Exploring Intrinsic Motivation and Satisfaction Using Self-Determination Theory A Study of Gig Workers on Knowledge-Based Service Platforms in China

Main Article Content

Liqian Yang
Chanchai Bunchapattanasakda

Abstract

Many scholars have discussed the gig economy and gig workers since 2015–2016. Knowledge-based service platforms are growing rapidly with the development of technology. In China, over the past seven years, there have been 1,021 financial investments totaling 131.3 billion Yuan in knowledge-based service platforms. Limited research has been undertaken on gig workers’ intrinsic motivation and satisfaction with such platforms. The present research study was based on self-determination theory to study gig workers’ intrinsic motivation and satisfaction on the six biggest knowledge-based service platforms in China. It fills a research gap in the study of gig workers’ motivation. A self-administrated questionnaire was distributed online to respondents to collect data. Finally, 1,049 valid responses were obtained. The research compared gig workers with and without full-time jobs. The Partial Least Squared method was applied to analyze and generate the results. It was found that self-determination and social capital positively influenced intrinsic motivation, and intrinsic motivation positively influenced gig workers’ satisfaction on knowledge-based service platforms. Also, older gig workers in full-time jobs showed less work satisfaction than the younger group. Some practical suggestions were made to platforms as well as to gig workers.

Article Details

Section
Research Articles

References

Altindis, S. (2011). Job motivation and organizational commitment among the health professionals: A questionnaire survey. African Journal of Business Management, 5(21), 8601–8609.

Ashford, S. J., Caza, B. B., & Reid, E. M. (2018). From surviving to thriving in the gig economy: A research agenda for individuals in the new world of work. Research in Organizational Behavior, 38, 23–41. https://doi.org/10.1016/j.riob.2018.11.001

Cammann, C., Fichman, M., Jenkins, D., & Klesh, J. (1979). The Michigan organizational assessment questionnaire. [Unpublished manuscript]. University of Michigan, Ann Arbor. https://doi.org/10.1037/ t01581-000

Chang, H., & Chuang, S. (2011). Social capital and individual motivations on knowledge sharing: Participant involvement as a moderator. Information & Management, 48(1), 9–18. https://doi.org/10.1016/j.im. 2010.11.001

Chen, Y. (2022). Want to transition online? How to choose a platform: Which is the best mainstream online education platform in China? https://zhuanlan.zhihu.com/p/38918897

Chow, W. S., & Chan, L. S. (2008). Social network, social trust and shared goals in organizational knowledge sharing. Information & Management, 45, 458–465. https://doi.org/10.1016/j.im.2008.06.007

D’Agostino, R. B. (2017). Tests for the normal distribution. In R. B. D'Agostino (Ed.), Goodness-of-fit techniques (pp. 367–420). Routledge.

Davidson, A., Habibi, M. R., & Laroche, M. (2018). Materialism and the sharing economy: A cross-cultural study of American and Indian consumers. Journal of Business Research, 82, 364–372. https://doi.org/10.1016/j.jbusres.2015.07.045

Deci, E. L., Olafsen, A. H., & Ryan, R. M. (2017). Self-determination theory in work organizations: The state of a science. Annual Review of Organizational Psychology and Organizational Behavior, 4, 19–43. https://doi.org/10.1146/ annurev-orgpsych-032516-113108

Deci, E. L., & Ryan, R. M. (2000). The" what" and" why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/ S15327965PLI1104_01

Dijkstra, T. K., & Henseler, J. (2015). Consistent and asymptotically normal PLS estimators for linear structural equations. Computational Statistics & Data Analysis, 81, 10–23. https://www.sciencedirect.com/ science/article/pii/S0167947314002126

Donovan, S. A., Bradley, D. H., & Shimabukuru, J. O. (2016, February 5). What does the gig economy mean for workers? Congressional Research Service (Report pp. 1–16). https://sgp.fas.org/crs/misc/R44365.pdf

Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app‐work in the gig economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114–132. https://doi.org/10.1111/1748-8583.12258

Gagné, M., & Deci, E.L. (2005). Self‐determination theory and work motivation. Journal of Organizational Behavior, 26(4), 331–362. https://doi.org/10.1002/job.322

Gagné, M., Deci, E. L., & Ryan, R. M. (2018). Self-determination theory applied to work motivation and organizational behavior. In D. S. Ones, N. Anderson, C. Viswesvaran, & H. K. Sinangil (Eds.), The SAGE handbook of industrial, work & organizational psychology: Organizational psychology (pp. 97–121). Sage Reference.

Hair, J. F., Money, A. H., Samouel, P., & Page, M. (2007). Research methods for business. Education + Training, 49(4), 336–337. https://doi.org/10.1108/et.2007.49.4.336.2

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long range planning: International Journal of Strategic Management, 46(2), 1–12.

Haivas, S., Hofmans, J., & Pepermans, R. (2012). Self-determination theory as a framework for exploring the impact of the organizational context on volunteer motivation. Nonprofit and Voluntary Sector Quarterly, 41(6), 1195–1214. https://doi.org/10.1177/0899764011433041

Hau, Y. S., Kim, B., Lee, H., & Kim, Y. G. (2013). The effects of individual motivations and social capital on employees’ tacit and explicit knowledge sharing intentions. International Journal of Information Management, 33(2), 356–366. https://doi.org/10.1016/j.ijinfomgt.2012.10.009

Hyman, L., Lamb, J., & Bulmer, M. (2006, April 24–26 ). The use of pre-existing survey questions: Implications for data quality [Paper presentation]. European Conference on Quality in Survey Statistics, Cardiff, United Kingdom. https://ec.europa.eu/eurostat/documents/64157/4374310/22-Use-of-pre-existing-survey-questions-implications-for-data-quality-2006.pdf/e953a39e-50be-40b3-910f-6c0d83f55ed4

Jabagi, N., Croteau, A.-M., Audebrand, L. K., & Marsan, J. (2019). Gig-workers’ motivation: thinking beyond carrots and sticks. Journal of Managerial Psychology, 34(4), 192–213. https://doi.org/10.1108/jmp-06-2018-0255

Lao, M. (2017). Workers in the gig economy: The case for extending the antitrust labor exemption. UCDL Review, 51(2), 1543–1587. https://lawreview.law.ucdavis.edu/issues/51/4/Articles/51-4_Lao.pdf

Litze, H., & Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.

Marquis, E. B., Kim, S., Alahmad, R., Pierce, C. S., & Robert Jr, L. P. (2018). Impacts of perceived behavior control and emotional labor on gig workers. In V. Evers, M. Naaman, G. Fitzpatrick, K. Karahalios, A. Lampinen, & A. Monroy-Hernandez (Eds.), CSCW '18: Computer supported cooperative work and social computing (pp. 241–244). Association for Computing Machinery. https://doi.org/10.1145/3272973. 3274065

Montgomery, T., & Baglioni, S. (2020). Defining the gig economy: Platform capitalism and the reinvention of precarious work. International Journal of Sociology and Social Policy, 9(10), 1012–1025. https://doi.org/10.1108/ijssp-08-2020-0400

Petriglieri, G., Ashford, S. J., & Wrzesniewski, A. (2019). Agony and ecstasy in the gig economy: Cultivating holding environments for precarious and personalized work identities. Administrative Science Quarterly, 64(1), 124–170. https://doi.org/10.1177/0001839218759646

Ramayah, T., Yeap, J. A., Ahmad, N. H., Halim, H. A., & Rahman, S. A. (2017). Testing a confirmatory model of Facebook usage in SmartPLS using consistent PLS. International Journal of Business and Innovation, 3(2), 1–14.

Rockmann, K., & Ballinger, G. (2017). Intrinsic motivation and organizational identification among on-demand workers. Journal of Applied Psychology, 102(9), 1305–1328. https://doi.org/10.1037/ apl0000224

Rožman, M., Grinkevich, A., & Tominc, P. (2019). Occupational stress, symptoms of burnout in the workplace and work satisfaction of the age-diverse employees. Organizacija, 52(1), 46–52. https://doi.org/ 10.2478/orga-2019-0005

Spanuth, T., & Wald, A. (2017). Understanding the antecedents of organizational commitment in the context of temporary organizations: An empirical study. Scandinavian Journal of Management, 33(3), 129–138. https://doi.org/10.1016/j.scaman.2017.06.002

Weerakoon, C., McMurray, A. J., Rametse, N. M., & Arenius, P. M. (2019). Social capital and innovativeness of social enterprises: Opportunity-motivation-ability and knowledge creation as mediators. Knowledge Management Research & Practice, 18(2), 147–161. https://doi:10.1080/14778238.2019.1590138

Wenhao, Y. (2020, October 10). CNNIC's latest report: My country's online education users reach 381 million. https://www.edu.cn/xxh/zyyyy/zxjy/202010/t20201010_2020613.shtml

Yamaguchi, I. (2013). A Japan–US cross-cultural study of relationships among team autonomy, organizational social capital, job satisfaction, and organizational commitment. International Journal of Intercultural Relations, 37(1), 58–71. https://doi.org/10.1016/j.ijintrel.2012.04.016

Zaman, U., Nawaz, S., Javed, A., & Rasul, T. (2020). Having a whale of a time: Linking self-determination theory (SDT), job characteristics model (JCM) and motivation to the joy of gig work. Cogent Business & Management, 7(1), 180–197. https://doi.org/10.1080/23311975.2020.1807707

Zhang, X., Liu, S, Chen, X., & Gong, Y. (2017). Social capital, motivations, and knowledge sharing intention in health Q&A communities. Management Decision, 55(7), 1536–1557. https://doi.org/10.1108/MD-10-2016-0739

Zikmund, W. G., Babin, B. J., Carr, J. C., & Griffin, M. (2013). Business research methods (3rd ed.). Cengage Learning.