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

profiles.

1 INTRODUCTION

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

https://orcid.org/0000-0001-8364-073X

https://orcid.org/0000-0002-9264-9127

https://orcid.org/0000-0001-6503-1447

https://orcid.org/0000-0001-7403-5036

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

642

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

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.

2 RELATED WORK

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

results.

The role of big data in intuition-based human

resource management is examined by (J. Kim et al.

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643

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).

3 INNOVATION MANAGEMENT

APPROACH

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

innovation.

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,

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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

645

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

Dates

Budget

Innovations Experts

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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.

4 CASE STUDY

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

647

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

session

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.

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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

649

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

90%;

 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.

5 CONCLUSION

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’

expectations.

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.

ACKNOWLEDGEMENTS

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.

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Early-identiﬁcation of Human Resource Trends and Innovations through Web-scraping Technology

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