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Article
Publication date: 11 June 2024

Hao-Fan Chumg, Sheng-Pao Shih, I-Hua Hung, Wen-Chin Tsao and Jui-Lung Chen

This research explores the complex interplay of multiple social factors with regard to what might encourage or inhibit users to interact with social commerce (SC).

Abstract

Purpose

This research explores the complex interplay of multiple social factors with regard to what might encourage or inhibit users to interact with social commerce (SC).

Design/methodology/approach

To investigate the phenomenon, we developed a model based on goal-directed behaviour and pluralistic ignorance theory (typically generated by universal behavioural adherence to social norms). Based on the 394 valid responses collected from a survey, partial least squares structural equation modelling (PLS-SEM), PROCESS and ANOVA were employed to examine the research hypotheses.

Findings

The results show that pluralistic ignorance and commercial desire positively influence SC intention. More importantly, our results show that the moderating effect of pluralistic ignorance dampens the positive relationship between social subjective norms and commercial desire. The findings also suggest that pluralistic ignorance mediates the relationships between: (1) social identity and SC intentions and (2) fear of isolation and SC intentions.

Originality/value

Consequently, this study reveals that SC intentions result from complex interactions between an individual’s psychology and social phenomena. Theoretical and managerial implications are also discussed to provide for the successful development of strategies regarding SC for researchers and SNSs operators.

Article
Publication date: 16 October 2018

Ya-Han Hu, Wen-Ming Shiau, Sheng-Pao Shih and Cho-Ju Chen

The purpose of this paper is to combine basic movie information factors, external factors and review factors, to predict box-office performance and identify the most crucial…

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Abstract

Purpose

The purpose of this paper is to combine basic movie information factors, external factors and review factors, to predict box-office performance and identify the most crucial factor of influence for box-office performance.

Design/methodology/approach

Five movie genres and first-week movie reviews found on IMDb were collected. The movie reviews were quantified using sentiment analysis tools SentiStrength and Stanford CoreNLP, in which quantified data were combined with basic movie information and external environment factors to predict movie box-office performance. A movie box-office performance prediction model was then developed using data mining (DM) technologies with M5 model trees (M5P), linear regression (LR) and support vector regression (SVR), after which movie box-office performance predictions were made.

Findings

The results of this paper showed that the inclusion of movie reviews generated more accurate prediction results. Concerning movie review-related factors, the one that exhibited the greatest effect on box-office performance was the number of movie reviews made, whereas movie review content only displayed an effect on box-office performance for specific movie genres.

Research limitations/implications

Because this paper collected movie data from the IMDb, the data were limited and primarily consisted of movies released in the USA; data pertaining to less popular movies or those released outside of the USA were, thus, insufficient.

Practical implications

This paper helps to verify whether the consideration of the features extracted from movie reviews can improve the performance of movie box-office.

Originality/value

Through various DM technologies, this paper shows that movie reviews enhanced the accuracy of box-office performance predictions and the content of movie reviews has an effect on box-office performance.

Details

The Electronic Library, vol. 36 no. 6
Type: Research Article
ISSN: 0264-0473

Keywords

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