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Record W3000665563 · doi:10.22055/slis.2019.30348.1632

شناسایی و تحلیل مولفههای اصلی مدیریت دانش در بخش عمومی و دولتی کشورها با رویکرد مطالعه تطبیقی

2019· article· fa· W3000665563 on OpenAlexaboutno aff
لیلا نامداریان, فرهاد شیرانی, تیمور مرجانی

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2019
Typearticle
Languagefa
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

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<p><strong>Background and Objectives</strong>: In the era of knowledge-based economy, most of large firms in the private sector are actively following; acknowledging, and implementing knowledge management techniques and instruments in order to reach competitive advantage, guarantees their survival and qualification. Knowledge management can foster the effectiveness and competitiveness of the government in the growingly changing environment. The public sectors and NGOs should face these challenges and take advantage of the opportunities resulting from globalization, knowledge-based economy and ICT development. However, lack of awareness of knowledge management is evident in the public sector. This might hinder the effective implementation of management actions in organizations for improving their performance. However, some public organizations have embraced the significance of knowledge management and offering services to the public and included knowledge management in their agenda. In Iran, the policy documents have highlighted knowledge management in the public sector. However, it is still an emerging area. Scrutinizing the experiences of various countries considering knowledge management in the public sector can highly contribute to its development in Iran. Hence, the current study primarily aimed to identify the factors contributing to the establishment of knowledge management in the public sector based on reviewing and exploring the knowledge management experiences in the public sector in various countries. To this end, the major research question was “What are the most important factors which should be considered for establishing knowledge management in the public sector?”<br><strong>Methodology</strong>: Taking into account that most knowledge management actions are included in research and development policies, to answer the research question, the nation-wide studies were divided into three categories, High R & D, Moderate R & D, and Low R & D according to the categorization presented by the European Union in 2007. As a result, such criteria as geographical location, percentage of R & D gross expenditures in GDP, their strategic importance, knowledge-based economy, and access to their information were taken into account in order to choose the countries for three R & D categories.In high R & D and with R & D budget over than 2.4% GDP, 6 countries were chosen from three groups including 3 countries from Europe (Swiss, Germany, and Austria), 2 countries from America (Canada, The United States), and 1 country from Asia (Korea),In moderate R & D and with R & D budget between 1.5% and 2.4% GDP, 2 countries were chosen from two groups including 1 country from Europe (England), and 1 country from Asia (China).In low R & D and with R & D budget less than 1.5% GDP, 4 countries were chosen from one group including 4 countries from Asia (India, Malaysia, Thailand, and Iran).The current study adopted a comparative qualitative approach and used framework analysis method. Having received the key concepts and ideas related to the research purposes, the researchers categorized them in a thematic framework. In order to form an analytical framework, the researchers used a continuous analysis for the qualitative data (coding summaries). Then, the experiences of the aforementioned countries were reviewed several times in order to discover the common meanings and patterns among their actions considering knowledge management. Afterwards, the actions related to knowledge management in the public sector were assigned codes and thereby, preliminary codes formed. Then, symmetrical codes were organized in the relevant thematic frameworks. The researchers finally defined, revised and analyzed the themes.<br><strong>Findings</strong>: According to the research findings, the most important components of knowledge management establishment in the public and governmental sectors are human resources and training, technological infrastructure, organizational culture, learning and innovation, and organizational structure.<br><strong>Discussion:</strong> According to the findings, those knowledge management dimensions which should be considered in the public sector were as follows:<br>•     Human resources and education- one of the most important components of knowledge management is human resources since it mainly relies on individuals’ tendency to share and reuse knowledge.<br>•     Technological substructures- establishing and using internal and external networks efficiently are important actions which should be considered by organizations. However, using internal and external networks can contribute to knowledge management when they are intended to serve this purpose. Otherwise, they would not be effective.<br>•     Organizational and leadership culture- sharing knowledge is not a natural action in public organizations and requires changing the individuals’ mental model. In order to change individuals’ attitudes and overcoming obstacles, knowledge sharing culture should be developed.<br>•     Learning and innovation- knowledge management plays a vital role in supporting organizational learning since effective sharing would facilitate collective wisdom. Moreover, there is a strong positive relationship between knowledge management, its human dimension and innovation.<br>•     Organizational structure- The structure of the public organization is traditional and entails numerous hierarchies. The term “silo” is probably the best choice for describing this structure. It mismatches the requirements of knowledge management. In other words, knowledge management requires an effective structure that is a motivating and productive structure.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.817
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0060.001
Scholarly communication0.0030.001
Open science0.0030.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.1090.196

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.270
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2019
Admission routes1
Has abstractyes

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