Innovative data analytic methods in human resource development: Recommendations for research design

• Date: 01 December 2018
• Author: Seung Won Yoon
• DOI: http://doi.org/10.1002/hrdq.21331
• Published date: 01 December 2018

EDITORIAL

Innovative data analytic methods in human

resource development: Recommendations for

research design

This editorial has two specific goals: (a) to provide helpful recommendations for authors submitting research manu-

scripts using innovative data analytic methods and (b) to invite greater interest and investment in data analytic

approaches in human resource development (HRD) research. Over the years, the HRDQ editorial team has made

intentional and strategic efforts to improve the quality, rigor, and relevance of research methods. Particularly helpful

are three recent editorials that include recommendations and quality criteria for qualitative, quantitative, and mixed-

methods research (Anderson, 2017; Nimon, 2017; Reio Jr & Werner, 2017). However, these were not the first rec-

ommendations; additional guidelines from earlier years can be found within the references section of these three edi-

torials. Notably, HRDQ created an additional submission category, methods paper, as another channel for studies

focusing on research methods (Nimon, 2016). To date, studies on mediated discourse analysis (Jones, Gold, & Clax-

ton, 2017) and design considerations for multiorganizational research (Saunders, Gray, & Bristow, 2017) have been

published as methods paper. An article in the current issue of HRDQ uses latent semantic analysis (LSA) in the con-

text of organizational survey (Arnulf, Dysvik, & Larsen, 2018). We would like to see the methods paper category

become a space for authors to share their innovative work.

Before discussing the recommendations for manuscript authors, I first clarify the relevance and importance of

innovative data analytic methods. Artificial intelligence (AI), also called machine learning, has become commonplace

in the workplace. Through recommendation systems, dashboards, wearable technologies, smartphone applications,

and even responsive robots, data-driven technologies are changing how people acquire information and interact with

others. Data-driven innovation and value creation are high priorities for strategic leaders. We also hear that forward-

thinking organizations are innovating by applying data analytics to various human resource (HR) practices, including

learning, hiring, and managing talent. Although interest in data analytics in the HR community is growing, research

seems to lag behind.

Data analytics refers to the examination of a large volume or fast-changing set of data that can be either struc-

tured (e.g., multiple spreadsheets and linked tables) or unstructured (e.g., text mining and click-streams), to extract

useful information so that stakeholders can make informed decisions (Chang, Kauffman, & Kwon, 2014). It relies

heavily on software and specialized programs. For this editorial, I include machine learning, HR analytics or people

analytics, and social or organizational network analysis as current HR-critical data analytic approaches. Although

these approaches originated from other disciplines, particularly mathematical computing and sociology, their applica-

tions in HRD are growing and will become more influential in the future. There are other terms that can also be

included in the list, such as computational social science (Alvarez, 2016) and cognitive architecture modeling (Sun,

Zhang, & Mathews, 2006), but capturing all data analytic approaches for these rapidly evolving areas is not the goal

of this editorial. Each approach illustrates an innovative use of data and advanced technologies to improve the effec-

tiveness of decision-making, and to find new insights from data or supplement shortcomings of opinion-driven

approaches. Although it may not be apparent, these data analytic approaches are conceptually and technically closely

related.

DOI: 10.1002/hrdq.21331

Human Resource Development Quarterly. 2018;29:299–306. wileyonlinelibrary.com/journal/hrdq © 2018 Wiley Periodicals, Inc. 299