) and Baruch Lev (New York University). In 2001, Thomas Stewart, former editor at FORTUNE Magazine, published an excellent cover story highlighting the importance of intellectual capital of organizations (Serenko et al. 2010).
After that, the KM discipline has started quickly evolving. Serenko and Bontis, in their meta-analysis of KM research predicted that the total number of KM works would exceed 10,000 by 2010 (Serenko & Bontis 2004). In fact, this number has quickly grew much faster. As of 2009, there were 20 distinct KM academic journals available (Serenko & Bontis 2009), with Journal of Knowledge Management and Journal of Intellectual Capital ranked as the leading A+ pure-KM outlets (Bontis & Serenko 2009). Dozens of national and international conferences were held with McMaster World Congress on the Management of Intellectual Capital and Innovation being the pioneering event (Serenko, Bontis & Grant 2009). A number of KM research centers were formed (e.g., The Monieson Centre, Queen’s University and Knowledge Management Research Centre, Hong Kong Polytechnic University). Graduate-level university courses were introduced since 2001 (Bontis, Hardie & Serenko 2008) (Bontis, Serenko & Biktimirov 2006).
Recently, a comprehensive scientometric analysis of the entire KM discipline was undertaken (Serenko et al. 2010). It was found that KM researchers tend to adapt methods of inquiry from reference disciplines, mostly from accounting, finance, human resources management, organizational behavior, psychology, and information systems. The methods of inquiry employed by KM researchers are: 1) framework, model, approach, principle, index, metrics, or tool development (32%); 2) case study (24%); 3) literature review (work based on existing literature) (11%); 4) survey (10%); and 5) use of secondary data (8%). Other methods, for instance, focus groups or field experiments are very rare in KM research. The most productive KM countries are USA, UK, Australia, Spain and Canada that generated over 50% of the word’s KM research output, with 21% coming solely from USA. The leading research institutions are Cranfield University, UK; Copenhagen Business School, Denmark; Macquarie University, Australia; University of Oviedo, Spain; and McMaster University, Canada. It was concluded that KM research may potentially contribute to the wealth of nations because the correlation between countries’ GDP per capita and their KM scholarly research output is strong (Spearman’s pho = 0.597, p < 0.000).
Since its establishment, the KM discipline has been gradually moving towards academic maturity. First, there is a trend towards higher cooperation among academics; particularly, there has been a drop in single-authored publications. Second, the role of practitioners has changed. Their contribution to academic research has been dramatically declining from 30% of overall contributions up to 2002, to only 10% by 2009. At the same time, this phenomenon is regrettable since academics may lose touch with practice and start producing research that is of less interest to industry professionals. In fact, the issue of relevance of academic research has been frequently raised in all fields, including KM. A series of interviews with a number of KM managers revealed that KM research is highly relevant to the needs of practice. However, there should be effective and efficient mechanisms to translate the findings presented in academic journals to a more comprehensible format accessible to non-academics (Booker, Bontis & Serenko 2008).
[edit] Research
A broad range of thoughts on the KM discipline exists with no unanimous agreement; approaches vary by author and school. As the discipline matures, academic debates have increased regarding both the theory and practice of KM, to include the following perspectives:
Techno-centric with a focus on technology, ideally those that enhance knowledge sharing and creation.
Organisational with a focus on how an organisation can be designed to facilitate knowledge processes best.
Ecological with a focus on the interaction of people, identity, knowledge, and environmental factors as a complex adaptive system akin to a natural ecosystem.
Regardless of the school of thought, core components of KM include People, Processes, Technology (or) Culture, Structure, Technology, depending on the specific perspective (Spender & Scherer 2007). Different KM schools of thought include various lenses through which KM can be viewed and explained, to include:
community of practice (Wenger, McDermott & Synder 2001) [2]
social network analysis [3]
intellectual capital (Bontis & Choo 2002) [4]
information theory [5] (McInerney 2002)
complexity science [6]
constructivism [7] (Nanjappa & Grant 2003)
[edit] Dimensions
Different frameworks for distinguishing between knowledge exist. One proposed framework for categorising the dimensions of knowledge distinguishes between tacit knowledge and explicit knowledge. Tacit knowledge represents internalised knowledge that an individual may not be consciously aware of, such as how he or she accomplishes particular tasks. At the opposite end of the spectrum, explicit knowledge represents knowledge that the individual holds consciously in mental focus, in a form that can easily be communicated to others.[8] (Alavi & Leidner 2001).
Early research suggested that a successful KM effort needs to convert internalised tacit knowledge into explicit knowledge in order to share it, but the same effort must also permit individuals to internalise and make personally meaningful any codified knowledge retrieved from the KM effort. Subsequent research into KM suggested that a distinction between tacit knowledge and explicit knowledge represented an oversimplification and that the notion of explicit knowledge is self-contradictory. Specifically, for knowledge to be made explicit, it must be translated into information (i.e., symbols outside of our heads) (Serenko & Bontis 2004). Later on, Ikujiro Nonaka proposed a model (SECI for Socialization, Externalization, Combination, Internalization) which considers a spiraling knowledge process interaction between explicit knowledge and tacit knowledge (Nonaka & Takeuchi 1995). In this model, knowledge follows a cycle in which implicit knowledge is 'extracted' to become explicit knowledge, and explicit knowledge is 'reinternalised' into implicit knowledge.
A second proposed framework for categorising the dimensions of knowledge distinguishes between embedded knowledge of a system outside of a human individual (e.g., an information system may have knowledge embedded into its design) and embodied knowledge representing a learned capability of a human body’s nervous and endocrine systems (Sensky 2002).
A third proposed framework for categorising the dimensions of knowledge distinguishes between the exploratory creation of "new knowledge" (i.e., innovation) vs. the transfer or exploitation of "established knowledge" within a group, organisation, or community. Collaborative environments such as communities of practice or the use of social computing tools can be used for both knowledge creation and transfer [9].
[edit] Strategies
Knowledge may be accessed at three stages: before, during, or after KM-related activities. Different organisations have tried various knowledge capture incentives, including making content submission mandatory and incorporating rewards into performance measurement plans. Considerable controversy exists over whether incentives work or not in this field and no consensus has emerged.
One strategy to KM involves actively managing knowledge (push strategy). In such an instance, individuals strive to explicitly encode their knowledge into a shared knowledge repository, such as a database, as well as retrieving knowledge they need that other individuals have provided to the repository [10]. This is also commonly known as the Codification approach to KM.
Another strategy to KM involves individuals making knowledge requests of experts associated with a particular subject on an ad hoc basis (pull strategy). In such an instance, expert individual(s) can provide their insights to the particular person or people needing this (Snowden 2002). This is also commonly known as the Personalization approach to KM.
Other knowledge management strategies for companies include:
rewards (as a means of motivating for knowledge sharing)
storytelling (as a means of transferring tacit knowledge)
cross-project learning
after action reviews
knowledge mapping (a map of knowledge repositories within a company accessible by all)
communities of practice
expert directories (to enable knowledge seeker to reach to the experts)
best practice transfer
competence management (systematic evaluation and planning of competences of individual organization members)
proximity & architecture (the physical situation of employees can be either conducive or obstructive to knowledge sharing)
master-apprentice relationship
collaborative technologies (groupware, etc)
knowledge repositories (databases, bookmarking engines, etc)
measuring and reporting intellectual capital (a way of making explicit knowledge for companies)
knowledge brokers (some organizational members take on responsibility for a specific "field" and act as first reference on whom to talk about a specific subject)
social software (wikis, social bookmarking, blogs, etc)
Particularly, the implementation of formal knowledge management practices is important in large organizations. When the number of employees exceeds 150, internal knowledge sharing dramatically decreases because of higher complexity in the formal organizational structure, weaker inter-employee relationships, lower trust, reduced connective efficacy, and less effective communication. As such, as the size of an organizational unit increases, the effectiveness of internal knowledge flows dramatically diminishes and the degree of intra-organizational knowledge sharing decreases (Serenko, Bontis & Hardie 2007).
[edit] Motivations
A number of claims exist as to the motivations leading organisations to undertake a KM effort [11]. Typical considerations driving a KM effort include:
Making available increased knowledge content in the development and provision of products and services
Achieving shorter new product development cycles
Facilitating and managing innovation and organizational learning
Leveraging the expertise of people across the organization
Increasing network connectivity between internal and external individuals
Managing business environments and allowing employees to obtain relevant insights and ideas appropriate to their work
Solving intractable or wicked problems
Managing intellectual capital and intellectual assets in the workforce (such as the expertise and know-how possessed by key individuals)
Debate exists whether KM is more than a passing fad, though increasing amount of research in this field may hopefully help to answer this question, as well as create consensus on what elements of KM help determine the success or failure of such efforts (Wilson 2002) [12].
[edit] Technologies
Early KM technologies included online corporate yellow pages as expertise locators and document management systems. Combined with the early development of collaborative technologies (in particular Lotus Notes), KM technologies expanded in the mid-1990s. Subsequent KM efforts leveraged semantic technologies for search and retrieval and the development of e-learning tools for communities of practice [13] (Capozzi 2007).
More recently, development of social computing tools (such as blogs and wikis) have allowed more unstructured, self-governing or ecosystem approaches to the transfer, capture and creation of knowledge, including the development of new forms of communities, networks, or matrixed organisations. However such tools for the most part are still based on text and code, and thus represent explicit knowledge transfer. These tools face challenges in distilling meaningful re-usable knowledge and ensuring that their content is transmissible through diverse channels [14](Andrus 2005).
Software tools in knowledge management are a collection of technologies and are not necessarily acquired as a single software solution. Furthermore, these knowledge management software tools have the advantage of using the organisation’s existing information technology infrastructure. Organisations and business decision makers spend a great deal of resources and make significant investments in the latest technology, systems and infrastructure to support knowledge management. It is imperative that these investments are validated properly, made wisely and that the most appropriate technologies and software tools are selected or combined to facilitate knowledge management. A set of characteristics that should support decision makers in the selection of software tools for knowledge management are available [15].
Knowledge management has also become a cornerstone in emerging business strategies such as Service Lifecycle Management (SLM) with companies increasingly turning to software vendors to enhance their efficiency in industries including, but not limited to, the aviation industry.[16]
[edit] See also
Chief knowledge officer
Community of practice
Competitive intelligence
Complexity theory and organizations
Computer supported cooperative work
Collective intelligence
Collective unconscious
Concept map
Data mining
DIKW
Enterprise content management
Enterprise 2.0
Enterprise bookmarking
Enterprise social software
Expert system
Explicit knowledge
Human-computer interaction
Information ecology
Knowledge
Knowledge base
Knowledge economy
Knowledge ecosystems
Knowledge engineering
Knowledge management software
Knowledge market
Knowledge representation
Knowledge tagging
Knowledge transfer
Knowledge worker
Knowledge-based theory of the firm
Management information system
Metaknowledge
Ontology
Organisational memory
Personal information management
Personal knowledge management
Sensemaking
Semantic web
Social network
Sociology of knowledge
Tacit knowledge
Value network analysis
[edit] References
Addicott, Rachael; McGivern, Gerry; Ferlie, Ewan (2006). "Networks, Organizational Learning and Knowledge Management: NHS Cancer Networks". Public Money & Management 26 (2): 87-94. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=889992.
Alavi, Maryam; Leidner, Dorothy E. (1999). "Knowledge management systems: issues, challenges, and benefits". Communications of the AIS 1 (2). http://portal.acm.org/citation.cfm?id=374117.
Alavi, Maryam; Leidner, Dorothy E. (2001). "Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues". MIS Quarterly 25 (1): 107-136. http://web.njit.edu/~jerry/CIS-677/Articles/Alavi-MISQ-2001.pdf.
Andrus, D. Calvin (2005). "The Wiki and the Blog: Toward a Complex Adaptive Intelligence Community". Studies in Intelligence 49 (3). http://ssrn.com/abstract=755904.
Bontis, Nick; Choo, Chun Wei (2002). The Strategic Management of Intellectual Capital and Organizational Knowledge. New York:Oxford University Press. ISBN 019513866X. http://choo.fis.toronto.edu/OUP/.
Bontis, Nick; Serenko, Alexander; Biktimirov, Ernest (2006). "MBA knowledge management course: Is there an impact after graduation?". International Journal of Knowledge and Learning 2 (3/4): 216-237. http://foba.lakeheadu.ca/serenko/papers/Bontis_Serenko_Biktimirov.pdf.
Bontis, Nick; Hardie, Tim; Serenko, Alexander (2008). "Self-efficacy and KM course weighting selection: Can students optimize their grades?". International Journal of Teaching and Case Studies 1 (3): 189-199. http://foba.lakeheadu.ca/serenko/papers/IJTCS_PUBLISHED.pdf.
Bontis, Nick; Serenko, Alexander (2009). "A follow-up ranking of academic journals". Journal of Knowledge Management 13 (1): 16-26. http://foba.lakeheadu.ca/serenko/papers/KM_Journal_Ranking_Bontis_Serenko.pdf.
Booker, Lorne; Bontis, Nick; Serenko, Alexander (2008). "The relevance of knowledge management and intellectual capital research". Knowledge and Process Management 15 (4): 235-246. http://foba.lakeheadu.ca/serenko/papers/Booker_Bontis_Serenko_KM_relevance.pdf.
Capozzi, Marla M. (2007). "Knowledge Management Architectures Beyond Technology". First Monday 12 (6). http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/1871/1754.
Davenport, Tom (2008). "Enterprise 2.0: The New, New Knowledge Management?". Harvard Business Online, Feb. 19, 2008. http://discussionleader.hbsp.com/davenport/2008/02/enterprise_20_the_new_new_know_1.html.
Lakhani, Andrew P.; McAfee (2007). "Case study on deleting "Enterprise 2.0" article". Courseware #9-607-712, Harvard Business School. http://courseware.hbs.edu/public/cases/wikipedia/.
McAdam, Rodney; McCreedy, Sandra (2000). "A Critique Of Knowledge Management: Using A Social Constructionist Model". New Technology, Work and Employment 15 (2). http://papers.ssrn.com/sol3/papers.cfm?abstract_id=239247.
McAfee, Andrew P. (2006). "Enterprise 2.0: The Dawn of Emergent Collaboration". Sloan Management Review 47 (3): 21-28. http://sloanreview.mit.edu/the-magazine/articles/2006/spring/47306/enterprise-the-dawn-of-emergent-collaboration/.
McInerney, Claire (2002). "Knowledge Management and the Dynamic Nature of Knowledge". Journal of the American Society for Information Science and Technology 53 (12): 1009–1018. http://www.scils.rutgers.edu/~clairemc/KM_dynamic_nature.pdf.
Morey, Daryl; Maybury, Mark; Thuraisingham, Bhavani (2002). Knowledge Management: Classic and Contemporary Works. Cambridge: MIT Press. pp. 451. http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8987.
Nanjappa, Aloka; Grant, Michael M. (2003). "Constructing on constructivism: The role of technology". Electronic Journal for the Integration of Technology in Education 2 (1). http://ejite.isu.edu/Volume2No1/nanjappa.pdf.
Nonaka, Ikujiro (1991). "The knowledge creating company". Harvard Business Review 69 (6 Nov-Dec): 96-104. http://hbr.harvardbusiness.org/2007/07/the-knowledge-creating-company/es.
Nonaka, Ikujiro; Takeuchi, Hirotaka (1995). The knowledge creating company: how Japanese companies create the dynamics of innovation. New York: Oxford University Press. pp. 284. http://books.google.com/books?id=B-qxrPaU1-MC.
Sensky, Tom (2002). "Knowledge Management". Advances in Psychiatric Treatment 8 (5): 387-395. http://apt.rcpsych.org/cgi/content/full/8/5/387.
Snowden, Dave (2002). "Complex Acts of Knowing - Paradox and Descriptive Self Awareness". Journal of Knowledge Management, Special Issue 6 (2): 100 - 111. doi:10.1108/13673270210424639. http://www.cognitive-edge.com/articledetails.php?articleid=13.
Spender, J.-C. & Andreas Georg Scherer (2007), "The Philosophical Foundations of Knowledge Management: Editors' Introduction", Organization 14 (1): 5-28, <http://ssrn.com/abstract=958768>
Serenko, Alexander & Nick Bontis (2004), "Meta-review of knowledge management and intellectual capital literature: citation impact and research productivity rankings", Knowledge and Process Management 11 (3): 185-198, DOI:10.1002/kpm.203, <http://www.business.mcmaster.ca/mktg/nbontis//ic/publications/KPMSerenkoBontis.pdf>
Serenko, Alexander; Nick Bontis & Lorne Booker et al. (2010), "A scientometric analysis of knowledge management and intellectual capital academic literature (1994-2008)", Journal of Knowledge Management in-press
Serenko, Alexander & Nick Bontis (2009), "Global ranking of knowledge management and intellectual capital academic journals", Journal of Knowledge Management 13 (1): 4-15, <http://foba.lakeheadu.ca/serenko/papers/KM_Journal_Ranking_Serenko_Bontis.pdf>
Serenko, Alexander; Nick Bontis & Josh Grant (2009), "A scientometric analysis of knowledge management and intellectual capital academic literature (1994-2008)", Journal of Intellectual Capital 10 (1): 8-21, <http://foba.lakeheadu.ca/serenko/papers/Serenko_Bontis_Grant.pdf>
Serenko, Alexander; Nick Bontis & Tim Hardie (2007), "Organizational size and knowledge flow: A proposed theoretical link", Journal of Intellectual Capital 8 (4): 610-627, <http://foba.lakeheadu.ca/serenko/papers/GitasRule_Published.pdf>
Thompson, Mark P.A. & Geoff Walsham (2004), "Placing Knowledge Management in Context", Journal of Management Studies 41 (5): 725-747, <http://papers.ssrn.com/sol3/papers.cfm?abstract_id=559300>
Wenger, Etienne; McDermott, Richard; Synder, Richard (2002). Cultivating Communities of Practice: A Guide to Managing Knowledge - Seven Principles for Cultivating Communities of Practice. Boston: Harvard Business School Press. pp. 107-136. ISBN 1578513308. http://hbswk.hbs.edu/archive/2855.html.
Wilson, T.D. (2002). "The nonsense of 'knowledge management'". Information Research 8 (1). http://informationr.net/ir/8-1/paper144.html.
Wright, Kirby (2005). "Personal knowledge management: supporting individual knowledge worker performance". Knowledge Management Research and Practice 3 (3): 156–165. doi:10.1057/palgrave.kmrp.8500061.
[edit] Notes
^ http://www.unc.edu/~sunnyliu/inls258/Introduction_to_Knowledge_Management.html
^ http://www.crito.uci.edu/noah/HOIT/HOIT%20Papers/TeacherBridge.pdf
^ http://www.ischool.washington.edu/mcdonald/ecscw03/papers/groth-ecscw03-ws.pdf
^ http://www.ndu.edu/sdcfp/reports/2007Reports/IBM07%20.doc
^ http://iakm.kent.edu/programs/information-use/iu-curriculum.html
^ http://papers.ssrn.com/sol3/papers.cfm?abstract_id=984600
^ http://citeseer.ist.psu.edu/wyssusek02sociopragmatic.html
^ http://papers.ssrn.com/sol3/papers.cfm?abstract_id=991169
^ http://papers.ssrn.com/sol3/papers.cfm?abstract_id=961043
^ http://www.cs.fiu.edu/~chens/PDF/IRI00_Rathau.pdf
^ http://tecom.cox.smu.edu/abasu/itom6032/kmlect.pdf
^ http://myweb.whitman.syr.edu/yogesh/papers/WhyKMSFail.pdf
^ http://elvis.slis.indiana.edu/irpub/HT/2001/pdf53.pdf
^ "Knowledge Management". www.systems-thinking.org. http://www.systems-thinking.org/kmgmt/kmgmt.htm. Retrieved 2009-02-26.
^ Smuts, Hanlie; Van der Merwe, AJ; Loock, M (2009), Key characteristics in selecting software tools for Knowledge Management, 11th International Conference on Enterprise Information Systems, Milan Italy (May 2009).
^ Aviation Industry Group. "Service life-cycle management", Aircraft Technology: Engineering & Maintenance, February-March, 2005.
[edit] External links
From Wikipedia, the free encyclopedia
Jump to: navigation, search
Knowledge management (KM) comprises a range of practices used in an organisation to identify, create, represent, distribute and enable adoption of insights and experiences. Such insights and experiences comprise knowledge, either embodied in individuals or embedded in organisational processes or practice.
An established discipline since 1991 (see Nonaka 1991), KM includes courses taught in the fields of business administration, information systems, management, and library and information sciences (Alavi & Leidner 1999). More recently, other fields have started contributing to KM research; these include information and media, computer science, public health, and public policy.
Many large companies and non-profit organisations have resources dedicated to internal KM efforts, often as a part of their 'business strategy', 'information technology', or 'human resource management' departments (Addicott, McGivern & Ferlie 2006). Several consulting companies also exist that provide strategy and advice regarding KM to these organisations.
KM efforts typically focus on organisational objectives such as improved performance, competitive advantage, innovation, the sharing of lessons learned, and continuous improvement of the organisation. KM efforts overlap with organisational learning, and may be distinguished from that by a greater focus on the management of knowledge as a strategic asset and a focus on encouraging the sharing of knowledge. KM efforts can help individuals and groups to share valuable organisational insights, to reduce redundant work, to avoid reinventing the wheel per se, to reduce training time for new employees, to retain intellectual capital as employees turnover in an organisation, and to adapt to changing environments and markets (McAdam & McCreedy 2000)(Thompson & Walsham 2004).
Contents[hide]
1 History
2 Knowledge management as an academic discipline
3 Research
3.1 Dimensions
3.2 Strategies
3.3 Motivations
3.4 Technologies
4 See also
5 References
5.1 Notes
6 External links
//
[edit] History
KM efforts have a long history, to include on-the-job discussions, formal apprenticeship, discussion forums, corporate libraries, professional training and mentoring programs. More recently, with increased use of computers in the second half of the 20th century, specific adaptations of technologies such as knowledge bases, expert systems, knowledge repositories, group decision support systems, intranets and computer supported cooperative work have been introduced to further enhance such efforts[1].
In 1999, the term personal knowledge management was introduced which refers to the management of knowledge at the individual level (Wright 2005).
In terms of the enterprise, early collections of case studies recognized the importance of knowledge management dimensions of strategy, process, and measurement (Morey, Maybury & Thuraisingham 2002). Key lessons learned included: people, and the cultures that influence their behaviors, are the single most critical resource for successful knowledge creation, dissemination, and application; cognitive, social, and organizational learning processes are essential to the success of a knowledge management strategy; and measurement, benchmarking, and incentives are essential to accelerate the learning process and to drive cultural change. In short, knowledge management programs can yield impressive benefits to individuals and organizations if they are purposeful, concrete, and action-oriented.
More recently with the advent of the Web 2.0, the concept of knowledge management has evolved towards a vision more based on people participation and emergence. This line of evolution is termed Enterprise 2.0 (McAfee 2006). However, there is an ongoing debate and discussions (Lakhani & McAfee 2007) as to whether Enterprise 2.0 is just a fad that does not bring anything new or useful or whether it is, indeed, the future of knowledge management (Davenport 2008).
[edit] Knowledge management as an academic discipline
KM emerged as a scientific discipline in the earlier 1990s. It was initially supported by only practitioners, when Scandia hired Leif Edvinsson of Sweden as the world’s first Chief Knowledge Officer (CKO). Hubert Saint-Onge (formerly of CIBC, Canada), started investigating various sides of KM long before that. The objective of CKOs is to manage and maximize the intangible assets of their organizations. Gradually, CKOs became interested in not only practical but also theoretical aspects of KM, and the new research field was formed. The KM ideas were quickly endorsed by several highly regarded academics, such as Ikujiro Nonaka (Hitotsubashi University), Hirotaka Takeuchi (Hitotsubashi University), Thomas H. Davenport (Babson College) and Baruch Lev (New York University). In 2001, Thomas Stewart, former editor at FORTUNE Magazine, published an excellent cover story highlighting the importance of intellectual capital of organizations (Serenko et al. 2010).
After that, the KM discipline has started quickly evolving. Serenko and Bontis, in their meta-analysis of KM research predicted that the total number of KM works would exceed 10,000 by 2010 (Serenko & Bontis 2004). In fact, this number has quickly grew much faster. As of 2009, there were 20 distinct KM academic journals available (Serenko & Bontis 2009), with Journal of Knowledge Management and Journal of Intellectual Capital ranked as the leading A+ pure-KM outlets (Bontis & Serenko 2009). Dozens of national and international conferences were held with McMaster World Congress on the Management of Intellectual Capital and Innovation being the pioneering event (Serenko, Bontis & Grant 2009). A number of KM research centers were formed (e.g., The Monieson Centre, Queen’s University and Knowledge Management Research Centre, Hong Kong Polytechnic University). Graduate-level university courses were introduced since 2001 (Bontis, Hardie & Serenko 2008) (Bontis, Serenko & Biktimirov 2006).
Recently, a comprehensive scientometric analysis of the entire KM discipline was undertaken (Serenko et al. 2010). It was found that KM researchers tend to adapt methods of inquiry from reference disciplines, mostly from accounting, finance, human resources management, organizational behavior, psychology, and information systems. The methods of inquiry employed by KM researchers are: 1) framework, model, approach, principle, index, metrics, or tool development (32%); 2) case study (24%); 3) literature review (work based on existing literature) (11%); 4) survey (10%); and 5) use of secondary data (8%). Other methods, for instance, focus groups or field experiments are very rare in KM research. The most productive KM countries are USA, UK, Australia, Spain and Canada that generated over 50% of the word’s KM research output, with 21% coming solely from USA. The leading research institutions are Cranfield University, UK; Copenhagen Business School, Denmark; Macquarie University, Australia; University of Oviedo, Spain; and McMaster University, Canada. It was concluded that KM research may potentially contribute to the wealth of nations because the correlation between countries’ GDP per capita and their KM scholarly research output is strong (Spearman’s pho = 0.597, p < 0.000).
Since its establishment, the KM discipline has been gradually moving towards academic maturity. First, there is a trend towards higher cooperation among academics; particularly, there has been a drop in single-authored publications. Second, the role of practitioners has changed. Their contribution to academic research has been dramatically declining from 30% of overall contributions up to 2002, to only 10% by 2009. At the same time, this phenomenon is regrettable since academics may lose touch with practice and start producing research that is of less interest to industry professionals. In fact, the issue of relevance of academic research has been frequently raised in all fields, including KM. A series of interviews with a number of KM managers revealed that KM research is highly relevant to the needs of practice. However, there should be effective and efficient mechanisms to translate the findings presented in academic journals to a more comprehensible format accessible to non-academics (Booker, Bontis & Serenko 2008).
[edit] Research
A broad range of thoughts on the KM discipline exists with no unanimous agreement; approaches vary by author and school. As the discipline matures, academic debates have increased regarding both the theory and practice of KM, to include the following perspectives:
Techno-centric with a focus on technology, ideally those that enhance knowledge sharing and creation.
Organisational with a focus on how an organisation can be designed to facilitate knowledge processes best.
Ecological with a focus on the interaction of people, identity, knowledge, and environmental factors as a complex adaptive system akin to a natural ecosystem.
Regardless of the school of thought, core components of KM include People, Processes, Technology (or) Culture, Structure, Technology, depending on the specific perspective (Spender & Scherer 2007). Different KM schools of thought include various lenses through which KM can be viewed and explained, to include:
community of practice (Wenger, McDermott & Synder 2001) [2]
social network analysis [3]
intellectual capital (Bontis & Choo 2002) [4]
information theory [5] (McInerney 2002)
complexity science [6]
constructivism [7] (Nanjappa & Grant 2003)
[edit] Dimensions
Different frameworks for distinguishing between knowledge exist. One proposed framework for categorising the dimensions of knowledge distinguishes between tacit knowledge and explicit knowledge. Tacit knowledge represents internalised knowledge that an individual may not be consciously aware of, such as how he or she accomplishes particular tasks. At the opposite end of the spectrum, explicit knowledge represents knowledge that the individual holds consciously in mental focus, in a form that can easily be communicated to others.[8] (Alavi & Leidner 2001).
Early research suggested that a successful KM effort needs to convert internalised tacit knowledge into explicit knowledge in order to share it, but the same effort must also permit individuals to internalise and make personally meaningful any codified knowledge retrieved from the KM effort. Subsequent research into KM suggested that a distinction between tacit knowledge and explicit knowledge represented an oversimplification and that the notion of explicit knowledge is self-contradictory. Specifically, for knowledge to be made explicit, it must be translated into information (i.e., symbols outside of our heads) (Serenko & Bontis 2004). Later on, Ikujiro Nonaka proposed a model (SECI for Socialization, Externalization, Combination, Internalization) which considers a spiraling knowledge process interaction between explicit knowledge and tacit knowledge (Nonaka & Takeuchi 1995). In this model, knowledge follows a cycle in which implicit knowledge is 'extracted' to become explicit knowledge, and explicit knowledge is 'reinternalised' into implicit knowledge.
A second proposed framework for categorising the dimensions of knowledge distinguishes between embedded knowledge of a system outside of a human individual (e.g., an information system may have knowledge embedded into its design) and embodied knowledge representing a learned capability of a human body’s nervous and endocrine systems (Sensky 2002).
A third proposed framework for categorising the dimensions of knowledge distinguishes between the exploratory creation of "new knowledge" (i.e., innovation) vs. the transfer or exploitation of "established knowledge" within a group, organisation, or community. Collaborative environments such as communities of practice or the use of social computing tools can be used for both knowledge creation and transfer [9].
[edit] Strategies
Knowledge may be accessed at three stages: before, during, or after KM-related activities. Different organisations have tried various knowledge capture incentives, including making content submission mandatory and incorporating rewards into performance measurement plans. Considerable controversy exists over whether incentives work or not in this field and no consensus has emerged.
One strategy to KM involves actively managing knowledge (push strategy). In such an instance, individuals strive to explicitly encode their knowledge into a shared knowledge repository, such as a database, as well as retrieving knowledge they need that other individuals have provided to the repository [10]. This is also commonly known as the Codification approach to KM.
Another strategy to KM involves individuals making knowledge requests of experts associated with a particular subject on an ad hoc basis (pull strategy). In such an instance, expert individual(s) can provide their insights to the particular person or people needing this (Snowden 2002). This is also commonly known as the Personalization approach to KM.
Other knowledge management strategies for companies include:
rewards (as a means of motivating for knowledge sharing)
storytelling (as a means of transferring tacit knowledge)
cross-project learning
after action reviews
knowledge mapping (a map of knowledge repositories within a company accessible by all)
communities of practice
expert directories (to enable knowledge seeker to reach to the experts)
best practice transfer
competence management (systematic evaluation and planning of competences of individual organization members)
proximity & architecture (the physical situation of employees can be either conducive or obstructive to knowledge sharing)
master-apprentice relationship
collaborative technologies (groupware, etc)
knowledge repositories (databases, bookmarking engines, etc)
measuring and reporting intellectual capital (a way of making explicit knowledge for companies)
knowledge brokers (some organizational members take on responsibility for a specific "field" and act as first reference on whom to talk about a specific subject)
social software (wikis, social bookmarking, blogs, etc)
Particularly, the implementation of formal knowledge management practices is important in large organizations. When the number of employees exceeds 150, internal knowledge sharing dramatically decreases because of higher complexity in the formal organizational structure, weaker inter-employee relationships, lower trust, reduced connective efficacy, and less effective communication. As such, as the size of an organizational unit increases, the effectiveness of internal knowledge flows dramatically diminishes and the degree of intra-organizational knowledge sharing decreases (Serenko, Bontis & Hardie 2007).
[edit] Motivations
A number of claims exist as to the motivations leading organisations to undertake a KM effort [11]. Typical considerations driving a KM effort include:
Making available increased knowledge content in the development and provision of products and services
Achieving shorter new product development cycles
Facilitating and managing innovation and organizational learning
Leveraging the expertise of people across the organization
Increasing network connectivity between internal and external individuals
Managing business environments and allowing employees to obtain relevant insights and ideas appropriate to their work
Solving intractable or wicked problems
Managing intellectual capital and intellectual assets in the workforce (such as the expertise and know-how possessed by key individuals)
Debate exists whether KM is more than a passing fad, though increasing amount of research in this field may hopefully help to answer this question, as well as create consensus on what elements of KM help determine the success or failure of such efforts (Wilson 2002) [12].
[edit] Technologies
Early KM technologies included online corporate yellow pages as expertise locators and document management systems. Combined with the early development of collaborative technologies (in particular Lotus Notes), KM technologies expanded in the mid-1990s. Subsequent KM efforts leveraged semantic technologies for search and retrieval and the development of e-learning tools for communities of practice [13] (Capozzi 2007).
More recently, development of social computing tools (such as blogs and wikis) have allowed more unstructured, self-governing or ecosystem approaches to the transfer, capture and creation of knowledge, including the development of new forms of communities, networks, or matrixed organisations. However such tools for the most part are still based on text and code, and thus represent explicit knowledge transfer. These tools face challenges in distilling meaningful re-usable knowledge and ensuring that their content is transmissible through diverse channels [14](Andrus 2005).
Software tools in knowledge management are a collection of technologies and are not necessarily acquired as a single software solution. Furthermore, these knowledge management software tools have the advantage of using the organisation’s existing information technology infrastructure. Organisations and business decision makers spend a great deal of resources and make significant investments in the latest technology, systems and infrastructure to support knowledge management. It is imperative that these investments are validated properly, made wisely and that the most appropriate technologies and software tools are selected or combined to facilitate knowledge management. A set of characteristics that should support decision makers in the selection of software tools for knowledge management are available [15].
Knowledge management has also become a cornerstone in emerging business strategies such as Service Lifecycle Management (SLM) with companies increasingly turning to software vendors to enhance their efficiency in industries including, but not limited to, the aviation industry.[16]
[edit] See also
Chief knowledge officer
Community of practice
Competitive intelligence
Complexity theory and organizations
Computer supported cooperative work
Collective intelligence
Collective unconscious
Concept map
Data mining
DIKW
Enterprise content management
Enterprise 2.0
Enterprise bookmarking
Enterprise social software
Expert system
Explicit knowledge
Human-computer interaction
Information ecology
Knowledge
Knowledge base
Knowledge economy
Knowledge ecosystems
Knowledge engineering
Knowledge management software
Knowledge market
Knowledge representation
Knowledge tagging
Knowledge transfer
Knowledge worker
Knowledge-based theory of the firm
Management information system
Metaknowledge
Ontology
Organisational memory
Personal information management
Personal knowledge management
Sensemaking
Semantic web
Social network
Sociology of knowledge
Tacit knowledge
Value network analysis
[edit] References
Addicott, Rachael; McGivern, Gerry; Ferlie, Ewan (2006). "Networks, Organizational Learning and Knowledge Management: NHS Cancer Networks". Public Money & Management 26 (2): 87-94. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=889992.
Alavi, Maryam; Leidner, Dorothy E. (1999). "Knowledge management systems: issues, challenges, and benefits". Communications of the AIS 1 (2). http://portal.acm.org/citation.cfm?id=374117.
Alavi, Maryam; Leidner, Dorothy E. (2001). "Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues". MIS Quarterly 25 (1): 107-136. http://web.njit.edu/~jerry/CIS-677/Articles/Alavi-MISQ-2001.pdf.
Andrus, D. Calvin (2005). "The Wiki and the Blog: Toward a Complex Adaptive Intelligence Community". Studies in Intelligence 49 (3). http://ssrn.com/abstract=755904.
Bontis, Nick; Choo, Chun Wei (2002). The Strategic Management of Intellectual Capital and Organizational Knowledge. New York:Oxford University Press. ISBN 019513866X. http://choo.fis.toronto.edu/OUP/.
Bontis, Nick; Serenko, Alexander; Biktimirov, Ernest (2006). "MBA knowledge management course: Is there an impact after graduation?". International Journal of Knowledge and Learning 2 (3/4): 216-237. http://foba.lakeheadu.ca/serenko/papers/Bontis_Serenko_Biktimirov.pdf.
Bontis, Nick; Hardie, Tim; Serenko, Alexander (2008). "Self-efficacy and KM course weighting selection: Can students optimize their grades?". International Journal of Teaching and Case Studies 1 (3): 189-199. http://foba.lakeheadu.ca/serenko/papers/IJTCS_PUBLISHED.pdf.
Bontis, Nick; Serenko, Alexander (2009). "A follow-up ranking of academic journals". Journal of Knowledge Management 13 (1): 16-26. http://foba.lakeheadu.ca/serenko/papers/KM_Journal_Ranking_Bontis_Serenko.pdf.
Booker, Lorne; Bontis, Nick; Serenko, Alexander (2008). "The relevance of knowledge management and intellectual capital research". Knowledge and Process Management 15 (4): 235-246. http://foba.lakeheadu.ca/serenko/papers/Booker_Bontis_Serenko_KM_relevance.pdf.
Capozzi, Marla M. (2007). "Knowledge Management Architectures Beyond Technology". First Monday 12 (6). http://firstmonday.org/htbin/cgiwrap/bin/ojs/index.php/fm/article/view/1871/1754.
Davenport, Tom (2008). "Enterprise 2.0: The New, New Knowledge Management?". Harvard Business Online, Feb. 19, 2008. http://discussionleader.hbsp.com/davenport/2008/02/enterprise_20_the_new_new_know_1.html.
Lakhani, Andrew P.; McAfee (2007). "Case study on deleting "Enterprise 2.0" article". Courseware #9-607-712, Harvard Business School. http://courseware.hbs.edu/public/cases/wikipedia/.
McAdam, Rodney; McCreedy, Sandra (2000). "A Critique Of Knowledge Management: Using A Social Constructionist Model". New Technology, Work and Employment 15 (2). http://papers.ssrn.com/sol3/papers.cfm?abstract_id=239247.
McAfee, Andrew P. (2006). "Enterprise 2.0: The Dawn of Emergent Collaboration". Sloan Management Review 47 (3): 21-28. http://sloanreview.mit.edu/the-magazine/articles/2006/spring/47306/enterprise-the-dawn-of-emergent-collaboration/.
McInerney, Claire (2002). "Knowledge Management and the Dynamic Nature of Knowledge". Journal of the American Society for Information Science and Technology 53 (12): 1009–1018. http://www.scils.rutgers.edu/~clairemc/KM_dynamic_nature.pdf.
Morey, Daryl; Maybury, Mark; Thuraisingham, Bhavani (2002). Knowledge Management: Classic and Contemporary Works. Cambridge: MIT Press. pp. 451. http://mitpress.mit.edu/catalog/item/default.asp?ttype=2&tid=8987.
Nanjappa, Aloka; Grant, Michael M. (2003). "Constructing on constructivism: The role of technology". Electronic Journal for the Integration of Technology in Education 2 (1). http://ejite.isu.edu/Volume2No1/nanjappa.pdf.
Nonaka, Ikujiro (1991). "The knowledge creating company". Harvard Business Review 69 (6 Nov-Dec): 96-104. http://hbr.harvardbusiness.org/2007/07/the-knowledge-creating-company/es.
Nonaka, Ikujiro; Takeuchi, Hirotaka (1995). The knowledge creating company: how Japanese companies create the dynamics of innovation. New York: Oxford University Press. pp. 284. http://books.google.com/books?id=B-qxrPaU1-MC.
Sensky, Tom (2002). "Knowledge Management". Advances in Psychiatric Treatment 8 (5): 387-395. http://apt.rcpsych.org/cgi/content/full/8/5/387.
Snowden, Dave (2002). "Complex Acts of Knowing - Paradox and Descriptive Self Awareness". Journal of Knowledge Management, Special Issue 6 (2): 100 - 111. doi:10.1108/13673270210424639. http://www.cognitive-edge.com/articledetails.php?articleid=13.
Spender, J.-C. & Andreas Georg Scherer (2007), "The Philosophical Foundations of Knowledge Management: Editors' Introduction", Organization 14 (1): 5-28, <http://ssrn.com/abstract=958768>
Serenko, Alexander & Nick Bontis (2004), "Meta-review of knowledge management and intellectual capital literature: citation impact and research productivity rankings", Knowledge and Process Management 11 (3): 185-198, DOI:10.1002/kpm.203, <http://www.business.mcmaster.ca/mktg/nbontis//ic/publications/KPMSerenkoBontis.pdf>
Serenko, Alexander; Nick Bontis & Lorne Booker et al. (2010), "A scientometric analysis of knowledge management and intellectual capital academic literature (1994-2008)", Journal of Knowledge Management in-press
Serenko, Alexander & Nick Bontis (2009), "Global ranking of knowledge management and intellectual capital academic journals", Journal of Knowledge Management 13 (1): 4-15, <http://foba.lakeheadu.ca/serenko/papers/KM_Journal_Ranking_Serenko_Bontis.pdf>
Serenko, Alexander; Nick Bontis & Josh Grant (2009), "A scientometric analysis of knowledge management and intellectual capital academic literature (1994-2008)", Journal of Intellectual Capital 10 (1): 8-21, <http://foba.lakeheadu.ca/serenko/papers/Serenko_Bontis_Grant.pdf>
Serenko, Alexander; Nick Bontis & Tim Hardie (2007), "Organizational size and knowledge flow: A proposed theoretical link", Journal of Intellectual Capital 8 (4): 610-627, <http://foba.lakeheadu.ca/serenko/papers/GitasRule_Published.pdf>
Thompson, Mark P.A. & Geoff Walsham (2004), "Placing Knowledge Management in Context", Journal of Management Studies 41 (5): 725-747, <http://papers.ssrn.com/sol3/papers.cfm?abstract_id=559300>
Wenger, Etienne; McDermott, Richard; Synder, Richard (2002). Cultivating Communities of Practice: A Guide to Managing Knowledge - Seven Principles for Cultivating Communities of Practice. Boston: Harvard Business School Press. pp. 107-136. ISBN 1578513308. http://hbswk.hbs.edu/archive/2855.html.
Wilson, T.D. (2002). "The nonsense of 'knowledge management'". Information Research 8 (1). http://informationr.net/ir/8-1/paper144.html.
Wright, Kirby (2005). "Personal knowledge management: supporting individual knowledge worker performance". Knowledge Management Research and Practice 3 (3): 156–165. doi:10.1057/palgrave.kmrp.8500061.
[edit] Notes
^ http://www.unc.edu/~sunnyliu/inls258/Introduction_to_Knowledge_Management.html
^ http://www.crito.uci.edu/noah/HOIT/HOIT%20Papers/TeacherBridge.pdf
^ http://www.ischool.washington.edu/mcdonald/ecscw03/papers/groth-ecscw03-ws.pdf
^ http://www.ndu.edu/sdcfp/reports/2007Reports/IBM07%20.doc
^ http://iakm.kent.edu/programs/information-use/iu-curriculum.html
^ http://papers.ssrn.com/sol3/papers.cfm?abstract_id=984600
^ http://citeseer.ist.psu.edu/wyssusek02sociopragmatic.html
^ http://papers.ssrn.com/sol3/papers.cfm?abstract_id=991169
^ http://papers.ssrn.com/sol3/papers.cfm?abstract_id=961043
^ http://www.cs.fiu.edu/~chens/PDF/IRI00_Rathau.pdf
^ http://tecom.cox.smu.edu/abasu/itom6032/kmlect.pdf
^ http://myweb.whitman.syr.edu/yogesh/papers/WhyKMSFail.pdf
^ http://elvis.slis.indiana.edu/irpub/HT/2001/pdf53.pdf
^ "Knowledge Management". www.systems-thinking.org. http://www.systems-thinking.org/kmgmt/kmgmt.htm. Retrieved 2009-02-26.
^ Smuts, Hanlie; Van der Merwe, AJ; Loock, M (2009), Key characteristics in selecting software tools for Knowledge Management, 11th International Conference on Enterprise Information Systems, Milan Italy (May 2009).
^ Aviation Industry Group. "Service life-cycle management", Aircraft Technology: Engineering & Maintenance, February-March, 2005.
[edit] External links
Subscribe to:
Posts (Atom)