Rediscovering the Forgotten Field of Industrial Applications in

Information Systems Research: A Literature Review of Industry 4.0

John O’Sullivan

, Brian O’Flaherty

and Tom O’Kane

Business Information Systems, Cork University Business School, 3rd Floor, O’Rahilly Building, University College Cork,

Western Road, Cork, Ireland

Keywords: Industry 4.0, Industrial Information Systems, Digital Transformation, Fourth Industrial Revolution, Smart

Factory, Manufacturing, Business Process Management.

Abstract: This paper is a literature review to determine if industrial applications are appropriately represented in

information systems (IS) scholarship. The field of Industry 4.0 was used as a representative sample of

industrial information systems and the Association for Information Systems (AIS) Senior Scholars’ Basket of

Journals was used as a representative, albeit highly ranked, sample of IS literature. Keywords representing

the eleven recognised technologies of Industry 4.0 were chosen and used to search the eight IS journals over

a time period corresponding with the lifecycle of Industry 4.0. This resulted in 1305 papers being discovered.

After calibrating the search terms, a second search yielded 770 papers. These papers were screened for

relevance to Industry 4.0 and for use of a manufacturing application. The resulting 20 papers were queried in

detail to establish the concepts used and a concept centric matrix was produced. The analysis shows that

industrial information applications are rarely used to undertake IS research in the academic field. The

dominant concept revealed was digital transformation resulting in changes to business processes. The

contribution to the literature is to highlight that substantial research studies can be conducted in the industrial

manufacturing arena, but very few have been conducted in the last decade. Therefore, it is an area worth

exploring for future IS research.

1 INTRODUCTION

When comparing a computer scientist to an

information system researcher, it has been written

that the computer scientist stands in front of the

technology and looks in, while the IS researcher

stands in the same place and looks at the world at

large (Avison et al., 2001). A similar comparison can

be drawn between the control and automation

engineer and the industrial information systems (IIS)

researcher. In this case the control and automation

engineer is standing in the factory and looking at the

technology while the industrial information

researcher is looking at the enterprise at large. This

paper, therefore focuses on industrial information

systems within the enterprise setting and assesses the

breadth and extent of research within the IS field.

Chiasson and Davidson have written that industry

“provides an important contextual "space" to build

https://orcid.org/0000-0003-3995-2968

https://orcid.org/0000-0003-2395-4950

https://orcid.org/0000-0003-2937-0753

new IS theory and to evaluate the boundaries of

existing IS theory” (Chiasson & Davidson, 2005).

While Chiasson and Davidson searched only two IS

journals (MIS Quarterly and Information Systems

Research), this paper searches all eight journals of the

AIS Senior Scholars’ Basket of Journals and defines

industry as the narrower manufacturing sector.

Industry is a significant part of the world economy

and fertile ground for IS research. In the EU, in 2017,

the manufacturing sector had a turnover of €7,230bn

and employed 28,531,906 persons (Eurostat). In the

same year in Ireland, an open economy with a large

exposure to foreign direct investment, industry

accounted for a turnover of €239bn (33% of the

business economy) while employing 242,966 persons

(only 14% of the persons engaged in the total business

economy) (CSO). Industry 4.0 and automation

enables this leverage of turnover. The motivation of

this research is to establish if the manufacturing

O’Sullivan, J., O’Flaherty, B. and O’Kane, T.

Rediscovering the Forgotten Field of Industrial Applications in Information Systems Research: A Literature Review of Industry 4.0.

DOI: 10.5220/0011258700003329

In Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics (IN4PL 2022), pages 15-25

ISBN: 978-989-758-612-5; ISSN: 2184-9285

industry is a valid arena in which to conduct IS

research.

This paper is structured as follows, with section

1.1 examining prior research. Section 2 gives the

background and definitions of Industry 4.0. In section

3, a methodology is designed and used to perform a

literature review. In section 3, the results of the search

and screening are presented. The emerging themes or

concepts of the literature are presented in section 4,

the literature review, containing section 4.1,

synthesis, or what do we know from the literature.

The methodological and quality state of the literature

is also presented in section 4.2, analysis, or how do

we know what we found in the literature. These

results are discussed in section 5 and future literature

review and research opportunities are outlined.

1.1 Previous Research

In their 2005 paper, titled ‘Taking Industry Seriously

in Information Systems Research’ (Chiasson &

Davidson, 2005), Mike Chiasson and Elizabeth

Davidson “consider various ways industry influences

IS activities”. They analysed two journals, MIS

Quarterly and Information Systems Research over

eight years from 1997 to 2004. It should be noted that

while they accepted that the Webster Dictionary

definition of industry is “any particular branch of

productive, especially manufacturing, enterprise”,

they take the “broad colloquial sense” of the word to

be synonymous with socio-economic sectors, e.g.,

retail industry and airline industry.

They posited that “increased attention to

industry” (or industries by their definition) “will

extend and refine IS knowledge”. They claim the

advantages for the IS field include:

• diffusing IS theory into other disciplines

• attending to the IS artefact embedded in its

social and technical context

• fostering new customers for IS knowledge

• and increasing the practical relevance of IS

research

This paper revisits this theme by replicating the

essence of the study and determining if there has been

any significant uptake in industry focused studies in

the period since 2011.

In this paper, the Webster Dictionary definition is

applied and the term “industry” is used to define the

manufacturing sector. It searches in the current

Basket of Eight journals, which expands on the two

used by Chiasson and Davidson, and searches from

2011 to 2020, a nine year period comparable to the

eight years of the other paper.

In their paper, Chiasson and Davidson present a

table of industry sectors returned in their search

(Table A-1, p. 605). In this paper, while screening

papers for relevance to Industry 4.0 and

manufacturing, the industry sectors were recorded so

that a comparable table could be presented. See Table

2, where it is evident that IS research in the industrial

manufacturing arena is declining.

2 INDUSTRY 4.0 BACKGROUND

Industry 4.0, or the fourth industrial revolution, is

meant to signify that we are experiencing a step in

capability equivalent to the previous industrial

revolutions. The first industrial revolution is

described as the advent of steam power in the late

eighteenth century which led to the textile industries

and railways (Duarte et al., 2018). The electrification

of the second industrial revolution at the turn of the

last century led to mass manufacturing, the assembly

line and household appliances (Klein & Crafts, 2020).

The third industrial revolution was born out of the

electronics industry and led to the automation of

manufacturing since the 1980s (Vasilash, 1995). The

essence of the fourth industrial revolution is the

interconnection of machines and cyber physical

systems (Lasi et al., 2014).

Industry 4.0 is a term that was first defined by the

German federal ministry of education and research

(Bundesministerium für Bildung und Forschung)

(BMBF, 2020) in 2011 and outlined in a research

agenda by the German Academy of Engineering

Sciences (Deutsche Akademie der

Technikwissenschaften) (Acatech, 2020), in 2013.

The synonymous term “fourth industrial revolution”

was first published by Klaus Schwab, Founder and

Executive Chairman of the World Economic Forum

(Schwab, 2012). Industry 4.0 has application-pull and

technology-push factors driving it. The pull factors

include short development periods, individualisation

on demand (“batch size one”), flexibility,

decentralisation and resource efficiency. The push

factors include automation, digitalisation, networking

and miniaturisation (Lasi et al., 2014).

2.1 Industry 4.0 Definition

Industry 4.0 is an all-encompassing term, which

envelops a number of existing and emergent

technologies and techniques. While there is a lack of

an agreed upon definition, academic, government and

consulting companies have attempted to define it and

the “definitional dimensions and sub-dimensions

IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics

characterizing Industry 4.0 in its technological and

non-technological aspects” (Culot et al., 2020). The

OECD has outlined the nine key technologies

enabling digital transformation as additive

manufacturing, autonomous machines and systems,

human machine integration, simulations, artificial

intelligence, system integration, big data, cloud

computing and the internet of things (OECD, 2017).

The Boston Consulting Group have outlined an

overlapping nine pillars of technological

advancement which also include augmented reality

and cybersecurity but not human machine integration

nor artificial intelligence (Lorenz et al., 2015). Figure

1 shows the specific terms used in each publication

and how the two lists overlap.

Figure 1: OECD’s nine key technologies enabling digital

transformation and the Boston Consulting Group’s nine

pillars of technological advancement.

Some of the seven shared terms have synonyms,

such as additive manufacturing and 3d printing, and

autonomous machines and autonomous robots. Other

terms have hypernyms, such as internet of things and

industrial internet of things, and big data and big data

analytics. Well known synonyms for artificial

intelligence and simulation; machine learning

(Samuel, 1962) and digital twin (Zhang et al., 2018)

respectively, are also included.

A comprehensive list of keywords was developed

from these definitions with which to search the

literature.

3 METHODOLOGY

3.1 Methodology Development

The literature review methodology was developed

from seminal IS literature review methodology

papers, and more recent papers (as examples for

specific steps and/or how to present the analyses).

After the pre-requisite of asking a research question,

the first task is the identification of the literature to be

searched (Webster & Watson, 2002). Within this task,

predetermined filters are chosen, i.e., the field of

research, the timescale of publications and sources,

such as journals, databases or conference

proceedings. The first iteration of keywords,

combination of keywords or phrases to be used in the

search are also listed. The overall structure of define,

search, select, analyse and present (Wolfswinkel et

al., 2013) is employed and modified.

In the select phase the researcher’s judgement is

used to further refine the sample obtained. In the

analyse phase, the quantitative data is collected and

the qualitative data is organised in content analysis

tables (dataset). After removing duplicate spurious

papers the flow of the filtration process is

documented (Pereira & Serrano, 2020). After

screening for relevance and applicability, the final

selection of papers is queried for research motivation,

objective, and method. The concepts in each paper are

established. The methodologies used in the final

resulting papers are presented and the papers are

assessed for quality.

This represents the overview of the analysis

process (Sammon, 2020). The data is organised with

the number of concepts, either by existence or

frequency. The other two analysis processes,

synthesis (compressing and presenting results

(Petersen et al., 2015)) and critique (highlighting

research deficits and future research directions), are

presented. Theoretical development involving the

development of a conceptual model with supporting

propositions (Webster & Watson, 2002) and the

evaluation of such a theory is beyond the scope of this

paper, but could be applicable for an expanded

literature review, or a separate coding paper.

3.1.1 Identify the Research Question

The research question is an explicit statement of what

the researcher wants to know about and phrasing it as

a question “forces the researcher to be more explicit

about what is to be investigated” (Bell et al., 2019).

The larger research question currently being

investigated is:

“How are industrial information systems topics

being reviewed, researched and communicated in the

IS body of peer-reviewed academic literature?”

To reduce the scope of the literature review for

this paper, the area of Industry 4.0 was chosen as a

sample of industrial topics and the AIS senior

scholars’ basket of journals (hereafter called the

“basket of eight”) was chosen as a well-respected and

Rediscovering the Forgotten Field of Industrial Applications in Information Systems Research: A Literature Review of Industry 4.0

highly ranked sample of IS academic literature (AIS,

2020) (Table 1). The refined and reduced research

question is:

“How are the topics of Industry 4.0 in a

manufacturing setting being used for IS research in

the basket of eight?”

3.1.2 Identify the Relevant Literature

The research question limited the field of study to

Industry 4.0 and limited the relevant literature to the

basket of eight.

The timescale of the search was contemporaneous

with the lifecycle of Industry 4.0; from 2011 to date.

Industry 4.0 as a general topic and all eleven

identified sub-topics outlined in section 2.1 were

included in the search. Some terms outside the OECD

and Boston Consulting Group list, identified during

unstructured searching, such as nanotechnology

(Duarte et al., 2018) and edge computing (Collett,

2020) were excluded.

Table 1: AIS Senior Scholars’ Basket of Journals.

AIS Senior Scholars' Basket of Journals

European Journal of Information Systems

Information Systems Journal

Information Systems Research

Journal of AIS

Journal of Information Technology

Journal of MIS

Journal of Strategic Information Systems

MIS Quarterly

Based on the terms identified, the following

keywords and keyword combinations were chosen:

High Level: "industry 4.0" OR "industrie 4.0" OR

"fourth industrial revolution" OR

"smart factory"

"digital transformation"

Detail Level: "additive manufacturing" OR "3d

printing"

“artificial intelligence” OR "machine learning"

"augmented reality"

"autonomous machine" OR "autonomous robot"

"big data" OR “big data analytics”

"cloud computing"

"cybersecurity" OR "cyber security"

"human machine interface"

"internet of things" OR "industrial internet of things"

“simulation” OR "digital twin"

"system integration"

3.1.3 Search

Online tools and resources, which have access to all

the relevant journals and databases, were selected for

the search (“UCC Library,” 2020).

The search method was to use the journal search

function and to enter each of the thirteen search terms

above into each of the basket of eight journals

individually. The following data was to be recorded

for each search: number of results per journal and

number of results per search term. The data was to be

recorded in a Microsoft Excel spreadsheet.

The citation of each resulting paper was also

saved to a reference management software package

(Zotero, 2020).

3.1.4 Select

After the first search, using the search terms above, it

was determined that the search terms were too broad

and the results could have non-industrial applications.

The search terms were then amended to include the

terms “industrial” and “manufacturing”. The search

was re-run with the new terms.

3.1.5 Analyse

The full dataset of all 1305 papers returned in the

search is available on request.

Duplicate papers, editorials and spurious papers

(e.g., “about the authors”) were eliminated.

At this stage, the abstract of every paper was read,

i.e. content-based analysis was conducted (Alhassan

et al., 2016). Introduction and conclusion were also

read, if necessary. Whether the paper covered the

topic in substantive manner or just in passing was

recorded. Whether the topic was covered with

reference to an industrial manufacturing application

was recorded. For each of these questions, a negative

resulted in elimination, i.e., two “yes” results were

required for the paper to proceed.

In some cases, the terms are misinterpreted or

misunderstood as they have alternative meanings.

e.g., the search term “system integration” is meant to

return results for the interconnection of technology

systems but results for mergers and acquisitions were

also returned. These were eliminated by the relevance

criterion.

The remaining papers were read in detail and

queried for research objective, motivation and

method. The particular manufacturing application

was also recorded. They were conceptualised and a

concept centric matrix was created (not displayed but

described in section 5.1). The methodologies used in

IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics

the final resulting papers were presented and the

papers were assessed for quality.

4 RESULTS

The result of the first search returned 1305 papers.

After the second search run, this was reduced to 770,

or 59.1% of the first run.

The number of results is reduced in all cases after

the second run. The next reduction in results was as a

consequence of removing duplicate and spurious

papers.

4.1 Content Analysis and Screening

To establish pertinence to the research question, the

dataset was queried for relevance to the search term,

i.e., is the term central to the paper, dealt with in a

comprehensive manner or just mentioned in passing. A

significant number of papers appear in multiple search

results and were eliminated as duplicates on their

second and subsequent appearances. The searches in

which they were analysed and the searches in which

they were eliminated were purely artefacts of the order

in which the searches were presented. For that reason,

when a paper was reviewed for relevance, it was

reviewed against all the search topics.

The papers were also, more importantly, checked

to establish if the term covered is related to the

manufacturing industry. For these two questions, two

“yes” answers were required for the paper to proceed.

If manufacturing was included as one sector among

other industry sectors, the paper was included.

With a view to continuing the research agenda as

proposed by Chiasson and Davidson (2005), the

industry sector covered in the paper, if any, was

recorded.

Editorials, spurious papers, e.g. “About the

Authors” and duplicate papers were removed from

the total. 770 papers remained. After duplicates,

editorials and spurious papers were removed, 459

papers remained. After reading the abstracts, and

further sections if required, papers that were not

substantively relevant to Industry 4.0 were removed

and 94 papers remained. Of these, only papers that

used an industrial manufacturing application were

allowed though the screening process. Thus 20 papers

remained. See Figure 2.

4.2 Content Analysis

The results, after screening, contain papers that

address one or more of the Industry 4.0 topics and use

an industrial manufacturing application. The resulting

20 papers were queried. The questions asked of each

paper, based on the six honest serving men (Sammon,

2020), are as follows: Who: Author(s), When: Year

of publication, What: Title, Where: Journal, What:

Research objective, Why: Research motivation, How:

Research method, What: Manufacturing application.

Of the 20 papers resulting, the plurality (six) were

from the European Journal of Information Systems.

The next most numerous source was the Journal of

MIS (four).

5 LITERATURE REVIEW

5.1 Synthesis

In the synthesis section, we examine what we know

from the literature. Each of the 20 papers was read in

full and a number of findings emerged.

Figure 2: Process flow of search, filtration, screening,

content analysis and conceptualisation.

Rediscovering the Forgotten Field of Industrial Applications in Information Systems Research: A Literature Review of Industry 4.0

These studies have examined data management

and usage, business processes, CIO practices, and

education. Within data management, one paper each

covered the topics of democratisation of information

(Clemons et al., 2017), external knowledge driving

innovation (Trantopoulos et al., 2017), blockchain

securing IOT data (Chanson et al., 2019), big data

stories (Boldosova, 2019) and information

completeness in tracking (Bardaki et al., 2011). In the

area of people, a paper covered the role of CIOs

(Kappelman et al., 2018) and another covered the

education required for virtual and augmented reality

adoption (Steffen et al., 2019). Under processes, the

following concepts were covered: modularity of

product design (Henfridsson et al., 2014), using big

data (Woerner & Wixom, 2015), how environment

affects cloud decisions (Kung et al., 2015), boundary

objects as socio-material linkages (Doolin &

McLeod, 2017) and blended approach to security

needs (Niemimaa & Niemimaa, 2019). The most

common concept was that of digital transformation

resulting in changing business processes (Baiyere et

al., 2020), (Kathuria et al., 2018), (Sandberg et al.,

2020), (Wamba & Chatfield, 2017), (Huang et al.,

2014), (Grover & Saeed, 2014), (Lee et al., 2020),

(Lyytinen et al., 2016).

Each paper was read in full and the following

concepts were extrapolated from the papers.

In Clemons et al. (2017), information flows

between four entities who create value: consumers,

producers, markets and society. The interaction is

determined by viability, networks and agency. The

information strategies of all entities are changing. For

consumers it is changing from ownership to a sharing

model. For

producers it is changing from push production to

customisation. For markets it is becoming more

disintermediated. This all results in a society that

values fairness as opposed to a ‘winner takes all’

model. The framework is one of democratisation of

information.

Trantopoulos et al., (2017) say that innovation in

firms is dependent on external knowledge (e.g.

customer, competitors, universities and consultants).

Information technology and network

interconnectivity moderate how a firm’s external

knowledge search benefits innovation.

For a firm undergoing digital transformation,

Baiyere, Salmela and Tapanainen (2020) explain that

the existing three logics associated with business

process management (BPM), process, infrastructure

and actors, experience tensions and should be

modified. Process should be modified from modelled

to light touch, infrastructure should be modified from

aligned to flexible and actors should change

behaviour from procedural to mindful.

European Chief Information Officers perform

both IT and business roles and are evaluated on their

performance in both spheres. Kappelman et al. (2018)

explain that there are differences in emphasis between

the USA and Europe. While the US CIOs spend more

on cloud computing, cybersecurity issues are more in

focus in Europe.

Design science research (DSR) was used to

instantiate a blockchain-based sensor data protection

system for IOT and two other projects were ex-post

evaluated by Chanson et al., (2019) suggesting that

the design successfully ensures tamper-resistant

gathering, processing and exchange of IOT data.

Storytelling patterns and types of stories can be

developed from big data analytics implementation

according to Boldosova (2019).

Henfridsson, Mathiassen and Svahn (2014)

outline how adopting and complementing two

architectural frames, network of parts and hierarchy

of patterns, allows modularity and decoupling,

leading to flexibility of redesign of digitised industrial

products.

Woerner and Wixom (2015) explain that big data

is not the solution to firms’ problems but used within

existing structures and processes, it can extend a

firm’s strategic toolbox.

Implementation of cloud computing must be

deployed in a hierarchical manner aligned with

business processes. Kathuria et al. (2018) consider

two options, cloud integration capability (internal)

and cloud service portfolio (external facing).

Sandberg, Holmström and Lyytinen (2020) say

that waves of digitisation can lead to product platform

transitions which can lead to organisational

outcomes, e.g. changed boundaries of platform scope,

scale and sources of value creation and extraction.

Radio frequency identification (RFID)

technology can be used as part of an IT system to

provide business benefits taking in to account five

contingency factors: environmental upheaval,

leadership, second-order organisational learning,

resource commitment and organisational

transformation. This is described in Wamba and

Chatfield (2017).

Boundary objects, e.g. a prototype device, can,

according to Doolin and McLeod (2017), act

sociomaterially to link diverse groups across

knowledge domains, cultures, languages and

locations.

Kung, Cegielski and Kung (2015) explain that

environmental factors, including mimetic pressure (to

copy other organisations) and perceived

IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics

technological complexity influence firms’ intentions

to adopt cloud technology, e.g. software as a service

(SaaS).

Operational agility is achieved through an

information processing network and organisational

control. Huang, Pan and Ouyang (2014) explain that

the information processing network depends on three

capabilities: information sensitivity, synergy and

fluidity.

Information received after implementation of an

integrated inter-organisational system (IOS) must be

used to make adjustments based on the information.

Grover and Saeed (2014) believe that if not, the

implementation has no benefit.

IOS integration results in manufacturer-supplier

flexibility, explain Lee, Wang and Grover (2020).

This aligned with IOS-enabled analytical ability

results in manufacturer agility.

Information security policy can be implemented

using abductive innovation, involving deductive

adoption, inductive adjustment and synthetic

innovation. This, according to Niemimaa and

Niemimaa (2019), blends best practices (top down)

and participatory development (bottom up).

Lyytinen, Yoo and Boland Jr. (2016) explain that

organisations can innovate using a framework of four

innovations networks: project, clan, federated, and

anarchic innovation networks.

Object tracking information ‘completeness’ is not

increased by increasing the number of capture points.

Rather, as found by Bardaki, Kourouthanassis, and

Pramatari (2011), it depends on the size (capture

points), breadth (location of capture points) and depth

(capability of capturing different objects).

Educating users on the affordances offered by

virtual reality (VR) and augmented reality (AR)

would result in greater adoption, as found by Steffen

et al.(2019).

5.2 New Insights into Industry Sector

Coverage in IS Research

While papers were screened for relevance, the

industry sector being covered in each paper was

recorded so that Table A-1 (Chiasson and Davidson,

2005, p.605) (2005) could be updated. The original

table covered eight years from 1997 to 2004 and

analysed only two papers: MIS Quarterly and

Information Systems Research. They “tallied the

number of articles in which empirical data were

drawn from a particular industry, using the industry

labels (e.g. manufacturing) used by the article

authors” (Chiasson & Davidson, 2005). This paper

covers nine years from 2011 to 2020 and analyses all

eight journals in the AIS Senior Scholar basket. This

paper also filters and screens the papers for relevance

to Industry 4.0. The number of articles referencing

industry sectors were tallied.

The papers specifying “Various” or “Various

Services” were excluded from the percentage

calculations of both papers to avoid discrepancy of

classification, i.e., the percentages for the original

table were recalculated without the “Various” entries.

The comparative results are in Table 2.

Comparing the percentage representation of papers

from Chiasson and Davidson (2005) to this paper, the

following industry sectors have increased visibility in

IS research: High-Tech/ IT Consulting / Telecomms,

Retail (albeit negligible increase), Health care,

Government and Utility. The following industry

sectors have decreased visibility: Manufacturing,

Banking/ Financial, Insurance (to zero), Distribution,

Education, Airline, Oil & Gas, Military, Law

(negligible decrease) and Real Estate (negligible

decrease), The following industry sectors were not

listed in the 2005 paper but were results in this 2020

search: Media/ Entertainment, Energy and

Agriculture.

The decrease in the coverage of manufacturing

from 21.8% to 16.9%, despite the latter search being

more focussed on manufacturing would indicate that

interest in researching manufacturing in the IS field is

decreasing.

5.3 Analysis

In the analysis section, we examine how we know

what we have gleaned from the literature by

reviewing the quality and methodological state of the

final papers.

5.3.1 Quality Analysis

The final 20 papers were reviewed from a quality

point of view and assessed for relevance,

accessibility, quality, and research method.

All papers were deemed relevant as they were the

result of a systematic search using predefined search

terms. All papers were deemed accessible as they

were sourced from the AIS senior scholars’ basket of

eight journals and were all accessible using the library

OneSearch tool. All journals were deemed to be of

sufficient authority for the same reason. The authority

of the authors was judged using their Google Scholar

citation count and h-index. The authority of paper

was judged by its Google Scholar citation count. The

quality of each paper was reviewed and assessed for

organisation, completeness and accuracy.

Rediscovering the Forgotten Field of Industrial Applications in Information Systems Research: A Literature Review of Industry 4.0

Based on the quality analysis above, all papers were

deemed to be of sufficient quality and none were

discarded.

5.3.2 Methodology Analysis

Case studies have been the most common type of

methodologies, accounting for twelve, or over half, of

the papers. Three papers proposed frameworks. Two

papers used ethnography. Design science research,

commentary and literature review accounted for one

paper each.

6 FUTURE RESEARCH

Eight of the final 20 papers covered the concept of

digitalisation causing changes to business processes.

A particular example in Sandberg et al. (2020)

outlines how digitalisation of a product platform

results in changes to organisational logic and raises

two interesting avenues for a future research agenda:

• “a more fine-grained classification of (digital)

platform types is essential for advancing

understanding of digitization’s impact on

product platforms.”

• “digitization covered substantial parts of ABB’s

and its customers’ operations (with more

significant and broader effects) and involved

multiple subsystems simultaneously. This

sequencing provides insights into how

digitization processes are likely to unfold in

product platforms and also suggests the need for

future research on their path dependencies.”

Topics other than industry and manufacturing

(e.g., COVID-19, social media, green IT,

entrepreneurship, agile IS, cryptocurrency, smart

cities) can be filtered or selected from the body of

papers in the analysis phase. An extra column could

be used to explicitly define the topic.

6.1 Comparative Analysis

The scope of the journal sources can be expanded

beyond IS journals to include equivalently ranked

journals in the field of Operations and Technology

Management, in order to compare how industrial

applications are covered in this field. These journals

were identified by choosing the top eight journals

ranked 4*, 4 or 3 in the Chartered Association of

Business Schools (CABS) Academic Journal Guide

(AJG) in the field of Operations and Technology

Management (CABS, 2022). The eight chosen

journals were used as a comparable sample.

Using the same search terms and the same

methodology, but searching in the operations and

technical management basket of journals the results

were 10,464 papers. This is more than an order of

magnitude more than the 770 papers found in the AIS

basket of eight. Without completing this literature

review, it is still obvious from the search results that

industrial manufacturing topics are more frequently

covered in Operations and Technology Management

journals than in IS journals, as expected.

7 CONCLUSION

This paper is a literature review to determine the level

at which industrial information systems (IIS) are

studied to research information systems (IS). The

field of Industry 4.0 was used as a representative

sample of IIS and the Association for Information

Systems (AIS) Senior Scholars’ Basket of Journals

was used as a representative, albeit highly ranked,

sample of IS literature. This literature review can be

leveraged by future research managers to investigate

the prevalence of industry and manufacturing as topic

of IS research outside the basket of eight journals.

Over the search period of 2011 to 2020, 20 papers

were found that include substantial reference to the

topics of Industry 4.0 and cover the manufacturing

arena.

The analysis shows that IIS is rarely used to

undertake IS research in the academic field compared

to other technical and operational fields. It was also

found that, compared to previous data on IS research

in industrial sectors, the coverage of manufacturing

has decreased.

The dominant concept revealed was that of digital

transformation resulting in changes to business

processes. The contribution to the literature is to

highlight that substantial research can be conducted

in the industrial manufacturing arena but few have

been conducted in the last decade. Therefore, it is an

area worth exploring for future IS research.

The authors are advocates of the role of the

researcher-practitioner, where practitioners trained

and qualified as researchers, can carry out research in

the industrial field and contribute to both academic

and practice knowledge. This type of researcher with

access to the industrial manufacturing sector has a lot

to offer IS research. IS researchers in academia

should encourage and advocate for the recruitment of

practitioners (in this case industrial manufacturing

professionals) as IS research students.

IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics

Industry in general and manufacturing in

particular offers a fertile area for IS research for many

reasons. The application of Industry 4.0 concepts in

the real world environment offers opportunities to

research the migration of data from the factory floor

to the cloud, the move from owning licences to

software as a service, the ability to use artificial

intelligence to optimise production, and the unveiling

of new information from big data sets.

The life science manufacturing sectors

(pharmaceutical, biotechnology and medical device)

offer the added advantage of requirement to

retirement lifecycle documentation suites and

traceability from a project, process and product point

of view, providing copious data to the prospective

researcher.

By encouraging cross pollination between

manufacturing engineers and IS practitioners, but also

between practitioners and researchers, the field of

industrial information systems can only grow

successfully in the coming years.

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IN4PL 2022 - 3rd International Conference on Innovative Intelligent Industrial Production and Logistics

APPENDIX

Table 2: Industries Examined. Comparison of industries returned in Chiasson and Davidson (2005) and this paper. *

Various/Various Services excluded.

Industry

Count (Chiasson

and Davidson,

2005)

Percent (Chiasson

and Davidson,

2005)

Count

(This paper)

Percent

(This paper)

Increase or

Decrease

Manufacturing 30 21.8% 44 16.9% Decrease

High-Tech/ IT

Consulting

/Telecomm

28 20.4% 81 31.0% Increase

Banking/

Financial

22 16.1% 30 11.5% Decrease

Retail 13 9.5% 25 9.6% Increase

Insurance 9 6.6% 0 0% Decrease

Health care 8 5.8% 33 12.6% Increase

Various/Various

Services*

7 N/A 61 N/A N/A

Government 5 3.6% 14 5.4% Increase

Distribution 5 3.6% 5 1.9% Decrease

Education 5 3.6% 5 1.9% Decrease

Airline 4 2.9% 4 1.5% Decrease

Oil & Gas 3 2.2% 1 0.4% Decrease

Military 2 1.5% 0 0% Decrease

Utility 1 0.7% 3 1.1% Increase

Law 1 0.7% 1 0.4% Decrease

Real Estate 1 0.7% 1 0.4% Decrease

Media/

Entertainment

0 0% 12 4.6% Increase

Energy 0 0% 1 0.4% Increase

Agriculture 0 0% 1 0.4% Increase

Total (excluding

Various/Various

Services)

137 100% 261 100%

Rediscovering the Forgotten Field of Industrial Applications in Information Systems Research: A Literature Review of Industry 4.0