The Impact of Fake Online Restaurant Reviews on Customers and the Fast-Food Industry in Egypt

The power of online reviews

3.1

Online reviews allow customers to quickly assess products or services by reading others’ opinions and applying heuristics to help them make decisions (Lee and Hong, 2019; Zhang et al., 2014). According to a Paget (2023, p. 6) report, ‘Nearly 95% of respondents read consumer reviews before shopping’. This report emphasizes the importance of negative reviews in decision-making by showing that most (82%) consumers actively read reviews before making purchases. Customers must assess the reliability of reviews containing experiences that may or may not have occurred before making a purchase. They may also use these reviews to form opinions about a company. Platforms like Yelp, Booking, and Google allow users to provide ratings and written reviews of their restaurant dining experiences. Such customer reviews are a great way for a company to find out what areas of its operations need improvement. However, companies frequently are unable to edit or verify reviews on platforms, so unfairly negative or inaccurate reviews are posted for everyone to see. Therefore, information can influence consumer purchase decisions, regardless of its quality or truth.

Fake online reviews

3.2

Kim and Park (2017) observed that, in the hospitality industry, social media reviews are a more significant indicator of hotel performance than traditional customer satisfaction evaluations. Furthermore, Wen et al. (2021) suggest that hotels and restaurants should take advantage of data mining in their review analyses, despite the widespread wariness of fake online reviews. The term ‘fake online reviews’ refers to inauthentic reviews that are published on various platforms by either genuine consumers or fictitious players (Wu et al., 2020; Hu et al., 2011). According to the report Fake Online Reviews 2021, fake reviews are defined as ‘any positive, neutral, or negative review that is not an actual consumer’s honest and impartial opinion and does not reflect a genuine experience of a product, service or business’ (Cavazos, 2021). The purpose of deceptive reviews is to mislead consumers (Zhang et al., 2016). Approximately 15–30% of reviews are fake, despite the anti-fraud efforts of review platforms (Belton, 2015; Luca & Zervas, 2016; Cao, 2023).

Understanding how consumers read, analyse, and are persuaded by reviews to make purchasing decisions is crucial for determining the authenticity of online reviews. Creating a false perception of a product or service can influence shoppers’ purchasing decisions both positively and negatively (Malbon, 2013). Reviews come from various sources, including customers, business owners, competitors, and even paid reviewers known as ‘shills’ (Ong et al., 2014). Additionally, roughly 5% of reviews on a given major retailer’s website were written by individuals who have never purchased the product they are supposedly for (Anderson & Simester, 2014). Fake reviews are particularly harmful in the restaurant industry, where consumer feedback is crucial (Larson & Denton, 2014; Wang et al., 2023). Fake social media identities can also exacerbate the issue, misleading consumers into making decisions based on inaccurate information (Romanov et al., 2017).

Factors that indicate a fake review

3.3

Fake online reviews can affect individuals who typically trust restaurants and consider them trustworthy. Such reviews may influence their dining choices and shape their overall experiences. The interaction between user perceptions and the authenticity of reviews highlights the complex nature of online reviews in the restaurant industry. According to Berry (2024), three primary factors help identify fake reviews: review authenticity, trust in content sources, and review tone. The study’s first hypothesis posits that the elements of fake online restaurant reviews posted by online reviewers have a positive influence on customers’ beliefs and behaviours.

H1: Fake online restaurant reviews positively influence customers’ beliefs and behaviours. To test this hypothesis, three indicators of fake reviews have been identified, accompanied by the following sub-hypotheses:

Fake online restaurant reviews and customer trust

3.4

Fake reviews can significantly erode customer trust in a product or service. When consumers realize that reviews might be false, they become wary of all feedback, making it challenging for them to make informed decisions (Cao, 2023). This loss of trust can result in reduced sales and damaged reputations for businesses found to be using or allowing fake reviews. Furthermore, once customers distrust the reviews, they may seek out competitors with more open and genuine review systems. In the end, the existence of fake reviews can significantly impact a company’s long-term credibility and success. As a result of the above-mentioned literature review, the first hypothesis was developed as follows:

H₂: There is a positive correlation between beliefs in fake online restaurant reviews and customer trust, as tested by analysing customer perceptions and trust levels in fake reviews.

Fake online restaurant reviews and brand reputation

3.5

Some users post fake reviews to intentionally boost or damage the reputation of a product or business. These reviews may contain false information and deceptive comments aiming to mislead potential customers. Such reviews are known as spam reviews, opinion spam, or bogus reviews, as referred to by Mohamed Sadom, et al. (2023). Some of these reviews are irrelevant, confusing customers about the actual products. In their research on ethnic restaurants, Ali et al. (2018) found that factors such as brand quality and image, customer satisfaction, trust, and loyalty significantly influence customer purchase intention. Meanwhile, Thomas et al. (2019) suggested that the authenticity of online reviews acts as a mediator for customer purchase intentions, alongside other factors such as the quality and quantity of online reviews, as well as related cues like the expertise of the reviewer; product or service ratings; and the reputation of the platform.

The example of United Airlines shows how public perception and customer feedback can greatly impact a brand’s image. The airline’s reputation suffered after a video of a passenger being forcibly removed from a flight was widely circulated, leading to a wave of negative reviews. Based on the literature review above, the following second hypothesis was developed:

H3: There is a positive correlation between customer trust and restaurant brand reputation.

This hypothesis aims to examine whether a strong correlation exists between the level of trust customers place in online reviews and their attitudes toward brands.

Purchase intent

3.6

Customers often consider the experiences shared by others online when making purchasing decisions. The rise of e-commerce and web improvements has increased consumer reliance on online reviews, which can significantly influence their trust, perceived usefulness, perceived risk, and perceived value of products or services (Wang et al., 2017; Lee & Hong, 2019; Ventre & Kolbe, 2020; Singh & Sarangal, 2021). Positive reviews can improve a product’s image and motivate consumers to make a purchase (Lee & Hong, 2019). Retailers and platforms themselves often control online reviews to increase sales volume and sometimes use fake reviews to protect a product’s reputation (Jensen & Yetgin, 2017). Advice and recommendations shared on social media platforms can also build customer trust and influence purchase decisions (Sulthana & Vasantha, 2021). Therefore, marketers should pay more attention to online reviews, since they can significantly influence potential customers’ purchasing decisions (Chakraborty, 2019).

In conclusion, online reviews, when credible, can influence people’s opinions about service providers and encourage purchases (Banerjee et al., 2017). Consequently, this research investigated the correlation between the two factors through the following hypotheses:

H4: There is a positive correlation between customers’ trust in restaurant reviews online and their willingness to make purchases.

The fourth hypothesis is crucial in understanding how consumer trust in online reviews influences dining decisions.

Study framework

3.7

This study examines the influence of fake online restaurant reviews on customer trust, as well as how customer trust affects restaurant brand reputation and customer purchases. A conceptual framework is presented in Figure 1, based on a comprehensive literature review. This study will specifically examine how trust in fake reviews affects customer trust, restaurant brand reputation, and purchase intentions, as shown in Figure 1.

Figure 1:

Questionnaire design

4.1

The research investigates factors influencing customers’ beliefs about fake online restaurant reviews, customer trust, the restaurant’s brand reputation, and their decision to dine there. Based on previous studies, specific research hypotheses have been developed to measure this phenomenon. The current study consists of four main variables adapted from previously studied models (Berry, 2024; Harrison-Walker & Jiang, 2023). Authenticity was measured via 19 items across four indicators. Five indicators concerned fake reviews and trust, brand reputation had three indicators, and purchase intention had four indicators, as presented in the Appendix. A five-point Likert scale was used for all constructions.

Sampling and data collection

4.2

This study employed ‘respondent-driven sampling’, an innovative methodology that empowers researchers to obtain nearly unbiased estimates of concealed populations. By leveraging the social networks of participants, this approach facilitates access to hard-to-reach groups, offering insights that traditional sampling methods may overlook (Saljanik & Heckathorn, 2004). The research also employed a quantitative approach and used Google Forms to conduct an online questionnaire aimed at gathering information from participants.

The questionnaire explored participants’ beliefs and trust in fake online restaurant reviews, their perceptions of brands, and their likelihood of dining at restaurants. This research was carried out in local and international fast-food restaurant chains located in the Cairo, Giza, Luxor, Alexandria, and South Sinai governorates in Egypt, and was made available in both English and Arabic. The bilingual survey included an informed consent page, and participants aged 18+ provided electronic consent before proceeding. Data collection spanned December 12, 2024 to February 13, 2025 and yielded 261 valid responses.

Demographic information of the participants

5.1

Table 1 presents a comprehensive overview of the demographic characteristics of participants. As presented in Table 1, out of 261 responses, the majority (68%) were male. Among the participants, 39.8% were aged between 30 and 39 years old. Most participants were single (57.9%). Regarding the level of education, 48.9% held a bachelor’s degree, 15.6% were high school graduates, and 35.5% had an associate’s degree.

Descriptive statistics

5.2

A comprehensive analysis of participant perceptions provides important insights into how Egyptian consumers evaluate online reviews (see Table 2). One key factor, ‘Trust in Content Sources’ (mean = 3.4, SD = 0.521), indicates that respondents were uncertain about the credibility of reviewers. This is a significant concern in Egypt’s collectivist culture, where verifying sources is crucial. This ambivalence suggests that consumers face challenges in authenticating the identities of reviewers on various platforms.

The ‘Review Authenticity’ score (mean = 3.7, SD = 0.637) shows moderate agreement among participants, as they question whether reviews genuinely reflect real experiences. Notably, the highest score was for the ‘Tone of Online Reviews’, with a mean of 4.3 and a standard deviation of 0.574. Respondents strongly agreed that extreme language, whether overly positive or aggressively negative, serves as a primary indicator of potential deception. Crucially, the high scores for ‘Customer trust’ (mean = 4.4, SD = 0.402) and ‘Brand reputation’ (mean = 4.4, SD = 0.551) indicate a consensus that fake reviews damage these qualities rather than improving them. The narrow standard deviations (0.402 - 0.551) demonstrate significant consistency across Egypt’s geographically diverse sample.

Reliability was confirmed through the statistics in Table 3. All constructs exceeded thresholds (CA > 0.7; CR > 0.8), with AVEs consistent with hospitality literature. Numerous studies have shown that AVE can be below 0.5 in the hospitality and tourism management field (Hair et al., 2021). According to the literature, a CR score exceeding 0.70 is also generally considered acceptable (Akgunduz & Eryilmaz, 2018; Karatepe et al., 2020), and that has been borne out in this study as well. In the present study, the Alpha coefficient was employed as a statistical measure to evaluate the internal consistency of the measurement items.

The results indicated that all Alpha values surpassed the acceptable threshold of 0.7, demonstrating that the study exhibits satisfactory internal consistency. This finding suggests that the measurement items effectively and reliably captured the intended constructs, thereby enhancing the overall validity of the study’s conclusion factors (Hair et al., 2021; Kock, 2020; Manley et al., 2021).

Correlation analysis

5.3

A correlation analysis examined relationships between variables. Pearson correlation results (Table 4) revealed significant positive relationships between exposure to fake online reviews and all study variables, confirming their interconnectedness in Egypt’s fast-food industry. The strongest correlation emerged with customer beliefs (r = 0.688, p < 0.01), indicating that greater exposure to deceptive reviews substantially heightens consumers’ recognition of their prevalence. This aligns with cultural dynamics in Egypt, where collective awareness of marketplace deception spreads rapidly through social networks.

The correlation between customer trust and fake reviews is moderate and statistically significant (r = 0.485, p = 0.002). This finding is particularly relevant in Egypt’s dining culture, where trust is crucial for making dining decisions. The moderate correlation suggests that Egyptian consumers may initially trust well-known local brands. However, this trust tends to diminish over time as they encounter more fake reviews. In contrast, there is a strong correlation between fake reviews and purchase intention (r = 0.680, p < 0.01). This indicates that fake reviews have a significant impact on buying decisions, especially taking into account a study that found 68% of consumers to be price-conscious (CAPMAS, 2023). All correlations were positive and significant at p < 0.01, confirming that fake reviews systematically harmed all measured dimensions of consumer perception.

Structural model testing

5.4

In this study, a multiple linear regression model was employed to investigate the relationships between study variables and hypotheses. The results, detailed in Table 5, present the main hypothesis of the study along with its three sub-hypotheses. A positive correlation was found between belief in fake online reviews and review authenticity, which corresponds to hypothesis H_1a. The results indicated a positive relationship between review authenticity and belief in fake online reviews, with a coefficient of [β = 0.215, t = 4.567 for H_1a].

Hypothesis H_1b suggests that trust in content sources positively correlates with belief in fake online reviews. As shown in Table 5, the data supported this hypothesis, indicating that trust in content sources positively influenced belief in fake online reviews, with a coefficient of β = 0.142, t = 2.307.

Hypothesis H_1c posits that the tone of online reviews also has a positive correlation with belief in fake online reviews, with a coefficient of β = 0.215, t = 2.160. All three hypotheses demonstrated a positive correlation with belief in fake online reviews, as shown in Figure 2.

Figure 2:

A structural equation modelling study of Egypt’s fast-food sector revealed that consumers’ perception of fake reviews is primarily driven by three factors: review authenticity (β = 0.215, p < 0.001), trust in content sources (β = 0.142, p < 0.001), and review tone (β = 0.215, p < 0.001), collectively explaining 41.6% of variance and thereby confirming H_1a-c. These perceptions significantly shape customer beliefs (β = 0.191, p < 0.01; 41.2% variance explained), fully supporting H1. Crucially, heightened fake review perceptions negatively impact customer trust, reducing it by 20.5% per unit increase (β = 0.205, p = 0.002). The model explains 23.6% of the variance in trust, validating H₂. This eroded trust subsequently damages brand reputation (β = 0.283, p < 0.001; R² = 31.2%), confirming H3, and reduces purchase intent by 68.1% per trust unit lost (β = 0.681, p < 0.001; 46.3% variance explained), thereby supporting H4. A summary of the hypotheses tested can be found in Table 5.

Discussion

5.5

The study’s findings reveal that fake restaurant reviews have a significant impact on customer trust, as participants agreed that the credibility and tone of online reviews enhance trust, while fake reviews lacking these elements are deemed weak and unhelpful. To maintain credibility, online reviews should avoid overlapping content and be well-written. Repeated phrases or grammatical errors can lead to perceptions of inauthenticity, diminishing trust. Additionally, reviews must include details about the reviewers, such as profiles or photos; otherwise, the reviewers may be viewed as fake, further harming trust. Misleading positive reviews can also lead consumers to incorrectly assume a restaurant provides high-quality food and service, influencing their dining choices (Choi et al., 2017).

This study also examined the factors influencing customer trust and their relationship to a restaurant’s reputation. According to the findings, customer trust in fake reviews strongly correlates with the restaurant’s reputation, thus supporting the second hypothesis. Dishonest marketing tactics, such as fake positive or negative reviews, can unfairly impact on the restaurant’s reputation, discouraging potential customers from visiting. This study’s results further suggest that fake restaurant reviews can significantly undermine consumer trust, leading to disappointment and distrust in online reviews.

Conclusion

5.6

The current study confirms that fake online reviews significantly undermine customer trust and distort restaurants’ reputations. Key factors that influence how consumers assess the credibility of reviews include authenticity, source credibility, and linguistic tone. This applies to reviews from both humans and AI, as supported by studies conducted by Cao (2023), Lee & Hong (2019), Ventre & Kolbe (2020), and Wang et al. (2017).

Overall, by actively managing online reviews, restaurants can foster customer loyalty, encourage repeat business, and benefit from positive word-of-mouth, ultimately gaining a competitive edge in the market. These findings offer valuable insights for managers of fast-food restaurants in Egypt, emphasizing the importance of actively monitoring reviews, correcting inaccuracies, and promoting transparency to build customer loyalty and gain a competitive edge.

Additionally, this study highlights significant theoretical and practical challenges that need to be addressed. Fake reviews expose the weaknesses in how trust is established in online marketplaces. When genuine indicators, such as detailed reviewer profiles and unique comments, are absent or easily replicated, traditional methods of building trust become ineffective, as noted by Berry (2024). This highlights the need for improved systems to detect deceit, particularly as AI-generated content makes it increasingly difficult to discern what is authentic. On a practical level, while proactively managing reviews is crucial, it presents challenges: Monitoring reviews manually on a large scale is difficult, and AI-generated fake reviews are becoming more sophisticated. Smaller restaurants also face limitations in resources. Relying solely on review platforms’ detection systems can be risky, as these systems are often unclear and inconsistent.

Theoretical implications

6.1

The study makes several considerable contributions to current literature. The research’s primary contribution is assessing how online reviews affect consumer trust and buying decisions. Online restaurant reviews play a crucial role in the restaurant industry by helping potential customers make informed decisions and motivating managers to enhance the quality of their products and services (Martinez-Torres & Toral, 2019). The first findings indicate that when a potential buyer reads a credible online review that has no overlap with other reviews, is composed properly, and does not contain repeated phrases or grammatical errors, it will significantly increase their trust in the product or service described by the review; this relationship also works in reverse (Berry, 2024). Therefore, these reviews should receive high priority on social media platforms, and marketers should consider them. The certainty of detecting fake reviews is essential for the success of any business (Reyes-Menendez et al., 2019; Moon et al., 2019; Akhtar et al., 2019; Ren & Ji, 2017; Hunt, 2015; Kim et al., 2015) and the literature strongly encourages restaurant managers to employ strategic methods to eliminate the potential damage caused by fake reviews (Mayzlin et al., 2014).

Customer trust is established through reviews about the quality of products or services, which can significantly influence a restaurant’s reputation, regardless of its popularity. This study highlights how customer trust in fake reviews negatively correlates with a restaurant’s reputation. Fake reviews can harm consumer trust, leading to disappointment and scepticism. Some businesses may even use fake reviews against competitors in an ‘online reputation war’ (Luca, 2016; Li et al., 2020).

In summary, trusting genuine reviews increases customer confidence in a restaurant and supports customer purchasing decisions. Our findings underscore the importance of intention in affecting outcomes, which is valuable to the restaurant industry. Overall, this study emphasizes the detrimental effects of fake reviews on trust and brand reputation and offers a framework for researching fake online reviews.

Managerial implications

6.2

Managers need to monitor online reviews and address fake reviews, including requesting their removal or verification. Moon et al. (2019) recommend adopting a ‘close strategy’, which allows for a more confidential review process that can reduce biased feedback and enhance trust in genuine customer opinions. Monitoring feedback from food critics and authentic customers is crucial for building credibility. By addressing concerns raised in reviews, restaurants can resolve issues and strengthen relationships with patrons. This approach fosters trust and shows a commitment to customer satisfaction, which can positively influence purchasing decisions. Additionally, restaurants should prioritize safety for customers providing negative feedback. Creating a safe space for genuine experience ensures that areas for service improvement are highlighted.

Limitations and future research

6.3

This research is constrained by its focus on local and international fast-food chains within five Egyptian governorates (Cairo, Giza, Luxor, Alexandria, and South Sinai), limiting generalisability beyond these geographic areas or beyond the fast-food sector. The reliance on 261 self-reported responses collected via a bilingual (English/Arabic) Google Form introduces potential biases, such as social desirability, and may not fully represent Egypt’s diverse population or regions outside the sampled governorates.

This study emphasizes the significant impact of fake online reviews on Egypt’s fast-food sector and highlights several areas for further research. Key recommendations include exploring cultural and psychological factors affecting consumers’ vulnerability to fake reviews; assessing trust in platforms and reviewer credibility; quantifying financial impacts on different business models; and evaluating localised counterstrategies. Additionally, a thorough review of Egypt’s regulatory framework concerning fake reviews is crucial, focusing on the effectiveness of current laws and platform governance.

Overall, this research is essential for formulating effective policy solutions. Future research should expand the structural model by investigating key moderating variables. Consumer expertise with a product’s brand or category likely weakens fake reviews’ impact, as knowledgeable consumers rely more on their own judgment (Chaterjee et al., 2023). Individual trait scepticism is another plausible moderator; highly sceptical consumers may more readily perceive reviews as fake. The nature of the product itself (e.g., search, experience, credence goods) also warrants investigation as a moderator, building on findings like those of Roman et al. (2023) review credibility differences.