Early-identification of Human Resource Trends and Innovations
through Web-scraping Technology
Alexander Smirnov
, Nikolay Shilov
, Alexey Kashevnik
, Mikhail Petrov
1,2 d
Simon Brugger
and Tefik Ismaili
St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), St. Petersburg, Russia
ITMO University, St. Petersburg, Russia
Festo SE & Co. KG, Esslingen, Germany
Keywords: Human Resource, Competencies, Innovations, Innovation Evaluation, Trend, Competence Management.
Abstract: The paper presents an innovation management approach within the human resources (HR) management area.
The approach provides possibilities to search and scrape HR trends and the joint global evaluation of those
trends by an expert community. We developed a platform that scrapes human resources websites and parses
the documents based on pre-defined keywords to support the innovation management approach and to identify
the innovations that are applicable to the HR domain. It is based on the analysis of term occurrence frequency
change during a period. The evaluations of innovations are done by HR Process Owners and the global HR
Expert Community. For project staffing, innovation-specific requirements are matched with employee skill
Keeping up with state-of-the-art trends in the area of
Human Resource (HR) management is important for
several reasons. First, the reasonable use of
innovations increases the efficiency of HR services
operating within the company. Second, the use of
modern technologies attracts potential employees
who are at the edge of technology development,
which is important for a company that pays
significant attention to innovations.
The current innovation search process is centered
around several experts who spend a significant part of
their working time studying what is currently going
on in the field, what technologies are in use, what
trends take place, and what can be potentially used in
the future (Figure 1, left part). The discovered trends
a then discussed and a group of experts decides,
which innovations can be potentially useful for the
company and initiate corresponding projects. As one
can see, all these processes (especially trend
discovering that takes a significant working time) are
currently manual.
The main research contribution of the paper is the
novel automated innovation management approach
that enables scraping and analysis of new HR trends
as well as joint evaluation of the trends by an expert
community. In the scope of the proposed approach,
we seek ways to automate parts of the processes
where human activity could be replaced through
technology in order to focus more on value-creation
along the process that cannot be taken over by
technology. As a result, a platform has been built with
the workflow shown in Figure 1 (right part). First, the
platform analyses new articles in the pre-defined
websites: it discovers new terms that currently
gaining popularity. Then, the list of potential
innovations is presented to an expert (a member of the
HR Process Owner community) who can decide if
they are really innovations or not. When a certain
amount of innovations is collected or a certain time
has passed, a group of experts is formed from the
global HR Expert Community
Smirnov, A., Shilov, N., Kashevnik, A., Petrov, M., Brugger, S. and Ismaili, T.
Early-identification of Human Resource Trends and Innovations through Web-scraping Technology.
DOI: 10.5220/0010438606420651
In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 1, pages 642-651
ISBN: 978-989-758-509-8
2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
Figure 1: Current and proposed innovation management process (automated operations are indicated by the double border).
Such group is based on matching expert
competencies against innovation descriptions.
Experts evaluate discovered innovations and decide
their priorities. In addition, every six months, a global
community of HR Experts evaluates innovations by
using the crowd-funding approach in order to identify
local and global innovation requirements. The most
promising innovations are implemented in pilot
processes by teams, which are also found via skill
matching. Once the innovations for pilot projects are
selected, innovation-specific skill requirements are
matched against a skill profile database of employees
to find out who has the best skill for the pilot project.
Since the platform is based on the earlier described
competence management platform (Petrov and
Kashevnik 2018), the skill matching stages are not
considered here in detail. All other steps are described
below. The paper is structured as follows. First, the
state of the art in the area of HR management is
presented. Then, the innovation search and analysis
approach is described. It is followed by the trend and
innovation evaluation procedure. Section IV presents
the implementation details and some platform interface
examples. The main results and future work are
discussed in the conclusion.
The human resources literature over the past 50 years
has been reviewed by (Cascio and Boudreau 2016) to
identify trends and analyze the evolution of human
resource and talent management. They reviewed the
history of international human resource management
and talent management and identified key topics that
were relevant at various times.
Discussion about the organization of the
interaction between employees and management is
described by (Černe, Batistič, and Kenda 2018). The
paper also considers the use of human resource
management systems to stimulate the generation of
ideas and innovation. They describe various
combinations of different styles and what
consequences they will lead to.
The factors that influence individual and team
creative ideas are investigated in (Amabile and Pratt
2016). According to their research, the generation and
management of innovations are dynamic processes
that are influenced by internal and external factors.
The impact of various parameters of human
resource flexibility on the effectiveness of research
and absorptive capacity of new knowledge is tested
by (Martínez-Sánchez, Vicente-Oliva, and Pérez-
Pérez 2020). Their findings point to the positive
impact of outsourcing, training core employees, and
applying innovation.
The impact of innovation and sustainable human
resource management practices on customer
satisfaction in hotels is investigated by (Wikhamn
2019). The paper shows how applying sustainable HR
practices drives innovation and leads to positive
The role of big data in intuition-based human
resource management is examined by (J. Kim et al.
Early-identification of Human Resource Trends and Innovations through Web-scraping Technology
2021). They analyzed data on baseball league clubs to
determine which is more important in human resource
management: intuition and experience or big data
analysis. Their results show that big data reduces the
bias associated with intuitive HR decision making.
However, these benefits are diminished by making
decisions based only on data analysis.
The factors that contribute to the transformation
of innovative aspirations into real results are explored
by (De Clercq, Thongpapanl, and Dimov 2011). Their
results show that high levels of organizational
autonomy, trust, and commitment strengthen the link
between innovation and organizational performance.
The impact of artificial intelligence progress on the
process of finding and managing innovation is studied
by (Haefner et al. 2021). The authors conclude that the
use of artificial intelligence helps to improve
information processing and new trend detection.
The impact of digitalization on innovation
organizations and their strategy is considered by
(Niewöhner et al. 2020). They show that innovation
organizations, culture, and strategy are influenced by
the changes in world trends.
Principles that are specific to innovation
organizations are discussed by (Solaimani, Haghighi
Talab, and van der Rhee 2019). They declare that
such organizations should have people and technical
equipment involved in innovation processes.
The conditions required for the effective
management of Big Data considered by (Caputo et al.
2020). Their results show that applying innovation
and employing staff trained to manage innovation
increases the profitability of companies in this area.
A flexible approach to project and innovation
management is offered by (Brandl, Kagerer, and
Reinhart 2018). It shows possible development
options and tasks that need to be solved.
The presented literature review showed that the
analysis of trends and existing practices for the search
and generation of innovations in HR is a relevant
topic (Cascio and Boudreau 2016). Human resource
management systems should support the generation
and implementation of ideas and innovations by
employees and management (Černe, Batistič, and
Kenda 2018). Stimulating creativity in innovation is
facilitated by clear goal setting, explicit interest in
innovation, the provision of resources, and a
reasonable time frame (Amabile and Pratt 2016). The
use of external sources of knowledge has a positive
effect on the development and dissemination of
knowledge in the organization (Martínez-Sánchez,
Vicente-Oliva, and Pérez-Pérez 2020). However,
innovation itself is not sufficient. The style and HR
practices used should be focused on the search and
application of innovations (Wikhamn 2019). Relying
only on big data or intuition is not enough for resource
management. A combination of approaches is more
effective (J. Kim et al. 2021). To successfully
implement innovations and get positive effects from
them, structural and relational mechanisms for cross-
functional exchange of ideas are required. Functional
managers who are responsible for finding and
applying innovations should have autonomy and trust
from management in decision-making (De Clercq,
Thongpapanl, and Dimov 2011). The development of
information technology and digitalization affect the
processes of searching and managing innovation and
improves their efficiency (Haefner et al. 2021;
Niewöhner et al. 2020). At the same time, it is
important to be able to combine everyday processes
and the development of new areas (Niewöhner et al.
2020). The use of technology and trained personnel to
search for innovations should be one of the basic
principles of a successful modern innovative
organization (Caputo et al. 2020; Solaimani,
Haghighi Talab, and van der Rhee 2019). Such
technologies should show possible options and their
requirements (Brandl, Kagerer, and Reinhart 2018).
In the paper, we proposed an approach to innovation
management that is aimed at innovation identification
in the HR area, the voting procedure of different
experts, and the project creation based on the
3.1 HR Trend and Innovation Search
and Analysis
The preliminary research on HR trend and innovation
search and analysis has been reported in (Shilov and
Teslia 2020). In accordance with its results, the
innovation is characterized by (i) radical novelty
relating the notion of emergence to a certain period of
time, and (ii) relatively fast growth assuming that the
attention paid to the innovation during the emergence
period growth significantly (Rotolo, Hicks, and
Martin 2015; Xu et al. 2019).
Finding innovations has been attracting the
attention of researchers for a while. In 2001 an
approach to emerging topic tracking via evaluation of
the Proportional Document Frequency measure
(TF*PDF) metric was proposed by (Bun and Ishizuka
2001). In the work presented by (K.-Y. Chen,
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Luesukprasert, and Chou 2007) another metric called
“Energy” was proposed that evaluates the
“importance” of a topic over different periods. These
two metrics have later been applied in different
domains (Ma 2011; Nguyen, Byung-Joo Shin, and
Seong Joon Yoo 2016).
Other approaches considered not only frequency
of term appearance but also references to the analyzed
documents (Y. Chen et al. 2013; Takahashi, Tomioka,
and Yamanishi 2014; Wang 2018) and document
clustering (Glänzel and Thijs 2012; Kasiviswanathan
et al. 2011; Small, Boyack, and Klavans 2014).
Whereas all the above techniques either apply certain
thresholds to separate emerging or new topics or use
the “Top N” approach, some works are aimed at the
application of machine learning techniques to analyze
the pre-computed metrics (D. Kim et al. 2019; Xu et
al. 2019).
Unfortunately, the latter techniques are most
applicable to significant innovations that form their
own fields during few years, since the innovation has
to be “invented” and proposed first, then noted by
others and cited in their publications. For the topic to
become noticeable, this cycle has to be repeated
several times that would take some years. In the case
of innovation search, this period is too long, so we
will have to rely only on the former metrics (Shilov
and Teslia 2020).
The methodology used was inspired by (Xu et al.
2019) and is shown in Figure 2. Firstly, the articles
related to the HR topics are scraped from the Web. The
relevance of the articles is evaluated by identifying
proper sources by the HR experts. Usually, these are
news sites and blogs related such as:
HBR (http://hbr.org),
Josh Bersin (http://joshbersin.com),
Deloitte (https://www2.deloitte.com),
HR Dive (https://www.hrdive.com),
HR Morning (https://www.hrmorning.com),
re:Work (https://rework.withgoogle.com),
and etc.
Then, the tokenization procedure is applied,
which selects “tokens” (“words”) in the text based on
the predefined rules. In this particular work, we
eliminate all non-letter symbols (they are replaced
with spaces) and identify single words, bi-grams, and
tri-grams as tokens. As a result, for example, we can
get such terms as “chatbot”, “talent marketplace”, or
“novel coronavirus pandemic”.
The resulting tokens are stemmed: the words are
reduced to their base form, and the stopwords (words
that are not significant for the analysis, such as articles
(“a”, “an”, “the”), prepositions (“in”, “on”, etc.),
conjunctions (“and”, “or”, etc.), pronouns (“we”, “it”,
etc.) and other) are filtered out and removed.
The above procedures result in vectors of
meaningful words for each article. After this, each
token (word or sequence of two or three words) is
considered as terms and a matrix is built, with rows
being articles, columns being terms, and values being
term occurrence numbers in the documents. This
matrix accompanied by article metadata (such as
publication date) is stored as an index.
As one can see even though the articles analyzed
are scraped from thematic Websites, not all of them
are related to HR innovations and a significant
amount of terms can be found that are not interesting
for innovation search (e.g., “novel coronavirus
pandemic” mentioned above). To filter out the
articles potentially related to innovations, the
following two aspects are considered.
On the one hand, the article should be related to
the appropriate current megatrends of the Gartner
Hype Cycle (e.g., connectivity, neo-ecology,
individualization). On the other hand, it has to be
related to HR categories (e.g., strategy and planning,
recruitment, talent management). The relationship of
an article to a megatrend or a category is evaluated
through the presence of keywords predefined by
experts for each megatrend and category.
Figure 2: The methodology of searching for HR innovations.
Early-identification of Human Resource Trends and Innovations through Web-scraping Technology
Since the amount of articles is rather big and
scraping and building an index is a time-consuming
operation, it is done on a nightly basis. At the moment
of paper writing, the database has information about
7000 articles, 1.2 million terms, and the index table
has nearly 3.5 million entries.
When the index is built, the system can propose
potential HR innovations and an expert can decide if
the suggested term is innovation or not. For this
purpose, the analysis of the dynamics of mentioning
the term in the articles related to HR innovations
(estimated via TF*PDF and Energy metrics) is done.
Based on the innovation features (radical novelty and
relatively fast growth) the dynamics of the
mentioning of the innovation should be similar to one
shown in Figure 3. One can see that before May 2019
this term almost had not been mentioned
(corresponding to the “radical novelty” feature),
however, after that its popularity has been growing up
significantly (corresponding to the “fast growth”
feature). Identified HR innovations are suggested by
the system to experts.
3.2 Trend and Innovation Evaluation
An objective importance and impact evaluation of the
trends and innovations is ensured through the
involvement of a global HR Expert Community. HR
Process Owner evaluates all innovations in the first
step. In a second and global step, the whole HR
Community votes on the top innovations. The set of
selected innovations and experts determines the
voting session, limited by the dates set by HR Process
Owners (see Figure 4). All experts participating in the
voting session have a virtual budget specified by the
HR Process Owners. They should distribute it among
the innovations selected for this session, according to
their importance and impact.
It is considered that the more value the expert
assigned to the innovation, the more important it is in
her/his opinion. The final result is determined for
each innovation selected for the session as the sum of
all values determined by the experts participating in
the session for this innovation. HR Process Owners
can use these results to determine the importance and
impact of innovation. An example of such results and
how they are displayed is shown in Section IV.
Information about innovations is stored in
innovation profiles. These profiles contain categories
and trends to which innovations belong, the terms
associated with them, and their impacts. Information
about the categories and impacts of the innovations is
displayed on the trend radar. An example of the trend
radar is shown in Section IV.
Figure 3: Dynamic of occurrence of the term “talent marketplace”.
Figure 4: General scheme of trend and innovation evaluation.
Process Owner
Potential innovation 1
Potential innovation n
Expert 1
Expert m
Voting session
Innovations Experts
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Figure 5: Innovation search workflow and hot terms identification.
3.3 Innovation Project Staffing
To find the group experts that have enough
competencies for innovation project estimation the
(Petrov and Kashevnik 2018) method has been
developed. The method allows finding the following
group types: most effective, most experienced group,
most available group. Based on the method HR
process owner has the possibility to find an effective
expert group to decide which innovation project
should be started.
In this section, we consider the use of the platform by
the innovation manager. The manager's goal is to
search for innovations, identify the most important
ones, and assign a project team to implement the
selected innovations.
The innovation search is carried out on the basis
of terms found in articles obtained from various
sources. The most relevant recent terms (hot topics)
are extracted fro m the articles as it is described in
Section III (Subsection A). These terms are shown for
each category (see Figure 5) and updated nightly. The
manager can choose a category or subcategory by
clicking on them. In this case, only terms that belong
to the selected category or subcategory will be shown.
If information about the specific term is required,
the manager can find it using an innovation search or
by clicking on that term. If the expert has not found
an innovation that he thinks needs to be added, he can
add it by clicking the "Add" button. The innovations
found and saved earlier are shown under the
innovation search.
Early-identification of Human Resource Trends and Innovations through Web-scraping Technology
The manager can view statistics for any of the
suggested hot topics or any found term (see Figure 6).
The graphs and the table show the absolute and
relative number of mentions of the term in the last few
months. This statistic shows how often the term has
been used in different periods (last 30 days, last 3
months, last year, or a number of occurrences for each
month in the last 5 years), and shows the articles in
which the term appears most frequently. The manager
can use this information to become more familiar
with the term and to understand how innovative it is.
If the manager has decided that some term is
innovation, he/she can create an innovation project
(see Figure 7). The manager should fill the required
fields and save the project. Some fields of the new
project (such as name, associated terms, and due date)
are filled in automatically and can be changed by the
manager. The innovation project contains
information about the innovation and associated
terms, as well as its impact. In addition, experts can
participate in the discussion of the project.
When several innovation projects have been
created, an innovation voting session is started to
decide which of them will be implemented. The
manager can select innovation projects and experts to
attend the session, and set a budget for the experts.
After the voting session is over, the manager uses the
voting results to identify the most important
innovations. The manager can select the required
and see the distribution of votes for various
Figure 6: Term occurrence statistics in the HR documents (to identify its novelty).
Figure 7: An innovation project creation workflow.
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
innovations in the form of a diagram or table (see
Figure 8). The most important innovations in different
categories are displayed on the trend radar (see Figure
9). The trend radar is divided into several sectors
corresponding to different categories. Each sector
contains innovations (in the form of points) that belong
to the corresponding category. The greater the impact
of an innovation, the closer it is to the center.
Trend radar is used to identify innovation projects to
be implemented. The closer the project is to the center,
the more important it is. Consequently, such projects are
more likely to be selected. When an innovation project
is selected for implementation, a project team must be
assigned. For this purpose, the requirements for the
competencies of team members should be determined.
When the requirements are determined, experts who
have the required competencies are automatically
matched (see Figure 10).
Figure 8: Innovations voting results.
Figure 9: The trend radar visualization.
Early-identification of Human Resource Trends and Innovations through Web-scraping Technology
Figure 10: Innovation project performers search.
They are presented in the form of groups that
managers can compare with each other and choose
the most suitable for current goals. Each group
contains information about its participants, their
availability, and efficiency. Also, the manager can see
the number of tasks performed by each participant at
the moment in order to assess their workload.
The developed system has been evaluated for the
HR department of Festo company. Several users
integrated the system workflow into their everyday
business processes. During the 5 month experience,
the following results have been tracked:
Manual effort of searching for interesting
articles related to HR innovation topic has been
decreased by 80%;
The effort for reading, validating, and
prioritizing innovation ideas has been
decreased by 40%;
The time of selecting relevant employees to
innovation projects has been decreased by
Actual HR trends database has been created in
the company;
HR trends and statistics are being used in the
HR Quarterly Review;
The global HR community is highly involved
in evaluating and implementing pilot projects;
The company has clear transparency of the
HR Community skills and gaps in the skills.
The paper presents an approach and case study to HR
trends and innovations identification through web-
scraping technology. We propose the method for
trend and innovation search that allows experts to
analyze them. Then the workflow is proposed to
implement the community voting for identification of
the trend innovativeness. We identify the following
assumptions: HR trends and innovations are always
related to the appropriate current megatrends of the
Gartner Hype Cycle and to HR Categories; the
content of the crawled web sites is of high quality and
consistent; the number of experts registered in the
system is enough for voting and team formation.
Also, we identify the following limitations for the
developed system: found by the system HR trends
and innovations strongly depend on the crawled
documents and sometimes do not meet experts’
The approach has been developed in the scope of
the joint research project with Festo SE & Co. KG.
Our goal was to automate the company processes for
trends and innovation search. The project was
successfully implemented and evaluated by the HR
department of Festo company.
The research is funded by the joint research project
between SPIIRAS and Festo AG & Co. KG.
Innovation project performers search has been
supported by RFBR project # 18-37-00377. The trend
and innovation evaluation method has been partly
supported by RFBR project # 19-07-00670.
Amabile, Teresa M., and Michael G. Pratt. 2016. “The
Dynamic Componential Model of Creativity and
Innovation in Organizations: Making Progress, Making
Meaning.” Research in Organizational Behavior 36:
Brandl, Felix J., Moritz Kagerer, and Gunther Reinhart.
2018. “A Hybrid Innovation Management Framework
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
for Manufacturing - Enablers for More Agility in
Plants.” In Procedia CIRP, Elsevier B.V., 1154–59.
Bun, Khoo Khyou, and M. Ishizuka. 2001. “Emerging
Topic Tracking System.” Proceedings - 3rd
International Workshop on Advanced Issues of E-
Commerce and Web-Based Information Systems,
WECWIS 2001: 2–11.
Caputo, Francesco, Alberto Mazzoleni, Anna Claudia
Pellicelli, and Jens Muller. 2020. “Over the Mask of
Innovation Management in the World of Big Data.”
Journal of Business Research 119: 330–38.
Cascio, Wayne F., and John W. Boudreau. 2016. “The
Search for Global Competence: From International HR
to Talent Management.” Journal of World Business
51(1): 103–14.
Černe, Matej, Saša Batistič, and Renata Kenda. 2018. “HR
Systems, Attachment Styles with Leaders, and the
Creativity–Innovation Nexus.” Human Resource
Management Review 28(3): 271–88.
Chen, Kuan-Yu, Luesak Luesukprasert, and Seng-cho T.
Chou. 2007. “Hot Topic Extraction Based on Timeline
Analysis and Multidimensional Sentence Modeling.”
IEEE Transactions on Knowledge and Data
Engineering 19(8): 1016–25.
Chen, Yan, Hadi Amiri, Zhoujun Li, and Tat-Seng Chua.
2013. “Emerging Topic Detection for Organizations
from Microblogs.” In Proceedings of the 36th
International ACM SIGIR Conference on Research and
Development in Information Retrieval - SIGIR ’13,
New York, New York, USA: ACM Press, 43.
De Clercq, Dirk, Narongsak Thongpapanl, and Dimo
Dimov. 2011. “The Moderating Role of Organizational
Context on the Relationship Between Innovation and
Firm Performance.” IEEE Transactions on Engineering
Management 58(3): 431–44.
Glänzel, Wolfgang, and Bart Thijs. 2012. “Using ‘Core
Documents’ for Detecting and Labelling New
Emerging Topics.” Scientometrics 91(2): 399–416.
Haefner, Naomi, Joakim Wincent, Vinit Parida, and Oliver
Gassmann. 2021. “Artificial Intelligence and
Innovation Management: A Review, Framework, and
Research Agenda
.” Technological Forecasting and
Social Change 162: 120392.
Kasiviswanathan, Shiva Prasad, Prem Melville, Arindam
Banerjee, and Vikas Sindhwani. 2011. “Emerging
Topic Detection Using Dictionary Learning.” In
Proceedings of the 20th ACM International Conference
on Information and Knowledge Management - CIKM
’11, New York, New York, USA: ACM Press, 745.
Kim, Donghwa, Deokseong Seo, Suhyoun Cho, and
Pilsung Kang. 2019. “Multi-Co-Training for Document
Classification Using Various Document
Representations: TF–IDF, LDA, and Doc2Vec.”
Information Sciences 477: 15–29.
Kim, Jaemin, Clay Dibrell, Ellen Kraft, and David
Marshall. 2021. “Data Analytics and Performance: The
Moderating Role of Intuition-Based HR Management
in Major League Baseball.” Journal of Business
Research 122: 204–16.
Ma, Hui-Fang. 2011. “Hot Topic Extraction Using Time
Window.” In 2011 International Conference on
Machine Learning and Cybernetics, IEEE, 56–60.
Martínez-Sánchez, Angel, Silvia Vicente-Oliva, and
Manuela Pérez-Pérez. 2020. “The Relationship
between R&D, the Absorptive Capacity of
Knowledge, Human Resource Flexibility and
Innovation: Mediator Effects on Industrial Firms.”
Journal of Business Research 118: 431–40.
Nguyen, Khanh-Ly, Byung-Joo Shin, and Seong Joon Yoo.
2016. “Hot Topic Detection and Technology Trend
Tracking for Patents Utilizing Term Frequency and
Proportional Document Frequency and Semantic
Information.” In 2016 International Conference on Big
Data and Smart Computing (BigComp), IEEE, 223–30.
Niewöhner, Nadine et al. 2020. “The Impact of the 4th
Industrial Revolution on the Design Fields of
Innovation Management.” In Procedia CIRP, Elsevier
B.V., 43–48.
Petrov, Mikhail, and Alexey Kashevnik. 2018. “Expert
Group Formation for Task Performing: Competence-
Based Method and Implementation.” In 2018 23rd
Conference of Open Innovations Association (FRUCT),
IEEE, 315–20.
Rotolo, Daniele, Diana Hicks, and Ben R. Martin. 2015.
“What Is an Emerging Technology?” Research Policy
44(10): 1827–43.
Shilov, Nikolay, and Nikolay Teslia. 2020. “Comparing
Statistical Measures for Discovering Emerging Terms
in Scopus Publications in the Area of Decision Support
in Smart City.” In 2020 27th Conference of Open
Innovations Association (FRUCT), IEEE, 1–8.
Small, Henry, Kevin W. Boyack, and Richard Klavans.
2014. “Identifying Emerging Topics in Science and
Technology.” Research Policy 43(8): 1450–67.
Solaimani, Sam, Ardalan Haghighi Talab, and Bo van der
Rhee. 2019. “An Integrative View on Lean Innovation
Management.” Journal of Business Research 105: 109–20.
Takahashi, Toshimitsu, Ryota Tomioka, and Kenji
Yamanishi. 2014. “Discovering Emerging Topics in
Social Streams via Link-Anomaly Detection.” IEEE
Transactions on Knowledge and Data Engineering
26(1): 120–30.
Wang, Qi. 2018. “A Bibliometric Model for Identifying
Emerging Research Topics.” Journal of the Association
for Information Science and Technology 69(2): 290–304.
Wikhamn, Wajda. 2019. “Innovation, Sustainable HRM
and Customer Satisfaction.” International Journal of
Hospitality Management 76: 102–10.
Xu, Shuo et al. 2019. “Emerging Research Topics
Detection with Multiple Machine Learning Models.”
Journal of Informetrics 13(4): 100983.
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