Application of latent semantic analysis in AIS academic

Contents lists available at ScienceDirect

International Journal of Accounting InformationSystems

journal homepage: www.elsevier.com/locate/accinf

Application of latent semantic analysis in AIS academic researchPaul D. Hutchisona,⁎, Ronald J. Daigleb, Benjamin GeorgecaUniversity Of North Texas, Department of Accounting, 1155 Union Circle #305219, Denton, TX 76203-5017, United States of Americab Sam Houston State University, College of Business, Department of Accounting, Huntsville, TX 77341-2056, United States of AmericacUniversity of South Dakota, Beacom School of Business, Department of Decision Sciences, 414 East Clark Street, Vermillion, SD 57069, United Statesof America

A R T I C L E I N F O

Keywords:Accounting information systems researchLatent semantic analysisResearch methodologyBig data

A B S T R A C T

This study provides insights about the historical, intellectual structure, and trends of academicresearch themes in journals specifically dedicated to Accounting Information Systems (AIS)research—International Journal of Accounting Information Systems (IJAIS), its predecessor journal,Advances in Accounting Information Systems (AiAIS), and Journal of Information Systems (JIS). UsingLatent Semantic Analysis, a statistical text analytics methodology that can uncover the con-ceptual content within unstructured data, this study identifies 14 prevalent academic researchthemes in AiAIS, IJAIS, and JIS from 1986 to 2015 and provides graphs that visualize thematictrends over time, including by journal. Certain themes have remained consistent in their studyover the timeframe, while others have increased or diminished. Certain themes have maturedwhile others appear to still be maturing at the end of the timeframe. Thematic trends by sourcejournal suggest that no journal has dominated publishing specific significant themes in AISacademic research.

1. Introduction

This study provides insights about the historical, intellectual structure and trends of themes in journals specifically dedicated toAccounting Information Systems (AIS) academic research—International Journal of Accounting Information Systems (IJAIS), its pre-decessor, Advances in Accounting Information Systems (AiAIS) and Journal of Information Systems (JIS). It is common to analyze theresearch literature of an academic discipline to gain fresh insights about its evolution, maturity, and potential future directions. Thisis true for AIS, whether it be:

• Of a particular theme (e.g., Guan et al., 2013; Yigitbasioglu and Velcu, 2012; Boritz and No, 2011; Kauffman et al., 2011;Konchitchki and O'Leary, 2011; Masli et al., 2011),• Select journal(s) dedicated to AIS (e.g., Guan et al., 2018; Moffitt et al., 2016; Hutchison et al., 2004; Samuels and Steinbart,2002), or• Across journals that publish AIS research (e.g., Daigle and Arnold, 2000).

This study utilizes a methodology that is quite different from most applied in prior AIS literature reviews. Traditional techniquesinclude thematic reviews, manual content analysis, and citation analysis. This study uses Latent Semantic Analysis (LSA), a statisticaltext analytics method that can uncover the conceptual content within unstructured data (Larsen and Monarchi, 2004). As a “big data”

https://doi.org/10.1016/j.accinf.2018.09.003Received 26 September 2017; Received in revised form 26 June 2018; Accepted 18 September 2018

⁎ Corresponding author.E-mail addresses: [email protected] (P.D. Hutchison), [email protected] (R.J. Daigle), [email protected] (B. George).

International Journal of Accounting Information Systems xxx (xxxx) xxx–xxx

1467-0895/ © 2018 Elsevier Inc. All rights reserved.

Please cite this article as: Hutchison, P.D., International Journal of Accounting Information Systems, https://doi.org/10.1016/j.accinf.2018.09.003

http://www.sciencedirect.com/science/journal/14670895

https://www.elsevier.com/locate/accinf

https://doi.org/10.1016/j.accinf.2018.09.003


mailto:[email protected]




tool, LSA has been used to analyze literature in Management Information Systems (MIS) (Love and Hirschheim, 2016; Sidorova et al.,2008), Operations Management (Kulkarni et al., 2014), and Real Estate (Winson-Geideman and Evangelopoulos, 2013a; Winson-Geideman and Evangelopoulos, 2013b). It has also recently been used to analyze the literature of a specific journal within a particularfield – JIS in AIS (Guan et al., 2018).

Chakraborty et al. (2014, 122) promote using “data mining techniques to automatically classify accounting research” and thisstudy meets that call. This research also complements and extends the LSA study by Guan et al. (2018) of JIS alone by examining AISarticle abstracts in AiAIS, IJAIS, and JIS from 1986 to 2015 (the same period studied in Guan et al., 2018).

LSA provides a unique benefit not found in many traditional literature review methods—a reduction in subjective bias whenperforming and interpreting the analysis. Like traditional factor analysis, LSA extracts words or collections of words from a text (e.g.,article abstracts, as in the present and relevant prior studies cited) through a mathematical dimension reduction technique. Keythemes are identified that can be analyzed, characterized, described, and labeled. This study identifies 14 prevalent academic re-search themes in AiAIS, IJAIS, and JIS from 1986 to 2015. The themes are validated through both relative strength of each extractedtheme's corresponding eigenvector and the resulting coverage of the abstracts dataset. This study also includes graphs that visualizethematic trends over time, including by journal.

The graphs suggest that certain themes have remained consistent over the period studied, while others have increased or di-minished. “Decision aids” (#2) and “Defining AIS” (#3) have consistently received more attention over time. “AIS investment on firmperformance” (#1) and “XBRL, taxonomies, and financial reporting” (#4) have received greater attention over time, especially nearthe end of the timeframe. “Group decisions with software” (#14) have received some attention over the timeframe, but not asconsistently as some other themes. “Expert systems” (#7) has declined in examination over the timeframe.

The graphs also show thematic trends over time by source journal. While there are some years in which research of a specifictheme is published in only one journal, results show that neither AiAIS-IJAIS nor JIS has dominated the publishing of a specific themeover the period studied. As a complement to Guan et al.'s (2018) study that focuses solely on JIS, this study shows that both AiAIS-IJAIS and JIS have equally served as publishing outlets over time for the same prevalent themes in AIS academic research.

This study does not suggest that themes having received steady or more attention over the timeframe studied should continuereceiving attention in the future, nor the future dismissal of themes that have received inconsistent or less frequent attention. Thismanuscript emphasizes that certain themes have matured and/or grown over the last three decades in the premier AIS journals, aswell as certain themes appearing to still be maturing. The results provide insights to researchers as they reflect, choose, and studyspecific themes, as well as evince that both IJAIS and JIS publish research covering the same major themes.

2. Latent semantic analysis

LSA is used due to its unique ability to uncover the conceptual content within unstructured data through a variety of mathe-matical dimension reduction techniques that estimate the linear combinations of the meaning of words and concepts (Kulkarni et al.,2014). It then uses that categorization method to allow subsequent in-depth analysis. LSA is a natural language processing methodthat extracts concepts from a sparse matrix of terms to produce an arrangement of terms that is bounded by the primary assumptionthat words similar in meaning will occur in analogous segments of text (Kulkarni et al., 2014; Deerwester et al., 1990).

A collection of contexts, identified as either specific individual unique words or a collection of specific meaningful terms inthe collected data, is extracted. A context can consist of unique terms and synonyms, as simple as “database” and “query” todescribe “database applications” or a single multiple-word term such as “accounting information systems.” More complexcontexts can be generated using a mixture of both single and multiple words such as “enterprise resource planning,” “ERP,”“implementation,” “plan,” and “system” to describe “ERP implementations.” Thus, in corollary with primary LSA assumptions,contexts with similar meaning will occur in similar meaning documents. Through a process analogous with traditional factoranalysis, LSA uses singular value decomposition (SVD) to determine unique terms that represent the underlying conceptsmanifested within the data.

A matrix containing counts of contexts per individual textual data segment (document, paragraph, or other designation ofgranularity) is constructed from the data. SVD then reduces this sparse matrix while preserving the similarity structure amongstcolumns representing each separate document. Like principal component analysis (PCA), SVD produces simultaneous principalcomponents for two sets of variables, contexts (U) and documents (VT). Two separate sets of factor loadings, one for the U andVT matrices, are produced with each latent factor associated with both a set of corresponding high-loading terms and paired setof corresponding high-loading documents. These two sets of results can then be interpreted concurrently to develop the fun-damental word usage and association patterns, which are termed Factors or Themes. Like traditional factor analysis, the re-searcher can indicate the number of factors within LSA to extract and therefore specify the level of granularity for themeextraction.

The resulting LSA analysis approximates the relationship of a word or group of words (contexts from the U matrix), to themeaning of a specific passage of text, illustrated by the VT matrix, and vice versa. The relationship these words have on the passage isthe interpretation of the derived associations between each individual word and word group utilized in the passage of text, and notthe individual frequencies of the words in the given corpus. Due to similarities with traditional factor analysis, cross-loadings, andthus overlapping, of themes can occur.

P.D. Hutchison et al. International Journal of Accounting Information Systems xxx (xxxx) xxx–xxx

2

2.1. Dimension reduction for theme extraction

Due to the complexity of high dimensional data, determining the number of themes to extract that provide a meaningful context isa complex exercise. Extant literature provides various approaches for investigating and reducing the dimensionality of componentmatrices. Many current techniques rely on various facets of the dimension reduction technique of PCA.

The three primary techniques currently used are: percent variance, scree plot analysis, and sequential testing (Jolliffe, 2002). Eachrelies on eigenvectors generated by the sample variance–covariance matrix of the data during PCA with the ordered eigenvaluesdetermining the rank of each extracted context. The percent variance approach attempts to simplify the selection process to a heuristicthat defines a cutoff value for which a predetermined percentage of documents within the library are included by the addition of eachsubsequent ranked context. Scree plot analysis is a visual interpretation method of the gaps or “elbows” identified upon the plottedmagnitudes of each eigenvector to isolate natural breaks within the extracted themes. Sequential testing tests repeatedly the differencebetween the ordered eigenvalues to locate statistically significant differences in the ordered array of eigenvalues. Each technique hastheir benefits and detractions, which complicates the selection of specific individual methodology.

The fundamental benefit of each technique is tied to the relative simplicity of analysis. As each digresses from simple heuristics tocomparative visual analysis and then parametric statistical tests, ease in application and interpretation decreases. The primarydetraction for both percent variance and scree plot analysis is the subjective nature of determining the threshold or gaps. In contrast,Zhu and Ghodsi (2006) highlight a problematic flaw regarding the validity of sequential testing as it relies on assuming the underlyingdata follows a multivariate normal distribution. Additionally Jolliffe (2002) expounds on this issue to surmise that “it's difficult to geteven an approximate idea of the overall significance level because the number of tests done is not fixed but random, and the tests arenot independent of each other” (Jolliffe, 2002, Section 6.1.4). Due to the variety of techniques available with no clear discerniblepredominant methodology and heavy criticism of the parametric option, our study employs a multifaceted approach of a scree plotanalysis in conjunction with percent variance to determine the final number of themes extracted.

The generated eigenvalues for each theme are an artifact of the frequency of a context that appears across the entire corpus library(abstracts from all three AIS journals), the frequency that the context appears per document, and the frequency that unique contextappears with other contexts within the theme. These individual eigenvalues are then ordered and subsequently analyzed using theaforementioned criteria. Due to the iterative nature of this approach, this study includes both the individual scree plot and thecorresponding coverage values for reader perusal (see Fig. 1).

After much deliberation amongst the authors, the number of themes extracted for this study was validated through both therelative strength of each theme's eigenvalue and resulting coverage provided to the dataset of abstracts. This results in coverage of86% of all abstracts and fixes the number of themes at 14 (see Fig. 1). The nature of any form of research itself provides a very strongand distinct research theme, as well as the interaction of the base theme with one or more additional themes. This combination ofthemes is a fundamental goal of research to extend the current body of knowledge, and therefore, a key pillar in the understandingthat multiple themes can be present in a given abstract. As evinced, a decrease in the cutoff eigenvalue would increase the number ofthemes extracted; however, due to the nature of overlapping themes, the increase would be at a rapidly diminishing rate, with littleadditional knowledge gleamed beyond the 86% coverage.

78%86%

90%92%

0

0.5

1

1.5

2

2.5

0 5 10 15 20 25

Eig

enva

lues

Number of Themes Extracted

Fig. 1. Eigenvalues of Correlation Matrix.


3

3. LSA applied to AIS academic research

3.1. AIS journal article abstracts

As this manuscript is primarily directed at AIS researchers, this study is limited to AiAIS and IJAIS, and JIS. These are the primaryjournals that AIS researchers regularly submit academic studies for consideration of publication, plus most of the editors of thesejournals primarily research in the AIS area.1 To include other Information Systems journals with authors from several other dis-ciplines would dilute the research themes identified and the purpose of this study.

Data for this study involves article abstracts obtained from the three journals historically dedicated to AIS research from 1986 to2015: AiAIS from 1992 to 1998, IJAIS from 2000 to 2015, and JIS from 1986 to 2015.2 Abstracts were obtained electronically for JISfrom Business Source Complete and IJAIS from Science Direct, and hand-collected from published editions for AiAIS.3 Since AiAIS is thepredecessor journal of IJAIS and their publication periods do not overlap, some results presented are for AiAIS and IJAIS combined(noted as AiAIS-IJAIS).

The quantities of abstracts from each journal vary due to the number of volumes, issues, and articles each journal published overthe period studied (see Table 1, Panel A). Overall, these three AIS journals published 54 volumes from 1986 to 2015: 25 by AiAIS-IJAIS (46.3%) and 29 by JIS (53.7%). There were 129 issues in these volumes: 69 in AiAIS-IJAIS (53.5%) and 60 in JIS (46.5%). Thus,both main publishing outlets for AIS research, AiAIS-IJAIS and JIS, published somewhat equally in terms of both volumes and issuesfor the period examined.

For all three AIS journals, an initial total of 1033 article abstracts were obtained (see Table 1, Panel B). This includes 400 fromAiAIS-IJAIS (38.7%) and 633 from JIS (61.3%). Since the focus of this study is on AIS academic research, 370 abstracts were thenremoved that are of following journal types: book review, commentary, discussion, dissertation summary, editorial, educationalmaterials, educational software, foreword, instructional case, instructional resource, literature review, panel discussion, Q&A session,replies, reports, symposium, etc. Items deleted were 107 (28.9%) for AiAIS-IJAIS and 263 for JIS (71.1%). After this reduction, thefinal number is 663 abstracts consisting of 293 (44.2%) from AiAIS-IJAIS and 370 (55.8%) from JIS.

The resulting final corpus library used in this study's analysis contains solely full-length abstracts, purposefully omitting theinclusion of predetermined keywords or manuscript title. Due to the matrices-driven approach of LSA, keywords and titles areredundant to the abstract and artificially inflate the frequency count of a term or group of terms. Eigenvalues generated throughanalysis of the eigenvectors can potentially be manipulated by both the frequency of terms within each abstract, as well as howfrequent that term or groups of terms are present throughout the corpus library. The S matrix (the frequency matrix ofFactors× Factors) provides the maximum number of eigenvectors within the analysis and therefore, the maximum possible numberof eigenvalues for rank-ordered analysis. By artificially enhancing the frequency of specific terms in relation to other terms within theS matrix, the compromised S matrix would bias the SVD solution for both the U and VT matrices, of which whose interpretations arethe foundation of LSA.

To mitigate the risk of misclassification, a variety of steps were taken to prepare the corpus library for analysis. Each term in everyabstract was identified for its textual attributes (i.e., numeric, alpha numeric, or characters) and associated part of speech. Terms withcorresponding synonyms were aggregated and non-essential descriptors (i.e., parts of speech, including but not limited to ab-breviations, conjunctions, adverbs, interjections, prepositions, and proper nouns) and numeric and punctuation attributes were ex-cluded. The tokenization process was further refined by applying additional stemming procedures to reduce the specific tokens to itsbase or root form. The remainder of parsed terms was additionally pruned to exclude extraneous information and limited frequencyterms. The iterative reduction in terms allows only the most valuable and relevant information to be retained for analysis. Uponcompletion of the data cleansing and preparation stages, theme extraction commenced.

3.2. Top AIS academic research themes

Using LSA, this study identifies the strongest 14 themes in AIS academic research published in AiAIS-IJAIS and JIS from 1986 to2015 (see Table 2 for ranking by eigenvalues).4 For each set of terms, each author independently determined a theme name. Dif-ferences were reconciled to determine an agreed upon theme name for each set of terms. No substantial differences existed betweenthe theme names identified by each author, thereby making it straightforward to reconcile and select final theme names. While somesubjectivity exists with the determination of a name for each theme, no subjectivity exists with the extraction of the 14 themes andtheir key terms – the LSA methodology determines these.

It should be noted that from 1986 to 2015, a theme may have been emphasized on occasion for an issue or section of an issue (see

1 Barrick et al. (2017), identify in Table 6 (p. 44) that JIS and IJAIS are the top two AIS academic research journals. Other journals in their AISjournal ranking list publish articles across several Accounting sub-disciplines (e.g., Financial Accounting, Tax Accounting, etc.).2 Abstracts are utilized as they present the research methodology and key research findings. Abstracts also allow comparability with other

research studies (e.g., Sidorova et al., 2008; Winson-Geideman and Evangelopoulos, 2013b; Kulkarni et al., 2014; Guan et al., 2018).3 Abstracts from nine articles retracted by these journals are not amongst those collected.4 Please note that each article can contain multiple AIS themes and some article abstracts may not be included in the 14 themes identified because

of the method described in Section 2.1. The Total Articles noted in Table 2 of 924 exceeds the 663 article abstracts used in this study because of theformer.


4

Table 3). While AiAIS did not have any, IJAIS had 11 and JIS had five during the period studied. Results are therefore interpretedconsidering this potential overriding effect on theme trends over time. In addition, these special theme issues/sections also provideinsights as to relevance of themes at specific points in time.

3.3. Trends of themes over time

To visualize themes, individual graphs of each theme over time by journal are provided and a trendline is included (see Fig. 2).Each graphic provides knowledge about the growth and maturity of each identified theme, whether a theme has continued to receiveattention in the literature or waned over time, and whether different trends exist for a particular theme by journal. The study'sexamination first focuses on trends in general over time, followed by discussion in the following section of trends by AIS journals.

The graph for “AIS investment on firm performance” shows very few articles over the first 13 years of the 30-year period.Beginning with 2000, however, more articles focused upon this theme and with a greater frequency through the remainder of theperiod, as demonstrated by the slope (0.2489). The trend shows that research in this theme was in its infancy in the early part of theperiod and grew substantially through the later part of the period.

In comparison, the graph for “Decision aids” shows a more consistent trend over the period. This is evinced by the slope (0.0805),which is close to zero. While there was a decline in the number of articles to zero in 2013, the number of articles increased over thelast two years of the timeframe. The graph for “Defining AIS” shows an even more consistent trend of articles. This consistency is alsoapparent by its slope (0.0176). While consistent, certain years (1987, 2000, 2003, and 2004) show “spikes” in articles published,thereby indicating heightened attention at certain times over the 30-year period.

While the last two themes show consistency over time, the trend in the graph for “XBRL, taxonomies, and financial reporting” islike that for “AIS investment on firm performance.” With XBRL created in 1998, it is worth noting that in the first 15 years of theperiod, there are two articles categorized under this theme, one in 1989 and the other in 1991. The first article studies a databaseapproach to corporate financial reporting, while the second studies the impact of financial statement presentation format on decision-making. These two taxonomy-type articles are therefore included with XBRL research. This theme's graph shows that the number ofarticles increased dramatically at the end of the period. The large number of articles in 2012 is due to two special issues on XBRL, onein IJIAS and the other in JIS. Activity appears to suggest that research in this theme will likely continue to grow and mature into thenear future.

The graph for “Internal control” shows articles published all throughout the period, with an inconsistent trend in the first half but

Table 1Descriptive statistics.

Panel A

AIS volumes and issues examined

1986–2015

Journal Volumes Volumes percent Issues Issues percent

Journal of Information Systems (JIS) 29 53.7% 60 46.5%Advances in Accounting Information Systems (AiAIS) 6 11.1% 6 4.7%International Journal of Accounting Information Systems (IJAIS) 19 35.2% 63 48.8%Subtotal 25 46.3% 69 53.5%Total 54 100.0% 129 100.0%

Panel B

AIS journal abstracts1

1986–2015

Journal Initial quantity Initial percent Deletedquantity

Deleted percent Reduced quantity Reduced percent

Journal of Information Systems (JIS) 633 61.3% 263 71.1% 370 55.8%Advances in Accounting Information Systems

(AiAIS)65 6.3% 15 4.0% 50 7.5%

International Journal of Accounting InformationSystems (IJAIS)

335 32.4% 92 24.9% 243 36.7%

Subtotal 400 38.7% 107 28.9% 293 44.2%Total 1033 100.0% 370 100.0% 663 100.0%

1 Abstracts for a(n) book review, commentary, discussion, dissertation summary, editorial, educational materials, educational software, fore-word, instructional case, instructional resource, literature review, panel discussion, Q&A session, replies, reports, symposium, etc. are not includedin this study.


5

Tabl

e2

AISacadem

icresearch

them

es:14factor

solution.

Term

sAiAIS-IJAIS

JIS

Totalarticles

%oftotal

Eigenvalues

Them

es

1+market,+firm,+

investment,+disclosure,+

announcement

4035

758.1%

2.28

AISinvestmenton

firmperformance

2+decision,+

aid,+decision

aid,+task,+

maker

3331

646.9%

2.19

Decisionaids

3+accountinginform

ationsystem

,accounting,system

s,inform

ation,+topic

3245

778.3%

1.68

Defining

AIS

4+xbrl,+

financial,+

taxonomy,sec,+report

1924

434.7%

1.58

XBRL,taxonom

ies,andfinancialreporting

5+control,+internal,+

internalcontrol,+internalauditor,+risk

2947

768.2%

1.44

Internalcontrol

6+erp,+implem

entation,+enterprise,+

erpsystem

,+plan

2718

454.9%

1.29

ERPsystem

s7

expert,+

expertsystem

,+explanation,+subject,+know

ledge

1525

404.3%

1.14

Expertsystem

s8

+user,+

satisfaction,+perceive,+

social,+

acceptance

3647

839.0%

1.11

Usersatisfactionandacceptance

ofAIS

9+assurance,+consum

er,+

online,+seal,+

purchase

2424

485.2%

1.05

Onlineassuranceservices

10+database,+

query,+query,data,+

error

1637

535.7%

1.04

Databases

11+governance,+

performance,+

business,+

capability,+organization

3825

636.8%

0.98

Organizationalgovernanceon

performance

12+process,+event,+application,data,+

model

4453

9710.5%

0.95

Datamodelingofprocessesandevents

13+audit,+audit,+continuous,+

assurance,ca

3546

818.8%

0.92

Continuous

auditingandassurance

14+student,+softw

are,+know

ledge,+group,+social

1465

798.5%

0.90

Group

decisionswith

softw

are

Totalarticles

402

522

924

100.0%


6

Tabl

e3

MainAISspecialthemes

1986–2015.

Journalofinformationsystem

s

2000

1999

Symposium

onISAssurance

2002

Inform

ationSystem

sAssurance

2008

2007

Research

Forum

2008

REA25th

Anniversary

SpecialSection

2012

XBRL

SpecialIssue

2013

SpecialIssue

onIT

Governance

2015

SocialMedia

Advancesinaccountinginform

ationsystem

sNone.

Internationaljournalofaccountinginform

ationsystem

s

Publicationdate

Special

2004

2003

Research

Symposium

onGovernance,Transparency

andIntegrity:T

heRoleofIT.U

niversity

ofWaterlooCentre

forInform

ationSystem

sAssurance.

2006

2005

Research

Symposium

onIntegrity,Privacy,Security

&Trustinan

ITContext.

2008

2007

Research

Symposium

onInform

ationSystem

sAssurance.

2010

2009

Research

Symposium

onInform

ationIntegrity

&Inform

ationSystem

sAssurance.

2011

SpecialIssue

onMethodologies

inAISResearch.

2012

XBRL:R

esearchImplications

andFutureDirections.

2012

2011

Research

Symposium

onInform

ationIntegrity

&Inform

ationSystem

sAssurance.

2013

Methodologies

inAISResearch.

2013

SpecialIssue:O

ntherelations

betweenmoderninform

ationtechnology,decisionmakingandmanagem

entcontrol.

2014

BusinessProcessModeling.

2014

2013

Research

Symposium

onInform

ationIntegrity

&Inform

ationSystem

sAssurance.


7

y = 0.2489x - 1.3586R² = 0.7198

0

2

4

6

8

10

12

1986 1990 1994 1998 2002 2006 2010 2014

Qua

ntity

Year

1. AIS investment on firm performance(+market,+firm,+investment,+disclosure,+announcement)

JISAiAIS + IJAISTrendline

y = 0.0805x + 0.8851R² = 0.1382

0

2

4

6

8

10

12

1986 1990 1994 1998 2002 2006 2010 2014

Qua

ntity

Year

2. Decision aids(+decision,+aid,+decision aid,+task,+maker)


y = 0.0176x + 2.2943R² = 0.0062

0

2

4

6

8

10

12

1986 1990 1994 1998 2002 2006 2010 2014

Qua

ntity

Year

3. Defining AIS(+accounting information system,accounting,systems,information,+topic)


y = 0.1751x - 1.2805R² = 0.4217

0

2

4

6

8

10

12

1986 1990 1994 1998 2002 2006 2010 2014

Qua

ntity

Year

4. XBRL, taxonomies, and financial reporting(+xbrl,+financial,+taxonomy,sec,+report)


Fig. 2. AIS academic research themes.


8

y = 0.166x - 0.0391R² = 0.5456

0

2

4

6

8

10

12

1986 1990 1994 1998 2002 2006 2010 2014

Qua

ntity

Year

5. Internal control(+control,+internal,+internal control,+internal auditor,+risk)


y = 0.1132x - 0.2552R² = 0.2784

0

2

4

6

8

10

12

1986 1990 1994 1998 2002 2006 2010 2014

Qua

ntity

Year

6. ERP systems(+erp,+implementation,+enterprise,+erp system,+plan)


y = -0.0556x + 2.1954R² = 0.0907

0

2

4

6

8

10

12

1986 1990 1994 1998 2002 2006 2010 2014

Qua

ntity

Year

7. Expert systems(expert,+expert system,+explanation,+subject,+knowledge)


y = 0.075x + 1.6046R² = 0.1058

0

2

4

6

8

10

12

1986 1990 1994 1998 2002 2006 2010 2014

Qua

ntity

Year

8. User satisfaction and acceptance of AIS(+user,+satisfaction,+perceive,+social,+acceptance)


Fig. 2. (continued)


9

y = 0.2552x - 1.8552R² = 0.5213

0

2

4

6

8

10

12

1986 1990 1994 1998 2002 2006 2010 2014

Qua

ntity

Year

11. Organizational governance on performance(+governance,+performance,+business,+capability,+organization)


y = 0.1061x + 1.5885R² = 0.1629

0

2

4

6

8

10

12

1986 1990 1994 1998 2002 2006 2010 2014

Qua

ntity

Year

12. Data modeling of processes and events(+process,+event,+application,data,+model)


y = 0.1157x - 0.1931R² = 0.213

0

2

4

6

8

10

12

1986 1990 1994 1998 2002 2006 2010 2014

Qua

ntity

Year

9. Online assurance services(+assurance,+consumer,+online,+seal,+purchase)


y = -0.0065x + 1.8667R² = 0.0012

0

2

4

6

8

10

12

1986 1990 1994 1998 2002 2006 2010 2014

Qua

ntity

Year

10. Databases(+database,+query,+query,data,+error)


Fig. 2. (continued)


10

a more consistent and steady trend in the second half. The years 2007–2015 show four to six articles per year on this theme. Similarly,the graph for “ERP systems” shows varying trend activity over the period. Only three articles were published before 1999, with manypublished afterward with some years having more articles than others.

The graph for “Expert systems” shows a much different trend than any of the other themes discussed so far. This theme receivedmore attention early in the period than these others. After reaching a peak of seven articles in 1997, the number of articles declinedover time, with multiple years having no published articles on the theme. This declining trend is seen in the theme's slope (−0.0556),with the flatness coming in 1997–2015. The graph for “User satisfaction and acceptance of AIS,” indicates a more consistent trendthan the last theme. The respective trends for both are very similar from 1986 to 1999. Beginning in 2000, however, more articleshave been published for “User satisfaction and acceptance of AIS,” with five in each of the last four years. This indicates a continued,more consistent focus on this theme over the entire timeframe.

The graph for “Online assurance services” shows no activity in the early part of the period. With the widespread use of the Internetfor ecommerce beginning in the mid-1990s, research on the theme followed soon afterward. The years 1999–2006 show severalarticles covering this theme, with the later years of 2009 and 2014 showing “spikes” in the number of articles published. In contrast,the graph for “Databases” shows a consistent trend across the timeframe. While the slope is negative (−0.0065), the pattern acrossthe period is consistent with some early years (1988, 1989, 1992, and 1993) and later years (2001–2006 and 2008) having four tofive published articles while other years throughout the period having no articles. The years 2012–2015 have seen only one to twoarticles published on this theme.

The graph for “Organizational governance on performance” is like some themes in which there are few articles early in the periodbut a larger number later through the end of the period. Looking specifically at the timeframe of 2010–2015, this theme appears to bethe most dominant of the 14 themes. The large number of articles (12) in 2013 is due to JIS publishing a “Special Issue on ITGovernance.” It appears research in this theme is continuing to mature through the end of the timeframe and into the near future.This theme's slope (0.2552) evinces its continuing maturity, which is the largest of any theme reported in this study.

The graph for “Data modeling of processes and events” shows a consistent and steady stream of published articles over the periodexamined. However, unlike the other 13 themes reported in this study, this is the only theme in which at least one article has beenpublished every year of the period studied, except for the first year of 1986. The substantial number of articles on this themepublished in 2014 is due to a special issue of IJAIS on “Business Process Modeling.”

The graph for “Continuous auditing and assurance” also shows a more consistent trend over the entire period, with an increase inthe number of published articles from 2004 to 2014. The increased number of articles published in 2012 is due to a special issue in

y = 0.1555x + 0.2897R² = 0.3665

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Fig. 2. (continued)


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IJAIS from the “2011 Research Symposium on Information Integrity & Information Systems Assurance.” Finally, the graph for “Groupdecisions with software” shows a varied pattern over time with more activity from 1986 to 1992 and 2002–2009 (especially in 2008).The years 2010–2013 show only one article per year, with an increase to two and four articles in the last two respective years of theperiod.

3.4. Trends of themes by journal

Further knowledge and insights about the top 14 AIS academic research themes can be gleamed by looking at their trends byjournal. As a reminder, because AiAIS is the predecessor of IJAIS, this study combines their abstracts to allow comparison to JIS.Results show that, in general, both journals have been outlets for the top themes over the entire timeframe from 1986 to 2015.

For eight of the 14 themes, both journals published at least one article in the same year at least 50% of the time for those years inwhich there is activity. For example, “Internal control” was a theme of articles published in 23 of the 30 years, with both journalspublishing at least one article on the theme in 15 of those years. As another example, “User satisfaction and acceptance of AIS” was atheme of articles published in 24 years, with both journals publishing at least one article on the theme in 13 of those years.

For four of the remaining six themes, each journal published at least one article in 50% or more of the years in which an articlewas published. For example, both journals published at least one article on “Defining AIS” in nine of the 27 years in which an articleon the theme was published. AiAIS-IJAIS was the only journal to publish an article on this theme in six other years (15 years in total),while JIS was the only journal to publish an article in 11 other years (18 years in total). As another example, both journals publishedat least one article on “XBRL, taxonomies, and financial reporting” in four of the 16 years in which an article on the theme waspublished. AiAIS-IJAIS was the only journal to publish an article on this theme in six other years (10 years in total), while JIS was theonly journal to publish an article in six other years (10 years in total).

For the two remaining themes, although both journals did not publish an article in most years in which an article on the themewas published, each journal did publish articles on the theme in a substantial number of years. For example, JIS published at least onearticle in 18 of the 21 years in which an article was published on “Databases,” while AiAIS-IJAIS published at least one article on thetheme in nine years. As another example, JIS published at least one article in 25 of the 27 years in which an article was published on“Group decisions with software,” while AiAIS-IJAIS published at least one article on the theme in nine years. While AiAIS-IJAIS didnot publish an article on either of these themes in most years in which an article on the theme was published, articles on these themeswere published in a substantial number of years. In summary, Fig. 2 shows no journal as the sole outlet for a specific theme, with bothjournals publishing a substantial number of articles on the top themes from 1986 to 2015.

3.5. AIS theme comparisons with Guan et al., 20185

Guan applies LSA to JIS abstracts from 1986 to 2015, while the current study employs the same methodology to both AiAIS-IJAISand JIS abstracts. Thus, some comparisons can be inferred between the two research studies. As an initial comment, clustering andtheme identification with LSA is quite sensitive to the amount of data utilized. Guan used 388 JIS abstracts, while this research uses370 JIS abstracts. Some difference is due to this study's exclusion of retracted articles (see footnote 4), as well as education related-abstracts (see Table 1, Panel B, footnote 1). In addition, this study uses 663 total abstracts due to the inclusion of AiAIS-IJAISabstracts. Keeping such differences in mind, Table 4 allows comparison of this study's 14 themes to the nine identified by Guan(Table 6, p. 74).

Several insights can be drawn from the comparisons in Table 4. This study identifies eight of the nine themes identified by Guan(with slightly different theme names). The one theme not identified is “Education and consulting” (#3 in Guan). This makes somesense due to the deletion of education-related articles and cases in the current study. Of the remaining eight themes identified in thisstudy, some similarities can be seen with those identified by Guan when comparing top loaded terms. For example, a comparison oftop loaded terms for “Internal control” in this study with those for “IT and audit” in Guan shows some similarity. The same can beseen from comparing top loaded terms for “ERP systems” in this study with those for “Online data assurance” in Guan.

As a further comment about the number of themes identified in each study, Guan also includes the results of a 25-factor model.While trying to identify more themes, Guan states that doing so can cause the creation of redundant themes, which is evinced by theresults of the 25-factor model. Guan therefore emphasizes the 9-factor model results (which we do so here, while slightly expandingthe number of identified themes with little, if any, redundancy).

Another insight can be drawn from comparing the rank ordering of similar themes between the two studies. Comparing thequantity of articles by theme between the two studies shows much variation which can directly impact both the themes identified andtheir rankings. In addition, the determination of abstracts to include plus coverage utilized (e.g., 86% in this study vs. 100% in Guan)can also directly impact both the themes identified and their rankings. As a further reminder, theme identification and rankingdifferences are also attributable to this study including AiAIS-IJAIS academic research. As an equally serving outlet for AIS academicresearch, this last point emphasizes a contribution of this study that compliments and extends Guan's study of JIS.

5 “Guan” instead of the full citation (Guan et al., 2018) is used in this section to improve narrative flow.


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4. Conclusions

This study uses LSA to identify and gain insights about academic research themes in three journals specifically dedicated to AISresearch —IJAIS, its predecessor, AiAIS, and JIS. This study complements and extends the research of Guan et al. (2018), who usedLSA to analyze JIS over the same period of this study, 1986–2015. By including AiAIS-IJAIS, the present study shows a wider view ofthe dominant themes across the premier AIS journals. Results suggest that certain themes have been consistently researched over thetimeframe (e.g., “Decision aids” (#2) and “Defining AIS” (#3)), while others have either increased (e.g., “AIS investment on firmperformance” (#1) and “XBRL, taxonomies, and financial reporting” (#4)) or decreased (e.g., “Expert systems” (#7)) in coverageover time. Results also indicate that the two premier AIS journals have been equivalent outlets for research covering the mostprevalent themes over the timeframe studied. These insights should be of interest to researchers when considering both futureresearch themes and AIS publication outlets.

This study does not suggest that certain themes will or should receive more attention, less attention, or equal attention in thefuture. Themes will be impacted by editorial decisions and researchers as they identify themes and show insightful findings thatfurther the AIS discipline. This study also cannot observe or suggest the existence of “gaps” in the literature because the analysis isdriven by the inputs (i.e., published articles) over the timeframe studied.

This study evinces the maturation and growth of certain themes over time. While making no suggestions about what attentioncertain themes should receive, one can scan articles in both JIS and IJAIS from 2016 and 2017 and see a theme like “XBRL, taxo-nomies, and financial reporting” still receiving strong continued attention. One can also see research regarding “Big Data,”“Blockchain,” and “AIS and Ethics” receiving recent attention (i.e., the last in a special section of six research articles in Vol. 31, no. 2of JIS in the Summer 2017). It will be interesting to see how the AIS discipline continues to mature and develop.

LSA is driven by the inputs, in this case journal abstracts, and therefore is still subject to the governing assumption that words thatare similar in meaning will occur in analogous segments of text. This assumption does hinder LSA when analyzing a corpus librarythat maintains a continually shifting lexicon of similar terms or context specific term groupings. Due to this limitation, classificationmethods employed through LSA still require individual interpretation of extracted contexts or themes. Multiple subject matter expertsare therefore necessitated when conducting the analysis and may provide analogous but not identical interpretations between variousreaders.

There are numerous research opportunities to further utilize LSA in AIS. LSA can be applied to any textual data, which can takemany different forms. These include verbal, print, or electronic form; obtained from narrative responses; open-ended survey ques-tions; interviews; focus groups; observations; or print media like articles, books, or manuals (Kondracki and Wellman, 2002). Withinformation output the final step of the data processing cycle of an AIS, LSA could be used to analyze the content of managementdiscussion and analysis and footnote disclosures in annual reports. By reducing the subjective bias when performing and interpretingresults, LSA shows great promise with assisting AIS researchers in their studies.

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Table 4Comparison of current study to Guan et al., 2018.

Current Study Guan et al., 2018

AIS investment on firm performance (1) Effect of IT investment and structure on performance (8)Decision aids (2) Decision support (5)Defining AIS (3)XBRL, taxonomies, and financial reporting (4) XBRL (2)Internal control (5)ERP systems (6)Expert systems (7)User satisfaction and acceptance of AIS (8) User satisfaction and participation (4)Online assurance services (9) Online data assurance (7)Databases (10)Organizational governance on performance (11) Governance strategy and risk control (9)Data modeling of processes and events (12) AIS modeling/REA (1)Continuous auditing and assurance (13) IT and audit (6)Group decisions with software (14)

This comparison utilizes Table 6 (p. 74) from Guan et al., 2018 and the nine-cluster model. The one theme not shown is“Education and consulting” (3), which is due to the exclusion of education-related abstracts from this study.


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https://ssrn.com/abstract=2848560

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