The DOI matches its Crossref record; 204 of the 211 references it cites were found in a registry or an index; 1 of the 53 sources its claims cite were checked for support.
Artificial intelligence in information systems research: A systematic literature review and research agenda
Open accessAbstract
AI has received increased attention from the information systems (IS) research community in recent years. There is, however, a growing concern that research on AI could experience a lack of cumulative building of knowledge, which has overshadowed IS research previously. This study addresses this concern, by conducting a systematic literature review of AI research in IS between 2005 and 2020. The search strategy resulted in 1877 studies, of which 98 were identified as primary studies and a synthesise of key themes that are pertinent to this study is presented. In doing so, this study makes important contributions, namely (i) an identification of the current reported business value and contributions of AI, (ii) research and practical implications on the use of AI and (iii) opportunities for future AI research in the form of a research agenda.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This systematic literature review analyzes 98 primary studies on AI in information systems research published between 2005 and 2020, drawn from 1,877 retrieved records. It finds fragmented AI definitions, a recent publication surge, dominance of machine learning and process automation, and gaps in tools/models and cumulative knowledge.
Key findings
- Of the 98 primary studies, 54 gave no clear AI definition; among the 44 that did, 7 cited no source, and as many as 28 definitions were used. Russel & Norvig was the most frequently cited source, though editions varied; LeCun et al. (2015) and Rai et al. (2019) each appeared twice.
- Publication and methods: 11 studies appeared between 2005 and 2015, while over half were published in 2019 and 2020. ICIS published 41 studies, ECIS 24, and IJIM 14. Of the 98 studies, 44 used quantitative methods, 31 qualitative, 13 were literature reviews, and 8 used mixed methods; data collection included 22 data mining, 21 experiments, 20 interviews, and 22 literary analysis.
- Contributions were largely “lessons learned” (39 studies; Fig. 5 reports 40), followed by methods (26), advice/implication (15), guidelines (6), tools (5), and models (6). Machine learning was the most common AI type (69 studies; Table 12 lists n=68), then expert systems (11), NLP (6), machine vision (5), robotics (3), and other (14).
- Reported business value was process automation (47 studies), cognitive insight (32), and cognitive engagement (17). The research agenda calls for clearer AI definitions, more research on societal/personal impacts, more work on non-ML AI types, and greater cumulative knowledge building.
Methods
- The review followed Okoli’s (2015) eight-step systematic literature review guide. Searches covered AIS eLibrary, Scopus, and ISI Web of Science, targeting nine IS journals and the top two IS conferences (ICIS and ECIS), using terms including AI, artificial intelligence, machine learning, neural networks, cognitive, automation, robot, and augment.
- Screening began with 1,877 retrieved studies; two authors independently removed duplicates, non-English studies, non-IS studies, and non-peer-reviewed publications. Inclusion/exclusion criteria were applied, yielding 98 primary studies. Analysis synthesized definitions of AI, publication year, publication channel, research methods and data collection, contribution types, AI types, and business value.
Limitations
- The paper states publication bias as a limitation because it focused on a select number of IS journals, excluding other IS conferences and non-IS outlets. It also reports addressing construct, external, internal, and conclusion validity through a structured protocol, external reviewers, an audit trail, and multiple authors, while noting conclusion bias cannot be eliminated.
- The text makes evident that 54 of 98 studies did not define AI and 7 defined it without citation, limiting comparability and cumulative knowledge. It also states that contribution types can be context-specific, especially methods, which may slow cumulative knowledge building.
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Claims the paper makes
79 Own result · 46 Not checked yet · 30 No basis given · 29 Own interpretation · 11 Own method · 1 Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“As this systematic study followed the eight-step guideline laid out by (Okoli, 2015), it is attributed to mitigating the threats to validity.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.2. Threats to validity and limitations of study
What the sources say
- Supportedcites Okoli, 2015 source
Section 1.4 identifies Okoli’s eight-step guideline and says all eight steps are essential for a scientifically rigorous review. The statement that following it mitigated threats to validity in this particular study is the citing authors’ assessment, not a finding attributed to Okoli. (LLM, conf=0.88)
plaindirect- Asserted: passes — The authors put it forward as true.
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- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The journal with the most studies was IJIM with 14 studies, especially notable as the scope for IJIM was five years in comparison to the 15 years of the other journals.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.2. RQ 2.2 What were the publication channels used?
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Due to the inclusion and exclusion criteria of this study, there were no studies on AI in Information Systems in the years 2007, 2008, 2010, and 2012.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.1. RQ 2.1. What number of IS academic studies on AI has been published between 2005 and 2020?
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The highest number of studies were published in ICIS, a total of 41 studies over the 15-year period.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.2. RQ 2.2 What were the publication channels used?
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The search strategy resulted in 1877 studies, of which 98 were identified as primary studies and a synthesise of key themes that are pertinent to this study is presented.”
- Also stated in: § 3.1. A systematic guide to literature review development · plain
“The search string used across the three databases retrieved 1877 studies (shown in Table 4).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > Abstract
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- Also stated in: § 3.1. A systematic guide to literature review development · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“2019 and 2020 show an immense surge in AI related studies in IS, signifying a much greater interest in the field.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.1. RQ 2.1. What number of IS academic studies on AI has been published between 2005 and 2020?
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Of the 98 primary studies, 54 of them gave no clear definition of the AI relevant to the study.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.1. RQ1: how is AI being defined in the field of IS?
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“And of the 44 studies that did give a definition, 7 of them cited no reference for the definition given.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.1. RQ1: how is AI being defined in the field of IS?
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Disregarding the seven studies that defined AI without citing a source, Russel & Norvig's book Artificial Intelligence: A Modern Approach was the most frequently cited source for defining AI, though the actual edition of the book varied, with each studies using the latest edition at the time.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.1. RQ1: how is AI being defined in the field of IS?
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This resulted in a total of 98 primary studies used as the basis of this SLR.”
- Also stated in: § Abstract · plain
“The search strategy resulted in 1877 studies, of which 98 were identified as primary studies and a synthesise of key themes that are pertinent to this study is presented.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
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- It may state two separate claims as one. 37
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Fig. 4. reveals that studies on AI remained relatively low for most of this period, with a total of 11 studies between the years 2005 and 2015.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.1. RQ 2.1. What number of IS academic studies on AI has been published between 2005 and 2020?
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Table 7 shows that 32 of the primary studies were published in journals and 65 were published in the top two IS conferences.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.2. RQ 2.2 What were the publication channels used?
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Four studies adopted focus groups to collect data, with three using workshops.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.3. RQ 2.3 research methods and data collection techniques used
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The most prominent contribution type among the 98 studies was categorised as "lessons learned" with 40 studies, closely followed by "methods" at 26 and to a lesser extent "advice or implication" at 15.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.3. Contribution mismatch
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By using a systematic literature review, we identified, classified, and analysed 1877 studies on AI and ML in IS that were published between 2005 and 2020.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5. Discussion
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Prior to commencing this task however, we had to consider the problem that the definitions of artificial intelligence were largely varied and ambiguous.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5. Discussion
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Literary analysis was used by 22 of the studies, and questionnaires incorporated by two.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.3. RQ 2.3 research methods and data collection techniques used
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Our findings show that while there was a lack of studies in the first decade of the relevant time, there has been a resurgence in recent years.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.2. The resurgence of AI in recent years
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Based on title, abstract and keyword, the 1786 studies were further narrowed down to 187.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“There was less range between the other types, expert systems having 11, machine vision with five and NLP at 6.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > Table 8
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“ICIS published 41 studies to AI in Information Systems in comparison to the 24 studies published in ECIS.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.2. The resurgence of AI in recent years
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Sample analysis was used by two studies.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.3. RQ 2.3 research methods and data collection techniques used
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Based on the removal of duplicates, non-scientific and non-English studies, 91 were removed, leaving 1786.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
plaindirect- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“IJIM published 14 primary studies, making them the most published regarding AI in IS journals used in the systematic review.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.2. The resurgence of AI in recent years
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Theory-as-discourse, Machine Learning subset selection, causal mapping and prediction markets were all used by a single study each.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.3. RQ 2.3 research methods and data collection techniques used
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“As previously shown in Fig. 4., there was a resurgence in interest in AI in 2017, before it seemed to really expand in 2019 and 2020.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.2. The resurgence of AI in recent years
hedged- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This study identified a lack of cohesion when defining AI, with as many as 28 definitions being used is the respective studies.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.1. Lack of cohesive definition of AI
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Looking at the definitions gathered from the primary studies, there seems to be a trend where AI is defined more in what its capabilities are rather than strictly defining what it is.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.1. Lack of cohesive definition of AI
hedged- Asserted: passes — The authors put it forward as true.
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In the paper
“An in-depth examination of the 187 remaining studies was undertook by the reviewers, which resulted in a further 90 studies being excluded.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The most common definition for AI seen was derived from Russel & Norvig, though the specific edition varied, followed by LeCun et al. (2015) and Rai et al. (2019) with two occurrences each.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.1. Lack of cohesive definition of AI
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In the paper
“Four studies adopted focus groups to collect data, with three using workshops.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.3. RQ 2.3 research methods and data collection techniques used
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“However, despite AI and Machine Learning being a large part of the primary studies, many did not provide a definition, or used definitions that were not cited (see Fig. 3).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.1. RQ1: how is AI being defined in the field of IS?
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Literary analysis was used by 22 of the studies, and questionnaires incorporated by two.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.3. RQ 2.3 research methods and data collection techniques used
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“10 studies adopted observation techniques and four collected data through documentation.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.3. RQ 2.3 research methods and data collection techniques used
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was less range between the other types, expert systems having 11, machine vision with five and NLP at 6.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > Table 8
plain- Asserted: passes — The authors put it forward as true.
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A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“AI is seeing a small consolidation in how it is defined; as something that exhibits human intelligence, which can be seen in the definitions used by the papers in section 4.1.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
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In the paper
“For expert systems, there seems to be a shift away from the "traditional" style of ES with just a few experts managing a system to a more hybrid knowledge base that uses a variety of AI systems (Wagner, 2017).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
What the sources say
- Not checked yetcites Wagner, 2017
hedged- Asserted: passes — The authors put it forward as true.
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In the paper
“This is a point that is mentioned also in the article of Davenport and Ronanki (2018) but is largely overlook in empirical studies.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.5. Focus on process automation
What the sources say
- Not checked yetcites Davenport and Ronanki (2018)
plain- Asserted: passes — The authors put it forward as true.
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- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The future agenda of AI seems set on the further advancement of”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Research into extensive interactions between advanced chatbots and humans is still immature, so researchers should take advantage of the increase in NLP capability and the usage of its technologies to do further research.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Despite the interest in AI in recent years, there remains gaps in knowledge.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, they seem focused on front facing interactions between these agents and customers, and less on actual employees working beside these agents.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.4. Focus on machine learning
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Researchers are increasingly using machine learning as a major component of their methodology when researching various topics, but currently there is a gap where much of this research could further expand on how and why they are using machine learning the way they are.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Finally, machine vision doesn't seem to be taking advantage of some of the recent technological advances seen in the new studies in machine learning, so there's a gap there for researchers to see if and how machine learning could be improved.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“From the findings and analysis of this SLR, it highlights a need for a more coherent working definition of AI among academia.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Researchers are increasingly using machine learning as a major component of their methodology when researching various topics, but currently there is a gap where much of this research could further expand on how and why they are using machine learning the way they are.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“This leaves a gap for researchers to consider the use of AI in other domains, such as agriculture sustainability (Nishant, Kennedy, & Corbertt, 2020) and the public sector (Dennehy, 2020;Abubakar, Behravesh, Rezapouraghda, & Yildiz, 2019).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
What the sources say
- Not checked yetcites Nishant, Kennedy, & Corbertt, 2020
- Not checked yetcites Dennehy, 2020
- Not checked yetcites Abubakar, Behravesh, Rezapouraghda, & Yildiz, 2019
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Many forms of automation, machine learning and intelligent agents are thus labelled as AI.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Specifically, on the use of theories in studying business value, much of the work that has been published to date remains untheoretical.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.5. Focus on process automation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
In the paper
“A specific domain that is currently lacking in research for robotics is technophobia; previous research has examined the fear of computers and have not accounted for new and evolving technologies such as robots (Sinha, Singh, Gupta, & Singh, 2020).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
What the sources say
- Not checked yetcites Sinha, Singh, Gupta, & Singh, 2020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Research into the full aspects of how service robots could potentially affect business and people is still lacking.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The future agenda of AI seems set on the further advancement of”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Chatbots and intelligent agents have made great advancements in recent years, while the effects of these advancements still need to be studied.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 7. Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A clear finding emerging from this systematic literature review is the need to (i) increase the number of rigorous academic studies on AI, especially regarding tools and models, (ii) be more detailed on the definition of AI used in studies, even when it is not the focus, and (iii) build on cumulative knowledge.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 7. Conclusion
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Machine vision seems to be lagging in advances compared to strides made in other AI functions.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 7. Conclusion
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Chatbots and intelligent agents have made great advancements in recent years, while the effects of these advancements still need to be studied.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 7. Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A clear finding emerging from this systematic literature review is the need to (i) increase the number of rigorous academic studies on AI, especially regarding tools and models, (ii) be more detailed on the definition of AI used in studies, even when it is not the focus, and (iii) build on cumulative knowledge.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 7. Conclusion
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Increase in use of machine learning as a methodology among researchers.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 7. Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A clear finding emerging from this systematic literature review is the need to (i) increase the number of rigorous academic studies on AI, especially regarding tools and models, (ii) be more detailed on the definition of AI used in studies, even when it is not the focus, and (iii) build on cumulative knowledge.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 7. Conclusion
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Effects of extended use of advanced service robots on people is still relatively undeveloped.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 7. Conclusion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The review conducted by Borges et al. (2021) focused more on specific AI interactions with organisational strategy and so misses some of the context in how it is being defined and how it creates value.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 2.2. Previous systematic literature reviews of AI in IS research
What the sources say
- Not checked yetcites Borges et al. (2021)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To further mitigate this threat, authors three and four were experienced in reviewing studies and acted as external reviewers to validate the research protocol.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.2. Threats to validity and limitations of study
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Once the literature searches in leading journals have commenced, the leading conferences of a field should follow (Webster & Watson, 2002).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
What the sources say
- Not checked yetcites Webster & Watson, 2002
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Yet, there is no consensus on what defines AI or what distinguishes it from other digital technologies (Bhatnagar et al., 2018;Monett & Lewis, 2018;Nilsson, 2009).”
- Also stated in: § 2.1. Evolution of AI definition · plain
“Despite the length of time the field has existed, there is still no commonly accepted definition (Allen, 1998;Bhatnagar et al., 2018;Brachman, 2006;Hearst & Hirsh, 2000;Nilsson, 2009).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 1. Introduction
What the sources say
- Not checked yetcites Bhatnagar et al., 2018
- Not checked yetcites Monett & Lewis, 2018
- Not checked yetcites Nilsson, 2009
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Also stated in: § 2.1. Evolution of AI definition · plain
None of the sources cited for this claim have been checked yet.
In the paper
“Screening of the retrieved studies was achieved by following the best practices proposed by Kitchenham (2004) and Dingsøyr and Dybå (2008).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
What the sources say
- Not checked yetcites Kitchenham (2004)
- Not checked yetcites Dingsøyr and Dybå (2008)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“three authors were involved in data extraction of the primary studies”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.2. Threats to validity and limitations of study
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The literature review conducted by Karger (2020) is focused narrowly on the relations between AI and blockchain and excludes everything else.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 2.2. Previous systematic literature reviews of AI in IS research
What the sources say
- Not checked yetcites Karger (2020)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Scopus was used as it claims to be largest database for abstracts and citations (Ballew, 2009;Kitchenham & Charters, 2007).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
What the sources say
- Not checked yetcites Ballew, 2009
- Not checked yetcites Kitchenham & Charters, 2007
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Two authors independently analysed the 1877 studies to remove (i) duplicate studies, (ii) non-English studies, (iii) non-IS studies, and (iv) non-peer reviewed scientific studies (books, book chapters, experience reports).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“ISI Web of Science was used as it is the largest citation database which stores over 800 million references (Manikandan & Amsaveni, 2016).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
What the sources say
- Not checked yetcites Manikandan & Amsaveni, 2016
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“conclusions drawn from analysis of the 98 primary studies involved all authors.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.2. Threats to validity and limitations of study
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“a full 'audit trail' from the initial 1877 studies to the identification of 98 primary studies was provided”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.2. Threats to validity and limitations of study
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This study includes all the relevant studies in a fifteen year period in a number of high quality journals and conferences, and includes studies that use AI in more oblique ways than these SLRs, such as studies that use machine learning approaches when researching their focus.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 2.2. Previous systematic literature reviews of AI in IS research
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: The nine selected IS journals focus on the socio-technological aspects of AI, which is the scope of this SLR.
In the paper
“The literature review conducted by Hofmann et al. (2019) was primarily concerned with the effects of AI and ML in the context of the radiology value chain, and so had a time span reflecting that, focusing on 2012 to 2018.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 2.2. Previous systematic literature reviews of AI in IS research
What the sources say
- Not checked yetcites Hofmann et al. (2019)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The two reviewers had to agree for a study to stay a primary study.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“A literature review's quality is dependent on the rigor of the search process (Vom Brocke et al., 2009).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
What the sources say
- Not checked yetcites Vom Brocke et al., 2009
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means there is less repetition of contributions and lessons learned, and that cumulative building of knowledge in this context may take time to accumulate.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.4. RQ 2.4. What kinds of contributions are provided by studies on AI in IS?
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: AI encompasses a broad array of uses and designs.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“As the aims of this study were not to establish a statistical causal relationship on AI in IS, other mitigations were used to combat it”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.2. Threats to validity and limitations of study
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“As noted by Davenport and Ronanki (2018), within IS, it may be more useful to look at AI through the lens of its business capabilities rather than its technologies.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.6. RQ3: What is the business value of AI?
What the sources say
- Not checked yetcites Davenport and Ronanki (2018)
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“After all, "Artificial Intelligence" should not be taken to mean "Artificial Human Intelligence", since "Intelligence" should be a more general notion than "Human Intelligence".”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.1. Lack of cohesive definition of AI
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“In 2016, the Economist declared in its June Special Report (The Economist, 2016) that "After many false starts, artificial intelligence has taken off."”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.2. The resurgence of AI in recent years
What the sources say
- Not checked yetcites The Economist, 2016
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“It is possible to combine a variety of these types together into a single AI system.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.5. RQ 2.5. What types of AI technologies are used by IS researchers?
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Therefore, the search strategy is best developed in concert with the research question.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This surge of interest in the use of AI among businesses and policy makers naturally resulted in a matching surge in interest among researchers, who now have much more to study due to AIs ever increasing use.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.2. The resurgence of AI in recent years
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Yet "intelligence" by its dictionary definition today was formed long before AI, and therefore strictly about human intelligence, where the various levels (conscious and subconscious) are unified, which is not the case for computer intelligence.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.1. Lack of cohesive definition of AI
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As AI is still a broad and relatively nebulous field in IS, performing experiments can provide rich data on AI usage in a variety of complex, contextual environments.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.3. RQ 2.3 research methods and data collection techniques used
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: AI is a broad and relatively nebulous field in IS.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For human intelligence, structure, behaviour, capability, and function are all relatively unified, while for AI these aspects are commonly pursued in different ways towards different goals.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.1. Lack of cohesive definition of AI
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This surge in interest in AI seems to correlate with our own findings, as researchers now have much more access to empirical evidence and AI in use with the advent of this "summer".”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.2. The resurgence of AI in recent years
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“such as regular meetings with all authors to explore any potential of bias.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.2. Threats to validity and limitations of study
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means that the definitions of AI often end up being quite similar, even if they are taken from a separate source.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.1. Lack of cohesive definition of AI
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Okoli (2015) propose a systematic review process that consists of 8 steps, namely planning (2 steps), selection (2 steps), extraction (2 steps) and execution (2 steps) that are completed across 4 phases (see Fig. 1.).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3. Research methodology
What the sources say
- Not checked yetcites Okoli (2015) source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“This systematic approach was chosen for its ability to offer reviews of high quality (Dingsøyr & Dybå, 2008) and transparent and replicable review (Leidner & Kayworth, 2006).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3. Research methodology
What the sources say
- Not checked yetcites Dingsøyr & Dybå, 2008
- Not checked yetcites Leidner & Kayworth, 2006
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“For example, IBM's Watson combines NLP, ML and machine vision techniques (Jarrahi, 2018a, 2018b).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.5. RQ 2.5. What types of AI technologies are used by IS researchers?
What the sources say
- Not checked yetcites Jarrahi, 2018a, 2018b
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“To know to what degree the results of a study can be generalised it is essential that the research process is described (Petersen & Wohlin, 2009).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.2. Threats to validity and limitations of study
What the sources say
- Not checked yetcites Petersen & Wohlin, 2009
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“A generally accepted approach to search string strategy is to base the search string on the research questions and include a list of synonyms, abbreviations, and alternative spellings (Kitchenham & Charters, 2007).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
What the sources say
- Not checked yetcites Kitchenham & Charters, 2007
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the IS community specifically, the increased feasibility of building empirical models from experimental data and using these models to make predictions (the goal of predictive analytics using big data) is likely another reason for the resurgence of interest.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.2. The resurgence of AI in recent years
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“A second reviewer was invited to analyse these studies as well, to pre-emptively combat potential bias.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 3.1. A systematic guide to literature review development
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Additionally, human intelligence is developed under certain evolutionary and biological restrictions, which are essential for human, but not really for intelligence in general.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.1. Lack of cohesive definition of AI
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, there may be issues with how IS research is approaching the defining of AI as there seems to be a believe that there is a true (i.e., real, natural) meaning of "intelligence" that AI research projects should abide by, at least among those that consider defining AI.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.1. Lack of cohesive definition of AI
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While it is not feasible to ask every researcher, who uses a machine learning technique in their research to dedicate an entire section to AI, the lack of sufficient detail seen in many of the primary studies showcases a possible lack in the cumulative building of knowledge.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.1. Lack of cohesive definition of AI
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The fact that machine learning applications are the most researched technology within the AI domain provides some indication about where future investments should be directed, as well as the expected type of business value that they can deliver.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.6. Implications for practice
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The specific applications and technologies of AI that are mostly researched provide practitioners some indication about the future deployments and common technologies in organisations.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.6. Implications for practice
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This machine learning dominance seems to be due to, at least in part, the wide variety of contexts in which machine learning is useful in comparison to the other types.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.4. Focus on machine learning
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This is likely partly due to the cognitive engagement dealing more with customers, and businesses being more conservative with customer facing technology (Davenport & Ronanki, 2018).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.5. Focus on process automation
What the sources say
- Not checked yetcites Davenport & Ronanki, 2018
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“That is, the advancement of technology making supervised learning from datasets and machine learning much more feasible means that researchers are now focused on this new foray, and "tools" and "methods" are left to languish because it seems the resurgence doesn't lift up all aspects of AI equally.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.3. Contribution mismatch
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These activities involve data analysis at such speeds and at volumes that no human would be possible to process.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.5. Focus on process automation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“This is likely due to process automation being both the least expensive and the easiest to implement of the three value types discussed here (Davenport & Ronanki, 2018).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.5. Focus on process automation
What the sources say
- Not checked yetcites Davenport & Ronanki, 2018
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Additionally, much of IS literature recognises that IS alone is ineffective in generating value, so complementary assets are key to realising value from IS (Shea, Dow, & Chong, 2019).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
What the sources say
- Not checked yetcites Shea, Dow, & Chong, 2019
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This was noted by (Wagner, 2017), who also theorised that the reason for this lack of impact is that the earlier developers were able to capitalise on the 'low hanging fruit' that had bigger impact for organisations.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.4. Focus on machine learning
What the sources say
- Not checked yetcites Wagner, 2017
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Cognitive insight was the second most common type of derived business value among the primary studies and was described by Davenport and Ronanki (2018) as "analytics on steroids".”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.5. Focus on process automation
What the sources say
- Not checked yetcites Davenport and Ronanki (2018)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, it has been noted by other studies that this shift hasn't resulted in a greater impact than earlier systems, despite the advances in technology.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.4. Focus on machine learning
What the sources say
- Not checked yetcites Wagner, 2017
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The idea of having frustrating conversions with a chat-bot that just can't seem to understand what the user is saying still quite strong in the public consciousness, never mind the high-profile failures such as Taybot (Badjatiya, Gupta, Gupta, & Varma, 2017).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.5. Focus on process automation
What the sources say
- Not checked yetcites Badjatiya, Gupta, Gupta, & Varma, 2017
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“A specific domain that is currently lacking in research for robotics is technophobia; previous research has examined the fear of computers and have not accounted for new and evolving technologies such as robots (Sinha, Singh, Gupta, & Singh, 2020).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
What the sources say
- Not checked yetcites Sinha, Singh, Gupta, & Singh, 2020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Additionally, much of IS literature recognises that IS alone is ineffective in generating value, so complementary assets are key to realising value from IS (Shea, Dow, & Chong, 2019).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
What the sources say
- Not checked yetcites Shea, Dow, & Chong, 2019
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This means that some of the other types of AI have received less research focus, though all of them can offer much both practically and academically.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.4. Focus on machine learning
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Adding to the above, major themes that have been under-reached remain difficult to identify with an absence of theoretically grounded work.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.5. Focus on process automation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This increased complexity may also mean it will take more time and effort than usual to see a return on the time and resources invested.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“Google Trends show that popularity of the search term for machine learning has surpassed the popularity of AI by almost twice as much (Google Trends, 2020).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.4. Focus on machine learning
What the sources say
- Not checked yetcites Google Trends, 2020
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“However, despite the great strides in AI noted in the, so called”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
What the sources say
- Not checked yetcites Kurzweil, 2005
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Adopters of RPA have noted the automation can radically transform back offices, delivering much lower costs while improving service quality, and decreasing delivery times, as well as freeing up employees from tedious tasks so they can focus on more important, challenging, and varied work (Lacity, Willcocks, & Andrew, 2015).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.5. Focus on process automation
What the sources say
- Not checked yetcites Lacity, Willcocks, & Andrew, 2015
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, while the prominence of machine learning may be understandable, it is still a concern.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.4. Focus on machine learning
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It seems likely that the reason for this lack of focus on tools or methods is related in some ways to the reasons for the AI resurgence itself noted in section 5.2.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.3. Contribution mismatch
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It also makes the comparison between findings and the identification of complementarities much more difficult.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.5. Focus on process automation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, the frequent appearance of machine learning as the AI application used in this paper can be attributed to the fact that it concerns a very broad spectrum of potential applications.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 5.4. Focus on machine learning
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“It has been noted that AI technology has tended to "become a somewhat broad church where many forms of automation and limited intelligent machines are labelled as AI" (Dwivedi et al., 2021).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
What the sources say
- Not checked yetcites Dwivedi et al., 2021 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The authors state this as an assumption.
The nine selected IS journals focus on the socio-technological aspects of AI, which is the scope of this SLR.
“These databases were used to retrieve studies from relevant IS journals, which were chosen as we belief these nine journals focus on the social-technological aspects of AI, which is the scope of this SLR.”
The paper relies on this without support.
There is a growing concern that research on AI could experience a lack of cumulative building of knowledge, which has overshadowed IS research previously.
“There is, however, a growing concern that research on AI could experience a lack of cumulative building of knowledge, which has overshadowed IS research previously.”
The paper relies on this without support.
AIS eLibrary is unique among the selected databases in being able to provide studies from the leading IS conferences.
“AIS eLibrary was used as of the databases used it was unique in being able to provide studies from the leading IS conferences.”
The paper relies on this without support.
AI is a broad and relatively nebulous field in IS.
“As AI is still a broad and relatively nebulous field in IS, performing experiments can provide rich data on AI usage in a variety of complex, contextual environments.”
The paper relies on this without support.
AI encompasses a broad array of uses and designs.
“This is compounded upon by AI encompassing such a broad array of uses and designs.”
The paper relies on this without support.
AI research tends to swing between its 'winters' and 'summers'.
“It's already been noted that AI research tends to swing between its "winters" and "summers", and it seems clear that we have headed into one of its summer periods in recent years.”
The paper relies on this without support.
AI applications cover a breadth of different techniques, technologies, and set a different set of requirements on data, infrastructure and leveraging them in the organisational setting.
“Since AI applications cover a breadth of different techniques, technologies, and set a different set of requirements on data, infrastructure and leveraging them in the organisational setting it is important that future research accurately frames the definition of AI that is used.”
The paper relies on this without support.
The subject and definition of human intelligence is still heavily debated even now.
“But the subject and definition of human intelligence is something that is still heavily debated even now.”
The paper relies on this without support.
AI has potential in all its forms.
“There is potential for AI in all its forms.”
The paper relies on this without support.
AI encompasses a broad array of ideas and practices, ranging from the specifics of the technology used to how it is defined.
“However, AI does tend to encompass a broad array of ideas and practises, ranging from the specifics of the technology used to even how it is defined.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“The value of AI in organizations varies based on the sector and the organization's activity(Collins et al., 2021).”
From Toward AI Governance: Identifying Best Practices and Potential Barriers and Outcomes (2023) · cites this paper as Collins et al., 2021
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 6. Future research agenda
“thus fulfil the fourth and final aim of this paper.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 1. Introduction
“1. identify the reported business value and contributions of AI 2. examine the practical implications on the use of AI 3. identify the opportunities for future AI research in IS.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 2.1. Evolution of AI definition
“their own attendance of the famous 1951 Dartmouth conference”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 2.1. Evolution of AI definition
“conference in Dartmouth College in 1956(McCorduck, 2004)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“Fig. 7. shows that the most common value type for AI was process automation (47 studies), followed by cognitive insight (32 studies).Cognitive engagement was reported with the lowest number of studies (17 studies).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > AI type Primary studies
Reported 98 · recomputed 96
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.5. RQ 2.5. What types of AI technologies are used by IS researchers?
“Fig. 6 shows that ML is the most popular form of AI used in the”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > AI type Primary studies
“Machine Learning(n=68)”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.3. RQ 2.3 research methods and data collection techniques used
“22 studies used data mining and 21 reported the use of experiments.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.3. RQ 2.3 research methods and data collection techniques used
“Experiments were the most popular choice for collecting data.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“Analysis was conducted to determine the research methods and data collection techniques of the 98 primary studies.Of the 98 primary studies, eight adopted a mixed method approach, thirteen took the form of a literature review, forty-four used a quantitative method and thirty-one used the qualitative method.”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.3. RQ 2.3 research methods and data collection techniques used
Reported 98 · recomputed 96
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.4. RQ 2.4. What kinds of contributions are provided by studies on AI in IS?
“contributions were largely made as 'lessons learned' (39 studies)”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.4. RQ 2.4. What kinds of contributions are provided by studies on AI in IS?
“Fig. 5. shows that 'lessons learned' (40 studies) and 'methods' (26 studies) are the most popular contribution of AI studies in IS research.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
“However, for the purpose of this SLR, the study will be categorised solely based on the primary AI type of the study (see Fig. 6.).”
§ Artificial intelligence in information systems research: A systematic literature review and research agenda > 4.2.5. RQ 2.5. What types of AI technologies are used by IS researchers?
Reported 98 · recomputed 108
Citations
Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,191
A logical calculus of the ideas immanent in nervous activity
McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
(2016) · Cited by 11,679
Artificial intelligence: A modern approach
Nilsson, N. J. (1996) · Artificial Intelligence · doi:10.1016/0004-3702(96)00007-0 · Cited by 10,730
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290
Reinforcement Learning: An Introduction
Johnson, J. D., Li, J., & Chen, Z. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00324-6 · Cited by 8,825
Analyzing the Past to Prepare for the Future: Writing a Literature Review
Webster, J., & Watson, R. T. (2002) · MIS Quarterly · doi:10.2307/4132319 · Cited by 7,449
Research works printed but not linked: 27
- Berente, N., Gu, B., Recker, J., & Santhanam, R. (2019). Managing AI. MIS Quarterly.
- Brachman, 2006, AA)AI—more than the sum of its parts, 2005 AAAI Presidential Address, 27, 19
- Buettner, R., Frick, J., & Rieg, T. (2019). High performance detection of epilepsy in seizure-free EEG recordings: A novel machine-learning approach using very specific epilectic EEG sub-bands. ICIS 2019 Proceedings.
- Dennehy, 2020, Ireland post-pandemic: Utilizing AI to kick-start economic recovery, Cutter Business Technology Journal, 33, 22
- Jarrahi, 2018, Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making, Business Horizons, 64, 577, 10.1016/j.bushor.2018.03.007 · DOI · Paper page
- Langley, P. (2011). Artificial Intelligence. AISB Quarterly.
- Li, Y. (2017). Deep Reinforcement Learning: An Overview. arXiv preprint arXiv.
- Merkert, J., Mueller, M., & Hubl, M. (2015). A survery of the application of machine learning in decisiion support syystems. ECIS 2015 Proceedings.
- Petersen, 2015, Guidelines for conducting systematic mapping studies in software engineering: an update, Information Software Technology, 64, 1, 10.1016/j.infsof.2015.03.007 · DOI · Paper page
- Rai, 2019, Next-generation digital platforms: Towards human-AI hybrids, MIS Quarterly, 43, 3
- Schwartz, 2019, Green AI, Commun. ACM, 63, 54
- Wilson, J., & Daugherty, P. R. (2018, July-August). Collaborative Intelligence: Humans and AI Are Joining Forces. Harvard Business Review.
- Meyer, 2014, A machine learning approach to improving dynamic decision making, Information Systems Research, 25, 239, 10.1287/isre.2014.0513 · DOI
- Mettler, 2017, Service robots in hospitals: New perspectives on niche evolution and technology affordances, European Journal of Information Systems, 26, 451, 10.1057/s41303-017-0046-1 · DOI · Paper page
- Nissen, 2006, Incorporating software agents into supply chains: Experimental investigation with a procurement task, MIS Quarterly: Management Information Systems, 30, 145, 10.2307/25148721 · DOI
- Yin, 2019, Regulating cryptocurrencies: A supervised machine learning approach to de-anonymizing the bitcoin blockchain, Journal of Management Information Systems, 36, 37, 10.1080/07421222.2018.1550550 · DOI
- P20. Chatterjee, S., Saeedfar, P., Tofangchi, S. & Kolbe, L. (2018). Intelligent road maintenance: a machine learning approach for surface defect detection. Research Papers. ECIS 2018 Proceedings.
- P25. Hofmann, P., Oesterle, S., Rust, P. & Urbach, N. (2019). Machine learning approaches along the radiology value chain – rethinking value propositions. ECIS 2019 Proceedings.
- P44. Rzepka, C. & Berger, B. (2018). User Interaction with AI-enabled Systems: A Systematic Review of IS Research. ICIS 2018 Proceedings.
- Abubakar, 2019, Applying artificial intelligence technique to predict knowledge hiding behavior, International Journal of Information Management, 49, 45, 10.1016/j.ijinfomgt.2019.02.006 · DOI · Paper page
- Dwivedi, 2019, Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy, International Journal of Information Management
- Nishant, 2020, Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda, International Journal of Information Management, 53, 10.1016/j.ijinfomgt.2020.102104 · DOI · Paper page
- Sipior, 2020, Considerations for development and use of AI in response to COVID-19, International Journal of Information Management, 55, 10.1016/j.ijinfomgt.2020.102170 · DOI
- Sinha, 2020, Robotics at workplace: An integrated Twitter analytics – SEM based approach for behavioral intention to accept, International Journal of Information Management, 55, 10.1016/j.ijinfomgt.2020.102210 · DOI
- Borges, 2020, The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions, International Journal of Information Management
- Coombs, 2020, Will COVID-19 be the tipping point for the Intelligent Automation of work? A review of the debate and implications for research, International Journal of Information Management, 55, 10.1016/j.ijinfomgt.2020.102182 · DOI · Paper page
- P83. Karger, E. (2020). Combining Blockchain and Artificial Intelligence – Literature Review and State of the Art. ICIS 2020 Research Papers.
Cited by
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Kalmatov, R., Karimovna, K. G., Zhamin, S., & Rahim, F. (2025) · Springer eBooks · doi:10.1007/978-3-031-40858-8_545-1
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Obianyo, I. I., Kelechi, S. E., Mohammed, A., Mahamata, A. A., & Onwualu, A. P. (2026) · Studies in systems, decision and control · doi:10.1007/978-3-032-07738-7_21
de Ribeiro, R. O., Santos, G. D. S., Batista, V. G., Peres, R. L. C., dos Santos, J. C. V., Ferreira, F. F. V. M., & Murari, T. B. (2025) · SAE technical papers on CD-ROM/SAE technical paper series · doi:10.4271/2025-36-0142
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Eskandar, K. (2025) · Annales Universitatis Paedagogicae Cracoviensis Studia Naturae · doi:10.24917/25438832.10.11
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Hammerschmidt, T. (2024) · doi:10.20378/irb-109667
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Manna, F., & Chaudhary, N. K. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-2196-8_3
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Adesola, A. E., Ojo, O. A., Aboutorabi, N., Iwendi, C., & Sharma, V. (2025) · IEEE International Conference on ICT in Business Industry & Government (ICTBIG) · doi:10.1109/ictbig68706.2025.11323513
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Manjunathareddy, G. R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1077-0.ch002
Zhu, X., & yu, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6118626
Mamora-Ogunlana, O., Ajibade, T., Ezeokonkwo, Ayodele, O. E., & Olusola, T. A. (2025) · NIPES Journal of Science and Technology Research · doi:10.37933/nipes/7.4.2025.si124
Rumlaklak, M. P., & Marie, I. A. (2025) · Tekmapro Journal of Industrial Engineering and Management · doi:10.33005/tekmapro.v20i1.888
Noor, W. H. M., Hanafi, H. F., Rufin, S. M. M., & Hamazah, A. H. (2025) · Journal of Information System and Technology Management · doi:10.35631/jistm.1041005
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Sharma, P. (2026) · Responsible leadership and sustainable management · doi:10.1007/978-981-95-5929-9_11
Hashmi, M., & Tubastuvi, N. (2026) · Pattimura Proceeding Conference of Science and Technology · doi:10.30598/pcst.2026.iconbe.p129-145
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Dani, A. A. H., Abduh, H., Restu, Eriyadi, M., Lubis, F. F., & Supangkat, S. H. (2025) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss66954.2025.11389492
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Masialeti, M., & Wang, W. (2025) · IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) · doi:10.1109/tps-isa67132.2025.00080
Aisyah, A. N., Junaidi, J., & Goso, G. (2026) · Golden Ratio of Finance Management · doi:10.52970/grfm.v6i1.2092
ADEWUNMI, M. C. (2026) · Open Books and Proceedings · doi:10.38140/obp4-2026-05
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Kariyana, I., & Sinkala, W. (2026) · Open Books and Proceedings · doi:10.38140/obp4-2026
Gutiérrez, L. H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6401112
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Pamfili, C., & Namli, N. I. (2026) · Dokuz Eylül Üniversitesi Sosyal Bilimler Enstitüsü Dergisi · doi:10.16953/deusosbil.1673372
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Cañete, P. A. V., Albarillo, R. A., Agsaullo, R. T., Bucayong, J. A., Cadano, M. A., Carajay, M. J. D., Dublin, J. A. M., Exilisi, P. D. A., Estepa, S. J. L., Hilot, L. R. A., et al. (2026) · International Journal of Research and Innovation in Social Science · doi:10.47772/ijriss.2026.10200313
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Scott, D. U. (2026) · IGI Global eBooks · doi:10.4018/979-8-3693-8302-5.ch013
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Metadata
- Authors
- Collins, C., Dennehy, D., Conboy, K., & Mikalef, P.
- Affiliations
- Ollscoil na Gaillimhe – University of Galway (IE)
- Norwegian University of Science and Technology (NO)
- Corresponding author
- Collins, Christopher
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 60
- Pages
- 102383
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W3178738300
- Semantic Scholar
- 52cf81382eebec7d73c0de2f19af81c7a7028ab3
- Cited by
- 783
- References
- 211
- Citations by year
- 2026: 127 · 2025: 232 · 2024: 193 · 2023: 133 · 2022: 79 · 2021: 18
- FWCI
- 7.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International Journal of Information Management (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
- Funders
- National University of Ireland, Galway
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Systems Theories and Implementation
- Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
- Keywords
- artificial intelligence, information systems research, research agenda, business value of AI, research trends
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-08, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-08 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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