Conducting Ethical Research With Big and Small Data: Key Questions for Practitioners

Dekas, Kathryn H.; McCune, Elizabeth A.

doi:10.1017/iop.2015.84

Cited by 6 publications

(7 citation statements)

References 2 publications

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“…In summary, building evidence-based and ethical HR analytics capabilities that foster trust and transparency is critically important to the success of any HR analytics effort. Each member of the HR analytics team plays a vital role as ethical researcher, analyst, interpreter, translator and educator (Dekas and McCune, 2015). Lastly, organizations need to think through how their HR analytical insights are derived, communicated and more importantly – used (Illingworth, 2015).…”

Section: Implications and Guidelines For Practicementioning

confidence: 99%

The HR analytics cycle: a seven-step process for building evidence-based and ethical HR analytics capabilities

Falletta

Combs

2020

JWAM

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PurposeThe purpose of the paper is to explore the meaning of HR analytics and introduce the HR analytics cycle as a proactive and systematic process for ethically gathering, analyzing, communicating and using evidence-based HR research and analytical insights to help organizations achieve their strategic objectives.Design/methodology/approachConceptual review of the current state and meaning of HR analytics. Using the HR analytics cycle as a framework, the authors describe a seven-step process for building evidence-based and ethical HR analytics capabilities.FindingsHR analytics is a nascent discipline and there are a multitude of monikers and competing definitions. With few exceptions, these definitions lack emphasis on evidence-based practice (i.e. the use of scientific research findings in adopting HR practices), ethical practice (i.e. ethically gathering and using HR data and insights) and the role of broader HR research and experimentation. More importantly, there are no practical models or frameworks available to help guide HR leaders and practitioners in doing HR analytics work.Practical implicationsThe HR analytics cycle encompasses a broader range of HR analytics practices and data sources including HR research and experimentation in the context of social, behavioral and organizational science.Originality/valueThis paper introduces the HR analytics cycle as a practical seven-step approach for making HR analytics work in organizations.

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Section: Implications and Guidelines For Practicementioning

confidence: 99%

The HR analytics cycle: a seven-step process for building evidence-based and ethical HR analytics capabilities

Falletta

Combs

2020

JWAM

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“…Note also that the ethical standards for informed consent are different for researchers (see Section 8.05 of the Code) developing selection tools than for those employing them. Guzzo, Fink, King, Tonidandel, and Landis (2015) discuss the implications of these requirements in some detail (see also Dekas & McCune, 2015 for further implications).…”

Section: Informed Consent In Assessmentsmentioning

confidence: 99%

Scientific, Legal, and Ethical Concerns about AI-Based Personnel Selection Tools: A Call to Action

Tippins¹,

Oswald²,

McPhail³

2021

Preprint

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Organizations are increasingly turning toward personnel selection tools that rely on artificial intelligence (AI) technologies and machine learning algorithms that, together, intend to predict the future success of employees better than traditional tools. These new forms of assessment include online games, video-based interviews, and the use of large amounts of big data pulled from many sources, including test responses, test-taking behavior, applications, resumes, and social media. Speedy processing, lower costs, convenient access, and applicant engagement are often and rightfully cited as the practical advantages for using these selection tools. At the same time, however, these tools raise serious concerns about their effectiveness in terms their conceptual relevance to the job; their basis in a job analysis to ensure job relevancy; their measurement characteristics (reliability and stability); their validity in predicting employee-relevant outcomes; their evidence and normative information being updated appropriately; and the associated ethical concerns around what information is being represented to employers and told to job candidates. This paper explores these concerns, concluding with an urgent call to industrial and organizational psychologists to extend existing professional standards for employment testing to these new AI and machine learning based forms of testing, including standards and requirements for their documentation.

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“…Managing data participation and privacy expectations Dekas and McCune (2015) summarised several crucial questions for practitioners interested in using employee data in their data efforts. One of the questions asks if employers can expect employees to accept data collection as a form of research, and thus "assume [that] the research and application of findings are an expected or reasonable part of operating a business/organization" (Dekas and McCune, 2015, p. 564).…”

Section: Ethics Education For Employers and Employeesmentioning

confidence: 99%

Big data: lessons for employers and employees

Jeske

Calvard

2020

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Purpose The purpose of this paper is to critically reflect on the pros and cons of using employee information in big data projects. Design/methodology/approach The authors reviewed papers in the area of big data that has immediate repercussions for the experiences of employees and employers. Findings The review of papers to date suggests that big data lessons based on employee data are still a relatively unknown area of employment literature. Particular attention is paid to discussion of employee rights, ethics, expectations and the implications employer conduct has on employment relationships and prospective benefits of big data analytics at work for work. Originality/value This viewpoint paper highlights the need for more discussion between employees and employers about the collection, use, storage and ownership of data in the workplace. A number of recommendations are put forward to support future data collection efforts in organisations.

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Conducting Ethical Research With Big and Small Data: Key Questions for Practitioners

Cited by 6 publications

References 2 publications

The HR analytics cycle: a seven-step process for building evidence-based and ethical HR analytics capabilities

The HR analytics cycle: a seven-step process for building evidence-based and ethical HR analytics capabilities

Scientific, Legal, and Ethical Concerns about AI-Based Personnel Selection Tools: A Call to Action

Big data: lessons for employers and employees

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