Click, order, dine: unpacking Gen Z's use of OFDAs with key determinants and moderating role of online ratings

The post-COVID-19 era has altered the landscape of company operations, which now heavily rely on online transactions, leading to a dramatic expansion of internet usage (Alaimo et al., 2020). According to Statista (2019a), Internet users in India as of March 2024 were more than 900 million. Today, close to 1 billion people use the Internet; however, forecasts for 2040 show a number of 1.5 billion, which points out the huge potential of the online market (Statista, 2023). During the outbreak of COVID-19, the food delivery and delivery men provided a necessary lifeline for the millions of people quarantined in various locations. An estimated 28.9% compound annual growth rate is anticipated for the Indian online meal delivery industry between 2022 and 2027, according to projections made by IMARC Group. Online Food Delivery Apps (OFDAs) enable local dining establishments such as restaurants, hotels, canteens and chefs to deliver takeout and meal parcels to consumers' doorsteps.

The rapid proliferation of OFDAs has transformed the dining experience, particularly among Gen Z, which is known for its digital fluency and affinity for convenience-driven solutions (Wood, 2013). The increased number of working youths in urban centers requires ready food delivery systems that can be practiced in their busy working lifestyle (Sharma and Saraf, 2020). The application of OFD has revived a new dimension to the dining sector through which users can easily order food from several nearby hotels, restaurants and chefs without using hard-copied menus or pamphlets (Lau and Ng, 2019). This convenient model enhances privacy and ensures the delivery of food as envisaged, since minimal contact is required between humans while ordering. Due to the fact that these apps can be downloaded directly onto a Smartphone right away, they are more easily accessible (Saxena, 2019). As customers increasingly rely on online applications for food, it is critical to understand the variables driving their decision to use this technology. In the past, many researchers have examined the determinants of technology adoption by using different models as a theoretical basis. These are the Theory of Planned Behavior, the Theory of Reasoned Action suitably proposed by Ajzen and Fishbein, Davis’ Technology Acceptance Model in 1989 and UTAUT by Venkatesh et al. (2003). Moreover, the new improved model, UTAUT2, has also been employed by a number of studies in the context of food delivery systems to assess behavioral intentions (Okumus et al., 2018; Alalwan, 2020; Muangmee et al., 2021).

However, there has not been enough research done on comprehending determinants of OFDAs, especially in relation to the Gen Z group of consumers. According to Persada et al. (2019), Gen Z refers to the generation that experienced the commercialization of the internet and was born after 1995. Often referred to as digital natives because of their constant exposure to technology, Gen Z is regarded as quite tech-savvy with a higher inclination towards digital-based solutions (Partono et al., 2024). Currently leading with 32% of the global population, mostly located in developing countries like India and China (Harari et al., 2023; Thangavel et al., 2021), Gen Z is a vital target audience for marketers (Naumovska, 2017). They even possess five to six times greater purchasing power than earlier generations, such as millennials (Djafarova and Foots, 2022). Thus, marketers are demonstrating a growing concern in the comprehension of Generation Z consumerism attitudes, motivations and perceptions (McColl et al., 2021). This research seeks to address this knowledge gap by examining the factors that lead Gen Z customers to execute their usage intentions with OFDAs.

Some of the variables identified in this study as predictors of users’ intention to use OFDAs include price-saving orientation, hedonic drive, perceived performance, social influence, perceived effort, time-saving orientation, perceived information quality and congruity with self-image. As highlighted by Ng et al. (2023), the strategic necessity of companies focusing on addressing internal operational gaps, such as the functionality and reliability of applications, as well as improving external features, including the efficiency of delivery and diversity of the menu, is vital to boost app performance. To effectively meet the needs of consumers, particularly younger and urban demographics who are increasingly attracted to OFD Services (Statista, 2019b), it is essential for operators and developers of these applications to optimize ease of use and operational efficiency. Generation Z is characterised as highly image-conscious and identity-driven while selecting brands or products (Smaliukiene et al., 2020), reflecting that self-image plays a critical role in shaping their attitudes towards OFDAs. Therefore, one important construct that emerged in this context is congruity with self-image, which assesses how well a brand or experience aligns with consumers' perceptions of their identities (Luna-Cortés et al., 2019). Self-image congruence becomes a critical attribute shaping their attitudes and intentions toward OFDS since this generation considers products or services that reflect their individualistic values and self-lifestyle. Understanding this alignment will not only improve user experience but also allow marketers to come up with strategies that will target this influential segment of the consumer market.

In the context of OFDAs, several studies have examined users’ intentions using established frameworks, identifying key predictors as perceived usefulness, ease of use, time-saving, convenience, and price-saving (Ray et al., 2019; Zhao and Bacao, 2020; Indriyarti et al., 2022). However, this line of research has predominantly treated consumers as a homogeneous group, without adequately accounting for generational differences in technology use, value orientation, and food-related behaviours. Consequently, Gen Z, the first fully digital-native cohort that exhibits significant differences from earlier cohorts across various dimensions, has not been sufficiently incorporated into existing OFD intention models. They exhibit high levels of connectivity, demonstrate significant reliance on online reviews, ratings, and user-generated content, and are inclined to seek social validation prior to making consumption decisions, in contrast to earlier generations (Indriyarti et al., 2022). This generation is marked by swift shifts in preferences, a strong inclination to explore diverse cuisines, actively sharing their opinions on social media, and an inclination towards snacking and on-demand food consumption (Shetu, 2024). There is a paucity of research that adapts UTAUT2 to Gen Z’s distinct characteristics, especially in the Indian context.

This study addresses this gap and expands on previous research in three ways. First, this study applies the extended UTAUT2 model (by including other motivational drivers such as information quality, time-saving orientation) to Generation Z consumers in India, who are driving the growth of digital commerce but have been little explored in prior research. Second, it introduces congruity with self-image into the UTAUT2 model to better examine how Generation Z’s choices reflect their identity in today’s world of personalized purchases. Finally, based on the Generation Cohort Theory, this study recognises the differences among generations driven by socio-cultural and technological experiences (Rungruangjit and Charoenpornpanichkul, 2024). This segment typically prioritises convenience and social validation, frequently compares prices, influences household purchasing decisions, and aligns their consumption with their self-image, making it particularly challenging to target (Shetu, 2025). Moreover, this study further aims to examine the moderating role of rating comments by other users between attitude and intention to use OFDAs, capturing how feedback from other users influences Gen Z buyers. In this regard, this paper aims to meet the following objectives:

1. To investigate the factors that affect Gen Z consumers’ behavioral intention towards the use of OFDAs.

2. To examine the role of online ratings as a moderator variable between Attitude towards OFDAs and intentions to use them.

OFD, as defined by Elvandari et al. (2018), utilizes the online platform to purchase meals from restaurants and fast-food establishments. Food delivery apps, often referred to as FDAs, are a new kind of mobile technology based on an online-to-offline model that acts as a middleman, connecting online ordering to caterers on one side and offline delivery options for customers on the other. These FDAs can be split into two categories, i.e. third-party platforms, such as Swiggy, Uber Eats and Zomato, and chains with brands, like Domino's, Pizza Hut and KFC that offer online ordering (Ray et al., 2019). Users download these apps and simply get access to restaurants, examine their menus, order food, and pay without interacting with restaurant workers. Through platform-based promotions and subsidised campaigns, food service providers may reduce costs and streamline delivery processes, potentially improving service efficiency and attracting more consumers and orders (Li et al., 2020).

Table 1 presents a summarized review of the frequently cited and relevant studies in the OFDAs domain. These studies were systematically retrieved from Scopus using keywords like “OFDAs”, “food delivery apps”, “meal delivery apps”, “food delivery applications”, “FDAs” and “usage intention”, “intention to use”, “propensity to use”, “willingness to use”. The search was restricted to publications within business management, social sciences, and multidisciplinary subject areas. Previous studies have investigated the adoption and usage intentions of OFDAs in a variety of countries, including the USA (Belanche et al., 2020), Bangladesh (Amin et al., 2021), India (Chakraborty et al., 2022; Kumar and Shah, 2021; Ray et al., 2019), China (Zhao and Bacao, 2020; Shahzad et al., 2023), South Korea (An et al., 2023), Thailand (Muangmee et al., 2021), and others. Notably, these studies rely on various theoretical perspectives, such as TPB, UTAUT, TAM, UGT and others.

For instance, Ray et al. (2019) applied Uses and Gratification theory along with a mixed-method approach to determine the reasons for FDA usage. It was found that user experience, usability, restaurant search capability, and listing significantly impacted the intention to use FDAs. Zhao and Bacao (2020) combined the UTAUT, Expectation Confirmation Model, and Task-Technology Fit frameworks, incorporating trust, to investigate FDA continuance usage amid COVID-19 in China. Their results underlined that satisfaction was the main driver, supported by performance expectation, trust, social influence, confirmation and task-technology fit. Kumar and Shah (2021) used the Pleasure-Arousal-Dominance model to examine how app aesthetics affect user mood and FDA use during the COVID-19 epidemic. It was found that app design triggers pleasure and dominance, which promotes sustained usage belief, thereby reflecting the psychological effect of app engagement. According to Muangmee et al. (2021), a study conducted in Bangkok, during COVID-19, people’s desire to use FDAs relies strongly on performance expectancy, trust, timeliness, effort expectancy, safety, and social influence. Belanche et al. (2020) applied TPB to investigate motivations for using and recommending FDAs in the U.S. They discovered that attitude and subjective norms affected users' intentions to use and communicate about OFDAs and perceived control was relevant for older users.

However, there is a paucity of research based on the UTAUT2 model looking at FDA usage, and those that are available rarely talk about Generation Z consumers in India. The purpose of this research is to elucidate key determinants of Gen Z’s behavioral intentions to use OFDAs in India. They are defined as the most challenging group because they tend to evaluate and compare products in great detail before deciding to buy (Indriyarti et al., 2022). Since they have grown up using technology, Gen Z wants digital companies to reflect their lifestyle and uphold their principles. This study thus aims to examine these dynamics and offer useful implications to service providers targeting Generation Z consumers.

The Technology Acceptance Model in social psychology has been applied in several studies to explain why individuals embrace technology. In particular, it has been used for the analysis of information systems related to e-commerce, mobile commerce, and online food delivery (Yeo et al., 2017; Roh and Park, 2019; Akram et al., 2020; Hong et al., 2021). Nevertheless, the model's examination of the correlations between variables in IT contexts is constrained since it is unable to sufficiently take into account the number of exogenous factors. It has also been criticized for failing to provide a wide grasp of work-technology settings (Morosan and DeFranco, 2016). Later, Venkatesh et al. (2003) developed the UTAUT by comparing eight behavioral research models in order to comprehend and categorise the behavior of adopting technology. As postulated in UTAUT, perceived and instrumental variables such as expectations in terms of performance and efforts, facilitating conditions, and social impact influence an individual’s willingness to accept or use technology. Building on the theoretical framework of UTAUT, Zhao and Bacao (2020) identified factors influencing the intention to persistently use FDAs during the COVID-19 timeframe.

However, the UTAUT was created with the organizational context in mind to better understand how and why employees embrace technology in their jobs, but there were still shortcomings in the model. Hence, in consumer technology adoption, the engagement efficacy expectation was expanded in the new UTAUT2 paradigm by including price value, hedonic encouragement, and habit in the UTAUT model as devised by Venkatesh et al. (2012). The food delivery system and behavioral intents have been the subject of several research studies that have employed the UTAUT 2 model (Okumus et al., 2018; Alalwan, 2020; Zhao and Bacao, 2020; Muangmee et al., 2021). Dwivedi et al. (2019) found some limitations per UTAUT-based theories, such as in their meta-UTAUT revised model, the authors integrated attitude as a mediating link and claimed that this addition had improved the model's explanation. Particularly in the early phases of adopting technology, the concept of attitude is crucial to determining an individual's behavioral intentions towards engaging in underlying behavior. This study will examine the determinants of intention to use FDAs with attitude as a connecting link while employing an extended UTAUT2 model among Gen Z consumers, which is less explored in the previous literature.

Of the UTAUT2 model, this study identified that performance expectation, effort expectancy, social impact and hedonic motivation are some of the core constructs affecting one’s choice to embrace technology. Performance expectancy is the term used to describe the user's anticipation that the system will be able to help them do their jobs (Venkatesh et al., 2003). Effort expectancy can be used to explain the user’s ease in utilizing a certain system. Hedonic motivation deals with the emotional aspect of a person while buying products and services. The final one is a social effect, which is the notion and attitude where people embrace and use the new system by getting influenced by others (Venkatesh et al., 2003). Facilitating circumstances were not maintained due to the rapidly growing food services industry in India (which is projected to double by 2025) and the approaching ubiquity of restaurant offerings and OFDS. Habits were not kept since the present research considered Z consumers' hedonic reasons and the notion of self-image congruence, thereby providing a more thorough foundation of a consumer's evaluations of their own self-image (Stets and Burke, 2003). This study included additional variables such as price and time-saving orientation to assess the time and cost influence on technology adoption behavior of consumers, considered as key attributes as per Attribution theory by Fiske and Taylor (1991). This theory explains how individuals interpret events and how these interpretations influence their subsequent behavior. Specifically, it addresses the cognitive process by which people attribute causes to outcomes based on the information available to them (Fiske and Taylor, 1991). In this study, Attribution Theory is used to reflect how consumers evaluate the utility of using FDAs in terms of convenience and economic benefit. Generation Z, particularly in urban settings or fast-paced cities, exhibits a strong preference for efficiency and instant gratification (Bashir et al., 2015). Considering lifestyle preferences, Gen Z is characterised by price sensitivity with a strong emphasis on time efficiency and information diversity (Keber, 2023). This makes price and time significant predictors of their OFD behavior, along with the function of information quality, which is relevant, particularly in the setting of an online platform (Lee et al., 2019). Moreover, the concept of self-image is included as Gen Z consumers (for example, those who are younger and live in cities) are probably more attracted to online services than previous generations (Statista, 2019b). To account for the underlying motivations that may lead to the use of OFDS among Gen Z consumers, this theoretical foundation thus required a construct expressing such motivational fabric. On that account, this study incorporated congruity with self-image as a determinant that quantifies the extent to which any offering or brand corresponds with the way that customers perceive themselves (Luna-Cortés et al., 2019). To improve the robustness of the research model, the study included attitude as a connecting link between predictors and the intention of using OFDAs. The proposed research model for the present study is illustrated in Figure 1.

As explained by Venkatesh et al. (2003), it is the expectation that a user has that a technology will enhance their performance at work. As a result, when people believe new technology will improve their ability to do their jobs, they are more prone to utilize it (Alalwan et al., 2018). Performance expectancy has been proven to be a reliable indicator of intentions to use in a variety of contexts, including mobile technologies such as banking and shopping contexts (Tam and Oliveira, 2016). The statistical evidence offered by Okumus et al. (2018) supported its importance in influencing the customer's intention to use mobile FDAs. OFDS was developed to make the process of placing an online meal order more convenient, either in place of or in addition to conventional methods. Customers may also optimize their orders on OFDAs by reviewing details about planned purchases before beginning work. In a similar vein, usability has been considered a related factor to expectations in terms of performance (Yeo et al., 2017). Thus, the following hypothesis is formed in light of the discussion above and recent literature:

Venkatesh et al. (2003) stated that effort expectancy denotes the extent of ease and minimum exertion an individual perceives while using a new technology or system. Customers prioritize ease of use and less effort when adopting new systems (Davis, 1989). FDAs are convenient and easy to use, since customers can order meals with the help of these apps in several simple clicks. Users can easily navigate through a wide variety of local restaurants and food options. In a previous study (Okumus et al., 2018), it was noted that effort expectations had an impact on users' intentions to utilize mobile applications. Customers are repeatedly seen to be particularly interested in how simple and easy a new technology is to use (Alalwan et al., 2018). Therefore, the following hypothesis is developed:

Another significant aspect is the social impact or the degree to which the opinions of other people in terms of peers, relatives and others matter in influencing the behavioral decision to embrace the new technology (Venkatesh et al., 2003). Literature has shown that consumers' behavioral attitudes and intentions to use new technology, products, and services were positively influenced by their social lives (Venkatesh et al., 2012; Chen et al., 2018). One way to conceptualize the social impact is the belief that using contemporary technology would enhance a person's social position or identity. On this basis, the following hypothesis was framed:

In addition to extrinsic factors like perceived utility and performance anticipation, intrinsic factors also play a significant role in influencing customers' intent and desire to adopt new apps and systems (Venkatesh et al., 2012). One of the intrinsic motivations is the sensorial, creative, and emotional aspects of using products or services, which is referred to as hedonic motivation. Literature has shown hedonic motivation as a key driver of users' intention to adopt mobile technologies (Baptista and Oliveira, 2015; Koenig-Lewis et al., 2015). It is related to the users' desire, amusement, and enjoyment to use new innovations or technology (Venkatesh et al., 2012). When it comes to FDAs, customers can order food using a mobile device without leaving their homes or places of business. Customers are encouraged to use the food delivery application because they enjoy using it. Thus, the hypothesis is developed as:

Information quality refers to the degree to which information provided by the system is precise, pertinent, timely, and exhaustive for efficient user decision-making (Zhou, 2011). It relates to the measurement of system performance to the extent that the user is provided with important and timely information (Zhao, 2019). Accuracy, up-to-dateness, dependability, completeness, applicability, clarity, and computerized formats are some of the desired characteristics of information quality (Swaid and Wigand, 2009). The quality of information influences the usage intentions of e-commerce platforms by enhancing customers' trust-based attitudes (Escobar-Rodriguez and Carvajal-Trujillo, 2014). As stated by Puriwat and Tripopsakul (2021), FDAs provide extensive information about menu, restaurant and location selections as well as multiphase order tracking. However, due to the lack of findings in the context of OFD services, especially among the consumers of the Z generation, this study developed the hypothesis as:

The term price-saving orientation describes how people use technology to save money by getting goods and services at lower costs. As highlighted by Jung (2014), customers would have savings in terms of both price and time while purchasing goods and services online. Comparative pricing, discounts and promotions have the potential to attract price-conscious customers who would choose a channel that gives the most value for their money. Price value was shown to be among the most important predictors of influencing a customer's decision-making to persistently use mobile websites (Venkatesh et al., 2012). According to Yeo et al. (2017), companies with more affordable pricing are perceived as having a more successful platform by users. Thus, the hypothesis is formulated as follows:

Online consumers are constantly seeking to save time while making purchases, thus reflecting a time-saving attitude. In the modern world with a fast-paced culture, people opt to have their meals delivered to them rather than deal with the hassle of dining out or the inconvenience of standing in the queue for food to be served (Prabowo and Nugroho, 2019). Moreover, as a digital native cohort, Gen Z, particularly those residing in urban settings, have a concern about saving their time and thus focus on doing the tasks in the least amount of time possible (Indriyarti et al., 2022). Customers' attitudes and intentions to use the online system are found to be related to time-saving oriented (Yeo et al., 2017). Due to the lack of findings in the context of online food delivery services, especially among young consumers, this study develops the hypothesis that:

According to Sirgy and Su (2000), congruity with self-image describes how well a product or brand aligns with customers' own self-perceptions in terms of their motivations for making purchases. Specifically, an individual might connect to tech, and over time, it becomes an integral part of their identity. Customers who exhibit strong intent to utilize OFDS may be those whose value system is highly congruent with them (Gunden et al., 2020). Even certain consumers (such as younger and urban consumers) are probably more drawn to OFDS (Statista, 2019b). Gen Z is transforming the ways of food preparation, ordering, packaging, and delivery, driven by their unique taste preferences, technology-centric lifestyle, and focus on efficiency (Akhmadi et al., 2021). Thus, the hypothesis was framed as:

An attitude is a predetermined behavior made up of ideas, emotions, feelings, and thoughts. As stated by Etiyawati et al. (2016), these personal opinions can either be favorable or unfavorable toward a certain behavior, which would influence the adoption of new technologies. People can be more inclined to embrace new technology and accept it with a positive attitude, thereby increasing their inclination to use it in the future if they see the benefits of utilizing it (Hwang et al., 2019). When it comes to the acceptability of mobile services, attitude has regularly been proven to be a strong determinant of intention to use them (Chen et al., 2018). Thus, this study developed the following hypothesis:

Online ratings have emerged as easily interpretable numerical indicators of service quality, offering quick evaluative cues about a restaurant or product (Filieri, 2015). Consumers assess products and services using a numerical rating system (e.g. a 5-star rating system) based on attributes (e.g. quality, price, accuracy, experience) provided by prior users on the basis of their experiences (King et al., 2014). With the expansion of online commerce, ratings have become increasingly influential in shaping consumer judgments, particularly for many Gen Zs, who display low institutional trust and rely heavily on peer-generated signals to build confidence in online services (Priporas et al., 2017). Customers’ reviews on products also influence both actual buying and repeat buying decisions to a large extent (Alalwan, 2020). The aggregation of multiple ratings generates a sense of consensus, which further influences purchase intentions by signalling social proof (Chen et al., 2021). Known as the e-Generation, Gen Z consumers are hyperconnected and heavily rely on the trustworthiness of reviews from previous users. This generation is characterised by rapid preference shifts, a strong inclination to explore diverse cuisines, and an active sharing of opinions on social media (Shetu, 2024). Considering this, the moderating effect of online ratings on the relationship between attitude and behavioral intention toward the usage of OFDAs has been examined in this study. Thus, the hypothesis is formed as:

The present study employed a cross-sectional, descriptive, and quantitative research approach. According to Rindfleisch et al. (2008), a cross-sectional technique is employed to capture the present behavioral patterns and views of customers by using data obtained at a specific moment in time, thus making it appropriate to understand key predictors that affect one’s intention to use OFDAs throughout the given period.

Cities exhibit unique characteristics that influence individual behavior differently (Long et al., 2023). As per Statista (2019b), younger and urban consumers are probably more drawn to OFDS. Chandigarh, designated as a Union Territory and serving as the capital for two Indian states, was selected for the study. It is characterised by significant mobile and internet penetration (89% of households), a diverse socio-economic landscape, and an evolving modern lifestyle (Kaur et al., 2018). These factors have contributed to an increased acceptance of ready-to-eat and delivered meals, particularly in urban settings (Sharma and Saraf, 2020).

The data was obtained by using a structured questionnaire through both online and offline modes. To collect the data online, the Google form was distributed via WhatsApp groups, while offline outreach targeted Gen Z via educational institutes, and local parks near Paying Guests (PGs) locations, as well as rented accommodations. People residing in these PGs are more likely to use OFDAs due to living away from family, busy schedules and limited access to cooking facilities. The sample focused on urban Generation Z consumers, a digitally native demographic characterised by significant technology adoption and a preference for convenience (Wood, 2013).

Andrade (2021) noted that purposive sampling is suitable when participants are deliberately chosen based on attributes pertinent to the goal of the investigation. Consequently, a purposive sampling technique was used, selecting respondents through a screening question: “Have you ever used any OFDAs in the past six months?” to ensure that only individuals with prior OFDA experience were included. According to Hair et al. (2019), the study's minimum sample size needs to be approximately five times the total number of parameters included in the research. Consequently, a minimum of 185 observations were needed. In the beginning, a total of 480 questionnaires were distributed, out of which 412 responses were gathered with a response rate of 85.8%. Moreover, analysis was conducted on a sample of 389 responses from the target group after excluding incomplete responses or those who had not used the OFDAs in the previous six months.

The questionnaire started with the screening question, followed by items measuring various determinants influencing attitude towards and intention of using OFDAs. A seven-point Likert scale, ranging from 1 (strongly disagree) to 7 (strongly agree), was used to measure the items, providing nuanced responses and adequate data variability. The measurement items were adapted from the previous studies as, attitude towards OFDAs from Childers et al. (2001), effort expectance from Palau-Saumell et al. (2019), information quality, social influence, hedonic motivation and performance expectancy from Lee et al. (2019), price saving orientation from Escobar-Rodriguez and Carvajal-Trujillo (2014), time saving orientation from Yeo et al. (2017), congruity with self-image from Antón et al., 2013, online ratings from Filieri (2015) and intention to use OFDAs from Goyal et al. (2023).

The SmartPLS software (version 4.1.0.0) was utilized to conduct the analysis of the proposed research framework and validate the hypotheses. SmartPLS makes it easier to assess the measurement model's psychometric qualities while evaluating the associations in the structural model, including moderating latent variables (Hair et al., 2014).

Analyzing the demographic structure of the respondents, the characteristics of the surveyed population can be seen in Table 2. As for the gender distribution, out of the total (389) responses, 53.5% of the sample were females (n = 208), while males were 46.5% of the participants (n = 181), representing a fair distribution. In the area of education, most of the respondents were postgraduates (46.01%), followed by those with an undergraduate degree (31.6%). The sample also included respondents who had vocational training (7.7%), a diploma (11.6%), and other educational levels (3.1%). The large proportion of those enrolled in or just graduated from college demonstrates a group that may require effective OFDAs to help them optimize their time and daily tasks efficiently. This is also consistent with the existing literature, which highlights that Gen Z is in their late teens and early to mid-twenties now and that a large section of this generation is either pursuing or has just finished higher education (Black et al., 2017; Priporas et al., 2017). The family’s monthly income distribution indicates that the respondents were a significant percentage of Indian middle-income families, i.e. 65.2% in the 30,000 to 60,000 bracket, with the above 60,000 coming in second (22.8%). In terms of birth year, the majority of respondents, i.e. 65.3%, were born between 1995–2000, followed by 31.9% between 2000–2005, with very few born after 2005. Birth year was used to reflect Gen Z as per generational theory, which groups cohorts by socio-cultural experiences rather than age, thus ensuring consistency with the literature. Digital fluency in Gen Z, also called iGeneration, leads even young people to demonstrate consumer decision-making skills due to internet exposure and interaction with technology at a very early age (Nguyen and Nguyen, 2023; Philip and Garcia, 2013). Therefore, the study didn't restrict participation based on adulthood status to capture the broader generational ethos influenced by shared digital environments, online habits, and content preferences. Respondents born after 2010, however, are typically categorized as Generation Alpha and are therefore excluded due to their unique socio-technological characteristics, developmental phases, and consumer socialization patterns (Aksar et al., 2025). Respondents were also asked about their experience with OFDAs, with 51.7% reporting 1–2 years of experience and 25.4% reporting 3–4 years. Those with more than four years of experience made up 22.9% of the sample.

Before testing for the model, the test for Common Method Bias (CMB) was employed, which may emerge when self-report data is collected from the respondents. To minimise bias, steps such as guaranteeing the anonymity of the respondent to reduce social desirability bias and promote honest responses were taken. For the statistical testing for common method variance, Harmon’s single-factor test was used. This test presupposes that a single factor should be the major contributor to the variance in the data if CMB is a significant concern. However, the output from the unrotated principal component factor analysis revealed that the first factor accounted for only 39.134% of the total variance. This percentage remains quite below the critical 50% threshold established in the literature for CMB concerns (Podsakoff et al., 2003; Fuller et al., 2016), suggesting that CMB does not significantly threaten the validity of the study findings.

The measurement model of the present study rigorously evaluated both the reliability and validity of the constructs. As reflected in Table 3, internal consistency or reliability has been established by evaluating factor loadings, Cronbach’s Alpha, and Composite Reliability (CR). Notably, all constructs had values higher than the threshold level of 0.50 (Hair et al., 2019). Convergent validity was also tested through Average Variance Extracted (AVE), which shows the proportion by which all the exogenous and endogenous constructs account for variabilities in their items. It must be 0.50 or higher, reflecting that at least half of the variance of the items was captured by the construct (Hair et al., 2014). The findings proved that AVE varied between 0.587 and 0.797, hence corroborating construct reliability and validity.

Discriminant validity was also assessed in the study, which is crucial for the identification of whether a specific construct is unique from other constructs in the model. The criterion for discriminant validity was tested by the Fornell-Larcker Criterion and the Heterotrait-Monotrait Ratio as expressed in Tables 4 and 5. The Fornell-Larcker Criterion assumes that the square root of each construct's AVE, represented by diagonal values, must exceed the corresponding off-diagonal correlations with other constructs (Fornell and Larcker, 1981). This model successfully confirms this criterion, thereby indicating adequate discriminant validity.

In a similar vein, the HTMT Ratio, which employs a threshold of 0.90 to evaluate discriminant validity, was also satisfied as HTMT values ranged from 0.05 to 0.859, implying that discriminant validity has been established (Henseler et al., 2015). Consequently, it can be concluded that the measurement model provided a robust foundation to proceed to test the structural model and hypothesis.

After confirming the measurement model, the structural model was created to evaluate the interactions between various factors impacting consumers' attitudes and behavioral intentions toward utilizing OFDAs. Prior to conducting the path analysis, a collinearity assessment was performed, ensuring that the constructs were not excessively correlated. Table 6 depicts that the Variance Inflation Factors (VIF) for the constructs were below the threshold of 5, confirming the absence of multicollinearity issues (Hair et al., 2014).

The coefficient of determination (R²) demonstrates the extent of influence of the independent variables on the dependent variables. Table 7 shows that more than 70% of the variance in attitude toward OFDAs was explained by factors like effort expectancy, social influence, hedonic motivation, quality of information, congruity of self-image, price-saving orientation, performance expectancy and time-saving orientation. Moreover, more than 50% of the variability in intention to use OFDAs could be accounted for by the model constructs. The literature defines values above 0.25 as a weak explanatory rate, values above 0.50 as a medium rate, and values greater than 0.75 as a strong rate (Muller and Cohen, 1989). Consequently, the proposed model exhibited a moderate to high level of explanatory or predictive efficacy for both attitude and behavioral intention.

The structural model was further assessed for significance using path coefficients, t-values, and standard deviation via the bootstrapping approach, which resamples the data 5,000 times (sample size = 389) to determine the results' stability. Based on the analyzed sample, a significance level of 0.05 was reported for the path coefficients (Table 8). Performance expectancy (PE), Social Influence (SI), Hedonic Motivation (HED), Price Saving Orientation (PS), Time Saving Orientation (TS), Congruity with Self Image (CS) had positive and significant association with Attitude towards OFDAs (ATT) with beta values of b = 0.202, b = 0.094, b = 0.095, b = 0.229, b = 0.140 and b = 0.197. Two variables were found to have an insignificant relationship, which were Effort Expectancy (EE) and Information Quality (IQ). The path coefficient of 0.608 indicated a strong positive relationship between consumers' attitudes towards OFDAs and their intention to use them, which is supported by the theory of planned behavior. Thus, H1, H3, H4, H6, H7, H8, H9 and H10 were supported. The validated model is shown in Figure 2 (software output).

The PLS-SEM indicator approach was utilized to estimate the degree to which online ratings moderated participants' attitudes towards and intentions to utilize OFDAs. As noted by Hair et al. (2014), t-values exceeding 1.64 are considered significant at the 5% level. The interaction term online ratings between attitude and intention had a path coefficient of 0.103, with p = 0.005, suggesting that it has a favorable moderating influence on the association between attitude formation and exhibiting intention to adopt OFDAs.

Figure 3 depicts a slope diagram illustrating the moderating impact of online ratings on the association between attitude towards OFDAs and intention to utilize them. The red line signifies the relationship when online ratings are low (−1 SD); on the other hand, the blue line represents an average level, while the green line reflects high online ratings (+1 SD). Notably, the slope of the green line is steeper than that of the blue and red lines. This suggests that, although online ratings are pivotal, their impact intensifies when paired with a positive attitude towards OFDAs—thus, they exert a more pronounced influence on the intention to use these services.

This study, particularly focusing on Z-generation consumers, examined several factors that influence their propensity to use OFDAs. The results showed that expectations in terms of performance had a significant and positive association with attitude and hence the behavioral intention to use OFDAs, thus supporting H1. The results align with the findings of Oliveira et al. (2014) and Shaikh et al. (2018), reinforcing the notion that users tend to adopt technologies that facilitate the completion of tasks more effectively. In the context of OFDAs, Gen Z is enticed by the useful benefits of such platforms, such as enhancing everyday efficiency and finding speedy solutions to issues, which resonates with the core of performance expectations. Regarding H2, concerning the effort expectation and the attitude towards OFDAs, results found no strong evidence of a statistical correlation between the two. We can conceptualize that the actual Gen Z people, who were raised in the digital sphere, do not believe that it requires much exertion to use OFDAs. This resonates with a study by Andrianto (2020), which reveals that consumers, particularly millennials, find internet services user-friendly. Therefore, it can be asserted that high levels of technology literacy characterize the Gen-Z population to view OFDAs more like simplistic tools; thus, ease is not a factor in attitudes toward the apps. Their adoption choices are more likely influenced by factors such as performance and convenience, which are more value-driven, rather than by effort-related considerations. Supporting H3, the study found a positive and significant association between social influence and attitudes regarding the usage of OFDAs in Gen Z. This supports the studies by Alalwan et al. (2018) and Pitchay et al. (2021) which highlighted the consideration of social influence in predicting technology use, especially those from a collectivistic culture who view the recommendations of their friends and relatives as influential. It can be concluded that Gen Z’s attitude towards OFDAs is informed by the perceptions of other people. Supporting H4, the study reported that hedonic motivation significantly improved attitudes towards the use of OFDAs, which is also consistent with Venkatesh et al. (2012), suggesting that pleasure and enjoyment should be linked to the adoption of technology. This implies that Generation Z focuses on fun, experience and entertainment in their aspect of consumption. Further, since the quality of the information had a non-significant impact on attitude towards using OFDAs, H5 was not accepted. Considering how trustworthy and precise information is crucial while making decisions about digital services, this outcome came as an unanticipated one. This finding is inconsistent with Noh and Lee (2015), who stated a positive association between the quality of information and use intention. One possible explanation is that Gen Z users, especially those in urban areas, have time constraints and thus prioritize convenience. As a result, they prefer quick indicators such as peer reviews, ratings, or suggestions over detailed or structured information from the app. Because of their high level of digital literacy, they can browse and make decisions effectively without relying too much on the quality of the information provided by the platform. The research further indicated that Gen Z user perceptions of OFDAs were favorably influenced by a focus on cost savings (H6). This finding is consistent with Ali et al. (2020). This shows that customers in Gen Z would appreciate OFDAs more when they offer greater value for the money and they might shift rapidly to the one with more cost-efficiency by comparing the prices of different providers.

Moreover, the current study found that emphasizing time-saving significantly and positively influenced individuals' perceptions of OFDAs, thereby validating H7. This finding is consistent with Pitchay et al. (2021) and Indriyarti et al. (2022), who indicate that perceived time-saving benefits greatly impact consumers' willingness to adopt online platforms. This reflects that Gen Z people tend to use OFDAs to avoid the hassle and streamline their busy lifestyles. This generation values time-saving features such as quick delivery, one-tap reordering, and intuitive interfaces while using online platforms. Through H8, the study revealed that congruity with self-image had a significant impact on Gen Z users’ attitude towards OFDAs. For this image-conscious and identity-driven generation, the usability and appearance of these applications significantly shape their intentions to engage with them. In other words, the design and marketing of OFDS are essential in motivating customers to evaluate the usability and visual appeal of these systems, which act as catalysts for shaping consumers' intentions. Belk (1988) found that features of commercial artefacts that facilitate people's establishment of behavioral pathways towards a certain product were significantly correlated with consumer self-concept. Finally, the study revealed that favorable attitudes about OFDAs significantly influenced the propensity to utilize them (H9). Consumers' attitudes about the service are influenced by the value that it brings, in the case of OFDAs, such as assisting them in choosing meal selections, which further encourages them to utilize these applications. Lastly, it was concluded that online ratings had a moderated effect on consumers’ attitude and intention toward using OFDAs (H10). This corroborates the social proof theory that posits that the chances of a consumer embarking on a particular behavior are higher still when there is some positive outside feedback, notably the ratings (Cialdini, 2007). This suggests that the probability of Gen Z people engaging with the OFDAs increases when positive sentiments are combined with online ratings.

This study expanded the UTAUT2 model by integrating and substantiating the congruity with self-image as an innovative determinant of attitudes toward OFDAs, thereby underscoring its significance in influencing consumer behavior. By examining the Gen Z intention to use OFDAs, this study enriches generational cohort theory by addressing the unique characteristics of this tech-savvy, convenience-driven segment (Rungruangjit and Charoenpornpanichkul, 2024). In addition, the moderation effect of online ratings on attitude and the intention to use OFDAs provided a nuanced understanding of the effect of external social validation, bearing a deeper comprehension of the decision-making of using OFDAs among Gen Z consumers. Thus, these results enrich the theoretical body of knowledge, notably by incorporating self-concept theory as well as revealing the importance of external information cues in the sphere of digital consumers. Findings show that Generation Z prioritises peer reviews and user cues over structured app content, challenging traditional quality notions. This requires a nuanced theoretical understanding of “informational trust” in Generation Z's decision-making, shifting focus from platform content to crowdsourced credibility.

The research underscored that OFDA providers seeking to captivate and maintain Gen Z customers must employ value-driven strategies. As digital natives, Gen Z users have higher expectations for efficiency, smooth navigation, and visually engaging interfaces; therefore, platform developers must ensure a seamless ordering process enhanced with high-resolution food images, real-time updates, and quick system responsiveness to reinforce performance expectations. Determinants such as price-saving and time-saving orientation indicated the necessity for discounts, cashback, loyalty programs, expedited delivery options, and real-time tracking to appeal to Gen Z's cost-conscious and time-sensitive behavior. The significance of performance expectancy and hedonic motivation suggests providing high-quality service while boosting app usage delight through gamification, captivating graphics, and interactive features to stimulate enjoyment and sustain user engagement. Gen Z values speed and efficiency. App developers should prioritise minimising load times, facilitating single-click reordering, implementing voice-based navigation, and predictive estimated travel time features. The moderating effect of online ratings highlighted the timely need to encourage customer reviews, display ratings prominently, and build customer trust through honest feedback and responsive customer service. Furthermore, the influence of social networks along with their alignment to personal identity indicates that marketing strategies need to correspond with the essence of Gen Z. Active social involvement in digital communities gives this generation a sense of belonging and self expression, therefore, the in-app social interaction features, peer recommendations, sharing options, user badges, and review incentives can enhance their engagement with the apps. Gen Z is more interested in sustainability and ethical behavior while reflecting online behavior (Brand et al., 2022); thus, leveraging influencer collaborations to promote these values would develop emotional ties with consumers. Gen Z, unlike previous generations, desires to experience personalised experiences in their online interactions to express their individuality and self-identity (Lim et al., 2024). Therefore, offering AI-driven user interfaces, tailored recommendations, and customised menus can foster a deeper brand connection and loyalty among Gen Z users.

Several limitations must also be highlighted in connection with this study. Initially, even though it studied key factors that affect the intention of Gen Z consumers to use OFDAs, it does not consider the other factors that might affect users’ intention, including cultural, geographical location and personal factors like lifestyle choices. Further, this study was confined to urban Generation Z individuals in India. The findings may not reflect the broader Gen Z views, especially in rural areas, due to economic and housing influences.

It is recommended that studies capture a more inclusive set of variables, such as demographic variations, lifestyle patterns, etc, in the future. Furthermore, this study employed a cross-sectional design to assess users’ intentions, which only reflect their attitudes and behaviours at a specific point in time. Nonetheless, longitudinal research studies offer an intricate understanding of the ways user intentions shift in response to evolving trends and technologies. In addition to that, the preferences of the rural Gen Z must be further explored, or a comparison between urban and rural categories can be made to derive broader insights.