JOURNAL ARTICLE

Malaysian University Students' Understanding and Perceptions of the Gig Economy.

  • Published In: Institutions & Economies, 2025, v. 17, n. 2. P. 121 1 of 3

  • Database: Sociology Source Ultimate 2 of 3

  • Authored By: Yosuke Uchiyama; Omar, Siti Aminah; Fumitaka Furuoka; Nikitina, Larisa; Pazim, Khairul Hanim; Lim, Beatrice 3 of 3

Abstract

Due to the development of digital technology and the diversification of working styles in the wake of the recession and pandemic, the demand for flexible task-based gig work is increasing in Malaysia. However, university students tend to be reluctant to enter the gig economy, preferring conservative full-time jobs. Knowledge of how well the younger generation understands and perceives the gig economy is lacking. Based on this gap, this paper investigates Malaysian students' understanding of and interest in the gig economy. Primary data collected through focus group interviews with five labour economics students were analysed using thematic analysis. The results revealed eight subtopics and three main themes, namely: (1) flexible and competitive open market, (2) recognition as a 'freelance gig', and (3) nature of labour diversification. Among practical implications, this study highlights the need for educators, policymakers and business entities to expand young people's knowledge and understanding of new economic realities, of which the gig economy and gig work are part. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Institutions & Economies. 2025/04, Vol. 17, Issue 2, p121
  • Document Type:Article
  • Subject Area:Technology
  • Publication Date:2025
  • ISSN:2232-1640
  • DOI:10.22452/IJIE.vol17no2.5
  • Accession Number:184591153
  • Copyright Statement:Copyright of Institutions & Economies is the property of University of Malaya, Faculty of Economics & Administration and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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