The DOI matches its Crossref record; 201 of the 201 references it cites were found in a registry or an index; 43 of the 206 sources its claims cite were checked for support.
Unlocking the value of artificial intelligence in human resource management through AI capability framework
Open accessOverview
AI snapshot
Written from the paper’s full text.
TL;DR
This systematic review synthesizes multidisciplinary literature to identify the organizational resources needed to build AI capability in HRM, arguing that technical infrastructure alone is insufficient. It proposes an AI capability framework centred on non-technical resources and AI-employee integration, and offers research propositions to advance AI scholarship in HRM.
Key findings
- The review distilled five themes: AI applications in HRM, collective intelligence, AI and employment/skills, drivers to AI adoption, and barriers to AI adoption.
- Organizations need complementary non-technical resources—human skills and competencies, leadership, team coordination, culture, innovation mindset, governance, knowledge management, and AI-employee integration—alongside technical resources such as data, infrastructure, and AI transparency.
- The review process began with 14,376 records, removed 1,564 duplicates, screened 12,812 records, retained 216 after title/abstract screening, identified 72 eligible articles, added 12 through cross-referencing, and included 84 studies; 18 topics were grouped into five themes.
- The proposed framework integrates resource-based view, knowledge-based view, and knowledge management strategies to explain how organizational readiness and context of AI use shape business and employee outcomes.
Methods
- Systematic literature review following established protocols, searching SCOPUS and EBSCO across 98 journals ranked 3, 4, or 4* in the ABS Journal Guide 2020, spanning HRM, general management, international business, information management, and operations management; last search date June 2021.
- Used an R script to remove duplicates, Latent Dirichlet Allocation topic modelling on titles and abstracts, team-based coding of topics, a document extraction table, and thematic synthesis to develop the AI capability framework.
- Final synthesis drew on 84 articles, organized into five research themes and used to propose research priorities and practical implications.
Limitations
- Stated: the review is limited to top-tier peer-reviewed journals (ABS 3–4*), so lower-ranked journals, non-peer-reviewed articles, and practitioner reports were excluded; the keyword and Boolean search may have missed relevant articles due to database unavailability or human error; future work should include Web of Science, DBLP, SSRN, SpringerLink, and law journals.
- Stated: selection of themes, research propositions, and resources relied on academic expert judgement and existing literature, so future studies should validate them with HR practitioners through interviews, Delphi studies, or AHP analysis.
- Evident: the framework is conceptual and not empirically tested in the paper; the conclusion reports reviewing 62 journals and 69 relevant articles, while the methodology reports 98 journals and 84 final articles, indicating an internal inconsistency in the reported review scope.
Model-written summary. Check it against the paper before relying on it.
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Verification
Claims the paper makes
43 of 206 cited sources checked · 17 supported · 0 flagged · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“AI can be useful to describe job requirements to attract the best suited candidates (Saling and Do, 2020)”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Supportedcites Saling and Do, 2020 source
The Abstract says analytic technology enables organizations to describe job requirements in detail so searches and recommendation tools can guide them toward the right person for a job. The Additional information section says organizations use AI to attract suitable employees and that AI can screen candidates against detailed requirements. (LLM, conf=0.88)
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“and employee motivation (Saling and Do, 2020)”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Supportedcites Saling and Do, 2020 source
The Additional information passage (page not supplied) proposes advanced sentiment monitoring to detect changes in employee satisfaction and morale and says that, with proper permissions, it could be a powerful tool for employee motivation. This supports the claim that AI-based sentiment analysis can be useful for that purpose. (LLM, conf=0.92)
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“to forecast absenteeism (Araujo et al., 2019)”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Supportedcites Araujo et al., 2019 source
The Abstract states that the authors applied an artificial-intelligence model to predict corporate absenteeism and that their experiments showed it could assist in that prediction. This supports the citing phrase, “to forecast absenteeism.” (LLM, conf=0.94)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“to support hiring decisions through screening and matching profiles with job roles (Peeters et al., 2020, Saling andDo, 2020)”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Partly supportedcites Peeters et al., 2020 sourceExperimental — accuracy not yet measured
The Data and IT infrastructure section says AI can be used in models that predict applicant performance or advise a recruiter whom to hire. The Introduction describes people analytics matching employees to vacancies, but does not say AI performed that matching; the paper also does not describe AI screening job candidates. Its mention of transaction screening concerns the work of a department, not hiring. (LLM, conf=0.85)
- Not matchedcites Saling andDo, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“AI can be combined with agent-based simulation to predict the overall situation of human resource development over time in a company to identify potential changes in recruitment strategies, the effect of promotion and development conditions, and the proportion of leavers under the conditions defined for the company (Pashkevich et al., 2019).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Could not verifycites Pashkevich et al., 2019 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“AI algorithms designed for human resource planning require well-structured decision objectives to provide good solutions (Meechang et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Could not verifycites Meechang et al., 2020 sourceExperimental — accuracy not yet measured
The Introduction and §2.1 describe a review of human acceptance of IT for disaster risk management, not AI algorithms for human resource planning. Searches for human resource planning, AI algorithms, and decision objectives found no passage addressing whether well-structured objectives are needed to produce good solutions. (LLM, conf=0.76)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“However, this raises significant questions about quality of the process itself, in particular an epistemological issue regarding the reliance on digital quantitative data from which the AI algorithms learn (Faraj et al., 2018).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Partly supportedcites Faraj et al., 2018 sourceExperimental — accuracy not yet measured
The Discussion & Conclusion (p. 11) questions decisions based primarily on digital data because they exclude qualitative, complex factors. Anticipatory quantification (pp. 4–5) likewise questions whether quantified data adequately represent people. The paper mentions algorithmic recruitment in Augmenting Coordination (p. 10), but does not specifically assess how this reliance affects the quality of recruitment processes. (LLM, conf=0.84)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Further, learning algorithms are reductionist in nature as they use predictive modelling based solely on correlational analysis of measured dimensions, thus reducing the individual to only those measured criterion (Faraj et al., 2018).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Partly supportedcites Faraj et al., 2018 sourceExperimental — accuracy not yet measured
In “Anticipatory quantification” (p. 5), the paper says predictive modelling builds on correlational analysis and that individuals may be reduced to measured dimensions. It says learning algorithms *can* be reductionist and calls that reduction a *possible* impact; it does not say they rely solely on correlational analysis. The citing sentence makes those points categorical. (LLM, conf=0.86)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The existing literature has also demonstrated appropriateness of RBV to be applied as a theoretical lens for developing distinctive and hard-to-imitate capabilities (such as AI implementation) in a turbulent and technology-driven business environment (Bromiley and Du, 2016;Mikalef and Gupta, 2021).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 4. Framework Development
What the sources say
- Not matchedcites Bromiley and Du, 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Supportedcites Mikalef and Gupta, 2021 source
The Introduction says RBT is an appropriate lens for dynamic and turbulent environments when organizations develop distinctive capabilities. Section 2.1 links the lens to hard-to-imitate capabilities and explicitly deems it appropriate for studying how firms develop AI capabilities. (LLM, conf=0.93)
Load-bearingStrength:boosted· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Not matched
Every source the paper cites for this claim was checked and supports it.
In the paper
“which supports the claim that successful AI implementation can only be fully achieved when there is an acceptance from decisionmakers for its use (Cao et al., 2021).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Supportedcites Cao et al., 2021 source
The introduction (p. 1) says the potential benefits of AI in organizational decision-making can only be fully realized if human decision-makers accept its use. This supports the citing sentence’s point that decision-maker acceptance is necessary for successful use of AI in that context. (LLM, conf=0.91)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Supported
- Probably not a substantial claim. 98
- It may state two separate claims as one. 80
Every source the paper cites for this claim was checked and supports it.
In the paper
“Baakeel (2020) examined the use of AI in recruitment, and found the potential for fast resume scanning, quickly and automatically responding candidate's queries, and virtual recruitment activities.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Supportedcites Baakeel (2020) source
The Conclusion and Recommendations section says AI can scan resumes very fast, answer candidates’ questions virtually, and interview and recruit people worldwide without in-person visits. These points support the cited sentence’s description of AI’s potential in recruitment. (LLM, conf=0.91)
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“AI is valuable in HR decision-making (case in point employees) because of the potential to avoid subjectivity, with more objective decisions guided by the information extracted through employee data mining (Chornous and Gura, 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Supportedcites Chornous and Gura, 2020 source
The Conclusions state that advanced workforce analytics can help avoid subjectivity in human-capital-management decisions and substantiate decisions with knowledge extracted by mining employee data. This directly supports the cited paraphrase, though the paper also cautions that people may interpret analytics through cognitive biases. (LLM, conf=0.94)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“This will allow organisations to develop the capability of workforce, improve teamwork, support flexible working and improve performance measurement (Chornous and Gura, 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Partly supportedcites Chornous and Gura, 2020 sourceExperimental — accuracy not yet measured
The Materials and Methodology section describes using workforce analytics for training and development and evaluating employees’ potential productivity. The Results section discusses HR metrics and reporting, which supports the performance-measurement part. Neither section establishes that these tools improve teamwork or support flexible working. (LLM, conf=0.86)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“there is currently no single universally accepted definition throughout the literature, which leads to a fundamental problem of coherent understanding of AI (Mikalef and Gupta, 2021).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 1. Introduction
What the sources say
- Supportedcites Mikalef and Gupta, 2021 source
Section 2.2, “Artificial intelligence,” explicitly says that the literature lacks a universally accepted definition of AI and that this has led to a fundamental problem of understanding AI in its entirety. This supports both parts of the citing sentence. (LLM, conf=0.95)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“and also analyzing financial data for payment and motivating personnel based on compensation (Kulkov, 2021)”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Supportedcites Kulkov, 2021 source
In the section on finding talents and salary formation, the paper describes an AI system for paying and motivating personnel. It says identifying mismatches between position and compensation helps reduce the risk of staff leaving. (LLM, conf=0.90)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“The recent theoretical framework building on the tenets of socio-technical systems theory and organisational socialisation framework by Makarius et al., 2020 integrating AI with employee within the business organisations has discussed the role of AI as a collaborator (new employee), where AI-employee collaboration can help the organisations to achieve competitive advantage.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
What the sources say
- Supportedcites Makarius et al., 2020 source
The Introduction defines sociotechnical capital as competitive advantage resulting from successful collaboration between AI and people. Section 2.3 also proposes treating AI as a collaborator and integrating it effectively to achieve competitive advantage for the adopting organization. (LLM, conf=0.94)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“AI teammates 'would be involved in complex problem solving: defining a problem, identifying root causes, proposing, and evaluating solutions, choosing among options, making plans, taking actions, learning from past interactions, and participating in after-action reviews' (Seeber et al, 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
What the sources say
- Partly supportedcites Seeber et al, 2020 sourceExperimental — accuracy not yet measured
The Abstract lists exactly these steps in a complex problem-solving process. It says effective machine teammates would need to engage in “at least some” of them, whereas the citing sentence omits that qualifier and can imply involvement in every listed step. (LLM, conf=0.86)
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These will enhance ability of employees to trust, use and adopt AI, productivity, and career satisfaction.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
What the sources say
- Partly supportedcites Makarius et al., 2020 sourceExperimental — accuracy not yet measured
Section 4.2 (page unavailable) says that understanding an AI system’s purpose and process can enhance employee trust, while section 4.4 predicts greater productivity from successful AI integration. Section 4.4 says understanding AI can influence career satisfaction, but does not say it will improve it. The paper presents these outcomes as expectations or possibilities, not assured effects. (LLM, conf=0.86)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The skills and expertise provide the organisations with capability to innovate, re-engineer and optimise both business processes and resources, efficiently, and therefore can their ability to dynamically adapt and remain responsive (Mikalef et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.3 AI and Employment
What the sources say
- Partly supportedcites Mikalef et al., 2020 sourceExperimental — accuracy not yet measured
Section 2.1 identifies technical and managerial skills as important to deriving value from big data analytics, while section 3 links analytics-derived insights to faster, more effective responses to change. But the paper addresses big data analytics rather than AI skills specifically, and section 6.2 says insight alone is insufficient: organizational action and flexibility are also required to achieve responsiveness. (LLM, conf=0.86)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Seeber et al (2020) went further and suggested that effective AI teammates required more than social robots and digital assistants.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
What the sources say
- Supportedcites Seeber et al (2020) source
The Abstract states that effective machine teammates must be more capable than today’s social robots and digital assistants. The citing sentence accurately paraphrases that point. (LLM, conf=0.94)
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“Kot et al. (2021) argue that the appropriate implementation of AI to support recruitment and retention can be essential to enhance employer brand and reputation as well.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Supportedcites Kot et al., 2021 source
Section 1.2 says AI-based HR systems assist in hiring and retaining talent and links AI-based recruitment to employer reputation. The paper’s Summary of results reports a significant positive association between AI recruitment and employer reputation. It studies employer reputation rather than using the term “employer brand,” but the claim is a reasonable paraphrase of its argument. (LLM, conf=0.88)
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“If handled appropriately, AI can offer a number of well-being benefits.For example, building well-being awareness, measuring well-being and the impact of interventions and changes, managing any AI risk on well-being, supporting positive well-being initiatives and providing employers insight into employee work-life-balance (Schiff et al, 2020.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Partly supportedcites Schiff et al, 2020 sourceExperimental — accuracy not yet measured
Section C, “Benefits of a well-being orientation” (page unavailable), supports building awareness, measuring impacts, managing AI-related risks, and improving well-being through AI design. It describes these as benefits of adopting a well-being orientation to AI, rather than benefits AI itself provides, and does not say that AI gives employers insight into employees’ work-life balance. (LLM, conf=0.88)
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Partly supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“In fact, several of the decisions made by humans are based on judgement and intuition (Meechang et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Could not verifycites Meechang et al., 2020 sourceExperimental — accuracy not yet measured
Section 2.1 discusses models of human behavior, and section 4.6 describes reliance on past experience in disaster preparedness. Neither passage says that human decisions are based on judgment and intuition; searches for that phrase and a paraphrase found no passage addressing the claim. Page numbers were not available in our copy. (LLM, conf=0.72)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Could not verify
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“An AI algorithm might identify a relevant criteria or relationship between criterion affecting the successful integration of employees into the company.However, that relationship may not be evident for decision-makers, which could question the reliability of the results (Meechang et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Could not verifycites Meechang et al., 2020 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Could not verify
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The predictive capacity of AI can be hindered by the challenges to accurately describe the complexity of human behavior (Pashkevich et al., 2019) and the effect of unknown external effects affecting the conditions of the environment and the company itself.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Partly supportedcites Pashkevich et al., 2019 sourceExperimental — accuracy not yet measured
Section 4.1 (Prediction Ability; page unavailable) reports weak predictive ability for some forklift fuel-consumption measures and says more explanatory variables could improve it. Section 5 (Discussion; page unavailable) identifies driver behavior as a significant factor. The paper does not say that difficulty accurately describing complex human behavior hinders AI prediction; the sentence’s uncited claim about unknown external effects is outside this verdict. (LLM, conf=0.76)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“(6) social performance by reducing human subjective bias in HR processes such as recruitment and employee appraisal, and improving employee experience and working conditions by analysing enterprise social media anonymous data (Toniolo et al., 2020);”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Figure 2: Framework summarising the review findings
What the sources say
- Partly supportedcites Toniolo et al., 2020 sourceExperimental — accuracy not yet measured
The Social sustainability section says AI can reduce recruitment bias through standardized candidate assessment and that reduced workload can improve working conditions and employee well-being. It does not support the more specific claims about bias in employee appraisal or improving employee experience and working conditions by analysing anonymous enterprise social-media data. It describes these effects as possibilities, rather than establishing that AI adoption leads to them. (LLM, conf=0.85)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The skills and expertise provide the organisations with capability to innovate, re-engineer and optimise both business processes and resources, efficiently, and therefore can their ability to dynamically adapt and remain responsive (Mikalef et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
What the sources say
- Partly supportedcites Mikalef et al., 2020 sourceExperimental — accuracy not yet measured
Section 2.1 says technical and managerial skills are required to derive value from big data, and Section 2.3 links organizational capabilities to efficient resource use, innovation, and reconfiguration in response to change. But Section 6.2 says knowledge alone is insufficient: responsiveness also requires organizational flexibility and action. The claim overstates what skills and expertise by themselves provide. Page numbers are unavailable in our copy. (LLM, conf=0.83)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Considering the new set of skills and capabilities needed for managers, employees, and AI to collaborate, there will be a necessity to redesign jobs, create new ones, transform business model and strategies (Toniolo et al., 2020;WambaTaguimdje et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
What the sources say
- Partly supportedcites Toniolo et al., 2020 sourceExperimental — accuracy not yet measured
The Social sustainability section says AI requires greater skills for remaining tasks, can support human–machine collaboration, and is changing employees’ roles. The Introduction and §1.3 say AI affects business models and strategy. The copy does not state that AI will necessitate creating new jobs; it discusses replacing jobs and moving workers into different roles instead. Page numbers were not available in our copy. (LLM, conf=0.84)
- Not matchedcites WambaTaguimdje et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The aim here is to minimize potential risks faced by organisations with regards to using AI and simultaneously protect the interests of business users (and/or whose data is collected) (Arrieta et al., 2020;Coombs et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
What the sources say
- Partly supportedcites Arrieta et al., 2020 sourceExperimental — accuracy not yet measured
Section 6.2.2 (p. 40) says AI impact assessments should identify and minimize potential negative impacts, and that audit reports can contribute to trustworthiness. Section 6.3.3 (p. 42) calls for protecting the privacy of people whose data is used. The paper does not specifically describe these reports as minimizing risks faced by organizations or protecting the interests of business users. (LLM, conf=0.82)
- Partly supportedcites Coombs et al., 2020 sourceExperimental — accuracy not yet measured
In the ‘Critical review of the literature and future research agenda,’ the paper discusses organisations’ legal liability for AI decisions and recommends governance to protect vulnerable people affected by automated decisions (page not provided). It does not establish that the aim of the reports mentioned in the citing sentence is to minimise organisational risks while protecting business users or people whose data is collected. (LLM, conf=0.78)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The authors have concluded that effective collaboration between human intelligence and AI will help to unlock the real potential of digital technology to solve pressing business and societal challenges, and this will require careful crafting of a strategy to re-define and recategorize existing jobs, by considering the skills-gap.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
What the sources say
- Not matchedcites Malone, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites Caputo et al., 2019 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Not matched
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“there is a lack of both understanding as well as knowhow on the best practices to effectively develop collaborative intelligence capability (Amabile, 2020;Ciechanowski et al., 2020;Makarius et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
What the sources say
- Not matchedcites Amabile, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Ciechanowski et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Supportedcites Makarius et al., 2020 source
The Introduction says that research on integrating AI with employees is underdeveloped and asks how organizations can do so successfully. Section 2.3 describes the relational study of AI as nascent and calls for greater understanding of how to integrate AI as a collaborator, supporting the paraphrase about a lack of established knowhow. (LLM, conf=0.88)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“Our review found that the adoption of AI within HRM processes and wider organisation will impact employees in different ways, such as job substitution, training needs, and uncertainty regarding roles and responsibilities (Frey & Osborne, 2017), limited understanding about how and why AI will be used (Raisch & Krakowski, 2020), trust and confidence (Gunning, 2017), formation of project teams where”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
What the sources say
- Not checked — no full text of the sourcecites Frey & Osborne, 2017 source
- Could not verifycites Raisch & Krakowski, 2020 sourceExperimental — accuracy not yet measured
The citation is attached specifically to employees’ limited understanding of how and why AI will be used. The Discussion (page not available) calls for scholars to better understand AI in management and discusses transparency, but does not say employees have that limited understanding. Searches for employee understanding, AI-use purposes, and explainability found no passage addressing the attributed point. (LLM, conf=0.72)
- Not matchedcites Gunning, 2017
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“The existing research studies have indicated the importance of AI-human collaboration (Fleming, 2019, Makarius et al., 2020), which has emerged as a new research theme in the literature (Murray et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
What the sources say
- Not checked — no full text of the sourcecites Fleming, 2019 source
- Supportedcites Makarius et al., 2020 source
The Introduction says that successful AI integration requires employees to interact with AI systems and that interaction between AI and people should be a focal point of management research. Section 2.3 likewise calls for studying AI as a collaborator. The separate claim about an emerging research theme is attached to Murray et al., not Makarius et al. (LLM, conf=0.92)
- Not checked — no full text of the sourcecites Murray et al., 2020 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Evaluating the internal expertise will help to identify employees having knowledge (technical and/or non-technical) to use AI, understand business processes and functions which can benefit, and how the benefits will serve both employees' and organisational goals/priorities (Mikalef and Gupta, 2021).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.3 Practical Implications
What the sources say
- Partly supportedcites Mikalef and Gupta, 2021 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In this context, existing research has identified the importance of collective intelligence, i.e., symbiotic partnership between AI and HI, and how this will also increase the capability of human workers, which will lead to higher employee productivity, better psychological outcomes, low turn-over and improved quality of outputs (Glikson and Wooley, 2020;Murray et al., 2020;von Krogh, 2018).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.3 Practical Implications
What the sources say
- Not checked — no full text of the sourcecites Glikson and Wooley, 2020 source
- Not checked — no full text of the sourcecites Murray et al., 2020 source
- Partly supportedcites von Krogh, 2018 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The existing literature has reported different types of AI systems within business organisations (Haenlein and Kaplan, 2019;Daugherty et al., 2019;Davenport and Bean, 2017): 1) automated intelligence, which involves automating routine and manual tasks, so that human workers can spend more time in non-trivial complex tasks.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Not checked — no full text of the sourcecites Haenlein and Kaplan, 2019 source
- Not matchedcites Daugherty et al., 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Davenport and Bean, 2017
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Our review showed the importance of understanding complementary organisational resources required to benefit from AI adoption in HRM, and exploring beyond the data and technological resources (Mikalef and Gupa, 2021).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI Organisational Resources
What the sources say
- Not matchedcites Mikalef and Gupa, 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
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
“We have not included autonomous intelligence because we are yet to come across such an AI system in HRM, which can function without any human involvement.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Governance and regulation
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“We found that recent reports published by the European Commission have emphasised on ethical and moral aspects surrounding AI, which has led to responsible development and use of AI (European Commission 2019a and2019b).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
What the sources say
- Not matchedcites European Commission 2019a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites European Commission 2019b
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
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, most studies reported in the literature and including HRM focus on multinational large organisations.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. 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 knowledge gap and our limited understanding exists in the HRM literature on the factors that will influence AI-employee collaboration, and the potential business impact of this collaboration.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
“While, many studies have reported the drivers and barriers related to adoption and implementation of AI, we consolidate these to propose AI capability theoretical framework.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 6. Conclusion and Future Direction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
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 review has identified five key themes in the literature: AI applications in HRM; collective intelligence; AI employment and skills; AI drivers and barriers to adoption.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 6. Conclusion and Future Direction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
“However, collective intelligence is nascent and emerging within academic research.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 6. Conclusion and Future Direction
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“We found that the current literature primarily has focussed on the applications of AI in HRM, anticipated benefits, impact of AI on jobs, and AI-driven decision-making augmenting human intelligence.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 6. Conclusion and Future Direction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“While, IM has primarily focussed on technical resources and infrastructure, and wider BAM research has focussed on non-technical resources, our capability framework objectively brings these two rather fragmented pieces of literature together (Loebbecke & Picot, 2015;Markus, 2015).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
What the sources say
- Not matchedcites Loebbecke & Picot, 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Markus, 2015 source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
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
“AI has a significant potential to support organizational research because of the capacity to analyze data and support decision-making.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Based on the model proposed by Mehrabad and Brojeny (2007), it can be used to select applicants from a pool of submitted applications (selection), make a decision based on the interview and organisational need (appointment) and propose a suitable salary and benefits based on their qualifications.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Not checked — no full text of the sourcecites Mehrabad and Brojeny (2007) source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“We conducted a systematic review of literature following the protocol suggested in the existing literature (Tranfield et al., 2003;Denyer and Tranfield, 2009;Wolfswinkel et al., 2013;Hopp et al., 2018) to ensure that the review process is transparent, easily reproducible, and critically analyses and systematises research themes (stemming from the review).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 2. Review Methodology
What the sources say
- Not checked — no full text of the sourcecites Tranfield et al., 2003 source
- Not matchedcites Denyer and Tranfield, 2009
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Wolfswinkel et al., 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
“our review sheds light on the importance of developing collective intelligence within the organisations, i.e., a collaborative working environment where AI and human intelligence (HI) can co-exist”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Citation
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Following the recommendations in Tranfield et al. 2003, this process ensured that the procedure is transparent, reproducible, and devoid of human errors.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Search Strategy
What the sources say
- Not checked — no full text of the sourcecites Tranfield et al. 2003 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. 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
“AI recruitment decreases the time taken to recruit individuals, enables organisations to respond to events more quickly and ultimately improve their competitive advantage through intangible assets of better recruited people.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“The use of technology to support HR has significant potential to support the business strategy (Kakkar and Kaushik, 2019).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Not matchedcites Kakkar and Kaushik, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“to identify personality traits from potential candidates and match them to the predominant traits found in the culture of the company (Lee and Ahn, 2020)”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Not checked — no full text of the sourcecites Lee and Ahn, 2020 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Sparrow, et.al. (2015) argue that this strategic focus needs to be contextualised within the organisation, rather than a more generic strategic focus.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Not matchedcites Sparrow, et.al. (2015)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
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 was initially done individually by each researcher and then consensus was reached through a group discussion to finalize the key themes emerging from the study.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Search Strategy
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“Next, all these documents were verified by each team member to ensure that the information is consistent with the literature and covered all relevant information (e.g., study protocol, or strategic implications relevant to our research question).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Search Strategy
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“to access a larger pool of candidates and reduce bias eliminating subjective criteria (Kshetri, 2020)”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.1 AI Applications
What the sources say
- Not checked — no full text of the sourcecites Kshetri, 2020 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“research has found that organisations often find it difficult to integrate AI within their business processes and systems, which often inhibits AI adoption (Davenport and Ronanki, 2018;Mikalef., et al., 2019).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Citation
What the sources say
- Not matchedcites Davenport and Ronanki, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Mikalef., et al., 2019 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“This will significantly impact the organisational processes, nature and meaning of work, work design structure within organisations, competencies and skills required in specific roles within the digital workforce, technology exposure, employer and employee expectations, work practices within the organisation and human resource strategy to bridge the skills gap to increase employee motivation and”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.3 AI and Employment
What the sources say
- Not matchedcites Schroeder et al., 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Wilson et al., 2017
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“The IM literature has reported that humans often reject and ignore a new technology, if they feel threatened by it (i.e., affects their financial and psychological wellbeing), irrespective of their interest and enthusiasm in it (Elkins et al., 2013).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
What the sources say
- Not checked — no full text of the sourcecites Elkins et al., 2013 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“AI algorithms can be combined with techniques such as data envelopment analysis to identify underperforming employees, their impact on the efficiency and effectiveness of the company, and the overall performance of the organization (Panteia, 2020)”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not matchedcites Panteia, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“The idea was first discussed by Malone (2018), who considered the intelligence of humans and that of machines as the intertwining of 'collective' intelligence.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
What the sources say
- Not matchedcites Malone (2018)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
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, potential benefits of such AI-employee collaboration established through a symbiotic partnership can be fully realised in practice (and beyond the theoretical narratives), if employees (human workers) understand, trust, and adopt AI.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“Furthermore, clarification about the limits and advantages of AI will help organizations to successfully integrate AI in human working environment (Malone, 2018).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
What the sources say
- Not matchedcites Malone, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Tsui et al., 2000 put forward how AI can be used for knowledge engineering that can aid knowledge searching and knowledge processes.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.3 AI and Employment
What the sources say
- Not checked — no full text of the sourcecites Tsui et al., 2000 source
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“For recruiting, AI can help writing job profiles, screening resumes, using enhanced video analysis to identify behavioral patterns of potential candidates, comparing them with the criteria required for the position (Belhadi et al., 2021), and identifying traits and skills of potential candidates to facilitate adaptation and enhance performance (Wilson and Gosiewska, 2014).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not matchedcites Belhadi et al., 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Wilson and Gosiewska, 2014 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
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 can be attributed to limited knowledge, skills and understanding among the workforce (both employees and managers) about AI capabilities, limitations, strategic initiatives, and integration with the existing business processes.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
“Jarrahi (2018) has suggested that the computational power and analytical capabilities of AI can be leveraged to deal with the complexity in decision-making process, with the intention to augmenting human intelligence (HI) and decision-making tasks, rather than replacing human from the process.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
What the sources say
- Not checked — no full text of the sourcecites Jarrahi (2018) source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Abubakar et al (2019) showed how AI could be used to highlight knowledge hiding.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.3 AI and Employment
What the sources say
- Not checked — no full text of the sourcecites Abubakar et al (2019) source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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 is achieved by automating trivial manual repetitive processes that require considerable time and human capital.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“AI-enabled systems will both automate and augment HRM decision-making in organisations, which has been a subject of both attention and fear among the corporate leaders (Janssen et al, 2019).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
What the sources say
- Not matchedcites Janssen et al, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“Fear of job losses and obscurity about AI-human responsibilities in a collaborative working environment will prevent organizations to concretize AI Adoption (Bughin, 2018).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
What the sources say
- Not matchedcites Bughin, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“Barro and Davenport, 2019 have argued that although organisations have acknowledged the potential benefits offered by AI adoption, they are yet to augment human intelligence or replace employees with AI expert systems.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
What the sources say
- Not matchedcites Barro and Davenport, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“AI capabilities can improve and optimize business operations and resources through task automation and augmenting human intelligence, which will reduce operational costs, lead production time, improve output/delivery response time (throughput), enhance creative process within organisations by freeing up employees' time to concentrate on non-trivial and complex procedures, resulting in performance”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not matchedcites Deb at al., 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Mishra and Pani, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Wamba-Taguimdje et al., 2020 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“Therefore, these skills will be strategic intangible resource that will be difficult to imitate by other firms, which will provide competitive advantage (Barney, 1991).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.3 AI and Employment
What the sources say
- Not checked — no full text of the sourcecites Barney, 1991 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“AI can provide more flexibility and work-related autonomy, promote creativity and innovation, and allow to streamline organizational processes (Malik et al., 2021).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not matchedcites Malik et al., 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“Bughin (2018) has discussed how the use of AI within business organisations is likely to increase the number of employees, rather than job losses, because the early adopters of AI are positioning themselves for growth which will stimulate employment.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.3 AI and Employment
What the sources say
- Not matchedcites Bughin (2018)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“For example, Liebowitz (2001), reported how AI enhanced knowledge management by facilitating effective knowledge sharing.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.3 AI and Employment
What the sources say
- Not checked — no full text of the sourcecites Liebowitz (2001) source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Additionally, AI can be useful for monitoring, performance measurement and tracking employee morale (Gaur and Riaz, 2019).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not checked — no full text of the sourcecites Gaur and Riaz, 2019 source
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Major drivers identified in the literature for the adoption of AI-enabled systems include the potential to be more objective, less likely to make mistakes, and the ability to predict future behavior through the identification of patterns in the historical datasets (Giermindl et al., 2021, Gaur andRiaz, 2019).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not matchedcites Giermindl et al., 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Gaur andRiaz, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“Such augmentation in any job context will boost the decision-making abilities of employees, increase the time devoted for non-trivial tasks, heighten creativity, and therefore enhance both employee and business productivity (Wilson and Daugherty, 2019).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.2 Collective Intelligence
What the sources say
- Not matchedcites Wilson and Daugherty, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
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 is important because it highlights that the source of bias in AI is associated to the bias of the people implementing the AI.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“Case in point the HR recruiters are relying on the meta data available on social media outlets to target potential candidates for roles within organisations.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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, the interpretability of results from AI and the potential clash with the human perspective can hinder the successful implementation of AI.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Ownership of the AI algorithm and its results may be placed on HR managers, and as such they would need to know the rationale for the data input choices and results (Davenport and Ronanki, 2018).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Not matchedcites Davenport and Ronanki, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
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
“For recruitment and hiring, this means that the core components of the organizational culture must be clearly delimited to obtain useful results.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
Strength:boosted· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“If users or HR managers do not understand the algorithms' affordances and variants, this can result in an inability to use the algorithms effectively to recruit and retain the best possible staff and to be swayed by prejudice (Chowdhury et al., 2020;Shin and Park, 2019).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Not matchedcites Chowdhury et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Shin and Park, 2019 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
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
“All these drivers will reduce bottlenecks and improve overall operational efficiency, which will enhance business productivity.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“Organizational resilience involves the capacity to absorb, respond and transform to capitalize on disruptions that threaten the organization (Lengnick-Hall et al., 2011).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not checked — no full text of the sourcecites Lengnick-Hall et al., 2011 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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 quantification has become a substitute for an individual's social life, personality, abilities and choices, attributes upon which recruitment decisions are being made.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“There are several reasons why transparency is particularly necessary in a HR context (Chowdhury et al., 2020;Silvernam, 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Not matchedcites Chowdhury et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Silvernam, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“The lowering of costs and easy access to the digital world, coupled with relative ease with which vast amounts of meta data can be processed, society is has become dominated with the logic of quantification (Espeland and Stevens, 2008).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Not checked — no full text of the sourcecites Espeland and Stevens, 2008 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The existing research has also indicated that IT adoption (such AI-enabled systems) within organisations will enhance dynamic capabilities which drives market capitalization, increased flexibility to adapt/re-engineer business operations and processes, agility, and responsiveness to address uncertain and evolving market demands and mitigate trade-offs within the organisation (Mikalef and Pateli, 2”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not checked — no full text of the sourcecites Mikalef and Pateli, 2017 source
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“Despite the recent investment on social responsibility programs, the general consensus is that companies are failing to tackle the SDGs because of limited visibility, flexibility and resilience (Bag et al., 2021a).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not matchedcites Bag et al., 2021a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Resilient organisations often rely on changes on the corporate culture and re-design of processes (Bag et al., 2021a).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not matchedcites Bag et al., 2021a
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Hence, the company must promote the development of skills that can use by employees to support the capacity of the organization to withstand disruptions through the introduction of HR management able to ensure human resources are managed effectively and that they can be employed at the right time and in the right conditions to help the company manage different circumstances (Slusarczyk, 2018).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not matchedcites Slusarczyk, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“On top of that, Giermindl et al. (2021) and Malik et al. (2021) highlight the importance of privacy and data protection concerns, issues stemming from constant tracking, and the potential of bias in the algorithms themselves.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Not matchedcites Giermindl et al. (2021)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Malik et al. (2021)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“This has led to the development of sustainable human resource management, which aims at accomplishing organizational sustainability supported by the design of HR policies, strategies and practices simultaneously underpinning social, environmental, and economic aspects (Wesley Ricardo de Souza et al., 2011).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not matchedcites Wesley Ricardo de Souza et al., 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
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
“Growing developments and usage of AI in everyday life and organisations has resulted in a need for HRM to consider the role and impact of AI on the well-being of employees.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Considering the importance of leveraging technology to increase the productivity of human resources to support sustainable development (Reddy et al., 2019), technology has been related to sustainable human resource management.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not matchedcites Reddy et al., 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Zhou, 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Organizational resilience is valuable to help businesses plan, prepare, strategize operations, and respond to unpredictable disruptions and efficiently recover from such disruptions (Scholten et al., 2019, Macdonald et al., 2018, Bag et al., 2021b).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not matchedcites Scholten et al., 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Macdonald et al., 2018 source
- Not matchedcites Bag et al., 2021b
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
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
“Particularly for HR recruitment the decision-making process of targeting potential candidates and taking them through the rudimentary checks, is accelerated using AI.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Other barriers include, assessing the data quality to ensure the decisions and recommendations are precise, accurate and relevant (Ransbotham, et al., 2020;Kiron and Schrage, 2019); optimal training dataset to reduce bias and reputational risks (Chowdhury et al., 2020;Glikson and Woolley, 2020); integrating existing systems with the AI implementation, to streamline information processing and”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Not matchedcites Ransbotham, et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Kiron and Schrage, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Chowdhury et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“Certain groups have been found to be disproportionality disadvantaged in AI algorithms, e.g., black faces associated as gorillas (Dougherty, 2015) and Asian people categorised as blinking (Wade, 2010).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Not matchedcites Dougherty, 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Wade, 2010
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
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
“Use of AI powered by the algorithms provides HR recruitment with efficient tools to both market jobs to an increasingly digitally based candidate and perform the rudimentary initial checks before candidates can be interviewed.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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.
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 has the potential of increasing the safety of the workforce and the sustainability of jobs in the long term”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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.
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 impact of these barriers in practice can be significant.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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
“Tambe et al. (2019)argues that the adoption of AI for HRM faces four major challenges, namely the complexity of the HR phenomena, small data, ethical constraints, and the reaction of employees to the implementation of artificial intelligence.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Not checked — no full text of the sourcecites Tambe et al. (2019) source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Issues such as bias (Chouldechova et al., 2018), misinformation (Little, 2018;Chesney and Citron, 2019), lack of understanding, intrusion, inequality (Buolamwini and Gebwu, 2018) and labour displacement (Autor, 2015) may impact on employees' well-being and their feelings of security, trust and privacy.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.4 Drivers to AI adoption
What the sources say
- Not checked — no full text of the sourcecites Chouldechova et al., 2018
- Not matchedcites Little, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Chesney and Citron, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“Drawing from the knowledge management literature, knowledge sharing has been identified by Hansen et al. (1999) as a key practice to create business value that will enhance economic performance of business organisations.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 4. Framework Development
What the sources say
- Not checked — no full text of the sourcecites Hansen et al. (1999)
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Thus, reducing individuals to a set of measured dimensions and avoiding dealing with a person's evolution and alternative explorations that may explain how one ends up in specific category (Ananny, 2015).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
What the sources say
- Not matchedcites Ananny, 2015
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
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
“which would suggest that there would be a demographic divide in openness to AI.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI Organisational Resources
Strength:hedged· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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, the capability of AI needs to be combined with the capacity of humans to empathize and understand the results within the global, organizational, and personal context.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
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
“For instance, AI has the potential to identify areas of lower performance based on the achievements of employees, but it would struggle to process the underpinning factors leading to low performance and therefore it could interpret a need for action in instances that may be temporary or affected by external variables.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: AI has shortcomings in creative and social intelligence.
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, in the context of AI knowledge sharing, codification will facilitate scaling up the knowledge dissemination across the organisation and re-using the knowledge, and personalization will promote trust and cooperative attitude of employees towards AI, through networking and discussion.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Governance and regulation
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“(8) developing successful business cases related to AI implementation which will enhance reputation among the business stakeholders, competitors, policy makers and consumers (Davenport and Ronanki, 2018).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Figure 2: Framework summarising the review findings
What the sources say
- Not matchedcites Davenport and Ronanki, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
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
“All of these will lead to developing a culture which fosters creativity, innovation capability, collaboration between teams, less resistance to change and conducive to the needs of human workers (where their human attributes such as intuitive intelligence, empathy, negotiation, and communication skills are acknowledged).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Governance and regulation
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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
“We envisage the context will determine type of AI (automation, augmentation and assisted).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Governance and regulation
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“(3) introducing new products and services, improving the quality of existing ones positively impacting operational performance and costs in terms of inventory management through optimising resource utilization (Wamba-Taguimdje et al., 2020;Alsheibani et al., 2020);”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Figure 2: Framework summarising the review findings
What the sources say
- Not checked — no full text of the sourcecites Wamba-Taguimdje et al., 2020 source
- Not matchedcites Alsheibani et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Both codification and personalization strategies have been used to build expertise and facilitate skills development in the context of platform ecosystems (Tiwana, 2013) and IT sectors (Garavelli et al., 2002).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Governance and regulation
What the sources say
- Not matchedcites Tiwana, 2013
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Garavelli et al., 2002 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
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 impact on employees will depend on the type of AI, which processes will be transformed, AI strategy of the organisation, knowledge creation, sharing and management mechanisms to develop skills, knowledge, and expertise of employees.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Governance and regulation
Strength:plain· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“The context of use will depend on the business problem and priority.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Governance and regulation
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, big tech companies like Facebook, Google and Amazon design algorithms that contributes to their success, however, these algorithms are closely guarded and protected as intellectual property (O'Neil, 2017).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI Organisational Resources
What the sources say
- Not matchedcites O'Neil, 2017
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
In the paper
“It is often considered as an extension of the RBV theory, and posits that knowledge created within the organisation is a critical asset which will help to produce sustainable competitive advantage in dynamic market environments because: (1) knowledge-based sources are socially complex to understand and embedded within the firm; (2) difficult to imitate by another organisation; (3) continuously”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 4. Framework Development
What the sources say
- Not checked — no full text of the sourcecites Grant, 1996 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. 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 organisational resources will determine the organisational readiness to utilize AI (i.e., resources that need financial investment and time to develop) and how it will be used (i.e., context which will lead to business process transformation).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Governance and regulation
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“RBV is one of the most widely applied theoretical perspectives to explain how resources within an organisation can help enhance business performance and competitiveness (Barney, 2001).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 4. Framework Development
What the sources say
- Not checked — no full text of the sourcecites Barney, 2001 source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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 inevitably raises questions about overreliance on AI decisions regarding targeted recruitment and following recruitment processes.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 3.5 Barriers to AI adoption.
Strength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“The development of skills and expertise in-house will provide role clarity in a collaborative AI-HI working environment, develop trust and confidence among the human workers, which will enhance their emotional engagement with AI.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > Governance and regulation
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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.
None of the sources cited for this claim have been checked yet.
In the paper
“It also helps to develop appropriate conditions, strategies, and resources within the organisation, which will allow the employees to harness new skills building on existing knowledge, facilitate access to relevant knowledge and expertise base, and finally encourage on sharing this knowledge with peers (Alsheibani et al., 2020;Demlehner & Laumer, 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
What the sources say
- Not matchedcites Alsheibani et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Demlehner & Laumer, 2020
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“Leadership plays an important role to intellectually stimulate employees' ability to perform their tasks and embrace change, through career development programmes (Nguyen and Mohamed, 2011).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
What the sources say
- Not matchedcites Nguyen and Mohamed, 2011
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“Therefore, AI can be considered as an innovative technology and organisational leadership will play a critical role to develop facilitating conditions to deploy, manage and evolve AI (Alsheibani et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
What the sources say
- Not matchedcites Alsheibani et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“A recent study has reported that data analysis, digital, complex cognitive, decision-making, and continuous learning skills are necessary for AI adoption in multinational corporations in India (Jaiswal et al., 2021).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
What the sources say
- Not checked — no full text of the sourcecites Jaiswal et al., 2021 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“we believe that the adoption of AI within HRM will also vary according to geographical regions (developed and developing economies) and industry sectors (such as manufacturing, construction, services, education creative industry and tourism) (Vrontis et al., 2021).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
What the sources say
- Not matchedcites Vrontis et al., 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Not matched
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
“AI-HI collaboration will inherently change organisational structure, roles and responsibilities and teams.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
“According to world bank, SMEs represent about 90% of businesses and more than 50% of employment worldwide, and therefore play a major role in job creation and global economic development (World Bank, 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
What the sources say
- Not matchedcites World Bank, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Not matched
None of the sources cited for this claim have been checked yet.
In the paper
“Such environment helps to create a supportive culture within the organisations that will enhance motivation and commitment of the employees to embrace innovative ideas, processes and strategies, helping the organisations to dynamically adapt and evolve (Lei et al., 2020;Al-Husseini et al., 2019).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
What the sources say
- Not matchedcites Lei et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites Al-Husseini et al., 2019 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
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
“crosscountry/cross-sector comparisons will bring new insights from International HRM perspective.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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
“the management of these competencies, skills and knowledge is considered an asset for organisations that can be supported using AI (Younis and Adel, 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
What the sources say
- Not checked — no full text of the sourcecites Younis and Adel, 2020 source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“Organizational structural issues will be influenced by AI systems in a variety of areas that are yet to be examined.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“research has shown that knowledge management practices (sharing, creation, co-creation, storage etc.) in the organisations will lead to knowledge integration and evolution which will enhance organisational dynamic capability and business competitiveness (Kearns and Sabherwal, 2006;Nickerson and Zenger, 2004;Grant, 1996).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > AI-Employee Collaboration
What the sources say
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“previous studies have also shown the decisive role of leadership to developing and shaping a positive culture within the organisations, which is conducive to introducing, implementing, and managing innovation within the organisations (Le and Lei, 2019;Le, 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
What the sources say
- Not matchedcites Le and Lei, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Le, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:boosted· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
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 capability framework will help organisations assess their readiness to leverage AI-based systems.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 6. Conclusion and Future Direction
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
In the paper
“Such communication mechanisms and strategy dissemination initiatives will require commitment from organisations' senior management (leadership) to enhance employee development, job satisfaction and workforce performance (Alsheibani et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.3 Practical Implications
What the sources say
- Not matchedcites Alsheibani et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“The ability to get explanations for the output responses will also reduce biases in business processes, operations, and decision-making, thus enhancing fairness (Satell and Sutton, 2019).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
What the sources say
- Not matchedcites Satell and Sutton, 2019
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Not matched
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 doing so the framework will help to objectively identify both technical and non-technical resources required to operationalise AI systems.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.3 Practical Implications
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
“On the contrary, AI is a technology and its adoption, implementation, and impact within HRM will depend on other resources and organisational strategies operationalising and governing these resources.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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 will be the pathway to augmenting human intelligence, rather than replacing it entirely within HRM decision-making processes.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
In the paper
“In this context, our review shows that innovative culture will stimulate employees to embrace AI, identify and seize new opportunities to use AI through their creative intellect, and dynamically respond to change resulting from HR business process and practice transformation, which will help to enhance business productivity and competitiveness (Brock and Von Wangenheim, 2019).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.3 Practical Implications
What the sources say
- Not checked — no full text of the sourcecites Brock and Von Wangenheim, 2019 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt 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
“Finally, while, in this review we relied on the judgement of academic experts and the existing academic literature to select the trending themes of AI research in HRM, research propositions (i.e., underresearched and emerging areas in the field), and resources to develop AI capability within organisations.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 6. Conclusion and Future Direction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably 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
“Secondly, our search (using the selected keywords and Boolean operators) may not have identified all the articles relevant to the topic due to issues related to database unavailability or human error.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 6. Conclusion and Future Direction
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“This will help these business users to confidently assess the accuracy of the responses based on their own tacit domain expertise, which will increase trust in these systems (Cowgill and Tucker, 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
What the sources say
- Not matchedcites Cowgill and Tucker, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“The primary goal of embedding transparency within AI-based ML models is to help the decision-making authorities understand what the AI system is doing, how it is generating the output responses and why a particular response is generated (Choudhury et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
What the sources say
- Not checked — no full text of the sourcecites Choudhury et al., 2020 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“Firstly, our review is restricted to studies published in top tier peer reviewed journals (ABS ranking -3, 3*, 4 and 4*).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 6. Conclusion and Future Direction
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
None of the sources cited for this claim could be matched to a paper in Crossref, so there is nothing to check it against.
In the paper
“However, these techniques currently lack transparency and explainability, which makes it difficult for managers to trust the output of AI (Agarrwal et al., 2020;Bieda et al., 2020).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.2 Theoretical Implications
What the sources say
- Not matchedcites Agarrwal et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Bieda et al., 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Not matched
In the paper
“This will lead to a better understanding of AI adoption context, and enhance trust and confidence in the management initiatives and strategy (Mahidhar and Davenport, 2018).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.3 Practical Implications
What the sources say
- Not matchedcites Mahidhar and Davenport, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
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
“Firstly, the AI capability framework proposed in this article can be used by HRM practitioners to assess the readiness of the organisations to adopt and implement AI systems.”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.3 Practical Implications
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
In the paper
“The extant business and management literature on AI have highlighted how the adoption will shape nature of work and job design within business organisations, and its positive impact on business productivity, whereas negatively for employees' emotional state and job performance (Fleming, 2019).”
§ Unlocking the Value of Artificial Intelligence in Human Resource Management through AI Capability Framework > 5.3 Practical Implications
What the sources say
- Not checked — no full text of the sourcecites Fleming, 2019 source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper relies on this without support.
Popular press and technology vendors have overinflated expectations about benefits and drivers to AI adoption and implementation.
“The popular press and technology vendors have overinflated expectations about benefits and drivers to AI adoption and implementation”
The authors state this as an assumption.
AI systems in HRM are manmade (manufactured by human) and not developed by AI itself.
“While calling AI as manmade may be debatable (Steels and Brooks, 2018), however we are yet to come across an AI system which is developed by AI itself in HRM.”
The authors state this as an assumption.
There is a way of controlling the input data and changing the outcome.
“It is assumed in this argument that there is a way of controlling the input data and changing the outcome.”
The authors state this as an assumption.
AI has shortcomings in creative and social intelligence.
“Additionally, AI is assumed to have shortcomings in creative and social intelligence (Mak et al., 2020).”
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“(e) the potential of GAI to change organizational norms and routines (e.g. the role of humans and AI in a team, job displacement, replacement, spill-over and new opportunities).”
From Generative Artificial Intelligence in Business: Towards a Strategic Human Resource Management Framework (2024) · cites this paper as Chowdhury et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“(d) the need for continuous learning and adaptation by the workforce to keep pace with GAI developments”
From Generative Artificial Intelligence in Business: Towards a Strategic Human Resource Management Framework (2024) · cites this paper as Chowdhury et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the past 5 years, human resource management (HRM) scholarship on AI has increased (Budhwar et al., 2022;Chowdhury et al., 2023;Edwards et al., 2022;Malik, Budhwar, & Kazmi, 2022).”
From Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT (2023) · cites this paper as Chowdhury et al., 2023
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“(c) the rapid advancement and evolution of GAI”
From Generative Artificial Intelligence in Business: Towards a Strategic Human Resource Management Framework (2024) · cites this paper as Chowdhury et al., 2023
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“(b) the superior capabilities of GAI compared to its predecessors”
From Generative Artificial Intelligence in Business: Towards a Strategic Human Resource Management Framework (2024) · cites this paper as Chowdhury et al., 2023
Experimental — accuracy not yet measured
In the citing paper
“Artificial intelligence will have an impact on human resources (Jia et al., 2018; Johansson and Herranen, 2019; Chowdhury et al., 2023).”
From Exploring the antecedents of AI adoption for effective HRM practices in the Indian pharmaceutical sector (2023) · cites this paper as Chowdhury et al., 2023
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.
Conclusion gives a different journal count
MinorThe methodology says the final journal list contained 98 journals, while the conclusion describes the review as covering 62 journals.
“Therefore, the final list comprised of 98 journals.”
“We reviewed potentially relevant studies in 62 top-tier HRM, GM, IB and IM journals”
Research-priority count changes from five to four
MinorThe discussion introduces five research priorities, whereas the conclusion says the review proposes four.
“we present five key research priorities and associated pathways”
“Based on the systematic review, we also propose four research priorities for theoretical and empirical advancement of scholarship on AI in HRM.”
Conclusion gives a different review size
MinorThe selection process reports 84 studies in the final review, but the conclusion says 69 articles were relevant. Both statements describe the completed review.
“Studies included in the final review (n=84)”
“based on the inclusion criteria as well the research question 69 articles were found to be relevant.”
Two different questions carry the A8 label
MinorThe skills-development question and the later ethics-and-governance question are both numbered A8, so the proposition labels are not unique.
“• A8: What are the factors that will help to determine skills, competencies and knowledge required by employees and managers?”
“• A8: How CSR principles and strategies within organisations can help account for AI ethics, governance, and regulations?”
Citations
Cites
Items 201–201 of 201
What’s Your Cognitive Technology Strategy?
Davenport, T. H., & Mahidhar, V. (2019) · Who Wins in a Digital World? · doi:10.7551/mitpress/11859.003.0012
Flagged first, then by citations
Research works printed but not linked: 18
- Amabile, 2020, Creativity, artificial intelligence, and a world of surprises, Academy of Management Discoveries, 6, 351
- Babic, 2021, Onboarding AI, Harvard Business Review, 98, 56
- Barro, 2019, People and machines: Partners in innovation, MIT Sloan Management Review, 60, 22
- Budhwar, 2020, Call for papers: Artificial intelligence challenges and opportunities for international HRM. The, International Journal of Human Resource Management.
- Bughin, 2018, Skill shift: Automation and the future of the workforce, McKinsey Global Institute, 1, 3
- Chesney, 2019, Deepfakes and the new disinformation war: The coming age of post-truth geopolitics, Foreign Aff., 98, 147
- Chui, 2018, Notes from the AI frontier: Insights from hundreds of use cases, McKinsey Global Institute, 1
- Davenport, 2017, How P&G and American Express are approaching AI, Harvard Business Review (blog)
- Fountaine, 2019, Building the AI-powered organization, Harvard Business Review, 97, 62
- Ghosh, 2019, Taking a Systems Aproach to Adopting AI, Harvard Business Review, Technology & Operations
- IEEE 7010, 2020
- Kakkar, 2019, Technology driven human resource measurement–A strategic perspective, International Journal on Emerging Technologies, 10, 179
- Ransbotham, 2018, Artificial intelligence in business gets real, MIT Sloan Management Review, 60280
- Ransbotham, 2019, Winning with AI, MIT Sloan Management Review and Boston Consulting Group
- Ransbotham, 2017, Reshaping business with artificial intelligence: Closing the gap between ambition and action, MIT Sloan Management Review, 59, 1
- Satell, 2019, We need AI that is explainable, auditable, and transparent, Harvard Business Review
- Singh, 2020, Workforce analytics: Increasing managerial efficiency in human resource, International Journal of Scientific and Technology Research, 9, 3260
- Wilson, 2018, Collaborative intelligence: humans and AI are joining forces, Harvard Business Review, 96, 114
Cited by
Sorted by citations
Budhwar, P., Chowdhury, S., Wood, G., Aguinis, H., Bamber, G. J., Beltran, J. R., Boselie, P., Lee Cooke, F., Decker, S., DeNisi, A., et al. (2023) · Human Resource Management Journal · doi:10.1111/1748-8583.12524 · Cited by 908
Self-citationConvolutional Neural Networks: A Survey
Krichen, M. (2023) · Computers · doi:10.3390/computers12080151 · Cited by 623
Perifanis, N.-A., & Kitsios, F. (2023) · Information · doi:10.3390/info14020085 · Cited by 407
Revolutionizing the circular economy through new technologies: A new era of sustainable progress
Sánchez‐García, E., Martínez‐Falcó, J., Marco‐Lajara, B., & Manresa‐Marhuenda, E. (2023) · Environmental Technology & Innovation · doi:10.1016/j.eti.2023.103509 · Cited by 376
Badghish, S., & Soomro, Y. A. (2024) · Sustainability · doi:10.3390/su16051864 · Cited by 261
Metadata
- Authors
- Chowdhury, S., Dey, P., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A., & Truong, L.
- Affiliations
- TBS Education (FR)
- Aston University (GB)
- Northeastern University London (GB)
- Corresponding author
- Chowdhury, Soumyadeb
- Venue
- Human Resource Management Review
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 33
- Issue
- 1
- Pages
- 100899
- ISSN
- 1053-4822 (ISSN-L)
- OpenAlex
- W4293229829
- Cited by
- 927
- References
- 156
- Citations by year
- 2026: 230 · 2025: 311 · 2024: 257 · 2023: 118
- FWCI
- 164.25
- Type
- journal article
- Language
- en
- Open access
- Licence
- Locations
- Human Resource Management Review (publishedVersion)
- Aston Publications Explorer (Aston University) (acceptedVersion)
- Repository@Nottingham (University of Nottingham) (acceptedVersion, other-oa)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Funders
- Aston University (2020-21)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Human Resource and Talent Management
- Keywords
- artificial intelligence in human resource management, organizational resources, artificial intelligence adoption, organizational readiness, non-technical skills, leadership, organizational culture, AI governance, resource-based view, knowledge-based view, research propositions
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 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (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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