TY - GEN
T1 - Discovering the influencing factors of physical gig economy usage
T2 - 11th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019
AU - Auditianto, Ari
AU - Sucahyo, Yudho Giri
AU - Gandhi, Arfive
AU - Ruldeviyani, Yova
PY - 2019/10
Y1 - 2019/10
N2 - Physical Gig Economy (PGE) in Indonesia has rapid growth in the last few years. Unfortunately, a large gap among PGE services occurred. Compared with ride-hailing services with highly frequent transactions, cleaning and mechanical services have had few transactions. This study aims to identify and analyze the factors that influence clients to use PGE services. Previous studies about users' intention were synthesized to develop the research model and hypothesis. Factors that are thought to have an influence on client behavior and intention are platform quality, trust, social influence, perceived risk, hedonic motivation, and economic benefits. Furthermore, a quantitative approach with Partial Least Squares Structural Equation Modeling (PLS-SEM) and 318 valid respondents is demonstrated. The results show that hedonic motivation is the most influencing factor followed by economic benefits, trust, and perceived platform quality. This study also informs that social influence only affects client on the early usage of PGE. Having the knowledge of these factors, PGE operators could develop the right strategies to further expand their business and attract new clients.
AB - Physical Gig Economy (PGE) in Indonesia has rapid growth in the last few years. Unfortunately, a large gap among PGE services occurred. Compared with ride-hailing services with highly frequent transactions, cleaning and mechanical services have had few transactions. This study aims to identify and analyze the factors that influence clients to use PGE services. Previous studies about users' intention were synthesized to develop the research model and hypothesis. Factors that are thought to have an influence on client behavior and intention are platform quality, trust, social influence, perceived risk, hedonic motivation, and economic benefits. Furthermore, a quantitative approach with Partial Least Squares Structural Equation Modeling (PLS-SEM) and 318 valid respondents is demonstrated. The results show that hedonic motivation is the most influencing factor followed by economic benefits, trust, and perceived platform quality. This study also informs that social influence only affects client on the early usage of PGE. Having the knowledge of these factors, PGE operators could develop the right strategies to further expand their business and attract new clients.
KW - Behavioral intention
KW - Influencing factors
KW - Physical gig economy
KW - PLS-SEM
UR - http://www.scopus.com/inward/record.url?scp=85081082332&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS47736.2019.8979958
DO - 10.1109/ICACSIS47736.2019.8979958
M3 - Conference contribution
T3 - 2019 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019
SP - 357
EP - 362
BT - 2019 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 12 October 2019 through 13 October 2019
ER -