Determinants of satisfaction with online food delivery providers and their impact on restaurant brands

Washington Macias (Facultad de Ciencias Sociales y Humanísticas, Escuela Superior Politecnica del Litoral, Guayaquil, Ecuador)
Katia Rodriguez (Facultad de Ciencias Sociales y Humanísticas, Escuela Superior Politecnica del Litoral, Guayaquil, Ecuador)
Holger Barriga (Facultad de Ciencias Sociales y Humanísticas, Escuela Superior Politecnica del Litoral, Guayaquil, Ecuador)

Journal of Hospitality and Tourism Technology

ISSN: 1757-9880

Article publication date: 14 June 2023

Issue publication date: 3 August 2023




This paper aims to analyze the spillover effects between online food delivery providers’ (OFDs) performance and restaurant brands. It proposed a model of three determinants for e-satisfaction with OFDs and related these variables to restaurants’ brand satisfaction, image and loyalty.


A survey was designed, and 332 responses from delivery apps users in Ecuador were collected. A partial least squares structural equations model was applied.


The three proposed determinants of OFD satisfaction were significant: e-service quality, personal aspects of delivery workers and perceived food quality. Regarding the spillover effects, e-service quality and personal aspects had an influence on perceived food quality, mediating a chain of effects on restaurants’ brand satisfaction, image and loyalty.

Research limitations/implications

Data were collected before COVID-19. Further studies will need to be undertaken in the context of the COVID-19 pandemic because minimal contact between delivery workers and customers is expected. In addition, food quality perceptions may include new concerns about biosafety norms.

Practical implications

Practices aiming to improve the service experience with OFDs are suggested, including proper training, supervision and improvement of delivery workers’ conditions. On the other hand, because restaurants do not control OFD’s performance, their contractual agreements should focus on avoiding service failures that erode restaurants’ brand equity.


This paper fulfills the need to study the spillover effects in the context of the collaborative economy, where delivery companies, delivery workers and restaurants work together to provide a service, and the performance of one of the parties impacts the consumers’ perceptions of the other party.


本调查旨在分析在线食品配送提供商 (OFD) 绩效与餐厅品牌之间的溢出效应。 它提出了 OFD 电子满意度的三个决定因素模型, 并将这些变量与餐厅的品牌满意度、形象和忠诚度相关联。


本研究设计了一项调查, 并收集了来自厄瓜多尔交付应用程序用户的 332 份回复。 应用PLS结构方程模型。


OFD 满意度的三个拟议决定因素非常重要:电子服务质量、送货员的个人方面和感知的食品质量。 关于溢出效应, 电子服务质量和个人方面对感知食品质量有影响, 调节对餐厅品牌满意度、形象和忠诚度的连锁影响。


本研究数据是在 COVID-19 之前收集。 由于预计送货员和顾客之间的接触最少, 因此需要在 COVID-19 大流行的背景下进行进一步研究。 此外, 食品质量认知可能包括生物安全规范的新担忧。


本研究建议采取旨在改善 OFD 服务体验的做法, 包括适当的培训、监督和改善送货工人的条件。 另一方面, 由于餐厅不控制 OFD 的绩效, 因此他们的合同协议应着重于避免服务失败而损害餐厅的品牌资产。


本文满足了研究协作经济背景下溢出效应的需要, 在这种情况下, 送货公司、送货工人和餐馆共同提供服务, 其中一方的表现会影响消费者的 对方的看法。



Macias, W., Rodriguez, K. and Barriga, H. (2023), "Determinants of satisfaction with online food delivery providers and their impact on restaurant brands", Journal of Hospitality and Tourism Technology, Vol. 14 No. 4, pp. 557-578.



Emerald Publishing Limited

Copyright © 2023, Washington Macias, Katia Rodriguez and Holger Barriga.


Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at

1. Introduction

Since the increase in online sales and connectivity worldwide, delivery services have gained great attention. In the USA, e-commerce, as a percentage of retail sales, has almost tripled in the past 10 years, reaching 14% in 2020 (U.S. Census Bureau, 2021). The rapid increase in connections has given immediacy and convenience a more prominent role among consumers, who can now order services and products, such as food, digitally (Euromonitor, 2019). Some studies report that the food ordering and delivery segment has emerged as the leader in instant delivery in the USA (Netzer et al., 2017) and Europe (Dablanc et al., 2017). By the end of 2022, online food delivery revenues were estimated at $323.3bn worldwide, with an expected cumulative growth of 44.2% through 2027, where the platform-to-consumer delivery segment would have the largest share (65%) (Statista, 2022). Some restaurants deliver their products with their own employees, and others make it through crowdsourced delivery, where independent people use their available resources – time and means of transportation – to complement the provision of the service, connected by an online delivery provider (OFD), such as Uber Eats or Glovo.

OFDs provide order services, payment, monitoring of the process and delivery, but they are not responsible for food preparation. OFDs have been inserted in the restaurant value chain, controlling most aspects of delivery, with the consequent appropriation of knowledge about consumer preferences (Rivera, 2019). However, restaurants have little control over OFD’s performance, which raises an interesting question. Which effects could the performance of OFDs have on restaurant brands? From the customers’ point of view, OFDs should be assessed based on their own experiences interacting with the platform and the delivery workers because the latter is recognized as one of the main factors of service evaluation (Wall and Berry, 2007; Voon et al., 2013; Alhelalat et al., 2017) without being sufficiently analyzed in OFDs. In this regard, a survey revealed that:

  • for most U.S. consumers, retailers are responsible for choosing a delivery partner that represents their brand and customer service values; and

  • a significant percentage of respondents (up to 79%) would not purchase from a retailer again due to delivery failures or unprofessional delivery workers (Dropoff, 2018).

Therefore, restaurant brand managers need to be aware of the risks faced in this collaborative context because the eventual failures of delivery partners may cause brand dilution (Loken and John, 2010).

Existing literature about the OFD segment is scarce and still developing (Shankar et al., 2022). Therefore, analyzing the trade-offs from the collaboration between OFDs and restaurants is a valuable research field that would give restaurant managers more comprehensive knowledge about the determinants of customers’ perceptions and attitudes in this novel context (Rivera, 2019). Based on these antecedents, this study aims:

  • to analyze the effect of three proposed determinants: e-service quality (e-SQ), personal aspects of delivery workers and perceived food quality (PFQ) on customer satisfaction with OFD; and

  • to determine whether these determinants affect restaurant brand satisfaction and brand equity outcomes.

Previous works have focused on the attitudes toward OFDs (Cho et al., 2019; Yeo et al., 2017), intention to use (Okumus et al., 2018; Yeo et al., 2017), adoption (Okumus and Bilgihan, 2014), choice (Saad, 2021) and the association between mobile food app attributes and final conversion (Kapoor and Vij, 2018). However, these studies examined the preconsumption stage. Our study instead analyzes customers who have already adopted OFDs, placed orders and consumed the food, further focusing on customer satisfaction with OFDs (e-satisfaction) and restaurants. Thus, motivations or precursors for adopting OFDs are not part of our research.

Regarding e-satisfaction, Wang et al. (2019) analyzed determinants from information systems and marketing mix, but only in same-restaurant apps. In addition, Alalwan (2020) incorporated detailed functional aspects of the OFD, while Suhartanto et al. (2019) and Annaraud and Berezina (2020) examined e-SQ and food quality as determinants of e-satisfaction. In contrast, our study includes personal aspects as a proposed determinant of OFD satisfaction, similar to the reliability dimension of the service quality scale for OFDs presented by Cheng et al. (2021). Moreover, our work differentiates itself from previous studies on OFD satisfaction by analyzing the eventual spillover effects on restaurant outcomes. Dirsehan and Cankat (2021) explored the relationship between OFD satisfaction and restaurant satisfaction and loyalty; however, our study separately analyzes the effects of the three proposed determinants on restaurant outcomes, providing a more complete picture of the phenomenon. Therefore, we have taken a marketing perspective to address this research problem, integrating e-service, satisfaction spillover and brand equity literature within a more comprehensive model.

Section 2 of the study presents the theoretical framework, followed by the proposed conceptual model and hypotheses development. Section 3 describes the applied methodology. Section 4 shows the results, and finally, Section 5 discusses the research findings and shows the conclusions.

2. Theoretical framework and hypotheses

Customer evaluation of service in electronic contexts has been theoretically described based on the Means-End-Chain (MEC) theory (Parasuraman et al., 2005; Fassnacht and Koese, 2006). MEC is a cognitive model that allows understanding consumer behavior by proposing that services or products are acquired as a means to achieve consumer ends or values. Product/service attributes are evaluated according to the consumer’s perceived consequences and experiences, which leads to the achievement of goals or values (Olson and Reynolds, 2001). The concept of e-SQ was coined to refer to the customer’s assessment of the quality of services provided in electronic contexts. This evaluation brings consequences at the level of the consumer’s mind, such as satisfaction and perceived value, which trigger intentions and behaviors (Parasuraman et al., 2005; Blut et al., 2015).

However, the concept of e-SQ is insufficient to assess the OFD service because there are other meeting points beyond the consumers’ interaction with the electronic app. Figure 1 depicts the service encounter for the OFD service, which also includes consumer interaction with delivery workers and the consumption of delivered food. Following Blut et al.’s (2015) conceptual framework, also rooted in MEC theory, customer satisfaction is an immediate outcome of service evaluation. Considering that two brands collaborate in the service provision (OFD and restaurant), we study the effects of three proposed determinants (e-SQ, personal aspects of delivery workers and PFQ) not only on OFD satisfaction but also on restaurant brand satisfaction and brand equity outcomes. Both this delimitation and description of the online food delivery service are necessary to propose a model that comprehensively evaluates the OFD service itself and its effects on restaurant brands.

To meet the two research objectives, some theories should be incorporated to describe the possible transmission of effects in the consumer’s mind from one brand to another and to explain the impact on restaurants’ brand equity outcomes, such as brand image and brand loyalty.

Satisfaction spillover theory mainly focuses on how satisfaction with one object or life domain can “spill over” other related objects or domains (Brady, 1980; Sirgy et al., 2001; Ilies et al., 2009). This theory has been used in several fields to explain the relationship between the satisfaction of neighboring or sub/supra-domains. For example, evidence of spillover effects has been found from job satisfaction to career satisfaction among physician assistants (Brady, 1980), from job satisfaction to daily marital satisfaction at home (Ilies et al., 2009), from employee perception of corporate social responsibility to job satisfaction and overall quality of life (Kim et al., 2018) and from employees IT satisfaction to job satisfaction (Wang et al., 2020). In the marketing field, Loken and John (2010) reported spillover effects in inconsistent brand extensions, and in brand alliances when the negative behavior of one brand spills over the other brand. Schumann et al. (2014) established that service failure by one program partner within coalition loyalty programs negatively impacts customer loyalty toward the whole program. Based on this framework, it is expected that the experience with one phase of the OFD service could affect customers’ perception regarding other phases and the entire experience.

The customer-based brand equity literature can explain how consumers’ exposure to the different stages of the service encounter affects the restaurant brand. Brand equity is defined as a perception of added value that a brand gives a product or service compared to the same unbranded product/service (Aaker, 1991; Keller, 1993; Davcik et al., 2015). Brand equity has a cognitive and affective basis in the customer’s mind, making brand image a concept of primary importance for building brand equity (Aaker, 1991; Keller, 1993). Brand image is the consumer perception of a brand supported by a broad set of brand associations regarding product attributes (e.g. food quality), benefits, sensations and experiences, which can vary in strength, favorability and distinctiveness (Aaker, 1991; Keller, 1993). It describes how consumers think and feel about a brand and is developed by satisfactory or unsatisfactory experiences (Ansary and Hashim, 2018).

Within brand equity literature, brand loyalty is recognized as an attitudinal or behavioral construct representing the extent to which the consumer is attached to the brand (Aaker, 1991). Oliver (1999) distinguishes two conceptualizations for loyalty: behavioral, related to repeated purchases, and psychological, associated with the commitment to consume brand products (Oliver, 1999) or the intention to select the brand as the first choice (Yoo and Donthu, 2001). Jacoby and Chestnut (1978) suggest studying “true” brand loyalty (psychological), arguing that the behavioral conceptualization could be invalid due to casual purchases, preference for convenience or seemingly inconsistent purchases when consumers are multibrand loyal. This study uses the psychological conceptualization of loyalty, consistent with several previous studies (Yoo and Donthu, 2001; Martínez et al., 2009; Buil et al., 2013). According to this framework, loyalty is supported by the belief that a brand is preferred over its competitors based on attribute information; reflects liking and positive attitudes toward the brand; and is consolidated from cumulatively satisfying usage occasions or experiences (Oliver, 1999).

2.1 Online food delivery provider satisfaction and e-service variables

OFD satisfaction is an overall assessment of OFD service performance compared to prior expectations (Suhartanto et al., 2019). Adapting previous definitions (Parasuraman et al., 2005) to the OFD context, we propose that e-SQ is the extent to which an OFD facilitates efficient and effective searching, purchasing and delivery of products and services. e-SQ can be broken down into four perceptual dimensions (Parasuraman et al., 2005): efficiency, the ease and speed of accessing and using the site; fulfillment, the extent to which the site’s promises about delivery and item availability are fulfilled; system availability, the correct technical functioning of the site; and privacy, the degree to which the site is safe and protects customer information. Perceptual-level attributes are preferred over concrete technical/design aspects influencing e-SQ evaluation because the formers are more enduring evaluative elements that do not change as fast as technological features (Parasuraman et al., 2005). Also, not all customers might be capable of evaluating design/technological features. In line with the MEC theory, which emphasizes customer experience in owning or using a product or service, perceptual attributes are more experiential than technical and, therefore, are more suitable to this theoretical framework. We also include delivery time as an element of particular importance for OFDs and restaurants. Faster delivery time is an attribute desired by consumers (Dropoff, 2018) and contributes to the OFD’s value proposition (Netzer et al., 2017).

It has been demonstrated that e-SQ positively influences customer satisfaction in online settings (Jeon and Jeong, 2017; Suhartanto et al., 2019; Annaraud and Berezina, 2020). Furthermore, specific characteristics such as OFD’s design, trustworthiness and variety of food options have significantly explained perceived value and attitudes toward OFDs (Aslam et al., 2021). Cheng et al. (2021) also assert that system operation, order traceability and timely and accurate order fulfillment positively impact customer satisfaction with OFDs. Therefore, the following hypothesis is proposed:


E-service quality positively influences satisfaction with OFDs.

Delivery workers’ personal aspects include neat and tidy appearance, personal hygiene, smile, friendliness, entertaining behavior, comfortable manners, politeness, linguistic skills and overall cordiality (Alhelalat et al., 2017). It is recognized that service providers’ look is a tool that shapes the brand value proposition and differentiates it from competitors by influencing consumers’ feelings, attitudes and behaviors (Pounders et al., 2015). Restaurant employees–customers interaction is crucial in evaluating service quality (Kim, 2014) and experience (Wall and Berry, 2007). Some studies show that service staff’s personal aspects more significantly contribute to customer satisfaction with restaurants than other service aspects (Alhelalat et al., 2017; Wall and Berry, 2007; Voon et al., 2013). In the USA, 75% of consumers agree that the professionalism of the delivery worker is important, and 70% of them consider that when the delivery worker is dressed in uniform, their trust in the service increases (Dropoff, 2018). It has been argued and demonstrated that the appearance and behavior of frontline staff influence customer satisfaction in the food industry (Alhelalat et al., 2017; Jang and Namkung, 2009; Wall and Berry, 2007). Saad (2021) found that the attitude of delivery workers is a relevant factor in choosing an OFD in Bangladesh. Also, Cheng et al. (2021) included a reliability dimension in the proposed service quality scale for OFDs related to the appearance and behavior of delivery workers, showing a positive relationship with customer satisfaction. Hence, the following hypothesis is formulated:


Personal aspects positively influence satisfaction with OFDs.

Perceived quality is one of the main drivers of brand equity (Davcik et al., 2015), defined as the overall perception of excellence or superiority of a brand over its competitors (Aaker, 1991). We refer to food quality as the overall performance of food to fulfill customer needs (Suhartanto et al., 2019), comprising several factors: menu variety, presentation, size, healthiness, taste, freshness and food temperature. Furthermore, there is evidence that PFQ influences satisfaction with OFDs (Suhartanto et al., 2019; Annaraud and Berezina, 2020), even though the restaurant brand provides the food. Therefore, the following hypothesis is proposed:


Perceived food quality positively influences satisfaction with OFDs.

2.2 Online food delivery providers’ performance, food quality and restaurants’ brand satisfaction

According to the spillover theory, satisfaction in one area of life may influence satisfaction in other aspects (Sirgy et al., 2001). In the context of this study, customers’ experiences in the initial or middle phases of the service encounter could affect their perception of the final stage (consumption) because customers likely incorporate information (good or bad experience) about one phase into their appraisal of other phases and the entire process. Based on this framework, Suhartanto et al. (2019) evidenced a spillover effect from e-SQ to PFQ, as we argue could also occur from delivery workers’ personal aspects to PFQ. In addition, satisfaction with the restaurant brand, conceptualized as an overall assessment, could also be influenced by the interaction experienced with the platform and the delivery workers. Therefore, we formulate these hypotheses:


E-service quality positively influences perceived food quality.


Personal aspects positively influence perceived food quality.


E-service quality positively influences restaurants’ brand satisfaction.


Personal aspects positively influence restaurants’ brand satisfaction.

Because satisfaction is a subjective assessment of product or service performance concerning prior expectations, PFQ should be essential in customer satisfaction with the restaurant brand. Several studies have shown that food quality affects customer experience (Liu et al., 2017) and satisfaction (Han and Hyun, 2017; Namin, 2017; Namkung and Jang, 2007) in the restaurant industry. Therefore, we pose the following hypothesis:


Perceived food quality positively influences restaurants’ brand satisfaction.

2.3 Impact on restaurants’ brand equity variables

Brand image was conceptualized as the consumer perception of a brand supported by a broad set of brand associations regarding product attributes (e.g. food quality), benefits, sensations and experiences, which can vary in strength, favorability and distinctiveness (Aaker, 1991; Keller, 1993). Etemad-Sajadi and Rizzuto (2013) explain that service quality influences restaurant brand image because image development is primarily experience-based; therefore, positive or negative disconfirmation may strengthen or weaken consumers’ impression of the brand. In the OFD setting, we propose that PFQ and satisfaction with the restaurant brand should influence postconsumption image development. Thus, the following hypotheses are presented:


Perceived food quality positively influences restaurant brand image.


Satisfaction with the restaurant brand positively influences its brand image.

We also hypothesize that the experience with the other aspects of service, i.e. e-SQ and personal aspects, could directly affect restaurant brand image. According to the associative network theory, which is the base for brand equity literature, the information in the consumer’s memory is stored in networks consisting of nodes (e.g. restaurant brand and its distinctive aspects) connected by links (Teichert and Schöntag, 2010). Associations could be attached to or modify an existing network by a triggering experience. Based on this theory, we propose that the sequential aspects of the whole service experience (interaction with platform and delivery workers, food unpacking and consumption) would confirm, add to, or modify some associations within the brand network either positively or negatively. Therefore, we propose these hypotheses:


E-service quality positively influences restaurant brand image.


Personal aspects positively influence restaurant brand image.

Regarding brand loyalty, we adopt the psychological definition as the long-term commitment to repurchase or repatronize a brand, involving favorable attitudes (Martínez et al., 2009). As an attitudinal construct, loyalty depends on the evaluative judgment and the strength of beliefs about the brand (Keller, 1993). A positive and strong brand image contributes to achieving a competitive advantage in the market, allowing brand differentiation in the consumer’s mind and triggering positive attitudes and behaviors toward the brand (Aaker, 1991; Keller, 1993; Buil et al., 2013). As brand image improves and customer satisfaction increases, we can suggest that consumers are more likely to choose a brand over its competitors and repeat this behavior in the future. Ansary and Hashim (2018) demonstrated that brand image positively affects brand attachment and attitudes. Macias and Cerviño (2018) found that the strength and favorable content of brand associations contribute to more brand loyalty. Moreover, the positive influence of brand image and customer satisfaction on loyalty to fast-food restaurants has been proven (Etemad-Sajadi and Rizzuto, 2013; Liu et al., 2017). Thus, we pose the following hypotheses:


Restaurant brand image is positively associated with loyalty.


Satisfaction with the restaurant brand is positively associated with loyalty.

The previous hypotheses are illustrated in Figure 2.

3. Methodology

3.1 Procedures and respondents

An unrestricted self-selective internet-based survey (Fricker, 2008) was used in the present study. This convenience sampling is justified considering the limitation to determining the sampling frame, given that there was no access to OFD databases and that OFD users are people with internet connectivity. Data were collected in February 2020 in Ecuador. Respondents used an OFD for ordering food within the 15 days before the survey.

3.2 Measurement

The scale of e-SQ was adopted from Suhartanto et al. (2019) and Ryu et al. (2012), suiting Parasuraman et al.’s (2005) conceptualization. The personal aspects factor was measured with Alhelalat et al.’s (2017) scale. The PFQ scale was based on Ryu et al. (2012) and included one item on food temperature from Namkung and Jang (2007). OFD and restaurant brand satisfaction (e-SAT, BSAT) were measured with scales from Möhlmann (2015) and Ryu et al. (2012), respectively. Martínez et al. (2009) provided the scales for restaurant brand image (IMA) and loyalty (LOY). Previous overall brand image was included as a control variable (single item based on Chang and Liu, 2009) for restaurant brand image. All items were measured on five-point Likert scales (ranging from 1 = strongly disagree to 5 = strongly agree) (Table 2).

First, items were translated from English to Spanish. Then, three experts in the field of research systematically judged whether each item contributed to the measurement of its construct and that no aspect of the domain was overlooked. Based on the experts’ feedback and after a pilot study (n = 18) aimed to check respondents’ understanding of the terminology used in the questionnaire, we made slight modifications to the initial version. Finally, the questionnaire was back-translated, and we found a high level of coincidence with the original English version of the items.

3.3 Evaluation of common method and social desirability biases

According to Podsakoff et al. (2003), a potential for common method bias (CMB) could be assessed because the measurements share the variance of the method. We carried out Harman’s one-factor test, finding that the total variance explained by the single factor is 37.56%, which suggests a low risk of CMB (Podsakoff et al., 2003). The social desirability bias (SDB) occurs when subjects change their answers for (a) impression management, IM (to look better to others), (b) self-deception, SD (to feel good about themselves) or (c) identity definition, ID (Larson, 2019). The variables in this study are not related to IM, SD or ID strategies. The SDB in surveys can be reduced by increasing survey anonymity, decreasing respondents’ concerns about the risks of truthfulness, raising awareness of the importance of the study and reducing interviewer bias (Krumpal, 2013; Wheeler et al., 2019). Because these recommendations were considered in this research’s design, we argue that the probability of SDB is minimal.

3.4 Data analysis

Structural equation modeling (SEM) was conducted by partial least squares (PLS) methods with SmartPLS (Ringle et al., 2015) to test the proposed model. PLS-SEM is suitable when the researcher aims to explore a new theory rather than confirming established theoretical models in complex models with many indicators and relationships, nonnormal data or small sample sizes (Hair et al., 2017). The measurement model was assessed by analyzing the reliability, convergent and discriminant validity of the constructs.

4. Results

The final sample size reached 332 observations. Table 1 shows the descriptive statistics. The most used OFD among respondents was Glovo (55.4%), and most orders were placed in McDonald’s (18.4%).

4.1 Measurement model

Construct reliability was assessed with Cronbach’s alpha and composite reliability (CR). Both indicators exceeded the 0.7 thresholds (Hair et al., 2017). The average variance extracted (AVE) for all the constructs was greater than 0.50 (Hair et al., 2017), showing convergent validity. Some items with low factor loadings were removed to meet this criterion, carefully ensuring not to affect content validity. For discriminant validity, the square root of the AVE was greater than its correlation with any other construct (Hair et al., 2017), and the heterotrait–monotrait ratios were below the maximum threshold of 0.9 (Henseler et al., 2015), after removing one item from LOY with high correlations with IMA items. Finally, all standardized factor loadings were significant. Tables 24 show these findings.

4.2 Structural model

Variance inflation factors were lower than 5, showing no multicollinearity problems (Hair et al., 2017). The PLS-SEM procedure used 5,000 bootstrap samples, as recommended by Hair et al. (2017). Table 5 reports path coefficients and significance tests. E-SQ, PASP and PFQ were significant determinants of e-SAT, supporting H1, H2 and H3. E-SQ and PASP showed spillover effects on PFQ, supporting H4 and H5. Another significant direct spillover effect was from e-SQ on BSAT (H6). BSAT was also influenced by PFQ (H8) but not by PASP (H7 not supported). None of the service components showed a significant direct influence over IMA (H9, H11 and H12, not supported); however, a positive and significant relation with BSAT was found (H10). IMA and BSAT had significant effects on LOY, providing evidence for H13 and H14, respectively. Finally, the control variable was significant.

The in-sample predictive power was assessed with the coefficient of determination (R2) of every endogenous variable in the model, ranging from 0.356 to 0.554 and indicating moderate to substantial power. Almost all statistically significant relationships showed meaningful effect sizes (f2 ≥ 0.02), except the direct relation between e-SQ and BSAT (f2 < 0.02). On the other hand, some of the relationships showed medium (0.15 ≤ f2 < 0.35) or large (f2 ≥ 0.35) effect sizes (Hair et al., 2017). The greatest effects were evidenced in the relationships between IMA → LOY and PFQ → BSAT. The predictive out-of-sample relevance (Q2) for all the endogenous constructs obtained with the blindfolding procedure resulted in values larger than 0, indicating predictive relevance (Hair et al., 2017). Table 6 shows the values for R2, f2 and Q2. Although some direct spillover relations were not significant, further analysis revealed significant total effects, which account for the sum of direct effects and total indirect effects (Hair et al., 2017), from e-SQ and PASP over BSAT, IMA and LOY, due to the existence of several mediating relationships in the model (Table 7). Based on all these criteria, the suggested model had a good performance.

5. Discussion and conclusions

5.1 Discussion

Reports show the increasing adoption of OFD services worldwide and project significant growth rates in the future (Statista, 2022). The COVID-19 pandemic further accelerated this adoption process, fostering OFD activity due to capacity restrictions for restaurants and consumers’ concerns about getting infected in enclosed places (Euromonitor, 2020). Despite the growth of this market, several academics sustain that only a few works have focused on the consumers of OFDs (Aslam et al., 2021; Rivera, 2019), and even fewer have analyzed how the performance of OFDs could impact restaurants (Dirsehan and Cankat, 2021). The fact that restaurants control little or nothing about the performance of OFDs (digital platforms and delivery workers) represents a risk if this performance affects restaurant brands. Therefore, in this paper, we proposed a model to assess the effect of satisfaction determinants of OFD on restaurants’ brand equity variables (spillover effects).

Our results show that OFD satisfaction is influenced by e-SQ, food quality and personal aspects, being e-SQ the most salient factor. Accordingly, previous research has found that the quality of restaurant apps (Wang et al., 2019), OFDs (Alalwan, 2020; Cheng et al., 2021; Suhartanto et al., 2019; Annaraud and Berezina, 2020), food quality (Suhartanto et al., 2019; Annaraud and Berezina, 2020) and the reliability dimension from Cheng et al.’s (2021) scale, related to the appearance and behavior of the delivery workers, positively influence e-satisfaction.

Regarding spillover effects, e-SQ and personal aspects influence how customers perceive food quality, and e-SQ affects satisfaction with restaurant brands. Other hypothesized spillover direct effects were not significant; however, indirect effects from e-SQ to restaurant brand image and loyalty and from personal aspects to brand satisfaction, image and loyalty were evidenced by the analysis of total effects. Previous studies have shown evidence of spillover effects in several fields (Schumann et al., 2014; Loken and John, 2010; Kim et al., 2018), including the impact of e-SQ on PFQ in the OFD context (Suhartanto et al., 2019). Our results provide new evidence that the two determinants controlled by the OFD (e-SQ and personal aspects) produce spillover effects on restaurants’ brand equity variables. The chain of effects is supported by the significant relationships between PFQ and restaurant brand satisfaction, brand satisfaction and brand image, and brand satisfaction and image on loyalty, which align with previous evidence on brand equity in the restaurant industry (Etemad-Sajadi and Rizzuto, 2013; Gallarza-Granizo et al., 2019; Liu et al., 2017).

5.2 Theoretical contributions

These results add to the rapidly growing field of OFD from a customer satisfaction and brand equity perspective. As a first contribution, our study proposed and demonstrated that delivery workers’ personal aspects is an independent and significant determinant of OFD satisfaction, consistent with several studies showing the importance of frontline staff’s personal aspects as contributors to satisfactory customer experiences. Many of these studies even found that personal aspects are more relevant than other service factors (Alhelalat et al., 2017; Voon et al., 2013; Wall and Berry, 2007). However, customer interaction with delivery workers in the OFD setting is more limited than in other types of services, which can explain why personal aspects was not the primary factor.

The second contribution is related to the spillover effects of OFD on restaurant brands. Prior to this work, no study had shown how the main aspects of the OFD service, such as e-SQ and personal aspects, could affect restaurants’ brand equity variables. To meet this purpose, our study used a theoretical framework that combined service quality literature, spillover theory and brand equity literature in an emerging context, such as the OFD service. As pointed out by Rivera (2019), there was a need to investigate the dynamics of the relationship between the OFD, the consumer and the restaurant, to gain a deep understanding of the effects that OFDs’ performance may have on restaurant brands. Therefore, our work filled a gap in the literature, showing that from a consumer perspective, a service failure by the OFD affects the aggregate experience, mining consumer satisfaction with the restaurant, its brand image and consumer loyalty.

Finally, our work sheds light on this issue in a developing country where the revenues and penetration of OFD are growing consistently and are expected to continue with such a tendency (Mentinno, 2021).

5.3 Practical implications

More restaurants are migrating to the online ordering model. Some are offering their service solely through delivery (cloud kitchens), and more apps are emerging (Dishman, 2020). However, this growth comes along with challenges for OFDs and restaurants. For Williams et al. (2020), complaints about the delivery service are presented in various aspects, such as app use, interaction with delivery workers and food delivered at inappropriate temperatures or conditions.

Our results demonstrate that OFD satisfaction is driven by several factors, such as platform quality, timely and accurate delivery, personal aspects of delivery workers and PFQ. Therefore, platforms must be easy to use, allowing customers to quickly find restaurants and food categories, make comparisons based on desired attributes, protect their personal information, rely on an efficient ordering and payment system and accurately track the order during delivery. In addition, OFDs must comply with promised delivery time. This factor also influences the PFQ because the temperature or conditions of delivered food could turn inappropriate if the delivery time exceeds a given threshold. Finally, a good service recovery policy is very appreciated by customers in case of service failures by OFDs (Aslam et al., 2021; Williams et al., 2020).

Regarding personal aspects, customers evaluate delivery workers’ physical appearance and friendliness when interacting by chat or telephone as part of their experience with the OFD. In the times of COVID-19, we consider that digital interaction would be more relevant than physical appearance because minimal physical contact is expected between customers and delivery workers. Therefore, we suggest OFDs train delivery workers with interaction skills to approach customers, open a better communication channel with them and supervise the fulfillment of their obligations, such as carefully handling packages, adhering to designated routes and complying with delivery time and hygiene standards. On the other hand, previous studies have evidenced delivery workers’ unsafe working conditions, underpayment, excessive working hours or multi-apping practices to reach a sufficient income (Figueroa et al., 2021). Thus, OFD companies should seriously consider improving delivery workers’ current conditions to foster their working motivation, responsible behavior toward consumers and general performance.

Considering the current consumers’ concerns about COVID-19, such as the implementation of hygiene standards in food preparation (Mehrolia et al., 2021), restaurants must communicate their biosafety measures to their clients to improve their perceived image. Likewise, restaurants should design packaging that, in addition to reflecting brand image, conveys a sense of cleanliness. They should also instruct delivery drivers on properly handling the packaged food they transport to minimize problems of mistreated packaging. The spillover effects evidenced in this work should encourage restaurant managers or restaurant associations to design collaboration terms that align the performance of the OFDs with restaurants’ brand image and to conduct a more careful search for partners. In this sense, they must ponder between being present on many platforms that may have many complaint records versus selecting fewer platforms with a lower number of orders but greater quality control. In addition, contact and feedback information from OFD customers must be available to restaurant managers to track customer perceptions of the whole service encounter process. Finally, because customers incorporate their experience with OFD’s platform and staff when evaluating the restaurant brand, restaurants must be aware of customers’ feedback and discuss failures in service provision with their partners to improve future customer experiences and preserve brand equity.

5.4 Limitations and future research

The most important limitation of this study is that it was conducted before COVID-19. During the pandemic, less interaction with delivery workers has been evidenced, and beliefs about food quality have changed because customers expect more biosafety standards in food preparation, packaging and delivery (Macias et al., 2021). Thus, during and post-pandemic, comparative studies are needed. We also consider as a limitation the fact that this is a cross-sectional study. In a longitudinal study, other researchers could explore if the spillover effects persist over time. Moreover, future investigations could search for moderator variables of the spillover effect between OFD and restaurants to better understand this phenomenon. Finally, the proposed model should be tested in other countries or regions to generalize this study’s conclusions.

In addition, it would be worth investigating how each element separately (physical appearance, clothing, cleanliness and manners) could affect customer evaluation. Besides satisfaction with the use of OFDs, attitudes toward the utilization of these platforms should be studied in more depth, considering the current concerns of society, such as food sustainability (that could be jeopardized due to immediacy and convenience as drivers of online food ordering), and the reduction of the precarious working conditions of delivery workers (Montgomery and Baglioni, 2020). Other issues not addressed in this study are whether price and meal discounts influence satisfaction with OFDs or restaurants. A natural progression of this work is to analyze these variables within a more extensive model, which could provide more explanatory power.

Our study focused on standard delivery (prepared meals), not on innovative food options in which the customer has to end-cook recipes based on some instructions and delivered ingredients. These creative options have been proven to generate greater experiential value for consumers (Gavilan et al., 2021) and could be an interesting segment to be investigated within the OFD topic. Also, analyzing consumer adoption and satisfaction with emerging ways of delivery, such as drone delivery (Shankar et al., 2022), would be a fruitful area for further work.


Components of service encounter in the ODP context

Figure 1.

Components of service encounter in the ODP context

Conceptual model

Figure 2.

Conceptual model

Profile of the respondents

Variables N %
Female 180 54.2
Male 151 45.5
Other 1 0.3
Primary school 2 0.6
Secondary education 112 33.7
Tertiary (bachelor’s degree) 124 37.3
Tertiary (graduate) 94 28.3
Monthly household income
$400 or less 26 7.8
$401–$700 41 12.3
$701–$1200 77 23.2
$1201–$2500 87 26.2
$2501–$4800 62 18.7
More than $4800 39 11.7
Mean 29.62
Median 27
Std. deviation 9.45
Most used OFDs
Glovo 184 55.4
Uber Eats 103 31.0
Rappi 10 3.0
Others 35 10.5
Most bought brands
McDonald’s 61 18.4
KFC 24 7.2
Domino’s Pizza 13 3.9
Others (120+ brands) 234 70.5

Source: Authors’ own creation

Items, factor loadings, reliability and convergent validity

Constructs and items Factor loading Cronbach’s α CR AVE
E-service quality (e-SQ) (Suhartanto et al., 2019; Ryu et al., 2012) 0.880 0.905 0.545
esq1 In the app, I can easily find what I need 0.763
esq2 The app makes it easy to get anything 0.747
esq3 The app is easy to use 0.778
esq4 Whenever I need, I can access the app 0.736
esq5 The app launches straight away 0.746
esq6 The app accurately informs the delivery time and conditions 0.748
esq7 The payment information is safe in this app 0.698
esq8 The ordered products were delivered within the estimated time 0.685
Personal aspects (PASP) (Alhelalat et al., 2017) 0.916 0.934 0.704
pas1 The delivery worker had a clean and well-kept physical appearance 0.783
pas2 The delivery worker’s clothes looked clean and tidy 0.765
pas3 The delivery worker showed friendly facial expressions 0.859
pas4 The delivery worker expressed himself in a friendly and warm way 0.889
pas5 The delivery worker expressed himself courteously and respectfully 0.849
pas6 Overall, the attitude of the delivery worker was cordial 0.881
Perceived food quality (PFQ) (Ryu et al., 2012; Namkung and Jang, 2007) 0.886 0.914 0.638
pfq1 The food was delicious 0.853
pfq2 [Brand] offered a variety of menu items 0.754
pfq3 [Brand] offered freshly prepared food 0.834
pfq4 The food was properly packed 0.779
pfq5 I received the food at the appropriate temperature 0.767
pfq6 The smell of the food was tempting 0.802
OFD satisfaction (e-SAT) (Möhlmann, 2015) 0.870 0.920 0.794
esa1 Overall, I am satisfied with the app 0.925
esa2 The last time I used the app it met my expectations 0.883
esa3 The app used represents the ideal version of an app to order food online 0.864
Restaurant brand satisfaction (BSAT) (Ryu et al., 2012) 0.907 0.941 0.843
bsa1 I am very satisfied with my overall consumption experience of [Brand] 0.916
bsa2 In general, consuming [Brand] puts me in a good mood 0.907
bsa3 I really enjoyed consuming [Brand] 0.931
Restaurant brand image (IMA) (Martínez et al., 2009) 0.876 0.906 0.617
ima1 The products and service of [Brand] have high quality 0.781
ima2 The products and service of [Brand] have better features than competitors’ 0.806
ima3 [Brand] is a brand that arouses sympathy 0.813
ima4 [Brand] conveys a personality that differentiates it from competing brands 0.743
ima5 [Brand] is a brand that does not disappoint its customers 0.776
ima6 [Brand] is one of the best brands in the sector 0.794
Loyalty toward restaurant brand (LOY) (Martínez et al., 2009) 0.831 0.922 0.856
loy1 I consider myself to be loyal to [Brand] 0.928
loy2 [Brand] would be my first choice 0.922
Overall previous brand image (PBI) (Chang and Liu, 2009)
pbi1 Before ordering the food through the app, the overall image that I had about [Brand] was fine

Source: Authors’ own creation

Correlation matrix

e-SQ 0.738
PASP 0.461 0.839
PFQ 0.513 0.507 0.799
e-SAT 0.623 0.493 0.570 0.891
BSAT 0.464 0.439 0.728 0.555 0.918
IMA 0.391 0.322 0.544 0.326 0.658 0.786
LOY 0.236 0.210 0.383 0.232 0.585 0.732 0.925

Square root of AVE on the diagonal

Source: Authors’ own creation

Heterotrait–monotrait ratio

PASP 0.506            
PFQ 0.582 0.557          
e-SAT 0.708 0.545 0.647        
BSAT 0.520 0.476 0.805 0.625      
IMA 0.443 0.357 0.608 0.371 0.735    
LOY 0.274 0.240 0.440 0.272 0.673 0.858  

Source: Authors’ own creation

Path coefficients for the structural model

Hypotheses Path coefficient SD Bootstrapping t p-value
H1 e-SQ → e-SAT 0.403 0.054 7.417 0.000
H2 PASP → e-SAT 0.166 0.057 2.928 0.003
H3 PFQ → e-SAT 0.279 0.061 4.590 0.000
H4 e-SQ → PFQ 0.355 0.057 6.260 0.000
H5 PASP → PFQ 0.343 0.058 5.890 0.000
H6 e-SQ → BSAT 0.105 0.051 2.087 0.037
H7 PASP → BSAT 0.067 0.055 1.216 0.224
H8 PFQ → BSAT 0.640 0.054 11.812 0.000
H9 PFQ → IMA 0.113 0.083 1.355 0.176
H10 BSAT → IMA 0.408 0.083 4.932 0.000
H11 e-SQ → IMA 0.039 0.062 0.637 0.524
H12 PASP → IMA −0.015 0.059 0.262 0.793
H13 IMA → LOY 0.612 0.055 11.204 0.000
H14 BSAT → LOY 0.182 0.059 3.101 0.002
Control PBI → IMA 0.267 0.071 3.742 0.000

p-values below 0.05 are given in italics

Source: Authors’ own creation

R2, f2 and Q2

R2 0.356 0.492 0.544 0.492 0.554
R2 adj 0.352 0.487 0.539 0.484 0.551
Q2 0.224 0.378 0.451 0.298 0.466
e-SQ 0.154 0.219 0.017 0.002
PASP 0.144 0.037 0.007 0.000
PFQ 0.099 0.577 0.010
BSAT 0.126 0.042
IMA 0.476
PBI 0.088

Source: Authors’ own creation

Total effects on spillover relationships

Relation Total effect SD Bootstrapping t p-value
e-SQ → BSAT 0.333 0.055 6.003 0.000
PASP → BSAT 0.286 0.058 4.915 0.000
e-SQ → IMA 0.215 0.062 3.489 0.000
PASP → IMA 0.140 0.059 2.366 0.018
e-SQ → LOY 0.192 0.043 4.463 0.000
PASP → LOY 0.138 0.042 3.290 0.001

p-values below 0.05 are given in italics

Source: Authors’ own creation


Aaker, D. (1991), Managing Brand Equity: Capitalizing on the Value of a Brand Name, The Free Press, New York, NY.

Alalwan, A. (2020), “Mobile food ordering apps: an empirical study of the factors affecting customer e-satisfaction and continued intention to reuse”, International Journal of Information Management, Vol. 50, pp. 28-44, doi: 10.1007/s11846-017-0235-2.

Alhelalat, J., Ma’moun, A. and Twaissi, N. (2017), “The impact of personal and functional aspects of restaurant employee service behaviour on customer satisfaction”, International Journal of Hospitality Management, Vol. 66, pp. 46-53, doi: 10.1016/j.ijhm.2017.07.001.

Annaraud, K. and Berezina, K. (2020), “Predicting satisfaction and intentions to use online food delivery: what really makes a difference?”, Journal of Foodservice Business Research, Vol. 23 No. 4, pp. 305-323, doi: 10.1080/15378020.2020.1768039.

Ansary, A. and Hashim, N. (2018), “Brand image and equity: the mediating role of brand equity drivers and moderating effects of product type and word of mouth”, Review of Managerial Science, Vol. 12 No. 4, pp. 969-1002, doi: 10.1007/s11846-017-0235-2.

Aslam, W., Ham, M. and Arif, I. (2021), “Technology at the dining table: linking perceived value, service recovery, and continuous intention to use food delivery applications”, Review of Business Management, Vol. 23 No. 4, pp. 600-618, doi: 10.7819/rbgn.v23i4.4135.

Blut, M., Chowdhry, N., Mittal, V. and Brock, C. (2015), “E-service quality: a meta-analytic review”, Journal of Retailing, Vol. 91 No. 4, pp. 679-700, doi: 10.1016/j.jretai.2015.05.004.

Brady, G. (1980), “Job to career satisfaction spillover among physician’s assistants”, Medical Care, Vol. 18 No. 10, pp. 1057-1062, doi: 10.1097/00005650-198010000-00009.

Buil, I., Martínez, E. and de Chernatony, L. (2013), “The influence of brand equity on consumer responses”, Journal of Consumer Marketing, Vol. 30 No. 1, pp. 62-74, doi: 10.1108/07363761311290849.

Chang, H. and Liu, Y. (2009), “The impact of brand equity on brand preference and purchase intentions in the service industries”, The Service Industries Journal, Vol. 29 No. 12, pp. 1687-1706, doi: 10.1080/02642060902793557.

Cheng, C., Chang, Y. and Chen, C. (2021), “Construction of a service quality scale for the online food delivery industry”, International Journal of Hospitality Management, Vol. 95, p. 102938, doi: 10.1016/j.ijhm.2021.102938.

Cho, M., Bonn, M. and Li, J. (2019), “Differences in perceptions about food delivery apps between single-person and multi-person households”, International Journal of Hospitality Management, Vol. 77, pp. 108-116, doi: 10.1016/j.ijhm.2018.06.019.

Dablanc, L., Morganti, E., Arvidsson, N., Woxenius, J., Browne, M. and Saidi, N. (2017), “The rise of on-demand ‘Instant Deliveries’ in European cities”, Supply Chain Forum: An International Journal, Vol. 18 No. 4, pp. 203-217, doi: 10.1080/16258312.2017.1375375.

Davcik, N., Vinhas da Silva, R. and Hair, J. (2015), “Towards a unified theory of brand equity: conceptualizations, taxonomy and avenues for future research”, Journal of Product and Brand Management, Vol. 24 No. 1, pp. 3-17, doi: 10.1108/JPBM-06-2014-0639.

Dirsehan, T. and Cankat, E. (2021), “Role of mobile food-ordering applications in developing restaurants’ brand satisfaction and loyalty in the pandemic period”, Journal of Retailing and Consumer Services, Vol. 62, p. 102608, doi: 10.1016/j.jretconser.2021.102608.

Dishman, L. (2020), “The delivery app landscape is changing and sustaining businesses during COVID-19”, available at: (accessed 3 May 2021).

Dropoff (2018), “Same-day delivery + the U.S. consumer, 3rd annual report, 2018 edition”.

Etemad-Sajadi, R. and Rizzuto, D. (2013), “The antecedents of consumer satisfaction and loyalty in fast food industry: a cross-national comparison between Chinese and Swiss consumers”, International Journal of Quality and Reliability Management, Vol. 30 No. 7, pp. 780-798, doi: 10.1108/IJQRM-May-2012-0069.

Euromonitor (2019), “Understanding the socioeconomic drivers of megatrends”, available at: (accessed 3 May 2021).

Euromonitor (2020), “Foodservice delivery in Latin America: the search for growth”, available at: (accessed 3 May 2021).

Fassnacht, M. and Koese, I. (2006), “Quality of electronic services: conceptualizing and testing a hierarchical model”, Journal of Service Research, Vol. 9 No. 1, pp. 19-37, doi: 10.1177/1094670506289531.

Figueroa, M., Guallpa, L., Wolf, A., Tsitouras, G. and Colón, H. (2021), “Essential but unprotected: app-based food couriers in New York City”, available at: (accessed 4 January 2022).

Fricker, R. (2008), “Sampling methods for web and e-mail surveys”, in Fielding, N., Lee, R. and Blank, G. (Eds), The Sage Handbook of Online Research Methods, SAGE Publications Ltd, London, pp. 195-219.

Gallarza-Granizo, M., Ruiz-Molina, M. and Schlosser, C. (2019), “Customer value in quick-service restaurants: a cross-cultural study”, International Journal of Hospitality Management, Vol. 85, p. 102351, doi: 10.1016/j.ijhm.2019.102351.

Gavilan, D., Balderas-Cejudo, A., Fernández-Lores, S. and Martinez-Navarro, G. (2021), “Innovation in online food delivery: learnings from COVID-19”, International Journal of Gastronomy and Food Science, Vol. 24, p. 100330, doi: 10.1016/j.ijgfs.2021.100330.

Hair, J., Jr, Hult, G., Ringle, C. and Sarstedt, M. (2017), A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), SAGE Publications, Inc, Los Angeles, CA.

Han, H. and Hyun, S. (2017), “Impact of hotel-restaurant image and quality of physical-environment, service, and food on satisfaction and intention”, International Journal of Hospitality Management, Vol. 63, pp. 82-92, doi: 10.1016/j.ijhm.2017.03.006.

Henseler, J., Ringle, C. and Sarstedt, M. (2015), “A new criterion for assessing discriminant validity in variance-based structural equation modeling”, Journal of the Academy of Marketing Science, Vol. 43 No. 1, pp. 115-135, doi: 10.1007/s11747-014-0403-8.

Ilies, R., Wilson, K.S. and Wagner, D.T. (2009), “The spillover of daily job satisfaction onto employees’ family lives: the facilitating role of work-family integration”, Academy of Management Journal, Vol. 52 No. 1, pp. 87-102, doi: 10.5465/AMJ.2009.36461938.

Jacoby, J. and Chestnut, R. (1978), Brand Loyalty: Measurement and Management, John Wiley and Sons Inc, New York, NY.

Jang, S. and Namkung, Y. (2009), “Perceived quality, emotions, and behavioral intentions: application of an extended Mehrabian–Russell model to restaurants”, Journal of Business Research, Vol. 62 No. 4, pp. 451-460, doi: 10.1016/j.jbusres.2008.01.038.

Jeon, M. and Jeong, M. (2017), “Customers’ perceived website service quality and its effects on e-loyalty”, International Journal of Contemporary Hospitality Management, Vol. 29 No. 1, pp. 438-457, doi: 10.1108/IJCHM-02-2015-0054.

Kapoor, A. and Vij, M. (2018), “Technology at the dinner table: ordering food online through mobile apps”, Journal of Retailing and Consumer Services, Vol. 43, pp. 342-351, doi: 10.1016/j.jretconser.2018.04.001.

Keller, K. (1993), “Conceptualizing, measuring, and managing customer-based brand equity”, Journal of Marketing, Vol. 57 No. 1, pp. 1-22, doi: 10.1177/00222429930570010.

Kim, G. (2014), “Applying service profit chain model to the Korean restaurant industry”, International Journal of Hospitality Management, Vol. 36, pp. 1-13, doi: 10.1016/j.ijhm.2013.07.008.

Kim, H., Woo, E., Uysal, M. and Kwon, N. (2018), “The effects of corporate social responsibility (CSR) on employee well-being in the hospitality industry”, International Journal of Contemporary Hospitality Management, Vol. 30 No. 3, pp. 1584-1600, doi: 10.1108/IJCHM-03-2016-0166.

Krumpal, I. (2013), “Determinants of social desirability bias in sensitive surveys: a literature review”, Quality and Quantity, Vol. 47 No. 4, pp. 2025-2047, doi: 10.1007/s11135-011-9640-9.

Larson, R.B. (2019), “Controlling social desirability bias”, International Journal of Market Research, Vol. 61 No. 5, pp. 534-547, doi: 10.1177/1470785318805305.

Liu, W., Lee, Y. and Hung, L. (2017), “The interrelationships among service quality, customer satisfaction, and customer loyalty: examination of the fast-food industry”, Journal of Foodservice Business Research, Vol. 20 No. 2, pp. 146-162, doi: 10.1080/15378020.2016.1201644.

Loken, B. and John, D. (2010), “When do bad things happen to good brands? Understanding internal and external sources of brand dilution”, in Loken, B., Ahluwalia, R. and Houston, M. (Eds), Brands and Brand Management: Contemporary Research Perspective, Taylor and Francis, New York, NY, pp. 233-270.

Macias, W. and Cerviño, J. (2018), “Unauthorized use of famous brands and its impact on brand equity dimensions”, Review of Marketing Science, Vol. 16 No. 1, pp. 1-24, doi: 10.1515/roms-2019-0005.

Macias, W., Rodriguez, K. and Barriga, H. (2021), “COVID-19 lockdown and the satisfaction with online food delivery providers”, Estudios Gerenciales, Vol. 37 No. 159, pp. 200-209, doi: 10.18046/j.estger.2021.159.4331.

Martínez, E., Montaner, T. and Pina, J. (2009), “Brand extension feedback: the role of advertising”, Journal of Business Research, Vol. 62 No. 3, pp. 305-313, doi: 10.1016/j.jbusres.2008.05.009.

Mehrolia, S., Alagarsamy, S. and Solaikutty, V.M. (2021), “Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression”, International Journal of Consumer Studies, Vol. 45 No. 3, pp. 396-408, doi: 10.1111/ijcs.12630.

Mentinno (2021), “Ecuador tendencias digitales 2021–2025”, available at: (accessed 4 January 2022).

Möhlmann, M. (2015), “Collaborative consumption: determinants of satisfaction and the likelihood of using a sharing economy option again”, Journal of Consumer Behaviour, Vol. 14 No. 3, pp. 193-207, doi: 10.1002/cb.1512.

Montgomery, T. and Baglioni, S. (2020), “Defining the gig economy: platform capitalism and the reinvention of precarious work”, International Journal of Sociology and Social Policy, Vol. 41 Nos 9/10, pp. 1012-1025, doi: 10.1108/IJSSP-08-2020-0400.

Namin, A. (2017), “Revisiting customers’ perception of service quality in fast food restaurants”, Journal of Retailing and Consumer Services, Vol. 34, pp. 70-81, doi: 10.1016/j.jretconser.2016.09.008.

Namkung, Y. and Jang, S. (2007), “Does food quality really matter in restaurants? Its impact on customer satisfaction and behavioral intentions”, Journal of Hospitality and Tourism Research, Vol. 31 No. 3, pp. 387-409, doi: 10.1177/1096348007299924.

Netzer, T., Krause, J., Hausmann, L., Bauer, F. and Ecker, T. (2017), “The urban delivery bet: USD 5 billion in venture capital at risk?”, McKinsey and Company, Travel, Transport and Logistics, Market report.

Okumus, B. and Bilgihan, A. (2014), “Proposing a model to test smartphone users’ intention to use smart applications when ordering food in restaurants”, Journal of Hospitality and Tourism Technology, Vol. 5 No. 1, pp. 31-49, doi: 10.1108/JHTT-01-2013-0003.

Okumus, B., Ali, F., Bilgihan, A. and Ozturk, A. (2018), “Psychological factors influencing customers’ acceptance of smartphone diet apps when ordering food at restaurants”, International Journal of Hospitality Management, Vol. 72, pp. 67-77, doi: 10.1016/j.ijhm.2018.01.001.

Oliver, R. (1999), “Whence consumer loyalty?”, Journal of Marketing, Vol. 63 No. 4_suppl1, pp. 33-44, doi: 10.2307/1252099.

Olson, J. and Reynolds, T. (2001), “The means-end approach to understanding consumer decision making”, in Reynolds, T. and Olson, J. (Eds), Understanding Consumer Decision Making: The Means-End Approach to Marketing and Advertising Strategy, Lawrence Erlbaum Associates, Publishers, Mahwah, NJ, pp. 3-20.

Parasuraman, A., Zeithaml, V.A. and Malhotra, A. (2005), “ES-QUAL: a multiple-item scale for assessing electronic service quality”, Journal of Service Research, Vol. 7 No. 3, pp. 213-233, doi: 10.1177/1094670504271156.

Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y. and Podsakoff, N.P. (2003), “Common method biases in behavioral research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, Vol. 88 No. 5, pp. 879-903, doi: 10.1037/0021-9010.88.5.879.

Pounders, K., Babin, B. and Close, A. (2015), “All the same to me: outcomes of aesthetic labor performed by frontline service providers”, Journal of the Academy of Marketing Science, Vol. 43 No. 6, pp. 670-693, doi: 10.1007/s11747-014-0407-4.

Ringle, C., Wende, S. Y. and Becker, J. (2015), SmartPLS 3 [Computer Software], SmartPLS, Bönningstedt, available at:

Rivera, M. (2019), “Online delivery provider (OFD) services: who is getting what from food deliveries?”, International Journal of Hospitality Management, Vol. 80, pp. A1-A2, doi: 10.1016/j.ijhm.2019.05.008.

Ryu, K., Lee, H. and Kim, W. (2012), “The influence of the quality of the physical environment, food, and service on restaurant image, customer perceived value, customer satisfaction, and behavioral intentions”, International Journal of Contemporary Hospitality Management, Vol. 24 No. 2, pp. 200-223, doi: 10.1108/09596111211206141.

Saad, A.T. (2021), “Factors affecting online food delivery service in Bangladesh: an empirical study”, British Food Journal, Vol. 123 No. 2, pp. 535-550, doi: 10.1108/BFJ-05-2020-0449.

Schumann, J., Wünderlich, N. and Evanschitzky, H. (2014), “Spillover effects of service failures in coalition loyalty programs: the buffering effect of special treatment benefits”, Journal of Retailing, Vol. 90 No. 1, pp. 111-118, doi: 10.1016/j.jretai.2013.06.005.

Shankar, A., Jebarajakirthy, C., Nayal, P., Maseeh, H.I., Kumar, A. and Sivapalan, A. (2022), “Online food delivery: a systematic synthesis of literature and a framework development”, International Journal of Hospitality Management, Vol. 104, p. 103240, doi: 10.1016/j.ijhm.2022.103240.

Sirgy, M., Efraty, D., Siegel, P. and Lee, D. (2001), “A new measure of quality of work life (QWL) based on need satisfaction and spillover theories”, Social Indicators Research, Vol. 55 No. 3, pp. 241-302, doi: 10.1023/A:1010986923468.

Statista (2022), “Online food delivery – worldwide”, available at: (accessed 31 August 2022).

Suhartanto, D., Helmi Ali, M., Tan, K.H., Sjahroeddin, F. and Kusdibyo, L. (2019), “Loyalty toward online food delivery service: the role of e-service quality and food quality”, Journal of Foodservice Business Research, Vol. 22 No. 1, pp. 81-97, doi: 10.1080/15378020.2018.1546076.

Teichert, T. and Schöntag, K. (2010), “Exploring consumer knowledge structures using associative network analysis”, Psychology and Marketing, Vol. 27 No. 4, pp. 369-398, doi: 10.1002/mar.20332.

U.S. Census Bureau (2021), “Quarterly retail e-commerce sales”, available at: (accessed 26 April 2021).

Voon, B., de Jager, J., Chitra, K., Kueh, K. and Jussem, P. (2013), “Human service matters: a cross-national study in restaurant industry”, Asian Journal of Business Research, Vol. 3 No. 2, pp. 1-11, doi: 10.14707/ajbr.130008.

Wall, E. and Berry, L. (2007), “The combined effects of the physical environment and employee behavior on customer perception of restaurant service quality”, Cornell Hotel Restaur. Adm. Q, Vol. 48 No. 1, pp. 59-69, doi: 10.1177/0010880406297246.

Wang, W., Wang, Y., Zhang, Y. and Ma, J. (2020), “Spillover of workplace IT satisfaction onto job satisfaction: the roles of job fit and professional fit”, International Journal of Information Management, Vol. 50, pp. 341-352, doi: 10.1016/j.ijinfomgt.2019.08.011.

Wang, Y., Tseng, T., Wang, W., Shih, Y. and Chan, P. (2019), “Developing and validating a mobile catering app success model”, International Journal of Hospitality Management, Vol. 77, pp. 19-30, doi: 10.1016/j.ijhm.2018.06.002.

Wheeler, S.A., Gregg, D. and Singh, M. (2019), “Understanding the role of social desirability bias and environmental attitudes and behaviour on South Australians’ stated purchase of organic foods”, Food Quality and Preference, Vol. 74, pp. 125-134, doi: 10.1016/j.foodqual.2019.01.007.

Williams, G., Tushev, M., Ebrahimi, F. and Mahmoud, A. (2020), “Modeling user concerns in sharing economy: the case of food delivery apps”, Automated Software Engineering, Vol. 27 Nos 3/4, pp. 229-263, doi: 10.1007/s10515-020-00274-7.

Yeo, V.C.S., Goh, S.K. and Rezaei, S. (2017), “Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services”, Journal of Retailing and Consumer Services, Vol. 35, pp. 150-162, doi: 10.1016/j.jretconser.2016.12.013.

Yoo, B. and Donthu, N. (2001), “Developing and validating a multidimensional consumer-based brand equity scale”, Journal of Business Research, Vol. 52 No. 1, pp. 1-14, doi: 10.1016/S0148-2963(99)00098-3.


The research was self-financed by the authors.

Corresponding author

Washington Macias is the corresponding author and can be contacted at:

About the authors

Washington Macias received a PhD degree in Management from University Carlos III de Madrid and is Associate Professor at Facultad de Ciencias Sociales y Humanísticas, Escuela Superior Politécnica del Litoral, ESPOL.

Katia Rodriguez is PhD in Business Administration, Instructor and General Coordinator of the Graduate Department, Facultad de Ciencias Sociales y Humanísticas, Escuela Superior Politécnica del Litoral, ESPOL.

Holger Barriga is Doctor in Business Administration and Instructor at Facultad de Ciencias Sociales y Humanísticas, Escuela Superior Politécnica del Litoral, ESPOL.

Related articles