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| Bodie et al (2017) - The Law and Policy of People Analytics. University of Colorado Law Review 88. discipline Law | People Analytics | Review ethics and bias in algorithmic recruiting | Review (Legal) [argument based on history, definition of people analytics & taylorism] | Thus, while the term people analytics can cover a variety of approaches to HR management, they, as a group, generally follow a particular pattern: (1) the search for new pools of quantitative data that are correlated with business and employment success, and (2) the use of such data to make workplace decisions and to replace subjective decision making by managers. | Computer Science | "more data" | Quantitative & statistical analysis & knowledge discovery & data mining & predictive analytics & NHST & Correlations | HR department Management | (Workplace) Decision making | scientific management & taylorism | Individual Group | Ethics & Legal & Bias | Not addressed | wider focus, takes into account all sources of data, looking to improve any people related organisational outcomes | jh: legal perspective, history of and definition of PA, more details on definition of PA | |
| Cheng (2017). Causal modeling in HR Analytics: a practical guide to models, pitfalls, and suggestions. Academy of Management Annual Meeting Proceedings, 1. discipline HR/management | HR Analytics | Review (technical) modeling and quasi-expermential designs | Review (technical) & Methodology | a tool that encompasses statistical models to add strategic influence in human resource management | PA as Tool IT provides Big Data | Big Data | Quantitative & statistical analysis & predictive analytics & regression & longitudinal multivariate models & quasi-experimental | HR department | Improve HR Decisions & Strategic Influence | not discussed & implied positivist | Individual | Statistical Errors & Robustness of Models | Not addressed | nucleus is HR function, which if moving beyond boundaries can provide strategic influence & different focus than us | jh: review of 8 papers on HR Analytics, provides info on pitfalls or current implementation, e.g. lack of academic rigour, pracititioners not sharing details & good for motivation & goal is providing overview and recommendations for modeling & only chose peerreviewed articles | |
| Marler, J. H., and Boudreau, J. W. (2017). An evidence-based review of HR Analytics. International Journal of Human Resource Management, 28(1), 3–26. | HR Analytics | Review | Review | A HR practice enabled by information technology that uses descriptive, visual, and statistical analyses of data related to HR processes, human capital, organizational performance, and external economic benchmarks to establish business impact and enable data-driven decision-making | IT as Enabler | HR Data Performance Data External Market Data | Quantitative & Descriptive & Visualisation & Statistical Analysis | HR department | (Data-driven) Decision making | RBV & HR Scorecard & Agency Theory | Individual Group | Not addressed | evidence-based integrative synthesis | sjacome: jh: main goal is review of HRA articles & how & why it works | ||
| Shrivastava, S. et.al (2018) Redefining HR using people analytics: the case of Google discipline HR/commercial | People Analytics & HR Analytics | Overview & Best Practices | Business case study | People analytics or human resource (HR) analytics refers to the use of analytical techniques such as data mining, predictive analytics and contextual analytics to enable managers to take better decisions related to their workforce | not mentioned | Performance Data | Quantitative & Predictive analytics & data mining & contextual analytics | Management | Decision making & Hiring & Retention & Collaboration & Performance | not discussed & implied positivist | Individual Group | Not addressed | Not addressed | wider focus | jh: pracitioner view, sales pitch for PA at google | |
| Singer, et al (2017) People Analytics in Software Development. Intl Summer School on Generative and Transformational Techniques in Software Engineering discipline tech | People Analytics | Present PA Tool für Software development | Design Science | the use of data, quantitative and qualitative analysis methods, and domain knowledge to discover insights about how people work together with the goal of improving collaboration. | IT as Enabler | Big Data | Quantitative and Qualitative | Developers | Collaboration | they discuss gaming the system, presentation of metrics more important than actual numbers | Individual | Privacy & Surveillance & Impression Management | Not addressed | focus on developer logs, fits to our model | jh: case study of software engerineering gamified dashboard, provides weak definition of PA, mostly focuses on their experiment & goal inquire how PA can improve software dev collaboration | |
| Tursunbayeva et al. (2018), People analytics - A scoping review of conceptual boundaries and value propositions | People Analytics | Review | Review | People Analytics is an area of HRM practice, research and innovation concerned with the use of information technologies, descriptive and predictive data analytics and visualisation tools for generating actionable insights about workforce dynamics, human capital, and individual and team performance that can be used strategically to optimise organisational effectiveness, efficiency and outcomes, and improve employee experience | IT as enabler | not mentioned | Quantitative & Descriptive & Visualisation & Predictive Analytics | HR department | Performance & Employee Experience | Not mentioned | Individual Group | Not addressed | Not addressed | sjacome: They adopt a broad approach to examine the PA topic, that according to them is poorely understood. | sjacome: jh: overview of terms, provides integrated definition, weak method, mostly based on frequency of words, no critical discussion | |
| Sprague (2015) - Welcome to the Machine Privacy and WorkplaceImplications of Predictive Analytics. Richmond Journal of Law and Technology discipline Law | Workplace Analytics | Review privacy concerns | Review (Legal) | Predictive Analytics use a method known as data mining to identify trends, patterns, or relationships among data, which can then be used to develop a predictive model & in many cases attempting to predict behavior." | IT as enabler | "more data" log data | Quantitative & Data mining & Predictive Analytics | Data scientists | Improve Management & Predict Behaviour | Taylorism | Individual Group | Privacy & Surveillance & Discrimination | too broad | sjacome: complemented. Jh: legal perspective, negative stance, provides analogy to taylorism, argues from big data analytics to PA, makes a point that employers must prove causation over mere correlation. | ||
| HR and analytics: why HR is set to fail the big data challenge [Angrave, David et al, 2016] | HR Analytics | Critique | academic opinion-piece | HR analytics involves complex multistage projects requiring question formulation, research design, data organisation, and statistical and econometric modelling of differing levels of complexity and rigour based on big data enables data driven decision making to improve performance of people-related processes | HRIS dashboards | HRIS big data longitudinal data (un)structured data surveys | Quantitative & Visualisation & Statistical analysis & Experiments, quasi-experiments & predictive analytics multivariate longitudinal analysis | HR department | Strategic value & Performance & Decision making | RBV & positivist & organizational psychology & critical realism | Organizational | lack of analytical capability & strategic impact & reduced wellbeing & privacy & ethics & data quality & does it work? | Not addressed | Mix the concepts of HR and data analytics | jh: critical perspective of HR developments by Angrave, opinion piece | |
| Optigrow: People Analytics for Job Transfers [Wei. D, 2015] | People Analytics | Propose big data approach for Job Transfers | Design Science & Methodology | No concept or definition used | Custom Algorithm | expertise assessments curricula vitae project tracking data and HR information | Quantitative & Custom Scoring Algorithm | HR department | Internal Hiring & Job Transfer | Not mentioned | Individual | Sponsorship | Not addressed | jh: case study at IBM, optigrow for re-hiring from internal employees & mathematical feasibility and organisational recommendations, e.g. stakeholder, management buy-in & no mention of PA, taylorism, pitfalls, etc. no definition & no causality or theory | ||
| The best practices to excel at people analytics, [Green. David, 2017] | People Analytics | Best practices | Practitioner's whitepaper | No concept or definition used | not mentioned | not mentioned | Anectodal references | HR department CHRO | Business Value | Not mentioned | Not clear | Sponsorship | Not addressed | not comparable | jh: no scientific rigour, lacks method, selection bias, only looks at top cases, no arguments for the points & may be useful for some anectodal references and further inquiry into the mentioned cases sjacome: the practices are described according to the expertise of the author. | |
| Smart HR 4.0 – how industry 4.0 is disrupting HR(Review) [Sivathanu, B. et. al, 2018] | Smart Human Resources | Best practices | business case study | SHR is characterized by innovations in digital technologies such as Internet of-Things, Big Data Analytics, and artificial intelligence (AI) and fast data networks such as 4G and 5G for the effective management of next-generation employees | IT as Enabler Internet of-Things fast data networks such as 4G and 5G | Big Data | Quantitative & Artificial Intelligence & Big Data Analytics | HR department | Improve Management & Hiring & Onboarding & Offboarding & Retention & Learning & Development | Not mentioned | Individual | Culture | Conceptual framework for talent on boarding | jh: some nice ideas, introduction no substance, and not related to specifics of PA & hypotheses are interesting, but not theoretically derived, i.e. arbitrary & 1 case study referenced (capgemini) & goal is conceptual framework, which depicts basic ideas on how big data can influence HR | ||
| Implicit bias in predictive data profiling within recruitments [Persson A, 2016] | People Analytics | Review ethics and bias in algorithmic recruiting | academic opinion-piece | Companies use data mining:machine learning with algorithms and statistical learning. This if often referredto as using “Big Data”, or “People Analytics” & i.e. using large datasets to identify parameters that represents the best candidates for various positions. The motivation for companies is often the claim to become more objective in their assessment. They can also be claimed to want to become more certain in their decision making for hiring. | not mentioned | Big Data | Quantitative & Statistical analysis & machine learning & Data mining | HR department Recruiters | Decision making | Not mentioned | Individual | Discrimination & Bias & Data Quality & Does it work? & Complexity of algorithms & Privacy & GDPR | Not addressed | jh: not very well theorized, development of argument is lacking warrants and evidence & repeats some ideas that are covered elsewhere & note it is IFIP pre conf paper by a phd student it is not clear where this paper is positioned and from what arguments it draws from & weak sources & goal is to analyse potential pitfalls and provide ideas how to deal with them in terms of big people data analytics | ||
| People Analytics: an organizational psychology perspective on data-oriented leadership [Reindl, 2016] | People Analytics | Overview | Review | People Analytics bezeichnen die systematische Analyse von Daten aus dem Personalwesen in Verbindung mit Daten aus anderen Unternehmensbereichen mit dem Ziel, Faktoren der Zusammenarbeit von Mitarbeitern und der Wettbewerbsfähigkeit von Unternehmen besser zu verstehen und gezielt zu fördern | IT as Enabler | "more data" HRIS employee count churn rates sick people personnel costs revenue curricula vitae longitudinal data sociometric badges | Quantitative & Clusteranalysis & Trend analysis & Regression analysis & SEM & SNA & predictive analytics | Management | hiring & retention & workforce planning & employee experience & competitive advantage & leadership & decision making & strategic alignment | organisational psychology | Individual | Legal & Ethics & Privacy & Change Management (Actionable Insights) | Sozial-, Emotions- und Motivationspsychologie sowie aus den Praxisbereichen Business Intelligence und Big Data. | jh: introduction to people analytics incl. definition, common methods, potential applications of PA & pitfalls and challenges & article is a call for more PA & overview is supported by selected sources, but lacking some sources & definitely useful article | ||
| Book: Human resources strategy and change: Essentials of workforce planning and controlling [Weiss, C., 2016] | Workforce Planning and Controlling | Best Practices | Practitioner's handbook | Workforce planning and controlling is the process of HR that ensures in a structured way that an organization always has the right number of people with the right competencies at the right moment at the right location and with the right costs. It analyzes the workforce demand, determines the workforce supply, and generates the insights to enable the relevant stakeholders to match demand with supply, allocate and schedule resources, identify workforce gaps, and develop action plans to fill or reduce the gaps. | Specialized workforce planning systems | workforce metrics skills employee count GPS costs KPIs | Quantitative & Descriptive & Predictive Analytics | HR department | Improve management & Performance | not mentioned & Positivist (controlling) | Individual | Compliance & Transparency & Data Quality & Effort & Adoption & Change resistance | Not addressed | jh: quickly touches workforce analytics as going beyond KPIs and reporting into predictive analytics & that's it & it is a practitioner's handbook, not research article | ||
| Is Your Company Ready for HR Analytics? [Baesens, Bart & De Winne, Sophie & Sels, Luc, 2017] | HR Analytics | Best practices | opinion-piece | HR analytics is “the new kid on the block” in busi-ness analytics applications, therefore practitioners can substantially benefit from lessons learned in applying an-alytics to customer-focused areas — and thus avoid many rookie mistakes and expensive beginner traps. | IT as Enabler | Big Data public data eg. Emails project data location skills | Quantitative & Statistical analysis & Social network analysis & Regression models | HR department | performance & hiring & retention & decision making | Not mentioned | Individual | Lack of analytical capability & actionable insights & privacy & ethics & diversity | Not addressed | benefit from lessons learned in applying analytics to customer-focused areas jh: mitsloan & provides advice for practitioners on how to use predictive models & extrapolated from experience in customer predictive models & look at business, safeguard privacy, constantly evaluate models, considers SNA/networks | ||
| HR analytics and performance appraisal system: A conceptual framework for employee performance improvement [Sharma and Sharma, 2017] | HR Analytics | Research employees willingness to improve under performance appraisal systems | Conceptual | HR analytics is more than just metrics and/or scorecards. It consists of various modeling tools such as behavioral modeling, predictive modeling, impact analysis, cost–benefit analysis and ROI analysis required for strategic HR decision-making | IT as Enabler | not mentioned | Quantitative & Behavioral modeling & Predictive analytics & Impact analysis & cost–benefit analysis & ROI analysis | HR department | performance & (objective) decision making | not mentioned | Individual | Bias & (perceived) Fairness & Discrimination | Not addressed | jh: posits that HRA is objective and can improve employees perception of appraisal system, coz "objective" & uses HRA as a blackbox term and does not address it any further & basically just "objective" theoretical development seems okay-ish, so probably a legit paper & quality is mediocre & paper name is wrong in column B | ||
| The rise (and fall?) of HR analytics A study into the future application, value, structure, and system support [van den Heuvel, Sjoerd, 2017] | HR Analytics & People Analytics | Explore future of HR Analytics | academic research paper (20 interviews) | the systematic identification and quantification of the people drivers of business outcomes, with the purpose to make better decisions "a tool for HR" digital personnel management | IT as an Enabler | "more data" internal and external integration from multiple sources | Quantitative & statistical analysis & descriptive & correlations & predictive analytics & visualisation | Any department HR department Management | absenteeism & diversity & evidence-based decision making & performance & efficiency & costs & leadership & hiring & succession planning & workforce planning & retention & learning & development & wellbeing & compensation & engagement | not addressed | Individual | legal & privacy & trust & strategic impact & sponsorship | Not addressed | "The label workforce analytics is actually detached from the HR function, but may have an exploitative association. Still, some leading software vendors such as Workday and SAP’s SuccessFactors use the term workforce analytics for their products. People analytics may the most neutral and employee friendly label, and is for example consistently used by Google, who in general avoids the term human resources and therefore named the HR department ‘People Operations’. Usage of any specific label will therefore mostly be a matter of consistency in specific product or business language and/or philosophy. The present study adopts the label HR Analytics, since the study was conducted in a Dutch context, where the dominant label is HR Analytics."p5 | jh: defines PA and has 20 survey/interview as sample on what defines PA & too long for me to read in detail, but seemed interesting enough & clear motivation (i.e. lack of academic articles on HRA) & needs more attention | |
| Learning from practice: how HR analytics avoids being a management fad [Rasmussen, Thomas, 2015] | HR Analytics | Critique | academic opinion-piece | Evidence-based decision making for HR function | Analytics | Qualitative and Quantitative | Quantitative & Multivariate Statistics | Organizations / HR Professionals | HR Decision making | Not mentioned | Organizational | Not addressed | Not addressed | "So far the published evidence supporting the alleged value of HR analytics is actually quite slim it is currently based more on belief than evidence, and most often published by consultants with a commercial interest in the HR analytics market" | negative: Lack of analytics about analytics It is not about data,but about data for informed decision-making Academic mindset in a business setting HR analytics run from an HR Center-of-Expertise(CoE) Hr analytics can be misused to maintain the status quo and drive a certain agenda jh: it maersk oil article & opinion piece with practical guidance & no scientific rigour & does provide interesting take on all our columns though & needs more attention | |
| "Personal social dashboard": A tool for measuring your social engagement effectiveness in the enterprise [Kremer-Davidson, S. et al , 2017] | Social Analytics | Present PA Tool for social analytics | design-science | Personal feedback on social activities via Dashboard | Dashboard | CACS Logs | Quantitative & Social Network Analysis & Activity Ranking | Employees | Employee engagement & Employee empowerment & Employee retention | not addressed | Individual | Privacy | Not addressed | Only employees themselves have access to their scores and even their managers cannot see them. This is crucial for protecting the privacy of employees and verifying that these scores cannot be used against them in any way. Interestingly, we see many employees sharing their scores through screen captures in the enterprise social network. Some employees mentioned they would like their managers to see their score improvement towards their yearly performance assessment jh: design science, social analytis, self-service & users improve engagement & does not consider wider discussion of social analytics & is relevant though & they do not argue why their tool is better than others & tool itself not evaluated | ||
| Raising your eminence inside the enterprise social network [Kremer-Davidson, S. et al , 2016] | Social Analytics | Overview | design-science | No definition provided | Tool | interview company's bloggers social behavioral patterns | Quantitative and Qualitative | Employees | Employee engagement | Graph Theory (?) | individual | Not addressed | Not addressed | jh: includes some descriptives on what makes high contributors sjacome: not relevant (?) | ||
| Inferring employee engagement from social media [Shami et al, 2015] | Social Analytics | Present Algorithm | design-science | Social Analytics using natural language processing for predicting social metrics such as employee engagement. | IT as Enabler Dashboard | CACS Logs | Quantitative & Natural Language Processing & Multivariate Statistics | Not addressed | Employee engagement | not discussed & implied positivist | Individual | Not addressed | Macey and Schneider model of employee engagement | Employee Engagement (employee willingness to apply discretionary effort towards organizational goals), state vs. trait jh: measures employee engagement, discusses that data traces can be me realtime than annual surveys, addresses trust, gaming the system & as such considers PA a little bit more broadly | ||
| Understanding employee social media chatter with enterprise social pulse [Shami et al, 2014] | Social Analytics | Present PA Tool | design-science | Social Analytics using natural language processing for predicting social metrics such as employee engagement. | Dashboard | CACS Logs | Quantitative & Natural Language Processing & Multivariate Statistics | Not addressed | Employee engagement | not discussed & implied positivist | Individual | Lack of managerial support & Privacy | Not addressed | We believe that ESP will be beneficial for companies with an active social media footprint and the business need to understand employee chatter. Technological challenges such as the limitations of sentiment analysis, and cultural barriers such as self-censorship and the lack of relevant social media content remain jh: text mining and computational linguistic analysis, evaluated results wiht survey & real-time automated analysis is beneficial for dynamic phenomena that are subject to frequent change & employees can compare themselves to the average & typically such an analysis requires activity in the given software & needs trust by employees, possibility of gaming the system & & Note from IBM research team | ||
| Finger on the pulse: The value of the activity stream in the enterprise [Guy, I., 2013] | Social Analytics | Present PA Tool | design-science | Social Analytics using natural language processing for predicting social metrics such as awareness | Dashboard | CACS Logs | Quantitative & Natural Language Processing | HR department Management IT | Awareness | not addressed | Individual Organizational | Not addressed | Not addressed | jh: social analytics, topic identification, sentiment analytics & evaluated with interviews & in particular HR is intested in slicing the data by characteristics & design science & IBM product dev | ||
| Using workforce analytics to improve strategy execution [Levenson, Alec, 2018] | Workforce Analytics | Introduce Workforce Analytics & Best Practices & Critique | Conceptual & Academic Case Study | Workforce analytics is using scientific means, i.e. data collection and rigorous analysis, for improving HR processes, strategy exectuion, and organizational effectiveness in general. | not mentioned | anonymized aggregate data interviews surveys internal IT systems HRIS | Qualitative & Quantitative & Multivariate Statistics & Multilevel modeling & Social Network Analysis | HR department Management | organizational management & strategic execution & organizational effectiveness & competitive advantage & recruiting & training & workforce planning & performance & engagement & compensation & learning & development | Systems approach & Social science | Individual Group Organizational | Lack of managerial support & senior sponsorship | referring to social scientists regarding importance | "In this article I argue that we need a different orientation to how workforce analytics is defined and conducted in organizations: a much greater emphasis on systems thinking and diagnostics, The approach I recommend is to start with the business strategy and the overarching objectives senior leaders aim to accomplish in the marketplace" jh: focus on competitive advantage and analysis on higher level e.g. org/group level. quantitivate not (always) needed there, instead qualitative analysis & addresses some nice ideas for analysis & provides depiction of approach & part of special issue | ||
| Workforce analytics: A case study of scholar-practitioner collaboration [Simon, Cristina & Ferreiro, Eva, 2018] | Workforce Analytics | Illustrate exchange between academic and practitioners in workforce analytics projects | academic case study | Workforce analytics is an effort that goes beyond applying statistical techniques to resolve practical managerial issues through the use of inferences and the development and testing of hypotheses based on workforce data. Question what role the workforce plays in supporting business optimize the contribution human capital makes to corporate performance | IT an Enabler Dashboard | "more data" Big Data Demographic Data Performance Data | Multivariate Statistics & Cluster Analysis & Regression & Correlation Analysis | HR department | Performance | Following the guidelines of the six-stage model of Talent Analytics development by Davenport, Harris, and Shapiro (2010), it could be stated that the company had shared definitions for their basic key indicators and had therefore reached the level of “Analytical HR” (Stage 2) capabilities, but did not have the expertise in making complex inferences or predictions about the contribution of their workforce to store performance. | Individual Group Organizational | Lack of managerial support & Lack of analytical capability & Change management | Not addressed | relationship between practitioners and academics | A successful Workforceanalytics infrastructure requires specific sets of knowledge on busi-ness research methods and rigorous analytical skills, as well as thedevelopment of a“questioning mind-set”that drives the design, col-lection, analysis, and further interpretation of the data (Angrave,Charlwood, Kirkpatrick, Lawrence, & Stuart, 2016) jh: case study on workforce analytics at inditex, quite interesting & addresses different methods and challenges when implementing such projects & storytelling & focus on competitive analytics & same group as Lawler, Boudreau, Levenson | |
| A strategic approach to workforce analytics: Integrating science and agility [ McIver, Derrick, et al., 2018] | Workforce Analytics | Introduce Workforce Analytics & Best Practices | Conceptual | Process to collect, manipulate, analyse HR related data and test hypotheses in a scientific manner. Data is integrated from different internal and external sources towards evidence-based decisions, not only for HR, but business in general. | IT as Enabler dashboard platform | structured unstructured CACS Logs Big Data internal external | Quantitative & Predictive Analytics & Statistics & Machine Learning | HR department Management | decision making & organizational outcomes & strategic value | mix of inductive/deductive & theory- and data-driven | Individual group organizational | Ethical & political issues & stakeholder support & change management & analytical capability | Not addressed | agile workforce analytics process: (1) prioritizing issues, (2) integrating deductive and inductive approaches, (3) preparing and validating data, (4) applying multiple methods in concert to support decisions, and (5) transforming insight into action to improve business outcomes jh: weak article & proposes concept of agile+PA as a process & provides understanding and methods of PA & can be well used. | ||
| Workforce assessment method for an urban police department Using analytics to estimate patrol staffing [Srinivasan, Sudharshana, et al., 2013] | Workforce Analytics | Propose Simulation Model | Simulation Study | improve business effectiveness by evidence-based decision making using qualitative and quantitative analysis of empirical big data to compare multiple solutions to a problem before the actual implementation of the proposed solution. | IT an Enabler | "more data" Big Data | Quantitative & Predictive Analytics & discrete-event simulation model | Not addressed | performance & staffing & business decisions | evidence-based decision making | Individual Group | Change resistance | Not addressed | police agencies in the US This research provides mathematical evidence to support the size of the patrol workforce needed to meet the RPD’s performance benchmarks jh: more data by tech innovations & analytics enables better decisions & but requires overcoming stakeholder resistance (and costs) & evidence based decision making is hype & very specialised context. | ||
| Book chapter: HR metrics and workforce analytics [KD Carlson, MJ Kavanagh, 2011] | Workforce Analytics & Predictive Analysis & HR metrics | Best practices | book chapter | measurement of human capital and the impact of people on organization processes to improve effectiveness & evidence-based management & workforce analytics is a system | IT as an Enabler HRIS dashboard | HRIS Big Data | Quantitative & realtime & metrics & benchmarking & predictive analytics & experiments (A/B testing) & balanced scorecard | HR department Management | decision-making & organizational effectiveness & recruiting & retention & promotions & succession planning & compensationb & employee engagement & performance & compliance & learning & development & workforce planning & strategc value | Scientific management (Taylorism) & Industrial & Organizational Psychology | Individual Group Organizational | Stakeholder Support & Change Management | Not addressed | As a result, organizations that make investments in internal human capital assessment resulting in useful HR metrics and workforce analytics will become less willing to share their knowledge with other organizations in their industry | ||
| Book: Handbook of Service Science: Workforce analytics for the services economy [Aleksandra Mojsilovi?Daniel Connors, 2010] | Workforce Analytics | Review | research paper | workforce optimization through improved planning, scheduling, deployment and resource management to yield greater business value and profits | IT as an Enabler | HRIS data ERP data | Quantitative & multivariate statistics, machine learning & clustering & simulations & social network analysis | HR department | manage skills & workforce planning & staffing & demand forecasting & talent management & recruiting & retention & knowledge management & promotion & learning & development & strategic value & innovation & | not addressed | Individual Group Organizational | Not addressed | Not addressed | IBM | ||
| Transforming HR in the digital era: Workforce analytics can move people specialists to the center of decision-making [Prerna Lal, 2015] | Workforce Analytics | Review | opinion-piece | Evidence-based decision making, informed by visualization and analysis of workforce data, to provide actionable and deep insights for driving workforce-related activities in the HR function and processes throughout the organization. | dashboard | HRIS data | Predictive Analytics & Descriptive Analytics & Visualization & forecast and scenario models | HR department | performance & evidence-based decision making & talent management & recruiting & employee retention & succession planning & learning & development & workforce planning & staffing & compensation & strategic value | Not mentioned | Individual | Data Integration | Not addressed | jh: opinion piece, weak arguments & but okay journal | ||
| Achieving Human Capital Management: Building the Workforce Analytics Infrastructure [J Barrete, 2004] | Workforce Analytics | Technical Overview of Data Warehouse | opinion-piece | From metrics to Analytics: Date Warehousing using internal and external HR and other function's data to improve people-related business functions | Data Warehouse OLAP cubes | external data internal data HRIS surveys public data sets | Quantitative & Data Warehousing & ETL & OLAP & descriptive analytics & what-if analysis | HR department Analysts | Talent Management & succession planning & employment satisfaction & workforce optimization | not addressed | Individual | Legal & Data Integration & Costs | Not addressed | jh: opinion piece & non academic & technical focus | ||
| Quantile Regression for Workforce Analytics [KN Ramamurthy, KR Varshney, 2013] | Workforce Analytics & Workforce Behaviour | Propose Algorithm | Methodological | Workforce analytics is a broad area comprising many scientific techniques that help in understanding and predicting the behavior of the workforce in a business using available data | not mentioned | HRIS data | Quantitative & Quantile Regression | Not addressed | Performance | implied positivist | Individual | Not addressed | Not addressed | develop frameworks based on quantile regression | jh: technical paper & IBM | |
| Latent Ability Model: A Generative Probabilistic Learning Framework for Workforce Analytics [Z Luo, L Liu, J Yin, Y Li, Z Wu, 2018] | Workforce Analytics | Propose Algorithm | Methodological | Workforce analytics is a data-driven statistical learning methodology that employs statistical models and machine learn-ing algorithms to worker-related activity data logs, enabling enterprise organizations to optimize their talent pools and transform human resource management | IT as an Enabler | CACS Logs | Quantitative & Machine Learning & Latent ability Model & Gradient Descent | HR department Analysts | Employee management & performance & improve HR processes | implied positivist | Individual | Not addressed | Not addressed | jh: technical paper, which estimates 3 latent parameters (performance, ability, matchup) for employees given their activity-event-log & rich dataset & | ||
| Adopting Analytics to Effectively Manage Workforce Needs [R Isherwood, M Seale, 2014] | Workforce Analytics | Review | Descriptive Report | quantitative rigour to effectively manage workforce (e.g. recruiting, retention, development) with internal & external data. apply marcoeconomic data to inform sound decision making Workforce analytics offer a fact-based approach to addressing workforce-related issues can reveal risks for employee segments and also the reasons | IT as an Enabler | Internal External | Quantitative & Predictive Analytics | HR department | recruiting & retention & learning & development & workforce planning & staffing & employee satisfaction & employee engagement & talent management & strategic alignment | implied positivist | Individual Group Organizational | Lack of analytical capability | Not addressed | Oil Companies |