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Consumers’ purchase intention and decision-making process through social networking sites: a social commerce construct

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  • Muhammad Usman Riaz
  • Luo Xiao Guang
  • Maria Zafar
  • Fakhar Shahzad
  • Muhammad Shahbaz
  • Majid Lateef

Abstract

The growing popularity of social commerce may transform the purchase behaviour of consumers. It is the need of time to investigate the factors that impact the consumers’ purchase intention in the social commerce environment, especially in a developing country like Pakistan. The study is a drive to investigate the factors influencing the purchase intentions of consumers in social commerce. By employing social learning theory, this study proposed a theoretical model to explore the factors affecting consumers’ purchase intention and decision making. A structured questionnaire-based survey was conducted for data collection, and 232 valid responses were analysed using structural equation modelling (SEM) to validate the proposed research model. The results of the study concluded that social commerce constructs in the form of learning from forums and communities, learning from ratings and reviews, and learning from social advertisements were significant predictors of social support constructs. Furthermore, social support constructs such as emotional and informational support significantly contribute to predicting consumer’s purchase intentions in social networking sites. In addition, this study revealed that special focus was needed to build social commerce constructs and social support by the managers of social commerce sites to attain consumers’ purchase intention.

Suggested Citation

  • Muhammad Usman Riaz & Luo Xiao Guang & Maria Zafar & Fakhar Shahzad & Muhammad Shahbaz & Majid Lateef, 2021. "Consumers’ purchase intention and decision-making process through social networking sites: a social commerce construct," Behaviour and Information Technology, Taylor & Francis Journals, vol. 40(1), pages 99-115, January.
  • Handle: RePEc:taf:tbitxx:v:40:y:2021:i:1:p:99-115
    DOI: 10.1080/0144929X.2020.1846790
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    Cited by:

    1. Zaghloul, Maha & Barakat, Sherif & Rezk, Amira, 2024. "Predicting E-commerce customer satisfaction: Traditional machine learning vs. deep learning approaches," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    2. Sun, Jianmin & Sarfraz, Muddassar & Ivascu, Larisa & Han, Heesup & Ozturk, Ilknur, 2024. "Live streaming and livelihoods: Decoding the creator Economy's influence on consumer attitude and digital behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 78(C).

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