Do PhD students’ research styles predict their perceived competencies?

Abstract Most empirical studies on PhD students’ competencies lack a conceptual framework, and no study has examined a comprehensive list of competencies necessary for both an academic and a non-academic career. The study pioneered the operationalization of (Shin’s Higher Education Forum, 16 , 27–43, 2019) conceptualization of the competencies that novice academics and knowledge professionals need to possess. Furthermore, the present study investigated how research styles, as a study resource, statistically predicted PhD students’ perceived competencies. Based on data from the 1,105 PhD students in Hong Kong (509 participants randomly assigned to the calibration group and 596 to the cross-validation group), a seven-scale inventory —the PhD Students’ Perceived Competencies Inventory (PSPCI)—was established. This inventory consists of 23 items across the following scales: core competencies (CC), job performance-novice academics (JP-NA), job performance-novice knowledge professionals (JP-NKP), understanding the work environment-novice academics (UWE-NA), understanding the work environment-novice knowledge professionals (UWE-NKP), identity development (ID), and professional habits (PH). Moreover, based on data obtained from the 1,105 PhD students, it was found that the six research styles (legislative, conservative, liberal, executive, hierarchical, and monarchic) measured by the Research Styles Inventory statistically predicted the seven scales in the PSPCI. These findings enrich the literature on PhD education and provide practical implications for students, supervisors, and programs.

Authors

Institutions

Publication Details

Journal
Higher Education
Published
2026-02-06
DOI
https://doi.org/10.1007/s10734-026-01629-x
Primary Topic
Doctoral Education Challenges and Solutions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Do PhD students’ research styles predict their perceived competencies?

Sook Mun Sim, Li-fang Zhang
Higher Education
Doctoral Education Challenges and Solutions
article

Do PhD students’ research styles predict their perceived competencies?

Sook Mun Sim, Li-fang Zhang
article en

Abstract

Abstract Most empirical studies on PhD students’ competencies lack a conceptual framework, and no study has examined a comprehensive list of competencies necessary for both an academic and a non-academic career. The study pioneered the operationalization of (Shin’s Higher Education Forum, 16 , 27–43, 2019) conceptualization of the competencies that novice academics and knowledge professionals need to possess. Furthermore, the present study investigated how research styles, as a study resource, statistically predicted PhD students’ perceived competencies. Based on data from the 1,105 PhD students in Hong Kong (509 participants randomly assigned to the calibration group and 596 to the cross-validation group), a seven-scale inventory —the PhD Students’ Perceived Competencies Inventory (PSPCI)—was established. This inventory consists of 23 items across the following scales: core competencies (CC), job performance-novice academics (JP-NA), job performance-novice knowledge professionals (JP-NKP), understanding the work environment-novice academics (UWE-NA), understanding the work environment-novice knowledge professionals (UWE-NKP), identity development (ID), and professional habits (PH). Moreover, based on data obtained from the 1,105 PhD students, it was found that the six research styles (legislative, conservative, liberal, executive, hierarchical, and monarchic) measured by the Research Styles Inventory statistically predicted the seven scales in the PSPCI. These findings enrich the literature on PhD education and provide practical implications for students, supervisors, and programs.

Higher Education
University of Hong Kong (HK)
Openalex Percentile: Top 68%
Doctoral Education Challenges and Solutions
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.