Access to this full-text is provided by MDPI.
Content available from Sustainability
This content is subject to copyright.
sustainability
Article
Positive and Negative Impacts of COVID-19 in
Digital Transformation
Radhakrishnan Subramaniam 1, Satya P. Singh 2, Parasuraman Padmanabhan 3 ,4 ,* , Balázs Gulyás3,4,5,
Prashobhan Palakkeel 6and Raja Sreedharan 7
Citation: Subramaniam, R.;
Singh, S.P.; Padmanabhan, P.;
Gulyás, B.; Palakkeel, P.; Sreedharan, R.
Positive and Negative Impacts of
COVID-19 in Digital Transformation.
Sustainability 2021,13, 9470. https://
doi.org/10.3390/su13169470
Academic Editors: Daryl Powell,
David Romero and Paolo Gaiardelli
Received: 15 July 2021
Accepted: 17 August 2021
Published: 23 August 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1School of Business, Amrita Vishwa Vidyapeetham University, Amritanagar 641112, India;
salemradha@gmail.com
2Department of Electronics and Communications Engineering, Netaji Subhas University of Technology,
New Delhi 110078, India; satya.prakash@nsut.ac.in
3Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore 608232, Singapore;
balazs.gulyas@ntu.edu.sg
4Cognitive Neuroimaging Centre, Nanyang Technological University, Singapore 636921, Singapore
5Department of Clinical Neuroscience, Karolinska Institute, 17176 Stockholm, Sweden
6Department of Management, Amrita Vishwa Vidyapeetham Bengaluru Campus, Karnataka 560035, India;
palakkeel@gmail.com
7Supply Chain Management, Rabat Business School, UniversitéInternationale de Rabat, Rabat 11103, Morocco;
raja.sreedharan@uir.ac.ma
*Correspondence: ppadmanabhan@ntu.edu.sg
Abstract:
This study was designed to research the impact of pandemic situations such as COVID-19
in digital transformation (DT). Our proposed study was designed to research whether COVID-19 is a
driver of digital transformation and to look at the three most positive and negative DT disruptors. Our
study suggests that COVID-19 is a driver of digital transformation, since 94 percent of respondents
agreed that COVID-19 is a driver of DT. The second phase of our study shows that technology,
automation, and collaboration (TAC) is the most positive significant factor which enables work from
anywhere (WFA) (or work from home) arrangements and also leads to the third positive factor of
a work-life balance (WLB). The top three negative factors are no work-life balance (NWL), social
employment issues (SEI), and data security and technology issues (DST). The negative factors show
a contradictory result since NWL is the most negative factor, even though WLB is the third most
positive factor. While the pandemic situation is leading to a positive situation for economies and
organizations at a micro level, the negative impacts, which will affect overall economic growth as
well as social, health, and wealth wellbeing, need to be kept in mind. The motivation of this study
was to research positive and negative effects of COVID-19 on DT, since COVID-19 is impacting
everyone and everyday life, including businesses. Our study developed a unique framework to
address both positive and negative adoption. Our study also highlights the need for organizations
and the economy to establish mitigation plans, as the pandemic has already been disrupting the
entire world for the past three quarters.
Keywords: pandemic; COVID-19; digital transformation; work-life balance; work from anywhere
1. Introduction
The year 2020 was a pandemic year for the whole world, since COVID-19 affected
everyone’s lives during that year. COVID-19 is a disease involving a coronavirus (“CO”
stands for corona, “VI” for a virus and “D” for disease) [
1
]. COVID-19 has had adverse
impacts for almost three quarters since the World Health Organization’s (WHO, Geneva,
Switzerland) announcement regarding the disease on 30 January 2020. The COVID-19 virus
is a severe acute respiratory syndrome related virus [
1
,
2
]. Entire economies are trying to
overcome the pandemic it has caused because it is a life-threatening issue for every nation.
As mentioned by the WHO, COVID-19 is one of the most serious global pandemics, akin
Sustainability 2021,13, 9470. https://doi.org/10.3390/su13169470 https://www.mdpi.com/journal/sustainability
Sustainability 2021,13, 9470 2 of 18
to the Spanish flu [
3
]. Under the prevailing circumstances, there is no solution available
to end or control this pandemic and the only solution is to follow the WHO guidelines as
much as possible [
3
]. Notably, there are vaccines such as the Pfizer and Moderna vaccines
which show efficient and promising results, as was mentioned by the WHO. Moreover,
Canada and many other countries have started vaccinating their senior citizens, and they
are expanding their vaccine roll outs to cover their entire populations, based on the latest
news on the topic (15 December 2020). As per the prevailing circumstances, there are
50 vaccines
that are currently in trials and the WHO is working with scientists and health
organizations worldwide [3].
Digital transformation (DT) has been defined in many ways, but generally it is the
use of information and communication technologies (ICT) along with their benefits [
4
]
to change business operating models, products, services, and organizational structures
to obtain a competitive advantage. According to Vial [
5
], “DT is a process that aims to
improve an entity by triggering significant changes to its properties through combinations
of information, computing, communication, and connectivity technologies”. There are
other definitions of DT [
6
] such as an “opportunity to create a new business model”. The
expectations and benefits involving DT are somewhat broader [
7
], since it is expected to
trigger changes in customer experiences, customer serendipity, business growth, increases
in customer touchpoints, changes to operating models, and so on.
DT is quite disruptive, as it changes the way that businesses operate and deliver goods
and services to the customer. DT has played a major role in daily business operations
during the last three-quarters when the COVID-19 pandemic was in effect. Hence, it is
crucial to study the correlation between COVID-19 and DT. Given the negative impacts
of COVID-19 on DT, whether we like it or not, survival is essential and even if businesses
cannot operate normally, we have to return the business sector back to normalcy where
possible [
8
]. Businesses are not operating as expected, as they are constantly being affected
by the state of this pandemic. The ways that businesses are constantly being affected is
pushing us towards a broader global recession, and hence there is a great need [
8
] to take
some effective steps now for the economic well-being of mankind. Tax reform and collection
processes are being delayed [
7
], as the “digital link” deadline for making tax digital (MTD)
is no longer a high priority. COVID-19 is initiating indirect tax and digitalization on
tax functions temporarily [
7
]. Due to COVID-19, tax reformation, collection processes,
underlying IT systems, and tax policies are being changed temporarily [
9
,
10
] at the macro
level, which is a burden to citizens and at the same time is difficult to use to maintain the
sustainability of the economy. In addition to tax reform for individuals, there are concerns
involving tax reform [
11
] for the top billionaires or large players such as Google, Amazon,
Facebook, and Apple (GAFA). Even though the world risks being affected by a broad
economic and democratic recession due to the pandemic, tax obligations on GAFA have
not been affected and their contribution is currently just 0.2 percent of their wealth [11].
The economic and social wellbeing of mankind is greatly affected by the COVID-19
pandemic in part because the insurance market is also being affected by COVID-19 [
12
],
which is a serious concern for social security at the macro level. Because customers are
constantly being locked down and, therefore, are strongly affected by the pandemic, a
change in digital business models and related technology components is needed to do
business while the pandemic persists. In other words, contactless payments can be very
much expected to increase under current digital business models [
13
]. Another emerging
situation in the field of education is to figure out the best way to resume schools, at least
online, which requires online learning platforms, changes in the learning process, and the
provision of e-contents [
14
]. Furthermore, the current role of teachers and students are also
to be changed. Because schooling is one of the basic needs for the next generation, COVID
is beginning a change in the school operating model. Keeping these technological changes
in the teaching methods, the related political issues should be resolved as soon as possible.
Unlike a local disease, as this epidemic is widespread, the expectation at the macro level is
a world of intercultural relationship and unity [
15
]. As the economy is fully affected, there
Sustainability 2021,13, 9470 3 of 18
is an identified unfair and deceptive trade service [
16
]. The business sector has identified
internal risks and changes in external market conditions [
17
] to be mitigated as well. In
addition to the economic [
8
] issue, social threats and changing communication practices
(telecommunication) are retarded [
18
] by the COVID-19 situation. Since the proliferation of
technology, information usage and security risks are high. The business and data are subject
to phishing and loss of information [
19
]. Data centralization is very much expected [
20
]
as the information is needed everywhere and anywhere to be accessible to employers
and employees. Another economic issue of reduction in trade interconnection [
1
] is also
identified as the impact of COVID-19.
As COVID-19 is something serious and affects every citizen in the entire world, DT
is somewhat of a buzzword that most of the business and community sectors are talking
about. As reviewed in the literatures cited in the above section, COVID-19 is leading to
both negative and positive impacts in the DT journey. However, it is unclear whether
the positive impact is on social and economic wellbeing, and at the same time the extent
of negative impact is unclear. As COVID-19 is impacting everyone and everyday life,
including businesses, DT is one of the key players which drive virtual business and social
community. COVID-19 has many effects in both positive and negative senses. This topic
of study is still quite new at this time, so it is quite important to research this topic to
contribute further to how the COVID-19 pandemic can be translated into positive aspects.
At the same time, it’s important to investigate the social wellbeing and business factors
which are negatively impacted so that they can be mitigated when this paper is utilized
properly. The authors of this papers are motivated to address these following questions.
•Is COVID-19 a driver of digital transformation?
•
What are the top three positive factors that COVID-19 is attributing to digital transformation?
•
What are the top three negative factors that COVID-19 is attributing to digital transformation?
To address our questions, a new adoption framework with homogenous factors is
needed. Our study will develop a model framework to identify and analyze the variables
responsible for the positive and negative effects of COVID-19 in DT. The domain identi-
fication and instrument development for the framework are proposed in the following
sections. This is then followed by data collection, analysis, testing of hypothesis, results,
discussions and implications, and conclusions in subsequent sections.
2. Literature Background
2.1. The Negative and Positive Impact on DT Due to COVID-19
The following are the consolidated negative impacts on DT which are due to the
COVID-19 pandemic (Table 1).
Table 1. Negative impacts on digital transformation (DT) due to COVID-19.
Item # Negative Impact on DT References
1
Reformation of the tax system and other IT components in tax
specific services
Digitalization in the tax function
[7,11]
2 Delay in digital link deadline for making tax digital (MTD) [21]
3
The expectation of the digital learning platform and digital ecosystem
Expected innovation in the way business is conducted through
information and communication technologies (ICT) usage
Lack of digital literacy
[8,22]
4
Tweaking IT systems of additional catastrophic requirement
Better communication channels
Expected new IT skill and platform for crisis management
[10]
Sustainability 2021,13, 9470 4 of 18
Table 1. Cont.
Item # Negative Impact on DT References
5
Expected contactless payment system
Expected digital business model
Contract tracing as a digital nurse
[13,22]
6 Expected online learning platform and online e-contents [14]
7
The emergence of data and insight
Virtual technologies
Telecom practice
[17,18]
8 Mitigating risk such as phishing attacks [19]
9 Expected telecare/telemedical service [10,23–25]
Looking at the positive impacts of COVID-19 in driving DT, there is a pressure to
enable a digital ecosystem, digital learning, and agile business models [
8
] for business
survival and sustainability. As identified by Sathya Nadella of Microsoft [
26
], keeping
the future in mind, the skill of DT is growing extremely fast. This is indicated by the
emergence of new opportunities in businesses [
27
–
29
] such as 5G, proliferated use of AI
and ML, filling gaps in B2B2C and B2B, and retarding cultural change by collaboration
and strong expectation or potential for quality management. There are a lot of other
IT-related business opportunities [
30
,
31
] in the space of IoT such as predictive analytics,
cloud computing, healthcare, mobility, social media and collaboration, use of automation
platform, robotics, medical imaging [
32
–
34
], wearables, and so on. Furthermore, trade
interconnection, telepresence, telecare, and telecommunication have other positive effects
on growth [1].
The management of disaster, pandemic, and emergencies are leading to tweaking the IT
system [
9
] which is a positive impact on driving DT. Digital learning and digital ecosystems
were part of the competitive factor, whereas they are now the basic requirement for most of
the economies since education and schooling are very much impacted [
8
,
35
]. The following
in Table 2are some of the positive impacts to drive DT but are not limited to these.
Table 2. Positive impact to drive DT.
Item # The Positive Impact to Drive DT References
1
The emergence of the digital ecosystem
Digital learning platform
Digital handshake between student and teacher
[8,14]
2
New business opportunity to tweak the IT system
New pandemic management system
The emergence of telecare service
[7,9,36]
3 Ecommerce and contactless payment system [13]
4 Digitalization of tax function [7]
5Virtual technologies, technology-based development, AI, and ML
Proximity deduction using Bluetooth devices [17,22,27–29]
6
System of collaboration management
System to manage emergencies, pandemic, safe distancing monitor
Hazard detection
[37–39]
7 Centralized data management and big data system [20]
8 Network, cloud, social media, IoT, and wearables [31,38]
2.2. Frameworks for Technology Adoption
The TOE model [
40
] was looking at the technological, organizational, and environ-
mental context to identify the influencing factor on innovation and adoption, whereas
innovation diffusion theory (IDT) [
41
] was focusing on economic, social, and communi-
cation contexts within the organization. Similarly, the MOA model [
12
] has been widely
applied in the management disciplines, whereas motivation refers to a willingness to act;
opportunity refers to the environmental or contextual mechanisms that enable motivation
Sustainability 2021,13, 9470 5 of 18
and the ability refers to the individual’s skills. RBV theory [
42
] is one of the classical
theories in the information systems based on Edith Penrose’s (1959) theory of firm growth.
Moreover, RBV focuses on the resources in the form of products, people, and processes.
Moreover, the technology-acceptance-model (TAM) [
43
] focuses on the acceptance and
adaptability of technology by an individual in the organization, whereas the unified-theory
of acceptance and use-of-technology (UTAUT) focuses on the user intentions towards
information systems [
44
]. Each of these models has unique factors or constructs as it is
meant for such specific usage in technology acceptance, diffusion, and usage. Our study is
looking at this model differently by allowing respondent to free flow positive or negative
attributes and perform meta-analysis to look at the appropriate factor/parameters for
further hypothesis. The outcome of this approach will be leading to factor identification
and modeling definition for this study. The resultant model and hypothesis will help to
test the research questions with the respective hypothesis.
3. Methods
As the purpose of this study is to address the questions discussed in the above section,
it is important to identify and develop a model to identify the hypothesis for further
analysis. This study uses Churchill’s approach [
45
] by categorizing variables, identifying
factors, and constructing a model for further testing and analysis. Refer to Figure 1for
the proposed approach in the process of developing the model, creating hypotheses, data
collection, validation, data analysis, hypothesis testing, results, and discussion.
Figure 1. A proposed approach to model development and testing.
3.1. Instrument Development
This is the process of developing questionnaires to conduct a survey and collect
appropriate data. Due to data privacy, the author decided to collect email address and
years of experience (0 to 10 years and greater than 10 years) to understand the profile of
respondents. The next question is to get a response on whether COVID-19 is a driver of DT.
This question is a checkbox of yes or no. “Yes” will direct respondents to the next section
for asking more questions on positive and negative impact. “No” directs the respondent
to stop here and submit an answer. The next section is for collecting responses on three
positive changes and three negative changes in DT. These six questions are of free text
to allow the respondent to provide the answer as the study does not want to predefine
to something which may not be appropriate. Refer to Table A1 for the questionnaires in
Appendix Aas defined here.
Sustainability 2021,13, 9470 6 of 18
3.2. Identification of Factors
As per the instrument developed in the above section, the six questions (each three
for positive and negative impact) are of free text answer. Our study will perform meta-
analysis [
46
] to identify appropriate contexts and group the responses together to come up
with the final factors for model development. The meta-analysis and factor identification
will be performed after the data collection.
3.3. Model Development
After the final output of factors in the identification stage, those factors will be used to
develop the model. The proposed model is stated in Figures 2and 3. Figure 2is to show
whether COVID-19 is a driver of DT with a single question with the expected mandatory
answer of yes or no, whereas Figure 3is to show the hypotheses for other questions relating
to the top three positive and negative impacts of DT due to COVID-19.
Figure 2. Is COVID-19 a Driver of DT?
Figure 3.
A proposed model. (Note: actual factor name will be identified at the data cleansing and factor
identification stage
).
3.4. Hypotheses Development
As stated in the literature review section, COVID-19 is quite disruptive, inhibiting
the whole world and affecting human wellbeing [
8
]. Almost everyone is working from
home because COVID-19 has affected three quarters so far and it seems almost impossible
to control. The DT in the form of change in IT systems and applications is regarded as
transactions in day-to-day life are getting changed by working from home, home-based
learning systems, and so on.
Hypothesis 1 (H1).
Is COVID-19 a Driver for Digital Transformation?” is to check whether
COVID-19 is a driver for DT. As this is a direct question through the survey questionnaires, data
validation will be just descriptive. The model in Figure 2provides a direct answer to this question.
Hypothesis 2 (H2).
What Are the Top Three Positive Factors That COVID-19 Is Attributing to
Digital Transformation?” is the extension of our testing when the respondent responded to question
1 “COVID-19 as a driver for DT” as yes. Questions 2 to 4 are collecting the positive impact of DT.
The factors will be identified based on meta-analysis as mentioned in “identification of factor” as
explained above. Then the data analysis will help to figure out the top three positive factors that
attribute to DT.
Sustainability 2021,13, 9470 7 of 18
Hypothesis 3 (H3).
What Are the Top Three Negative Factors That COVID-19 Is Attributing
to Digital Transformation?” is the extension of our testing to identify top three negative factors.
Questions 5 to 7 are collecting responses to the negative impact of DT. The factors will be identified
based on meta-analysis as mentioned in “identification of factor” as explained above. Then the data
analysis will help us to figure out the top three negative factors that attribute to DT.
3.5. Data Collection
The respondents were chosen using purposive sampling. The study is performed
with employees in an enterprise environment with different levels of experience. There is a
wide distribution of experience levels since the categories (less than 10 years and more than
10 years
) were used in our instrument. This survey was sent to 135 recipients and received
acceptable unique responses of 87 percent for this empirical analysis [
47
]. Looking at
their employment profiles, 79 percent of them had greater than 10 years of experience and
21 percent
of them had less than 10 years of experience (Table 3). Respondents reported
that it took them less than 2 min to complete the survey.
Table 3. Respondent’s employment profile.
Year of Experience in Employment Number of Respondents
Greater than 10 Years 91
Between 0 and 10 years 94
Data Cleansing and Factor Identification
The data are collected to allow the respondent to answer in free text for three positive
and three negative changes of DT due to COVID-19. The meta-analysis was to group these
responses to form the construct for further analysis. The responses are codified
(Figure 4
)
as part of the meta-analysis. The data in Table 4are the categorization and grouping of
various responses from the outcome of meta-analysis. There are very few answers which
fall outside of DT related factors and are eliminated as false positives in the data analysis.
Table 4. Independent variables (IVs) and factor identification.
Item # Positive and Negative
Variables to Drive DT Various Responses
Positive factor/Construct
1IV: Work from anywhere
(WFA)
Work from home (WFH)
Lessor no travel to the office
WFA
More productivity
Remote working
Flexibility in work
Pollution-free
Time-saving as no commuting needed
Spend long working hours
Do multitask
No global barrier
2 IV: Work-life balance (WLB)
Work-life balance
Flexible work arrangement
Spend time with family or children
Review work culture
Balanced lifestyle
Stay together
Connect with friends
Sustainability 2021,13, 9470 8 of 18
Table 4. Cont.
Item # Positive and Negative
Variables to Drive DT Various Responses
3
IV: Innovative business model
(IBM)
Business model innovation
An alternate channel of work
Evolution of product, service and processes
Run business remotely
Change in customer engagement
Essential service
Service personalization
Driving innovationInnovative solution
Business goes digital
4IV: Technologies, automation,
and collaboration (TAC)
Customer ready to explore new technologies
Importance of automation
Cloud technology adoption
Online transaction improvement
Online business
Digitalization
Online learning
Video, conference, virtual meeting, Tele conversation
Telecommuting
IT security
Virtual workplace
Mobility
Globalized skill sharing
Business process improvement
The wise use of scrum
Negative factor/Construct
5IV: No work-life balance
(NWL)
No work-life balance
No-defined working hours/long hours
Health
Lack of physical/social interaction
Too many distractions
Overwork
Back-to-back virtual meeting/unscheduled
Less confidence
6IV: Social and employment
issue (SEI)
Unemployment
Loss of Job
Job insecurity
Stress
Low income
More home expenses
Divide of rich and poor
Buying unwanted things
No social life
7IV: Data, security, technology
issues (DST)
Security vulnerability/breach
Technology reliant
Slowness/network issue
Data privacy/cybersecurity
Additional technology skillset
Misuse of technology by family members
Lack of technology infra robustness
Cybercrime/online fraud
Technology complexity
Sustainability 2021,13, 9470 9 of 18
Table 4. Cont.
Item # Positive and Negative
Variables to Drive DT Various Responses
8IV: Business model change
(BMC)
Bricks and mortar business suffer
Economic volatility
The long-run business sustainability question
Human touch is not possible by technology
Manpower reduction
Sales decline
Figure 4. Cont.
Sustainability 2021,13, 9470 10 of 18
Figure 4. Raw responses. (Note: converted raw response is presented as pictured).
Refer to the Figure 5for the model as defined as the outcome of the factor identification.
Figure 5. Proposed conceptual model.
3.6. Data Analysis
The data were converted to CSV file for the analysis using R application (an opensource
statistical and data science scripting platform). The version of R is 3.6 which is the latest
version at the time of this analysis. The author has validated sample size using semTools
(formula as below) and found that the sample size required is 97 as compared to actual
sample of 117 (appropriate for SEM analysis in R).
findRMSEAsamplesize(rmsea0 = 0.05, rmseaA = 0.08, df = 100, power = 0.80)
The libraries of Lavaan 0.6–7 [
48
], sem, semPlot, and Lavaan were loaded for the
analysis. The reliability and consistency were tested, and the overall Cronbach’s alpha
is ~0.60 (acceptable) since the number of questions is less than 10 [
45
]. The data analysis
was performed using structural equation modeling (Lavaan-SEM). This method is widely
used in the IT and information system field of research to evaluate the suitable prediction
between the proposed constructs and the dependent variables [
49
]. The model fit analysis
was evaluated based on the Lavaan-SEM analysis. It ended normally after 132 iterations,
and it showed convergence properly as shown in Table 5.
The overall summary information of goodness-of-fit-analysis shows very positive
results, since GFI = 0.95, AGFI = 0.90, TLI = 0.94, CFI = 0.9, RMSEA = 0.07, and
SRMR = 0.10
.
Refer to Table 6for details of the references. Refer to the following Figure 6for the value of
significance as analyzed in this study.
Sustainability 2021,13, 9470 11 of 18
Table 5. Model fit analysis/proper convergence.
SEM Output
Ended normally after 132 iterations 132 iterations
Default estimator Maximum likelihood
Optimization method NLMINB
Free parameters 19
Observations 117
Model Test User Model:
Test statistic 27.309
DF 17
Chi Square p-value 0.054
Model Test Baseline Model:
Test statistic 314.904
DF 28
p-value 0.000
User Model versus Baseline Model:
Comparative Fit Index 0.964
Tucker-Lewis Index 0.941
Loglikelihood and Information Criteria:
Loglikelihood user model (H0) 729.494
Loglikelihood unrestricted model (H1) 715.839
Akaike 1496.988
Bayesian 1549.469
Sample-size adjusted Bayesian 1489.408
Root Mean Square Error of Approximation:
Root Mean Square Error 0.072
90 Percent confidence interval—lower 0.000
90 Percent confidence interval—upper 0.120
p-value RMSEA ≤0.05 0.217
Standardized Root Mean Square Residual:
Standard Root Mean Square 0.099
Table 6. Good-to-fit analysis.
Measure Norms (for a Good Fit) Analysis Output Reference
x2p-value > 0.05 0.054 Hooper. 2018 and [36]
GFI/AGFI GFI ≥0.99
AGFI > 0.90
GFI = 0.95
AGFI = 0.90 Hooper. 2018 Tetteh. 2015, and Kline. 2010
TLI TLI ≥0.95 0.94 Tetteh. 2015, and [36]
CFI CFI ≥0.90 0.96 Tetteh. 2015, [50], and [36]
RMSEA RMSEA < 0.08 0.07 Hooper. 2018, Tetteh. 2015, and [36]
SRMR
SRMR < 0.08
*** 0 to 0.1 deemed acceptable
(Hooper. 2018)
0.1 Tetteh. 2015, Hooper. 2018, and [36]
*** Note: According to Hooper (2018) the acceptable SRMR value is 0 to 0.10.
Figure 6. Significance diagram.
Sustainability 2021,13, 9470 12 of 18
3.7. Testing the Hypothesis
3.7.1. Hypotheses H1: Is COVID-19 a Driver of DT?
We will start by looking at the data analysis for hypothesis (H1), which is a simple
descriptive statistic with an expected answer of yes or no. The descriptive statistic shows
that this hypothesis is true as the answer is yes for 94 percent and no for 6 percent only.
As such, 94 percent of the respondent responded that COVID-19 is a driver of DT in a
pandemic situation. The driver of DT can be positive or negative, which is further tested
in H2 and H3. As the DT is quite disruptive [
4
] in the changes to business and operating
models, COVID-19 is one of the enablers of DT as tested in this hypothesis. COVID-19 is
the enabler of the DT journey at the macro level since the data collection in our study is at
the macro level.
3.7.2. Hypotheses H2: What Are the Top Three Positive Factors That COVID-19 Is
Attributing to Digital Transformation?
The top three positive factors are technology, automation, and collaboration (TAC);
work from anywhere (WFA); and work-life balance (WLB). The study also shows that
innovative business model (IBM) is also at the same level of prediction as WLB. WLB was
baselined to 1.0 and TAC is 5.02, which is far more significant than WLB. Moreover, WFA is
4.26 is far more significant than WLB. IBM is more or less at the same level of significance
as WLB. However, descriptive statistics of WLB is 21 as compared to IBM 18. Hence the top
three positive factors are TAC, WFA, and WLB. Refer to the discussion section for further
discussion and literature.
3.7.3. Hypotheses H3: What Are the Top Three Negative Factors That COVID-19 Is
Attributing to Digital Transformation?
The top three significant factors which are negatively attributed to DT are no work-life
balance (NWL); social and employment issues (SEI); and data, security, and technology
issues (DST). When NWL is baselined to 1.00, SEI is 0.32 and DST is 0.20. Refer to the
discussion section for further discussion and literature.
4. Results
The first hypothesis is all about whether COVID-19 a driver for DT. Based on the
direct answers from the respondent, there are 110 responses of “yes” (COVID-19 is a driver
of DT) and 7 responses of “no” (not a driver for DT). This study was conducted globally
during this peak period of COVID-19 (within last 12 months) and received a response rate
of 87 percent since the subject of this study was of interest to most of the respondents. The
result for H1 is just a descriptive analysis which shows that COVID-19 is a driver of DT.
The responses for the subsequent questions are only when the answer to hypothesis one
(H1) was “yes”.
H2 was to check the top three positive factors that COVID-19 is attributing to digital
transformation. The analysis shows that the top three positive significant factors are (as in
Figure 6) TAC (technologies, automation, and collaboration), WFA, and WLB. Additionally,
looking at the descriptive data, responses are TAC = 151, WFA = 134, WLB = 21, and
IBM = 18.
In contrast, H3 was to check the top three negative factors through which COVID-19
is affecting digital transformation. The top three negative significant (Figure 6) factors are
NWL = 1.00, SEI = 0.32, and DST = 0.20. Looking at the descriptive data, responses for
NWL = 196, SEI = 68, and DST = 41 and business model change (BMC) = 10. NWL is rated
as high by most of the responses as the responses are 196. This is because working from
home removes limits for working hours and schedule or control of time for the work.
Additionally, SEI is significant in a negative aspect and there are 68 responses for
SEI. There are few studies [11,51,52] justifying social as well as employment issues due to
pandemic. If we combine overall positive and negative relation, the pandemic is leading to
Sustainability 2021,13, 9470 13 of 18
positive disruption in DT, but it also leads to negative disruption as there is no work-life
balance, as well as other and social and employment issues.
5. Discussion and Implications
The results clearly show that COVID-19 disruption is leading to technology adoption
and disruption in the form of automation and collaboration [
8
,
14
,
31
]. In addition to
TAC, COVID-19 is leading to working from anywhere [
53
] which removes the geographic
barriers and office dependency. The geographic barrier may help to improve employment
in developing countries. DT is enabling WFA as it depends on technology but removes
brick and mortar kind of work culture. TAC (responses: 151) leads to WFA (responses:
134) which leads to WLB (responses: 21) as per descriptive responses. The vision of
anywhere and anytime is not new [
17
] as it was visioned in the early 2000s. WFH leads to
connecting employees 24
×
7 through mobility [
54
] or even sometimes connected through
collaboration platforms 24
×
7. WFA has a direct negative effect on work-life balance as
responses for NWL is 196. This is more than each of every other factor. Of the respondents,
21 reported that work-life balance was good, but 196 responses indicated a lack of work-life
balance, which is quite alarming as NWL is leading to social issues.
It is important to mitigate social issues arising from NWL balance [
55
] by having
a work schedule in a controlled manner. Many studies are suggesting having a better
work-life balance [56] for health and wealth wellbeing. NWL is [57] rated highly satisfied
by the young and old age group of employees but not by the middle age group. This
study [
57
] was done during 2002, but our study shows that the majority of responses
says NWL is rated high even though 79 percent of the respondent profiles have more
than 10 years of employment. This means that over a period of time work-life balance
deteriorates as a result of disruptions like COVID-19 together with the mainstreaming of
digital technologies.
5.1. Practical Implication
The instrument development is done by having a survey form to collect data rather
than performing some form of qualitative studies due to the current stay-at-home orders
and social distancing. In addition, there are numerous challenges presented by COVID
to the supply chain [
58
], which are all implications for managers and policymakers. As
this pandemic has lasted for over one year, it is not going to be eradicated immediately.
Quantitative data collection is the only way to collect data and perform studies like this. The
quantitative data (using survey form) were collected at the time of the pandemic situation
in which the reaction from respondents may not reflect the facts, since negative motivation
may take place. The quantitative data collection helps authors of this paper to collect
sentiments of employees through the survey questionnaires rather than qualitive studies
like face-to-face interaction which is not allowed during this pandemic situation. COVID-
19 has created a mass disruption which erodes entire economies, leading to thinking of
recovery of life rather than the economy. The responses may not be uniform across poorly
to highly managed economies. A lot of other socio-economic issues may mix into COVID-
19 issues. The responses are collected during COVID pandemic stage which may not be
reflective of current situation since emotion erodes fairness in responses. The level or
spread of pandemic is not uniform across the globe as the management of pandemic varied
across economies. If the study of this impact analysis is needed at specific regional or
economic levels, it would better to perform in specific economy or region.
The organization sustainability is relying on enabling DT as quickly as possible to
run the business and help employees and customers to get connected. The positive or
negative effect of DT is depending on not just common factors but also on the economy,
digital level, and control of COVID-19 pandemic. If country levels of DT’s impact in times
of COVID-19 disruption is needed, this study can be extended to specific economies using
this same research approach. Social sustainability depends upon how quickly COVID-19 is
solved and how well society is connected seamlessly with or without this kind of pandemic
Sustainability 2021,13, 9470 14 of 18
situation. Again, the measurement of social sustainability with the impact of COVID-19 in
DT can be researched further in specific economies if needed.
5.2. Theoretical Implication
The theoretical model is derived based on collecting user responses in free text and
grouping the responses into various factors by doing a meta-analysis of responses. The
instrument development was done to collect responses for three positive and negative
impacts of COVID-19 in DT. The responses are in free text. Then free text was analyzed
using meta-analysis as in Table 5and various responses in column 3 are codified as eight IVs,
like “WFA, WLB, IBM, TAC, NWL, SEI, DST, and BMC” in column 2. The meta-analysis
from the responses is based on evidence based practice as explained in Kitchenham’s
approach [
59
]. The various authors in this paper scrutinized responses by variables in
both positive and negative factors in Table 5. The collected data are not reflective of non-
employment pools such as homemakers, students, and so on, since data are only collected
from employees. The identification of factors was from the outcome of responses with the
knowledge that the author collected from limited literature in COVID-19 at this point.
6. Conclusions
There is no doubt that the testing of H1 proves that COVID-19 is the driver of digital
transformation. Further analysis relating to H2 and H3 proves that the significance of
positive impacts is greater than negative impacts. These impacts are directly related to
employment and workplaces as the study is among employees. The study shows that
technology, automation, and collaboration (TAC) is rated high within positive impacts of
DT. This is then followed by working from anywhere (WFA) and new business models
(IBM) in positive impacts of DT. This pandemic disruption cannot be avoided as business
needs to continue as usual to some extent and it requires employees to work from home or
anywhere. WFH or WFA require fundamental technology (TAC) to allow seamless working
with the coworkers and customers. Technology, automation through technologies, and
collaborative technologies are the important IT platforms as needed for every employee
in the organization. Not only for employees, but eLearning acceptance [
60
] by students
proves that COVID-19 is enabling DT directly. Changes in operation or business model [
61
]
is very much driven by DT in the form on industry 4.0 in COVID-19 situation. TAC is
one of the positive outcomes of COVID-19. The next significant factor is WFA which
is expected to be a new normal in the COVID-19 situation. Overall, COVID-19 brings
positive impact in WLB, WFA, and TAC. There will be some form of change in the business
model or IBM is expected while the respondents rated work-life balance as a positive
change. The contribution from this study is that DT is important to mitigate the pandemic
situation as business needs to run as usual. Since the pandemic is widespread and global,
employees need to connect globally which requires collaborative technologies. This study
informs employers and businesses of opportunities as new business models have emerged
to mitigate the pandemic. The study also informs employers that mitigation in the form of
technology transformation (DT) is a must for employees or even customer connections.
On the other hand, there is negative disruption as descriptive statistics show the nega-
tive impact in the COVID-19 situation since base-lined of NWL
(no work life balance) = 1.00
as fixed parameter. This indicates that employee work-life balance is affected and important
to be recovered by maintaining due diligence of employee engagement in the pandemic. It
is important to engage employees within office hours and give room for balancing their
life. NWL leads to social and employment issues (SEI = 0.32). The contribution of this
study indicates clearly that employers must maintain due diligence of engagement to
maintain work life balance. This will also improve social and health well-being. Societies,
organizations, and economies are to be mindful of social and health well-being while using
positive disruption as opportunities.
Looking at the result and implications, a qualitative study can be done once the
COVID-19 regulations are relaxed. If the disruption of the pandemic in DT impact needs to
Sustainability 2021,13, 9470 15 of 18
be studied for specific regions or economies, this study’s approach can be extended further
for specific regions or economies. As the study is specific to the COVID-19 disruption in
DT, it may not be directly related to other types of pandemics as the nature of disruption
may vary.
Author Contributions:
Conceptualization, R.S. (Radhakrishnan Subramaniam); methodology, R.S.
(Raja Sreedharan); software P.P. (Prashobhan Palakkeel); validation, R.S. (Radhakrishnan Subrama-
niam), S.P.S. and P.P. (Parasuraman Padmanabhan); formal analysis, R.S. (Radhakrishnan Subrama-
niam); investigation, R.S. (Radhakrishnan Subramaniam) and S.P.S.; resources, R.S. (Radhakrishnan
Subramaniam); data curation R.S. (Radhakrishnan Subramaniam); writing—original draft prepara-
tion, R.S. (Radhakrishnan Subramaniam); writing—review and editing, R.S. (Radhakrishnan Subra-
maniam) and B.G.; visualization, R.S. (Radhakrishnan Subramaniam); supervision, P.P. (Prashobhan
Palakkeel); project administration, P.P. (Parasuraman Padmanabhan); funding acquisition, B.G. All
authors have read and agreed to the published version of the manuscript.
Funding:
Authors acknowledge the support from Lee Kong Chian School of Medicine and Data
Science and AI Research (DSAIR) center of Nanyang Technological University Singapore (Project
Number ADH-11/2017-DSAIR). P.P. (Parasuraman Padmanabhan) and B.G. also acknowledge the
support from the Cognitive Neuro Imaging Centre (CONIC) at NTU, Singapore.
Institutional Review Board Statement:
The ethical committee from Amrita Vishwa Vidyapeetham,
India has reviewed “IEC-AIMS-2021-DOM-026, dated: 18-02-2021” and granted approval by stating
that no ethical issues are involved in this study.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Questionnaires.
Section 1 Is COVID-19 a Digital Transformation Driver?
Email Address:
1. Year of Experience in Employment
Option 1: 0 to 10 years
Option 2: > 10 years
2. Is COVID-19 a driver of digital transformation?
Option: 1 yes (Go to Section 2 if yes)
Option 2: No
Section 2 Top three positive and negative changes in digital transformation due to COVID-19
3. First positive change in digital transformation (Please provide in maximum 5 words)
(Example, work from home)
4. Second positive change in digital transformation (Please provide in maximum 5 words)
(Example, work from home)
5. Third positive change in digital transformation (Please provide in maximum 5 words)
(Example, work from home)
6. A first negative change in digital transformation (Please provide in maximum 5 words)
(Example, no work-life balance)
7. A second negative change in digital transformation (Please provide in maximum 5 words)
(Example, no work-life balance)
8. A third negative change in digital transformation (Please provide in maximum 5 words)
(Example, no work-life balance)
Sustainability 2021,13, 9470 16 of 18
References
1.
Vidya, C.T.; Prabheesh, K.P. Implications of COVID-19 Pandemic on the Global Trade Networks. Emerg. Mark. Financ. Trade
2020
,
56, 2408–2421. [CrossRef]
2.
Bragazzi, N.L. Digital Technologies-Enabled Smart Manufacturing and Industry 4.0 in the Post-COVID-19 Era: Lessons Learnt
from a Pandemic. Int. J. Environ. Res. Public Health 2020,17, 4785. [CrossRef] [PubMed]
3.
Freudenberger, E. No Business as Usual. Available online: http://www.bannedthought.net/International/RIM/AWTW/1985-3
/AWTW-03-RCP-NBAU.pdf (accessed on 15 January 2021).
4. Matt, C.; Hess, T.; Benlian, A. Digital Transformation Strategies. Bus. Inf. Syst. Eng. 2015,57, 339–343. [CrossRef]
5.
Vial, G. Understanding digital transformation: A review and a research agenda. J. Strateg. Inf. Syst.
2019
,28, 118–144. [CrossRef]
6. Berman, S.J. Digital transformation: Opportunities to create new business models. Strateg. Leadersh. 2012,40, 16–24. [CrossRef]
7.
Westerman, G.; Bonnet, D.; Mcafee, A. The Nine Elements of Digital Transformation Opinion & Analysis. 2014. Available online:
https://sloanreview.mit.edu/article/the-nine-elements-of-digital-transformation/ (accessed on 15 January 2021).
8.
Maritz, A. A Multi-Disciplinary Business Approach to COVID-19: La Trobe Business School Perspectives. Int. J. Organ. Innov.
2020,13, 45–62.
9.
Mehboob, D. Tax Departments Value Negotiating and IT Skills in Crisis Management—ProQuest. Available online: https://search.
proquest.com/openview/f6d02763c6b40204d1ccdfde4366cf10/1?pq-origsite=gscholar&cbl=30282 (accessed on
15 January 2021).
10.
Mehboob, D.; Cano, M.C. Taxpayers Fear COVID-19 Will Trigger New DSTs—ProQuest. Available online: https://search.
proquest.com/openview/82067560d0b18e176cab756cea46abc1/1?pq-origsite=gscholar&cbl=30282 (accessed on 15 January 2021).
11.
Yu, X. Study on The Communication Impact of Live Streaming E-Commerce Mode in China. Available online: https://www.
diva-portal.org/smash/get/diva2:1443560/FULLTEXT01.pdf (accessed on 15 January 2021).
12.
Wang, Y.; Zhang, D.; Wang, X.; Fu, Q. How Does COVID-19 Affect China’s Insurance Market? Emerg. Mark. Financ. Trade
2020
,56,
2350–2362. [CrossRef]
13.
Kerigan, N. The consumer in lockdown: Consumer—Merchant payments in a mobility-constrained environment. J. Paym. Strateg.
Syst. 2020,14, 113–119.
14.
Verawardina, U.; Asnur, L.; Lubis, A.L.; Hendriyani, Y.; Ramadhani, D.; Dewi, I.P.; Darni, R.; Betri, T.J.; Susanti, W.; Sriwahyuni, T.
Reviewing Online Learning Facing the COVID-19 Outbreak. Available online: https://www.iratde.com/index.php/jtde/article/
view/281 (accessed on 15 January 2021).
15.
Xin, J. How can we communicate interculturally? Response and reflection from global communication scholars1 on the COVID-19
epidemic. China Media Res. 2020,16, 91–121.
16.
Marmor, R.R. COVID-19 Highlights New Privacy Challenges for Big Tech, Lawmakers. Intellect. Prop. Technol. Law J.
2020
,32, 5–7.
17.
Sivan, Y.; Nunes, F.B.; Feng, M.; Milik, O. The Dark Side of Virtual Worlds Issue Editors Angie Cox (Prime) Coordinating Editor
Tzafnat Shpak. J. Virtual Worlds Res. 2020,13. [CrossRef]
18.
Kishan, S.S.; Ramakrishnan, S.; Qureshi, M.I.; Khan, N. Pandemic thoughts, civil infrastructure and sustainable development:
Five insights from COVID-19 across travel lenses Mergers and Acquisitions View project Systematic Literature review and Meta
Analysis View project. J. Talent Dev. Excell. 2020,12, 1690–1696.
19. Brown, E. How Companies Can Use Their Trademarks to Combat Covid-19-Related Phishing. J. Internet Law 2020,23, 4–5.
20.
Khahro, S.H.; Ali, T.H.; Vighio, A.A.; Khahro, Q.H.; Moriyani, M.A. Post Pandemic Project Management Key Skills and Challenges:
Discovery Service for Amrita Vishwa Vidyapeetham. Talent Dev. Excell. 2020,12, 2253–2260.
21.
Cano, M.C. HMRC Delays ‘Digital Link’ Deadline for MTD. Available online: https://search.proquest.com/openview/23a926c3
7b9e5b28c043e0d234323126/1?pq-origsite=gscholar&cbl=30282 (accessed on 15 January 2021).
22.
Anderez, D.O.; Kanjo, E.; Pogrebna, G.; Kaiwartya, O.; Johnson, S.D.; Hunt, J.A. A COVID-19-Based Modified Epidemiological
Model and Technological Approaches to Help Vulnerable Individuals Emerge from the Lockdown in the UK. Sensors
2020
,20,
4967. [CrossRef] [PubMed]
23.
Betancourt, J.A.; Rosenberg, M.A.; Zevallos, A.; Brown, J.R.; Mileski, M. The Impact of COVID-19 on Telemedicine Utilization
Across Multiple Service Lines in the United States. Healthcare 2020,8, 380. [CrossRef]
24.
Wang, S.; Parsons, M.; Stone-McLean, J.; Rogers, P.; Boyd, S.; Hoover, K.; Meruvia-Pastor, O.; Gong, M.; Smith, A. Augmented
Reality as a Telemedicine Platform for Remote Procedural Training. Sensors 2017,17, 2294. [CrossRef] [PubMed]
25.
Palozzi, G.; Schettini, I.; Chirico, A. Enhancing the Sustainable Goal of Access to Healthcare: Findings from a Literature Review
on Telemedicine Employment in Rural Areas. Sustainability 2020,12, 3318. [CrossRef]
26.
Nadella, S.; Euchner, J. Navigating Digital Transformation: An Interview with Satya Nadella. Res. Technol. Manag.
2018
,61,
11–15. [CrossRef]
27. Chaniot, E. Tools for Transformation: Michelin’s Digital Journey. Res. Technol. Manag. 2019,62, 31–35. [CrossRef]
28.
Ponsignon, F.; Kleinhans, S.; Bressolles, G. The contribution of quality management to an organisation’s digital transformation:
A qualitative study. Total Qual. Manag. Bus. Excell. 2019,30, S17–S34. [CrossRef]
29.
Kane, G. The Technology Fallacy: People Are the Real Key to Digital Transformation. Res. Technol. Manag.
2019
,62,
44–49. [CrossRef]
Sustainability 2021,13, 9470 17 of 18
30.
Ardolino, M.; Rapaccini, M.; Saccani, N.; Gaiardelli, P.; Crespi, G.; Ruggeri, C. The role of digital technologies for the service
transformation of industrial companies. Int. J. Prod. Res. 2018,56, 2116–2132. [CrossRef]
31.
Mugge, P.; Abbu, H.; Michaelis, T.L.; Kwiatkowski, A.; Gudergan, G. Patterns of Digitization: A Practical Guide to Digital
Transformation. Res. Technol. Manag. 2020,63, 27–35. [CrossRef]
32.
Singh, S.P.; Wang, L.; Gupta, S.; Goli, H.; Padmanabhan, P.; Gulyás, B. 3D Deep Learning on Medical Images: A Review. Sensors
2020,20, 5097. [CrossRef] [PubMed]
33.
Urooj, S.; Singh, S.P. Geometric Invariant Feature Extraction of Medical Images Using Hu’s Invariants. In Proceedings of the
10th INDIACom; 2016 3rd International Conference on Computing for Sustainable Global Development, New Delhi, India,
16–18 March 2016; pp. 1560–1562.
34.
Urooj, S.; Singh, S.P. Rotation Invariant Detection of Benign and Malignant Masses Using PHT. In Proceedings of the 2015
International Conference on Computing for Sustainable Global Development, New Delhi, India, 11–13 March 2015;
pp. 1627–1632.
35.
Mhlanga, D.; Moloi, T. COVID-19 and the Digital Transformation of Education: What Are We Learning on 4IR in South Africa?
Educ. Sci. 2020,10, 180. [CrossRef]
36.
Auerbach, J.; Miller, B. COVID-19 Exposes the Cracks in Our Already Fragile Mental Health System. Am. J. Public Health
2020
,
971–973. [CrossRef]
37. Mrsa, V. Open Science in Closed Societies: EBSCOhost. Food Technol. Biotechnol. 2020,58, 2–4.
38.
Martinez, M.; Yang, K.; Constantinescu, A.; Stiefelhagen, R. Helping the Blind to Get through COVID-19: Social Distancing
Assistant Using Real-Time Semantic Segmentation on RGB-D Video. Sensors 2020,20, 5202. [CrossRef]
39.
Uddin, M.I.; Shah, S.A.A.; Al-Khasawneh, M.A. A Novel Deep Convolutional Neural Network Model to Monitor People
following Guidelines to Avoid COVID-19. J. Sens. 2020,2020, 8856801. [CrossRef]
40.
Baker, J. The Technology–Organization–Environment Framework. In Information Systems and Theory; Springer: New York, NY,
USA, 2012; pp. 231–245.
41.
Khan, I.U.; Hameed, Z.; Khan, S.U. Undefined Understanding online banking adoption in a developing country: UTAUT2 with
cultural moderators. J. Glob. Inf. Manag. 2017,25, 43–65. [CrossRef]
42. Vargas, J.R.C.; Mantilla, C.E.M. Sustainable supply chain management capabilities: A review from the resource-based view, the
dynamic capabilities and stakeholder theories. Lat. Am. J. Manag. Sustain. Dev. 2014,1, 323. [CrossRef]
43.
Gangwar, H.; Date, H.; Ramaswamy, R. Understanding determinants of cloud computing adoption using an integrated TAM-TOE
model. J. Enterp. Inf. Manag. 2015,28, 107–130. [CrossRef]
44.
Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q.
2003,27, 425–478. [CrossRef]
45. Churchill, G.A. A Paradigm for Developing Better Measures of Marketing Constructs. J. Mark. Res. 1979,16, 64–73. [CrossRef]
46.
Blut, M.; Wang, C. Technology readiness: A meta-analysis of conceptualizations of the construct & its impact on technology usage.
J. Acad. Mark. Sci. 2020,48, 649–669.
47.
Dubey, R.; Gunasekaran, A.; Childe, S.J.; Blome, C.; Papadopoulos, T. Big Data and Predictive Analytics and Manufactur-
ing Performance: Integrating Institutional Theory, Resource-Based View and Big Data Culture. Br. J. Manag.
2019
,30,
341–361. [CrossRef]
48.
Rosseel, Y. Lavaan: An R Package for Structural Equation Modeling. Available online: https://www.jstatsoft.org/article/view/
v048i02 (accessed on 15 January 2021).
49.
Jakliˇc, J.; Grublješiˇc, T.; Popoviˇc, A. The role of compatibility in predicting business intelligence and analytics use intentions. Int. J.
Inf. Manag. 2018,43, 305–318. [CrossRef]
50.
Markman, S.A.; Sawilowsky, B. Limitations in the systematic analysis of structural equation model fit indices. J. Mod. Appl. Stat.
Methods 2017,16, 69–85. [CrossRef]
51. Donthu, N.; Gustafsson, A. Effects of COVID-19 on business and research. J. Bus. Res. 2020,117, 284. [CrossRef]
52.
Baum, T.; Mooney, S.K.K.; Robinson, R.N.S.; Solnet, D. COVID-19’s impact on the hospitality workforce—New crisis or
amplification of the norm? Int. J. Contemp. Hosp. Manag. 2020,32, 2813–2829. [CrossRef]
53.
Choudhury, P.; Foroughi, C.; Larson, B.Z. Work-from-Anywhere: The Productivity Effects of Geographic Flexibility. Acad. Manag.
Proc. 2020,2020, 21199. [CrossRef]
54.
Lal, B.; Dwivedi, Y.K. Investigating homeworkers’ inclination to remain connected to work at “anytime, anywhere” via mobile
phones. J. Enterp. Inf. Manag. 2010,23, 759–774. [CrossRef]
55.
Tausig, M.; Fenwick, R. Unbinding time: Alternate work schedules and work-life balance. J. Fam. Econ. Issues
2001
,22,
101–119. [CrossRef]
56.
Zheng, C.; Molineux, J.; Mirshekary, S.; Scarparo, S. Developing individual and organisational work-life balance strategies to
improve employee health and wellbeing. Empl. Relat. 2015,37, 354–379. [CrossRef]
57.
Oswald, A. Are You Happy at Work? Job Satisfaction and Work-Life Balance in the US and Europe; University of Warwick: Coventry,
UK, 2020.
58.
Khan, M.A.; Nabi, M.K.; Khojah, M.; Tahir, M. Students’ Perception towards E-Learning during COVID-19 Pandemic in India:
An Empirical Study. Sustainability 2021,13, 57.
Sustainability 2021,13, 9470 18 of 18
59.
Meirun, T.; Makhloufi, L.; Ghozali Hassan, M. Environmental Outcomes of Green Entrepreneurship Harmonization. Sustainability
2020,12, 10615. [CrossRef]
60.
Alam, S.T.; Ahmed, S.; Ali, S.M.; Sarker, S.; Kabir, G.; Ul-Islam, A. Challenges to COVID-19 vaccine supply chain: Implications for
sustainable development goals. Int. J. Prod. Econ. 2021,239, 108193. [CrossRef]
61.
Kitchenham, B.A.; Dyba, T.; Jorgensen, M. Evidence-Based Software Engineering. In Proceedings of the 26th International
Conference on Software Engineering, Edinburgh, UK, 23–28 May 2004; pp. 273–281.
Available via license: CC BY 4.0
Content may be subject to copyright.
