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A structured literature review of
scientometric research of the knowledge
management discipline: a 2021 update
Alexander Serenko
Abstract
Purpose –The purpose of this study is to conduct a structured literature review of scientometric research
of the knowledge management (KM) discipline for the 2012–2019 time period.
Design/methodology/approach –A total of 175 scientometric studies of the KM discipline were
identified and analyzed.
Findings –Scientometric KM research has entered the maturity stage: its volume has been growing,
reaching six publications per month in 2019. Scientometric KM research has become highly specialized,
which explains many inconsistent findings, and the interests of scientometric KM researchers and their
preferred inquiry methods have changed over time. There is a dangerous trend toward a monopoly of the
scholarly publishing market which affects researchers’ behavior. To create a list of keywords for
database searches, scientometric KM scholars should rely on the formal KM keyword classification
schemes, and KM-centric peer-reviewed journals should continue welcoming manuscripts on
scientometric topics.
Practical implications –Stakeholders should realize that the KM discipline may successfully exist as a
cluster of divergent schools of thought under an overarching KM umbrella and that the notion of
intradisciplinary cohesion and consistency should be abandoned. Journal of Knowledge Management is
unanimously recognized as a leading KM journal, but KM researchers should not limit their focus to the
body of knowledge documented in the KM-centric publication forums. The top six most productive
countries are the USA, the UK, Taiwan, Canada, Australia and China. There is a need for knowledge
brokers that may deliver the KM academic body of knowledge to practitioners.
Originality/value –This is the most comprehensive, up-to-date analysis of the KM discipline.
Keywords Scientometrics, Academic research, Structured literature review, Discipline identity,
Knowledge management
Paper type Research paper
1. Introduction and purpose of the study
Study the past, if you would divine the future. (Confucius, 551–479 BC)
The knowledge management (KM) discipline has deep historical roots (Lambe, 2011;
Massingham, 2020). The first KM principles were used by the ancient Greeks more than
4,000 years ago. In 400–300 BC, Plato and Aristotle discussed the nature of empirical
knowledge and learning. Many Western philosophers and thinkers, including John Locke
(1632-1704) and George Berkeley (1685-1753), wrote on the topics of knowing, mind,
reality, learning, existence and experience. More than 200 years ago, Westerman (1768)
identified personal knowledge of workers as a source of competitive advantage. Senior
(1836) hypothesized that intellectual capital (IC) is the key quality of the laborer. In the
beginning of the 20th century, Schumpeter (1912/1934) proposed a theory of economic
development which emphasized the importance of managing organizational resources to
Alexander Serenko is
based at the Faculty of
Business and IT, University
of Ontario Institute of
Technology, Oshawa,
Canada.
Received 29 September 2020
Revised 17 November 2020
Accepted 20 December 2020
DOI 10.1108/JKM-09-2020-0730 VOL. 25 NO. 8 2021, pp. 1889-1925, ©Emerald Publishing Limited, ISSN 1367-3270 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1889
respond to external pressures. Eventually, Schumpeter’s theory formed a conceptual
foundation for contemporary KM ideas (Biranvand et al.,2017). Paton (1922) justified the
importance of intangible assets, and Dewey (1929) established a basis for a practical side
of knowledge application and argued that a disconnect between theory and practice may
impede human progress.
After this, several management theorists introduced various KM- and IC-related terms, such
as the stock of knowledge (Penrose, 1959), a tacit dimension of knowledge (Polanyi, 1958),
the knowledge industry (Machlup, 1962), the knowledge worker (Drucker, 1969)and
organizational learning (Argyris and Schon, 1978). Kronfeld and Rock (1958) theorized that
IC is the most significant factor in stock price appraisals, and Henry (1974) further
explicated the importance of knowledge in the development of public policy and advocated
formal KM principles. In 1975, Chaparral Steel established official KM practices and, in
1980, Digital Equipment Corporation installed the first large-scale KM system (Wiig, 1997a).
Soon after that, Kellogg (1983) proposed an AI-based KM application, and, in 1987, the
inaugural KM book was published (Sveiby and Lloyd, 1987).
The 1990s witnessed a surge in KM publications inspired by the pioneering works of Senge
(1990),Nonaka and Takeuchi (1995),Grant (1996),Wiig (1997b), Davenport and Prusak
(1998) and others. In 1993, the first KM conference was organized in Boston (Prusak, 2001)
and, in 1994, The Learning Organization journal was established. In 1996, McMaster
University launched the World Congress on the Management of Intellectual Capital and
Innovation (which also included KM topics) (Serenko et al., 2009), followed by numerous
other scholarly meetings. In 1997, Journal of Knowledge Management published its
inaugural issue, which expedited a further recognition of KM within the larger academic
community. Since then, KM has made remarkable progress to establish itself as a well-
recognized academic discipline with a strong theoretical and practical base.
Table 1 presents a list of six characteristics that define the identity of an academic discipline
(Krishnan, 2009;Junghans and Olsson, 2014) in the context of KM. It shows that KM meets
these criteria and may be formally referred to as an academic discipline. As the body of
knowledge has been growing, many scholars have inquired into the past, present and
future development of the KM discipline to better understand its identity and define its
trajectory. As a result, many scientometric studies of the KM discipline have been
conducted.
Table 1 Characteristics of the knowledge management discipline
Characteristics The KM discipline Related works
Object of research Soft (i.e. human-centered) and hard (i.e. IT-focused) KM
artifacts studied at individual, group, organizational, inter-
organizational and national levels
Bedford (2015a); Fteimi and Lehner (2018);
Mariano and Awazu (2016)
Unique specialist
knowledge not shared
with other disciplines
KM-centric peer-reviewed journals, conference
proceedings, books, textbooks and citation classics that
accumulate the scientific body of knowledge and transfer it
to practice
Fteimi and Lehner (2016);Qiu and Lv (2014);
Serenko and Bontis (2017);Serenko and
Dumay (2015b,2017)
Concepts and theories Various KM frameworks, topics (e.g. knowledge creation,
sharing and transfer; counterproductive knowledge
behavior), models and theories (e.g. the “ba” concept; the
SECI model)
Heisig (2009);Nonaka and Konno (1998);
Nonaka and Takeuchi (1995)
Technical language Specific terminology adjusted to the nature of the KM
artifact (e.g. knowledge hoarding, hiding and sabotage;
KM maturity; KM systems)
Del Giudice and Della Peruta (2016);
Kuriakose et al. (2010);Serenko (2019,2020);
Trusson et al. (2017)
Research methods Emphasis on case studies, surveys and interviews Ngulube (2019)
Institutional manifestation Place in formal academic curricula, research centers and
professional associations
Bedford (2013);Katus
ˇ
c
akov
a and Jase
ckov
a
(2019)
PAGE 1890 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 25 NO. 8 2021
Scientometrics is a science about science (Price, 1963): a systematic approach to explore
the past, present and future directions of a scholarly domain. Scientometrics has always
attracted the attention of the research community because it helps scholars better
understand the idiosyncrasies of their discipline and its identity (Hassan and Loebbecke,
2017). The role of scientometric studies is to sensitize, inform and educate internal and
external stakeholders on the discipline’s state-of-the-art and to propose a potential
corrective action of needed trajectory (Serenko and Jiao, 2012). However, even the highly
motivated stakeholder may find it challenging to locate, critically analyze and use the body
of knowledge documented in the vast ocean of somewhat disparate scientometric KM
publications.
First, the volume of scientometric KM research has been constantly growing, reaching 108
individual publications by 2012. Second, most scientometric studies focus on a single issue
which does not allow the reader to form a complete picture of the various intricacies of the
KM domain. Third, many scientometric KM studies are conducted in isolation and rarely
situate their findings in the context of previous scientometric KM research. Fourth, the
results of scientometric KM works are often mixed and inconsistent (Serenko, 2013). Thus,
KM discipline stakeholders would benefit from a comprehensive analysis of the previous
KM scientometric research. Serenko (2013) conducted such a study by examining 108
scientometric KM publications and developed 19 implications of interest to various KM
stakeholders. This study has been well received by the scientific community as manifested
in its citation count and numerous inquiries from internal and external discipline
stakeholders. However, as the volume of scientometric research has been further
accumulating, it is an appropriate time to revisit and update the Serenko’s (2013) findings.
To ensure the methodological rigor of this update, the present investigation uses the
structured literature review [structured literature reviews (SLR] approach.
The SLR is a “method for studying a corpus of scholarly literature, to develop insights,
critical reflections, future research paths and research questions” (Massaro et al., 2016a,
p. 767) which relies on a formalized and well-articulated approach to identify and analyze
relevant works rather than on the skills of a group of researchers (Dumay et al.,2016). SLR
investigators follow an explicit set of steps described in Section 2. The sequence of these
steps is not “cast in stone”: researchers should consider the process a guided journey
rather than a rigid path, and they may deviate from the prescribed steps depending on their
study’s context.
As an empirical approach, the SLR has many advantages over other literature review
techniques, such as traditional literature reviews, narrative reviews, meta-analyses and
research syntheses (Massaro et al., 2016a). First and foremost, in the SLR, researchers
follow a prescribed set of rules instead of relying on their personal, subjective opinion
regarding which sources to select and discuss. By following the SLR method, researchers
are more likely to identify all seminal works in their domain because they do not solely rely
on their personal knowledge of a specific corpus of literature. As a result, the SLR
decreases the degree of subjectivity inherent in the research process and the reported
findings. Second, traditional literature reviews require researchers to possess a great
degree of expertise in the domain under investigation: they are expected to be aware of all
major works, authors, terms and research streams a priori. On the one hand, seasoned
scholars may offer valuable insights on the state of the literature. On the other hand, they
may consciously or subconsciously introduce personal biases and present a distorted view
of a scientific domain. In contrast, one does not have to be a senior scholar to conduct the
SLR. Thus, the SLR opens new horizons for academics at various stages of their careers,
especially students and emerging scholars, and brings in some “new blood” to brighten up
the stagnant domain. As well, by following the SLR approach, experienced academics may
further validate their view on the state of the literature and discover paths for future
research.
VOL. 25 NO. 8 2021 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1891
Third, because the SLR process is transparent and is possible to replicate precisely,
researchers may easily defend their conclusions and recommendations. The method’s
replicability also ensures the future continuity of the study. Fourth, the SLR analysis process
is more rigorous, including reliability and validity checks which reduce (yet not completely
eliminate) personal researchers’ biases. For example, researchers may address reliability
by involving multiple coders who use the same codebook and assure validity by situating
their findings in theory. Fifth, researchers may use various information technology (IT) tools
to analyze the data and create forecasts. Sixth, the SLR allows the use of quantitative
measures to analyze and present the results, which further reduces the subjectivity inherent
in literature analysis studies. For these reasons, the SLR has established itself as a popular
method of inquiry in KM research (Paoloni et al.,2020). Thus, it may also serve as a rigorous
method of inquiry in scientometric KM projects. Therefore, the purpose of the present study
is to conduct the SLR of scientometric KM research to update the findings previously
reported by Serenko (2013).
2. Methodology
The SLR method was implemented by following the guidelines proposed by Massaro et al.
(2016a). The process was adjusted to fit the specific requirements of this study. The
literature review protocol was based on the previous study by Serenko (2013),who
described the state-of-the-art of scientometric KM research. However, as the volume of
such research has been accumulating, various discipline stakeholders would benefit from
an updated view. Because of the various advantages of the SLR, this method provides the
most comprehensive and valid description of the idiosyncrasies of the KM domain. The
general research question was: What is the current state of scientometric KM research, and
what implications can be drawn for the various stakeholders of the KM discipline? The type
of included works encompassed all peer-reviewed publications on scientometrics in KM,
such as journal articles, conference proceedings papers and refereed book chapters. To
identify such works, the following five-step search process was developed:
1. Step 1. A manual review of all KM-centric journals listed by Serenko and Bontis
(2017).
2. Step 2. A keyword search of the following databases: Emerald, ScienceDirect, ProQuest,
JSTOR, Web of Science (WoS), IEEE Xplore and Google Scholar, based on the pairs of KM
and scientometrics keywords. The KM keywords were selected from the KM classification
scheme by Fteimi and Lehner (2018, pp. 1540–1554) from A.1; C.6.15; D.2.13; E.2; H.8.9;
L.4.1.; L.4.15; and L.4.26 categories. The scientometrics keywords were adapted from
Serenko (2013) and included “scientometric(s)”, “bibliometric(s)”, “informetric(s)”,
“ranking”, “productivity”, “impact”, “relevance”, “citation analysis”, “co-citation analysis”,
“network analysis”, “collaboration”, “research”, “research policy”, “discipline past”,
“discipline future”, “research trend(s)”, “paradigm”, “method”, “management fashion/fad”,
etc.
3. Step 3: Analysis of the citing works. Google Scholar citations to all papers identified
earlier were manually analyzed to discover additional relevant publications.
4. Step 4. Bibliography analysis. Within all publications collected so far, the bibliography
lists (i.e. cited works) were analyzed to discover additional relevant works.
5. Step 5. For all newly discovered works, Steps 3 and 4 were repeated.
Because Serenko (2013) analyzed the works published from 1997 to August 2012
(inclusive), the search process focused on the period from September 2012 to August 2019
(inclusive).
The following analysis was done with the help of analytical frameworks:
PAGE 1892 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 25 NO. 8 2021
1. The general context of each examined scientometric study was analyzed (see Table 2).
After establishing the broader context of the scientometric study, its topic (i.e. the
actual objective of the examined works) was coded (see Table 3).
2. The scientometric methods used in the analyzed works were coded. Table 4 offers the
codebook. Note that, for topics and methods, multiple codes were assigned when
necessary because one work may pursue multiple objectives and use several methods,
which is common practice in scientometrics.
3. The quality of implications presented in the examined works –defined as the extent
to which the publication builds upon its findings to develop insights, offer
recommendations and provide guidance for KM discipline stakeholders and/or
scientometric KM researchers –was documented by using the codebook described in
Table 5.
4. Coverage comprehensiveness was defined as the extent to which the data used in the
empirical part of the examined work covered the KM domain. It was assessed by using
two criteria:
䊏the data source (where the analyzed data came from, e.g. the target database);
and
䊏the search criteria (e.g. the keywords applied to locate the data).
Table 3 Codebook –the purpose of the examined scientometric studies
Purpose Description
Analysis and/or ranking of KM journals
and/or conferences
Analysis and/or ranking of journals and/or conferences publishing KM research
Collaboration analysis Collaboration patterns of KM researchers, institutions, funding bodies and countries
Intellectual core of the KM discipline State, identity, structure, theoretical foundations and intellectual core of the KM discipline
(including analysis of research topics, classification schemes and ontologies)
Productivity and impact Analysis of productivity and impact of KM researchers, institutions and countries
Research paradigms and research
methods
Analysis of research paradigms, empirical research methods, methodology trends and
methodology agendas
Research relevance, knowledge
translation and brokering
Impact of academic KM research on the state of practice, practical relevance of academic
KM research and the dissemination of academic and scholarly knowledge to non-academic
stakeholders
Retrospective analysis and the future of
KM
KM history, origin, historical roots and potential future development
Table 2 Codebook –the context in which the examined studies were conducted
Context Description
General The entire KM discipline; conclusions generalize to the overall KM domain
Geographic
location
A particular country or geographic region; conclusions are limited to the selected
location only
Topic A specific research stream or a sub-domain within the KM discipline; conclusions
pertain to the area (e.g. topic) of interest only
Publication
forum
An individual journal or the proceedings of a specific conference; conclusions are
drawn in the context of a single publication forum
Group of
people
A particular category of stakeholders or participants within the KM domain;
conclusions refer to a unique group of people
VOL. 25 NO. 8 2021 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1893
5. The impact of the examined scientometric KM works was measured by analyzing citations
generated by Google Scholar. Works published in 2019 were excluded from citation
analysis because they have not had sufficient time to demonstrate their citation impact.
6. Authors’ awareness of previous scientometric KM research is defined as the degree to
which the authors are aware of the prior publications in this research domain. Those
scientometric KM scholars who are aware of previous works in this domain should cite
such publications to acknowledge the intellectual contribution of their predecessors
(Hassan and Serenko, 2019). Thus, the authors’ awareness of previous scientometric
KM research was measured by the number of citations to previous scientometric KM
works in their papers’ bibliography.
7. Author and paper characteristics were analyzed by:
䊏compiling a list of the most productive authors of scientometric KM works by
means of the direct count method (i.e. each author of the publication receives a
score of one regardless of the number of authors listed in the work);
䊏calculating the number of authors per paper; and
䊏creating a list of outlets where the examined scientometric KM works appeared.
SLR reliability was ensured by involving two coders who had advanced graduate-level
training in qualitative research and who used the same codebook. All discrepancies were
Table 4 Codebook –methodology used in the examined scientometric studies
Method Description
Bibliometric laws and models The application of bibliometric models and laws such as Lotka’s law, Bradford’s law and the Bass
diffusion model
Case study A formal case study of the KM discipline
Citation analysis Any type of citation analysis excluding co-citation analysis
Co-citation analysis Analysis of co-citations in the bibliography lists of examined works
Content analysis of academic
publications
Analysis of the content of academic publications including the title, abstract, full text, etc. but
excluding citations and keywords
Content analysis of documents Analysis of the content of non-academic documents such as email messages, social media posts,
websites, course outlines, public reports and job postings
Counting techniques Counting publications, authors, institutions and countries
Expert opinion Solicitation of expert opinion by using surveys, interviews, the Delphi method and focus groups
Formal literature review Systematic literature review, structured literature review and literature meta-analysis
Keywords analysis Analysis of keywords in academic publications and article classification categories (i.e. subjects)
selected from scholarly databases without analyzing the title, abstract and full text
Network analysis Application of network analysis and network visualization techniques (excluding keywords analysis)
Personal opinion Most of the ideas are based on the author’s personal experience, opinion, views and beliefs, which are
not supported by literature and/or empirical evidence
Traditional literature review Most of the ideas are based on academic literature without doing a systematic/structured literature
review, meta-analysis or empirical analysis
Table 5 Codebook –the quality of implications
Ranking Description
None The work proposes no implications for KM discipline stakeholders
Some The work briefly mentions some implications for KM discipline stakeholders, but it does
not elaborate on them in detail and does not develop actionable recommendations (e.g.
the implications part is limited to a few short sentences)
Extensive The work presents very distinct, detailed, comprehensive, actionable and easy-to-follow
implications for KM discipline stakeholders and elaborates on them
PAGE 1894 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 25 NO. 8 2021
identified and discussed by the coders in person, and agreement on the classification of all
items was reached. The Krippendorff’s (1980) reliability coefficient exceeded 0.8. SLR validity
was ensured by comparing the findings with those reported in KM literature and with the
theoretical insights in the previous Serenko’s (2013) study. Finally, numerous implications for
scientometric KM researchers and KM discipline stakeholders were proposed.
3. Results
Previously, Serenko (2013) analyzed 108 scientometric KM works for the period from 1997
to August 2012 (inclusive). In this study, 175 additional works were discovered. Of these,
eight pertained to the period from 1996 to August 2012 (i.e. these were missed in the
previous study and were included in the analyzed data set) and 167 to the period from
September 2012 to August 2019 (inclusive). Note that one of the newly discovered papers
that was omitted by Serenko (2013) appeared in 1996; thus, the previous period is now
referred to as 1996–2012. The large number of additional scientometric KM works has
further confirmed the need for a follow-up study: in total, at least 283 scientometric KM
papers have been published since the birth of the discipline. All works pertaining to the
2012–2019 period have been cited in this paper.
3.1 Topics and methods in scientometric knowledge management works
Figure 1 depicts the constantly growing volume of scientometric KM research which, by
2019, had reached 74 publications per year. Figure 2 visualizes the context in which the
examined studies were situated. Only 43% of them focused on the entire KM discipline; and
40% pertained to scientometric research on specific topics. Examples include KM for
development (Ergazakis et al.,2013), IT in KM (KM software, data mining, big data)
(Mu
¨hlburger et al., 2017), innovation (Torugsa and O’Donohue, 2016), the role of library and
information science (LIS) in KM (Agarwal and Islam, 2018), KM in franchising (Iddy
and Alon, 2019), process capital (Matthies, 2014), knowledge commercialization (Biranvand
et al.,2017), and knowledge sharing, exchange and transfer (Chou and Tang, 2014). In
total, 11% of studies were conducted in the context of a single journal or conference (Barik
and Jena, 2013) using both KM and non-KM outlets (Bedford and Bekbalaeva, 2018;
Potgieter, 2018). A smaller fraction of the projects (3%) was done in the context of specific
countries or regions, e.g. India (Chakraborty and Verma, 2018), China (Li et al.,2013), Latin
America and the Caribbean (Galvis-Lista et al.,2014), and 3% was conducted in the
context of a particular group of people, namely, KM professionals.
Figure 1 Number of scientometric KM works published per year
VOL. 25 NO. 8 2021 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1895
Figure 3 presents the topics of scientometric KM works, and Figure 4 compares the two
time periods. There was a noticeable increase in some topics, such as the intellectual core
of the KM field; productivity and impact studies; and collaboration patterns. This growth
took place at the expense of studies on KM research paradigms and methods, the
retrospective analysis and the future of KM, the nature of KM publication venues and the
practical relevance of KM research.
Figure 5 outlines the methods of inquiry used in the examined scientometric works, and
Figure 6 shows that there was a substantial change over time. Particularly, there was an
increase in the use of advanced methods, such as formal literature reviews (e.g. systematic
and SLRs), keywords analysis, citation analysis and network analysis at the expense of less
rigorous techniques such as expert opinion, personal opinion and traditional literature
reviews. In addition, a new category emerged, which pertains to the application of
bibliometric models and laws (e.g. Lotka’s Law, Bradford’s Law and the Bass diffusion
model) and which was used in 4% of the examined works. The largest decline (14%) was
observed in the analysis of the content of academic publications, including their title,
abstract and full text [1].
Previously, Serenko (2013) discussed three distinct phases of scientometric KM research:
Phase I –Initiation (1996-2001); Phase II –Early Development (2002-2006); and Phase III –
Rigor and Consolidation (2007-2012). The current study identified two additional phases:
Figure 3 Topics of scientometric research of the KM discipline
Figure 2 Context of scientometric research of the KM discipline
PAGE 1896 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 25 NO. 8 2021
Phase IV –Methodological Advancement (2013-2016) and Phase V –Maturity (2017-2019).
During the Methodological Advancement stage, scientometric KM scholars honed their
skills by focusing on more innovative, leading edge, and advanced research approaches.
In the Maturity phase, they continued using these advanced methods while simultaneously
reducing the application of basic techniques such as expert opinion, personal opinion and
traditional literature reviews. The biggest decline in the use of less advanced methods took
place during the Maturity stage. During the Maturity phase, research on retrospective
analysis and the future of KM (KM history, origin, roots and future development) also
received less attention. Figure 7 visualizes the phases of scientometric KM research. A
unique attribute of the Methodological Advancement and Maturity phases is a high degree
of specialization when over half of all studies are conducted in a unique context of specific
topics, publication forums, geographic regions and groups of people.
Scientometric KM research follows a cumulative research tradition wherein most topics and
methods used at one phase reappear at the following stages. At the same time, there is a
Figure 5 Methods used in scientometric research of the KM discipline
Figure 4 Changes in the topics of scientometric research of the KM discipline
VOL. 25 NO. 8 2021 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1897
gradual tendency toward continuous improvement and refinement of both topics and
methods. For instance, initially, traditional literature reviews played an essential role.
Eventually, however, this method has been replaced by more rigorous systematic and
SLRs: given the large volume of KM publications, it is difficult to ensure an adequate
coverage of the phenomenon by doing a literature review without following a formal inquiry
method. Similarly, scientometric KM scholars initially engaged in a retrospective analysis of
the discipline to understand its history and future. By the Maturity phase, though, many
scholars had lost interest in this topic. Instead, at the Methodological Advancement and
Maturity phases, in addition to developing KM journal rankings, researchers began
exploring various facets of individual KM journals and conferences, which further attests to
the evolution of the scientometric KM domain. Thus, the cumulative research tradition is
accompanied by gradual changes in topics and methods which ensures a continuous
progression of scientometric KM research.
3.2 Quality of implications and/or recommendations
In 48% of all papers, researchers proposed new research questions, summarized their
recommendations in easy-to-comprehend tables and developed detailed, actionable
courses of action, followed by a comprehensive elaboration (for exemplars, see Manhart
and Thalmann, 2015;Fellnhofer, 2018). Unfortunately, this was not always the case. In 13%
of all works, implications were only briefly mentioned in a few short sentences, which was
not enough to apply these studies’ findings. A total of 39% had no recommendations and/or
implications for theory and practice: the authors merely presented and summarized the
results and left it up to the reader to draw his or her own conclusions and develop
actionable items. In other words, over half of all scientometric KM works failed to properly
inform the reader and propose a course of action.
Figure 6 Changes in the methods used in scientometric research of the KM discipline
PAGE 1898 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 25 NO. 8 2021
3.3 Coverage comprehensiveness
Coverage comprehensiveness was assessed through:
䊏the data source; and
䊏the search criteria.
Analysis of the data source revealed that, first, almost half of the examined data sets were
retrieved from WoS, Scopus, EBSCO, ScienceDirect and ProQuest–ABI/INFORM, which
signifies a trend toward the monopolization of the search space by a few dominant players
(Table 6). EBSCO has recently included Emerald and ScienceDirect, which reinforces the state
of monopoly. Second, since 2012, the role of Scopus has increased from 1.2% to 8.8% of all
searches. Third, the number of single-use specialized databases (e.g. CINAHL, EconBiz,
MEDLINE) has increased. Such databases may be successfully used to locate works on
unique research topics. Fourth, the role of non-KM-centric peer-reviewed journals has
decreased whereas that of KM-centric peer-reviewed journals has increased. The role of peer-
reviewed conference proceedings has dropped from 10% to 1.8%. Fifth, 57% of studies relied
on the use of a single database. Only a small fraction of studies complemented the use of
databases with the search of KM-centric journals that were predominantly selected from the
ranking lists by Serenko and Bontis (2009;2013a;2017). As a data source, the most frequent
KM journals were Journal of Knowledge Management,Knowledge Management Research &
Practice,Journal of Information & Knowledge Management,International Journal of Knowledge
Management,Journal of Intellectual Capital,andKnowledge and Process Management.
With respect to search criteria, the keyword coverage has shown some improvement: 55%
of the studies that relied on automatic database searches used a single keyword, mostly
Figure 7 Evolution of scientometric research of the KM discipline
VOL. 25 NO. 8 2021 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1899
“knowledge management,” compared to 67% reported for the 1996–2012 period, but most
searches were quite basic and limited to the title, abstract and keywords. Given the
interdisciplinary nature of KM, many articles do not contain “knowledge management” in
their titles, abstracts or keywords, and these were missed by the authors of these studies.
Analysis of several of the latest issues of volume 24 of Journal of Knowledge Management
further confirmed that most of the articles do not contain “knowledge management” in their
titles, abstracts or keywords. Yet, scientometric researchers used a variety of keywords
(Nyamasege et al.,2019) or manually examined each paper to determine its suitability
(Massaro et al., 2015) only in rare cases.
To summarize, there is a trend toward the consolidation of searches around a smaller group
of major databases while occasionally increasing the breadth of coverage with the use of
specialized databases and journals. Many researchers still rely on a single “KM” keyword in
automated searches, which may not ensure an adequate coverage of the domain and so
result in biased findings. Nevertheless, there is some improvement in the breadth of
keyword coverage.
3.4 Impact of scientometric knowledge management works
The 283 examined works received 20,211 citations on Google Scholar. Scientometric KM
papers were cited at the rate of 7.38 citations per year, on average [2]. Out of 34 works with
no citations, 17 appeared in 2018 and have not yet had enough time to get noticed and
cited. Table 7 shows a ranking of the most frequently cited works that attracted 13,742
(68%) of all citations. Of these papers, all except one were published in the previously
examined time period (i.e. 1996-2012), which shows that accumulating citations is a time-
consuming process. The citation impact of scientometric KM research is extremely skewed
because a small number of older works attract a disproportionate number of citations,
Table 6 Data sources of scientometric KM works
Category (%)
Indexes and databases
Clarivate Analytics Web of Science Collection (20.4%)
Scopus (8.8%)
EBSCO Research Databases (7.5%)
ScienceDirect (6.6%)
ProQuest –ABI/INFORM (5.3%)
Google Scholar (4.9%)
Emerald (4.4%)
IEEE Xplore (3.5%)
ACM Digital Library (2.2%)
SpringerLink (1.8%)
AISeL (1.3%)
Compendex (0.9%)
JSTOR (0.9%)
Other (9.7%) (China Doctoral Dissertations Full-text Database; CINAHL Complete; CWTS
Journal Indicators; EconBiz; Embase; INFORMS; Ingenta Connect; MEDLINE; NDLTD;
NISTEP; Oria; OST; Prozesskapital; PsycINFO; ResearchGate; SAGE Journals; SciVerse;
Taylor & Francis Online; The Cochrane Library; The United States Patent and Trademark
Office; TPAC Database; Wiley Online Library)
78.3
Other sources –direct search
KM-centric peer-reviewed journals (13.7%)
Non-KM-centric peer-reviewed journals –predominantly IS, IT and general management
(4.9%)
Peer-reviewed conference proceedings (1.8%)
Doctoral dissertations (0.9%)
Books (0.4%)
21.7
PAGE 1900 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 25 NO. 8 2021
which does not bode well for a healthy scientific domain. On a positive note, many new
promising works have been recently published (see Table 8). For example, the recent
articles by Inkinen (2016) and Girard and Girard (2015) have been attracting more than 30
citations per year and are likely to become the future KM citation classics.
3.5 Authors’ awareness of prior scientometric research
There has been a steady trend toward the improvement of the citing behavior of
scientometric KM scholars (see Table 9 and Figure 8). In the 2012-2019 period, the
proportion of papers that cited no previous relevant works decreased by 40%, and the
average number of relevant citations almost tripled. At the same time, there is room for
improvement. For instance, during 2017-2019, scientometric KM works cited 5.72 relevant
publications on average, which is a good sign, but 19% of papers contained no relevant
references, and 14% cited only a single scientometric KM paper. There were two distinct
types of citation-deficient publications. The first category pertained to narrow scientometric
Table 7 Most frequently cited scientometric KM works (top 10% –28 works)
Work
Total # of
citations
# of Citations
per year Work
Total # of
citations
# of Citations
per year
Wilson (2002) 1,355 79.71 Serenko and Bontis (2004) 378 25.20
Grover and Davenport (2001) 1,163 64.61 Durst and Edvardsson (2012) 373 53.29
Wiig (1997b) 1,144 52.00 Crossan and Guatto (1996) 335 14.57
Easterby-Smith et al. (2000) 1,142 60.11 Argote (2011) 313 39.13
Schultze and Leidner (2002) 811 47.71 Serenko et al. (2010) 287 31.89
Prusak (2001) 726 40.33 Nonaka and Peltokorpi (2006) 264 20.31
Bapuji and Crossan (2004) 632 42.13 Ponzi and Koenig (2002) 262 15.41
Heisig (2009) 522 52.20 Hallin and Marnburg (2008) 251 22.82
Scarbrough et al. (1999) 492 24.60 Kebede (2010) 245 27.22
Baskerville and Dulipovici (2006) 408 31.38 Ragab and Arisha (2013) 236 39.33
Teece (1998) 402 19.14 Scholl et al. (2004) 231 15.40
Bjørnson and Dingsøyr (2008) 396 36.00 Metaxiotis et al. (2005) 229 16.36
Scarbrough and Swan (2001) 395 21.94 Ponzi (2002) 190 11.18
Wiig (1999) 379 18.95 Spender (2008) 180 16.36
Table 8 Top ten works for the 2012-2019 period with the largest number of citations
per year (not included in Table 7)
Work
# of Citations
per year Work
# of Citations
per year
Inkinen (2016) 31.33 Serenko and Bontis (2013a) 21.17
Girard and Girard (2015) 31.00 Trindade et al. (2017) 20.00
Fazey et al. (2013) 29.00 Serenko and Dumay (2015b) 18.75
Akhavan et al. (2016) 27.67 Massaro et al. (2016b) 18.33
Massaro et al. (2015) 26.75 Manhart and Thalmann (2015) 18.25
Table 9 Authors’awareness of prior scientometric KM research
1996–Aug 2012 Sept 2012–2019 1996–2019
% of papers with no relevant citations 30.36 18.24 23.05
% of papers with only one relevant citation 25.89 21.76 23.40
Average number of relevant citations 2.01 5.39 4.05
VOL. 25 NO. 8 2021 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1901
topics –for instance, to the investigation of a particular attribute of the KM discipline –where
no (citeable) prior research on this topic had been done. The second type of publications
was devoted to general scientometric KM issues for which a comprehensive body of prior
knowledge was simply ignored. For example, a 2019 study that analyzed Journal of
Knowledge Management overlooked all relevant previous publications that also scrutinized
this journal. Another 2017 study on the impact of academic KM research missed all
previous publications on the practical relevance and dissemination of scientific KM output.
3.6 Publication forums
The vast majority of scientometric KM works were published in peer-reviewed journals,
followed by conference proceedings and book chapters (Table 10). There has been a shift
toward peer-reviewed KM-centric journals, particularly Journal of Knowledge Management,
Knowledge Management Research & Practice and VINE: The Journal of Information and
Knowledge Management Systems, and fewer scientometric KM publications appeared in
non-KM-centric journals. More works were published in the proceedings of KM-centric
conferences, especially in the proceedings of the European Conference on Knowledge
Management.
3.7 Author characteristics
Table 11 shows an increasing trend in co-authorship in scientometric KM works. For
instance, in the years 2018–2019, only 20% of all publications were single authored. The
degree of collaboration, which is “the ratio of the number of collaborative research papers
to the total number of research papers published in the discipline during a certain period of
time” (Subramanyam, 1983, p. 37), further attests to this claim. Since 1996, 486 KM
researchers have contributed to scientometric KM research. Table 12 presents a list of 32
productive authors [3]. Of them, only 13 appeared in the 1996–2012 list. This implies that
there has been an infusion of new talent in scientometric KM research.
The observed productivity distribution was analyzed by using Lotka’s law (Lotka, 1926). It
suggests that the number of authors within a particular scientific domain publishing a
certain number of papers is a fixed ratio to the number of researchers who published only a
single paper. It was found that the predicted number of authors with multiple works
dramatically exceeds the observed number of authors. Moreover, out of 486 authors,
317 one-time contributors are expected, whereas their actual number is 394. In addition,
Figure 8 Average number of citations to prior KM works in the set of examined publications
PAGE 1902 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 25 NO. 8 2021
Table 10 Publication forums of scientometric KM works
#Category (%)
1 Peer-reviewed journals
–KM-centric (43.46%)
–Journal of Knowledge Management (15.20%)
–Knowledge Management Research & Practice (6.01%)
–International Journal of Knowledge Management (3.53%)
–Knowledge & Process Management (3.53%)
–VINE: The Journal of Information and Knowledge Management Systems (3.18%)
–Journal of Information & Knowledge Management (2.83%)
–Electronic Journal of Knowledge Management (2.47%)
–Knowledge Management for Development (2.12%)
–Other (4.95%)
–Non-KM-centric –predominantly IS/IT, management, scientometrics, and information
& library science (36.74%)
–Scientometrics (2.12%)
–Library Philosophy and Practice (1.41%)
–Other (33.57%)
80.56
2 Peer-reviewed conference proceedings
–KM-centric (6.01%)
–European Conference on Knowledge Management (2.83%)
–Other (3.18%)
–Non-KM-centric (6.01%)
12.00
3 Book chapters 6.37
4 Technical reports/working papers 1.07
Table 11 Co-authorship preferences of scientometric KM researchers
1996–Aug 2012 Sept 2012–2019 1996–2019
Average number of authors 2.08 2.48 2.31
Maximum number of authors 6 21 21
% of single-authored papers 33.34 21.56 27.20
Degree of collaboration 0.67 0.78 0.73
Table 12 The most productive authors of scientometric KM works
Rank Name # of papers Rank Name # of papers
1 Bontis, N.16 17 Chen, T.T.3
2 Durst, S. 8 17 Crossan, M. 3
3 Handzic, M. 7 17 Croasdell, D.3
4 Heisig, P.6 17 Hall, D.3
5 Booker, L.5 17 Jennex, M.3
5 Dumay, J. 5 17 Lee, M.R.3
7 Bedford, D.A.D. 4 17 Malik, B.A. 3
7 Edvardsson, I.R. 4 17 Mariano, S. 3
7 Ergazakis, K.4 17 Massaro, M. 3
7 Ferenhof, H.A. 4 17 Nakamori, Y.3
7 Fteimi, N. 4 17 Onyancha, O.B. 3
7 Lehner, F. 4 17 Ribie
`re, V. 3
7 Metaxiotis, K.4 17 Sahoo, J. 3
7 Rechberg, I.D.W. 4 17 Spender, J.-C. 3
7 Scarbrough, H.4 17 Swan, J.3
7 Syed, J. 4 17 Walter, C. 3
Note: The author appeared in the 1996-2012 list of scientometric KM researchers
VOL. 25 NO. 8 2021 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1903
79 rather than 59 scholars were supposed to contribute twice. Thus, the productivity
distribution of scientometric KM scholars does not follow Lotka’s law.
3.8 Major insights of scientometric knowledge management works
A review of the major findings documented in the examined KM works for the 2012-2019
period sheds some light on the current state and identity of the KM discipline.
3.8.1 Knowledge management journals and conferences. Journal ranking studies indicate
that Journal of Knowledge Management is the leading outlet, followed by The Learning
Organization,Knowledge Management Research & Practice,Knowledge and Process
Management,VINE: The Journal of Information and Knowledge Management Systems and
International Journal of Knowledge Management (Serenko and Bontis, 2017). A vast
majority of studies that explored various facets of KM publication forums focused on Journal
of Knowledge Management (e.g. see Gaviria-Marin et al.,2018). They concluded that it is
the most productive (Breznik, 2018), highly influential (Teixeira and Oliveira, 2018),
balanced (Handzic, 2015) and geographically inclusive (Handzic and Durmic, 2013) journal
that publishes positivist empirical papers (Ngulube, 2015) and emphasizes knowledge
sharing and transfer topics (Raza and Malik, 2018). Other studies identified Knowledge
Management Research & Practice as a promising and highly productive KM outlet
(Aitouche et al.,2018;Gaviria-Marin et al.,2019) reporting on knowledge sharing,
knowledge transfer, situated learning, research methods, KM foundations and IC issues
(Ribie
`re and Walter, 2013;Walter and Ribie
`re, 2013). Other journals such as Electronic
Journal of Knowledge Management and Journal of Information & Knowledge Management
also underwent some scrutiny (Thanuskodi and Umamaheswari, 2013;Sahoo et al., 2017a;
Alajmi and Alhaji, 2018). Oddly enough, KM pioneers –Nonaka, I., Takeuchi, H., Davenport,
T. and Prusak, L. –do not generally publish in top KM-centric journals (Handzic and
Durmic, 2013).
Over half of all KM publications appear in conference proceedings (Qiu and Lv, 2014;
Sahoo et al., 2017b). Of particular importance are the European Conference on Knowledge
Management (Fteimi and Lehner, 2016), the International Conference on Intellectual Capital,
Knowledge Management, and Organizational Learning (Silva et al., 2017) and the KM track
at the Hawaii International Conference on System Sciences (Dittes et al.,2016).
3.8.2 Collaboration patterns. Studies of collaboration patterns of KM researchers,
institutions, funding bodies and countries reached several conclusions. First, research
collaboration must be encouraged because it results in a higher quality and quantity of
scientific output (Sedighi and Jalalimanesh, 2014;Ceballos et al.,2017;Sahoo and Pati,
2018). Second, there has been a steady increase in collaboration which is manifested in a
growing number of authors per paper (Wang et al.,2018), but authors’ extent of
collaboration varies depending on the geographic locations, publication forums and time
periods. Third, even though a small number of researchers have extensive collaboration
networks (Bontis, N., O’Donnell, D. and Voelpel, S.C.), the overall level of domestic and
international cooperation is disappointing (Zuo et al., 2012;Massaro et al., 2016b). The
extent of collaboration among the most productive institutions (Qiu and Lv, 2014),
developed countries (Wang et al.,2018) and funding agencies (Z
ˇlahti
cet al.,2017)isalso
insufficient.
3.8.3 Productivity and impact. Based on the overall volume of publication output, the USA is
consistently ranked the most productive country: it is ranked the top one in 76% of all
studies and is included in the top three lists in 90% of all studies. Other countries included
in the top three lists are the UK (76%), Taiwan (24%), Canada (21%), Australia (17%) and
China (17%). There is some level of consistency among the country-level research
productivity studies. Of 29 institutions in the top three productivity lists, only five appeared
more than once: Hong Kong Polytechnic University, HK (4); National Cheng Kung
PAGE 1904 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 25 NO. 8 2021
University, Taiwan (4); City University of Hong Kong, HK (3); Lakehead University, Canada
(3 times); and Nanyang Technological University, Singapore (2).
Of 34 researchers identified in the top three lists of individual research productivity, eight
appeared multiple times [4]: Bontis, N.; Carrillo, F.J.; Ergazakis, K.; Gottschalk, P.;
Nonaka, I.; Tseng, M.L.; and Yigitcanlar, T. In these lists, only Bontis, N. was mentioned
three times and the others only twice. The institutional and individual productivity lists reveal
a high level of inconsistency among the studies. Hong Kong and Singapore, which were
mentioned in the top three institutional lists, were excluded from the top three country
rankings. Of the 27 most frequently cited works that were ranked top three, only five were
listed in multiple studies: Alavi and Leidner (2001) (two), Davenport and Prusak (1998)
(two), Kogut and Zander (1992) (two), Nonaka (1994) (five) and Nonaka and Takeuchi
(1995) (four). Except for a few seminal works, there was little consistency on the most
frequently cited works. This suggests that the findings of the institutional and individual
productivity rankings as well as citation impact studies depend on their methodology,
namely, on the source from which the analyzed data set is selected. At the same time, the
effect of a data source becomes less salient at the country level of assessment.
A truly disturbing finding is that Lotka’s Law (Lotka, 1926) does not apply to the productivity
distribution of KM authors: only one scientometric study of the KM discipline empirically
supported it (Wallace, 2012), whereas four did not (Kumar and Mohindra, 2015;Muzzammil
and Asad, 2016;Sahoo et al., 2017a;Maity and Sahu, 2019). Given that the study by
Wallace (2012) is the oldest of these, it is possible that Wallace’s conclusion is dated and
does not apply to the current state of KM research. The reason for this finding is that more
than 80% of KM authors contributed to the discpline only once (i.e. published only a single
KM paper) (Ergazakis et al.,2013;Handzic and Durmic, 2013;Tsai, 2013;Maity and Sahu,
2019).
3.8.4 Research paradigms and research methods. Studies that examined KM research
paradigms concluded that the discipline is dominated by positivist epistemologies but
researchers rarely explicitly state their philosophical assumptions (Hustad et al., 2017;Ngulube,
2019). With respect to the popular methods of inquiry, there was a general conclusion that KM
research is empirical by its nature while the number of conceptual works has been gradually
declining (Ngulube, 2015). A large number of projects identified case studies as a leading
(Handzic, 2015;Durst, 2019) and highly credible (Patil and Kant, 2014) research method.
Another substantial pool of studies reported surveys are a top choice of KM researchers
(Edvardsson and Durst, 2014;Ferenhof, 2016), followed by interviews (Ko
¨r, 2017). At the same
time, other methods, such as action research, ethnography, mixed-methods and the use of
secondary data were generally underrepresented (Durst et al., 2015;Rechberg, 2018).
3.8.5 Research relevance, knowledge translation and knowledge brokering. Studies on KM
research relevance, knowledge translation and knowledge brokering reached several
conclusions. It has been generally agreed that the findings reported in the academic KM
literature are of high value to practitioners (Moshonsky et al., 2014;Edvardsson and Durst,
2017) and that industry–academia collaboration and dialogue lead to knowledge creation
(Fabbe-Costes, 2018). Regrettably, despite the potential usefulness of KM research,
practitioners show little interest in the domain, and KM has become a purely academic
discipline (Hislop et al.,2018) where scientific research output is targeted at academics
rather than industry professionals.
The KM discipline was established as a field of practice. However, the participation of
practitioners in academic research has dramatically declined and remains very low
(Wallace, 2012;Massaro et al., 2016b;Akakandelwa, 2017). Academics and practitioners
have divergent views on the need for more theoretical KM research, and they disagree on
various KM issues (Sagsan and Medeni, 2012;Heisig, 2015). There are also dramatic
differences between academic and practitioner-oriented journals (Ribeiro-Soriano and
Berbegal-Mirabent, 2017). The KM scholarly body of knowledge remains under-used by
VOL. 25 NO. 8 2021 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1905
professionals: their current awareness of KM models, methods and theories is very shallow
and fragmented (Bedford, 2015b), and very few practitioners ever apply recommendations
from academic articles in their work (Booker et al., 2013). This results in a gap between
theory and practice (Ragab and Arisha, 2013).
Currently, KM practitioners stay up-to-date with the state of the discipline by means of
online forums, discussion groups, colleagues, and, only occasionally, academic literature
(Booker et al.,2013). As a result, there have been calls to strengthen the relationship
between academic research and the needs of industry practitioners (Wang et al.,2018)and
embed scholarly recommendations into routine managerial practices (Lo
¨nnqvist, 2017). The
best solution to bridge the gap between KM theory and practice is to implement knowledge
translation mechanisms which aggregate the academic body of knowledge and deliver it to
busy practitioners in an efficient, easy-to-comprehend format. This may be achieved by
introducing formal positions of knowledge brokers and associations governing the process
of knowledge translation (Bedford, 2015b;Cummings et al.,2019). The use of metaphors
may also prove to be useful in reaching a wider stakeholder audience (Gu
¨ndu
¨z, 2019). At
the same time, more research on the efficacy of knowledge transfer mechanisms is
warranted (Barbour et al.,2018).
3.8.6 Retrospective analysis and the future of knowledge management. KM takes its
conceptual roots from the works of Joseph Schumpeter, Friedrich Hayek, Gilbert Ryle,
Claude Shannon, Gilbert Ryle, Michael Polanyi, James March, Herbert Simon, Mark S.
Granovetter and Chris Argyris (Khasseh and Mokhtarpour, 2016). The discipline has gone
through three stages of development: fragmentation (when the KM discipline was
represented by distinct schools of thought), integration (when the KM discipline was
represented by a holistic view, common vocabulary and analytical approaches) and fusion
(when the KM discipline converged with other scientific domains, theories and principles)
(Handzic, 2016;Handzic, 2017). Yet, despite a continued interest in KM topics (O’Leary,
2016), the field is full of confusion, sharp divides and disintegration (Spender, 2013;
Spender, 2015). Nevertheless, there will likely be a renewed interest in KM concepts and
tools in the future (Schmitt, 2015), and other scientific domains offer promising opportunities
to incorporate KM ideas (Lee et al.,2016).
The future of KM may evolve into three emerging trends referred to as extension (increasing
the depth and breadth of current KM research), specialization (creating sub-domains within
a larger KM paradigm) and reconceptualization (revisiting fundamentals and restructuring
the entire discipline) (Handzic, 2017). KM researchers should engage more in
interdisciplinary research (Brahma and Mishra, 2015). The term “KM” may potentially evolve
into “knowledge science,” and the field may merge with subjects from strategic
management, information economics, artificial intelligence, education, philosophy, industrial
and organizational psychology, LIS, human resource management, and information
systems (IS) (Kabir, 2014). KM concepts may be also transformed into knowledge design
thinking (Boersma, 2017).
3.8.7 Intellectual core of the knowledge management discipline. Analysis of the
scientometric works that focused on the intellectual core of the KM discipline identified four
findings. First, the overall volume of yearly KM publications had reached its peak between
2011 and 2015 and started to decline (Muzzammil and Asad, 2016;Ko
¨r and Mutlutu
¨rk,
2017;Breznik, 2018;Khiste and Awate, 2018). This, however, does not indicate the collapse
of KM (Garlatti and Massaro, 2016) because there has been an increasing research output
in the various niches of KM research. Specifically, there has been a steady growth in
publications on knowledge sharing (Goswami and Agrawal, 2018), KM in health care
(Lopes da Cruz et al., 2017), knowledge-based development (Fombad and Onyancha,
2017), knowledge-based view in franchising (Tsai et al., 2017), KM in data mining (Tsai,
2013), KM in small and medium-sized enterprises (Massaro et al., 2016b) and KM in start-
ups (Centobelli et al.,2017). It seems that, instead of increasing its sheer volume, KM
PAGE 1906 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 25 NO. 8 2021
research has extended its interdisciplinary reach and transformed itself into numerous
streams that are being explored in detail. The number of KM research institutions and
countries participating in KM research has also been growing. KM is considered a stable,
distinct program of study in higher education institutions (Cervone, 2017), and KM curricula
has already reached maturity (Bedford, 2013). Almost half of LIS schools have implemented
formal KM education in their programs, which suggests that KM is progressing well from
theory to practice (Katus
ˇ
c
akov
a and Jase
ckov
a, 2019). Even though KM is the youngest
management field, it is not a fad, and it is progressing well toward maturity and recognition
(Serenko and Bontis, 2013b;Tzortzaki and Mihiotis, 2014).
Nevertheless, there is room for improvement. Presently, KM exhibits an insufficient level of
intradisciplinary consensus, cohesion and communication (Teixeira and Oliveira, 2018). It
lacks a common vocabulary, definitions and terminology (Fteimi and Lehner, 2013;
Gavrilova and Kubelskiy, 2018): more than 100 definitions of KM exist which vary
depending on the application context (Girard and Girard, 2015). There are arguments that
the discipline is dominated by a technocratic school of thought (Girard and Ribie
`re, 2016).
The interdisciplinarity of KM (Zavaraqi, 2016) further contributes to its lack of consistency,
uniformity and structure. It seems that KM is represented by a number of unique research
themes that are expected to continuously evolve (Powell et al.,2016;Sa
gsan et al.,2016).
Second, most studies confirmed the leading role of Journal of Knowledge Management as a
flagship KM outlet that publishes the largest number of influential papers on both general
and specialized KM topics (Mariano and Walter, 2015). Other KM-centric journals,
especially Knowledge Management Research & Practice, also play a vital role in the
preservation and dissemination of KM research (Ahmadi and Nazim, 2018). At the same
time, there are many non-KM-centric journals that publish a large volume of KM studies on
specialized topics and serve as a bridge between KM and other domains (Silva et al.,
2017). For instance, Research Policy dominates knowledge transfer topics (Chou and Tang,
2014), BMC Health Services Research,Implementation Science and Journal of Advanced
Nursing dominate knoweldge sharing in health care (Lopes da Cruz et al.,2017), R&D
Management dominates open innovation in KM (Natalicchio et al.,2017), Journal of
Economic Geography and Journal of Development Economics dominate knowledge
leakage and spillover (Ferenhof, 2016)andCities,Journal of Cleaner Production,and
Technological Forecasting & Social Change dominate KM for smart cities and sustainability
(Trindade et al.,2017). Computer science (CS), IS and LIS journals (e.g. Expert Systems
with Applications,Computers in Human Behavior,MIS Quarterly,Decision Support
Systems,Journal of the Association for Information Science and Technology and Lecture
Notes in Computer Science) play an important role in the accumulation and distribution of
KM research far beyond the KM discipline (Landrum et al., 2014;Akhavan et al.,2016;
Huang et al.,2018;Ali et al.,2019). At the same time, a macro-level distribution of KM works
is still poorly understood: one study confirmed that the productivity of journals publishing
KM research follows Bradford’s law (Ali et al.,2019), whereas another failed to do so (S and
Sevukan, 2014). Third, most studies reported that knowledge sharing is by far the most
popular keyword in KM papers, followed by knowledge transfer, KM systems and
innovation (Mariano and Awazu, 2016;Ahmadi and Nazim, 2018). Other keywords pertain
to IT (e.g. data mining, information and communication technologies) (Fombad and
Onyancha, 2017;Ko
¨r and Mutlutu
¨rk, 2017). For some reason, more than half of all studies
mentioned “knowledge management” as one of the most frequent keywords in KM
publications.
Analysis of prevalent and expanding research streams indicated a more comprehensive
state of KM research directions. Even though knowledge sharing and transfer topics topped
the list again (Kennedy and Burford, 2013;Costa and Monteiro, 2016;Ferguson, 2016;
Fteimi et al.,2019), other popular themes signaled the breadth and further advancement of
KM research. Examples of popular and growing research streams include communities of
VOL. 25 NO. 8 2021 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1907
practice (Bolisani and Scarso, 2014), the consequences of knowledge spillover, loss and
leakage (Ferenhof, 2016), knowledge-based urban development (Edvardsson et al., 2016),
KM success factors (Fteimi and Lehner, 2018), public sector KM (Jussila et al.,2017), KM in
project management (Cabral et al.,2014;Handzic and Durmic, 2015;Sareminia et al.,
2016), adoption, use and diffusion of KM systems (Matayong and Mahmood, 2013),
process capital (Matthies, 2014), social media for knowledge sharing (Sarka and Ipsen,
2017), the role of KM in innovation (Leon and Bolisani, 2016) and the intersection of KM and
IT (Iskandar et al., 2017;Khan and Vorley, 2017;Usai et al.,2018). KM also includes
knowledge-based development (Akude and Grunewald, 2014) and organizational learning
(Song et al., 2014;Adz
ˇi
c, 2018;Castaneda et al., 2018) research streams, and it is closely
connected to IC topics (Pereira and Machado, 2019). At the same time, there are several
underrepresented or poorly understood KM themes: outsourcing of knowledge processes
(Edvardsson and Durst, 2014), knowledge waste (Ferenhof et al., 2015), the role of an
individual (Rechberg and Syed, 2012;Rechberg and Syed, 2014a;Rechberg and Syed,
2014b), business outcomes of KM (e.g. the impact of KM on performance) (Heisig et al.,
2016), unlearning and forgetting (Klammer and Gueldenberg, 2019), the management of
knowledge risks (Durst et al.,2016), various aspects of customer KM (Wilhelm and
Gueldenberg, 2014;Khosravi and Hussin, 2018) and human factors in KM technologies
(Sarka et al.,2019).
Fourth, the KM discipline has deep historical roots (Khasseh and Mokhtarpour, 2016), and it
has progressed through many stages of development from technological to strategic to
sociological (Gonz
alez-Valiente et al.,2019). It draws upon and extends the knowledge
base from various domains, including CS, management (especially accounting and
organizational behavior), engineering, economics, social sciences (particularly psychology)
and mathematics (Tome
´and Gonzalez-Loureiro, 2014;Malik and Ali, 2018), but it
distinguishes itself from the other disciplines (Harper, 2013). Most importantly, KM has been
showing a steady process of moving away from borrowing knowledge from other
(reference) disciplines toward the development of its own body of knowledge (Dulipovici
and Baskerville, 2015). For example, KM works are frequently cited in CS and social
sciences (Alajmi and Alhaji, 2018), and KM offers much value to humanities researchers
(Handzic and Dizdar, 2016), which is a sign of disciplinary maturity.
4. Implications
4.1 Implications for scientometric knowledge management research
Implication #1: Scientometrics represents a fruitful research avenue for KM scholars.The
overall volume of scientometric KM publications has continued growing, and the KM
scientometric research has been continuously attracting the attention of the research
community. As of 2019, on average, six scientometric KM works were published per month,
which confirms the status of KM as a recognized scientific discipline that is worth exploring
further.
Implication #2: Scientometric KM researchers should engage in highly specialized studies.
Only 43% of scientometric KM studies focus on the entire KM discipline, whereas 57%
explore specific topics, publications forums, geographic locations or groups of people.
There is a strong trend toward highly specialized scientometric KM projects. As the volume
of KM research grows, it becomes difficult to explore the entire KM discipline in a single
study. As a result, researchers investigate a particular facet of KM and draw their
conclusions in the context of a particular attribute that they study, which represents a
natural progression of scientometric KM research.
Implication #3: It is expected that scientometric KM researchers gradually change their
interests and preferred inquiry methods. Recently, scientometric KM scholars have become
less interested in exploring research paradigms and methods and have started focusing
PAGE 1908 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 25 NO. 8 2021
more on the discipline’s intellectual core. They reduced their reliance on traditional literature
reviews and publications’ content analysis. This shift is understandable because these
topics and inquiry methods have reached a saturation point. It is likely that other similar
changes will persist in the future.
Implication #4: Scientometric KM research has entered the maturity stage. Scientometric KM
research has progressed through four phases of development –Initiation, Early Development,
Rigor and Consolidation and Methodological Advancement –and has entered the Maturity
stage. The Maturity stage is accompanied by a decline in the use of less rigorous methods –
personal opinion, traditional literature reviews and expert opinion –and an increase in
advanced approaches, including SLRs, keywords analysis, counting techniques, citation
analysis, network analysis and the use of bibliometric laws and models. During this phase,
researchers also pay less attention to the retrospective analysis of the KM field.
Implication #5: Scientometric KM projects should not be considered a methodological
exercise. Instead, their objective must be to develop actionable implications and
recommendations for various KM stakeholders. On the one hand, 48% of all scientometric
KM works offer evidence-based implications and recommendations. On the other hand,
in 13% of the works, implications are extremely limited, and, in 39%, are missing.
Scientometric scholars should always keep in mind that the ultimate goal of their work is to
inform busy readers who may lack the expertise to interpret the findings by themselves, and
so the scholars should go beyond the mere documentation of their method and the results.
In other words, they should always return to the “so what” question and answer it from the
perspective of the discipline’s stakeholders.
Implication #6: A trend toward a monopoly of the scholarly publishing market is reflected in
the behavior of scientometric KM researchers. Several recent partnerships and acquisitions
among the leading scholarly publishers suggest a trend toward a monopoly of the for-profit
academic publishing market. Scientometric KM scholars have also consolidated their
selection of data sources around five major databases: WoS, Scopus, EBSCO,
ScienceDirect, and ProQuest –ABI/INFORM. Given the recent addition of ScienceDirect
and Emerald searchable content to EBSCO, this trend is likely to persist. On the one hand,
an ability to conduct a comprehensive search for academic literature by using a single
interface offers efficiency and increases search breadth. On the other hand, it may reinforce
a state of monopoly and create a dangerous precedent in the academic world that is
supposed to strive toward democracy and debate.
Implication #7: Scientometric KM scholars should further improve the rigor of their literature
search approaches. Scientometric KM researchers have increased the coverage
comprehensiveness of the literature included in their empirical analyses: during the
2012–2019 period, 57% of all data retrieval methods relied on a single database (vs 64% for
the 1996–2012 period), and 55% of all database searches used a single keyword (vs 67%
for the 1996–2012 period). Though this improvement is encouraging, it is not sufficient to
ensure an adequate coverage of the entire KM domain. For example, as of September
2020, only 11 out of 26 KM-centric journals were covered by WoS. Thus, relying on
Clarivate’s products exclusively, which was done in 20.4% of all cases, may bias the
findings and should be discouraged.
Implication #8: To create a list of keywords for database searches, scientometric KM scholars
should rely on the KM keyword classification scheme. In the previous study, Serenko (2013)
emphasized a need for the development of a unified KM keyword classification scheme.
Bedford (2015a)andFteimi and Lehner (2018) answered his call in a very rigorous way, and
future scientometric KM scholars are strongly recommended to make use of their work.
Implication #9: KM-centric peer-reviewed journals should continue welcoming manuscripts
on scientometric topics. Compared to the previous period (i.e. 1996-2012), a larger
proportion of all scientometric KM works have appeared in KM-centric journals. This is
VOL. 25 NO. 8 2021 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1909
encouraging because the works documented in the outlets that cater to the discipline’s
target audience are more likely to be used by KM scholars. However, 36.7% of such
works appeared in non-KM-centric outlets, and these are less likely to reach KM readers.
Thus, scientometric KM scholars are advised to submit their manuscripts to KM-centric
journals, and these outlets are advised to welcome such submissions. A notable example
is Journal of Knowledge Management that has published 15.2% of the entire
scientometric KM research output.
Implication #10: Scientometric KM researchers should continue improving their awareness
of the existing body of knowledge and use it in their work. This study observed that a
growing number of scientometric KM authors conduct comprehensive literature reviews to
form a theoretical and methodological foundation for their studies and to situate their
findings in light of prior research, which is reflected in their citation behavior. Regrettably,
some scholars still ignore the very tenet of academic research –standing on the shoulders
of the giants who have gone before (Merton, 1993): 19% of all publications failed to cite
prior works, and 14% cited only one relevant paper. At this stage of scientometric KM
research, it is unlikely that no pertinent publications exist. Those who work on niche topics
where “no giants have gone before” may, at the bare minimum, relate their findings to the
overall state of the entire KM discipline.
Implication #11: Citation impact of scientometric KM research is highly skewed.
Scientometric KM publications achieve high impact: an average work is cited 7.38 times per
year. At the same time, the top 10% of all works have attracted a disproportionately high
volume of citations –an astonishing 68% (i.e. 13,742 citations for 28 papers, or 490 citations
per paper, on average). On the one hand, the skewness of science is a well-established
fact, and it may be hypothesized that scientometric KM research exhibits the attributes of
other scholarly domains. On the other hand, a more equal distribution of citation impact is
desirable to ensure that no relevant works remain unnoticed. Moreover, there is a danger
that some scientometric KM scholars may engage in counterproductive citation behavior by
citing prior works without reading, understanding, and properly using them merely because
these papers have been cited by others. For example, 30% of all citations in the KM
discipline are problematic (Serenko and Dumay, 2015a), and it is possible that
scientometric KM scholars are not immune to effects of this problem.
Implication #12: A large-scale, comprehensive investigation of KM publication forums is
warranted. Serenko (2013) called for further research into the nature of KM publication
forums because “many scientometric investigations of the outlets publishing academic KM
works lacked methodological rigor and, as a result, produced highly inconsistent findings”
(p. 790). The present study identified many rigorous studies of KM publication forums, most
of which focused on a single journal or conference. However, these studies were done in
relative isolation which, in most cases, was a methodological necessity. As a result, they
offer a very narrow view of the KM discipline and do not help the reader form a holistic
perspective of the entire spectrum of KM publication forums. For example, it is reasonable
to expect that the studies focusing exclusively on Journal of Knowledge Management,
Knowledge Management Research & Practice or Electronic Journal of Knowledge
Management offer a unique scientometric portrait of a respective outlet which may lead to
different conclusions on the state of the entire KM discipline. Thus, KM discipline
stakeholders would benefit from a large-scale, comprehensive assessment of the entire
data base that is used to preserve the discipline’s body of knowledge.
Implication #13: Scientometric KM researchers should continue engaging in inter-
departmental and international research collaboration. Earlier, Serenko (2013) emphasized
a need for more internal and external collaboration among scientometric KM scholars. This
study revealed an increasing collaboration trend manifested in a higher number of authors
per paper and a lower fraction of single-authored publications, which ultimately improves
the quality, impact and rigor of scientometric KM works.
PAGE 1910 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 25 NO. 8 2021
Implication #14: It is important to understand whether the productivity distribution of
scientometric KM researchers is expected to follow Lotka’s law. The productivity distribution
of scientometric KM scholars does not follow Lotka’s law because too many scholars
contribute to the research area only once or very rarely. Two explanations are proposed.
First, this may reveal a somewhat unhealthy state of scientometric KM research which is
represented by many scholars who contribute only once or at least less frequently than
Lotka’s law predicts. Presently, only 36% of active KM researchers consider KM their
primary research domain (Serenko and Bontis, 2017). As an interdisciplinary field, KM is
represented by scholars from IS, organizational behavior, human resources, strategy, etc.
who have ample opportunities to pursue non-KM topics. After contributing only once or
twice, they abandon KM in favor of other research areas.
Second, scientometric publications require a lot of mechanical work which involves manual
and extremely time-consuming processes of data collection, aggregation, verification,
coding, analysis, etc. It is possible that such type of work is allocated to research assistants
(e.g. graduate students) in exchange for authorship. In this case, many such research
assistants may have no interest in working in the scientometric KM domain in the future, but
they agree to take part in the project as an opportunity to gain some research experience
and secure a publication. In this case, the fact that the authorship distribution patterns of
scientometric KM scholars deviate from Lotka’s law does not indicate the domain’s
immaturity. It is critical, therefore, to empirically investigate the propositions above by
surveying or interviewing KM authors.
Implication #15: The inconsistency observed in the findings of scientometric KM works may
be attributed to a high degree of the specialization of most studies. Serenko (2013)
concluded that “the results reported in scientometric KM studies are inconsistent” (p. 789).
A high level of inconsistency was also observed in the present study. For example, there are
dramatic discrepancies among the studies in terms of productivity rankings of individuals
and institutions, lists of the most impactful works, longitudinal fluctuations in the overall KM
research output (e.g. see Cardenas and Udo, 2013), and popular research topics. A closer
examination of the entire body of scientometric KM research shows that these
inconsistencies are a natural product of the high degree of specialization of a majority of
scientometric KM studies. For instance, the field comprises general, knowledge-based
development and organizational learning topics, and it is this specialization that naturally
results in a high discrepancy in these studies’ conclusions.
4.2 Implications for the knowledge management discipline
Implication #1: Scholars should realize that the KM discipline may successfully exist as a
cluster of divergent schools of thought under an overarching KM umbrella and that the
notion of intradisciplinary cohesion and consistency should be abandoned. Hannabuss
(1987) envisioned the interdisciplinary nature of the KM discipline, and it seems that his
prediction has materialized: there is a general consensus that the field has progressed in an
interdisciplinary direction. Interdisciplinary research leads to creativity, value, impact and
high scientific output. Thus, as an interdisciplinary field of science, KM has great potential to
contribute to the state of theory and practice, and the scientific KM community should fully
embrace the notion of interdisciplinarity, a divergence of opinion and a multiplicity of co-
existing paradigms.
Implication #2: A recent decline in the overall volume of yearly KM publications reported by
scientometric KM studies does not signify a diminished interest in the discipline. A majority
of scientometric KM studies have agreed that, between 2011 and 2015, the overall volume
of yearly studies on KM topics reached its peak and started to decline. This statement,
however, does not accurately reflect the status of the KM discipline. First, KM has extended
its interdisciplinary reach, and KM topics have been increasingly incorporated in
publications in other scientific domains; thus, it is difficult to distinguish between a “KM” and
VOL. 25 NO. 8 2021 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1911
a “non-KM” paper and decide whether to count it as a part of the KM research output. In
particular, there has been a growing interest in niche KM topics that intersect with other
management domains –for instance, innovation, IS and health care. It is unclear whether
one should consider these papers KM-centric publications. Second, most studies
measuring the volume of KM research use “knowledge management” as a major keyword.
By doing so, they miss a large number of relevant KM papers because a growing number of
KM publications omit the phrase “knowledge management” in their titles, abstracts and
keywords. Thus, the interdisciplinary progression of KM makes it more difficult to identify
and measure, but the interest in KM topics is very strong.
Implication #3: Journal of Knowledge Management is unanimously recognized as the
discipline’s leading publication forum. There is consensus that Journal of Knowledge
Management is the discipline’s flagship journal: it is a highly productive, influential,
innovative, balanced and geographically inclusive outlet that has consistently topped KM
journal rankings and has achieved recognition both within and outside the KM discipline. In
2019, it was ranked A according to the Australian Business Deans Council (ABDC) Journal
Quality List and received the Journal Impact Factor of 4.745, which exceeds that of more
than half of all journals included in the Financial Times 50 list.
Implication #4: KM researchers should not limit their interest to the body of knowledge
documented in the KM-centric sources. The KM body of knowledge is documented and
preserved in both KM-centric and non-KM-centric journals and conference proceedings. As
of September 2020, there were 26 KM-centric journals and several conferences that focus
specifically on various KM topics. However, a large share of KM works is also preserved in
outlets catering to other disciplines, especially to CS, IS, LIS, health care and economics.
Implication #5: It is important to understand why so many researchers publish only a single
KM work. The application of Lotka’s law to the productivity patterns of KM scholars revealed
that more than 80% of KM authors publish only a single KM paper, which is a truly
disturbing sign. However, before discussing this fact in the context of the discipline’s health,
it is critical to understand why this phenomenon takes place, and this represents an
important research avenue.
Implication #6: The top six most productive countries are the USA, the UK, Taiwan, Canada,
Australia and China. Irrespective of the method, there was general agreement on the list of
the countries that generated the largest number of KM publications. These countries have
already achieved a high standard of living or have been progressing well toward achieving
one. Though a causal directional relationship between the number of research articles
published and economic growth is more complicated than it seems (Ntuli et al.,2015),
evidence suggests that KM research activity is directly linked to a country’s economic
prosperity, which highlights the relationship between knowledge and wealth (Ramy et al.,
2018). This further confirms the importance of supporting KM research at a national level.
Implication #7: KM scholars should continue increasing their research collaboration.Ontheone
hand, KM scholars have improved their collaboration behavior. On the other hand, the extent of
collaboration depends on geographic locations, publication venues and time periods. Only a
few leading KM scholars have developed extensive collaborative networks, and collaborative
efforts rarely cross international borders. By engaging in national and international collaboration,
KM scholars may reach multiple populations of individuals and organizations, stimulate their
creativity, ensure an effective use of their research expertise, expedite the process of scientific
discovery, enhance the visibility of their research output, avoid effort duplication and infuse the
latest academic knowledge into the knowledge base of developing countries. In addition, having
strong, well-developed collaborative networks serves as a sign of disciplinary maturity.
Implication #8: There is life beyond case studies, surveys and interviews as research
methods. In the early days of KM research, case studies, surveys and interviews
represented the most common empirical approaches, but it seems that this trend has
PAGE 1912 jJOURNAL OF KNOWLEDGE MANAGEMENT jVOL. 25 NO. 8 2021
prevailed throughout the entire lifespan of KM research. As such, the role of these methods
in the development and cultivation of KM research is unarguable. At the same time, other
methods of inquiry which are dramatically under-represented in KM research –for example,
action research, laboratory experiments, design science, ethnography, field studies, field
experiments, mixed-methods and the use of secondary data –may not only fill the gaps in
our knowledge but also cause a paradigm shift. Thus, it is strongly recommended that
researchers embark on the use of these methodologies and that journal editors and
reviewers welcome these submissions.
Implication #9: There is a need for knowledge brokers that may deliver the KM academic
body of knowledge to practitioners. Previously, Serenko (2013) identified a growing gap
between KM academics and practitioners which, as the present study showed, has only
widened. At this stage, it is obvious that the direct knowledge dissemination channel –
which assumes that practitioners directly access, read and benefit from academic
publications –does not function. A proposed solution includes the introduction of formal
and informal positions of knowledge brokers who aggregate, summarize and deliver the
academic knowledge scattered across disparate publication venues to busy practitioners in
an easy-to-comprehend format. For this, the KM discipline may follow the basic KM
principles and adapt the model that has been successfully pioneered in the medical field
under the general term of translational research.
Implication #10: The KM discipline has been progressing well toward maturity and
recognition, but it has been a bumpy ride. During its relatively short history, KM has made
remarkable progress by drawing upon and extending knowledge from reference disciplines
such as CS, management, engineering, economics, social sciences and mathematics.
Because of its interdisciplinary nature, it is difficult to take a precise snapshot of the
discipline’s state, which is evident in the many inconsistencies reported in the findings of
the 175 scientometric studies analyzed in this investigation. However, it is evident that
KM is not a management fad and, as a discipline, it has gained recognition within the
broader scientific community. Most importantly, there are signs that KM has started
infusing knowledge into other disciplines, and KM topics appear in non-KM-centric
management and even non-management journals. In the future, it is likely that KM will
undergo a further transformation process on its bumpy ride toward full academic
maturity.
5. Limitations and conclusions
No scientific endeavor is flawless, and this study is no exception. First, despite the use of a
rigorous SLR method, it is possible that some relevant studies were missed. Examples
include publications existing in non-electronic form at only or those that were not indexed by
academic databases, including Google Scholar. Second, this study focused on peer-
reviewed works only, such as refereed journal articles, conference proceedings papers and
book chapters. However, non-academic sources –for instance, practitioner magazines –
may also contain valuable insights on the state of the KM discipline. Third, the search was
limited to English-language publications, but it is possible that works published in other
languages may portray a different picture of the KM discipline. Fourth, the KM discipline
has close ties with the IC domain, and it is difficult to study KM in isolation from IC. Thus,
future researchers should keep these issues in mind when building upon the findings
reported in this study.
The purpose of this study was to conduct the SLR to update the findings of a previous
project by Serenko (2013) who examined 108 scientometric KM works. In this study, 175
additional publications were analyzed to form an updated picture of the discipline’s identity.
The growth in the volume of scientometric KM research signifies the interest in the KM
discipline and further confirms its status as a recognized management discipline. Based on
the findings, 15 distinct implications for scientometric KM researchers and 10 distinct
VOL. 25 NO. 8 2021 jJOURNAL OF KNOWLEDGE MANAGEMENT jPAGE 1913
implications for KM discipline stakeholders were proposed. Among the major findings is the
fact that it is an appropriate time to recognize that the KM discipline may successfully
evolve as a cluster of distinct schools of thought under an overarching KM framework, and
its body of knowledge may eventually merge with that existing in other management
disciplines and beyond. It seems that Wilson (2002), who claimed that KM is nonsense, was
wrong: even though, as he envisioned, KM may be viewed as an umbrella term because of
its interdisciplinary nature, KM concepts and activities have never evolved into a “nonsense
science”.
Notes
1. In Figures 5 and 6, the sum may differ from zero because of rounding.
2. Works published in 2019 were excluded from citation analysis.
3. The author of this study excluded himself from this list to remain impartial.
4. The author of this study excluded himself from this list to remain impartial. It is for this reason, only
seven names are mentioned.
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About the author
Dr Alexander Serenko is an Associate Professor of Management Information Systems in the
Faculty of Business and IT, University of Ontario Institute of Technology and a Lecturer in
the Faculty of Information, University of Toronto. Dr Serenko holds a PhD in Management
Information Systems from McMaster University. His research interests pertain to
scientometrics, knowledge management, technology addiction and implicit cognitive
processes. Alexander has published more than 90 articles in refereed journals, including
MIS Quarterly,European Journal of Information Systems, Information & Management,
Communications of the ACM and Journal of Knowledge Management, and his works have
received more than 8,500 citations. Alexander has also won six Best Paper awards at
Canadian and international conferences. In 2018, he was ranked one of the most productive
and influential academics in the knowledge management discipline. Alexander Serenko
can be contacted at: a.serenko@utoronto.ca
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