Using online data and network-based text analysis in HRM research. Issue 1 (28th September 2017)

• Record Type: Journal Article
• Authors: Platanou, Kalliopi; Mäkelä, Kristiina; Beletskiy, Anton; Colicev, Anatoli
• Is Part Of: Journal of organizational effectiveness. Volume 5:Issue 1(2018)
• Page Start: 81
• Page End: 97
• Publication Date: 2017-09-28
• Subjects: Network text analysis -- Online data -- HRM research; Organizational effectiveness -- Periodicals; Personnel management -- Periodicals; 658.314
• DOI: 10.1108/JOEPP-01-2017-0007
• Languages: English
• ISSNs: 2051-6614

Abstract

Abstract : Purpose: The purpose of this paper is to propose new directions for human resource management (HRM) research by drawing attention to online data as a complementary data source to traditional quantitative and qualitative data, and introducing network text analysis as a method for large quantities of textual material. Design/methodology/approach: The paper first presents the added value and potential challenges of utilising online data in HRM research, and then proposes a four-step process for analysing online data with network text analysis. Findings: Online data represent a naturally occuring source of real-time behavioural data that do not suffer from researcher intervention or hindsight bias. The authors argue that as such, this type of data provides a promising yet currently largely untapped empirical context for HRM research that is particularly suited for examining discourses and behavioural and social patterns over time. Practical implications: While online data hold promise for many novel research questions, it is less appropriate for research questions that seek to establish causality between variables. When using online data, particular attention must be paid to ethical considerations, as well as the validity and representativeness of the sample. Originality/value: The authors introduce online data and network text analysis as a new avenue for HRM research, with potential to address novel research questions at micro-, meso- and macro-levels of analysis.