Foodborne illness outbreaks in restaurants and patrons’ propensity to return

Abstract

Purpose This study aims to investigate the decision-making process consumers engage in when choosing to return to a restaurant that has experienced a foodborne illness outbreak. Design/methodology/approach A scenario-based survey was conducted to collect data from 1,025 respondents on their propensity to return to restaurants that have been cited for serving foods that caused a foodborne illness outbreak. Partial least squares-based structural equation modeling was used to analyze the data. Findings The findings of this study show perceived vulnerability, perceived severity and attitude are statistically significant, whereas subjective norms and perceived behavioral control are not statistically significant predictors of consumers’ intention to patronize a restaurant that has experienced a foodborne illness outbreak. Research limitations/implications This study examined consumers’ intentions to return to a restaurant that has experienced a foodborne illness outbreak. Findings are based on a scenario-based survey, and thus, the results cannot be generalized. Originality/value By implementing protection motivation theory (PMT) and the theory of planned behavior (TPB), findings from this study extend the understanding of patrons’ revisit intentions regarding restaurants with foodborne illness outbreak.

Understanding the Eco-Friendly Role of Drone Food Delivery Services: Deepening the Theory of Planned Behavior

“…The composite reliability was examined to confirm consistency among the measurement items for seven constructs (see Table 2). The values of composite reliability for the seven constructs were over 0.60, which confirmed that all measurement items are highly consistent [46]. Furthermore, all average variance extracted (AVE) values exceeded 0.50, which indicated that there was no problem with the convergent validity [47].…”

International Tourists’ Protection Intentions to Use Food Hygiene Cues in the Choice of Destination Local Restaurants

“…The second part was designed to address the protection intentions of international tourists to use food hygiene indicators in their choice of local restaurant using a seven-point Likert scale, where 1= strongly disagree and 7= strongly agree. Fifteen food hygiene indicators were adopted and adapted from the previous literature (Aksoydan, 2007;Ali et al, 2019;Bai et al, 2019;Barber & Scarcelli, 2009;Barber et al, 2011;Choi et al, 2019;Harris et al, 2018;Henson et al, 2006;Fleetwood, 2019;Park et al, 2016;Yeung &Yee, 2013). The study instrument was given to a panel of experts in the fields of food safety and hospitality management to evaluate the face and content validity of the food hygiene measures.…”

“…Previous studies also supported different sources of information used by consumers to assess the safety of food and resulting impacts on restaurant selection. These are restaurant inspection notices, press media, and broadcasting, friends and relatives' recommendations, and internet-based information (Ali et al, 2019;Bai et al, 2019;Choi et al, 2013;Choi et al, 2019;Harris et al, 2018;Seo et al, 2015;Uggioni & Salay, 2014;Wong et al, 2015;Yannopoulou et al, 2011).…”

“…Word of mouth (WOM) also has a strong effect on consumer evaluation of food safety in a particular restaurant and future decision-making. This now includes both the traditional form of WOM made by a small group of friends and family (Ali et al, 2019;Bai et al, 2019;Harris et al, 2018) and the online word of mouth (eWOM) generated by social media networks (Seo et al, 2015;Zhang et al, 2017). Most specifically in the context of travel, Yeung and Yee's study (2013) indicated that international tourists frequently seek tour guide advice in the choice of destination restaurants to reduce food safety risks.…”

How to Meet the Diverse Needs of Consumers: Big Data Mining based on Online Review

“…The system is used by the New York City Department of Health and Mental Hygiene (DOHMH) to monitor Yelp for foodborne illness complaints [41,42,43]. It helped food safety regulators improve regulatory efficiency [44].…”