Cited by 78Corporate Management and LeadershipAI and HR TechnologiesHuman Resource Development and Performance Evaluation

Indispensable skills for human employees in the age of robots and AI

Chuang, Szufang·2024·European Journal of Training and Development

Abstract

Purpose: Based on sociotechnical systems theory, social (human) and technological sub-systems in an organization should be taken in account when making strategic decisions and designed to fit the demands of the environment for organizational effectiveness. Yet there is very limited information in literature on whether employees are well equipped with indispensable (human) skills to prepare them combating challenges caused by advanced technology. The purpose of this study is to empirically investigate employees’ human skills that are critical for success in the Age of Robots and Artificial Intelligence from human resource development’s perspective. Design/methodology/approach: A questionnaire was developed for the purpose of this exploratory study. A total of 422 US Midwest employees were surveyed on their human skills level that are critical for success in the Industry 4.0 transformation. Findings: In general, the respondents could perform all the measured human skills (which can be categorized into social skillset and decision-making skillset) more than adequate but may vary by education level and gender. To strengthen one’s human skills, organizations may begin with facilitating employees on relationship building to create a support system and a strong sense of belonging, which will promote their social sensitivity and collaboration skill development, as well as decision-making skillset. Originality/value: The findings of this study can be used for techno-structural interventions and employee development programs. This study highlights the importance of investigating human skills to cope with the changing nature of work and make upskilling more feasible and flexible for workers to be robot-proof.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1108/ejtd-06-2022-0062

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Chuang, S.
Affiliations
Indiana State University (US)
Corresponding author
Chuang, Szufang
Venue
European Journal of Training and Development
Publisher
Emerald
Published
2022-11-14
Volume
48
Issue
1/2
Pages
179-195
ISSN
2046-9012 (ISSN-L)
OpenAlex
W4308917458
Cited by
78
References
2
Citations by year
2026: 22 · 2025: 32 · 2024: 20 · 2023: 4
FWCI
13.23
Type
journal article
Language
en
Open access
closed
Locations
European journal of training and development (publishedVersion)
Topics
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Corporate Management and Leadership
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
  • Social Sciences › Psychology › Applied Psychology › Human Resource Development and Performance Evaluation
Keywords
soft skills, Industry 4.0, socio-technical systems theory, human resource development, social skills, decision-making skills, upskilling, employee development programs, gender differences, educational attainment, relationship building, organizational effectiveness

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review