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Predicting intention to follow online restaurant community advice: a trust-integrated technology acceptance model

Predicting intention to follow online restaurant community advice: a trust-integrated technology... This research investigates consumer intention to follow online community advice. Applying the technology acceptance model (TAM) to the context of online restaurant communities, the study empirically examines the effects of perceived usefulness, perceived ease of use, attitude and trust on the intention to follow online advice.Design/methodology/approachThe data were collected from 360 members of online restaurant communities on Facebook and analyzed using structural equation modeling (SEM).FindingsThe findings revealed that trust, perceived usefulness and attitude are key predictors of the intention to follow online restaurant community advice.Originality/valueExtant research on the influence of online reviews on consumer behavior in the restaurant industry has largely focused on the characteristics of the review, reviewers or readers. Moreover, other studies have investigated consumers' motivations to write online restaurant reviews. This study, however, takes a different approach and examines what drives consumers to follow the advice from online restaurant communities. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Journal of Management and Business Economics Emerald Publishing

Predicting intention to follow online restaurant community advice: a trust-integrated technology acceptance model

Predicting intention to follow online restaurant community advice: a trust-integrated technology acceptance model

European Journal of Management and Business Economics , Volume 32 (2): 18 – May 12, 2023

Abstract

This research investigates consumer intention to follow online community advice. Applying the technology acceptance model (TAM) to the context of online restaurant communities, the study empirically examines the effects of perceived usefulness, perceived ease of use, attitude and trust on the intention to follow online advice.Design/methodology/approachThe data were collected from 360 members of online restaurant communities on Facebook and analyzed using structural equation modeling (SEM).FindingsThe findings revealed that trust, perceived usefulness and attitude are key predictors of the intention to follow online restaurant community advice.Originality/valueExtant research on the influence of online reviews on consumer behavior in the restaurant industry has largely focused on the characteristics of the review, reviewers or readers. Moreover, other studies have investigated consumers' motivations to write online restaurant reviews. This study, however, takes a different approach and examines what drives consumers to follow the advice from online restaurant communities.

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References (78)

Publisher
Emerald Publishing
Copyright
© Aya K. Shaker, Rasha H.A. Mostafa and Reham I. Elseidi
ISSN
2444-8451
DOI
10.1108/ejmbe-01-2021-0036
Publisher site
See Article on Publisher Site

Abstract

This research investigates consumer intention to follow online community advice. Applying the technology acceptance model (TAM) to the context of online restaurant communities, the study empirically examines the effects of perceived usefulness, perceived ease of use, attitude and trust on the intention to follow online advice.Design/methodology/approachThe data were collected from 360 members of online restaurant communities on Facebook and analyzed using structural equation modeling (SEM).FindingsThe findings revealed that trust, perceived usefulness and attitude are key predictors of the intention to follow online restaurant community advice.Originality/valueExtant research on the influence of online reviews on consumer behavior in the restaurant industry has largely focused on the characteristics of the review, reviewers or readers. Moreover, other studies have investigated consumers' motivations to write online restaurant reviews. This study, however, takes a different approach and examines what drives consumers to follow the advice from online restaurant communities.

Journal

European Journal of Management and Business EconomicsEmerald Publishing

Published: May 12, 2023

Keywords: Online reviews; Restaurants; Trust; Online communities; Technology acceptance model (TAM)

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