research on knowledge management

• Browse All Articles
• Newsletter Sign-Up

KnowledgeManagement →

No results found in working knowledge.

• Were any results found in one of the other content buckets on the left?
• Try removing some search filters.
• Use different search filters.

A review of knowledge management about theoretical conception and designing approaches

International Journal of Crowd Science

ISSN : 2398-7294

Article publication date: 11 April 2018

Issue publication date: 10 July 2018

The main purpose of this paper is to conduct an in-depth theoretical review and analysis for the fields of knowledge management (KM) and investigate the future research trend about KM.

Design/methodology/approach

At first, few theoretical basis about KM which include definitions and stages about KM have been summarized and analyzed. Then a comprehensive review about the major approaches for designing the KM system from different perspectives including knowledge representation and organization, knowledge sharing and performance measure for KM has been conducted.

The contributions of this paper will be useful for both academics and practitioners for the study of KM.

Originality/value

For this research, the focus is on conducting an in-depth theoretical review and analysis of KM.

• Knowledge management
• Literature review
• Design approaches

Gao, T. , Chai, Y. and Liu, Y. (2018), "A review of knowledge management about theoretical conception and designing approaches", International Journal of Crowd Science , Vol. 2 No. 1, pp. 42-51. https://doi.org/10.1108/IJCS-08-2017-0023

Emerald Publishing Limited

Copyright © 2018, Tingwei Gao, Yueting Chai and Yi Liu.

Published in the International Journal of Crowd Science . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

In recent years, knowledge has been widely recognized the most crucial competitive asset ( Palacios and Garrigos, 2006 ). Knowledge refers to a theoretical or practical understanding of a subject. Knowledge management (KM) has become a very common term in the twenty-first century, as it has been applied to a wide spectrum of activities and areas with the purpose of managing, creating and enhancing intellectual assets ( Shannak, 2009 ). And it has become enriched with a huge wealth of contributions from many scholars and an extensive accumulation of experiences. From a deeper point of view, KM should be a kind of working method and philosophy. KM is a part of the field of management studies, but it is also closely integrated with information and communication technologies ( Mihalca et al. , 2008 ). In fact, KM can be observed from several perspectives, as there are a number of fields that contribute to it. Prominent among them are the fields of philosophy, cognitive science, social science, management science, information science, knowledge engineering, artificial intelligence and economics ( Kakabadse et al. , 2003 ).

Why the need to manage knowledge? Nowadays we are in the era of knowledge. The reason of increased importance of knowledge lies in the fact that effective management of knowledge brings many positive outcomes to improve learning efficiency. And we implement KM initiatives with the expectation that it will result in increased competitive advantage. KM is used to capture, document, retrieve and reuse knowledge, as well as to create, transfer and exchange it ( Dayan and Evans, 2006 ). There is no limit to where KM can be applied, ranging from individual learning, small enterprises to large multinational corporations: KM has become increasingly more important for individuals to understand what information is essential, how to administer this essential information and how to transform essential information into permanent knowledge ( Tseng et al. , 2012 ); KM plays a fundamental role in the success of an organization’s activities and strategies ( Castrogiovanni et al. , 2016 ). Therefore managing and using knowledge effectively is vital for both individuals and organizations to take full advantage of the value of knowledge.

During the past decade, numerous publications dealing with KM reviews from different perspectives have been published. Ragab and Arisha (2013) categorized different branches of KM research. Serenko (2013) analyzed the stock of KM publications and identified citation classics in KM field. Makhsousi et al. (2013) reviewed recent advances on the implementation of KM in different areas and discussed why some of KM implementations fail and how they could turn into a successful one. Arisha and Ragab (2013) provided a literature review and categorized the analysis of the rapidly growing number of KM publications, and they offered a comprehensive reference for newcomers embarking on research in the field. Matayong and Mahmood (2013) reviewed the current literature of KM systems studies in organizations. Chiliban et al. (2014) reviewed different KM models based on their strengths and weaknesses. Tzortzaki and Mihiotis (2014) studied how the theory revolving around KM has developed over the years. Omotayo (2015) reviewed the literature in the area of KM to bring out the importance of KM in an organization. Asrar-ul-Haq and Anwar (2016) reviewed the attempts to provide the evidence base concerning knowledge sharing and KM in organizational settings.

Based on the above-described scenario, in this research, we aim to provide a systemic overview of KM. And we accomplish this task by a series analysis approaches, such as literature bibliometric, theoretical basic analysis and designing approaches’ re-view. At last, our main contributions can be related to the Streams (A) and (B) as follows: (A) we summarize and analyze some major theoretical conceptions about KM and (B) we give a comprehensive review about the approaches for designing the KM system. The remainder of this paper is organized as follows. In Section 2, we review the major conception of KM. Section 3 shows and analyzes the approaches to design KM system. Finally, conclusions are presented in Section 4.

2. Theoretical conception of knowledge management

2.1 definition of knowledge management.

There are a number of approaches to the conception about knowledge, as it is both a complex and abstract term. Actually, the definition of knowledge is a matter of ongoing debate among philosophers in the field of epistemology. One of the most accepted definitions about knowledge is that knowledge is a dynamic human resource of justification of the personal beliefs to obtain the truth ( Nonaka, 1994 ). It can then be stated that knowledge is an invisible or intangible asset, in which its acquisition involves complex cognitive processes of perception, learning, communication, association and reasoning ( Epetimehin and Ekundayo, 2011 ). Knowledge is the concept, skill, experience and vision that provides a framework for creating, evaluating and using the information ( Soltani and Navimipour, 2016 ). Generally, knowledge can be divided into two types, tacit and explicit ( Hubert, 1996 ). Tacit knowledge is the personal and context-specific knowledge of a person that resides in the human mind, behavior and perception ( Duffy, 2000 ). Koenig (2012) suggested that explicit knowledge means information or knowledge that is set out in tangible form.

Also there are many definitions and descriptions about KM written by different scholars from various fields. These definitions are somewhat unclear and have different meanings depending on the authors’ views. To have a deep understanding of KM, we should re-visit some fundamentals of KM, such as the theoretical understanding of the concept of knowledge despite the abundance of theoretical and conceptual work. We have reviewed some major conceptions of KM and summarized them in Table I . When reviewing the definitions about KM, there are some terms that seem more central and fundamental than others, such as organization and information. In summary, despite the various versions of the definition and descriptions about KM, their essence is to help individuals improve learning efficiency and integrate different information resources to improve competitiveness advantages. And KM is capable of providing the individual with the tools and techniques they need to surmount the overwhelming information they encounter and to enable them to improve learning efficacy and increase competitive advantage.

2.2 Process and stages of knowledge management

KM is viewed as a process, where many related activities are formed to carry out key elements of strategy and operations for KM. During the past two decades, a vast number of KM processes have been introduced by researchers from different perspectives. And we reviewed and summarized some major descriptions about KM process. Table II shows this result. Although there are various descriptions about KM process, some words seem more central and fundamental than others, such as creation, storage, transfer and application.

Knowledge creation refers to how new knowledge is created. This stage involves the developing of new content or the replacing of existing content within the tacit and explicit knowledge ( Ajmal and Koskinen, 2008 ). Knowledge storage refers to the process of recording knowledge and storing it in the repositories such as archives, databases and filing systems. And it aims to transfer the knowledge to the individual, groups or units that need to apply it ( Johannsen, 2000 ). Knowledge transfer is an important process of KM and refers to the transfer of knowledge to locations where it is needed and can be used ( Pirkkalainen and Pawlowski, 2013 ). This phase is critical for the success of the KM process, as the transfer must produce changes in the knowledge base ( Argote and Ingram, 2000 ). Knowledge application refers to the actualizing of knowledge. This process can be used to adjust strategic direction, solve new problems, improve efficiency and reduce costs ( Newell et al. , 2004 ). And this stage is used to make good use of the created knowledge such as implementing a best practice.

3. Designing approaches for knowledge management

3.1 knowledge representation and organization.

Knowledge representation and organization is a technique that increasing efficiency of an explaining associations of knowledge bodies with the purpose of managing knowledge by creating similar content associations. During the past decade, the semantic link network (SLN) has been widely used in the field of KM. SLN is a network that represents semantic relations between concepts. And it is always used as a form of knowledge representation. It consists of vertices, which represent concepts, and edges, which represent semantic relations between concepts ( Hai, 2011 ).

Kravchenko et al. (2017) designed a new approach for semantic similarity estimation to solve some problems about KM. They developed the genetic algorithm for semantic similarity estimation in accordance with the knowledge graph model. Xiao et al. (2016) proposed a new model for knowledge semantic representation (KSR) to produce semantic interpretable representations, which is used for explicitly representing knowledge. Che Cob et al. (2016) proposed a KM model based on semantic to support collaborative learning environment. Cob et al. (2015) discussed the application of SLN to enhance the KM and proposed a semantic KM model to support collaborative learning environment. Liu et al. (2014) described the development of a semantic-based KM platform for Web-enabled environments featuring intelligence and insight capabilities.

Among the applications of SLN in KM, the most widely used method is ontology. Ontology was taken from philosophy, where it means a systematic explanation of being. An ontology is a catalog of existing concepts in a field, which contains predicates, semantics of concepts and terms and how they relate to one another (Natalya et al. , 2001). Ontology has wide application potential in the classification of information, the construction of information and knowledge database, as well as the research and development of intelligent search engine. As shown in Table III , the applications of ontology to the field of KM have aroused the concern of many researchers during the past decade.

3.2 Knowledge sharing

One of the major challenges in KM is how to promote to share knowledge with others. In fact, effective KM relies on successful knowledge sharing ( Swacha, 2015 ). Knowledge sharing can be defined as “the exchange of knowledge between and among individuals.” And it aims at bringing knowledge sources together and manipulating into new knowledge structures or routines. Knowledge sharing and knowledge transfer are sometimes used synonymously or are considered to have overlapping content ( Dan and Sunesson, 2012 ). Following the bulk of literature, we shall consider knowledge sharing to be semantically the same as knowledge transfer ( Paulin and Suneson, 2012 ). The success of knowledge sharing relied on the degree to which the knowledge is recreated in the recipient.

Swacha (2015) defined a system of appropriate gamification rules which makes use of a number of purposely selected gamification components, and aimed at motivating individuals for various activities related to knowledge sharing. Yong (2013) provided new findings of the respective impacts of organizational rewards, reciprocity, enjoyment and social capital on individuals’ knowledge sharing intentions, which prior research has ignored so far. Their new findings will be very useful to deepening and widening our understanding of the respective role of individual motivations and social capital in individuals’ knowledge-sharing intentions. Ma and Yuen (2011) proposed an online knowledge-sharing model and tested among undergraduate students using an online learning environment. And this model introduces two new constructs – perceived online attachment motivation and perceived online relationship commitment. Hung et al. (2011) investigated the effects of intrinsic motivation and extrinsic motivation on knowledge sharing in a group meeting. Results of their experiment showed that the KM system with built-in reputation feedback is crucial to support successful knowledge sharing. Tohidinia and Mosakhani (2010) evaluated the influence of a series of potential factors on knowledge-sharing behavior and suggested a systematic effort to improve knowledge-sharing behavior in organizations, an effort in which relevant factors from different perspectives are considered.

3.3 Performance measure for knowledge management

Performance measurement is a crucial part in KM ( Wang et al. , 2015 ). By this process of measure, we can assess the effectiveness of KM practices and judge whether the current knowledge process can meet the our learning needs and whether it can provide feedback of information on KM to carry out continuous improvement on KM. KM performance evaluation includes the design of KM performance evaluation criteria and the selection of the evaluation methods ( Wang and Zheng, 2010 ). This process consists of qualitative analysis and quantitative analysis. The common qualitative approaches for KM evaluation include open-ended questionnaires ( Changchit et al. , 2001 ), expert interviews ( Booker et al. , 2008 ), case studies and surveys ( Darroch and McNaughton, 2002 ). While, the quantitative analysis is always used to measure the explicit knowledge with a series of indicators which include both financial and non-financial ( Chen and Chen, 2005 ).

Wang et al. (2016) proposed an index system of KM, which includes four components: the KM process, the organizational knowledge structure, the economic benefits and the efficiency. Wang et al. (2015) categorized the performance measures into three categories: knowledge resources, KM processes, and the factors that affect KM. Zhang (2010) applied the Balanced Scorecard into the performance assessment of KM on the basis of the analysis of the Balanced Scorecard and KM and carried out the detailed analysis to measure the performance of KM tools from four aspects – financial, customer, internal processes and learning and growth. Wang and Zheng (2010) proposed a KM performance evaluation method that includes knowledge system, structure capital, human capital, mental capital and market capital. Wu et al. (2009) developed an evaluation method of KM performance based on the principal component analysis. And the measure index consists of knowledge stocks, maturity degree of the learning organizations, information management and marketing capability. Tseng (2008) proposed a categorization matrix that classifies the performance indicators for potential use in KM performance measurements. And the evaluation criteria of this method include process, human and IT.

4. Conclusions

For this research, we focus on providing a deep theoretical review and analysis of KM. First, we summarized and analyzed the theoretical conceptions of KM, which include conception and stages. Then we reviewed some major approaches for designing the KM system from different perspectives including knowledge representation and organization, knowledge sharing and performance measure for KM.

Major definitions about KM

Different descriptions about KM process

Ontology for knowledge representation

Ajmal , M.M. and Koskinen , K.U. ( 2008 ), “ Knowledge transfer in project-based organizations: an organizational culture perspective ”, Project Management Journal , Vol. 39 No. 1 , pp. 7 - 15 .

Arisha , A. and Ragab , M.A.F. ( 2013 ), “ Knowledge management and measurement: a critical review ”, Journal of Knowledge Management , Vol. 17 No. 6 , pp. 873 - 901 .

Argote , L. and Ingram , P. ( 2000 ), “ Knowledge transfer: a basis for competitive advantage in firms ”, Organizational Behavior and Human Decision Processes , Vol. 82 No. 1 , pp. 150 - 169 .

Asrar-ul-Haq , M. and Anwar , S. ( 2016 ), “ A systematic review of knowledge management and knowledge sharing: trends, issues, and challenges ”, Cogent Business & Management , Vol. 3 No. 1 , p. 1127744 .

Booker , L.D. , Bontis , N. and Serenko , A. ( 2008 ), “ The relevance of knowledge management and intellectual Capital research ”, Knowledge and Process Management , Vol. 15 No. 4 , pp. 235 - 246 .

Castrogiovanni , G. , Ribeiro-Soriano , D. , Mas-Tur , A. and Roig-Tierno , N. ( 2016 ), “ Where to acquire knowledge: adapting knowledge management to financial institutions ⋆ ”, Journal of Business Research , Vol. 69 No. 5 , pp. 1812 - 1816 .

Changchit , C. , Holsapple , C.W. and Viator , R.E. ( 2001 ), “ Transferring auditors’ internal control evaluation knowledge to management ”, Expert Systems with Applications , Vol. 20 No. 3 , pp. 275 - 291 .

Chen , A.P. and Chen , M.Y. ( 2005 ), “ A review of survey research in knowledge management performance measurement: 1995–2004 ”, Journal of Universal Knowledge Management , No. 1 , pp. 4 - 12 .

Che Cob , Z. , Abdullah , R. , Mohd Drus , S. and Ali , N.A. ( 2016 ), System Requirement Specifications for a Semantic Knowledge Management System for Collaborative Learning Environment .

Chiliban , B. , Baral , L.M. and Kifor , C.V. ( 2014 ), “ Review of knowledge management models for implementation within advanced product quality planning ”, Knowledge Science, Engineering and Management , Springer International Publishing .

Cob , C. , Abdullah , R. , Risidi , H. and Mohd , N.M. ( 2015 ), “ Preliminary study on semantic knowledge management model for collaborative learning ”, ARPN Journal of Engineering and Applied Sciences , Vol. 10 No. 2 , pp. 442 - 450 .

Dan , P. and Sunesson , K. ( 2012 ), “ Knowledge transfer, knowledge sharing and knowledge barriers – three blurry terms in km ”, Electronic Journal of Knowledge Management , Vol. 10 No. 1 , pp. 82 - 92 .

Darroch , J. and McNaughton , R. ( 2002 ), “ Developing a measure of knowledge management ”, In: Bontis , N. (Ed.), World Congress on Intellectual Capital Readings , Butterworth-Heinemann , Boston, MA , pp. 226 - 242 .

Dayan , R. and Evans , S. ( 2006 ), “ KM your way to CMMI ”, Journal of Knowledge Management , Vol. 10 No. 1 , pp. 69 - 80 .

Duffy , J. ( 2000 ), “ Knowledge management: to be or not to be? ”, Information Management Journal , Vol. 34 No. 1 , pp. 64 - 67 .

Epetimehin , F.M. and Ekundayo , O. ( 2011 ), “ Organisational knowledge management: survival strategy for Nigeria insurance industry ”, Interdisciplinary Review of Economics and Management , Vol. 1 No. 2 , pp. 9 - 15 .

Hai , Z. ( 2011 ), “ Semantic linking through spaces for cyber-physical-socio intelligence: a methodology ⋆ ”, Artificial Intelligence , Vol. 175 No. 5 , pp. 988 - 1019 .

Hubert , S.O. ( 1996 ), “ Tacit knowledge: the key to the strategic aliment of intellectual capital ”, Strategy and Leadership , Vol. 24 No. 2 , pp. 10 - 16 .

Hung , S.Y. , Durcikova , A. , Lai , H.M. and Lin , W.M. ( 2011 ), “ The influence of intrinsic and extrinsic motivation on individuals’ knowledge sharing behavior ”, International Journal of Human-Computer Studies , Vol. 69 No. 6 , pp. 415 - 427 .

Johannsen , C.G. ( 2000 ), “ Total quality management in a knowledge management perspective ”, Journal of Documentation , Vol. 56 No. 1 , pp. 42 - 54 .

Kakabadse , N.K. , Kakabadse , A. and Kouzmin , N. ( 2003 ), “ Reviewing the knowledge management literature: towards a taxonomy ”, Journal of Knowledge Management , Vol. 7 No. 4 , pp. 75 - 91 .

Koenig , M.E.D. ( 2012 ), “ What is KM? Knowledge management explained ”, KM World, May, 2012 , available at: www.kmworld.com/Articles/Editorial/What-Is-…/What-is-KM-Knowledge-Management-Explained-82405.aspx (accessed 8 February 2013 ).

Kravchenko , Y. , Kursitys , I. and Bova , V. ( 2017 ), The Development of Genetic Algorithm for Semantic Similarity Estimation in Terms of Knowledge Management Problems .

Liu , Y. , Yang , D. and Wang , Y. ( 2014 ), A Semantic-Based Knowledge Management Platform , Pacific Asia Conference on Information Systems (PACIS) , p. 163 .

Ma , W.W.K. and Yuen , A.H.K. ( 2011 ), “ Understanding online knowledge sharing: an interpersonal relationship perspective ”, Computers & Education , Vol. 56 No. 1 , pp. 210 - 219 .

Makhsousi , A. , Sadaghiani , J. and Amiri , M. ( 2013 ), “ A review on recent advances on knowledge management implementations ”, Management Science Letters , Vol. 3 No. 3 , pp. 861 - 866 .

Matayong , S. and Mahmood , A.K. ( 2013 ), “ The review of approaches to knowledge management system studies ”, Journal of Knowledge Management , Vol. 17 No. 3 , pp. 472 - 490 .

Mihalca , R. , Uta , A. , Intorsureanu , I. and Andreescu , A.I. ( 2008 ), “ Knowledge management in e-learning systems ”, Informatica Economica Journal , Vol. 12 No. 2 , pp. 365 - 369 .

Newell , S. , Tansley , C. and Huang , J. ( 2004 ), “ Social capital and knowledge integration in an ERP project team: the importance of bridging and bonding ”, British Journal of Management , Vol. 15 No. Suppl 1 , pp. S43 - S57 .

Nonaka , I. ( 1994 ), “ A dynamic theory of organizational knowledge creation ”, Organization Science , Vol. 5 No. 1 , pp. 14 - 37 .

Omotayo , F.O. ( 2015 ), “ Knowledge management as an important tool in organisational management: a review of literature ”, Library Philosophy & Practice .

Palacios , M.D. and Garrigos , S.F. ( 2006 ), “ The effect of knowledge management practices on firm performance ”, Journal of Knowledge Management , Vol. 10 No. 3 , pp. 143 - 156 .

Paulin , D. and Suneson , K. ( 2012 ), “ Knowledge transfer, knowledge sharing and knowledge barriers – three blurry terms in KM ”, Electronic Journal of Knowledge Management , Vol. 10 No. 1 .

Pirkkalainen , H. and Pawlowski , J. ( 2013 ), “ Global social knowledge management: from barriers to the selection of social tools ”, Electronic Journal of Knowledge Management , Vol. 11 No. 1 , pp. 3 - 17 .

Ragab , M.A.F. and Arisha , A. ( 2013 ), “ Knowledge management and measurement: a critical review ”, Journal of Knowledge Management , Vol. 17 No. 6 , pp. 873 - 901 .

Serenko , A. ( 2013 ), “ Meta-analysis of scientometric research of knowledge management: discovering the identity of the discipline ”, Journal of Knowledge Management , Vol. 17 No. 5 , pp. 773 - 812 .

Shannak , R.O. ( 2009 ), “ Measuring knowledge management performance ”, European Journal of Scientific Research , Vol. 35 No. 2 , pp. 242 - 253 .

Soltani , Z. and Navimipour , N.J. ( 2016 ), “ Customer relationship management mechanisms: a systematic review of the state of the art literature and recommendations for future research ”, Computers in Human Behavior , Vol. 61 , pp. 667 - 688 .

Swacha , J. ( 2015 ), “ Gamification in knowledge management: motivating for knowledge sharing ”, Polish Journal of Management Studies , p. 12 .

Tohidinia , Z. and Mosakhani , M. ( 2010 ), “ Knowledge sharing behavior and its predictors ”, Industrial Management & Data Systems , Vol. 110 No. 4 , pp. 611 - 631 .

Tseng , K.H. , Chang , C.C. , Lou , S.J. , Tan , Y. and Chiu , C.J. ( 2012 ), “ How concept-mapping perception navigates student knowledge transfer performance[J] ”, Educational Technology & Society , Vol. 15 No. 1 , pp. 102 - 115 .

Tseng , S.M. ( 2008 ), “ Knowledge management system performance measure index ”, Expert Systems with Applications , Vol. 34 No. 1 , pp. 734 - 745 .

Tzortzaki , A.M. and Mihiotis , A. ( 2014 ), “ A review of knowledge management theory and future directions ”, Knowledge and Process Management , Vol. 21 No. 1 , pp. 29 - 41 .

Wang , J. , Ding , D. , Liu , O. and Li , M. ( 2016 ), “ A synthetic method for knowledge management performance evaluation based on triangular fuzzy number and group support systems ”, Applied Soft Computing , Vol. 39 , pp. 11 - 20 .

Wang , Y. and Zheng , J. ( 2010 ), “ Knowledge management performance evaluation based on triangular fuzzy number ”, Procedia Engineering , Vol. 7 No. 8 , pp. 38 - 45 .

Wang , K.Y. , Tan , L.P. , Cheng , S.L. and Wong , W.P. ( 2015 ), “ Knowledge management performance measurement: measures, approaches, trends and future directions ”, Information Development , Vol. 31 No. 3 .

Wu , Y.L. , Wang , X. and Wu , H.S. ( 2009 ), “ Research on the performance measurement of knowledge management based on principal component analysis ”, International Workshop on Intelligent Systems and Applications , IEEE , pp. 1 - 4 .

Xiao , H. , Huang , M. and Zhu , X. ( 2016 ), “ Knowledge semantic representation: a generative model for interpretable knowledge graph embedding ”, arXiv preprint arXiv:1608.07685 .

Zhang , R. ( 2010 ), “ The application of the balanced scorecard in performance assessment of knowledge management ”, The, IEEE International Conference on Information Management and Engineering , IEEE , pp. 443 - 447 .

Further reading

Hau , Y.S. , Kim , B. , Lee , H. and Kim , Y.G. ( 2013 ), “ The effects of individual motivations and social capital on employees’ tacit and explicit knowledge sharing intentions ”, International Journal of Information Management , Vol. 33 , pp. 356 - 366 .

Noy , N.F. and McGuinness , D.L. ( 2001 ), Ontology development 101: a guide to creating your first ontology , Stanford University , Stanford, CA, 94305 .

Corresponding author

Related articles, we’re listening — tell us what you think, something didn’t work….

Report bugs here

All feedback is valuable

Please share your general feedback

Join us on our journey

Platform update page.

Visit emeraldpublishing.com/platformupdate to discover the latest news and updates

Questions & More Information

Answers to the most commonly asked questions here

A descriptive framework for the field of knowledge management

• Regular Paper
• Published: 16 July 2020
• Volume 62 , pages 4481–4508, ( 2020 )

Cite this article

• Yousra Harb   ORCID: orcid.org/0000-0002-0906-9165 1 &
• Emad Abu-Shanab 2  

1379 Accesses

15 Citations

Explore all metrics

Despite the extensive evolution of knowledge management (KM), the field lacks an integrated description. This situation leads to difficulties in research, teaching, and learning. To bridge this gap, this study surveys 2842 articles from top-ranked KM journals to provide a descriptive framework that guides future research in the field of knowledge management. This study also seeks to provide a comprehensive depiction of current research in the field and categorizes these research activities into higher-level categories using grounded theory approach and topic modeling technique. The results show that KM studies are classified into four core research categories: technological, business, people, and domains/applications dimensions. An additional concern addressed in this study is the major research methodologies used in this field. The results raise awareness of the development of KM discipline and hold implications for research methodologies and research trends in the selected KM journals. The results obtained from this study also provide practitioners with a useful quality reference source. The framework and the components included provide researchers, practitioners, and educators with an ontology of KM topics, where they can cover deficiencies in research and provide an agenda for future research.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Knowledge Management in Organizations - A Bibliometric Analysis of Research Trends

Knowledge Management: (Potential) Future Research Directions

The Pervasive Identity of Knowledge Management: Consolidation or Dilution?

Khasawneh R, Abu-Shanab E (2013) Using KMS as a tool to improve decision making process. In: Saeed S, Alsmadi I (eds) Knowledge-based processes in software development. IGI Global, New York, pp 1–11

Google Scholar  

Moro S, Cortez P, Rita P (2015) Business intelligence in banking: a literature analysis from 2002 to 2013 using text mining and latent Dirichlet allocation. Expert Syst Appl 42(3):1314–1324

Delen D, Crossland MD (2008) Seeding the survey and analysis of research literature with text mining. Expert Syst Appl 34(3):1707–1720

Bragge J, Korhonen P, Wallenius H, Wallenius J (2012) Scholarly communities of research in multiple criteria decision making: a bibliometric research profiling study. Int J Inf Technol Decis Mak 11(2):401–426

MATH   Google Scholar  

Serenko A, Bontis N (2013) Global ranking of knowledge management and intellectual capital academic journals: 2013 update. J Knowl Manag 17(2):307–326

Mariano S, Awazu Y (2016) Artifacts in knowledge management research: a systematic literature review and future research directions. J Knowl Manag 20(6):1333–1352

Handzic M (2015) A descriptive analysis of knowledge management research: period from 1997 to 2012. In: Bolisani E, Handzic M (eds) Advances in knowledge management. Knowledge management and organizational learning, vol 1. Springer, Cham, pp 45–64

Walter C, Ribière V (2013) A citation and co-citation analysis of 10 years of KM theory and practices. Knowl Manag Res Pract 11(3):221–229

Strauss A, Corbin J (eds) (1990) Basic of qualitative research: grounded theory procedures and techniques. Sage, Newbury Park

Fteimi N (2015) Analyzing the literature on knowledge management frameworks: towards a normative knowledge management classification schema. In: Proceedings of 23rd European conference on information systems (ECIS), Münster

Abdullah R, Selamat MH, Sahibudin S, Alias RA (2005) A framework for knowledge management system implementation in collaborative environment for higher learning institution. J Knowl Manag Pract 6(1):1–8

Meso P, Smith R (2005) A resource-based view of organizational knowledge management systems. J Knowl Manag 4(3):224–234

Fteimi N, Lehner F (2016) Main research topics in knowledge management: a content analysis of ECKM publications. Electron J Knowl Manag 14(1):5–17

Serenko A, Bontis N (2013) The intellectual core and impact of the knowledge management academic discipline. J Knowl Manag 17(1):137–155

Serenko A, Dumay J (2015) Citation classics published in knowledge management journals. Part I: articles and their characteristics. J Knowl Manag 19(2):401–431

Serenko A, Bontis N, Booker L, Sadeddin K, Hardie T (2010) A scientometric analysis of knowledge management and intellectual capital academic literature (1994–2008). J Knowl Manag 14(1):3–23

Wallace DP, Fleet C, Downs LJ (2011) The research core of the knowledge management literature. Int J Inf Manage 31(1):14–20

Dwivedi YK, Venkitachalam K, Sharif AM, Al-Karaghouli W, Weerakkody V (2011) Research trends in knowledge management: analyzing the past and predicting the future. Inf Syst Manag 28(1):43–56

Ma Z, Yu K (2010) Research paradigms of contemporary knowledge management studies: 1998–2007. J Knowl Manag 14(2):175–189

Webster J, Watson R (2002) Analyzing the past to prepare for the future: writing a literature review. MIS Q 26(2):8–23

Guzzo RA, Jackson SE, Katzell RA (1987) Meta-analysis analysis. Res Organ Behav 9(1):407–442

King WR, He J (2005) Understanding the role and methods of meta-analysis in IS research meta-analysis in is research. Commun Assoc Inf Syst 16(October):665–686

Bentley RA (2008) Random drift versus selection in academic vocabulary: an evolutionary analysis of published keywords. PLoS ONE 3(8):3057

Yang H, Tate M (2012) A descriptive literature review and classification of cloud computing research. Commun Assoc Inf Syst 31(2):35–60

Martin P, Turner B (1986) Grounded theory and organizational research. J Appl Behav Sci 22(2):141–157

Wolfswinkel JF, Furtmueller E, Wilderom CP (2013) Using grounded theory as a method for rigorously reviewing literature. Eur J Inf Syst 22(1):45–55

Lawrence JE, Tar UA (2018) Factors that influence teachers’ adoption and integration of ICT in teaching/learning process. Educ Med Int 55(1):79–105

Orlikowski W (1993) CASE tools as organizational change: investigating incremental and radical changes in systems development. MIS Q 17(3):309–340

Peng Y et al (2008) A descriptive framework for the field of data mining and knowledge discovery. Int J Inf Technol Decis Mak 7(4):639–682

Bacon CJ, Fitzgerald B (2001) Framework for the field of information systems. ACM SIGMIS Datab DATABASE Adv Inf Syst 32(2):46–67

Blei D, Ng A, Jordan M (2003) Latent Dirichlet allocation. J Mach Learn Res 3(Jan):993–1022

Eickhoff M, Wieneke R (2018) Understanding topic models in context: a mixed-methods approach to the meaningful analysis of large document collections. In: Proceedings of the 51st Hawaii international conference on system sciences, Hawaii, pp 903–912

Janasik N, Honkela T, Bruun H (2009) Text mining in qualitative research: application of an unsupervised learning method. Organ Res Methods 12(3):436–460

Baumer E, Mimno D, Guha S, Quan E, Gay G (2017) Comparing grounded theory and topic modeling: extreme divergence or unlikely convergence? J Assoc Inf Sci Technol 68(6):1397–1410

Al-Ramahi M, El-Gayar O, Liu J (2016) Discovering design principles for persuasive systems: a grounded theory and text mining approach. In: Proceedings of 2016 49th Hawaii international conference on system sciences (HICSS). IEEE, Koloa, pp 3074–3083

Lin YC, Hsu CL, Lin MH, Yang HF, Hong CF (2011) Using grounded theory and text mining to find interesting reading materials for slow EFL learners. In: International conference on human–computer interaction. Springer, Berlin, pp 77–85

Corbin J, Strauss A (1990) Grounded theory research: procedures, canons, and evaluative criteria. Qual Sociol 13(1):3–21

Chiovitti R, Piran N (2003) Rigour and grounded theory research. J Adv Nurs 44(4):427–435

Yu CH, Jannasch-Pennell S, DiGangi A (2011) Compatibility between text mining and qualitative research in the perspectives of grounded theory, content analysis, and reliability. Qual Rep 16(3):730–744

Lin FR, Hsieh LS, Chuang FT (2009) Discovering genres of online discussion threads via text mining. Comput Educ 52(2):481–495

Haddi E, Liu X, Shi Y (2013) The role of text pre-processing in sentiment analysis. Proc Comput Sci 17(2013):26–32

Wang X, Mccallum A (2006) Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 424–433

Bird S, Klein E, Loper E (eds) (2009) Natural language processing with python, 1st edn. O’Reilly Media Inc., New York

Ngulube P (2015) Trends in research methodological procedures used in knowledge management studies. Afr J Lib Arch Inf Sci 25(2):125–143

Creswell JW (ed) (2013) Qualitative inquiry and research design: choosing among five approaches, 3rd edn. Sage, Los Angeles

Myers MD (ed) (2009) Qualitative research in business and management. Sage, London

Abu-Shanab E, Harb Y (2019) E-government research insights: text mining analysis. Electron Commer Res Appl 38(November–December):100892

Lau JH, Grieser K, Newman D, Baldwin T (2011) Automatic labelling of topic models. In: Proceedings of the 49th annual meeting of the association for computational linguistics, Portland, Oregon, pp 1536–1545

Velcin J et al (2011) Readitopics: make your topic models readable via labeling and browsing. In: Proceedings of IJCAI: international joint conference on artificial intelligence, pp 5874–5876

Chang J, Boyd-Graber J, Gerrish S, Wang C, Blei D (2009) Reading tea leaves: how humans interpret topic models. In: Proceedings of advances in neural information processing systems. Vancouver, BC, pp 288–296. http://umiacs.umd.edu/~jbg//docs/nips2009-rtl.pdf

Abu-Shanab E, Knight MB, Haddad M (2014) Knowledge sharing practices and the learning organization: a study. IUP J Knowl Manag 12(2):16–27

Eriksson P, Kovalaine A (eds) (2008) Grounded theory research. Qualitative methods in business research. Sage, Thousand Oaks

Intezari A, Gressel S (2017) Information and reformation in KM systems: big data and strategic decision-making. J Knowl Manag 21(1):71–91

Khan Z, Vorley T (2017) Big data text analytics: an enabler of knowledge management. J Knowl Manag 21(1):18–34

Yang JT (2007) The impact of knowledge sharing on organizational learning and effectiveness. J Knowl Manag 11(2):83–90

MathSciNet   Google Scholar  

Lopes I, Rodrigues A (2005) Intangible assets identification and valuation—a theoretical framework approach to the portuguese airlines companies. Electron J Knowl Manag 5(2):193–202

Evangelou C, Karacapilidis N (2005) On the interaction between humans and knowledge management systems: a framework of knowledge sharing catalysts. Knowl Manag Res Pract 3(4):253–261

Lopes I, Martins M, Nunes M (2005) Towards the knowledge economy: the technological innovation and education impact on the value creation process. Electron J Knowl Manag 3(2):129–138

Diefenbach T (2006) Intangible resources: a categorial system of knowledge and other intangible assets. J Intell Cap 7(3):406–420

Preece A et al (2001) Better knowledge management through knowledge engineering. IEEE Intell Syst 16(1):36–43

Mohammadi K, Khanlari A, Sohrabi B (2010) Organizational readiness assessment for knowledge management. Int J Knowl Manag (IJKM) 5(1):29–45

Goh S (2002) Managing effective knowledge transfer: an integrative framework and some practice implications. J Knowl Manag 6(1):23–30

Jeenger P, Kant R (2013) Understanding the knowledge sharing barriers in organisation: a fuzzy AHP approach. J Inf Knowl Manag 12(1):1350003

Alavi M, Leidner D (2001) Review: knowledge management and knowledge management systems: conceptual foundation and research issues. MIS Q 25(1):107–136

Biloslavo R, Zornada M (2004) Development of a knowledge management framework within the systems context. In: Proceedings of the 5th European conference on organizational knowledge, learning and capabilities, 2–3 April, Innsbruck

Sureephong P, Chakpitak N, Ouzrout Y, Bouras A (2008) An ontology-based knowledge management system for industry clusters. In: Proceedings of global design to gain a competitive edge. Springer, London, pp 333–342

Mentzas G, Apostolou D (1998) Towards a holistic knowledge leveraging infrastructure: the KnowNet approach. In: Proceedings of the 2nd international conference on practical aspects of knowledge management, Basel, pp 1–12

Asrar-ul-haq M, Anwar S (2016) A systematic review of knowledge management and knowledge sharing: trends, issues, and challenges. Cog Bus Manag 14(1):1–17

Nonaka I, Takeuchi H (eds) (1995) The knowledge-creating company: how Japanese companies create the dynamics of innovation. Oxford University Press, New York

Ahmed Y, Ahmad M, Ahmad N, Zakaria NH (2018) Social media for knowledge-sharing: a systematic literature review. Telemat Inform 37(February 2019):72–112

Charband Y, Navimipour NJ (2016) Online knowledge sharing mechanisms: a systematic review of the state of the art literature and recommendations for future. Inf Syst Front 18(6):1131–1151

Singh M, Kant R (2008) Knowledge management barriers: an interpretive structural modeling approach. Int J Manag Sci Eng Manag 3(2):141–150

Ferraris A, Mazzolen A, Devalle A, Couturier J (2018) Big data analytics capabilities and knowledge management: impact on firm performance. Manag Decis 57(8):1923–1936

Hasani K, Sheikhesmaeili S (2016) Knowledge management and employee empowerment. Kybernetes 45(2):337–355

Attia A, Salama I (2018) Knowledge management capability and supply chain management practices in the Saudi food industry. Bus Process Manag J 24(2):459–477

Im Y, Park J, Kim M, Park K (2019) Comparative study on perceived trust of topic modeling based on affective level of educational text. Appl Sci 9(21):4565

Hussinki H, Ritala P, Vanhala M, Kianto A (2017) Intellectual capital, knowledge management practices and firm performance. J Intell Cap 18(4):904–922

Barão A, de Vasconcelos JB, Rocha A, Pereira R (2017) A knowledge management approach to capture organizational learning networks. Int J Inf Manag 37(6):735–740

Gupta V, Chopra M (2018) Gauging the impact of knowledge management practices on organizational performance—a balanced scorecard perspective. VINE J Inf Knowl Manag Syst 48(1):21–46

Torres A, Ferraz S, Santos-Rodrigues H (2018) The impact of knowledge management factors in organizational sustainable competitive advantage. J Intell Cap 19(2):453–472. https://doi.org/10.1108/JIC-12-2016-0143

Article   Google Scholar  

Abualoush SH, Obeidat AM, Tarhini A, Masa’deh R, Al-Badi A (2018) The role of employees’ empowerment as an intermediary variable between knowledge management and information systems on employees’ performance. VINE J Inf Knowl Manag Syst 48(2):217–237

Durst S, Pietro E (2018) Exploring knowledge management practices in third-party logistics service providers. VINE J Inf Knowl Manag Syst 48(2):162–177

Tsang YP et al (2018) A fuzzy association rule-based knowledge management system for occupational safety and health programs in cold storage facilities. VINE J Inf Knowl Manag Syst 48(2):199–216

Lejla T, Nijaz B (2018) Information technology, knowledge management and human resource management. VINE J Inf Knowl Manag Syst 48(2):255–276

Hwang Y, Lin H, Shin D (2018) Knowledge system commitment and knowledge sharing intention: the role of personal information management motivation. Int J Inf Manag 39(April):220–227

Daňa J, Caputo F, Ráček J (2018) Complex network analysis for knowledge management and organizational intelligence. J Knowl Econ 11:405–424

Download references

Author information

Authors and affiliations.

Business Information Technology, Yarmouk University, Irbid, Jordan

Yousra Harb

Accounting and Information Systems, Qatar University, Doha, Qatar

Emad Abu-Shanab

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Yousra Harb .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

See Table  4 .

Rights and permissions

Reprints and permissions

About this article

Harb, Y., Abu-Shanab, E. A descriptive framework for the field of knowledge management. Knowl Inf Syst 62 , 4481–4508 (2020). https://doi.org/10.1007/s10115-020-01492-x

Download citation

Received : 22 November 2019

Accepted : 06 July 2020

Published : 16 July 2020

Issue Date : December 2020

DOI : https://doi.org/10.1007/s10115-020-01492-x

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Knowledge management
• Research direction
• Grounded theory
• Topic modeling
• Text mining
• Find a journal
• Publish with us
• Track your research

Knowledge management (KM) is the process of identifying, organizing, storing and disseminating information within an organization.

When knowledge is not easily accessible within an organization, it can be incredibly costly to a business as valuable time is spent seeking out relevant information versus completing outcome-focused tasks.

A knowledge management system (KMS) harnesses the collective knowledge of the organization, leading to better operational efficiencies. These systems are supported by the use of a knowledge base. They are usually critical to successful knowledge management, providing a centralized place to store information and access it readily.

Companies with a knowledge management strategy achieve business outcomes more quickly as increased organizational learning and collaboration among team members facilitates faster decision-making across the business. It also streamlines more organizational processes, such as training and on-boarding, leading to reports of higher employee satisfaction and retention.

Use this model selection framework to choose the most appropriate model while balancing your performance requirements with cost, risks and deployment needs.

Register for the ebook on generative AI

The definition of knowledge management also includes three types of knowledge—tacit, implicit, and explicit knowledge. These types of knowledge are largely distinguished by the codification of the information.

• Tacit knowledge:  This type of knowledge is typically acquired through experience, and it is intuitively understood. As a result, it is challenging to articulate and codify, making it difficult to transfer this information to other individuals. Examples of tacit knowledge can include language, facial recognition, or leadership skills.
• Implicit knowledge:  While some literature equivocates implicit knowledge to tacit knowledge, some academics break out this type separately, expressing that the definition of tactic knowledge is more nuanced. While tacit knowledge is difficult to codify, implicit knowledge does not necessarily have this problem. Instead, implicit information has yet to be documented. It tends to exist within processes, and it can be referred to as “know-how” knowledge.
• Explicit knowledge:  Explicit knowledge is captured within various document types such as manuals, reports, and guides, allowing organizations to easily share knowledge across teams. This type of knowledge is perhaps the most well-known and examples of it include knowledge assets such as databases, white papers, and case studies. This form of knowledge is important to retain intellectual capital within an organization as well as facilitate successful knowledge transfer to new employees.

While some  academics  (link resides outside ibm.com) summarize the knowledge management process as involving knowledge acquisition, creation, refinement, storage, transfer, sharing and utilization. This process can be synthesized this a little further. Effective knowledge management system typically goes through three main steps:

• Knowledge Creation:  During this step, organizations identify and document any existing or new knowledge that they want to circulate across the company.
• Knowledge Storage:  During this stage, an information technology system is typically used to host organizational knowledge for distribution. Information may need to be formatted in a particular way to meet the requirements of that repository.
• Knowledge Sharing:  In this final stage, processes to share knowledge are communicated broadly across the organization. The rate in which information spreads will vary depending on organizational culture. Companies that encourage and reward this behavior will certainly have a competitive advantage over other ones in their industry. 

There are a number tools that organizations utilize to reap the benefits of knowledge management. Examples of knowledge management systems can include:

• Document management systems  act as a centralized storage system for digital documents, such as PDFs, images, and word processing files. These systems enhance employee workflows by enabling easy retrieval of documents, such as lessons learned.
• Content management systems (CMS) are applications which manage web content where end users can edit and publish content. These are commonly confused with document management systems, but CMSs can support other media types, such as audio and video.   
• Intranets  are private networks that exist solely within an organization, which enable the sharing of enablement, tools, and processes within internal stakeholders. While they can be time-consuming and costly to maintain, they provide a number of groupware services, such as internal directories and search, which facilitate collaboration.
• Wikis  can be a popular knowledge management tool given its ease of use. They make it easy to upload and edit information, but this ease can lead to concerns about misinformation as workers may update them with incorrect or outdated information.
• Data warehouses  aggregate data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence (AI), and machine learning. Data is extracted from these repositories so that companies can derive insights, empowering employees to make data-driven decisions.

While knowledge management solutions can be helpful in facilitating knowledge transfer across teams and individuals, they also depend on user adoption to generate positive outcomes. As a result, organizations should not minimize the value of human elements that enable success around knowledge management.

• Organizational Culture:  Management practices will affect the type of organization that executives lead. Managers can build learning organizations by rewarding and encouraging knowledge sharing behaviors across their teams. This type of leadership sets the groundwork for teams to trust each other and communicate more openly to achieve business outcomes.
• Communities of practice:  Centers of excellence in specific disciplines provide employees with a forum to ask questions, facilitating learning and knowledge transfer. In this way, organizations increase the number of subject matter experts in a given area of the company, reducing dependencies on specific individuals to execute certain tasks.

Armed with the right tools and strategies, knowledge management practices have seen success in specific applications, such as:

• Onboarding employees:  Knowledge management systems help to address the huge learning curve for new hires. Instead of overwhelming new hires with a ‘data dump’ in their first weeks, continually support them with knowledge tools that will give them useful information at any time.  Learn more
• Day-to-day employee tasks:  Enable every employee to have access to accurate answers and critical information. Access to highly relevant answers at the right time, for the right person, allows workforces to spend less time looking for information and more time on activities that drive business.  Learn more
• Self-serve customer service:  Customers repeatedly say they’d prefer to find an answer themselves, rather than pick up the phone to call support.  When done well, a knowledge management system helps businesses decrease customer support costs and increase customer satisfaction.  Learn more

Companies experience a number of benefits when they embrace knowledge management strategies. Some key advantages include:

• Identification of skill gaps:  When teams create relevant documentation around implicit or tacit knowledge or consolidate explicit knowledge, it can highlight gaps in core competencies across teams. This provides valuable information to management to form new organizational structures or hire additional resources.
• Make better informed decisions:  Knowledge management systems arm individuals and departments with knowledge. By improving accessibility to current and historical enterprise knowledge, your teams can upskill and make more information-driven decisions that support business goals.
• Maintains enterprise knowledge:  If your most knowledgeable employees left tomorrow, what would your business do? Practicing internal knowledge management enables businesses to create an organizational memory. Knowledge held by your long-term employees and other experts, then make it accessible to your wider team.
• Operational efficiencies:  Knowledge management systems create a go-to place that enable knowledge workers to find relevant information more quickly. This, in turn, reduces the amount of time on research, leading to faster decision-making and cost-savings through operational efficiencies.  Increase productivity not only saves time, but also reduces costs.
• Increased collaboration and communication:  Knowledge management systems and organizational cultures work together to build trust among team members. These information systems provide more transparency among workers, creating more understanding and alignment around common goals. Engaged leadership and open communication create an environment for teams to embrace innovation and feedback.
• Data Security:  Knowledge management systems enable organizations to customize permission control, viewership control and the level of document-security to ensure that information is shared only in the correct channels or with selected individuals. Give your employees the autonomy access knowledge safely and with confidence.

Experiment with foundation models and build machine learning models automatically in our next-generation studio for AI builders.

Watson Discovery is an award-winning AI-powered search technology that eliminates data silos and retrieves information buried inside enterprise data.

How to drive your DataOps framework with a single layer of business knowledge that supports self-service, data governance, AI, natural language processing, and accelerated data lake generation.

Train, validate, tune and deploy generative AI, foundation models and machine learning capabilities with IBM watsonx.ai, a next-generation enterprise studio for AI builders. Build AI applications in a fraction of the time with a fraction of the data.

A program that focuses on the study of knowledge management in government agencies and corporations for the purpose of supporting stated organizational goals and objectives, and prepares individuals to function as information resource managers. Includes instruction in information technology, principles of computer and information systems, management information systems, applicable policy and regulations, and operations and personnel management.

A student with a previous master’s degree must earn a minimum of 60 semester hours of graduate credit beyond the master’s degree in organized course work, independent study and the dissertation. Additional courses above the 60 hours also may be stipulated as needed, such as the research tool and subject tool requirement. Courses counted toward the doctorate must be numbered 5000 or above (except INFO 5000   , INFO 5080   , INFO 5200    or INFO 5600   ) and must be chosen with the approval of a faculty academic advisor.

A student with no previous master’s degree must earn a minimum of 72 semester hours of graduate course work in organized course work, independent study and the dissertation. Additional courses above the 72 hours also may be stipulated as needed. Courses counted toward the doctorate must be numbered 5000 or above and must be chosen with the approval of a faculty academic advisor. The student formally concludes course work by passing the qualifying examination before fully engaging in dissertation research.

Required core, 12 hours

• INFO 6000 - Seminar in Information Science
• INFO 6660 - Readings in Information Science
• INFO 6700 - Seminar in Communication and Use of Information
• INFO 6945 - Trends and Issues in Information Science

Methods core, 12 hours

• INFO 6940 - Research Methodology in Information Science

Quantitative Research Methods/Statistics    6 hours

Qualitative Research Methods    3 hours

Concentration core with guided electives, 15 hours

Completion of at least two of the following prescribed courses (6 hours) with the guidance of their academic advisor:

• ADTA 5100 - Fundamentals of Data Analytics
• INFO 5306 - Project Management for Information Systems
• INFO 5307 - Knowledge Management Tools and Technologies
• INFO 5500 - Foundational Principles in Knowledge Management
• INFO 5503 - Knowledge Management Processes and Practices

Completion of at least three of the following prescribed courses (9 hours) from the list below or other electives as approved by advisor or Major Professor:

• CSCE 5300 - Introduction to Big Data and Data Science
• DTSC 5502 - Principles and Techniques for Data Science
• INFO 5223 - Metadata for Information Organization and Retrieval I
• INFO 5224 - Metadata for Information Organization and Retrieval II
• INFO 5230 - Documents and Records Management
• INFO 5347 - Digital Citizenship
• INFO 5441 - Advanced Storytelling
• INFO 5709 - Data Visualization and Communication
• INFO 5711 - Internet Applications, Services and Management for Information Professionals
• INFO 5810 - Data Analysis and Knowledge Discovery
• INFO 5841 - Data Curation and Management
• INFO 6740 - Scholarly and Scientific Communication
• INFO 6930 - Information and Communication Measurement
• JOUR 5280 - Media Management

Concentration electives, 9 hours

Electives as approved by advisor or Major Professor.

Dissertation, 12 hours

• INFO 6950 - Doctoral Dissertation

Research tool requirement

Students must demonstrate proficiency in research methods or statistics prior to or within the first semester of beginning doctoral course work. This requirement can be met by successfully completing the courses listed below or an equivalent course, or by passing a proficiency exam. A course accepted for this requirement cannot count toward the 60 (or 72) hours required for the doctoral degree.

• COMM 5185 - Quantitative Research Methods in Communication
• DSCI 5180 - Introduction to the Business Decision Process
• EPSY 5210 - Educational Statistics
• INFO 5080 - Research Methods and Analysis

Doctoral Committee

The doctoral committee comprises at least three faculty members who represent at least two academic units, one of which is the Department of Information Science. The committee is formed by the student and serves to evaluate the student’s work at the qualifying examination, dissertation proposal, and dissertation stages.

Progress toward the degree

The student must maintain a minimum grade point average of 3. 0 (B) on all course work on the degree plan.

In addition,

• all core courses must be completed with a grade of A or B;
• no more than two C’s in the non-core program requirements will count toward the degree; and
• no course with a grade below C will count toward the degree.

The maximum time allowed for completing the doctoral degree is 8 years. A faculty academic advisor meets with each student at least annually to review the student’s progress in the program. The student is eligible to sit for the qualifying examination when he or she has designated a doctoral committee, met all degree plan requirements except dissertation hours, and cleared any incomplete grades. When a student passes the qualifying examination, he or she is admitted to candidacy. The doctoral candidate must write and successfully defend a dissertation proposal and a completed dissertation in order to complete the degree.

Postdoc Danielle Bovenberg Honored for Research on Technical Knowledge Diffusion

In an analysis of three semiconductor R&D facilities, Bovenberg showed how instrument technicians help innovators in competition share solutions to common problems without divulging industry secrets.

Danielle Bovenberg, a postdoctoral research associate in organizational behavior, has won the 2024 Giarratini Rising Star Best Paper Award from the Industry Studies Association (ISA) for her working paper “Sharing Solutions without Spilling Secrets: The Role of Technicians in the Diffusion of Knowledge at Innovation Frontiers.”

The award recognizes a paper by an early-career scholar that demonstrates “significant personal investment” in researching a particular industry’s institutions and markets.

The diffusion of technical knowledge is important for innovation at innovation frontiers such as green energy and pharmaceutical development. But competition among companies can disincentivize individual researchers from collaborating.

In a multi-year ethnographic study, Bovenberg focused on three semiconductor R&D facilities used by different organizations including startups, universities, and Fortune 500 companies. She found that these facilities' staff technicians and engineers, who possess expertise in shared equipment but are not affiliated with specific innovators, are able to provide solutions to common problems without compromising the proprietary knowledge and strategies of individual researchers. While technical support occupations are often overlooked in scholarship on high-tech innovation, Bovenberg argued that they play a vital role in accelerating industry-wide advancements.

Bovenberg, who came to Yale SOM after earning her PhD in technology management from the University of California, Santa Barbara, was recently named a Rising Star by Stanford’s Management Science and Engineering Department. She is also a finalist for the ISA’s annual dissertation award. Her research focuses on the diffusion of knowledge in technologically complex industries.