International Journal of Information Management 37 (2017) 1380–1390
Contents lists available at ScienceDirect
International Journal of Information Management
journal homepage: www.elsevier.com/locate/ijinfomgt
Revisiting the information audit: A systematic literature review and
synthesis
Robert B. Frost, Chun Wei Choo∗
Faculty of Information, University of Toronto, 140 St. George. St., Toronto, ON M5S 3G6, Canada
a r t i c l e i n f o
Article history:
Received 25 May 2016
Received in revised form 8 September 2016
Accepted 11 October 2016
Available online 25 October 2016
Keywords:
Information audit
Information management
Information quality
Quality management
Literature review methodology
a b s t r a c t
The purpose of this paper is to revitalize the theory and practice of the Information Audit (IA) by connecting
it with recent developments in information management theories and methods While the IA is
a powerful information management practice, the methods and applications of IA have not been wedded
to recent developments in the study of information management capability and information quality
management. This study addresses that gap. The paper also introduces and applies a systematic methodology
for conducting literature reviews that combines concept mapping, review scoping, and a structured
search and analysis process. The resulting search in Scopus and Proquest and subsequent analysis of the
recent literature (2011–2016) on IA and quality, evaluation, measurement, and maturity in the context
of information management yielded the following findings and recommendations. IA research and
practice could do well to: pursue contingency frameworks rather than seek universal standardization;
investigate the relationship between IA and the dimensions of information quality and information management
quality; undertake case studies that apply more foundational IA methodologies in full; develop
theories of IA maturity and IA maturity modelling methods; recognize that measurement and evaluation
of information management quality and information quality are necessary elements of the IA and should
be explicitly incorporated into IA methodology.
© 2016 Elsevier Ltd. All rights reserved.
1. Introduction
The information audit (IA) has been defined by Buchanan and
Gibb (2007) as “a holistic approach to identifying and evaluating an
organization’s information resources and information flow, in order
to facilitate effective and efficient information systems” (p. 171).
The IA provides “an invaluable structure of knowledge” in formulating
an organizational information strategy (Orna, 2004; p. 105),
and as Buchanan and Gibb (2008) note, the IA’s influence on information
management, technology, systems, and content are well
established in much of the foundational literature on IA (Buchanan
& Gibb, 1998; Burk & Horton, 1998; Henczel, 2001; Orna, 1999).
In its fullest form, the IA encompasses all the methods and tools
needed to catalogue, model, evaluate, quality-control, and analyze
an organization’s information assets and information management.
In their comparative analysis of the common IA methodologies
established in the late 1990s and early 2000s, Buchanan and Gibb
(2008) propose a seven step methodological baseline for the IA and
find that the methodologies of Orna (1999) and Henczel (2001)
∗ Corresponding author.
E-mail addresses: robert@rbfrost.com (R.B. Frost), cw.choo@utoronto.ca
(C.W. Choo).
cover every step of the baseline. Henczel’s methodology involves
seven stages (pp. 18–19):
1) Planning the audit by setting objectives, identifying stakeholders,
scoping the project and allocating resources, selecting a
methodology, and developing communications and business
strategies.
2) Collecting data in an information resources database, designing
and distributing questionnaires, holding focus groups, and
conducting personal interviews.
3) Analyzing the collected data and research.
4) Evaluating gaps and duplications in information, mapping and
interpreting information flows, formulating recommendations,
and developing a change management plan.
5) Communicating recommendations to stakeholders through
written reports, presentations and seminars, webpages, and personal
feedback.
6) Implementing recommendations through implementation
programs, formal change plans, post-implementation strategies,
and information policies.
7) Ongoing information service management to measure and
assess the changes through a regular information audit and service
evaluation cycle.
http://dx.doi.org/10.1016/j.ijinfomgt.2016.10.001
0268-4012/© 2016 Elsevier Ltd. All rights reserved.
R.B. Frost, C.W. Choo / International Journal of Information Management 37 (2017) 1380–1390 1381
Other established methodologies cover all seven stages of the
baseline in different sequences or with added stages (e.g. Orna,
1999), do not provide guidance for the planning the audit (e.g.
Buchanan & Gibb, 1998), or do not provide guidance for planning
or change management following a report of the audit’s findings
(e.g. Burk & Horton, 1998). Griffiths (2012) notes that the traditional,
established IA methods are generally concerned with Hard
IA, involving “notions of compliance, regulation and accuracy” (p.
43), rather than improving the usability of information assets,
increasing the efficiency of information use, or finding opportunities
for business innovation by changing information management
practices (what Griffiths calls Soft IA). Henczel’s later emphasis on
user-centric interviews and focus groups, information flow mapping,
as well as integration with change management and service
management showed that the IA could have applications beyond
the “hard” realm of accountability and compliance.
While the IA is a powerful information management practice,
the methods of both hard and soft IA described in the literature have
not been wedded to recent developments in the study of information
management capability (as described by Mithas, Ramasubbu,
& Sambamurthy, 2011) and information quality management (as
conceptualized in the framework of Baskarada & Koronios, 2014).
Furthermore, measurement and evaluation techniques such as
information asset registration and maturity modelling remain
largely absent from most IA methodologies, their synthesis with IA
representing a direction for further research (Griffiths, 2012). With
the advent of widespread digital transformation and the rise of big
data, it is now more important than ever for organizations to have
methods and tools for auditing and evaluating their information
assets.
With the aim of better connecting the IA literature with recent
information management theories and methods, this paper poses
three research questions for investigation:
RQ1: What recent research (from 2011 to 2016) has been done
on IA?
RQ2: What recent research (from 2011 to 2016) has been done
on quality, evaluation, measurement, and maturity in the context
of information management?
RQ3: In the future, how might IA researchers and practitioners
synthesize the recent research on IA with the recent research on
information management quality, evaluation, measurement, and
maturity?
This paper will address the above three research questions
through a systematic literature review, present and discuss the
results of the review, outline the implications of the review for
IA researchers and practitioners, and describe the limitations of
the review. To better aggregate the large volume of articles under
review, the discussion of results will provide a high-level overview
of recent trends in IA methods and theories rather than a complete
analysis of every reviewed article. The paper offers unique
contributionsin both its literature review methodology and its findings
on recent trends in IA and information management quality,
evaluation, measurement, and maturity.
2. Concept mapping
In preparation for the systematic literature review, a conceptual
map of the linkages between IA, information quality, information
management quality, evaluation, measurement, and maturity was
created through a preliminary analysis of pivotal works from the
IA and information management literature. Mindful of the fact that
researchers and practitioners in different domains often utilize different
terminologies for similar concepts, the need to establish
working understandings of quality, evaluation, measurement, and
maturity was recognized. Applying the working understandings to
the conceptual mapping process led to the development of an analytical
framework, which would ensure a consistent approach to
analysis in later stages of the literature review.
Two perspectives on quality were considered: information quality
and information management quality. Information quality was
understood with respect to Floridi’s (2013) characterization of
information quality as the categories, dimensions, purpose-depth,
and purpose-scope that shape a unit of information. Information
management quality was understood with respect to Mithas,
Ramasubbu, and Sambamurthy’s (2011) characterization of highquality
information management capability as “the ability to
provide data and information to users with the appropriate levels
of accuracy, timeliness, reliability, security, confidentiality, connectivity,
and access and the ability to tailor these in response to
changing business needs and directions” (p. 238). Measurement
and evaluation were understood interchangeably as appraisals of
a quality criterion with reference to a specific performance indicator
(e.g. the appraisal of a form’s accessibility with reference
to the quantity or severity of access barriers it contains). Maturity
was understood with respect to Marchand, Kettinger, and
Rollins’ (2001) view of information orientation maturity as a mixture
of highly developed information capabilities. Benchmarking
was understood as an alternative approach to maturity modelling
in which an organization’s internal measurements are compared to
external measurements, rather than compared to internal targets.
With the key concepts for analysis defined, their links to an IA
were established in relation to Buchanan and Gibb’s (2007) description
of the IA as “a holistic approach to identifying and evaluating
an organization’s information resources and information flow, in
order to facilitate effective and efficient organizational information
systems” (p. 171). In deconstructing Buchanan and Gibb’s description
of the IA process, four implications for the linkages between an
IA and the other concepts under investigation in this paper become
evident:
Implication 1: The IA requires an evaluation of information
resources and flows.
Implication 2: The evaluation of information resources entails
a measurement of information quality.
Implication 3: The evaluation of information flows entails a
measurement of information management quality.
Implication 4: The measures of information quality and
information management quality are determinants of maturity,
contributing to effective and efficient information systems.
The four linkages derived from Buchanan and Gibb’s (2007) definition
were formalized in a concept map, shown in Fig. 1. The
concept map illustrates the causal relationships between each of
the concepts under analysis, and also provides a basic rationale
model of IA goals and IA-maturity links for use in future maturity
modelling.
With working understandings of the concepts under investigation
established and formalized in the concept map as a result of the
preliminary analysis, it was possible to proceed with the creation
literature review using a well-defined and consistent analytical
framework.
3. Methodology
The literature review was systematic and performed in accordance
with the methodology described by vom Brocke et al. (2009).
The methodology is rigorous in its approach, prescribing five phases
to the systematic literature review: scoping, conceptualization,
literature search, analysis/synthesis, and stating the expected contributions
of the review to the broader research agenda. This
literature review largely followed the prescriptions of vom Brocke
1382 R.B. Frost, C.W. Choo / International Journal of Information Management 37 (2017) 1380–1390
Fig. 1. Concept map of the linkages between IA and the other concepts under investigation in this paper, with reference to Buchanan and Gibb’s (2007) definition of the IA.
et al., with some liberties being taken to adapt the methodology to
the specific needs of this study.
To scope the review, vom Brocke et al. recommend using the
taxonomy of literature review characteristics proposed by Cooper
(1988). Table 1 utilizes Cooper’s scoping table to outline the overall
scope of this review, producing the following methodological
decisions:
Decision 1: To maintain a focus on methodological and conceptual
trends, the review would focus on methods and theories
of IA, information quality, information management quality, measurement,
evaluation, maturity, and benchmarking. A thorough
discussion of the individuals, industries, and/or sectors involved
in the application of methods and theories would be beyond the
scope of the review, as would detailed analysis and criticism of the
research outcomes of an empirical study.
Decision 2: The goal of the review would be to integrate those
methods and theories into insights on recent developments in IA
and points of connection between IA and other recent areas of
information management research.
Decision 3: The results and discussion of the review would be
organized conceptually, with reference to the conceptual mapping
performed during the review’s preliminary analysis.
Decision 4: The perspective of the review would ultimately be
argumentative rather than a neutral representation of the literature,
espousing a position on the best paths forward for future IA
research based on the review’s results.
Decision 5: The audience for the review would be specialized IA
scholars and practitioners.
Decision 6: The extent of the review’s coverage of the literature
would be representative, seeking a large enough sample of
the literature to be representative of current trends rather than an
exhaustive search of all recent literature.
After scoping the review, vom Brocke et al. recommend conceptualizing
the topic(s) to be studied—this phase of the methodology
development was already performed in the preliminary analysis,
with the creation of the concept map. Accordingly, the concept map
provided a conceptualization of the topics, which could be used to
inform the analysis and synthesis phase.
In developing the literature search process, an adaptation of the
search process proposed by vom Brocke et al. was used, specifically,
the adaptation used by Kowalczyk, Buxmann, and Besier (2013) in
their systematic review of the business intelligence and analytics
literature. The literature search process began with the creation
of inclusion criteria to determine the conditions under which an
Table 1
The scope of this review using the scoping table recommended by Cooper (1988), with this review’s characteristics highlighted in grey.
R.B. Frost, C.W. Choo / International Journal of Information Management 37 (2017) 1380–1390 1383
Fig. 2. The information flow of the literature search process used in this review, with the entire process being performed once to address RQ1 and a second time to address
RQ2.
article would be included in or excluded from analysis. The five
inclusion criteria decided upon were:
Criterion 1: The article was published in a peer-reviewed scholarly
journal, a trade journal, or the proceedings of an academic or
trade conference.
Criterion 2: The queried data source provided full text of the
article or a link to the full text of the article.
Criterion 3: The article was written in English.
Criterion 4: The article was published between 2011 and 2016.
Criterion 5: The article examines one or more of the concepts
included in the concept map from an information management perspective
(papers on the topic of maturity or quality measurement
from an operations management perspective with no relationship
to information management, for example, were excluded).
Next, query structures were iteratively developed through trial
and error. Query structures were experimentally entered into the
search engines of Scopus and ProQuest until a set of query structures,
which were optimal for addressing the research questions
and inclusion criteria, had been identified. Two structured queries
were created to extract articles from Scopus and ProQuest, which
could provide potential answers to RQ1, and another two structured
queries were created to extract articles from Scopus and
ProQuest, which could provide potential answers to RQ2. All four
query structures are documented in Appendix A of this paper. RQ3
was to be addressed through a discussion of the literature review’s
results rather than through a query of its own.
To ensure the results more accurately reflected the inclusion
criteria, the queries were accompanied by manual selections of
inclusion criteria in the Scopus and ProQuest user interfaces: only
articles published between 2011 and 2016 in scholarly journals,
trade journals, and conference proceedings were included in the
search results.
With the query structures optimized and defined, two separate
instantiations of the literature search process were carried out in
response to RQ1 and RQ2, respectively. Both search processes were
carried out in careful conformance with a predefined process model
(depicted in Fig. 2). Both search processes consisted of five steps:
Step 1: After querying the Scopus and ProQuest search engines,
article information was extracted from Scopus and ProQuest and
results counts were recorded in an extraction log.
Step 2: The extracted articles had their titles and abstracts analyzed
to assess their degree of fit with the concept map and their
potential value in answering the research question at hand. Articles
that were not well-suited to the conceptualization of the topic
or had little value in answering the research question at hand
were excluded from further consideration, and the count of articles
remaining after the title and abstract analysis was recorded in
the extraction log.
Step 3: The remaining articles had their full text analyzed in
full to assess their degree of conceptual fit and potential value in
greater detail. Again, articles that were not well-suited conceptually
or had little value in answering the research question at hand
were excluded from further consideration. The count of articles
remaining after the full text analysis was recorded in the extraction
log.
Table 2
Concept matrix template used to record and count conceptual information about the articles. The actual concept matrix resulting from the review can be found in Appendix
B.
Concept Matrix Template
Year Author(s) Research
Focus
Info. Audit IM Quality Info. Quality Measure-
ment/Evaluation
Maturity/
Bench-
marking
(Year) (Authors)
1384 R.B. Frost, C.W. Choo / International Journal of Information Management 37 (2017) 1380–1390
Table 3
Extraction log of the results from each step of both the literature search addressing RQ1 and the literature search addressing RQ2.
Extraction Log
Research Question Database Name Structured Query Results Title/Abstract Results Full Text Results
RQ1 Scopus 98 18 9
RQ1 ProQuest 194 6 4
RQ2 Scopus 289 5 4
RQ2 ProQuest 416 11 6
997 40 22
Table 4
Total counts of the amount of articles adopting particular research and conceptual
foci from the pool of 22 articles remaining after the full text analysis.
Concept Matrix Totals
Research Focus Article Count
Outcomes 4
Methods 15
Theories 14
Applications 9
Concept Matrix Totals
Conceptual Focus Article Count
Information Audit 12
Information Management Quality 3
Information Quality 10
Measurement/Evaluation 13
Maturity/Benchmarking 5
Step 4: All of the articles remaining after the full text analysis had
their authorship information and year of publication recorded in
an adaptation of the concept matrix template (depicted in Table 2)
provided by Webster and Watson (2002). The articles were then
reviewed in full a second time to discern their focus or foci with
reference to the categories the scoping table: outcomes and results
of research studies, methods of conducting research and analysis,
theories that conceptually model and explain the nature of a target
of analysis, or applications of research methods and theories to specific
case studies or contexts. The second full text review was also
used to discern the extent to which they analyzed the concepts outlined
in the concept map. The research focus/foci and concept map
relationships of each article were recorded in the concept matrix.
Step 5: The counts of each research focus and concept map relationship
were tallied and used for analysis and synthesis in Section
5 of this review.
The literature searches in response to RQ1 and RQ2 were both
carried out over the span of one week in March 2016. The rigor,
reproducibility, and research question orientation of the methodology
was expected to answer all three research questions posed
by this study, offering three sets of unique contributions to the IA
research agenda.
4. Results
Between both search processes, 997 initial results were
returned, 40 remained after title and abstract analysis, and 22
remained after full text analysis. In accordance with the methodology,
a breakdown of the step-by-step results was recorded in an
extraction log, which can be found in Table 3.
All 22 of the articles remaining after the full text analysis had
their authorship information and year of publication entered into
a concept matrix. Their full text was then reviewed a second time
to determine the research and conceptual foci of each article. After
the research and conceptual foci of each article had been recorded
in the concept matrix, the results of the review were sorted in
descending order based on year of publication in order to facilitate
analysis of trends in the literature over time, and the results of
the research focus and conceptual reviewing for each article were
tallied.
Table 4 depicts the total counts for each research and conceptual
focus, which were observed in each of the 22 reviewed articles.
Note that it is possible for a single article to have multiple research
or conceptual foci, and thus the research and conceptual foci counts
do not add up to 22. The research foci most often represented in the
22 reviewed articles were methods and theories, with 15 articles
adopting a methods focus and 14 articles adopting a theories focus.
The conceptual foci most often represented in the 22 reviewed articles
were measurement/evaluation and information audit, with 13
articles analyzing and discussing measurement/evaluation in detail
and 12 articles analyzing and discussing information audit in detail.
A more detailed discussion of the trends revealed by the concept
matrix and the implications of those trends for researchers and
practitioners will be presented in later sections of this paper. A full
version of the concept matrix with research and conceptual foci
counts for each of the 22 articles can be found in Appendix B of this
paper.
Considering the initial queries for RQ1 and RQ2 resulted in a total
of 997 articles being extracted from Scopus and ProQuest, it may
seem surprising that so few articles remained after full text analysis
was performed. The significant gap between initial query results
and full text results is in large part attributable to the fact that many
initial query results did not approach the concepts under investigation
from an information management perspective, and as a
result, use terms such as “information audit”, “information quality”,
and “measurement” in senses, which are unrelated to the prevalent
conceptualizations of those terms within the information management
literature. For example, in one excluded article, which had
its title and abstract analyzed in response to RQ1, Yanjun (2013)
discusses highly technical methods of auditing the functionality of
computer systems, and in doing so, uses the phrase “massive information
audit” (p. 409) to describe the need for computer system
auditors to develop new computer auditing methods in response
to the big data paradigm. In another example of an excluded article,
which had its title and abstract analyzed in response to RQ2, Orozco,
Tarhini, Masa’deh, and Tarhini (2015) briefly discuss concepts of
measurement, evaluation, and maturity, but only in the context of
improving IT governance architectures through increasing information
system and business alignment. The article does not analyze
those concepts of measurement, evaluation, and maturity models
in much detail, nor does it link its domain-specific perspective to a
broader information management perspective or the relationships
expressed in this paper’s concept map.
Despite a smaller sample of results remaining after the full text
analysis than was initially anticipated, the results are representative
enough of recent trends in the IA literature and literature
related to the concept map established in the preliminary analysis
to be able to answer RQ1 and RQ2. Recent developments and
trends in IA literature (addressing RQ1) and relevant information
management literature (addressing RQ2) will be discussed in the
following section with reference to the literature search processes,
and ultimately, the findings from the two bodies of literature will
R.B. Frost, C.W. Choo / International Journal of Information Management 37 (2017) 1380–1390 1385
be synthesized into implications for researchers and practitioners,
which address RQ3.
5. Discussion
Given the number of papers found in the literature search, it
is not feasible to elaborate on the content of each item. Instead,
we provide a big-picture view, which highlights the main contours
of the IA and information management terrain covered by recent
published research. In analyzing the literature, we sought out the
main areas of research emphasis, as well as the areas that require
research but have hitherto received little attention.
5.1. Recent literature on IA
To address the first research question (RQ1), our analysis of the
literature on IA leads us to make the following four observations
that we believe characterize the state of recent research on the
subject.
5.2. Firstly, much more attention has been given to theories and
methods of IA than IA research outcomes and applications
The recent theory-building observed in the IA literature always
goes hand-in-hand with more prescriptive guidelines and methods
of IA practice. For example, Fraser-Arnott (2014) develops a
theoretical framework of technical, interpersonal, analytical, and
knowledge competencies within librarianship, which—when fused
with knowledge management techniques—form domain-specific
methods and tools of knowledge auditing. A second example
of IA theories and methods being fused include Buchanan and
McMenemy’s (2012) discussion of the nature of elicitation, decomposition,
and representation in process modelling methods used
to model digital library information flow. In a third example,
Griffiths (2012) analyzes of the fundamental, common characteristics
between IA methodologies, the division between “hard” IA
focused on compliance and “soft” IA focused on information mapping,
and the manner in which that division and the characteristics
of hard and soft IA are expressed in practical techniques of IA such
as compliance management, information systems audit, and information
asset registration.
5.3. Secondly, there has been limited research that focuses on IA
applications and case studies
Among the 22 articles remaining after the full text analysis, only
five have a focus on IA applications (Ariffin, Latif„ Faudzi, Shariff, &
Nadzir, 2014; Jones, Mutch, & Valero-Silva, 2013; Mircea, GhilicMicu,
& Stoica, 2012; Ocholla, 2011; Vo-Tran, 2011). Of those five
articles, only Jones et al. (2013) have a pure focus on application,
applying the well-established IA methodology of Buchanan and
Gibb (1998) to a case study of information flow mapping at the
Nottingham City Homes public housing service. Their case study
follows three steps: identifying the organizational context and process
structure by reviewing business documentation; interviewing
staff members to determine the processes/activities involved in
their role, the information flows they mediate, and the information
resources they utilize; analyzing and reporting on recommendations,
then seeking the implementation of those recommendations.
Jones, Mutch, and Valero-Silva conclude that the results of their
application “affirm the value of the [IA] approach” (p. 297), but
suggest that challenges exist in convincing key stakeholders to recognize
the value of and engage with the information audit process.
5.4. Thirdly, there have been few notable attempts to extend or
modify established IA methodologies for specific industries or
specialized domain areas
The research foci of the remaining four IA applications are
divided between methods and applications, with the authors
proposing their own extensions or modifications of established IA
methodologies, then applying their modified methods to a case
study. Ariffin et al. (2014) propose modifications of IA methods
to better suit the electricity supply industry, determining that
IA implementation is currently a challenge in the industry due
to the limited linkage of existing IA frameworks with systems
development processes, the lack of scoping or resource sampling
guidelines in most IA methods, the lack of standardized IA methodologies,
and the lack of change management guidelines to facilitate
the implementation of IA-driven recommendations. Mircea et al.
(2012) devise a variation of the IA referred to as a “decision process
audit” with five steps: planning the audit, identification of
key decisions, description of decision processes, evaluation of decision
processes, and drafting the audit report. They then apply the
decision process audit method to a case study of a public procurement
contract for photocopy repair and maintenance services
in the Bucharest University of Economic Studies. Ocholla (2011)
leverages IA methods such as process mapping and resource inventory
to propose research audit methods for mapping and auditing
scholarly research, then conducts a research audit of the research
projects and research output of the University of Zululand between
1994 and 2008. Vo-Tran (2011) combines Henczel’s (2001) IA
methodology with an action research methodology, modifying the
“implementing recommendations” stage of Henczel’s methodology
to be better applicable to architectural design. Vo-Tran then applies
the hybrid methodology to a case study of the architectural design
process of a new building at an Australian university.
5.5. Fourthly, there is a near absence of research that focuses on
IA implementation and outcomes
Only one article extracted from the literature search has a focus
on IA research outcomes: Carvalho and Esteban-Navarro’s (2016)
study of intelligence system auditing, in which the results of a
literature review, participant observation, and case study are triangulated,
enabling the authors to propose empirically-grounded
methods of auditing the informational and operational aspects of
business intelligence systems. In their literature review, Carvalho
and Esteban-Navarro separate audit methods into information
resources audit, information audit, knowledge audit, communication
audit, and intelligence audit, recognizing the unique value of
each set of audit methods as part of a more holistic audit of intelligence
systems. Their incorporation of “information ecology theory,
information asset audits, user studies, knowledge management and
intelligence studies” (p. 58) into traditional IA methods, inspired
by the outcome of their research, implies that traditional IA methods
must be responsive and amenable to the growing importance
of knowledge management and business intelligence systems if IA
is to remain a valuable information management practice in the
current business environment.
The lack of any outcome-focused empirical studies of IA implementation
beyond Carvalho and Esteban-Navarro’s indicates a
significant research gap for IA. Although many articles focus on
methods and theories, which extend the capability of the traditional
IA methods, there is little research being done to analyze,
criticize, and develop new methods and theories out of the planning
and implementation of specific IA projects. In studying applications
of IA methods and theories, it is worth noting that compared to the
early IA methodologies, the objectives of audits are increasingly
taking a hybrid approach, auditing for hard IA reasons of compli-
1386 R.B. Frost, C.W. Choo / International Journal of Information Management 37 (2017) 1380–1390
ance and accountability, as well as soft IA reasons of usability and
innovation. However, there does not yet exist a contingency model
outlining which hard IA or soft IA methods an organization should
adopt based on the audit context.
5.6. Recent literature on quality, measurement/evaluation, &
maturity/benchmarking
In response to the second research question (RQ2), and
based on our analysis of the recent literature, we again make
a number of observations that we believe characterize the state
of recent research on quality, measurement/evaluation, maturity/benchmarking
in the information management context.
First, we note that the greatest research attention has been given
to measurement/evaluation. Of the concepts from the concept map
which were investigated in the literature search process addressing
RQ2, the concept with the greatest attention paid to it was measurement/evaluation,
with a total of 13 articles including it as a
conceptual focus (see Appendix B for the full mapping of every article
aligned with the measurement/evaluation focus). Of those 13
articles, however, only four discuss measurement/evaluation from
an information management perspective in addition to discussing
the concept of IA (Buchanan & McMenemy, 2012; Jones et al., 2013;
Mircea et al., 2012; Shamel, 2014). Shamel (2014) situates the IA
within a larger knowledge assessment process, first formulated
by Henczel (2000), in which the information audit is preceded
by needs analysis and followed by a knowledge audit. Shamel
then suggests methods of measuring the gap between information
and knowledge management goals and the actual performance
revealed by the audit, as well as a list of common strengths and
weaknesses to be vigilant for during evaluation of the audit results.
Jones et al. (2013) describe the methods that they used to have staff
members of an organization, which is undergoing an IA, evaluate
their organization’s information resource usage with reference to
a set of best practices. Buchanan and McMenemy (2012) discuss
measures of success and common pitfalls in modelling information
flows as part of an IA, such as failing to elicit vital information from
staff, failing to decompose information flow processes appropriately,
and underrepresenting less tangible activities or resources.
Mircea et al. (2012) discuss the five steps of evaluating decision
process quality as part of a decision process audit: identifying the
dimensions of the managerial situation, developing alternatives,
analyzing and evaluating alternatives, choosing and implementing
the best alternative, and evaluating the overall quality of the
decision.
Other articles in the segment of measurement/evaluation literature,
which do not share a conceptual focus on IA, tend to
focus on the management of information systems. For example,
Visser, van Biljon, and Herselman (2013) analyze frameworks and
survey tools for evaluating the performance of management information
systems in Further Education and Training colleges. Petter,
DeLone, and McLean (2012) review the historical evolution of information
systems success measures followed by the suggestion of
research directions for new success measures. Mohammed and
Yusof (2012) offer criteria for effective information quality management
in health information systems. All of these articles present
novel approaches to the measurement and evaluation of information
systems, but are not connected to the literature on IA. However,
all three of the above examples share an additional conceptual
focus on information quality, indicating the importance of not only
managing information systems, but managing the information they
contain as well.
Second, we note a surprising lack of overlap between information
quality and IA as conceptual foci in the literature. On the whole,
information quality also received a great deal of attention in the
reviewed literature: 10 articles, which analyzed or discussed information
quality, were reviewed in total. Of those 10 articles, seven
conceptually overlap with measurement/evaluation (including the
three examples of information systems management articles noted
above). This close relationship between information quality and
measurement/evaluation is somewhat unsurprising, given the fact
that information quality is aptly defined by one of those 10 articles
as “a measure [emphasis added] of where the information consistently
meet [sic] all user expectations” (Gunawan & Suhardi, 2014,
p. 1). The lack of overlap between information quality and IA conceptual
foci, though, is more surprising: out of all of the articles
reviewed, only Griffiths (2012) discusses both information quality
and IA. In fact, in discussing the need to combine business
intelligence and compliance functions to better manage information
quality, Griffiths voices “concern that IA practitioners may be
placing emphasis on the quality of the subject organization’s data
processing when the key element should be an assessment of the
quality of the data itself” (p. 45). While Griffiths does not provide
a framework for assessing information quality as part of an organization’s
IA, his concern about data and information quality being
overlooked within IA practice signals the need for researchers and
practitioners to begin bridging this conceptual gap.
Third, there has been limited research attention on maturity/benchmarking
in the information management context. In
contrast to measurement/evaluation and information quality,
the concept of maturity/benchmarking in the context of information
management received relatively little attention in the
reviewed literature: only five articles analyze and discuss maturity/benchmarking
(Chuah, 2014; Gunawan & Suhardi, 2014; Zou,
Flanagan, Jewell, & Tang, 2013; Chuah & Wong, 2012; Pranicevi´c,
Alfirevi´c, & ˇStemberger, 2011). Following an initial study (Chuah
& Wong, 2012), which determined that further exploration of the
dimensions of business intelligence quality and maturity were
needed, Chuah (2014) constructs a maturity model for enterprise
business intelligence, linking the development of organizational
and information-managerial capabilities to the maturity level of
the enterprise business intelligence system. Gunawan and Suhardi
(2014) create a theoretical and methodological framework for total
information quality management, combining quality management
principles from the total quality management and information
quality management schools into an evaluable, five-level maturity
model. Zou et al. (2013) create an information maturity model tailored
to the decision-making needs of the construction industry,
proposing that a unit of information’s overall maturity is composed
of four measures: its format, informational value, informational
quality, and the timeliness of its delivery to end users. Based on
their empirical study of the relationship between information system
maturity and hotel performance measurement, Pranicevi´c et al.
(2011) introduce a maturity model for information systems in the
hospitality industry. Despite the breadth of domain-specific theories
and methods of maturity modelling displayed throughout the
reviewed literature, no article was found with conceptual overlap
between IA and maturity/benchmarking, leaving a gap in the
research to be filled by a maturity model of IA practice.
Finally, the concept with the least attention paid to it was information
management quality. Only three articles analyze and discuss the
concept (Fraser-Arnott, 2014; Sheriff, Bouchlaghem, El-Hamalawi,
& Yeomans, 2012 ;Griffiths, 2012). Sheriff et al. (2012) explore the
drivers of and barriers to effective information management in
UK-based architecture and engineering organizations, determining
from the outcome of their study that the ability to improve end
products, the ability to improve processes, the effective transfer
of learning, legal and regulatory compliance, and risk mitigation
are all drivers of information management quality. However, Sheriff
et al.’s unique perspective on information management quality
does not share a conceptual focus on IFraser-Arnott (2014) and
Griffiths (2012) do exhibit conceptual foci on both IA and informa-
R.B. Frost, C.W. Choo / International Journal of Information Management 37 (2017) 1380–1390 1387
tion management quality, but their engagement with the drivers,
barriers, and dimensions of information management quality is not
as direct and thorough as Sheriff et al.’s. As a result, there is a gap
in the research to be filled by an outcome-focused empirical study
of the causal relationships between IA methods and information
management quality.
6. Implications for researchers
Our characterization of the recent literature in IA and information
management allows us to identify the areas of emphasis in the
research, as well as the gaps and opportunities for further research.
Based on the discussion in the last section, we propose four directions
for future IA research that would address RQ3, that is, how
might IA researchers better synthesize the recent research on IA
and information management?
6.1. Direction 1: pursue contingency frameworks rather than
standardization
Ariffin et al. (2014) and Griffiths (2012) have recently lamented
that the lack of standardization in IA methodologies is a central reason
for IA failing to be put into widespread practice. Buchanan and
Gibb’s (2008) “methodological baseline” for IA practice contends
that IA methodology selection should be based on organizational
requirements and auditor experience, but stops short of building
a contingency framework of context-specific methods and tools,
which could supplement the baseline. This review’s finding that
most recent IA research focuses on methods, signals that the proliferation
of different methodological variations and permutations of
the IA has become too spread out between different use cases and
industry-specific needs to be effectively standardized. In light of
that finding, it appears that the pursuit of cross-industry or industry
standardization is a dead end.
Future research should instead focus on designing contingency
frameworks for IA practice, with different sets of methods and
tools being prescribed for different industries, business scenarios,
and information cultures. For example, aligning particular sets of
methods and tools with the relationship-based, risk-taking, rulefollowing,
and results-oriented information cultures typologized
by Choo (2013) could prove more widely valuable and applicable
than a one-size-fits-all approach to IA methodology.
6.2. Direction 2: explore the relationship between IA and quality
dimensions in more detail
Despite the close conceptual link between IA, information management
quality, and information quality established in this paper,
the relationship between those concepts is scarcely explored in the
reviewed literature. Only Fraser-Arnott (2014) and Griffiths (2012)
discuss the nexus of IA and information management quality, and
only Griffiths discusses the nexus of IA and information quality.
Future research, which seeks a deeper understanding of the causal
relationships between IA and quality, will not only provide novel
contributions to the research agenda, but would also validate the
power of the IA if a causal link between IA practice and heightened
quality could be empirically demonstrated.
6.3. Direction 3: apply more foundational IA methodologies in full
to case studies
There is a shortage of case studies applying foundational IA
methodologies such as Burk and Horton (1998), Buchanan and Gibb
(1998), Orna (1999), and Henczel (2001). In the reviewed literature,
researchers will often propose their own modifications of foundational
IA methodologies and apply the modified components to
a case study—as exemplified in Ariffin et al. (2014), Mircea et al.
(2012), Ocholla (2011), and Vo-Tran (2011)—but there is only one
instance of a foundational IA methodology being applied to a case
study in full, as exemplified in Jones et al. (2013). Moreover, there
are few articles focused on the research outcomes of empirical
studies of IA implementation compared to articles focused on IA
methods, theories, and applications. Future research focusing on
the outcomes of applying foundational and recent IA methods and
theories could make a significant contribution to the IA literature
by reporting on the strengths and weaknesses of IA methodologies
in different use contexts, resulting in more empirical studies of IA
outcomes.
6.4. Direction 4: develop theories of IA maturity and IA maturity
modelling methods
In the reviewed literature, there is no article which exhibits
conceptual overlap between IA and maturity. Griffiths (2012) has
suggested that the development of a maturity model represents a
future direction in his research, and indeed, this review suggests
that theories of IA maturity and IA maturity modelling methods
are future research directions, which still remain unrealized.
6.5. Contribution to research methodology
In addition to its implications for the content of future IA
research, this literature review claims a unique methodological
contribution, which stands to benefit all information management
researchers. vom Brocke et al. (2009) provided the overall structure
of this review’s methodology, Kowalczyk et al. (2013) provided a
refinement of the literature search process, Webster and Watson
(2002) provided the concept matrix template, and Cooper (1988)
provided the scoping table template. However, to the knowledge
of the author, the creation of a concept map and its direct integration
into research questions, an extraction log, concept matrix, and
results-driven conceptual analysis and synthesis has not previously
been performed in the same manner as this review. Additionally,
this review’s use of a detailed data flow model to illustrate the specific
actions involved in the literature search process and make the
process easily replicable from the diagram alone is unique in the
information management literature. This methodology is ideal and
easily replicable for potentially any literature review, which aims
to sample only a representative portion of the literature and synthesize
the findings, but strive for a high degree of rigor in doing
so.
Because this methodology is so easily replicable, it is an ideal
candidate for the methodology of future IA reviews. An update to
this literature review could be conducted years after the publication
of this review using the exact same methodology, and an expansion
of this literature review to analyze more data sources and concepts
could easily be done by making minor adjustments to the concept
map, extraction log, and concept matrix.
7. Implications for practitioners
In a series of post-hoc discussions, the authors of the paper
reflected further on the findings of our literature survey, and arrived
at three recommendations for IA practitioners.
Recommendation 1: Measurement and evaluation of information
management quality and information quality are necessary
parts of the IA. Define information management quality and information
quality dimensions in advance of the audit, then measure
and evaluate those quality dimensions as part of the audit. Mithas
et al. (2011) implicitly offer insights into the quality dimensions of
information management through their examination of information
management capability factors; Floridi (2013) offers a readily
1388 R.B. Frost, C.W. Choo / International Journal of Information Management 37 (2017) 1380–1390
useable typology of information quality dimensions. Measurement
and evaluation of the quality dimensions can then be incorporated
into the phase of an IA methodology, which involves evaluation
and qualitative judgment of the audit’s findings, such as Henczel’s
(2001) “data evaluation” phase or Buchanan and Gibb’s (2008)
“account” phase.
Recommendation 2: In the reviewed literature, modelling
and diagramming techniques are rarely recommended or situated
within a larger IA process, but the use of these techniques
is essential to the holistic mapping of information resources and
flow. Observing Buchanan and Gibb’s (2007) separation of resource,
process, and strategic perspectives when conducting the IA, there
is a need for using different modelling and diagramming techniques
when auditing within each of the three perspectives.
Use entity-relationship diagrams and/or classification schemes to
model information resources when working in the resource perspective;
use data flow, UML, and/or BPMN diagrams to model
information flows when working in the process perspective; use
Allee’s (2000) value network diagram and/or the strategic view diagram
template found in Buchanan and Gibb (2007, p. 169) to model
alignment between information resources, flows, and strategies
when working the strategic perspective.
Recommendation 3: Document and publish more applications
of IA methods so that researchers and practitioners have a greater
knowledge base to draw from in studying the performance of different
IA method and in innovating new IA methods and tools.
8. Limitations
In advance of this review being conducted, the coverage of
this literature review was predefined as representative rather than
exhaustive, as outlined in Section 3. Accordingly, only two major
academic databases (Scopus and ProQuest) were queried as part of
the literature review. The body of literature on IA is quite small and
highly specialized, so an exhaustive review encompassing more
data sources will likely not produce any significant insights into
RQ1 beyond what has been covered in this review. Nonetheless,
this review’s coverage of only two data sources does constitute a
limitation of the study.
A more significant limitation arises from the query structures,
which were used to address RQ2 (found in Appendix A). Those
query structures were designed to limit the query results to articles,
which discussed information management, information quality,
and one other conceptual keyword, with the assumption that those
articles would be of greatest relevance to the scope of this literature
review. In the experimental development of the query
structures; query structures seeking a match for only one conceptual
keyword were tested. Unfortunately; each of those query
structures resulted in tens of thousands of results being returned
from both Scopus and ProQuest. If the number of data sources
were expanded beyond two; the total number of query results pertaining
to any one conceptual focus could easily extend into the
hundreds of thousands. Although an exhaustive review of the literature
on information management quality; information quality;
and measurement/evaluation/maturity/benchmarking in the context
of information management would surely reveal enormous
potential for cross-pollination with IA theories and methods; such
a review would likely not be possible without a data mining and
analysis tool custom-built for the research project.
9. Conclusion
This literature review posed and answered three research questions,
which aimed to better discern recent developments in the
IA literature, recent developments in quality, evaluation, measurement,
and maturity in the information management literature,
and how those two sets of developments could be synthesized
with one another. A conceptualization of the topics was
mapped out, and a systematic literature review methodology was
described. The review was conducted and the results of the review
were presented and discussed, with special attention paid to
the research foci and conceptual foci of the reviewed literature.
Implications of the review for researchers and practitioners were
then outlined, and the limitations of the review were acknowl-
edged.
This literature review indicates that interest in IA research has
remained highly specialized yet steady since 2011. Embedding
recent developments in information management, information
quality, measurement and evaluation, and maturity and benchmarking
more deeply into IA theories, methods, and applications
could propel interest in IA research up to higher levels. The
practicality of IA methodologies could be improved by having
more IA case studies on record, as well as through the design
of IA contingency frameworks, IA maturity models, and information/information
management quality measurement techniques.
The IA is clearly an extremely valuable information management
practice, but there is still much work to be done by both IA
researchers and practitioners to make the practice more widely
known and used. It is unfortunate that at a time when information
management has become a necessity in nearly every industry and
sector, IA research and practice have lagged behind other facets
of information management. There now exists a great opportunity
for an intellectual renewal of IA research and practice, and
it is hoped that this literature review will serve as a guidepost for
anyone seeking to push the IA agenda forward.
Funding
This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit sectors.
Appendix A. Query Structures
The query structure used to extract articles from Scopus in
response to RQ1 was structured as follows:
{information audit}
The query structure used to extract articles from ProQuest in
response to RQ1 was structured as follows:
“information audit” NOT auditory
The query structure used to extract articles from Scopus in
response to RQ2 was structured as follows:
{information management} AND {information quality} AND
(“evaluat*” OR “measure*”) AND (“maturity” OR “benchmark*”)
The query structure used to extract articles from ProQuest in
response to RQ2 was structured as follows:
(“information management” AND “information quality”) AND
(“evaluat*” or “measure*”) AND (“maturity” OR “benchmark*”)
R.B. Frost, C.W. Choo / International Journal of Information Management 37 (2017) 1380–1390 1389
Appendix B. Full Concept Matrix
Concept Matrix
Reference Research Focus Info. Audit IM Quality Info. Quality Measure-
ment/Evaluation
Maturity/Bench-
marking
Carvalho and Esteban-Navarro (2016) Outcomes, Methods x
Ayyash (2015) Theories x
Fraser-Arnott (2014) Theories, Methods x x
Ariffin et al. (2014) Methods, Application x
Shamel (2014) Theories, Methods x x
Chuah (2014) Theories x x
Gunawan and Suhardi (2014) Theories, Methods x x x
Bastos, Moreira, Bruno, Filho, and Filho (2014) Theories, Methods, Application x x
Jones, Mutch, and Valero-Silva (2013) Application x x
Zhi-hong, Da-wei, and Hong-yun (2013) Methods x
Visser et al. (2013) Theories, Methods, Application x x
Zou et al. (2013) Theories, Application x x x
Mohammed and Yusof (2013) Methods, Application x x
Sheriff et al. (2012) Outcomes, Theories x
Reifsnider (2012) Methods x
Buchanan and McMenemy (2012) Theories, Methods x x
Griffiths (2012) Theories, Methods x x x
Chuah and Wong (2012) Outcomes x x x
Petter et al. (2012) Theories x x
Mircea et al. (2012) Methods, Application x x
Ocholla (2011) Theories, Methods, Application x
Vo-Tran (2011) Methods, Application x
Pranicevi´c et al. (2011) Outcomes, Theories x x
12 3 10 13 5
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Robert B. Frost is an information management researcher with a Master of Information
degree from the University of Toronto. His research interests include methods
of auditing information assets, quality dimensions of information management, and
methods of leveraging information assets to improve service quality. His Masters
degree culminated in a capstone project, which involved the design and pilottesting
of a holistic audit framework for the cataloguing, modelling, and quality
management of information assets.
Chun Wei Choo is Professor of the Faculty of Information at the University of
Toronto. His recent books include The Inquiring Organization (2016, Oxford University
Press); The Knowing Organization (2nd ed., 2006, Oxford University Press);
The Strategic Management of Intellectual Capital and Organizational Knowledge
(2002, Oxford University Press); and Information Management for the Intelligent
Organization (3rd ed., 2002, Information Today).