Unlocking the value of artificial intelligence in human resource management through AI capability framework

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

AI in recruitment has the potential for fast resume scanning, quickly and automatically responding to candidate queries, and virtual recruitment activities.

“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.”

• cites Baakeel (2020) sourceSupported

May combine two claims.

AI is valuable in HR decision-making because of the potential to avoid subjectivity, with more objective decisions guided by information extracted through employee data mining.

“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).”

• cites Chornous and Gura, 2020 sourceSupported

Classified as probably not a substantial claim. May combine two claims.

AI-driven HR analytics allows organisations to develop workforce capability, improve teamwork, support flexible working, and improve performance measurement.

“This will allow organisations to develop the capability of workforce, improve teamwork, support flexible working and improve performance measurement (Chornous and Gura, 2020).”

• cites Chornous and Gura, 2020 sourcePartly supported

  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)

  Experimental — accuracy not yet measured

Classified as probably not a substantial claim.

There is currently no single universally accepted definition of AI throughout the literature.

“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).”

• cites Mikalef and Gupta, 2021 sourceSupported

May combine two claims.

Use of AI for monitoring can boost employee retention by analyzing financial data for payment and motivating personnel based on compensation.

“and also analyzing financial data for payment and motivating personnel based on compensation (Kulkov, 2021)”

• cites Kulkov, 2021 sourceSupported

Classified as probably not a substantial claim.

AI-employee collaboration can help organisations achieve competitive advantage.

“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.”

• cites Makarius et al., 2020 sourceSupported

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

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.

“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).”

• cites Seeber et al, 2020 sourcePartly supported

  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)

  Experimental — accuracy not yet measured

May combine two claims.

AI socialisation will enhance employees' ability to trust, use and adopt AI, productivity, and career satisfaction.

“These will enhance ability of employees to trust, use and adopt AI, productivity, and career satisfaction.”

• cites Makarius et al., 2020 sourcePartly supported

  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)

  Experimental — accuracy not yet measured

May combine two claims.

AI skills and expertise provide organisations with capability to innovate, re-engineer and optimise both business processes and resources efficiently, and therefore can enhance their ability to dynamically adapt and remain responsive.

“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).”

• cites Mikalef et al., 2020 sourcePartly supported

  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)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

Effective AI teammates require more than social robots and digital assistants.

“Seeber et al (2020) went further and suggested that effective AI teammates required more than social robots and digital assistants.”

• cites Seeber et al (2020) sourceSupported

Probably not a substantial claim: may not pass S3 (load-bearing).

Appropriate implementation of AI to support recruitment and retention can be essential to enhance employer brand and reputation.

“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.”

• cites Kot et al., 2021 sourceSupported

May combine two claims.

If handled appropriately, AI can offer well-being benefits, including building well-being awareness, measuring well-being and the impact of interventions and changes, managing AI risk on well-being, supporting positive well-being initiatives, and providing employers insight into employee work-life-balance.

“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.”

• cites Schiff et al, 2020 sourcePartly supported

  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)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

Several decisions made by humans are based on judgement and intuition.

“In fact, several of the decisions made by humans are based on judgement and intuition (Meechang et al., 2020).”

• cites Meechang et al., 2020 sourceCould not verify

  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)

  Experimental — accuracy not yet measured

May combine two claims.

An AI algorithm might identify a relevant criterion or relationship affecting successful integration of employees into the company, but that relationship may not be evident for decision-makers, which could question the reliability of the results.

“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).”

• cites Meechang et al., 2020 sourceCould not verify

  Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured

May combine two claims.

The predictive capacity of AI can be hindered by challenges to accurately describe the complexity of human behavior and the effect of unknown external effects affecting the conditions of the environment and the company itself.

“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.”

• cites Pashkevich et al., 2019 sourcePartly supported

  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)

  Experimental — accuracy not yet measured

May combine two claims.

AI adoption in HRM leads to social performance by reducing human subjective bias in HR processes such as recruitment and employee appraisal, and improving employee experience and working conditions.

“(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);”

• cites Toniolo et al., 2020 sourcePartly supported

  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)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Skills and expertise provide 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.

“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).”

• cites Mikalef et al., 2020 sourcePartly supported

  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)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

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.

“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).”

• cites Toniolo et al., 2020 sourcePartly supported

  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)

  Experimental — accuracy not yet measured
• cites WambaTaguimdje et al., 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The aim of these reports is to minimize potential risks faced by organisations with regards to using AI and simultaneously protect the interests of business users.

“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).”

• cites Arrieta et al., 2020 sourcePartly supported

  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)

  Experimental — accuracy not yet measured
• cites Coombs et al., 2020 sourcePartly supported

  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)

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S2 (checkable). May combine two claims.

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.

“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.”

• cites Malone, 2018Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Caputo et al., 2019 sourcePartly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured

May combine two claims.

There is a lack of both understanding and knowhow on best practices to effectively develop collaborative intelligence capability.

“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).”

• cites Amabile, 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Ciechanowski et al., 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Makarius et al., 2020 sourceSupported

May combine two claims.

The adoption of AI within HRM processes and wider organisation will impact employees in different ways, such as job substitution, training needs, uncertainty regarding roles and responsibilities, limited understanding about how and why AI will be used, trust and confidence, formation of project teams where AI and HI co-exist, and concerns about career progression and development.

“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”

• cites Frey & Osborne, 2017 sourceNot checked — no full text of the source
• cites Raisch & Krakowski, 2020 sourceCould not verify

  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)

  Experimental — accuracy not yet measured
• cites Gunning, 2017Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

Existing research studies have indicated the importance of AI-human collaboration, which has emerged as a new research theme in the literature.

“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).”

• cites Fleming, 2019 sourceNot checked — no full text of the source
• cites Makarius et al., 2020 sourceSupported
• cites Murray et al., 2020 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Evaluating internal expertise helps identify employees with technical and/or non-technical knowledge to use AI, understand business processes and functions that can benefit, and how benefits will serve both employees' and organisational goals/priorities.

“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).”

• cites Mikalef and Gupta, 2021 sourcePartly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Existing research has identified that collective intelligence-a symbiotic partnership between AI and human intelligence-increases the capability of human workers, leading to higher employee productivity, better psychological outcomes, low turnover, and improved quality of outputs.

“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).”

• cites Glikson and Wooley, 2020 sourceNot checked — no full text of the source
• cites Murray et al., 2020 sourceNot checked — no full text of the source
• cites von Krogh, 2018 sourcePartly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured

Probably not a substantial claim: may not pass S3 (load-bearing).

The existing literature reports different types of AI systems within business organisations, including automated, assisted, augmented, and autonomous intelligence.

“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.”

• cites Haenlein and Kaplan, 2019 sourceNot checked — no full text of the source
• cites Daugherty et al., 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Davenport and Bean, 2017Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

The review showed the importance of understanding complementary organisational resources required to benefit from AI adoption in HRM, and exploring beyond data and technological resources.

“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).”

• cites Mikalef and Gupa, 2021Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Classified as probably not a substantial claim.

Autonomous intelligence is not included because the authors have not come across an AI system in HRM that can function without human involvement.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Recent reports published by the European Commission have emphasised ethical and moral aspects surrounding AI, which has led to responsible development and use of AI.

“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).”

• cites European Commission 2019aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites European Commission 2019bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S4 (based).

Most studies reported in the literature and including HRM focus on multinational large organisations.

“Finally, most studies reported in the literature and including HRM focus on multinational large organisations.”

Classified as probably not a substantial claim. May combine two claims.

There is a knowledge gap and limited understanding in HRM literature on factors that will influence AI-employee collaboration and its potential business impact.

“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.”

Classified as probably not a substantial claim.

Many studies have reported the drivers and barriers related to adoption and implementation of AI.

“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.”

Classified as probably not a substantial claim.

The review identified five key themes in the literature: AI applications in HRM; collective intelligence; AI employment and skills; AI drivers and barriers to adoption.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Collective intelligence is nascent and emerging within academic research.

“However, collective intelligence is nascent and emerging within academic research.”

May combine two claims.

The current literature primarily has focused on the applications of AI in HRM, anticipated benefits, impact of AI on jobs, and AI-driven decision-making augmenting human intelligence.

“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.”

May combine two claims.

IM has primarily focussed on technical resources and infrastructure, and wider BAM research has focussed on non-technical resources.

“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).”

• cites Loebbecke & Picot, 2015Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Markus, 2015 sourceNot checked — no full text of the source

Classified as probably not a substantial claim.

AI has significant potential to support organizational research because of its capacity to analyze data and support decision-making.

“AI has a significant potential to support organizational research because of the capacity to analyze data and support decision-making.”

May combine two claims.

AI can be used for various aspects of recruitment, including selecting applicants from a pool of submitted applications, making appointment decisions based on interview and organisational need, and proposing suitable salary and benefits based on qualifications.

“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.”

• cites Mehrabad and Brojeny (2007) sourceNot checked — no full text of the source

Classified as probably not a substantial claim.

The systematic review followed a protocol to ensure the review process is transparent, easily reproducible, and critically analyses and systematises research themes.

“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).”

• cites Tranfield et al., 2003 sourceNot checked — no full text of the source
• cites Denyer and Tranfield, 2009Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Wolfswinkel et al., 2013Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S2 (checkable).

Developing collective intelligence, a collaborative working environment where AI and human intelligence can co-exist, is important for organisations.

“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”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The review process was transparent, reproducible, and devoid of human errors.

“Following the recommendations in Tranfield et al. 2003, this process ensured that the procedure is transparent, reproducible, and devoid of human errors.”

• cites Tranfield et al. 2003 sourceNot checked — no full text of the source

May combine two claims.

AI recruitment decreases the time taken to recruit individuals, enables organisations to respond to events more quickly, and improves competitive advantage through better recruited people.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The use of technology to support HR has significant potential to support business strategy.

“The use of technology to support HR has significant potential to support the business strategy (Kakkar and Kaushik, 2019).”

• cites Kakkar and Kaushik, 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

AI can identify personality traits from potential candidates and match them to the predominant traits found in the culture of the company.

“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)”

• cites Lee and Ahn, 2020 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

Strategic focus for AI-driven HR analytics needs to be contextualised within the organisation, rather than a more generic strategic focus.

“Sparrow, et.al. (2015) argue that this strategic focus needs to be contextualised within the organisation, rather than a more generic strategic focus.”

• cites Sparrow, et.al. (2015)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

The research team used individual coding followed by group consensus to finalize the key themes emerging from the study.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

All documents were verified by each team member to ensure information consistency with the literature and coverage of all relevant information.

“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).”

May combine two claims.

AI can access a larger pool of candidates and reduce bias by eliminating subjective criteria.

“to access a larger pool of candidates and reduce bias eliminating subjective criteria (Kshetri, 2020)”

• cites Kshetri, 2020 sourceNot checked — no full text of the source

May combine two claims.

Organisations often find it difficult to integrate AI within their business processes and systems, which often inhibits AI adoption.

“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).”

• cites Davenport and Ronanki, 2018Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Mikalef., et al., 2019 sourceNot checked — no full text of the source

May combine two claims.

The digital workforce will significantly impact organisational processes, nature and meaning of work, work design structure, competencies and skills, technology exposure, employer and employee expectations, work practices, and HR strategy to bridge the skills gap to increase employee motivation and productivity, leading to business productivity.

“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”

• cites Schroeder et al., 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Wilson et al., 2017Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

Humans often reject and ignore a new technology if they feel threatened by it, irrespective of their interest and enthusiasm in it.

“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).”

• cites Elkins et al., 2013 sourceNot checked — no full text of the source

May combine two claims.

AI algorithms can be combined with data envelopment analysis to identify underperforming employees, their impact on efficiency and effectiveness of the company, and overall performance of the organization.

“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)”

• cites Panteia, 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

The intelligence of humans and machines can be considered as the intertwining of 'collective' intelligence.

“The idea was first discussed by Malone (2018), who considered the intelligence of humans and that of machines as the intertwining of 'collective' intelligence.”

• cites Malone (2018)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Classified as probably not a substantial claim.

Potential benefits of AI-employee collaboration established through a symbiotic partnership can be fully realised in practice only if employees understand, trust, and adopt AI.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Clarification about the limits and advantages of AI will help organizations successfully integrate AI in the human working environment.

“Furthermore, clarification about the limits and advantages of AI will help organizations to successfully integrate AI in human working environment (Malone, 2018).”

• cites Malone, 2018Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

AI can be used for knowledge engineering that can aid knowledge searching and knowledge processes.

“Tsui et al., 2000 put forward how AI can be used for knowledge engineering that can aid knowledge searching and knowledge processes.”

• cites Tsui et al., 2000 sourceNot checked — no full text of the source

May combine two claims.

For recruiting, AI can help writing job profiles, screening resumes, using enhanced video analysis to identify behavioral patterns of potential candidates, comparing them with criteria required for the position, and identifying traits and skills of potential candidates to facilitate adaptation and enhance performance.

“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).”

• cites Belhadi et al., 2021Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Wilson and Gosiewska, 2014 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

Limited knowledge, skills and understanding among the workforce about AI capabilities, limitations, strategic initiatives, and integration with existing business processes can be attributed to organisations not augmenting human intelligence or replacing employees with AI expert systems.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Computational power and analytical capabilities of AI can be leveraged to deal with complexity in decision-making, with the intention to augment human intelligence and decision-making tasks rather than replacing humans.

“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.”

• cites Jarrahi (2018) sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

AI could be used to highlight knowledge hiding.

“Abubakar et al (2019) showed how AI could be used to highlight knowledge hiding.”

• cites Abubakar et al (2019) sourceNot checked — no full text of the source

May combine two claims.

AI achieves flexibility, autonomy, creativity, innovation, and streamlined processes by automating trivial manual repetitive processes that require considerable time and human capital.

“This is achieved by automating trivial manual repetitive processes that require considerable time and human capital.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

AI-enabled systems will both automate and augment HRM decision-making in organisations.

“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).”

• cites Janssen et al, 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

Fear of job losses and obscurity about AI-human responsibilities in a collaborative working environment will prevent organizations from concretizing AI adoption.

“Fear of job losses and obscurity about AI-human responsibilities in a collaborative working environment will prevent organizations to concretize AI Adoption (Bughin, 2018).”

• cites Bughin, 2018Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Although organisations have acknowledged potential benefits offered by AI adoption, they are yet to augment human intelligence or replace employees with AI expert systems.

“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.”

• cites Barro and Davenport, 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

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 throughput, enhance creative process by freeing up employees' time to concentrate on non-trivial and complex procedures, resulting in performance gains.

“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”

• cites Deb at al., 2018Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Mishra and Pani, 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Wamba-Taguimdje et al., 2020 sourceNot checked — no full text of the source

May combine two claims.

AI skills will be a strategic intangible resource that is difficult to imitate by other firms, providing competitive advantage.

“Therefore, these skills will be strategic intangible resource that will be difficult to imitate by other firms, which will provide competitive advantage (Barney, 1991).”

• cites Barney, 1991 sourceNot checked — no full text of the source

May combine two claims.

AI can provide more flexibility and work-related autonomy, promote creativity and innovation, and allow streamlining of organizational processes.

“AI can provide more flexibility and work-related autonomy, promote creativity and innovation, and allow to streamline organizational processes (Malik et al., 2021).”

• cites Malik et al., 2021Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

Use of AI within business organisations is likely to increase the number of employees, rather than job losses, because early adopters of AI are positioning themselves for growth which will stimulate employment.

“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.”

• cites Bughin (2018)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

AI enhanced knowledge management by facilitating effective knowledge sharing.

“For example, Liebowitz (2001), reported how AI enhanced knowledge management by facilitating effective knowledge sharing.”

• cites Liebowitz (2001) sourceNot checked — no full text of the source

May combine two claims.

AI can be useful for monitoring, performance measurement and tracking employee morale.

“Additionally, AI can be useful for monitoring, performance measurement and tracking employee morale (Gaur and Riaz, 2019).”

• cites Gaur and Riaz, 2019 sourceNot checked — no full text of the source

May combine two claims.

Major drivers for 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 identification of patterns in historical datasets.

“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).”

• cites Giermindl et al., 2021Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Gaur andRiaz, 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Augmentation of AI in any job context will boost decision-making abilities of employees, increase time devoted to non-trivial tasks, heighten creativity, and enhance both employee and business productivity.

“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).”

• cites Wilson and Daugherty, 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

The source of bias in AI is associated with the bias of the people implementing the AI.

“This is important because it highlights that the source of bias in AI is associated to the bias of the people implementing the AI.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

HR recruiters are relying on meta data available on social media outlets to target potential candidates for roles within organisations.

“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.”

May combine two claims.

The interpretability of results from AI and the potential clash with the human perspective can hinder successful implementation of AI.

“Therefore, the interpretability of results from AI and the potential clash with the human perspective can hinder the successful implementation of AI.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

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.

“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).”

• cites Davenport and Ronanki, 2018Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

For recruitment and hiring, the core components of the organizational culture must be clearly delimited to obtain useful results.

“For recruitment and hiring, this means that the core components of the organizational culture must be clearly delimited to obtain useful results.”

May combine two claims.

If users or HR managers do not understand 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.

“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).”

• cites Chowdhury et al., 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Shin and Park, 2019 sourceNot checked — no full text of the source

Classified as probably not a substantial claim. May combine two claims.

All these drivers will reduce bottlenecks and improve overall operational efficiency, which will enhance business productivity.

“All these drivers will reduce bottlenecks and improve overall operational efficiency, which will enhance business productivity.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Organizational resilience involves the capacity to absorb, respond and transform to capitalize on disruptions that threaten the organization.

“Organizational resilience involves the capacity to absorb, respond and transform to capitalize on disruptions that threaten the organization (Lengnick-Hall et al., 2011).”

• cites Lengnick-Hall et al., 2011 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

Quantification has become a substitute for an individual's social life, personality, abilities and choices, attributes upon which recruitment decisions are being made.

“This quantification has become a substitute for an individual's social life, personality, abilities and choices, attributes upon which recruitment decisions are being made.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Transparency is particularly necessary in a HR context.

“There are several reasons why transparency is particularly necessary in a HR context (Chowdhury et al., 2020;Silvernam, 2020).”

• cites Chowdhury et al., 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Silvernam, 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

Society has become dominated with the logic of quantification due to lowering costs and easy access to the digital world and the ease of processing vast amounts of meta data.

“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).”

• cites Espeland and Stevens, 2008 sourceNot checked — no full text of the source

May combine two claims.

IT adoption, such as AI-enabled systems, within organisations will enhance dynamic capabilities which drives market capitalization, increased flexibility to adapt or re-engineer business operations and processes, agility, and responsiveness to address uncertain and evolving market demands and mitigate trade-offs within the organisation.

“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”

• cites Mikalef and Pateli, 2017 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

Companies are failing to tackle the SDGs because of limited visibility, flexibility and resilience.

“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).”

• cites Bag et al., 2021aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

Resilient organisations often rely on changes in corporate culture and re-design of processes.

“Resilient organisations often rely on changes on the corporate culture and re-design of processes (Bag et al., 2021a).”

• cites Bag et al., 2021aNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

A company must promote the development of skills that employees can use to support the organization's capacity to withstand disruptions through HR management that ensures human resources are managed effectively and employed at the right time and conditions.

“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).”

• cites Slusarczyk, 2018Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

Privacy and data protection concerns, issues stemming from constant tracking, and the potential of bias in algorithms themselves are important barriers to AI adoption.

“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.”

• cites Giermindl et al. (2021)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Malik et al. (2021)Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

Sustainable human resource management aims at accomplishing organizational sustainability supported by the design of HR policies, strategies and practices simultaneously underpinning social, environmental, and economic aspects.

“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).”

• cites Wesley Ricardo de Souza et al., 2011Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Classified as probably not a substantial claim.

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.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Technology has been related to sustainable human resource management.

“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.”

• cites Reddy et al., 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Zhou, 2021Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

Organizational resilience is valuable to help businesses plan, prepare, strategize operations, and respond to unpredictable disruptions and efficiently recover from such disruptions.

“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).”

• cites Scholten et al., 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Macdonald et al., 2018 sourceNot checked — no full text of the source
• cites Bag et al., 2021bNot matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

For HR recruitment, the decision-making process of targeting potential candidates and taking them through rudimentary checks is accelerated using AI.

“Particularly for HR recruitment the decision-making process of targeting potential candidates and taking them through the rudimentary checks, is accelerated using AI.”

May combine two claims.

Other barriers to AI adoption include assessing data quality to ensure decisions and recommendations are precise, accurate and relevant; optimal training dataset to reduce bias and reputational risks; integrating existing systems with AI implementation; developing a data-centric culture; and technology turbulence.

“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”

• cites Ransbotham, et al., 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Kiron and Schrage, 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Chowdhury et al., 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

Certain groups have been found to be disproportionately disadvantaged in AI algorithms, e.g., black faces associated as gorillas and Asian people categorised as blinking.

“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).”

• cites Dougherty, 2015Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Wade, 2010Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Use of AI powered by algorithms provides HR recruitment with efficient tools to both market jobs to an increasingly digitally based candidate and perform rudimentary initial checks before candidates can be interviewed.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

Using AI to predict vulnerable employees and make adjustments has the potential of increasing the safety of the workforce and the sustainability of jobs in the long term.

“This has the potential of increasing the safety of the workforce and the sustainability of jobs in the long term”

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

The impact of these barriers in practice can be significant.

“The impact of these barriers in practice can be significant.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Adoption of AI for HRM faces four major challenges: complexity of HR phenomena, small data, ethical constraints, and reaction of employees to the implementation of AI.

“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.”

• cites Tambe et al. (2019) sourceNot checked — no full text of the source

May combine two claims.

Issues such as bias, misinformation, lack of understanding, intrusion, inequality and labour displacement may impact employees' well-being and their feelings of security, trust and privacy.

“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.”

• cites Chouldechova et al., 2018Not checked — no full text of the source
• cites Little, 2018Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Chesney and Citron, 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

Knowledge sharing is a key practice to create business value that will enhance economic performance of business organisations.

“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.”

• cites Hansen et al. (1999)Not checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

Reducing individuals to a set of measured dimensions avoids dealing with a person's evolution and alternative explorations that may explain how one ends up in a specific category.

“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).”

• cites Ananny, 2015Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

There would be a demographic divide in openness to AI.

“which would suggest that there would be a demographic divide in openness to AI.”

Probably not a substantial claim: may not pass S2 (checkable).

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.

“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.”

May combine two claims.

AI has the potential to identify areas of lower performance based on employee achievements, but it would struggle to process the underpinning factors leading to low performance and therefore could interpret a need for action in instances that may be temporary or affected by external variables.

“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.”

• AssumptionThe authors state this as an assumption: AI has shortcomings in creative and social intelligence.

May combine two claims.

In the context of AI knowledge sharing, codification will facilitate scaling up 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.

“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.”

May combine two claims.

AI adoption in HRM leads to developing successful business cases related to AI implementation which will enhance reputation among business stakeholders, competitors, policy makers and consumers.

“(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).”

• cites Davenport and Ronanki, 2018Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based). May combine two claims.

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.

“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).”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

The context will determine the type of AI (automation, augmentation and assisted).

“We envisage the context will determine type of AI (automation, augmentation and assisted).”

May combine two claims.

AI adoption in HRM leads to introducing new products and services, improving quality of existing ones, positively impacting operational performance and costs through optimising resource utilization.

“(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);”

• cites Wamba-Taguimdje et al., 2020 sourceNot checked — no full text of the source
• cites Alsheibani et al., 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

Both codification and personalization strategies have been used to build expertise and facilitate skills development in platform ecosystems and IT sectors.

“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).”

• cites Tiwana, 2013Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Garavelli et al., 2002 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

The impact on employees will depend on the type of AI, which processes will be transformed, AI strategy of the organisation, and knowledge creation, sharing and management mechanisms.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The context of use depends on the business problem and priority.

“The context of use will depend on the business problem and priority.”

Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

Big tech companies design algorithms that contribute to their success, but these algorithms are closely guarded and protected as intellectual property.

“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).”

• cites O'Neil, 2017Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Classified as probably not a substantial claim. May combine two claims.

KBV posits that knowledge created within the organisation is a critical asset which will help to produce sustainable competitive advantage in dynamic market environments because knowledge-based sources are socially complex, difficult to imitate, and continuously evolve and are co-created within the organisation.

“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”

• cites Grant, 1996 sourceNot checked — no full text of the source

May combine two claims.

Organisational resources determine organisational readiness to utilize AI and how it will be used.

“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).”

Classified as probably not a substantial claim.

RBV is one of the most widely applied theoretical perspectives to explain how resources within an organisation can help enhance business performance and competitiveness.

“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).”

• cites Barney, 2001 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

Overreliance on AI decisions regarding targeted recruitment and following recruitment processes is a concern.

“This inevitably raises questions about overreliance on AI decisions regarding targeted recruitment and following recruitment processes.”

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based). May combine two claims.

The development of skills and expertise in-house will provide role clarity in a collaborative AI-HI working environment, develop trust and confidence among human workers, and enhance their emotional engagement with AI.

“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.”

May combine two claims.

Leadership helps to develop appropriate conditions, strategies, and resources within the organisation, which allow employees to harness new skills, facilitate access to relevant knowledge and expertise base, and encourage sharing this knowledge with peers.

“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).”

• cites Alsheibani et al., 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Demlehner & Laumer, 2020Not checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

Leadership plays an important role to intellectually stimulate employees' ability to perform their tasks and embrace change, through career development programmes.

“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).”

• cites Nguyen and Mohamed, 2011Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

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.

“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).”

• cites Alsheibani et al., 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

Data analysis, digital, complex cognitive, decision-making, and continuous learning skills are necessary for AI adoption in multinational corporations in India.

“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).”

• cites Jaiswal et al., 2021 sourceNot checked — no full text of the source

Classified as probably not a substantial claim. May combine two claims.

The adoption of AI within HRM will vary according to geographical regions and industry sectors.

“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).”

• cites Vrontis et al., 2021Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

AI-HI collaboration will inherently change organisational structure, roles and responsibilities and teams.

“AI-HI collaboration will inherently change organisational structure, roles and responsibilities and teams.”

Classified as probably not a substantial claim. May combine two claims.

According to World Bank, SMEs represent about 90% of businesses and more than 50% of employment worldwide, and play a major role in job creation and global economic development.

“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).”

• cites World Bank, 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

A supportive culture within organisations enhances motivation and commitment of employees to embrace innovative ideas, processes and strategies, helping organisations to dynamically adapt and evolve.

“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).”

• cites Lei et al., 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Al-Husseini et al., 2019 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

Cross-country/cross-sector comparisons will bring new insights from International HRM perspective.

“crosscountry/cross-sector comparisons will bring new insights from International HRM perspective.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The management of competencies, skills and knowledge is considered an asset for organisations that can be supported using AI.

“the management of these competencies, skills and knowledge is considered an asset for organisations that can be supported using AI (Younis and Adel, 2020).”

• cites Younis and Adel, 2020 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing), S4 (based).

AI systems will influence organizational structural issues in a variety of areas.

“Organizational structural issues will be influenced by AI systems in a variety of areas that are yet to be examined.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Knowledge management practices in organisations lead to knowledge integration and evolution which enhance organisational dynamic capability and business competitiveness.

“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).”

• cites Kearns and Sabherwal, 2006 sourceNot checked — no full text of the source
• cites Nickerson and Zenger, 2004 sourceNot checked — no full text of the source
• cites Grant, 1996 sourceNot checked — no full text of the source

Probably not a substantial claim: may not pass S3 (load-bearing).

Previous studies have shown the decisive role of leadership in developing and shaping a positive culture within organisations, which is conducive to introducing, implementing, and managing innovation.

“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).”

• cites Le and Lei, 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

• cites Le, 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S2 (checkable).

The AI capability framework will help organisations assess their readiness to leverage AI-based systems.

“The capability framework will help organisations assess their readiness to leverage AI-based systems.”

Probably not a substantial claim: may not pass S3 (load-bearing).

Commitment from organisations' senior management (leadership) is required to enhance employee development, job satisfaction, and workforce performance.

“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).”

• cites Alsheibani et al., 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

May combine two claims.

The ability to get explanations for output responses will reduce biases in business processes, operations, and decision-making, thus enhancing fairness.

“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).”

• cites Satell and Sutton, 2019Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Classified as probably not a substantial claim.

The AI capability framework helps objectively identify both technical and non-technical resources required to operationalise AI systems.

“In doing so the framework will help to objectively identify both technical and non-technical resources required to operationalise AI systems.”

May combine two claims.

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.

“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.”

Probably not a substantial claim: may not pass S2 (checkable).

This will be the pathway to augmenting human intelligence, rather than replacing it entirely within HRM decision-making processes.

“This will be the pathway to augmenting human intelligence, rather than replacing it entirely within HRM decision-making processes.”

May combine two claims.

Innovative culture stimulates 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 helps enhance business productivity and competitiveness.

“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).”

• cites Brock and Von Wangenheim, 2019 sourceNot checked — no full text of the source

Classified as probably not a substantial claim.

In the review, the authors relied on the judgement of academic experts and the existing academic literature to select the trending themes of AI research in HRM, research propositions, and resources to develop AI capability within organisations.

“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.”

Probably not a substantial claim: may not pass S3 (load-bearing).

The review's search using selected keywords and Boolean operators may not have identified all articles relevant to the topic due to database unavailability or human error.

“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.”

May combine two claims.

Transparency will help business users to confidently assess the accuracy of responses based on their own tacit domain expertise, which will increase trust in these systems.

“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).”

• cites Cowgill and Tucker, 2020Not matched

  Couldn’t be matched to a paper in Crossref, so it can’t be checked.

Probably not a substantial claim: may not pass S3 (load-bearing).

The primary goal of embedding transparency within AI-based ML models is to help decision-making authorities understand what the AI system is doing, how it is generating output responses and why a particular response is generated.

“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).”

• cites Choudhury et al., 2020 sourceNot checked — no full text of the source

Classified as probably not a substantial claim.

The review is restricted to studies published in top tier peer reviewed journals (ABS ranking 3, 3*, 4 and 4*).

“Firstly, our review is restricted to studies published in top tier peer reviewed journals (ABS ranking -3, 3*, 4 and 4*).”

May combine two claims.

These techniques currently lack transparency and explainability, which makes it difficult for managers to trust the output of AI.

“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).”