Knowledge management
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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
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[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
Thursday, November 19, 2009
Monday, September 7, 2009
philosophical / discusive
1 Philosophical/discursive
This may cover a variety of approaches, but will draw primarily on existing literature, rather than new empirical data. A discursive study could examine a particular issue, perhaps from an alternative perspective (eg feminist). Alternatively, it might put forward a particular argument or examine a methodological issue.
Examples:
· Davies, P. (1999) ‘What is Evidence-Based Education?’ British Journal of Educational Studies, 47 (2), 108-121. [A discussion of the meaning of ‘evidence-based education’ and its relevance to research and policy]
· Pring, R. (2000) ‘The ‘False Dualism’ of Educational Research’. Journal of Philosophy of Education, 34, 2, 247-260. [An argument against the idea that qualitative and quantitative research are from rigidly distinct paradigms]
2 Literature review
This may be an attempt to summarise or comment on what is already known about a particular topic. By collecting different sources together, synthesising and analysing critically, it essentially creates new knowledge or perspectives. There are a number of different forms a literature review might take.
A ‘systematic’ review will generally go to great lengths to ensure that all relevant sources (whether published or not) have been included. Details of the search strategies used and the criteria for inclusion must be made clear. A systematic review will often make a quantitative synthesis of the results of all the studies, for example by meta-analysis.
Where a literature field is not sufficiently well conceptualised to allow this kind of synthesis, or where findings are largely qualitative (or inadequately quantified), it may not be appropriate to attempt a systematic review. In this case a literature review may help to clarify the key concepts without attempting to be systematic. It may also offer critical or alternative perspectives to those previously put forward.
Examples:
· Adair, J.G, Sharpe, D. and Huynh, C-L. (1990) ‘Hawthorne Control Procedures in Educational Experiments: A reconsideration of their use and effectiveness’ Review of Educational Research, 59, 2, 215-228. [A systematic review and meta-analysis of studies that have tried to measure the ‘Hawthorne Effect’]
· Black, P. and Wiliam, D. (1998) ‘Assessment and classroom learning’. Assessment in Education, 5, 1, 7-74. [Quite a long article, but it includes an excellent summary of a large field of research]
· Brown, M., Askew, M., Baker, D., Denvir, H and Millett, A. (1998) ‘Is the National Numeracy Strategy Research-Based?’ British Journal of Educational Studies, 46, 4, 362-385 [A review of the evidence for and against the numeracy strategy]
3 Case study
This will involve collecting empirical data, generally from only one or a small number of cases. It usually provides rich detail about those cases, of a predominantly qualitative nature. There are a number of different approaches to case study work (eg ethnographic, hermeneutic, ethogenic, etc) and the principles and methods followed should be made clear.
A case study generally aims to provide insight into a particular situation and often stresses the experiences and interpretations of those involved. It may generate new understandings, explanations or hypotheses. However, it does not usually claim representativeness and should be careful not to over-generalise.
Examples:
· Jimenez, R.T. and Gersten, R. (1999) ‘Lessons and Dilemmas derived from the Literacy Instruction of two Latina/o Teachers’. American Educational Research Journal, 36, 2, 265-302. [A detailed study of the behaviour and experiences of two teachers of English to minority students]
· Ball, S. (1981) Beachside Comprehensive: a case study of secondary schooling.
4 Survey
Where an empirical study involves collecting information from a larger number of cases, perhaps using questionnaires, it is usually described as a survey. Alternatively, a survey might make use of already available data, collected for another purpose. A survey may be cross-sectional (data collected at one time) or longitudinal (collected over a period). Because of the larger number of cases, a survey will generally involve some quantitative analysis.
Issues of generalisablity are usually important in presenting survey results, so it is vital to report how samples were chosen, what response rates were achieved and to comment on the validity and reliability of any instruments used.
Examples:
· Francis, B. (2000) ‘The Gendered Subject: students’ subject preferences and discussions of gender and subject ability’.
· Denscombe, M. (2000) ‘Social Conditions for Stress: young people’s experience of doing GCSEs’ British Educational Research Journal, 26,. 3, 359-374.
5 Evaluation
This might be an evaluation of a curriculum innovation or organisational change. An evaluation can be formative (designed to inform the process of development) or summative (to judge the effects). Often an evaluation will have elements of both. If an evaluation relates to a situation in which the researcher is also a participant it may be described as ‘action research’. Evaluations will often make use of case study and survey methods and a summative evaluation will ideally also use experimental methods.
Examples:
· Burden, R. and Nichols, L. (2000) ‘Evaluating the process of introducing a thinking skills programme into the secondary school curriculum’. Research Papers in Education, 15, 3, 259-74.
· Ruddock, J. Berry, M., Brown, N. and Frost, D. (2000) ‘Schools learning from other schools: cooperation in a climate of competition’. Research Papers in Education, 15, 3, 293-306.
6 Experiment
This involves the deliberate manipulation of an intervention in order to determine its effects. The intervention might involve individual pupils, teachers, schools or some other unit. Again, if the researcher is also a participant (eg a teacher) this could be described as ‘action research’. An experiment may compare a number of interventions with each other, or may compare one (or more) to a control group. If allocation to these different ‘treatment groups’ is decided at random it may be called a true experiment; if allocation is on any other basis (eg using naturally arising or self-selected groups) it is usually called a ‘quasi-experiment’.
Issues of generalisablity (often called ‘external validity’) are usually important in an experiment, so the same attention must be given to sampling, response rates and instrumentation as in a survey (see above). It is also important to establish causality (‘internal validity’) by demonstrating the initial equivalence of the groups (or attempting to make suitable allowances), presenting evidence about how the different interventions were actually implemented and attempting to rule out any other factors that might have influenced the result.
Examples:
· Finn, J.D. and 2, 4, 520-4. [A smaller study which investigates how the kinds of feedback students are given affects their achievement]
· Achilles, C.M. (1990) ‘Answers and questions about class size: A statewide experiment.’ American Educational Research Journal, 27, 557-577. [A large-scale classic experiment to determine the effects of small classes on achievement]
Slavin, R.E. (1980) ‘Effects of individual learning expectations on student achievement’. Journal RJC
EdD Research Methods
of Educational Psychology, 72002
Educational Qualification
M.A (History)
MLIS(LIBRARY INFORMATION SCIENCE)
B.P.ED (PHYSICAL DEPARTMENT)
MLIS(LIBRARY INFORMATION SCIENCE)
B.P.ED (PHYSICAL DEPARTMENT)
Friday, July 31, 2009
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