Understanding and predicting customers’ intentions to use smartphone-based online games: a deep-learning-based dual-stage modelling analysis

Muhammad Sabbir Rahman*, Surajit Bag, Farhana Habib Zinnia, Nripendra P. Rana, Mohammad Osman Gani

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Building upon flow theory, the present research empirically investigates the impact of customers' attitudes on their intention to use smartphone-based online gaming. It also examines the mediating effects of customers’ perceived flow and engagement between attitudes and customer's intention to use smartphone-based online gaming. Furthermore, customers' cognitive involvement was also examined as a moderator in between attitude and perceived flow. The data were analysed using a dual-stage hybrid method embedded with PLS-SEM and ANN. A sample of 688 smartphone-based online game players was surveyed, and the results support the notion that customers' attitudes, considered as a formative construct, significantly influence intention to use smartphone-based online gaming. The study further confirms that perceived flow and customer engagement mediate the relationship between attitudes and intention to use smartphone-based online gaming. The study also identifies the role of cognitive involvement as a significant moderator in between customers' attitudes and perceived flow. Contributions for both theory and practice are presented in the concluding sections.

Original languageEnglish
Article number108083
Number of pages15
JournalComputers in human behavior
Volume152
DOIs
Publication statusPublished - Mar 2024
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Artificial neural network
  • Attitude
  • Customer intention
  • Online gaming
  • Perceived flow
  • PLS-SEM
  • Smartphone

ASJC Scopus subject areas

  • Arts and Humanities (miscellaneous)
  • Human-Computer Interaction
  • General Psychology

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