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The value of novel technologies in context to performance measurement and
management: A systematic review and future research directions
Rajan Kumar V K, Juhani Ukko
*
, Tero Rantala, Minna Saunila
LUT University, School of Engineering Sciences, Department of Industrial Engineering and Management, Mukkulankatu 19, FI-15210, Lahti, Finland
ARTICLE INFO
Keywords:
Performance measurement
Novel technology
Digital twins
Cyber physical systems
Artificial intelligence
Blockchain
Internet of things
ABSTRACT
The purpose of this paper is to systematically review research related to the value of new technologies in the
context of performance measurement and management. The review synthesizes findings from previous studies to
provide a comprehensive understanding of the current state-of-the-art of various novel technologies and how they
assist performance measurement and management across various industries. Scopus was selected as the main
electronic database for this study. As a principal result, we found that research related to the value of novel
technologies in the context of performance measurement and management is formed around six research streams:
automation techniques, predictive analytics, real-time data collection, visualization, collaboration tools, and
blockchain technology. As a conclusion, the research presents that novel technologies can be used to track and
monitor performance, automate processes, and streamline operations. The findings of this review will bring more
coherent value to the current state of the literature and contribute to a diverse body of knowledge.
1. Introduction
Continuous organizational growth and learning emphasizes the
measurement of performance, such as workload, efficiency, effective-
ness, adaptability, and productivity. As competition between companies
increases, measuring organizational performance requires an intelligent
and innovative strategic approach (Guo &Lyu, 2021). In a rapidly
growing competitive environment, it is important to find innovative
ways to develop performance measurement and management (PMM) and
utilize the measurement results: data are everywhere with the emergence
of new technology (Bhatti et al., 2021;Kumar et al., 2021). In many
industries, technology has almost displaced traditional mechanisms,
changed the dynamics of the entire work environment, and especially
modernized the operations of companies to achieve better performance
(Agrawal, 2019). Novel technologies have become commonplace in
many organizational functions, including PMM systems (Tas
¸kin &Yazar,
2020;Ukko et al., 2022). Technologies such as artificial intelligence (AI),
machine learning (ML), the internet of things (IoT), cyber physical sys-
tems (CPS), digital twins, cloud computing, and blockchain have been
considered as major technologies in Industry 4.0 context (Choi et al.,
2022), and, thus, have the potential to improve the accuracy and effi-
ciency of PMM. These are defined as novel technologies as they result
unparalleled mix of existing solutions. In today's competitive market,
novel technologies are transcending traditional business models and
reshaping organizational structures (Suuronen et al., 2022). At the same
time, technology is notorious for becoming outdated quickly and can thus
turn many people against the digital world (Sahlin &Angelis, 2019, pp.
16–21). Therefore, it is important for companies to understand the value
of new technologies, such as AI, digital twins, CPS, cloud computing, and
augmented reality, to improve performance and processes for building
business around customers and ahead of competitors.
Prior research has identified various benefits of such technologies
(Shmueli et al., 2016;Kumar, 2019;Masood &Hashmi, 2019;Mello &
Martins, 2019;Holopainen et al., 2022;Olan et al., 2022;Saunila et al.,
2022). Thus, with the rapid growth of technology, measuring and man-
aging performance has become more important than ever, improving
PMM. This is because companies cannot gain competitive advantages by
not participating in this development. For example, emerging technolo-
gies can offer useful information for viewing the most urgent needs of
customers. An organization's ability to identify these needs is one of the
most important areas for development in PMM (Holopainen et al., 2023)
and, if successful, can lead to better performance in several measurement
areas related to understanding customers (Sahlin &Angelis, 2019).
Collecting and analyzing data becomes an essential part of extracting
vital information, and even though data analysis is a complex process
requiring demanding calculations, new technologies can offer many
* Corresponding author.
E-mail addresses: Rajan.VK@lut.fi(R.K. V K), juhani.ukko@lut.fi(J. Ukko), tero.rantala@lut.fi(T. Rantala), minna.saunila@lut.fi(M. Saunila).
Contents lists available at ScienceDirect
Data and Information Management
journal homepage: www.journals.elsevier.com/data-and-information-management
https://doi.org/10.1016/j.dim.2023.100054
Received 26 June 2023; Received in revised form 28 August 2023; Accepted 7 October 2023
2543-9251/©2023 The Authors. Published by Elsevier Ltd on behalf of School of Information Management Wuhan University. This is an open access article under the
CC BY license (http://creativecommons.org/licenses/by/4.0/).
Data and Information Management xxx (xxxx) xxx
Please cite this article as: V K, R. K. et al., The value of novel technologies in context to performance measurement and management: A systematic
review and future research directions, Data and Information Management, https://doi.org/10.1016/j.dim.2023.100054
opportunities, such as automatic decision making and real-time presen-
tation of results. Kumar (2019) and Kumar et al. (2021) have shown that
new technology, such as blockchain, has improved potential to transform
PMM by providing a secure and transparent system for collecting, stor-
ing, and analyzing data and enabling the automation of PMM processes.
Similarly, with the advancement of novel technologies like AI, ML,
visualization, big data techniques, and the IoT, real-time data collection
and performance management have become increasingly important in
many fields, such as service systems, manufacturing, production opera-
tions and healthcare (Olan et al., 2022;Reyes et al., 2022). These tech-
nologies have the potential to revolutionize the way organizations
measure and manage performance by providing more accurate, timely,
and relevant information.
In summary, the integration of new technologies such as AI, big data
analytics, and cloud computing has become a growing trend in organi-
zations, where the central goal is also to improve PMM (Monod et al.,
2022). Some studies have shown positive impacts on efficiency and
effectiveness, while others reported mixed results or raised concerns
about the potential downsides of these technologies, such as increased
data privacy and security risks (Agrawal, 2021;Chen et al., 2012;Tao
et al., 2019). Despite this trend, research is limited exploring the impact
of these technologies on PMM and their outcomes. Given the growing
importance of PMM in organizations, it is crucial to systematically review
existing literature to gain a deeper understanding of the value of these
technologies in this context. This research aims to fill this gap with a
systematic review of existing literature on the value of novel technologies
in the context of PMM.
This article is organized as follows: the first part is an introduction
that sheds light on the background information and context of the study
and presents the research gap and research question. The second part
presents a purposeful methodology, by which the authors followed a
protocol that allowed data to be imported from reliable sources. The third
section summarizes the descriptive results of the research, and the fourth
section describes the most important findings, relevant limitations, as
well as future research avenues. Finally, the fifth part presents the con-
clusions based on the results of the study.
2. Methodology
This review employs a rigorous approach in the form of a systematic
literature review (SLR). SLR represents an independent genre of litera-
ture review conducted with a systematic, precise, clear and reproducible
methodology designed for a careful and thorough process of collecting,
analyzing and summarizing research studies on a given topic (Ham-
mersley, 2020;Kraus et al., 2022;Suri, 2020). This, in turn, is useful
when selecting and evaluating studies very carefully in order to form a
complete picture of a topic known in a given field (Linnenluecke et al.,
2020;Kraus et al., 2022). The use of the SLR method enables the iden-
tification of gaps in the existing literature and assists in suggesting new
opportunities for future research (Hammersley, 2020). The SLR process is
described step by step below and the process for literature selection is
shown in Fig. 1.
2.1. Eligibility criteria
This step is concerned with the establishment of inclusion and
exclusion criteria. To extract the relevant studies and answer the pro-
posed questions in this systematic review, Scopus was chosen as the main
electronic database. The main goal of developing eligibility criteria is to
increase intra-and inter-reviewer reliability in the study selection, in-
crease reproducibility, minimize bias, and provide transparency for users
(Nelson, 2014). Thus, we developed the following inclusion criteria:
Studies published in peer-reviewed journals since 1994.
Fig. 1. Process flow diagram of literature selection.
R.K. V K et al. Data and Information Management xxx (xxxx) xxx
2
Studies in the final stage of publication.
Studies published in English.
The extracted articles based on the selection criteria were further
analyzed in two steps. First, we analyzed each article's title, abstract, and
keywords. Those articles that did not address the proposed research
questions were eliminated. Second, we read the remaining articles in
detail and excluded articles that did not provide information about uti-
lizing novel technologies in PMM that could contribute to the research
aim. Additionally, all duplicate articles found in the multiple searches
were excluded.
2.2. Information sources
Scopus was used as the main database for identifying potentially
relevant documents. Scopus was selected because it is the largest elec-
tronic citation and abstract database for peer-reviewed literature
(Elsevier, 2022). Scopus database as source for scientific articles and a
basement for the literature reviews is well-suited for capturing pertinent
literature within the domain under investigation, and it is commonly use
as a main or sole source for the documents (e.g., Malanski et al., 2021;
Oliveira et al., 2018). The search strategies were drafted and further
refined through team discussion.
2.3. Search strategy
This section defines the keywords that were used to extract relevant
information from the Scopus database that could contribute to the
research aim. This section describes the entire search strategy for the
Scopus database in Table 1 and Table 2.
Group 1 keywords were mainly oriented toward managing,
measuring, and control activities, while Group 2 keywords were used
specifically for novel technologies that are used to control, manage, and
measure organizational performance. These groups were then combined
using logical Boolean operators in the Scopus search engine platform.
The search queries are given in Table 2, where algorithmic search queries
are shown.
After combining Group 1 and Group 2 to set up a search algorithm in
the Scopus platform, the search engine initially yielded 1472 hits. The
protocol set up for this systematic review provides some useful advan-
tages, like speeding up the search within the assigned domain, control-
lability to the desired selection, manageability of the retrieved
information, and quick identification of the selected keyword for detailed
analysis.
2.4. Selection process
The article search process was limited to titles, abstracts, and key-
words in the search bar of the Scopus platform. Two groups of keywords
were created to process the selection procedure. The entry typed in the
search bar is shown in Table 2. For the second group of keywords, the
wildcard (*) was added to ensure that all forms of a term related to the
word being searched would be found. The wild card entry keyword was
applied to digital twin and cyber physical system. In the first search, the
Scopus platform yielded 1472 articles as a result of the algorithm in
Table 2.
Because the keyword incorporated numerous hits from subjects like
mathematics, biology, ecology, physics, chemistry, and psychology, a
layer of subject limitation was added to the search criteria. Consequent
searches were designed to target more relevant and specific subject areas
(business, management and accounting, computer science, engineering,
and decision science) that are concerned to this systematic review. This
yielded 754 articles relevant to the subject. The titles of these 754 doc-
uments were further analyzed, yielding 235 articles to move forward
with. It was necessary to filter more documents not related to the
research questions of this systematic review. The abstracts of the
remaining 235 journal articles were read to determine if the articles were
relevant to the proposed research questions. The result of this process
yielded 99 peer-reviewed journal articles for full text reading.
After reading the full texts of these 99 articles, 54 articles were
selected for further analysis. Fifteen articles, after further cross-checking
and reading, were found not to be closely relevant to the scope of this
systematic review. Thirty-nine articles were ultimately selected from this
procedure. During the systematic literature search process, it is likely that
some important scientific work may not be found. Non-probabilistic
sampling, like snowball sampling, is needed to accommodate more
work in relevant fields (Bakker, 2010). Therefore, snowball sampling was
applied to extract other relevant articles.
The procedure for snowball sampling was dependent on in-depth
readings and the understanding of the researcher instead of consid-
ering the inclusion and exclusion criteria set up for this systematic re-
view. It was necessary to identify the experts in the domain and become
familiar with their work. Google Scholar and Scopus were utilized to
search for additional documents during the non-probabilistic sampling
for this study. The snowball sampling helped to identify 28 additional
relevant articles, leading to total of 67 articles in the final selection.
A systematic breakdown of the searching process for the potential
literature is shown in the process flow diagram in Fig. 1.
2.5. Final sample
Chronologically, the descriptive analysis suggests that novel tech-
nology as a cutting-edge development for performance management is a
very recent and fragmented issue. Fig. 2 represents the number of
selected studies published per year within the review period from 1994
to the present. The descriptive analysis shows that more attention has
been given to this subject in the last three to four years, with growth still
trending. This means that the relationship between novel technologies
with respect to PMM has been receiving more interest from researchers.
Table 1
Keywords used for the search queries.
Group Keywords
Group
1
performance management, performance measurement, organizational
control, management control
Group
2
artificial intelligence, AI, augmented reality, AR, IoT, 5G, digital twins,
cyber physical system, cloud computing, blockchain
Table 2
Algorithm for search queries.
Digital
Library
Group Algorithm
Scopus Group 1 (TITLE-ABS-KEY (“Performance Management”)OR
TITLE-ABS-KEY (“Performance Measurement”)OR
TITLE-ABS-KEY (“Organizational Control”)OR
TITLE-ABS-KEY (“Management Control")
Group 2 TITLE-ABS-KEY (“Artificial Intelligence”) OR TITLE-
ABS-KEY(“AI”) OR TITLE-ABS-KEY (“Augmented
Reality”) OR TITLE-ABS-KEY(“AR”) OR TITLE-ABS-
KEY (“Digital Twins*") OR TITLE-ABS-KEY (“Cyber
Physical System*") OR TITLE-ABS-KEY (“Cloud
Computing”) OR TITLE-ABS-KEY (“Blockchain”)OR
TITLE-ABS-KEY (“IoT”) OR TITLE-ABS-KEY (“5G00 )
Group 1 and
Group 2
(TITLE-ABS-KEY (“Performance Management”)OR
TITLE-ABS-KEY (“Performance Measurement”)OR
TITLE-ABS-KEY (“Organizational Control”)OR
TITLE-ABS-KEY (“Management Control”) AND
TITLE-ABS-KEY (“Artificial Intelligence”) OR TITLE-
ABS-KEY (“AI”) OR TITLE-ABS-KEY (“Augmented
Reality”) OR TITLE-ABS-KEY (“AR”) OR TITLE-ABS-
KEY (“Digital Twins*") OR TITLE-ABS-KEY (“Cyber
Physical System*") OR TITLE-ABS-KEY (“Cloud
Computing”) OR TITLE-ABS-KEY(“Blockchain”)OR
TITLE-ABS-KEY (“IoT”) OR TITLE-ABS-KEY (“5G00 ))
R.K. V K et al. Data and Information Management xxx (xxxx) xxx
3
Since the trend in Fig. 2 shows that relevant research has mostly been
conducted in recent years, the snowball sampling was carefully reviewed
from 2017 to the present. After careful readings, only 28 articles from
2017 to the present were considered for the analysis, with the purpose of
adding more value to the proposed research questions of this review.
In the next section, we discuss the findings of our thematic analysis of
carefully and systematically chosen literature from 1994 to the present.
The choice of 1994 as the starting point for this review is based on the
scarcity of scientific research, as searches with the selected criteria
showed that not much research about the use of novel technologies in
PMM was performed before that year (see Fig. 2).
2.6. Analysis and validity
The analysis was conducted iteratively and collaboratively by a team
of four researchers. Our collaborative effort allowed us to approach the
subject matter from various perspectives, ensuring a comprehensive
analysis of the literature and themes related to novel technologies in
PMM. At first, a preliminary research themes in the literature were
generated, meaning that the themes were allowed to emerge explora-
torily. The final research themes were formulated through a cyclic pro-
cess as follows: (1) reading the results of the articles (included in the final
sample) and interpreting the results; (2) classifying their content into
themes; and (3) consolidating themes into main and subcategories. This
iterative inquiry ended in a situation where no new information was
found in the final sample. The diverse expertise of the researchers that
participated in the analysis enriched the rigor and depth of our review,
contributing to the robustness of the identified themes and future
research directions. In terms of validity, our study rigorously followed
established research methodologies for systematic reviews. We main-
tained transparency in our approach by detailing the process of article
selection, inclusion and exclusion criteria, and the databases utilized for
our search.
3. Research streams of PMM in relation to novel technologies
This section highlights the primary research streams in the PMM field,
supplemented by the domain of management control and organizational
control. The discussion is structured according to six themes. Many of the
selected themes from the studies in this systematic literature review can
be categorized in more than one sub-theme because they have multiple
overlapping advantages for PMM. The themes are categorized in Table 3.
3.1. Automation techniques and PMM (theme 1)
Papers grouped around this theme are mostly focused on the use of
technologies like AI, ML, the IoT, cloud computing, and CPS to automate
processes in various industries (e.g., Heilbrunn et al., 2017;Monod et al.,
2022;Olan et al., 2022). Automation techniques facilitate the creation of
information that is more easily interpreted by humans and can be used to
automatically generate reports, manage customer data, track inventory,
and improve customer service (Finch, 2002). Such automation
Fig. 2. Number of selected studies per year (snowball sampling is not included here).
Table 3
Themes emerging from reviewing prior research.
Theme Definition References
1. Automation
techniques and
PMM
Use of technology to automate
processes in PMM
Teimoury, Fathian and
Chambar, 2013;Heilbrunn
et al., 2017;Kumar &Singh,
2019;Nawaz &Mary, 2019;
Siderska, 2020;Agrawal,
2021;Monod et al., 2022;
Olan et al., 2022
2. Predictive
analytics and
PMM
Use of past data to build
models to predict future trends
and behaviors
Chen et al., 2012;Shmueli
et al., 2016;Abolbashari et al.,
2018;Kumar &Garg, 2018;
Dubey et al., 2019;Masood &
Hashmi, 2019;Van Calster
et al., 2019;Psarras et al.,
2020;Hassan et al., 2021
3. Real-time data
collection and
PMM
Process of gathering and
analyzing data generated and/
or received in real time
Yuldoshev et al., 2018;
Barricelli et al., 2019;Sahlin &
Angelis, 2019;Agrawal, 2021;
Dweekat and Al Alomar, 2021;
Tokat et al., 2021;Bertoni &
Bertoni, 2022;Holopainen
et al., 2022;Saunila et al.,
2022
4. Visualization and
PMM
Software application or
program enabling graphical
representations of data
Bititci et al., 2016;Perkel,
2018;Naslund &Norrman,
2019;Manville et al., 2019;
van Assen &de Mast, 2019;
Batt et al., 2020;Sarram &
Ivey, 2022.
5. Collaboration
tools and PMM
Software application/platform
that enables teams to work
together
Al-Hakim &Lu, 2017;Garrick,
2018;Bhatia &Malhotra,
2019;Song et al., 2019;
Marion and Fixon, 2020;
Marques et al., 2022
6. Blockchain
technology and
PMM
Decentralized and distributed
digital ledger technology
Zheng et al., 2019;Salah et al.,
2020;Reyes et al., 2022;Yuan
et al., 2020;Kumar et al., 2021
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4
techniques support PMM in various ways. The findings from several
studies (Teimoury et al., 2013;Agrawal, 2021;Heilbrunn et al., 2017;
Monod et al., 2022;Nawaz &Mary, 2019;Olan et al., 2022) suggest that
automation can help to improve the efficiency and quality of PMM,
reduce delays in PMM, and produce information patterns that are easier
for humans to understand.
When it comes to improving the efficiency and quality of PMM,
process automation involves the use of various software programs to
automate administrative tasks, such as creating and managing customer
databases, which can help streamline operations. It can also be used to
facilitate the tracking of performance metrics, such as customer satis-
faction and employee engagement (Kumar &Singh, 2019). For example,
robotic process automation is a growing technology that automates re-
petitive tasks using software robots (Siderska, 2020). The author argues
that robotic process automation can be a significant driver of digital
transformation by streamlining processes, improving productivity, and
reducing costs. In addition, by utilizing AI and ML algorithms, supply
chain processes can be automated for better visibility and accuracy to
increase efficiency, reduce costs, and reduce risks (Teimoury et al.,
2013). Olan et al. (2022) argue that automated techniques like chatbots
powered by AI can guide users through complex processes or workflows,
providing personalized recommendations based on user behavior.
Regarding the reduction of PMM delays, Nawaz and Mary (2019)
argue that chatbots can help to reduce the amount of time needed to
complete tasks, which can in turn help to improve organizational effi-
ciency by providing real-time feedback and support to employees.
Meanwhile, Agrawal (2021) suggests that automatic cloud computing
can provide efficient management by allocating and deallocating re-
sources based on system demands, which can optimize resource utiliza-
tion and reduce costs. She adds that automatic cloud computing can
provide security solutions (such as automatic backup and security
monitoring) for cloud-based systems. Monod et al. (2022) found that AI
systems can enable service providers to have more control over customer
experiences by automating certain aspects of the customer service pro-
cess, such as training inquiries and providing automated responses. All
these assist in reducing delays in PMM.
By producing information patterns for PMM, automation techniques,
especially ML techniques, can be applied to automatically identify pat-
terns and configurations in complex data sets, which can help humans
make informed decisions and identify the best planning strategies
(Heilbrunn et al., 2017). The authors introduce “kernel-based machine
learning methods”to detect mutual configurations of applied planning
strategies and performances in small- and medium-sized businesses.
These methods involve using mathematical kernels to map data into a
higher-dimensional space where a linear classifier can be used to identify
patterns in the data. The authors argue that the proposed model can be
used to automatically and accurately predict future performance based
on the detected configurations. Olan et al. (2022) suggest that promising
tools like chatbots powered by AI could be effective for knowledge
sharing and could simplify human tasks, such as benefits administration,
employee engagement, and onboarding.
3.2. Predictive analytics and PMM (theme 2)
Predictive analytics is a set of techniques and tools that use data,
statistical algorithms, and machine learning to analyze historical data
and make predictions about future events or outcomes (Abolbashari
et al., 2018;Kumar &Garg, 2018). Based on historical data, predictive
analytics identifies patterns and trends that can be used to develop pre-
dictive models that can accurately forecast future outcomes, enabling
organizations to anticipate and plan for the future (Shmueli et al., 2016).
Prior research on this theme (e.g., Dubey et al., 2019;Hassan et al., 2021;
Psarras et al., 2020;Van Calster et al., 2019) suggests that predictive
analytics can help to facilitate decision-making based on PMM infor-
mation, assist in risk management and failure detection, and improve
performance based on PMM information.
For various business operations, managers need insights and infor-
mation to make informed decisions and optimize their operations. Pre-
dictive analytics can be a valuable tool for them in doing so (Dubey et al.,
2019;Kamble et al., 2020;Kumar &Garg, 2018). Psarras et al. (2020)
examined the balanced scorecard, a tool for PMM, that can be used
together with predictive analytics to improve the decision-making pro-
cess for funding programs. The authors argue that the balanced scorecard
provides a framework for identifying key performance indicators across
different areas of the program, while predictive analytics helps to identify
trends and patterns in the data that can be used to make predictions
about future performance. Similarly, in manufacturing plants, predictive
analytics can be used to predict equipment failures and can help orga-
nizations make data-driven decisions and improve performance out-
comes (Dubey et al., 2019). One major challenge is the accuracy and
reliability of predictive analytics results, which are often influenced by
factors such as data quality and the selection of machine learning algo-
rithms (Masood &Hashmi, 2019).
When it comes to managing risks and detecting failures, predictive
analytics can play an important role in PMM by providing organizations
with the tools and insights needed to forecast future performance and
identify opportunities for improvement (Shmueli et al., 2016;Chen et al.,
2012). For example, analyzing financial data and identifying patterns
and trends can benefit the financial industry in a variety of ways, such as
risk management, investment strategies, and predicting customer
behavior (Chen et al., 2012). Alharthi (2018), and Hassan et al. (2021)
have shown that predictive analytics can be applied to identify areas for
improvement and potential risks in the health industry. For example, it
can be used to identify patients who are at high risk for readmission,
allowing healthcare organizations to take proactive measures to reduce
readmission rates and improve patient outcomes. Zhao et al. (2019)
introduced a comprehensive methodology for measuring and managing
the performance of supply chain networks using predictive analytics. By
analyzing network topology, the authors developed a predictive model
that can help identify potential failures in a company's supply chain
network before they occur, which can enable companies to improve their
supply chain resilience and reduce the impact of disruptions. Moreover,
they developed a set of metrics to evaluate the performance of a network
under different scenarios, including disruptions and capacity constraints.
Regarding the enhancement of performance based on PMM infor-
mation, predictive models are used to assess how the performance of a
unit relates to its attributes (Kumar &Garg, 2018). The model predicts
how likely it is that a similar unit in a different sample will show specific
performance. It is used in marketing to predict how customers will
behave during transactions (Kumar &Garg, 2018). Additionally, predi-
cative analytics can be used to optimize resource allocation and staffing
by identifying periods of high demand and allocating resources accord-
ingly (Van Calster et al., 2019). Another example of the application of
predictive analytics is found in research by Kamble et al. (2020), who
argue that traditional approaches to supply chain PMM are limited in
their ability to provide real-time insights and accurate predictions of
future performance. They suggest that by leveraging big data and pre-
dictive analytics techniques, organizations can develop more compre-
hensive and accurate understandings of their supply chains’
performance.
3.3. Real-time data collection and PMM (theme 3)
Real-time data collection is a very important aspect of PMM across
various industries and novel technologies like the IoT, cloud computing,
digital twins, and AI (e.g., Barricelli et al., 2019;Bertoni &Bertoni, 2022;
Sahlin &Angelis, 2019). Previous studies on this theme (e.g., Agrawal,
2021;Dweekat &Al Alomar, 2021;Holopainen et al., 2022;Yuldoshev
et al., 2018) show that real-time data collection facilitates PMM by
enhancing timely decision making, assisting in the prediction of trends
and responses to changing environments, and improving the optimiza-
tion of current operations.
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5
In a rapidly changing environment, a large volume of data is gener-
ated by the system everywhere, and it is of utmost importance to collect
these data in real time and analyze them to make informed and deliberate
decisions from a managerial perspective (Sahlin &Angelis, 2019). Ac-
cording to Yuldoshev et al. (2018), AI and ML techniques can automate
the real-time data collection process and provide real-time data analysis
and insights, allowing managers to make organizational decisions in a
timely manner. Furthermore, they add that the integration of these
technologies with visualization tools can enhance the interpretability and
transparency of performance predications. Holopainen et al. (2022)
argue that digital twin technology also provides real-time data insights
that can help organizations make more informed decisions and respond
quickly to changing circumstances. They show that companies are
increasingly actively searching for the possibilities to support innovation
through the utilization of digital twins. Further, Tokat et al. (2021)
propose a method for designing and developing key performance in-
dicators using fuzzy c-means clustering to analyze real-time data in
warehouse loading operations. The fuzzy c-means clustering algorithms
can identify patterns and relationships in the data to help managers make
timely, informed decisions.
Real-time data collection also has potential for identifying trends and
measures to respond to changing environments. Real-time data collection
allows organizations to quickly measure performance with the aim of
identifying trends and responding to changing environments (Agrawal,
2021;Sahlin &Angelis, 2019). Sahlin and Angelis (2019) introduced
various digital technologies and tools that can be used to facilitate the
collection of data in real time, such as digital dashboards, analytics
platforms, and automation. These tools can help organizations to mea-
sure and track performance more accurately and quickly, as well as
identify areas for improvement that can produce competitive advantages.
Holopainen et al. (2022) argue that one specific technology, digital
twins, can help organizations understand their operations in real time,
identify areas for improvement, and predict and prevent problems before
they occur. Similarly, Saunila et al. (2022) also found that digital twins
can be a powerful tool to develop PMM for the digital organizational
transformation. They add that digital twins can help collect real-time
data by creating virtual replicas of physical objects, processes, or sys-
tems. In addition, the authors mention that digital twins can be equipped
with sensors that can collect data in real time and transmit them to a
central platform for analysis in real time to identify patterns, trends, and
anomalies.
Finally, real-time data collection assists in optimizing current opera-
tions. For example, a digital twin of a manufacturing process could be
used to monitor and control the production line in real time, adjusting
settings as needed to optimize performance (Barricelli et al., 2019;Ber-
toni &Bertoni, 2022). Other novel technologies, like cloud computing,
play an important role in managing and processing large volumes of data
in real time as they utilize virtualization technology to host and manage
multiple virtual computers using a single physical computing platform.
This enables users to access computing resources from anywhere,
anytime, and eliminates the need for physical infrastructure (Agrawal,
2021). The author further explains that by using real-time data collection
and analysis, automatic cloud computing systems can monitor and
optimize performance in real time. Dweekat and Al Alomar (2021) pro-
vide valuable insight into the potential of IoT technology to improve the
quality and safety of perishable dairy products. The study emphasizes the
importance of using simulation-based approaches to assess the perfor-
mance of IoT systems in different scenarios, which can help select the
optimal setup for different applications. That in turn plays a significant
role in optimizing operations.
3.4. Visualization and PMM (theme 4)
Visualization tools, such as graphs, charts, and maps that display data
in an easily understandable way that allows users to quickly identify
trends, patterns, outliers, and other insights, can be powerful tools for
PMM (Naslund &Norrman, 2019;Perkel, 2018). Prior research on
visualization and PMM (Batt et al., 2020;Manville et al., 2019;Naslund
&Norrman, 2019) emphasizes the importance of visualization tools such
as dashboards and charts in performance measurement systems, partic-
ularly to provide effective communication and visualization of perfor-
mance information (Sarram &Ivey, 2022).
The utilization of visualization tools can support the implementation
and utilization of performance measurement approaches, such as
balanced scorecards (Manville et al., 2019;van Assen &de Mast, 2019).
Visualization tools, such as radar charts and histograms (Batt et al., 2020;
Perkel, 2018) that present performance data in a clear and concise
manner, can support the importance of effective communication and
visualization tools in the implementation of a balanced scorecard
approach. Visualization tools can also support the PMM of organizations’
change initiatives, stakeholder involvement, and effective communica-
tion (Naslund &Norrman, 2019). According to Bititci et al. (2016),
visualized PMM activities support strategy development and imple-
mentation in contemporary business environments, improve both inter-
nal and external communication, and also enable people to engage in
strategic thinking.
In today's rapidly growing and digitizing business environment, data
visualization is a key component of data literacy, providing a handful of
skills for managers and employers to support their PMM activities (Batt
et al., 2020). The use of performance measurement dashboards (Batt
et al., 2020) to visualize performance data and monitor progress toward
goals emphasizes the importance of data visualization for providing in-
sights and understanding complex data sets,which can aid in making
data-driven decisions (Manville et al., 2019;Naslund &Norrman, 2019;
Perkel, 2018). Visualization tools can also help in visualizing various data
sets, such as financial data, customer data, and operational data, and
foster organizational understanding of how these visualizations can
provide valuable insights into the performance of different aspects of an
organization (Bititci et al., 2016;Batt et al., 2020;Manville et al., 2019).
Visualization tools can also be used to create interactive dashboards and
reports, which can help in monitoring and measuring key performance
indicators and tracking progress toward goals (Batt et al., 2020;Naslund
&Norrman, 2019). Visualized PMM practices can also support the lean
management philosophy in digitizing business environments (van Assen
&de Mast, 2019).
3.5. Collaboration tools and PMM (theme 5)
Collaboration tools are software applications that can provide orga-
nizations with platforms to share their performance data in real time and
to manage and track progress and make data-driven decisions (Al-Hakim
&Lu, 2017;Garrick, 2018;Song et al., 2019). This real-time data sharing
capability provided by collaboration tools can lead to more efficient goal
setting and performance tracking, as well as more timely feedback, which
can improve performance outcomes. By providing a platform for orga-
nizations to share information and coordinate their efforts, collaboration
tools can help to break down silos and improve cross-functional collab-
oration, which can be crucial for achieving organizational goals (Al-Ha-
kim &Lu, 2017;Bhatia &Malhotra, 2019;Garrick, 2018). For example,
according to Marion and Fixon (2020), these collaboration tools have
become increasingly more sophisticated while being easier to use and are
integrated earlier in organizational processes, such as innovation pro-
cesses. According to Marion and Fixon (2020), collaboration tools sup-
porting organizational PMM not only affect output and process efficiency
but also increase the depth and breadth of the work of individual em-
ployees and rewrite the rules of how knowledge management acts as a
critical competitive capability.
The utilization of collaboration tools (online collaboration platforms,
messaging apps, and video conferencing software) can also support the
successful management of tacit knowledge, which is critical for
improving organizational performance (Garrick, 2018). The utilization of
modern collaboration tools, such as social media, online collaboration
R.K. V K et al. Data and Information Management xxx (xxxx) xxx
6
platforms, and other digital communication tools, can facilitate interac-
tion and communication in digitizing business environments and thus
support the transfer and management of tacit knowledge (Bhatia &
Malhotra, 2019). Collaboration tools can also support organizations’
PMM activities by bringing together remote team members, who might
face increased complexity in their daily tasks and require mechanisms
with adaptive capabilities, to share and combine knowledge (Marques
et al., 2022). According to Marques et al. (2022), in industry 4.0,
augmented reality as a collaborative tool for PMM is one of the most
promising solutions available, allowing the seamless integration of vir-
tual and real-world objects, which can be used to provide a shared un-
derstanding of tasks and contexts.
The utilization of collaboration tools is not limited to managing and
evaluating the internal operations of organizations (Al-Hakim &Lu,
2017;Garrick, 2018;Song et al., 2019). By using collaboration tools to
share information and work together, stakeholders can improve the
speed and accuracy of their PMM activities, which can in turn improve
organizational performance (Bhatia &Malhotra, 2019).
3.6. Blockchain technology and PMM (theme 6)
In recent years, there has been a growing interest in using blockchain
technology, which is a decentralized ledger that enables secure and
transparent data sharing, to support the PMM activities of industrial or-
ganizations (Reyes et al., 2022;Salah et al., 2020;Zheng et al., 2019).
Blockchain technology has the potential to support the development of
performance measurement by providing a secure and transparent system
for collecting, storing, and analyzing data. As a decentralized ledger,
blockchain allows for the creation of a tamper-proof and immutable re-
cord of performance data (Zheng et al., 2019;Salah et al., 2020).
Blockchain technology can also reduce the risk of fraud and improve trust
between stakeholders, which can contribute to better performance out-
comes (Salah et al., 2020).
The use of blockchain technology can enable more accurate and real-
time tracking of supply chain performance metrics, such as delivery
times, inventory levels, and quality control measures (Reyes et al., 2022).
The transparency and security provided by blockchain technology can
also facilitate more effective performance evaluation and management of
supply chain partners, enabling organizations to better monitor and
improve their supply chain operations (Yuan et al., 2020). Kumar et al.
(2021) explore the challenges of implementing the industrial IoT and
proposes the use of blockchain technology to mitigate these challenges.
They found that the use of blockchain in the industrial IoT can also
enable better tracking and monitoring of key performance indicators in
real time. For example, blockchain can enable the creation of smart
contracts that automatically execute when certain conditions are met,
allowing for automated PMM.
While there is growing interest in using blockchain for performance
measurement, there is a need for further research into how this tech-
nology can be effectively integrated into existing performance mea-
surement systems and processes. Additionally, there is a need for
empirical studies to investigate the actual impact of blockchain tech-
nology on performance outcomes (Zheng et al., 2019;Salah et al., 2020;
Kumar et al., 2021).
4. Research gaps and future research directions
The recent advancement in novel technology has brought numerous
opportunities to improve PMM in various industries. Table 4 presents the
research gaps identified for future research.
5. Conclusions
In today's fast-paced business environment, staying ahead of the
competition requires organizations to be able to conduct PMM effec-
tively. Novel technologies can provide organizations with the necessary
Table 4
Themes and future research.
Theme Key literature
streams/existing
research
Future research
directions
References
Automation
techniques
and PMM
Efficiency and
quality
improvement
Reduction of
delays
Production of
information
What are the main
challenges associated
with utilizing
automation
techniques in PMM?
How should issues
such as lack of
standardization, the
need for intelligent
and adaptive
mechanisms, and the
importance of user
privacy and data
protection be
considered?
Agrawal (2021)
What ethical
considerations
should be considered
when implementing
automation
techniques in PMM,
and what are the
potential impacts on
organizational
culture and values?
Majumder &
Mondal, 2021;
Monod et al.,
2022;Olan et al.,
2022
How can
organizations design
effective knowledge
sharing strategies
that consider user
experience and ease
of use while
incorporating
automation
techniques?
Majumder and
Mondal (2021)
Predictive
analytics and
PMM
Decision making
and optimization
Risk and failure
management
Performance
improvement
based on PMM
information
What are the
potential benefits and
challenges of
integrating predictive
analytics with other
management tools,
such as project
management
software, to improve
PMM?
Psarras et al.
(2020)
What ethical and
privacy concerns are
associated with the
use of predictive
analytics in PMM,
and how can they be
addressed?
Salazar-Reyna
et al. (2022)
How does the quality,
integration, and
visualization of the
data ensure the
success of the
predictive models for
PMM?
Mello &Martins,
2019;Masood &
Hashmi, 2019
Real-time data
collection
and PMM
Making informed
and deliberate
decisions
Trend
identification and
response
Optimization of
current
operations
How can
organizations ensure
data privacy and
security when using
real-time data
collection in PMM?
Agrawal (2021)
What is the
comparative
effectiveness of AI-
and ML-based real-
time data collection
and analysis
techniques against
Yuldoshev et al.
(2018)
(continued on next page)
R.K. V K et al. Data and Information Management xxx (xxxx) xxx
7
tools to do so. As these technologies continue to evolve, organizations
will be well positioned to adapt and take advantage of new opportunities
for performance improvement. In a nutshell, the integration of these
novel technologies has the potential to revolutionize PMM.
In this paper, we present a systematic review of studies related to the
value of novel technologies in the context of PMM. We aimed to establish
the current state-of-the-art of various novel technologies and how they
can assist in developing PMM across various industries. Research related
to the value of novel technologies in the context of PMM was grouped
around the following research streams: automation techniques, predic-
tive analytics, real-time data collection, visualization, collaboration
tools, and blockchain technology.
5.1. Theoretical implications
The findings of this study provide increased theoretical understand-
ing to prior knowledge on PMM. In addition, the findings of the study
generate some theoretical insights into dealing with novel technologies,
such as collaboration tools or blockchain technology. While demon-
strating the interplay between novel technologies and PMM, this study
contributes to theory development by suggesting important research
avenues to further development of the theories in PMM. While the results
of the study demonstrate the increasing interaction between automation
techniques and PMM, predictive analysis and PMM, real-time data
collection and PMM, collaboration tools and PMM as well as blockchain
technology and PMM, there are open questions relating to each of these
themes, that can be used as a guideline for further studies and theoretical
development. Based on the results of this study, there is a need for more
in-depth understanding about the ethical considerations, as well as data
privacy and standardization while the novel technologies are utilized in
PMM.
5.2. Managerial implications
The results of the study show the wide role of opportunities that novel
technologies provide organizations to support their PMM. Research
related to the value of novel technologies in the context of PMM was
grouped around six research streams and it was shown how novel tech-
nologies can be used for PMM in a variety of ways. These technologies
can be used to track and monitor performance, automate processes, and
streamline operations. For example, analytics tools can be used to track
employee performance and identify areas for improvement. Data visu-
alization tools can be used to create easy-to-understand representations
of performance data, allowing for better decision making. Automation
tools can be used to automate processes such as payroll or employee
onboarding, making them faster and more efficient.
5.3. Limitations and future research directions
Like all the academic studies, this study is not without its limitations.
The chosen methodology relies on data sourced solely from the Scopus
database as it is well-suited for capturing pertinent literature within the
domain under investigation. Furthermore, the analysis is confined to
publications in the English language, potentially introducing linguistic
predispositions. As the data for this study is gathered from one academic
database, although considered sufficient source, further studies could be
conducted to explore the phenomenon with the data from alternate
sources such as Web of Science, JSTOR, and ScienceDirect.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
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Theme Key literature
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