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\begin{document}

\title{Evidence-based Information Systems (IS) research: the case of systematic
literature review (SLR)}



\author[1]{Salifu Yusif}%
\author[2]{Abdul  Hafeez-Baig}%
\affil[1]{University of Southern Queensland}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 27, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Over the last decades there have been increased in the application of
information systems (IS) in the form of digital health technologies to
enhance access to healthcare services, improve healthcare quality and
efficiency of healthcare systems. Evidence on the efficiency of digital
health technologies in clinical settings to enable the adoption and
scaling up of such technologies is buried in ever increasing and complex
extant literature including grey due to lack of the application of
robust evidence-based research in IS. By and large, in the medical and
health domain, decision-making is based on evidence-based research. In
this study, we advocate for the increased use of evidence-based research
using systematic literature review (SLR) as reference point in IS
interventions especially in healthcare settings as digital health
interventions increase. We believe the increased use/application of
evidence-based research in the domain of IS will be key to identifying
and scaling up relevant digital health technologies, and one step
forward from ``potential'' as they are known for. We demonstrate this by
liking IS research to medical research in various aspects. We also
provide a step-by-step guide to conducting systematic review in IS
including strategies on how to harness evidence through strategic use of
the contentious grey literature.%
\end{abstract}%



\sloppy


\textbf{Evidence-based Information Systems (IS) research: the case of
systematic literature review (SLR)}

Salifu Yusif\textsuperscript{*}, BELA, School of Management and
Enterprise University of Southern Queensland 37 Sinnathamby Blvd,
Springfield Central QLD 4300 Australia salifusf5@gmail.com +61 4 7052
1113

Abdul Hafeez-Baig\selectlanguage{ngerman}¹

¹School of Management and Enterprise University of Southern Queensland
Toowoomba QLD Australia 4350

\textbf{Abstract}

Over the last decades there have been increased in the application of
information systems (IS) in the form of digital health technologies to
enhance access to healthcare services, improve healthcare quality and
efficiency of healthcare systems. Evidence on the efficiency of digital
health technologies in clinical settings to enable the adoption and
scaling up of such technologies is buried in ever increasing and complex
extant literature including grey due to lack of the application of
robust evidence-based research in IS. By and large, in the medical and
health domain, decision-making is based on evidence-based research. In
this study, we advocate for the increased use of evidence-based research
using systematic literature review (SLR) as reference point in IS
interventions especially in healthcare settings as digital health
interventions increase. We believe the increased use/application of
evidence-based research in the domain of IS will be key to identifying
and scaling up relevant digital health technologies, and one step
forward from ``potential'' as they are known for. We demonstrate this by
liking IS research to medical research in various aspects. We also
provide a step-by-step guide to conducting systematic review in IS
including strategies on how to harness evidence through strategic use of
the contentious grey literature.

\textbf{Keywords: IS, Digital health technologies, Evidence-based
research, Health/Medical research, Systematic Review}

\textbf{Introduction}

The discipline of information systems (IS) has embraced a continuum of
research approaches to enable it to continue to expand and acquire
knowledge, contain its rapid innovation and to continue to provide
understanding to new concepts as they emerge. Empirically, qualitative,
quantitative, mixed research methods, case research strategy etc. have
been extensively and successfully applied to the field of IS. IS reviews
have generally been narrative and criticised for being singular
descriptive accounts of the contributions of scholars in the discipline.
However, literature reviews play critical role in establishing the
foundation of academic inquiries. It collates and synthesises literature
to establish roadmap for research -- establish research agenda and
identify gaps through the understanding of the length and breadth of
existing body of knowledge (Webster \& Watson, 2002). True and valuable
gaps can only be identified after exhaustive search and synthesis of
literature in targeted domains and IS is indifferent. As such,
approaches to scientific inquiries and in IS for that matter need to be
valid, reliable, and repeatable and literature review research is
foundational to these (Xiao \& Watson, 2019).

IS research has drawn its methodologies largely from behavioural
theories and practices that explain human~behaviour~by examining the
experiences and their impact. This is present in the individual's
environment and the learned associations he or she has acquired through
previous experience to develop IS that seek to solve specific practical
world problems. On the other hand, design-science paradigm seeks to
extend the boundaries of human and organizational capabilities by
creating new and innovative artifact (Chatterjee \& Hevner, 2010)
(Hevner, March, Park, \& Ram, 2004). Okoli and Schabram (2010) assert
that information systems' research methods are different from those of
the health sciences. Information Systems is a combination of social
science, business, and computing science, whose research methods are
different from those of the health sciences, from which the systematic
literature review (SLR) or systematic reviewed (SR) methodology has
largely been developed. For example, qualitative, quantitative, and
mixed research methodologies have all been extensively applied in
research in IS domain and in health as well. However, with the advent of
digital health or health information systems and the increased in the
innovation and application of information technologies to improve health
care and health outcome, evidence-based research has become imperative
in IS research. In fact, evidence-based research outcomes will serve as
the basis for decisions on the development, adoption, and use of IS that
solves problems, especially in patient care and health systems in
general will be based on convincing evidence from rigorous research.
Thus, the main aim of this article is to develop guidelines for
conducting evidence-based research in IS given its increased application
in healthcare in the context digital health using SLR as the gold
standard for evidence-based research. We believe this will help advance
from the ``potential'' to actual capabilities of digital health
technologies, adoption and scaling up in relevant settings. To achieve
this objective, we demonstrate with various components of research
design on how IS research can be adapted to those of health and medical
sciences and develop a step-by-step guide on how to conduct rigorous
systematic literature review in this context and beyond.

Systematic investigation used in both research and operational
investigations collects or analyses data to answer a specific question
about a phenomenon. Thus, a systematic investigation designed for the
purpose of expanding (i.e., for the purpose of developing or
contributing to) the knowledge base of a scientific discipline
constitutes research. In this context, this is about establishing the
needed evidence regarding the application and outcomes of health
technologies. This is done to ensuring the validity and integrity of
trial data, customizing the plan for monitoring intervention fidelity
(i.e., including all potentially variable aspects of intervention
fidelity, including delivery, receipt, acceptance, and intention to
use), and adopting strategies to keep pace with technology change
(DeVito Dabbs et al., 2013). In other words, it is about the utilization
of these technologies to create and test the equipoise and dynamism of
information technology and health research theories to advance safety
and adaptability in the application of information systems and
technologies in healthcare (Riley et al., 2011). As such, the
establishment of facts/evidence regarding the efficacy/reliability of
such technologies relies heavily on data and methods of analysing such
data.

For example, with clinical trials of health interventions, clinical
evidence on the use of these technologies is still patchy due to lack of
evidence-based research. In fact, despite the proliferation of digital
health interventions, descriptions of the unique considerations for
conducting randomized trials of health IT interventions and intended
consequences for use on patients are lacking (DeVito Dabbs et al.,
2013). It is perceived that when comparing the pros and cons of digital
health technologies, the pros outweigh the cons. However, an unintended
consequence or errors from digital health systems such as computerised
physician order entry (CPOE) could be fatal in addition to its known
association with increased time for completion of selected physician
workflow and financial challenges. For example, in the past, studies
such as Berger and Kichak (2004) discovered a convincing level of
association between the implementation and use of CPOE systems and
expanded medical errors due to inadequately designed system interfaces
as with lack of system end-user training and support.

On the one hand, IS research is defined as the inquiry into the impact
from the adoption and use of IS on people's behaviour, performance of
their duties, groups, and organizations (Galliers, Markus, \& Newell,
2007). On the other hand, management information systems (MIS) research
is defined as ``the systematic investigation of the development,
operations, use and/or impact of an information (sub) system in an
organizational environment'' (Ives, Hamilton, and Davis 1980, p.11).
Both definitions emphasis on the impact of information on organizations.
Research in health and medical sciences systematically investigates any
impact an intervention may have on identified problem or case.

As opposed to ontology and epistemology, critical realism has no
preference for methods as this largely depends upon the research
question and its accompanying characteristics such as causal mechanisms.
On this, Mingers, Mutch, and Willcocks (2013) contend that critical
realism offers potential opportunities to shift attention away from a
focus on the data and methods of analysis towards real problems and
their causes. However, real problems do not occur or exist in isolation.
The data provide source to the problem including identifying their
causes when it is analysed using appropriate method. Grover and Lyytinen
(2015) proposed that one way to overcome the challenges of agonizing
over the dearth of original and bold theorizing over IT is to ``generate
richer data and theory and engage in faster knowledge production
cycles---cycles that feed back into stronger mid-range theorizing''
(P.288). When integrating theory with empirics by creating
disproportionate mid-range models that are difficult to consolidate, it
is critical we examine and debate the negative impacts of the field's
dominant epistemic scripts and relax them by permitting IS scholarship
that more fluidly accommodates alternative forms of knowledge
production. One of such alternatives is literature review research that
leads to conceptualisation of concepts and testing them empirically.
Again, there is the need to move beyond statistical generalizability as
one can observe that different researchers and philosophers have used
the term generalizability to mean different things - Whether research is
conducted quantitatively or qualitatively, there is only one
scientifically acceptable way to establish a theory's generalizability
to a new setting: thus, it is for the theory to survive an empirical
test in that setting (Lee \& Baskerville, 2003).

However, with all these, as foundational, methodological review of past
literature as a crucial endeavour for any academic research of which IS
no exemption (King \& Rodriguez, 1978; Levy \& Ellis, 2006; Snyder,
2019) is lacking and in IS discipline. Literature review is taken for
granted resulting in scanty evidence for the formation of insight into
the rest of the research processes. In fact, there is a real dearth of
scientific literature involving the systematic evaluation of information
systems (King \& Rodriguez, 1978). This has not improved since Webster
and Watson (2002), the initial senior editors for MISQ Review identified
that methods of structuring and presenting literature reviews remain a
challenge. Nonetheless, IS continues to evolve, resulting in the
proliferation and advancement of IS technologies. Empirical studies also
continue without evidence-based foundations informing such advancements
and future studies. Surely, that there have been some attempts to
providing guidelines for conducting literature review in the IS
discipline. For example, systematic literature review (Okoli, 2015b;
Okoli \& Schabram, 2010), standalone literature review (Okoli, 2015a),
literature review (Bandara, Furtmueller, Gorbacheva, Miskon, \&
Beekhuyzen, 2015), to mention but just a few. We will summarise the key
outputs of these works and more a little later. Worst yet, leading
information systems journals such as MISQ, Informs, etc. do not appear
to have interest in calling for such studies.

SLR provides an evidence-based research outcomes and serves as
foundation for the advancement of IS development and application. For
this study, we will stick to SLR. This research will contend that
systematic literature review as a rigorous research method could serve
as a theoretical foundation for empirical research and will be useful in
linking IS research to policy and practice. Additionally, whilst
systematic literature reviews provide exhaustive literature search and
synthesis, there appears not any guide for building conceptual
frameworks/models, which serve as foundational bedrock for testing,
refining, building, and validating expanded existing and new theories in
the domain of IS. We are not making the case for the literature review
of every study be systematic given its very tedious nature. In fact, by
far systematic literature review papers are some of the most frequently
cited papers across disciplines. What we are encouraging is an increased
in rigorous SLR studies that can provide reliable insight for future
studies that researchers can reference to. As with a few other
researchers who made attempt to address literature review shortcomings
in literature review we concur with Brocke et al. (2009, p.2) in
contending ``that the process of searching the literature must be
comprehensibly described. Only then can readers assess the
exhaustiveness of the review and other scholars in the field can more
confidently (re)use the results in their own research''.

But first, we provide an update on the trend of methodologically focused
research in IS published in MISQ as a leading journal in the discipline.
As a result, we retrieved 21 MISQ IS methodology-related studies and 1
from Informs for detail review to provide a trajectory for future
related studies as the field continue to evolve. We did not find any
systematic literature review study in the history of MISQ. In Table 1
below, we summarised characteristics of retrieved publications for
detail analysis.

With its origins from the social sciences, mixed methods have now been
embraced by other disciplines including but not limited to medical and
health sciences, nursing, social and mental health, etc. (Wisdom \&
Creswell, 2013) and in IS as a social science discipline. Mixed research
methodology provides the needed environment for researchers to achieve a
better understanding of a phenomenon being researched by exposing
relevant ``connections'' in the context of complementarity,
completeness, developmental, expansion, corroboration/confirmation,
compensation, and diversity (Venkatesh, Brown, \& Bala, 2013). Better
still, mixed research methods also help in uncovering ``contradictions''
in the context of gaps, unexplained findings and inconclusions that
exist between qualitative and quantitative data (Shorten \& Smith,
2017). The opportunity to integrate data allows for triangulation,
laying the foundation for rigorous analysis and interpretations of
phenomenon from wide and far, resulting in the development of novel or
grounded theory that can be tested. The debate on when mixed methods
research is most appropriate is on-going. However, one understanding is
clear -- the ability of mixed methods research to offer rich insight
into various phenomena and develop novel theoretical perspectives. As
suggested Venkatesh et al. (2013), if little attention can be paid to
paradigmatic or cultural incommensurability when the research question,
purpose, and context requirements have all been met, the questions that
need to address this broad proposition in the context of IS are: what
research questions can appropriately be answered by mixed methods
research? What research purpose can be addressed using mixed research
methods? And of course, the question of in what context in IS can mixed
research methods be found to be appropriately useful or applicable?

Whilst advocating for case research strategy, Benbasat, Goldstein, and
Mead (1987) reiterated that the goals of the researcher and the nature
of the research topic influence the selection of a strategy (Benbasat,
1984). Following the recommendations by Yin (2003) on when to
appropriately apply case study approach, Benbasat et al. (1987)
emphasised on three situations/or reasons to validate the application of
case study research in IS. They are: IS in practice or use in its
natural settings, e.g., in organizations to help answering the ``how''
and ``why'' questions to gain insight into the nature and complexity of
IS in use; and to research an under-research area. The relevance of case
study is its potential to link research to practice. Case study research
in IS is likened to systems requirements elicitation for systems
development. Over time, a holistic/comprehensive and exhaustive
secondary research on case study research will produce valuable outcomes
that will enable academics in partnership with practitioners to offer
insight into the future of IS as with guidelines describing how
organizations could adopt, digitally transform, and manage the
transition process sustainably given the technological change and
innovative nature of IS.

As pioneers of case study research approach, Stake (1995) and Yin (2003)
premised their philosophy on case study on constructivist paradigm.
Constructivists claim that truth is relative and that it is dependent on
one's perspective. Although constructivists are inclined on saying that
truth is subjective and dependent on the experience of the individuals,
there is a limit to the degree of subjectivity, which does not allow a
sweeping shift in the meaning of the context of a phenomenon being
studied (Baxter \& Jack, 2008).

IS discipline is known for its dynamism and enduring unabating change
and innovation. There is acknowledgement for the need to cope with such
dynamism as with keeping up with change and innovation. By and large IS
research has been based on quantitative and positivist experimental
models to determine relationships among variables (factors) and their
impacts and the overarching impact of this relations on outcome
variables or a concept of interest (Kaplan \& Duchon, 1988). These
relationships have generally been among technology, processes, people,
and organizations. The interpretations and conclusions from these
relationships provide insight for IS improvement, development, and
advancement. As such, instruments used in establishing, determining, and
measuring these relationships need to be credible -- would have gone
through validation process with high level of fidelity. 11 years after
Straub (1989) called for new efforts to validate the positivist,
quantitative instruments that IS researchers were using, it is unclear
whether the 17\% that reported reliability, 13\% that validated their
constructs or 19\% that either utilizing pre-test or a pilot test has
improved both in terms of percentages and rigorousness. Given the
dynamic and innovative nature of IS, relying on archaic research
methodologies and other older research resources, then we might be
losing track of the real progress and advancement made in the discipline
and those yet to be made. To determine the progress made in instrument
validation, there need an exhaustive or comprehensive review of
literature over a period determined to hold relevant milestones in the
history the discipline to establish a new starting point for the future
of the discipline.

Generalizability has the tendency to be used inappropriately. However,
in a profound critique on this matter, Lee and Baskerville (2003)
concluded that be it in case study environment or statistical premise,
theory may never be generalized to a setting where it was never tested
empirically tested and confirmed as with no sample size or the number of
sites in a multisite case study would be an indicator of greater
generalizability of a theory to new settings. Indeed, depending on the
of individuals and the peculiarity of such experiences, unknown biases
in collected data in both scenarios limited opportunities for subjects
to freely say what they feel about a research subject. Do people choose
answers that best relate to their understanding or that is there could
have been a more suitable words/phrases as pertaining to individuals.
And if these two are not sufficient to validate generalizability why
would researchers not stop generalizing their research findings? What
are the best research approaches those researchers can confidently use
to generalize findings?

Behavioural and design sciences are the hallmark of IS research (Hevner
et al., 2004). Hevner and colleagues expound that whilst behavioural
science paradigm sought understanding and establishment of theories of
human and/or organizational behaviour, design science sought to advance
the boundaries of both human and organizational capabilities by creating
new innovative products. As briefly touched on early, the relationships
between humans, technology and organization epitomises the discipline of
IS. Given their very abstract nature, the scopes of the concepts of
human and organization encompass the understanding of creative nature of
human and vision of organization and the sustainability of such
creativity and the realization such vision. The ability of IS to create
value lies in designing and developing products that support such
creativities and vision and adapting their changing nature. Hevner et
al. (2004) note that Benbasat and Zmud (1999) ``argue that the relevance
of IS research is directly related to its applicability in design,
stating that the implications of empirical IS research should be
implementable, \ldots{} synthesize an existing body of research,
{[}or{]} stimulate critical thinking among IS practitioners. For this to
be achieved, there need an exhaustive synthesis of existing knowledge on
relevant empirical IS research to gain insight into the trend of
existing technologies to establish evidence sufficient to warrant
implementation of outcomes of empirical studies. This exhaustive
synthesis would be multi-facet/level structured but adaptable in its
approach to establish facts that encourage critical thinking among IS
practitioners.

No one approach to information systems research can provide the richness
that information systems, as a discipline needs for further advancement.
Recently, top journals including MISQ have focused intensely on theory
expansions and development and less on research methods. If this study
as with many others is anything to go by, we can attest to the aged of
existing research that renews energy for research methods in IS.

Literature review as a research methodology in IS discipline has not
received sufficient attention. At the same time, extant literature from
empirical studies is growing at an exponential rate with rich knowledge
that need to be collated and synthesised for purposes not limited to
identifying needs for further research, develop theories, provide
answers to formulated questions, and gain insight into research trend.
Some common examples of literature review are the narrative review, SLR,
the semi-SLR, and the integrative review.

Narrative literature reviews are ``are comprehensive narrative syntheses
of previously published information'' (Green, Johnson, \& Adams, 2006,
p.103). Other types of narrative literature review are commentary and
editorial (Carey, 1998; Gray \& Chambers, 1997). The commentary type of
narrative review that are expression of an opinion and are usually
shorter than full length review articles. Authors usually synthesis
extant literature that aligns or justifies the opinion of the author.
The very ``opinion'' nature of commentaries make them bias given that
they tend to focus on the perception or philosophy of the commentator.
Editorials, as the name suggests, are usually written by editors of
journals in which they synthesis existing literature on current issues.
They are short and narrowly focused. Depending on how they are written,
editorials are not always narrative reviews (Green et al., 2006, p.103).

Integrative review is usually a literature review that is conducted for
the purpose of developing/discovering theoretical framework (use of this
theory). Theoretical frameworks provide the required structure within
which researchers can situate their research paradigms in the context of
philosophy, epistemology, methodology and critical analysis (Adom,
Hussein, \& Agyem, 2018; Grant) in synthesising relevant literature to
derive concepts.

SLR, which we are advocating for in IS research is the gold standard for
evidence-based review developed within the medical discipline given its
convergent nature when gathering facts for evidence-based practice.
Information systems reinforces evidence-based practice given its ability
to capture, process, and store data as evidence for decision-making.
``If the raw data is valid, then the processed data, or ``information'',
can be considered as equivalent to evidence'' (Rodrigues, 2000, p.1344).
Information systems used in clinical settings are used as interventions
for improved healthcare delivery.

We retrieved 10 MISQ IS research methodology-related studies for detail
review to provide a trajectory for future related studies as the field
continue to evolve. In the table below, we summarised the
characteristics of retrieved publications.

\textbf{Table 1: MISQ IS research method related studies}

\textbf{Source: Developed for this study}

Extant guidelines for conducting literature reviews in various
disciplines including business Snyder (2019), psychology (Baumeister \&
Leary, 1997), management (Tranfield, Denyer, \& Smart, 2003), human
resource (Torraco, 2005), medicine (Liberati et al., 2009; Wong,
Greenhalgh, Westhorp, Buckingham, \& Pawson, 2013), social sciences
(Davis, Mengersen, Bennett, \& Mazerolle, 2014), marketing (Palmatier,
Houston, \& Hulland, 2018), and (Onwuegbuzie \& Frels, 2016). For IS, we
found the works of Okoli and Schabram (2010), and Okoli (2015a). A
phrase we consistently came across in literature was ``comprehensive
literature review''. Specifically, we were concerned about the
term/concept ``comprehensive''. This term seemed to be used
interchangeably with ``systematic''. However, they do not mean entirely
the same. We perceive that in the context of narrative literature
review, ``comprehensive'' may be the most appropriate terms whilst in
systematic literature review, ``exhaustive'' may serve a better purpose.
Unfortunately, majority of authors do not comprehensively search and
synthesis extant literature, which effectively impact the
generalizability of the findings of such studies. As one editor-in-chief
asserts in response to a narrative review/conceptual paper we wrote:

\emph{``Nowadays, systematic reviews have increasingly replaced
traditional narrative reviews and expert commentaries as a way of
summarising research evidence. Examination of quantitative and if
possible, with qualitative evidence or a mixed-method systematic reviews
are highly desired''.}

The rigorous standardized methodology for conducting a SLR that has
developed from the health sciences and other fields is virtually unknown
in IS research (Okoli \& Schabram, 2010). However, more and more
healthcare services now heavily rely on IS for improved delivery. For
example, the increased use of digital health to support evidence-based
practice (Agarwal, Gao, DesRoches, \& Jha, 2011) and to better generate
evidence and deliver evidence-based care.

\textbf{Evidence-based Research in IS -- the like of Medical Research}

We expounded the themes used by when comparing medical research and
management research

2.1 The nature of the IS discipline

Drawing on sociology of science foundations, Taylor, Dillon, and Van
Wingen (2010) posit that to survive and prosper, health applied
disciplines must meet the dual demands of academic and practitioner
audiences by demonstrating both focus and diversity in their research.
The nature of the IS discipline can be likened to that of medical field
in the context of convergent and management in divergency. Convergent in
the sense of gathering and collating facts and relationships among key
findings in relevant studies. Thus, the extent to which two measures of
variable that theoretically should be related are indeed related is a
consideration often used in sociology, psychology, and behavioural
sciences. The objective of this concept lies in the need for existing
relationship between/or among key findings from selected multiple
studies from practice and interventions to serve as evidence for
decision-making. In fact, IS and the continuous emergence of
Internet-based health information technologies and other decision
support systems provide evidence-based knowledge. It is the power of
being able to attribute causes to effects through accrued evidence. For
example, it would have been useful if there was sufficient evidence to
support providers to know when to appropriately order telemetry and
appropriately discontinue telemetry when it was no longer medically
indicated.

IS is a combination of social science, business, and computing science
and increasingly of health sciences. Divergent thinking capability has
become increasingly important for innovation and problem solving and in
a spontaneous, free-flowing manner, many ideas are generated in an
emergent cognitive fashion (Ni, Yang, Chen, Chen, \& Li, 2014). The
discipline of IS, particularly IS, e.g., IS Strategy tends to be more
abstract and exploratory with flexibility to respond to the ongoing
rapidly changing needs of businesses. This can be achieved through the
exploration of emerging business trends with new evolving models for the
development, implementation, and use of systems in various application
domains: IS strategy and business outcomes; Internet applications,
computer-supported collaborative work, virtual teams, and knowledge
management (Taylor et al., 2010). The nexus between medical research and
management research is IS research. On this, the study by Tranfield et
al. (2003) provides the difference between medical research and
management research. This allows researchers in IS to operationalize
both convergent and divergent in the context of ``Nature of the
discipline''. We demonstrate this by adapting these differences. Early
on we contended that the definition of IS was narrow due to its
exclusion of health sciences and given the increasing role of IS in
healthcare domain.

2.2 Research culture

IS has demonstrated both rigorous scientific evaluation and the split
between positivist and phenomenological perspectives. In fact, ``the
essence of Information Systems as design science, for example, lies in
the scientific evaluation of artifacts'' (Iivari, 2005) in (Chatterjee
\& Hevner, 2010) (Hevner \& Chatterjee, 2010). According to Smith
(1998), positivists attitudes toward sociology are of the view that
objective or hard facts and the relationship between these facts are
foundational to scientific facts. For positivists, such laws have the
status of truth and social objects that can be studied in much the same
way as natural objects.' These hard facts are the objective
(quantitative) and the technical aspects of IS that are logically
established and programmed into systems with defined functionalities or
outputs and excludes any priori speculations. For example, embedded
business rules, primary keys, and other information systems
functionalities that are not subjected to broad discourse and varying
outputs. On the contrary, phenomenology is generally qualitative and
subjective, more abstract and draws on divergence and exploratory.
Again, in IS non-technical and managerial decision-making based on the
outputs from sets of computerised set of systems and procedures. For
example, digital transformation, IS Strategy and other management
related issues.

2.3 Research questions

In medical field, research questions are based on high level of
unanimity given defined healthcare cases. The formulation of research
questions is based on evidence gathered from scoping and systematic
literature reviews. Scoping reviews are used to mapping broad topics,
identify knowledge gaps, scope a body of literature, clarify concepts,
investigate research conduct, or to inform a SLR (Munn et al., 2018;
Pham et al., 2014). These are part of the evidence-based guidelines
(EBG) and Model Process and Grading of Recommendations Assessment,
Development, and Evaluation (GRADE) framework for developing,
implementing, and evaluating EBGs in the prehospital care setting
(Patterson et al., 2017). Similar models and processes for formulating
research questions are developing in IS with digital health reshaping
how healthcare is delivered and their evaluations using gold standards
such as randomised controlled trials (RCTs) to determine how efficacious
with evidence these systems are for scaling and implementation/adoption.
See for example, Fatehi, Smith, Maeder, Wade, and Gray (2017). We see
the use of common IS products in various service and production
industries. Similar approaches could be used for formulating relevant
questions for research in IS.

2.4 Interventions and evaluation

In the medical discipline, interventions can be measured through
experiments -- laid down procedures agreed on consensus to be followed
to make discoveries, and test hypotheses. This is applicable to the
implementation and use of IS given their technical nature in several
settings although digital health interventions, for example, are
characteristically complex with multiple components and potentially
multiple goals, which give rise to evaluation methodological challenges,
they can be measured through experiments. Whilst experiment does look
less applicable in the management domain, it does also imply that the
introduction of IS is not always a technical issue, necessitating the
addressing of softer or management issues. In IS, interventions may not
follow laid down procedures to measure the extent to which such systems
influenced or changed identified/existing and targeted situations play
similar roles. For example, IS can serve as intervention or be used to
facilitate processes that improve behavioural health lifestyle through
capturing relevant data (Penn, Goffe, Haste, \& Moffatt, 2019). In
addition to capturing data, there are other IS interventions that seek
to ensuring that such data have met required quality through specific
interventions. For example, interventions to improve routine health
information system data quality and use for decision-making (Lemma,
Janson, Persson, Wickremasinghe, \& Källestål, 2020). Such an
intervention can be evaluated by investigating actual system use, user
needs, varying user experiences, user expertise and system user
friendliness, and training. Again, in some cases, ISs are introduced to
improve operations in organizations (Maguire \& Ojiako, 2007).
Generally, scholars have used IS Success Model as a theoretical base in
a bid to understand the impact IS technologies on user behaviour and how
that translates into overall improvement in services. Such improvement
could be efficient resource allocation, improved hospital admission
process in the context of reduced waiting time/queue to admission in the
hospital. Again, user satisfaction and systems usage are by far the most
widely used factors to evaluate the effectiveness of IS systems. Whilst
the effectiveness of such user systems may vary depending on a range of
factors including user acceptance and support as with training and
change management issues, there could be the development of standardized
system evaluative tools for various systems in the equivalence of
medical practice guidelines established on consensus and by
authoritative bodies. Similar roles could be assumed by accredited IS
bodies.

2.5 Research designs

The objective of research design is to inform details of how a research
is being conducted. It talks about ``how'' every activity in the
research was carried out (procedures) including but not limited to the
source of data and how it will be collected, how research questions will
be formulated, how they be answered and theories where applicable. A
design would have explicitly provided information about the study type,
described the study population and sample size with justification, the
unit of analysis and measuring technique. Several research designs have
been adopted in practice to help evaluate IS interventions, in
particular digital health interventions. Research designs in the medical
field are influenced by a hierarchy of evidence from existing
literature. This evidence must be based on exhaustive search and
collation of literature in a systematic manner. Similarly, in IS, the
achievement of validity and reliability are fundamental when using
quantitative research and questionnaire instrument for that matter. When
using qualitative research method, validity and reliability are achieved
through triangulation, the use of multiple data source with a succinct
explanation of strategies used in collecting data including meticulous
design of interview guide. Of the four triangulation types, namely
method, investigator, theory, and data source type, the data sourced
type is used by most qualitative research on human phenomena in many
cases to test validity (Carter, Bryant-Lukosius, DiCenso, Blythe, \&
Neville, 2014). However, the challenge with multiple data types is scope
and the level of consistency or fidelity regarding the instruments used
in gathering the data. IS research has enjoyed designs from both medical
and management.

2.6 Theory

A theory not only explains known facts; it also allows scientists to
make predictions of what they should observe if a theory is true. Theory
expounds facts by providing scientist with the framework to ask
questions about the reality. However, the ``lack of consensus on exactly
what theory is may explain why it is so difficult to develop strong
theory in the behavioural sciences {[}given the growing scholarship on
that{]} references, data, variables, diagrams, and hypotheses that are
not theory'' (Sutton \& Staw, 1995). Additionally, over time IS as a
discipline has been gaining traction in the medical field. In medical
field, attention has been paid to what works, why it worked and how it
worked. For example, did the intervention brought about the expected
change or improvement in accordance with implementation fidelity or
integrity. This logic of whether a digital health intervention brought
about any expected change, why and under what conditions could be of
great importance in generalizing the outcomes of IS interventions.

2.7 Aims of policy

Decision-making across building blocks of all health systems is based on
reliable information that is available to authorised persons in the
context of data-generation, compilation, analysis, and communication and
use (World Health Organization, 2009) to provide scientific evidence.
Reliable information is also fundamental to developing health systems
policies and adoption, health research as with governance and
regulation. For example, IS systems are used to capture relevant
information about health interventions. To achieve this, such IS systems
need to follow existing medical intervention structured procedure. As
opposed to multiple and competing policy aims of IS in non-medical
disciplines, IS interventions in the behavioural sciences are now
beginning to streamline towards achieving specific outcomes.
Overwhelmingly, these outcomes are all geared towards improving
organizational performance as with the aim of digital health. Digital
health improved used of digital technologies including data and
information technologies to improve healthcare delivery by helping to
live heathier lives whilst taking greater control of their lives. In
fact, the policies and aims of digital health transcend reducing illness
and death to include sustaining such achievements by offering people
greater control over through access to relevant health information.
Where there are multiple policy aims, aspects of larger systems would be
tailored for various components. For example, a Business architecture,
Information architecture, Technology architecture

2.8 Methods

In the domain of IS, two dominant methodologies have been used by
researchers: Action Design Research and Design Science Research
(Chatterjee \& Hevner, 2010). These main research methods lack
operational processes and decision support tools that are rigorous
(Mandran \& Dupuy-Chessa, 2018) with any fidelity that can be verified.
For example, the problem with the extensive use of end-user
characteristics is the lack of evaluative process to verify the validity
of relevant qualitative data collection tools and whether the outcomes
of such studies are generalizable. It is often said that quantitative
research is a theory in search of data, while qualitative research is
data in search of a theory. To improve system interactions with users,
ISO9241210 has outlined three main user-centred design. They are
requirement elicitation from users, which uses mainly qualitative data
collection method; the design phase that develop systems in
collaboration with users through workshops and focus groups; and the
quantitative evaluation (experiment). The shortcoming of this user
centred design is the lack of baseline to measure the impact of such
systems. A baseline could be the established post-phase one of the
user-centred design. As a result, an exploratory sequential mixed
method, for example, prior to phase two to develop rigorous instrument
to confirm the findings of phase one is very useful. This will improve
the reliability of findings of the evaluative or experimental phase.

2.9 Literature review

Literature review is defined as the collection, synthesis, and
evaluation of published studies. Published studies could be primary,
secondary, and tertiary. Primary literature review makes use of
materials encompassing empirical studies -- ranging from observation,
interviews, to experimental and is meant to provide an assessment of
what has been published on a topic of interest mainly of actual
practices. The secondary literature derived from the exposition of the
primary literature sources are generally reviewed studies. These studies
could be narrative, systematic, semi-systematic and integrative. Authors
of these studies integrate primary literature studies through synthesis,
thus, further expanding on the findings of primary literature studies.
The outcomes of this type of literature review have the potential to be
generalizable. The tertiary source encompasses the distillation and
collection of materials derived from the hybrid of primary and secondary
literature sources, for example, textbooks, guidebooks, encyclopedia.
Thus, the purpose of the tertiary literature is to offer an impression
of important findings of existing studies or research. One of the
importance of a tertiary literature review is its ability to introduce
principles to practices in a discipline. By far this type of literature
review is the least used one in academia. Literature can either be
narrative or systematic. Narrative review is the conventional and the
oldest means of studying extant studies and is generally qualitative
with no particular ``formal'' guide for undertaking it. As a result, it
does not generally seek generalization. Below, we provide an overview of
systematic literature review.

Literature review in the medical field has largely been systematic and
meta-analysis providing a quantitative evidence for reliable conclusion
and generalizability (Tranfield et al., 2003). Narrative literature
review has been used extensively in the non-medical field to provide
basis for further exploration of multi-facet and ongoing changes of
situations and a timely response to the experiences of people/subjects
and situations. Whilst not generally generalizable, it serves a great
purpose for providing immediate overview/understanding of existing and
new phenomena using varied methodologies including socio-cultural,
natural and library and processes in the context of data collection and
conception, data analysis, and data reporting (McAlpine, 2016). It is
interesting how researchers can draw conclusion from a narrative
literature study given unstructured nature. There must be some assurance
that the study is comprehensive and has covered prominent research of
the given time. The selection of and critically analysing large set of
literature from varying sources is appearing to be relevant to produce
generalizable results.

Often, and generally, SLR studies selection criteria have been one that
is based on published empirical materials. A concern with this criterion
emanates from the fact that ``there is a growing recognition that often
evidence is difficult to find because of decisions that are made about
where, how, and when to publish the results of studies based on the
findings of those studies'' (Balshem et al., 2013, p.1). This makes room
for undetected biases in publish materials. One of such sources of
materials is the grey literature. Grey literature is literature from
unpublished reports from government and others such as dissertation that
is now being touted as becoming an important additional source of
literature towards evidence-based studies. The Institute of Medicine and
the Agency for Healthcare Research and Quality (AHRQ) and current
Cochrane guidance made recommendations to this effect. Generally, useful
interventions are published far more than interventions that produced
negative poor outcomes. Whilst the inclusion of grey literature is being
encouraged, the impact of their exclusion has been found to be
relatively small or negligible. Given both technical (structured) and
non-technical (abstract) nature of IS, the careful collation and
consolidation of materials from grey literature and unpublished
dissertation and theses will be useful. Like in management this will
help respond directly and timely to changing practice and policy needs.
Whilst majority of SLR studies searched for non-English and unpublished
papers, less than 5\% of these studies are included for full review
(Hartling et al., 2017).

As research continue to grow and information explodes in the discipline
of IS, literature reviews have increasingly become crucial in the
definition and understanding of IS for academics, practitioners, and
policymakers alike.

\textbf{Overview of systematic literature review}

In the medical and public health fields, SLR has been hailed as a gold
standard methodology for evidence-based research since the establishment
of Cochrane Collaboration in 1993. In the discipline of IS, a rigorous
systematic literature review study would be critical to collate and
summarise evidence on how IS technologies, specifically digital health
technologies are improving healthcare delivery and outcomes. Currently,
digital health technologies are generally known for their ``potential''.

SLR approach is already in use in the IS research. SLR requires thought,
probing of concepts, interrogation of assumptions and expectations, and
a considered appraisal of where a SLR might fit into the general scheme
of knowledge and keep expanding in the domain of research that apply it.
Can machines be trained to apply the criteria of inclusion and
exclusion? Can machines be trained on how to apply assessment of bias?
(Gough, Oliver, \& Thomas, 2017). Theory-based SLRs, which summarise
evidence on what works, when and why, strive for more than greater
policy relevance. In fact, they strive for premise for decision-making
by top management in practice. For example, in the assessment of the
evaluation process of MIS products, King and Rodriguez (1978)
categorised assessments into four categories -- attitudes, value
perceptions, information usage, and decision performance. Holistically,
each of these categories requires a systematic evaluation that is
exhaustive of existing literature to gather evidence on these categories
to avoid the pitfalls of common scientific basis, fallacy of affirming
the consequent, summative validity and called for basic reasoning that
accommodates the latest developments in positivist, interpretive,
action, and design research (Lee \& Hubona, 2009). This ultimately leads
to technology adoption mindfulness in an era populated with mundane and
bleeding edge technologies. This is the case for digital health
technologies. These technologies still lack rigorous evaluations as with
methods used in evaluating them to provide sufficient/reliable evidence
on their functionalities and the most suitable circumstances in which
they will be more beneficial. Reviews that answer these questions adopt
a mixed methods approach and draw on a range of study types. Answering
the `what works' and `what doesn't' questions mean drawing on
effectiveness studies, conducted to standards of high-quality impact
evaluation. But in formulating answers to the `when' and `why' questions
require a broader range of evidence from both quantitative and
qualitative research (Snilstveit, 2012) for policy relevance. Ideally,
this would normally be theory-based SLRs, which are usually mixed
method-based reviews (White, 2018). According to White, mixed methods
review is one which draws on a variety of evidence, factual and
counterfactual, qualitative and quantitative, to address different
questions along the causal chain (p.1). Drawing on different types of
studies theory-based SLRs are policy-focused (Snilstveit, 2012). Both
theory-based SLR and health technology assessment (HTA) are all
theory-focused evaluations.

Let us look at the concept of Health Technology Assessment (HTA), which
is the application of organized knowledge and skills in the form of
devices, medicines, vaccines, procedures, and systems developed to solve
a health problem and improve quality of lives'' given the rampant
medical errors and system inefficiencies during the pre-digitization.
The World Bank also defined HTA as a complete policy-focused research
that evaluates short and long-term impact from the application
operationalization of technology, which include but not limited to
benefits, costs, risks (World Bank, 1995), and
accessibility/availability. With advanced health information
technologies such as electronic health/medical records and many more the
domain of health care has witnessed improvement in every facet of its
management and administration. From improved communications between
healthcare provider and consumer to improved medication safety,
tracking, and reporting; and promoting quality of care through optimized
access to and adherence to guidelines'' (American College of
Obstetricians and Gynecologists, 2015, p.1). Health technologies can
range from medicine and medical devices, and pharmaceuticals as with
computer-aided information systems such as electronic healthcare records
(EHR) systems, health information exchange (HIE), personal health
records (PHR), national information networks, 3D printing, Artificial
intelligence (AI), etc are examples of computer- supported IS. Again, a
revisit to the definition of IS provides an understanding of the
social-technical aspect of IS vis-à-vis people and their roles to
accomplish task using technology. The challenge remains with the
availability of limited contribution of high-quality evaluations
resulting in ungeneralizable reviews in the context of policy and
practice implications, necessitating the need for further rigorous
studies. Whilst substantial progress has been made, however, there
challenges with the wider adoption of health information technologies
given the lack of concrete evidence to answer `when' and `why' questions
in the adoption of these technologies, costs and risks associated with
such technologies would need a structured-procedure/process-based
evaluative methodology with fidelity. These requirements fit well into
SLR.

There are not many studies that have provided guidance for conducting
SLR in general. Existing ones include (Okoli \& Schabram, 2010;
Onwuegbuzie \& Frels, 2016; Tranfield et al., 2003). The guide/steps
recommended by these authors are summarised in the Table 2 below.

\textbf{Table 2: Existing works on how to conduct SLR}

Whilst these guides do provide straight-forward steps for conducting
standard SLR, we believe they appear too general. Systematic literature
review needs to be structured in the true sense of structure -- specific
and procedural in ways that allow for easy replication for similar
outcomes/conclusion, which is the essence of evidence. For example, what
are the pre-SLR preparations? What are the strategies that are used for
searching the literature? Many more concerns need to be
addressed/transparent and form part of methodological or studies given
SLRs help in the systematic and exhaustive search for,
collation/organization of relevant literature for analysis and
dissemination of evidence?

In the IS discipline, gathering evidence has become paramount as data
and information have increasingly become critical for every service
improvement. SLRs aid the analysis and dissemination of evidence, using
rigorous and transparent methods to generate empirically attained
answers to focused research questions. Identifying all evidence relevant
to the research questions whilst an essential component, is also a
challenge of SLRs. Below, we present steps to follow when conducting SLR
not limited to the discipline of IS.

\textbf{Step 1: Pre-SLR preparations}

3.1.1 Select belief and topic that align with your research interests.

To begin any literature research, researchers must first identify the
need for such a research -- topics and what problem(s) have been
identified. In other words, researchers need to establish the purpose
for such a research, thus, the summarising all extant information about
a phenomenon to establish evidence capable of being generalized.
Literature review is used to justify a need for research through A
thorough identification of the need for the research sets the basis for
step 2 below. In fact, it is the identification of research gaps that
need to fill. To identify research gaps, a reasonable amount of reading
is necessary to establish research gaps. When doing this reading,
researchers tend to touch on wide range of extant literature, which
enable them to narrow down to focused areas.

Conduct scoping reviews on published and unpublished (grey) literature

As the name suggests, scoping reviews are carried out to map out the
extent of available literature on a chosen topic and their general
overview, preparing grounds for SLRs without necessarily paying
attention to quality and other specifics. Scoping reviews are focused on
helping to answer the question: ``What information has been presented on
this topic in the literature?''~and for gathering and assessing
information prior to conducting a SLR (Munn et al., 2018). Thus, scoping
review is used to derive specific questions to answer in SLR. Steps
involved in conducting scoping review has been outlined by Munn et al.
(2018).

Grey literature

Grey literature is literature or evidence not published in commercial
publications or journals outlets. Among others, grey literature could
range from theses and dissertations, academic materials, ongoing
research, working papers, briefings, conference papers, discussion
papers, government reports, and evaluations. There is debate among
evidence-based researchers on the inclusion of grey literature when
conducting SLR given their very nature -- not peer-reviewed. We also
need to be aware that more recently, Open Access journals are prone to
publishing research that were rejected by non-open access journals for
poor quality due to exorbitant article processing charges as they are
increasingly doing so. We contend that the challenges of including grey
literature in SLRs are not embedded in the quality of the materials, but
rather in the search strategy. A carefully thought-out grey literature
search strategy may be an invaluable component of a SLR by thusly
reducing publication bias, increase reviews' comprehensiveness and
timeliness, and foster a balanced picture of available evidence (Paez,
2017). Grey literature is a key part of the evidence produced and used
for public policy and practice as posit Lawrence, Houghton, Thomas, and
Weldon (2014). The authors found that ``the most important or very
important resources that policy makers use are reports (81\%), journal
articles (75\%), discussion papers (69\%), briefings, reviews and guides
(66\%) and data sets (61\%) demonstrating the relevance of grey
literature in policy and practice. The findings of Lawrence and
colleagues only intensifies the ongoing debate on whether to include
grey literature or not in SLR. These said, the main challenges of
including grey literature are how to search or locate them as with how
they meet the methodological requirements of articles to be included.
Whilst we do not necessarily subscribe to any camp of this debate, we
believe a scoping review on grey literature will provide an invaluable
insight into the broader evidence out there and help set some
expectations in scoping review on published literature. The outcome from
these reviews will inform a more relevant research question for the
actual SLR.

\textbf{3.2 Step 2: Set your inclusion and exclusion criteria.}

Prior to commencing the actual search activity, the researchers need to
develop a protocol to guide the selection of would-include and -exclude
study materials. This is determined after the research question has been
formulated and is used to set the delineations -- in/out-scope of the
study. Inclusion criteria are the factors that an extant study must have
to answer the focused research question set. On the contrary, exclusion
criteria are all the characteristics of extant study that do not in any
way contribute to answering the research question. These criteria could
be study objective or purpose, year study was carried out or published,
peer-reviewed, study methodology, study population and sample size, age,
language in which study was published, geographical location of study,
theory-/non-theory-based review etc.

When the study is theory-based, inclusion and exclusion criteria should
focus on IS interventions that have been evaluated and the use of theory
of change. Over the last decade or so, IS has seen increased in
implementation science, an equivalent of process evaluation to verify
the fidelity of system implementation activities. For example, there is
no doubt about the effectiveness of digital health. However, little is
known about the unintended consequences of digital health from a system
point of view, making it difficult to institutionalize eHealth into
routine care as with the challenge of reforming health policies in this
regard.

In IS, evaluation is the appraisal of the quality and value of the
characteristics/functions of systems and their impact on end-user and on
business operations in an organization. It is ``the act of measuring or
exploring properties of a health information system (in planning,
development, implementation, or operation) (Ammenwerth et al., 2004,
p.480). Conventionally, evaluations in IS has been impact evaluation, an
assessment of how an IS intervention been assessed affects existing
conditions to bring about needed change. For example, when evaluating
the effectiveness of an IS, attention is focused on the impact of an
implemented system on organizational/business operations in the context
of the effectiveness of users and other outputs (Hamilton \& Chervany,
1981). Again, this could have also been how the feature/functionality of
a system impacted end-user perception. Furthermore, the evaluation of
health information technologies for high-quality, safe and
cost-effectiveness to improve quality of and increase access to
healthcare. IS requires rigorous and effective evaluation with fidelity.
``Interestingly, change theory has not been much explored in IS
research.

A SLR protocol provides guides for undertaking systematic literature
review study and help to minimise risks of bias whilst optimising
standardization/uniformity resulting in increased transparency and
fidelity. It is a mini-guide or reference within the broader guide for
conducting SLR. In fact, the protocol services as a quick reference tool
for the researcher(s). Components of the study protocol include but not
limited to the research question to be answered, study selection
criteria and procedures, eligibility, checklist for study quality
assessment, search strategy, etc. (Kitchenham, 2004) and above all the
methods for performing synthesis (Ryan, 2019). Examples of tools that
help in developing protocol are PRISMA checklist, Theta Collaborative
tool, Cochrane Review handbook, AMSTAR checklist.

\textbf{3.3 Step 3: Search strategy/define your search terms/phrases.}

An important characteristic of SLR is replicability. However,
formulating search strategies to be used for SLRs, finding the best
balance between sensitivity and specificity, and translating search
strategies between databases is challenging (Bramer, de Jonge,
Rethlefsen, Mast, \& Kleijnen, 2018). It takes thorough preparation,
including scoping reviews to understand the extent of scope and relevant
concepts to derive research questions and ultimately, search strategies
that captures every part of the research question. To achieve
replicability, there is the need for consistency in search strategy that
must be exhaustive and efficient. Overarchingly, search strategy is
influenced by a clear and concise research question around which
phrases/key words are derived to be used for searching through relevant
databases. It is important the search phrases/words, which are usually
key concepts addresses various aspects of the research question. It is
critical that the same key concepts/phrases are used throughout the
search across all search databases and search engines. The search
activity needs to be well documented including where the search in
taking place, the keywords used for search, the output/out in terms of
number of initial studies to be included.

\textbf{3.4 Step 4: Practically screen your organized literature in
accordance with step 3 above}

Executing this guide implies that author(s) would need to appraise all
potential papers to be included for full review (Okoli, 2015a). This
takes place after protocol and getting participating authors on the same
understanding on all the requirements of the studies (Fink, 2019) based
on their inclusion and exclusion criteria set above in step 3. Arguably,
this step determines the quality of the review study to be conducted. To
achieve this, authors would need to adhere to protocols and ensure that
all authors strictly follow through. An important outcome from this
guide is ``screened down'' potential paper that are most likely to be
included. It is important to note here that the outcome of this exercise
does not seal the faith of paper that met the initial elimination
criteria. A further screening would need to be conducted when authors
provide further clarification around potential papers on why they should
be included or not be included at all. Some of the fundamental criteria
suggested by Fink (2019) to include content (topics or variables),
publication language, journal, authors, study setting, participants or
subjects, research design or sampling methodology, publication date,
source of financial support. Authors would notice that the criteria
recommended by Fink are superficial and might only help in the initial
elimination stage. Other important criteria such as quality of study,
study objectives/purpose, study focus, length of paper, theory-free or
theory-based study, intervention, etc will later need to be considered
as further inclusion/exclusion criteria. To minimise the tediousness of
this step, the use of referencing program or software is recommended.
For example, when using EndNote, authors can create group folders that
enable them to put likely papers together whist attaching core/important
articles and other relevant materials as needed for full review later.
The advantages of the refencing programs include the minimization of
referencing errors and the alphabetical arrangement of articles. It
automatically identifies categories of papers during referencing. Note
that practically screening papers does not encompass assessing the
quality of papers.

\textbf{3.5 Step 5: Data extraction and synthesis}

Depending on your research aims and question, at least, there could be
two phases of this exercise. It is the act of locating and
acquiring/accessing appropriate materials deemed relevant for the study
and all sources of information well documented. There are many
referencing systems that help in organizing and documenting information,
for example, EndNote. First, specific characteristics of studies among
others such as geographical location, study objectives, methods, study
design, population, and theory where relevant will be looked at. This
step is about bringing ideas and information together for the purpose of
identifying the overall pattern and how each pattern fits into the
whole. The outcome sought here is an overview of the research focus.
This outcome crucial in confirming/or refining formulated research
questions. Attention is paid to the connections among the extracted
materials in the context of agreements and disagreement, flow among
relevant materials for the purpose of drawing conclusion as organizing
proposition for the synthesis The second phase could be extracting the
actual results/findings of IS implemented as an intervention Inclusion
and exclusion criteria.

\textbf{3.6 Step 6: Appraise the quality of studies to include.}

For this section, we define quality as the extent to which various parts
of studies coherently fit the whole. Thus, the match among various parts
of research design -- research question and method, selection of
research subjects, sample size data collection process, measurements,
analysis, and reporting. In this section, particular attention needs to
be paid to the quality of each section of the entire research design.
For example, to prioritize papers according to their quality and to
exclude certain papers deemed not useful due to inferior methodological
quality (Okoli, 2015a). Okoli (2015a) posits that ``perhaps the most
significant distinction between classes of quality appraisal methods is
whether the primary studies are quantitative (i.e., they obtain
knowledge by measuring numbers) or qualitative (i.e., use text or other
non-numeric data with discussion and argumentation to understand the
phenomenon); hence'' (p.896).

When appraising the quality of qualitative studies, Okoli highlights
that study reviewers should endeavour to distinguish the lines of
arguments in the context of inference, assertion, or supposition drawing
on logics of how arguments were developed. For theoretical papers,
particularly those that aimed at deriving or discovering theories such
as grounded theory, the need to identify whether authors premised their
arguments along deductive or inductive reasoning is of great importance.

Research quality in IS when focused on digital health has become a
matter of life or death given the dearth of knowledge on the unintended
consequences and side effects of the application of digital health
technologies. As the primary objective of this study, to we posit that,
given the increased application of IS in the healthcare environment,
there is a growing evidence of identical interventions in the
application of IS in the healthcare domain. This warrants stricter
methodological quality of the highest standard. The implication of this
new evidence unlike Fink's, is that the advocacy of Petticrew and
Roberts (2008) for ``leniency of hierarchy of evidence'' when conducting
SLR in social sciences no longer really hold much on which to reckon,
particularly for IS.

On incorporating non-peer reviewed/unpublished papers, we suggested
above a scoping review on grey literature for insight or broader idea on
the topic being researched as a starting point for systematic literature
review. Additionally, high-quality reports with quality/well-cited
references could be of great use. Again, as opposed to Fink (2005),
Okoli broke ranks with these differences to assert that both qualitative
and quantitative lend themselves to rigorous empirical structured
methodology of SR to achieve explicit, comprehensive exhaustive, and
reproducibility (Okoli, 2015a). To achieve the same level of quality
among multiple authors, a standard form developed by all authors would
be relevant. See an example developed by Fink.

\textbf{3.7 Step 7: Results}

In the results section, researchers/authors need to describe study
selection and the characteristics of studies found as with the process
involved in appraising those studies. Here, it is ideal to talk about
the overall studies identified, how many were examined in the context of
titles, abstracts, and other relevant characteristics such as
theory-based and non-theory papers, methodologies, etc. Where relevant,
authors need to clarify issues relating to any identified risk of bias
with individual studies. Part of this section would have also
demonstrated the application of inclusion and exclusion criteria set
above. How many papers met initial inclusion criteria, why additional
papers were later excluded, etc. The roles of authors/researchers
involved also needed to be mentioned in this section. For example, how
many authors appraised the quality of the papers.

\textbf{3.8 Step 8: Data synthesis}

Data synthesis is the combined evaluation of extracted data --
qualitative, quantitative, or mixed for evidence and decision-making. It
is about the organization and presentation of data from the findings of
the actual review to draw conclusion about a body of evidence. The
implication of this stage/activity in SLR is the systematic and holistic
assessment of results of individual study included with particular
attention paid to key features of the studies, which may include but may
not be limited to study design, study subjects, etc. Depending on what
the purpose and the criteria set for the study are, data from both
qualitative and quantitative studies can serve complementing or
triangulating role for the purpose of exploratory or explanatory of
evidence. For qualitative data, a narrative or meta-synthesis allows the
SLR and combination findings and ``offer an appropriate balance between
an objective framework, a rigorously scientific approach to data
analysis and the necessary contribution of the researcher's subjectivity
in the construction of the final work'' (Lachal, Revah-Levy, Orri, \&
Moro, 2017. p.1). Meta-synthesis builds a rigorous foundation for
reporting given that it is not constrained to synthesising studies for
the purpose of comparison, which is largely great when reporting
findings. For quantitative data, a statistical synthesis is employed in
the context of meta-analysis, the combination of statistical results
from multiple studies. Like in the medical field, in the domain of IS
the implementation of new systems and the application of digital health
interventions carry potential for change. As such the application of
data synthesis methodologies such as realistic synthesis helps in the
categorizations of these changes for evidence-based decision making. A
good SLR should make it easier for the practitioner to understand the
research by synthesizing extensive primary research papers from which it
was derived (Tranfield et al., 2003).

\textbf{3.9 Step 9: Discussion and conclusion}

When aligning or relating discussion in IS research to that of medical
discipline as we are advocating for in this study, it implies the need
for not only a simple account of facts, but rather a logical
presentation of arguments of available facts (Jenicek, 2006). It implies
a thorough evaluation of extant literature from IS implementations or
cases. Remember, ``no one has thought as long and as hard about your
study as you have'' and that your discussion should start off with the
major findings of the study (\selectlanguage{polish}Ş\selectlanguage{english}anl\selectlanguage{polish}ı, \selectlanguage{english}Erdem, \& Tefik, 2013, p.1239). The
fundamental function of the discussion for systematic literature review
is to lead readers in understanding the main findings from your review.
This emanates from the results and synthesis exercises above and
implications for practice theory, literature, and policy. Thus, your
discussion will seek to raise and answer questions that include but are
not limited to the extent to which your findings or evidence challenge
existing literature, theory, practice, and policy. When interpreting
your findings, ensure to situate and relate back to the overall results
of the study, how the findings agree, disagree and or complement with
existing body of knowledge or literature (Masic, 2018). Whilst highly
tempting, it is important that both the results and synthesis are not
repeated here. The focus of this section is to summarise the key
findings, highlighting the strength/weakness for each of the main
outcomes. Remember the results above only presented the characteristics
without detailing their implications for the study whilst considering
alliterative explanation of the findings. As such, at this stage,
authors now have the room to discuss in detail and describe the place of
their findings from the results and synthesis of literature and
demonstrate how the results build on existing evidence or body of
knowledge and suggestions on how to use/apply the results in the context
of specific use settings and generalization. In fact, when we relate
This section also discusses any identified limitations of study and how
that might impact/have impacted the findings of the study. These
sections also point out the implications as with suggestions for further
studies.

\textbf{Conclusion}

First, this paper advocated for and has demonstrated the need for
adapting IS research to that of medical/health research and echoed the
growing importance of evidence-based research influenced by increased
use of IS in the healthcare domain using SLR as the case. The paper did
this by looking at the commonalities between IS medical/health domains
in various contexts, which included the nature of the disciplines of IS
and medicine/health, research culture, research questions, interventions
and evaluations, research designs, theory and aims of policy, methods
literature review. Secondly, the paper emphasised and made a new call
for increased importance of literature review studies particularly SLR
studies as a gold standard for gathering evidence and for the purpose of
generalizability of findings. We then provided a step-by-step guide on
how to conduct systematic literature review including the use of grey
literature. To include grey literature in SLR, we suggested that a
scoping review on grey literature be conducted. The outcomes of scoping
literature review on grey literature provides the extent of evidence
available in non-published materials, which can be validated with
scoping review on mainstream literature.

\textbf{Disclosure Statement}

The author has no conflict of interest to report

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