The DOI matches its Crossref record; 55 of the 55 references it cites were found in a registry or an index; 18 of the 92 sources its claims cite were checked for support.
Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM
Open accessAbstract
Across different disciplinary boundaries, research into algorithmic surveillance (Newlands, 2020), people analytics (Gal et al., 2020; Marler & Boudreau, 2017; Tursunbayeva et al., 2018), human res...
Overview
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Written from the paper’s full text.
TL;DR
This editorial proposes "Algorithmic Human Resource Management" (algorithmic HRM) as a narrower, more clearly defined term than "digital HRM," defined as the use of software algorithms operating on digital data to augment HR-related decisions and/or automate HRM activities. It introduces four special-issue papers and lays out a future research agenda comparing organizations transitioning to algorithmic HRM with "digital native" online labor platforms.
Key findings
- The authors define algorithmic HRM as "the use of software algorithms that operate on the basis of digital data to augment HR-related decisions and/or to automate HRM activities," highlighting three features: (1) generation and use of digital data, (2) deployment of software algorithms that process digital data, and (3) partial or full automation of HRM-related decision-making. The third feature distinguishes algorithmic HRM from other forms of digital HRM.
- Algorithmic HRM supports decision-making in two ways: augmentation (algorithms provide information/predictions that inform human decisions, as in HR analytics) and automation (prescriptive algorithms select and execute a course of action with little or no human involvement, most prevalent on online labor platforms such as Uber, Upwork and Deliveroo).
- Citing a Deloitte (2020) survey of almost 9,000 business and HR leaders, the authors report that most organizations collect worker data mainly for descriptive purposes — turnover (82%), salary costs (68%), workforce composition (53%) — while predictive and prescriptive HRM algorithms are rarely used.
- The four special-issue papers are summarized: Myllymäki (socio-material theoretical framing of digital HRM); Wiblen and Marler (talent identification in a large multi-divisional professional services firm, where algorithmic HRM's role varied across divisions); Ellmer and Reichel (HR analytics in a large German corporation, showing "epistemic alignment" practices that enhance HR managers' credibility and status); and Waldkirch, Bucher, Schou and Grünwald (freelance gig workers on Upwork, analyzed via Reddit discussions, finding selection, performance management, compensation and employee relations automated, while training and development is informally outsourced to peer chat communities).
- The authors note that algorithmic HRM can reinforce discrimination on the basis of gender, ethnicity or age at scale, and that opacity and lack of "explainability" are recurring concerns; they call for research into the "dark(er) side" of HRM algorithms.
Methods
- Conceptual/editorial synthesis: the authors review and integrate literature across HRM, sociology of labor, organization studies and business ethics to propose and define the construct "algorithmic HRM."
- The paper introduces and discusses four papers in the special issue, drawing on their findings to illustrate augmentation versus automation across different organizational contexts.
- Illustrative examples and secondary data are cited (e.g., Amazon warehouse/delivery worker data; Upwork's Job Success Score; Didi's 30 million taxi drivers in 2018; the Deloitte 2020 survey), rather than original empirical data collection.
Limitations
- The paper does not state explicit limitations; it is an editorial introduction rather than an empirical study, so it presents no primary data, sample, or analysis of its own.
- Evident from the text: the special-issue papers are summarized rather than re-analyzed, and several supporting figures (e.g., the Deloitte survey, the Didi figure) are cited secondhand from other sources.
- The authors frame many of their propositions as open questions for future research, indicating that the comparative claims about digital natives versus organizations "becoming digital" are not yet empirically tested.
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Claims the paper makes
25 Sources not found · 17 Own interpretation · 9 Partly supported · 9 No full text of the sources · 9 Not checked yet · 9 No basis given · 3 Supported so far · 2 Couldn’t confirm · 1 Supported · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Thus, algorithmic HRM has the potential to reinforce discrimination on the basis of, for example, gender, ethnicity or age at scale (Ge et al., 2016;Hannák et al., 2017) if it learns these are parameters that are associated with human decisions.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and the role of automated data processing
What the sources say
- Not matchedcites Ge et al., 2016
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Partly supportedcites Hannák et al., 2017 sourceExperimental — accuracy not yet measured
The Search Ranking Bias and Summary of Results passages report that perceived gender and race correlate with TaskRabbit search rankings and suggest that biased customer reviews or other behavior may feed into those rankings. The Mechanisms of Discrimination section says biased rankings can reinforce social biases. The paper does not examine age discrimination or establish that an algorithm learns demographic parameters from human decisions; its evidence is limited to two freelance marketplaces, not algorithmic HRM at scale. (LLM, conf=0.86)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“More generally, IT developers and therefore end-user employees, managers or independent contractors are not aware of the basis upon which computer-programmed steps are taken and which data are drawn on when HRM algorithms generate a selected type of output (Gal et al., 2020;Leicht-Deobald et al., 2019).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and the role of automated data processing
What the sources say
- Not checked — no full text of the sourcecites Gal et al., 2020 source
- Partly supportedcites Leicht-Deobald et al., 2019 sourceExperimental — accuracy not yet measured
In “A Brief History of Algorithm-Based HR Decision-Making” (page unavailable), the paper says the complexity of prescriptive algorithms makes it virtually impossible for a human to understand exactly how they turn information into a decision. In “The Assumption of Algorithm’s Objectivity” (page unavailable), it says proprietary code makes it difficult for HR managers to examine the code or training data. These passages support limited transparency, but not the categorical claim that IT developers and all the listed end users are unaware of the steps and data used for HRM algorithm outputs. (LLM, conf=0.84)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The 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
“Notwithstanding their benefits such as adaptability, the use of software algorithms in HRM creates challenges (Gal et al., 2020;Leicht-Deobald et al., 2019;Tambe et al., 2019).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and the role of automated data processing
What the sources say
- Not checked — no full text of the sourcecites Gal et al., 2020 source
- Supportedcites Leicht-Deobald et al., 2019 source
The Conclusion (page unavailable) says algorithm-based HR decision-making can be ethically problematic and identifies challenges including potential bias and harm to employees’ personal integrity. This directly supports the broad claim that using algorithms in HRM creates challenges. (LLM, conf=0.94)
- Not checked — no full text of the sourcecites Tambe et al., 2019 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Every source the paper cites for this claim was checked and supports it.
In the paper
“In fact, OLPs are an extreme example of algorithmic HRM where many (parts of) HRM processes are automated (Duggan et al., 2020).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Avenues for future research into algorithmic HRM
What the sources say
- Supportedcites Duggan et al., 2020 source
Section 3 describes algorithmic management as commonplace in app-work and says algorithms select and manage labour with limited human involvement. The Conclusion identifies work assignment and performance management as HRM-like activities in app-work and says algorithms undertake roles traditionally performed by HR professionals. Together, these passages support the characterization of platforms as an extensive example of automated HRM. (LLM, conf=0.90)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“OLPs also employ community managers and marketing specialists to manage communication within and outside of the platform (Gegenhuber et al., 2020;Meijerink & Keegan, 2019).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM, worker status and employment relationships
What the sources say
- Not checked — no full text of the sourcecites Gegenhuber et al., 2020 source
- Could not verifycites Meijerink & Keegan, 2019 sourceExperimental — accuracy not yet measured
The available passages contain bibliographic metadata (labeled Abstract, page unavailable), not discussion of platform staffing. Searches for community managers, marketing specialists, and internal or external platform communication found no passage addressing the claim. (LLM, conf=0.72)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“As labor market intermediaries, OLPs create online marketplaces where supply and demand for contingent labor meet electronically (Duggan et al., 2020;Kuhn & Maleki, 2017;Meijerink et al., 2021).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and strategy execution
What the sources say
- Supportedcites Duggan et al., 2020 source
The Introduction describes the gig economy as using online platforms to digitally connect workers with consumers. Section 2 describes digital platforms as intermediaries that facilitate interactions between workers and customers. Together, these passages support the claim that platforms electronically bring together demand for services and contingent workers who supply them. (LLM, conf=0.92)
- Not checked — no full text of the sourcecites Kuhn & Maleki, 2017 source
- Supportedcites Meijerink et al., 2021 source
The Literature review and theoretical background describes online labor platforms as labor-market intermediaries that use technology to create online marketplaces connecting requesters with on-demand gig workers. This supports the paraphrase; the supplied passage has no page number. (LLM, conf=0.94)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
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
“However, OLPs nevertheless still need to control their freelance workforce to align their behavior to strategic objectives of the OLP and clients' interests (Frenken et al., 2020;Gandini, 2019;Meijerink et al., 2021).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM, worker status and employment relationships
What the sources say
- Not checked — no full text of the sourcecites Frenken et al., 2020 source
- Not checked — no full text of the sourcecites Gandini, 2019 source
- Supportedcites Meijerink et al., 2021 source
The HRM practices section says platforms need to control gig workers to pursue market-share and revenue growth. The Performance appraisal section says meal deliverers need to meet the expectations of consumers and restaurants, and describes how platforms use their ratings to appraise workers. Page numbers are unavailable in our copy. (LLM, conf=0.92)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Currently, in HR Analytics, algorithmic HRM primarily augments HRM decision-making in that human decision makers may choose not to act on algorithmic-enabled insights or choose alternatives that are not necessarily predicted to be optimal by an algorithm (Rasmussen & Ulrich, 2015;Tursunbayeva et al., 2018).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and automated decision-making as output
What the sources say
- Partly supportedcites Rasmussen & Ulrich, 2015 sourceExperimental — accuracy not yet measured
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
- Partly supportedcites Tursunbayeva et al., 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.
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
In the paper
“In the context of a large German corporation, the authors show how the introduction of HR analytics, and the use of HRM algorithms to augment human decision making, is a deeply socio-material process.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
What the sources say
- Not matchedcites Ellmer & Reichel, this issue
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
In the paper
“In a study of a large multi-divisional professional services firm implementing a digitalized talent management technology, Wiblen and Marler (this issue) highlight how the interaction between HR managers, operational managers and the algorithms that augment the identification of future talent in the organization is not deterministic or completely predictable.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
What the sources say
- Not matchedcites Wiblen & Marler, this issue
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Not matched
- Probably not a substantial claim. 27
- It may state two separate claims as one. 22
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Using this definition, we regard algorithmic HRM as a specific subset/type of digital, or electronic, HRM (Bondarouk et al., 2017;Strohmeier, 2020b).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic human resource management
What the sources say
- Partly supportedcites Bondarouk et al., 2017 sourceExperimental — accuracy not yet measured
The Introduction treats electronic HRM as a broad category encompassing digital forms and HRM content shared through IT. It does not identify algorithmic HRM as a subset; that specific classification is the citing authors’ inference, not a point stated in this source. (LLM, conf=0.84)
- Not checked — no full text of the sourcecites Strohmeier, 2020b source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Software algorithms support HRM decision making in two ways, through providing information (or augmentation) and with automation (Leicht-Deobald et al., 2019;Raisch & Krakowski, in press).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and automated decision-making as output
What the sources say
- Supportedcites Leicht-Deobald et al., 2019 source
In “A Brief History of Algorithm-Based HR Decision-Making” (page not available), the paper says prescriptive algorithms serve two functions: decision support and decision automation. It describes automation as decision-making without human involvement, supporting the citing sentence’s distinction. (LLM, conf=0.90)
- Not matchedcites Raisch & Krakowski, in press
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“These so-called predictive algorithms operate regression-based forecasting techniques that, for example, help managers predict which employees are likely to leave the organization (and thus how to make decisions about retention), or to predict the future performance of a job candidate (and thus help hiring managers with selection decisions) (Cheng & Hackett, 2021;Leicht-Deobald et al., 2019).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and automated decision-making as output
What the sources say
- Not checked — no full text of the sourcecites Cheng & Hackett, 2021 source
- Partly supportedcites Leicht-Deobald et al., 2019 sourceExperimental — accuracy not yet measured
In “A Brief History of Algorithm-Based HR Decision-Making” (page unavailable), the paper says predictive algorithms use advanced regression techniques and describes a hiring algorithm that scores how well applicants would fit a job. It does not describe using predictive algorithms to identify employees likely to leave or to guide retention decisions; its mention of turnover concerns a descriptive scorecard. (LLM, conf=0.87)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The cited sources that could be checked couldn’t confirm or dispute this claim.
In the paper
“algorithms, human agency and social processes in OLPs are likely to differ due to their business model and how they interact with (platform) workers (Gegenhuber et al., 2020;Kuhn & Maleki, 2017;Meijerink & Keegan, 2019;Wood et al., 2019).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Avenues for future research into algorithmic HRM
What the sources say
- Not checked — no full text of the sourcecites Gegenhuber et al., 2020 source
- Not checked — no full text of the sourcecites Kuhn & Maleki, 2017 source
- Could not verifycites Meijerink & Keegan, 2019 sourceExperimental — accuracy not yet measured
The available indexed passages are bibliographic metadata (some labeled Abstract; no page numbers), not discussion of platform practices. Searches for algorithms, human agency, business models, and interactions with platform workers found no passage addressing the claim. (LLM, conf=0.72)
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“For instance, rather than use organizational hierarchy to control employee behavior through physical oversight, OLPs substitute algorithmic HRM and in doing so, need to strike a balance between autonomy and control (Frenken et al., 2020;Meijerink et al., 2021;Wood et al., 2019).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM, worker status and employment relationships
What the sources say
- Not checked — no full text of the sourcecites Frenken et al., 2020 source
- Partly supportedcites Meijerink et al., 2021 sourceExperimental — accuracy not yet measured
The Introduction describes algorithm-based compensation and other HRM practices that control gig workers, while ‘Institutional complexity of HRM for freelancers’ describes the tension between worker autonomy and control. The paper does not say that algorithmic HRM substitutes for organizational hierarchy or physical oversight; its discussion of the corporation logic instead likens platform control to organizational routines and hierarchy. Page numbers are unavailable in our copy. (LLM, conf=0.84)
- Partly supportedcites Wood et al., 2019 sourceExperimental — accuracy not yet measured
The Findings and discussion section says direct supervision of remote gig workers was limited by distance, while platform ratings and rankings provided effective algorithmic control and left workers substantial discretion. It also suggests that extinguishing that discretion would undermine service quality. The paper does not explicitly say platforms need to strike a balance between autonomy and control, so that prescriptive part goes beyond its findings. (LLM, conf=0.82)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Moreover, OLPs rely on opaque HRM algorithms in an attempt to replace HR managers and disguise the control that is exercised over the behavior of their freelance workforce (Meijerink et al., 2021).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM, worker status and employment relationships
What the sources say
- Partly supportedcites Meijerink et al., 2021 sourceExperimental — accuracy not yet measured
The Abstract and Implications for theory and practice describe technology-enabled, covert HRM practices that control freelance workers while preserving their freelance status. Limitations and directions for future research says Uber Eats and Deliveroo withheld details of their algorithms. The copy does not say that the algorithms are intended to replace HR managers or that their opacity itself disguises control. (LLM, conf=0.86)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“In contrast with 'traditional' contexts, platform workers are not supervised by human (line) managers tasked with implementing HRM activities (Bos-Nehles et al., 2013) or HR specialists on HR analytics teams (Ellmer & Reichl, this issue; Wiblen & Marler, this issue).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM, worker status and employment relationships
What the sources say
- Supportedcites Bos-Nehles et al., 2013 source
The Introduction passage (indexed under Abstract; page not provided) says HRM responsibilities have increasingly been devolved to line managers, who implement HRM practices in their daily work with employees. This supports the role attributed to Bos-Nehles et al.; the separate point about platform workers is the citing authors’ contrast, not a finding attributed to this source. (LLM, conf=0.91)
- Not matchedcites Ellmer & Reichl, this issue
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not matchedcites Wiblen & Marler, this issue
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Supported
In the paper
“Their analysis highlights the epistemic practices of boundary spanning, customizing dashboards, and speaking a language of numbers.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
What the sources say
- Not matchedcites Ellmer & Reichel, this issue
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
In the paper
“Gig workers who work with online labor platforms spend a lot of time trying to converse with humans as well as figuring out how to challenge, resist or game the controls and constraints built into the technology.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
What the sources say
- Not matchedcites Waldkirch et al., this issue
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“They show how HR managers see and use HRM algorithms to demonstrate their quantitative and business knowledge, and therefore enhance their credibility and status within the organization.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
What the sources say
- Not matchedcites Ellmer & Reichel, this issue
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
- Not matched
In the paper
“From these data, the authors find that HRM activities such as selection, performance management, compensation and employee relations are automated by HRM algorithms.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
What the sources say
- Not matchedcites Waldkirch et al., this issue
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirectSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“Taken together, these practices reflect what the authors refer to as epistemic alignment, where members in an HR analytics team align algorithmic output with the way organizational decision-makers perceive business reality.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
What the sources say
- Not matchedcites Ellmer & Reichel, this issue
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plainSingle case- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
- Not matched
In the paper
“The final paper in this special issue provides an example of online labor platforms as a context where algorithmic HRM fully automates HRM decision-making.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
What the sources say
- Not matchedcites Waldkirch et al., this issue
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- 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
“Digitization by means of sensors and smart devices allows for the real-time collection of digital data that come in large amounts and from a variety of sources.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and the creation and use of digital data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Studies into HR analytics ( Marler & Boudreau, 2017;Minbaeva, 2017;Tursunbayeva et al., 2018;Van den Heuvel & Bondarouk et al., 2017 ), HRM algorithms (Cheng & Hackett, 2021;Leicht-Deobald et al., 2019), and artificial intelligence (AI) deployed in HRM practices (Strohmeier & Piazza, 2015;Vrontis et al., 2021), while beginning to coalesce around key issues, tend to use different terms to describe”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Introduction
What the sources say
- Not checked — no full text of the sourcecites Suddaby, 2010 source
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“As such, algorithmic HRM requires what (Strohmeier, 2020b) refers to as digitization -a process by which information is turned into digital data.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and the creation and use of digital data
What the sources say
- Not checked — no full text of the sourcecites Strohmeier, 2020b source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Accordingly, its first two features (i.e.digital data and use of software algorithms) imply that algorithmic HRM falls within the realm of digital HRM, while the third feature, automation of HRM-related decision-making, distinguishes algorithmic HRM from other forms of digital HRM.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic human resource management
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Accordingly, algorithms process data and by so doing they produce”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and the role of automated data processing
What the sources say
- Not matchedcites Pachidi et al., 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Not matched
In the paper
“For example, software algorithms are (or can be made) opaque to workers, and it may not be entirely clear how these algorithms learn, or the exact source of their learning, and how they add parameters or adjust parameter weights using the data to which they are dynamically adapting (Burrell, 2016).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and the role of automated data processing
What the sources say
- Not checked — no full text of the sourcecites Burrell, 2016 source
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this sense, the use of AI in HRM is a specific form of algorithmic HRM.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and the role of automated data processing
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is where software algorithms come into play for transforming data in an automated and efficient manner.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and the role of automated data processing
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For software algorithms to be able to”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and the creation and use of digital data
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Given the sheer size and speed at which they are collected, Big Data are difficult to process efficiently in a manual manner.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and the role of automated data processing
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“As noted by Tursunbayeva et al. (2021), research on the ethical issues of HR analytics -where HRM algorithms are mostly used for augmenting decision making -is still at an early stage and focused mainly on ethical risks such as discrimination, bias, privacy concerns and excessive surveillance (Tursunbayeva et al., 2021).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Ethical implications of algorithmic HRM
What the sources say
- Not matchedcites Tursunbayeva et al., 2021
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- 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
“As such, these organizations do not need to pay attention to balancing autonomy-control tensions that OLPs incorporate into their HRM algorithms.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM, worker status and employment relationships
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“While almost all organizations today are part of the information technology revolution, most organizations are still in a digitalization process, albeit some at a more advanced stage than others (Strohmeier, 2020b).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Avenues for future research into algorithmic HRM
What the sources say
- Not checked — no full text of the sourcecites Strohmeier, 2020b source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. 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
“Moreover, employees may not be directly affected by algorithmic HRM in cases where a human manager and HRM algorithms both interact to enact HRM activities, yet where humans make the final decision.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM, worker status and employment relationships
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
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
“Vice versa, the actions of workers generate digital data that HRM algorithms processes, thereby triggering changes in the materiality of algorithmic HRM.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“As these examples show, algorithmic HRM includes the use of a broad array of digital HRM activities that now goes well beyond the use of descriptive/predictive algorithms that HR analytics scholars frequently study.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and automated decision-making as output
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
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
“Seen through a socio-material lens, HRM algorithms are performative while triggering changes in social processes.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Given the size of most workforces operating on OLPs, the execution of HRM activities by human managers would simply be too time-consuming and limit platforms' capacity to scale rapidly and perform well.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and strategy execution
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“For instance, a recent survey of almost 9,000 business and HR leaders shows that the majority of the organizations mostly collect worker data for descriptive purposes regarding turnover (82%), salary costs (68%) or workforce composition (53%), while predictive and prescriptive HRM algorithms are rarely used (Deloitte, 2020).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and strategy execution
What the sources say
- Not matchedcites Deloitte, 2020
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“The gig workers on the Upwork platform are independent contractors and along with its completely digital business model, most of the HRM practices are automated.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
What the sources say
- Not matchedcites Waldkirch et al., this issue
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- 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
“Prescriptive algorithms that automate HRM activities not only forecast what can be done in different scenarios, but also select and execute a course of action without (much) involvement by a human decision maker.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and automated decision-making as output
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In contrast to most existing organizations in which work is performed by employees who exchange their labor as part of an open-ended employment relationship, OLPs work with freelancers whose alternative employment relationship is temporary and transactional (Prassl, 2018).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM, worker status and employment relationships
What the sources say
- Not matchedcites Prassl, 2018
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Not matched
In the paper
“Although the workforce that performs tasks through OLPs generally consists of freelance workers, OLPs also have employees, for example software engineers and data specialists, who are responsible for designing the algorithmic HRM activities that automate HRM decisions regarding platform workers (Kuhn, Meijerink & Keegan, 2021).”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM, worker status and employment relationships
What the sources say
- Not checked — no full text of the sourcecites Kuhn, Meijerink & Keegan, 2021 source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In this special issue, Waldkirch et al. describe how HR algorithms based on artificial intelligence and machine learning render the performance evaluation of freelancers completely opaque and prevent them from autonomously managing or marketing their capabilities.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Ethical implications of algorithmic HRM
What the sources say
- Not matchedcites Waldkirch et al.
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- 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
“But with increasingly sophisticated algorithms, particularly with advances in AI algorithms, some HR decisions are now made without human”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and automated decision-making as output
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably 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
“Labor sociologists have shown how the use of prescriptive HRM algorithms enables employers to capture excessive economic value from workers' labor.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Ethical implications of algorithmic HRM
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Put differently, HRM algorithms can both automate and augment HRM decision making.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM and automated decision-making as output
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In contrast, organizations that 'become digital' are more likely to have conventional employment contracts along with managerial oversights that serves that the primary control mechanism.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Algorithmic HRM, worker status and employment relationships
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. 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
“Besides differing in the material agency of HRM algorithms, human agency and social processes in OLPs are likely to differ due to their business model and how they interact with (platform) workers”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Avenues for future research into algorithmic HRM
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
Human decision makers are involved in designing prescriptive algorithms.
“We acknowledge that human decision makers are involved in designing prescriptive algorithms and that prescriptive algorithms often rely on digital data that that is generated by humans.”
The paper relies on this without support.
Prescriptive algorithms often rely on digital data that is generated by humans.
“We acknowledge that human decision makers are involved in designing prescriptive algorithms and that prescriptive algorithms often rely on digital data that that is generated by humans.”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
“In the third paper, Ellmer and Reichel (this issue) illustrate how the credibility and status of HR managers is influenced by the ways they work with HRM algorithms used for HR analytical purposes.”
§ Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM > Overview of papers included in the special issue
“Ellmer & Reichl (this issue) contribute to the literature by showing how epistemic practices -as a form of human agency -affords the augmentation of HRM-related decision making by human managers.”
Citations
Cites
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How the machine “thinks”: Understanding opacity in machine learning algorithms
Burrell, J. (2016) · Big Data & Society · doi:10.1177/2053951715622512 · Cited by 3,196 · cited 1×
“For example, software algorithms are (or can be made) opaque to workers, and it may not be entirely clear how these algorithms learn, or the exact source of their learning, and how they add parameters or adjust parameter weights using the data to which they are dynamically adapting (Burrell, 2016).”
BackgroundArtificial Intelligence and Management: The Automation–Augmentation Paradox
Raisch, S., & Krakowski, S. (2021) · Academy of Management Review · doi:10.5465/amr.2018.0072 · Cited by 2,755
Algorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732
Systems Competition and Network Effects
Katz, M. L., & Shapiro, C. (1994) · Journal of Economic Perspectives · doi:10.1257/jep.8.2.93 · Cited by 2,646
Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy
Wood, A. J., Graham, M., Lehdonvirta, V., & Hjorth, I. (2019) · Work, Employment and Society · doi:10.1177/0950017018785616 · Cited by 2,209
XAI—Explainable artificial intelligence
Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., & Yang, G.-Z. (2019) · Science Robotics · doi:10.1126/scirobotics.aay7120 · Cited by 2,053
Artificial Intelligence in Human Resources Management: Challenges and a Path Forward
Tambe, P., Cappelli, P., & Yakubovich, V. (2019) · California Management Review · doi:10.1177/0008125619867910 · Cited by 1,609
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- Cappelli P., 2017, Harvard Business Review, 2
- Ellmer M., 2015, Momentum Quarterly, 4, 174
- Newlands G., 2020, Organization Studies, 1
- Parry E., 2014, Employee Relations, 36
- Rosenblat A., 2016, International Journal of Communication, 10, 3758
Cited by
Sorted by citations
Meijerink, J., & Bondarouk, T. V. (2021) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100876 · Cited by 420
Self-citationArtificial intelligence (AI)-assisted HRM: Towards an extended strategic framework
Malik, A., Budhwar, P. S., & Kazmi, B. A. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2022.100940 · Cited by 340
Algorithmic HRM control in the gig economy: The app‐worker perspective
Duggan, J., Carbery, R., McDonnell, A., & Sherman, U. (2023) · Human Resource Management · doi:10.1002/hrm.22168 · Cited by 164
Creation of the algorithmic management questionnaire: A six‐phase scale development process
Parent‐Rocheleau, X., Parker, S. K., Bujold, A., & Gaudet, M. (2023) · Human Resource Management · doi:10.1002/hrm.22185 · Cited by 177
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Metadata
- Authors
- Meijerink, J., Boons, M., Keegan, A., & Marler, J.
- Affiliations
- University of Twente (NL)
- Vrije Universiteit Amsterdam (NL)
- University College Dublin (IE)
- State University of New York (US)
- Corresponding author
- Meijerink, Jeroen
- Venue
- The International Journal of Human Resource Management
- Publisher
- Informa UK Limited
- Published
- 2021-05-13
- Volume
- 32
- Issue
- 12
- Pages
- 2545-2562
- ISSN
- 0958-5192 (ISSN-L)
- 1466-4399
- OpenAlex
- W3163237151
- Cited by
- 267
- References
- 47
- Citations by year
- 2026: 92 · 2025: 59 · 2024: 68 · 2023: 29 · 2022: 17 · 2021: 2
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The International Journal of Human Resource Management (publishedVersion, cc-by-nc-nd)
- VU Research Portal (publishedVersion, other-oa)
- University of Twente Research Information (publishedVersion, cc-by-nc-nd)
- Research Repository UCD (University College Dublin) (acceptedVersion, cc-by-nc-nd)
- University of Twente Research Information (submittedVersion, cc-by-nc-nd)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
- Keywords
- digital HRM, algorithmic surveillance, HR analytics, artificial intelligence in HR, employee monitoring
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 2026-11-03, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
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