Enablers and barriers to using HR analytics in employee onboarding and competence development
Guskova, Margarita (2026)
2026
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-202604126187
https://urn.fi/URN:NBN:fi:amk-202604126187
Tiivistelmä
This thesis explored how human resource (HR) analytics can enhance personalized onboarding and competence development while balancing data-driven approaches with a human-centred focus. The study aimed to identify factors that enable or constrain HR analytics adoption and to develop a framework linking technical, organizational, and individual conditions. Using semi-structured interviews with HR professionals and thematic analysis, the research examined patterns related to system usability, social influence, analytical skills, and organizational readiness.
The findings show that although HR analytics is widely valued, its adoption is limited by fragmented systems, insufficient analytical capabilities, and a lack of leadership support. Motivation alone proved insufficient without clear processes, appropriate tools, and allocated resources. The results emphasize that effective, data-driven onboarding requires alignment between technology, organizational culture, and leadership commitment, as well as professionals’ ability, motivation, and opportunity to use analytics in practice.
To concretize these findings, a small Python prototype was developed to demonstrate how basic analytics can support onboarding personalization. The prototype illustrates that even simple personalization depends on structured data, data quality, accessible tools, and a foundational level of analytics literacy, thereby reinforcing the framework derived from the empirical results.
The findings show that although HR analytics is widely valued, its adoption is limited by fragmented systems, insufficient analytical capabilities, and a lack of leadership support. Motivation alone proved insufficient without clear processes, appropriate tools, and allocated resources. The results emphasize that effective, data-driven onboarding requires alignment between technology, organizational culture, and leadership commitment, as well as professionals’ ability, motivation, and opportunity to use analytics in practice.
To concretize these findings, a small Python prototype was developed to demonstrate how basic analytics can support onboarding personalization. The prototype illustrates that even simple personalization depends on structured data, data quality, accessible tools, and a foundational level of analytics literacy, thereby reinforcing the framework derived from the empirical results.