BPM in the Era of Industry 4.0: A Bibliometric Analysis

Hadjer Khider

1,2 a

, Slimane Hammoudi

, Abdelkrim Meziane

and Alfredo Cuzzocrea

Information Systems and Multimedia Systems Department, CERIST, Algiers, Algeria

Computer Science Department, Faculty of Exact Sciences,University of Bejaia, Bejaia 06000, Algeria

ERIS Team, Computer Science Department – ESEO, Angers, France

iDEA Lab, University of Calabria, Rende, Italy

Keywords: Industry 4.0, Digital Transformation, BPM, Bibliometric Analysis.

Abstracts: In the age of today's technological development, with the advancement of the digitization of organizations,

Industry 4.0 (I4.0) has evolved as a consequence of the fourth industrial revolution, leading industry to face

a digital transformation (DT). This transformation is based on the use of cyber-physical systems (CPS) and

information and communication technologies (ICT), in particular artificial intelligence (AI) and the Internet

of Things (IoT). This new paradigm has brought changes in various areas of the functioning of the

organization through a DT that holistically affects business processes, products, relationships and

competencies which is a major challenge for organizations. This paper is dedicated to analyze the literature

on BPM in the digital industry era through a bibliometric analysis, in order to analyze the impact of the I4.0

concepts and their associated technologies on the BPM, which will allow to determine the main BPM issues.

1 INTRODUCTION

Business Process Management (BPM) is at present

one of the most often implemented methods of

management within organizations (Szelagowski et

al., 2020). According to Gartner in (Gartner, 2018),

BPM software is a technology solution that enables

organizations to design, analyze, execute, monitor

and optimize important processes. It aims at

providing techniques and software to design, enact,

control, and analyze business processes involving

humans, organizations, papers and other sources of

information (Di Ciccio et al., 2015) . BPM presents a

valuable and an advantageous tool through cost

reduction, process excellence, and continuous

process improvement. This important revolution has

been stirred-up by recent advancements in the big

data research area (Leung et al., 2018; Leung, Braun,

et al., 2019; Leung, Chen, Hoi, Shang, & Cuzzocrea,

2020; Leung, Chen, Hoi, Shang, Wen, et al., 2020;

Leung, Cuzzocrea, et al., 2019). In the age of today's

technological development, with the advancement of

the digitization of organizations, Industry 4.0 (I4.0)

has evolved as a consequence of the fourth industrial

revolution (Mohanta et al., 2020). The Fourth

https://orcid.org/0000-0002-0566-923

Industrial Revolution is being realized through the

combination of various technologies, such as artificial

intelligence, cloud computing, adaptive robotics,

augmented reality, additive manufacturing and the

Internet of Things (IoT) (Queiroz et al., 2022). This

new paradigm has brought changes in various areas

of the functioning of the organization through a

digital transformation (DT) that holistically affects

business models, processes, products, relationships

and competencies, which is a major challenge for

organizations (Flechsig et al., 2022), and which

requires a fail fast culture due to the current business

environment and competitive factors (Szelągowski et

al., 2022). In line with the aforementioned concepts,

this paper presents a bibliometric analysis on BPM in

the era of I4.0 and DT. This study has three main

objectives: (i) to analyze the nature and evolution of

the literature related to BPM in the era of digital

industry and (ii) to identify the thematic areas related

to I4.0 and its impact on BPM, and (iii) to identify the

challenges of BPM in the era of DT. To achieve this,

a bibliometric analysis of 231 papers listed in the

Scopus was conducted.

Structurally, this paper is organized as follows:

Section 2 shows a summarized background on I4.0

Khider, H., Hammoudi, S., Meziane, A. and Cuzzocrea, A.

BPM in the Era of Industry 4.0: A Bibliometric Analysis.

DOI: 10.5220/0011995200003467

In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS 2023) - Volume 2, pages 651-659

ISBN: 978-989-758-648-4; ISSN: 2184-4992

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

651

and DT, Section 3 presents a bibliometric analysis on

the BPM in the era of I4.0 and DT, Section 4

discusses the results and findings on the Knowledge

base of BPM in the digital Age. Section 5 presents

discussion, synthesis and positioning. Finally, in

Section 6, we conclude with a summary of the

findings, limitations of the study, and some directions

of the future research.

2 BACKGROUNDS

In this Section, we provide an overview of

backgrounds that are relevant to our research:

Industry 4.0 and Digital Transformation. These will

represent the relevant scientific humus for the

remaining part of the paper.

2.1 Industry 4.0

The term Industry 4.0 refers to a new model of

organization and control of the value chain through

the product life cycle supported by information

technologies that is, it is the application to the model

industry (Okano, 2017). Various technologies or

techniques can be used for implementing Industry

4.0. These technologies include Cyber Physical

Systems, IoT, cloud computing, blockchain,

industrial information integration and other related

technologies (Xu et al., 2018).

2.2 Digital Transformation

The ongoing worldwide digital transformation,

triggered by the Industry 4.0 initiative, has brought

new concepts and emerging technologies to the

surface (Pires et al., 2019). This new paradigm has

historically been defined as the use of technology to

profoundly and rapidly transform business activities,

processes, competencies, and models to fully

leverage the changes and opportunities brought by

digital technologies and their impact across society in

a strategic and prioritized way, to meet changing

business and market requirements, and to lead an

organization into the digital future (Albert, 2020).

The digital transformation of an organization is much

more than just digitalization, as defined by (Schwab,

2016), it is the result of an organizational change

where people, processes and the entire business

model understand technology as a tool to generate

value among its consumers and collaborators.

3 BIBLIOMETRIC ANALYSIS: A

TECHNIQUE OF SYSTEMATIC

LITERATURE REVIEW

One of the most important methods of discovering

knowledge is by synthesizing the results of previous

studies. We conduct a bibliometric analytic approach

to investigate the knowledge base on BPM and

analyzing the impact of the I4.0 concepts and their

associated technologies in the management of

organizations and their business processes (BPs).

Recourse to bibliometric analysis has become

increasingly popular. It facilitates the identification of

frequently referenced authors and related

publications, and the keywords most commonly used

in a given study field (Zehra et al., 2022). It also

facilitates the review of the literature by bringing the

researcher to influential research works or

publications, in a given study field (Zupic et al.,

2015). (Donthu et al., 2021) discussed in detail the

methodology to conduct bibliometric analysis.

3.1 Evaluative Techniques

Evaluation techniques are used to analyze the relative

influence and academic impact of a topic. These

include various measures of productivity, such as

evaluation of the historical evolution of the number

of publications, the distribution of papers by field,

journal and author, and the analysis of the most cited

papers (Hall, 2011). These analyses have been carried

out by means of the VOSviewer (van Eck et al., 2010)

software, as well as by means of the information that

Scopus itself has generated.

3.2 Visualization Software

The use of bibliometric analysis often involves using

network visualization software (Donthu et al., 2021),

ranging from software based entirely on graphical

user interfaces, such as VOSviewer (van Eck et al.,

2010), to software based on commands, such as the

Bibliometrix package in R (Aria et al., 2017). For our

bibliometric analysis we used the VOSviewer

software.

3.3 Study Design: Methodology

The goal of this study was to achieve two purposes.

The first was the analysis of the BPM literature in the

era of I4.0 and DT. This, will help BPM researchers

determine BPM issues, and the second was to

determine research trends over time. To achieve our

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goals, we combined both bibliometric methodologies;

performance analysis and science mapping. The

techniques for bibliometric analysis manifest across

two categories: (1) performance analysis and (2)

science mapping. (Donthu et al., 2021). As

mentioned in (Donthu et al., 2021), performance

analysis examines the contributions of research

constituents to a given field (Cobo et al., 2011), while

science mapping focuses(Baker et al., 2021) on the

relationships between research constituents.

Table 1: Data Collection.

Features Values

Database Scopus

Search Criterion Topic

Kind of Paper Article, conference book chapter

(published or early access)

Time Range 2016-2023

Keywords ("business process management" OR

"BPM") AND (“Industry 4.0” OR

“Digital transformation”)

Number of Initial

Papers

231

Filter Criterion Duplicated papers. Paper not in

English, Papers not related with the

topic

Number of final

Papers

222

In accordance with the general guidelines for the

conduct of a bibliometric analysis presented in(Baker

et al., 2021), the next five key steps have been

followed:

 Research Design: Various types of

methodologies have been developed to

construct a bibliometric analysis, we have

considered citation and co-citation analysis

(by author and journal) (Appio et al., 2014),

co-word analysis, and network analysis .

 Bibliometric Data Collection: we have

browsed the Scopus database accessed via our

SNDL

portal, which gives us access to rich

and diverse international electronic

documentation. which has offered a total of

231 papers for examination. The search terms

("business process management" OR "BPM")

AND (“Industry 4.0” OR “Digital

transformation”) were used to extract

bibliometric data as mentioned in Table 1.

 Inclusion Criteria: There were three

inclusion rules that were followed: Journal

articles, conference papers, and book chapters

in which one of the keywords appears in the

article title, abstract, or keywords. The

https://www.sndl.cerist.dz/index.php?p=9

publication date ranges from 2016 to 2023. If

they met all inclusion criteria, English

language abstracts were included in the

bibliometric review.

 Exclusion Criteria: All papers with a core

subject of industry 4.0 or DT but not relevant

to the field of BPM were left out of the

analysis. After filtering the data based on the

inclusion and exclusion criteria, a total of 231

papers were gathered (see Table 1). We have

excluded the paper that are not in English.

which has limited to a total of 222.

 Methodology and Software: We have used

the Scopus data set in the analysis and the

approaches listed above, in addition to the

VOSviewer

(van Eck et al., 2010) as a

software for visual and quantitative analysis.

4 KNOWLEDGE BASE OF BPM

IN THE DIGITAL AGE:

RESULTS AND FINDINGS

4.1 Productivity Assessment

Figure 1 presents the annual scientific production in

I4.0 and DT fields related to BPM. A fundamental

jump in the number of works occurred in 2017 and

2018, while the number of works extremely increased

in 2022. Table 2 shows the top productive nations.

Figure 1: Annual scientific output on BPM and industry 4.0:

A Scopus database analysis (2016–2023).

As shown in Figure 2, Germany is one of the

countries that has played an important and crucial role

in the ongoing progress of the field, followed by

Russian Federation. Austria, Portugal and Italy.

https://www.vosviewer.com/

2016 2017 2018 2019 2020 2021 2022 2023

Year of publication

Document per year

Annual scientific output

BPM in the Era of Industry 4.0: A Bibliometric Analysis

653

Table 2: Distribution of papers per country.

Country Num. of

Papers

Country Num. of

Papers

Germany 45 Australia 3

Russian

Federation

21 Liechtenstein 3

Austria 18 Denmark 2

Portugal 17 Hungary 2

Italy 17 Japan 2

United States 14 Latvia 2

Brazil 11 Sweden 2

Belgium 9 Turkey 2

India 7 Azerbaijan 2

U. Kingdom 7 Finland 2

South Africa 6 Slovenia 1

France 6 Tunisia 1

South Africa 6 Colombia 1

Czech 5 Cyprus 1

Netherlands 5 Ecuador 1

Switzerland 5 Greece 1

Croatia 4 Ireland 1

Lithuania 4 Mexico 1

Peru 2 Namibia 1

Romania 2 Norway 1

South Korea 2 Saudi A. 1

Thailand 3 Bangladesh 1

Ukraine 3 Argentina 1

Cambodia 2 U.A.E. 1

Canada 2 Slovakia 1

China 2

Figure 2: Ranking of countries based on scientific output on

BPM in the era of industry 4.0 and digital transformation

(2016-2023).

4.1.1 Most Productive Authors

The most productive authors working on the topic of

I4.0 and the DT in relation to BPM are shown in

Figure 3. As we can see in Figure 3, German

researchers such as Flechsig and Schmidt W. are the

most active contributors (see Figure 3), Schmidt W.,

which emphasizes the agility required by business

processes to support process execution for the digital

transformation of organizations (Fleischmann et al.,

2021), followed by Imgrund, F. (Marcus Fischer et

al., 2020; Rehse et al., 2021), and Fischer, M.

followed by Janiesch C. in (M Fischer et al., 2019;

Marcus Fischer et al., 2020, 2021; Imgrund et al.,

2018, 2019) and Fettke, P. (Niesen et al., 2016; Rehse

et al., 2021) who focused on the great potential of

BPM techniques to meet I4.0 challenges.

Figure 3: An overview of the most productive authors.

4.1.2 Most Influential Publications

Table 3: Most cited authors.

Author Paper per

Author

Citations

Fettke P. 4 91

Imgrund F. 5 85

Janiesch C. 5 85

Fischer M. 4 82

Winkelmann A. 4 82

Lederer M. 4 53

Van looy A. 4 44

Santoro F.M. 4 34

Kirchmer M. 4 28

Stary C. 5 24

Telukdarie A. 4 24

Teixeira L. 4 18

Schmidt W. 6 17

Barata J. 4 8

Schmidt, W.

Lederer, M.

Massaroni, C.

Abasova, S.

Erasmus, J.

Herbig, N.

Mendes, M.

Pogolski, C.

Schimak, M.

Vidgof, M.

Al Sawadi, O.

Analide, C.

Astapenko, E.O.

Barbosa, C.E.

Belo, O.

Bondarenko, M.

Butt, J.

Cipriani, V.

Number of Paper bublished

Most productive authors

Distribution of papers per Author

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Table 3 shows the most cited among the 222 papers,

for example, the most cited paper is Industry 4.0:

State of the art and future trends, by (Xu et al., 2018)

with 1428 local citations and 2398 global citations,

followed by the paper, Blockchain

based business process management framework for

service composition in industry 4.0 by

(Viriyasitavat et al., 2020) with 115 and 264 local and

global citations, respectively.

Intriguingly, although (Baiyere et al., 2020)

receives 89 local citations, this work has 209 global

citations followed by Imgrund F. et al. (2020).

Strategy archetypes for digital transformation:

Defining meta objectives using business process

management, with 58 local citations and 179 global

citations. It examines how the large companies use

BPM to implement digital transformation.

Table 4: Distribution of paper by type of publication.

Publication type Papers Frequency (%)

Conference Paper 132 59.45

Article 63 28.37

Conference Review 14 6.30

Book Chapter 6 2.70

Review 4 1.80

Book 2 0.90

Editorial 1 0.45

Table 4 shows that the 222 selected papers were

included in 2752 publications. Specifically, the

distribution according to the type of publication is as

follows: 132 (59,45%) were published in conference

papers, 63 (28,37%) in journals, 14 in conference

reviews (6.30%), 6 (2.70%) in book chapters, 4

(1.80%) in reviews, 2 (0.90%) in books and 1 paper

in editorial (0,45%). The distribution of papers by

type of publication is shown in Figure 4.

Figure 4: Distribution of papers by type of publication.

Figure 5 presents the distribution of papers according

to the source. Of the 222 selected papers, 63 are

published in journals. It can be seen from Table 5,

that the Business Process Management Journal has

the highest number of publications on the subject of

I4.0 and DT related to BPM in the Scopus index,

followed by Sustainability Switzerland , Applied

Sciences Switzerland, Business And Information

Systems Engineering, Business And Information ,

Business And Information Systems Engineering

Systems Engineering, IEEE Access, Information And

Management, Information Systems, International

Journal of Engineering And Advanced Technology

and Journal On Data Semantics.

Figure 5: Most Relevant Papers Sources.

Table 5: Most Relevant Journal Sources. (Total of 63).

Journal Name Nub. of

publications

Assigned

quartile

Business Process

Management Journal

10 Q1

Sustainability Switzerland 4 Q2

Applied Sciences

Switzerlan

2 Q2

Business And Information

stems En

ineerin

2 Q1

IEEE Access 2 Q1

Information And

Mana

ement

Information Systems 2 Q1

International Journal of

Engineering and Advanced

Technolog

2 No yet

assigned

quartile

Journal On Data Semantics 2 Q3

As Table 6 reflects, most of the papers of this

selection (31.9%) have been published in the

computer science area, followed by Some other

relevant areas as Engineering (17,0%), Business,

Management and Accounting (14,5%), Decision

Sciences (11,2%), Mathematics (11,0%), and Other

Science Technology Topics. Thus, there appear to be

four perspectives from which to approach I4.0 and

DT: Technology, Engineering, Management and

59,01%

27,03%

6,31%

2,70%

1,80% 0,90%

0,45%

Distribution of papers by type

Conference Paper Article

Conference Review Book Chapter

Review Book

Editorial Erratum

Lecture…

Communic…

IFIP…

Applied…

Informatio…

Aip…

Decision…

Estudios…

IEEE…

IT…

Internation…

Iop…

Journal Of…

Procedia…

Revista De…

Distribution of papers according to

source

BPM in the Era of Industry 4.0: A Bibliometric Analysis

655

Science. The distribution of papers by research area

is presented in the Figure 6 below.

Figure 6: Distribution of papers by research area.

Table 6: Distribution of papers by research area.

Research areas Papers

(N)

Percentage

(N/222)

Com

uter Science 165 31,9

ineerin

88 17,0

Business, Management and

Accountin

75 14,5

Decision Sciences 58 11,2

Mathematics 57 11,0

Energ

16 3,1

Physics and Astronomy 11 2,1

Social Sciences 10 1,9

Economics, Econometrics

and Finance

9 1,7

Figure 7: Distribution of papers by affiliation.

4.2 Network Analysis

The use of bibliometric analysis often involves using

network visualization software (Donthu et al., 2021),

ranging from fully graphical user interface-based

software such as VOSviewer (van Eck & Waltman,

2010) to command-based software such as the

Bibliometrix package in R (Aria et al., 2017). We

have used the VOSviewer software to carry out our

bibliometric analysis. Each node in a network

represents an entity (e.g. article, author, country,

institution, keyword, journal). The nodes and links in

this cluster can be used to explain the coverage of the

theme (cluster) by the topics (nodes) and the

relationships (links) between the topics (nodes)

manifested under this theme (cluster).

4.2.1 Collaboration Network Analysis

Figure 8 shows the collaboration matrix. Among the

total of 57 countries in the original dataset, the

collaboration of the researchers by country showed

that the authors have collaborated with researchers

from the same country as well as with researchers

from other countries. As shown in Figure 8, the sizes

of most nodes in the yellow cluster (Germany node),

the green cluster (Russian Federation), the sea green

cluster (Austria and Portugal nodes), and the orange

cluster (Italy node) are larger than in other clusters.

This visualization indicates that most of the selected

researchers in these clusters have published more

articles than other researchers (as we can see in Figure

8, Germany is the country with more publications).

The distance between the nodes shows the

partnerships between the yellow, green, sea green and

the orange clusters. Of all these clusters, the dark

orange cluster is in the center.

Figure 8: Collaboration among researchers from different

countries (visualized by VOSviewer).

165

Distribution of papers by research area

Computer Science

Engineering

Business, Management and

Accounting

Decision Sciences

Mathematics

Energy

Physics and Astronomy

Social Sciences

Economics, Econometrics

and Finance

University of Johannesburg

Instituto de Engenharia…

Chulalongkorn University

Phactum

Hochschule Aalen University of…

Peter the Great St. Petersburg…

Universität Wien

Vilniaus Universitetas

Center for Research &…

Juraj Dobrila University of Pula

QUA-LiS NRW

Södra Skog

Supply Chain development and…

Talpasolutions GmbH

Dr. Albert Fleischmann & Partner

IIC University of Technology

Lusófona University

National Technical University of…

Hochschule Karlsruhe - Technik…

Pusan National University

Number of paper

Affiliation

Distribution of documents per

affiliation

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4.2.2 Keyword Co-Occurrence Network

Analysis

Figure 9. shows the co-occurrence matrix. Each node

in a network represents a keyword, in which: The size

of the node is an indication of the occurrence of the

keyword (i.e. the number of occurrences of the

keyword), The link between nodes represents

keyword co-occurrence (i.e. keywords that occur

together), The thickness of the link signals the

occurrence of keyword co-occurrences (i.e. the

number of times the keywords occur together), Larger

nodes indicate more occurrences of the keyword, The

thicker the link between the nodes, the greater the

number of co-occurrences between keywords. Each

color represents a thematic cluster. As seen in Figure

9, the main keywords in the research topic are:

“business process management (117 occurrences and

554 total link strength), “enterprise resource

management” (116 occurrences and 634 total link

strength), “digital transformation” (84 occurrences

and 395 total link strength), “industry 4.0” (76

occurrences and 317 total link strength), “business

process” (46 occurrences and 266 total link strength),

“Internet Of Things “(17 occurrences and 101 total

link strength) and “digitalization” (13 occurrences

and 79 total link strength).

Figure 9: Keyword co-occurrence network using

VOSviewer.

Research trends and hot topics emerge from the co-

word analysis of the most frequent keywords (Li et

al., 2016), As can be seen in Figure 10, in our sample

of 222 papers, we detected 1,501 keywords. We only

considered the 19 keywords that appear in at least 12

publications. The nodes illustrate the occurrence of

the keywords, while the links between the nodes

represent the number of times that the words appear

together.

Figure 10: The main keywords in the research topic of BPM

and I4.0 and DT.

Figure 11: Co-authorship network in the I4.0 and DT

related to BPM field.

The co-authorship analysis included researchers

with at least one publication (fractional counting)

regarding I4.0 and DT related to BPM. 603

researchers met this threshold and were selected for

network analysis. The co-authorship network in I4.0

and DT related to BPM is mapped in Figure 11, and

this map can be used to focus on research areas that

are shared by many currently active authors. As can

be seen in Figure 11, the co-authorship network can

be divided into seven groups. Of all these clusters, the

red cluster is at the center. 603 met this threshold and

were selected for network analysis. Among these 603

researchers, only 48 of them collaborated directly or

indirectly. The authors separated these 48 researchers

into seven clusters to form a network of co-

authorship. As shown in Figure 11, the sizes of most

nodes in the red cluster yellow cluster and green

cluster are bigger than in other clusters. This

visualization indicates that most of the selected

researchers in these clusters have published more

articles than other researchers. The distance between

the nodes shows partnerships among the red, yellow,

green blue, orange light blue and purple clusters. Of

all these clusters, the red cluster is at the center. This

represents the frequent collaboration between authors

who are interested in the same research area.

BPM in the Era of Industry 4.0: A Bibliometric Analysis

657

5 SYNTHESIS, DISCUSSION

AND POSITIONING

This paper presents a bibliometric analysis study on

BPM in the I4.0 era. In this study, we analyzed a total

of 231 papers published in 63 journals, 8 books, and

132 conference titles from 2016 to 2023. We have

explored some interesting results concerning the

BPM-related publications. The results of this study

confirm the important role of BPM in the success of

the I4.0 and DT. The growing number of publications

shows that many research centers and universities are

starting to explore this topic. Many countries, such as

Germany, followed by the Russian Federation,

Austria, Portugal and Italy, have played a crucial role

in the ongoing progress of the field which proves the

importance of this discipline. However, the number

of papers is still very low compared to the importance

and novelty of Industry 4.0, and most of the papers do

not propose technologies and methods for BPM

implementation that can support the implementation

of the I4.0 concept. New research opportunities and

challenges arise from the concept of I4.0. One of

these key challenges is the future of the BPM and how

it can be implemented for the I4.0 and the DT.

6 CONCLUSIONS

The aim of the bibliographic analysis was to

investigate the current research trends and challenges

in the field of BPM in the era of the new digital

industry and the challenges it faces. According to the

study’s findings, the research on the topic of I4.0 and

DT is growing at a rapid pace. Researchers have

published their findings in different types of

publications: research papers, books, book chapters

and conference papers, increasingly collaborating to

improve their quality. Germany, followed by the

Russian Federation, Austria, Portugal and Italy, are

among the countries that have played an important

and decisive role in the ongoing progress of the field.

The keywords most frequently used by researchers

(e.g. Industry 4.0, BPM, Digital Transformation,

Digitalization and Internet of Things) indicate the

hotspots in BPM research. The main purpose of

adopting different bibliometric analysis methods was

to reveal research trends and published papers. The

bibliometric analysis we have presented is limited as

we have only used Scopus as a knowledge base. In

the future we intend to extend the bibliometric

analysis to another knowledge base such as Web of

Science.

REFERENCES

Albert, M. (2020). Digitalization and Digital

transformation in Finance Operations. December. doi:

10.3389/fpsyg.2022.1081595

Appio, F. P., Cesaroni, F., & Di Minin, A. (2014).

Visualizing the structure and bridges of the intellectual

property management and strategy literature: a

document co-citation analysis. Scientometrics, 101(1),

623–661. doi: 10.1007/s11192-014-1329-0

Aria, M., & Cuccurullo, C. (2017). bibliometrix : An R-tool

for comprehensive science mapping analysis. Journal

of Informetrics, 11(4), 959–975. doi:

10.1016/j.joi.2017.08.007

Baiyere, A., Salmela, H., & Tapanainen, T. (2020). Digital

transformation and the new logics of business process

management. European Journal of Information

Systems, 29(3), 238–259. doi:

10.1080/0960085X.2020.1718007

Baker, H. K., Kumar, S., & Pandey, N. (2021). Forty years

of the Journal of Futures Markets : A bibliometric

overview. Journal of Futures Markets, 41(7), 1027–

1054. doi: 10.1002/fut.22211

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., &

Herrera, F. (2011). An approach for detecting,

quantifying, and visualizing the evolution of a research

field: A practical application to the Fuzzy Sets Theory

field. Journal of Informetrics, 5(1), 146–166. doi:

10.1016/j.joi.2010.10.002

Di Ciccio, C., Marrella, A., & Russo, A. (2015).

Knowledge-Intensive Processes: Characteristics,

Requirements and Analysis of Contemporary

Approaches. Journal on Data Semantics, 4(1), 29–57.

doi: 10.1007/s13740-014-0038-4

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim,

W. M. (2021). How to conduct a bibliometric analysis:

An overview and guidelines. Journal of Business

Research, 133(March), 285–296. doi:

10.1016/j.jbusres.2021.04.070

Fischer, M, Imgrund, F., Janiesch, C., & Winkelmann, A.

(2019). Directions for future research on the integration

of SOA, BPM, and BRM. Business Process

Management Journal, 25(7), 1491–1519. doi:

10.1108/BPMJ-05-2018-0130

Fischer, Marcus, Hofmann, A., Imgrund, F., Janiesch, C.,

& Winkelmann, A. (2021). On the composition of the

long tail of business processes: Implications from a

process mining study. Information Systems, 97,

101689. doi: 10.1016/j.is.2020.101689

Fischer, Marcus, Imgrund, F., Janiesch, C., & Winkelmann,

A. (2020). Strategy archetypes for digital

transformation: Defining meta objectives using

business process management. Information &

Management, 57(5), 103262. doi:

10.1016/j.im.2019.103262

Flechsig, C., Lohmer, J., Voß, R., & Lasch, R. (2022).

Business Process Maturity Model for Digital

Transformation: An Action Design Research Study on

The Integration of Information Technology.

ICEIS 2023 - 25th International Conference on Enterprise Information Systems

658

International Journal of Innovation Management,

26(03). doi: 10.1142/S1363919622400126

Fleischmann, A., Friedl, A., Großmann, D., & Schmidt, W.

(2021). Modeling and Implementing of Industrie 4.0

Scenarios. In Communications in Computer and

Information Science: Vol. 1401 CCIS (pp. 90–112).

doi: 10.1007/978-3-030-72696-6_4

Gartner. (2018). Gartner Says BPM is Critical for Business

Transformation Success. Retrieved from

https://solutionsreview.com/business-process-

management/gartner-says-bpm-is-critical-for-business-

transformation-success/

Hall, M. (2011). Publish and perish? Bibliometric analysis,

journal ranking and the assessment of research quality

in tourism. Tourism Management, 32(1), 16–27. doi:

10.1016/j.tourman.2010.07.001

Imgrund, F., Fischer, M., Janiesch, C., & Winkelmann, A.

(2018). Approaching digitalization with business

process management. MKWI 2018 - Multikonferenz

Wirtschaftsinformatik, 2018-March, 1725–1736.

Imgrund, F., & Janiesch, C. (2019). Understanding the

Need for New Perspectives on BPM in the Digital Age:

An Empirical Analysis. Lecture Notes in Business

Information Processing, 362 LNBIP, 288–300. doi:

10.1007/978-3-030-37453-2_24

Leung, C. K., Braun, P., Hoi, C. S. H., Souza, J., &

Cuzzocrea, A. (2019). Urban Analytics of Big

Transportation Data for Supporting Smart Cities. In

Lecture Notes in Computer Science: Vol. 11708 LNCS

(pp. 24–33). Springer International Publishing. doi:

10.1007/978-3-030-27520-4_3

Leung, C. K., Chen, Y., Hoi, C. S. H., Shang, S., &

Cuzzocrea, A. (2020). Machine Learning and OLAP on

Big COVID-19 Data. 2020 IEEE International

Conference on Big Data (Big Data), 5118–5127. doi:

10.1109/BigData50022.2020.9378407

Leung, C. K., Chen, Y., Hoi, C. S. H., Shang, S., Wen, Y.,

& Cuzzocrea, A. (2020). Big Data Visualization and

Visual Analytics of COVID-19 Data. 2020 24th

International Conference Information Visualisation

(IV), 2020-Septe(Iv), 415–420. doi:

10.1109/IV51561.2020.00073

Leung, C. K., Cuzzocrea, A., Mai, J. J., Deng, D., & Jiang,

F. (2019). Personalized DeepInf: Enhanced Social

Influence Prediction with Deep Learning and Transfer

Learning. Proceedings - 2019 IEEE International

Conference on Big Data, Big Data 2019, 2871–2880.

doi: 10.1109/BigData47090.2019.9005969

Leung, C. K., Hoi, C. S. H., Pazdor, A. G. M., Wodi, B. H.,

& Cuzzocrea, A. (2018). Privacy-Preserving Frequent

Pattern Mining from Big Uncertain Data. 2018 IEEE

International Conference on Big Data, 5101–5110. doi:

10.1109/BigData.2018.8622260

Li, H., An, H., Wang, Y., Huang, J., & Gao, X. (2016).

Evolutionary features of academic articles co-keyword

network and keywords co-occurrence network: Based

on two-mode affiliation network. Physica A: Statistical

Mechanics and Its Applications,

450, 657–669. doi:

10.1016/j.physa.2016.01.017

Mohanta, B., Nanda, P., & Patnaik, S. (2020). Management

of V.U.C.A. Using Machine Learning Techniques in

Industry 4.0 Paradigm. In New Paradigm of Industry

4.0 Internet (Vol. 64, pp. 1–24). doi: 10.1007/978-3-

030-25778-1_1

Niesen, T., Houy, C., Fettke, P., & Loos, P. (2016).

Towards an integrative big data analysis framework for

data-driven risk management in industry 4.0.

Proceedings of the Annual Hawaii International

Conference on System Sciences, 2016-March, 5065–

5074. doi: 10.1109/HICSS.2016.627

Okano, M. T. (2017). IOT and Industry 4 . 0 : The Industrial

New Revolution. International Conference on

Management and Information Systems, 26.

Pires, F., Cachada, A., Barbosa, J., Moreira, A. P., & Leitao,

P. (2019). Digital Twin in Industry 4.0: Technologies,

Applications and Challenges. 2019 IEEE 17th

International Conference on Industrial Informatics

(INDIN), 2019-July, 721–726. doi:

10.1109/INDIN41052.2019.8972134

Queiroz, M. M., & Wamba, S. F. (2022). Managing the

Digital Transformation. In Managing the Digital

Transformation. Boca Raton: CRC Press. doi:

10.1201/9781003226468

Rehse, J.-R., Dadashnia, S., & Fettke, P. (2021). Business

process management for Industry 4.0-Three application

cases in the DFKI-Smart-Lego-Factory. IT -

Information Technology, 60(3), 133–141. doi:

10.1515/itit-2018-0006

Schwab, K. (2016). The Fourth Industrial Revolution: what

it means and how to respond. World Economic Forum,

1–7.

Szelagowski, M., & Lupeikiene, A. (2020). Business

Process Management Systems: Evolution and

Development Trends. Informatica (Netherlands),

31(3), 579–595. doi: 10.15388/20-INFOR429

van Eck, N. J., & Waltman, L. (2010). Software survey:

VOSviewer, a computer program for bibliometric

mapping. Scientometrics, 84(2), 523–538. doi:

10.1007/s11192-009-0146-3

Viriyasitavat, W., Da Xu, L., Bi, Z., & Sapsomboon, A.

(2020). Blockchain-based business process

management (BPM) framework for service

composition in industry 4.0. Journal of Intelligent

Manufacturing, 31(7), 1737–1748. doi:

10.1007/s10845-018-1422-y

Xu, L. Da, Xu, E. L., & Li, L. (2018). Industry 4.0: state of

the art and future trends. International Journal of

Production Research, 56(8), 2941–2962. doi:

10.1080/00207543.2018.1444806

Zehra, A., & Urooj, A. (2022). A Bibliometric Analysis of

the Developments and Research Frontiers of Agent-

Based Modelling in Economics. Economies, 10(7), 171.

doi: 10.3390/economies10070171

Zupic, I., & Čater, T. (2015). Bibliometric Methods in

Management and Organization. Organizational

Research Methods, 18(3), 429–472. doi:

10.1177/1094428114562629

BPM in the Era of Industry 4.0: A Bibliometric Analysis

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