1. Introduction
The notion of online food delivery is not new to the world. Recent years have witnessed a huge shift in customer behavior from traditional dining to online food deliveries. The omnipresent effect of technology and a widespread rise in disposable income has prompted customers to have their food with effortless ordering through food delivery applications [1]. The global food industry is undergoing a fundamental shift as new digital platforms are rapidly capturing markets with a strong customer base. From traditional offline food ordering to a dedicated advanced mobile phone app, the overall method of ordering food has now become the story of a single click. In the current scenario, customers’ increasing willingness to purchase through mobile applications is constantly reshaping market dynamics. Food delivery application has become a phenomenal disruptor in the food delivery industry. Food delivery platforms have also been seen as the most convenient feature for customers during the global COVID-19 pandemic. Customers demand a better and more consistent experience when making most purchases, including in food choices. They expect the assurance of timely delivery, discount, offers, and freedom of order customization [2]. It is no surprising fact that Asia holds the biggest chunk with a 55% share in the global food delivery market [3]. In addition, online food delivery through mobile apps has also increased in Pakistan. The restaurant industry in Pakistan has a fundamental growth rate of approximately 20–25% per year and it explicitly shows growth potential for the next decade. Exponential growth has also been recorded in restaurant businesses linked with digital platforms dealing in food deliveries. Despite the unavailability of authentic data, the industry experts estimate the market to be $150 billion a year [4]. However, the success of these online food delivery applications depends upon customer retention. According to [5], one of the key benchmarks in creating satisfied customers is to enhance their app experience. Mobile applications have the potential to create a positive customer experience as they allow businesses to obtain a deeper understanding of the customer shopping journey. However, the main objective of mobile applications is not just to provide a better customer experience, but also to build long-term customer relationships. Therefore, it is imperative for food retailers to understand customers’ application experience and how it might affect their purchase intention [6]. Customer experience is a universal concept that can be examined in different forms. Former studies have mainly discussed the cognitive and affective as the two main dimensions of online customer experience [7,8,9]. However, there is scant literature on customer experience in the context of mobile app shopping [10,11].
Regardless of the importance of cognitive and affective customer experience in the retail landscape, there is a dearth of studies that simultaneously focus on both perspectives in online food delivery applications [12,13]. Even though food delivery applications have been driving considerable attention, only limited studies have discussed related customer behavior. The majority of the research into food delivery applications has analyzed customer intention, perception, and initial adoption [14,15]. Despite the increasing adaptation of food delivery applications, knowledge regarding the importance of cognitive and affective experiences in application satisfaction and repurchase intention has not been fully considered and examined by academic researchers. Therefore, the current study was conducted to examine the impact of cognitive and affective experiences in terms of application satisfaction and repurchase intention. To address the above-mentioned knowledge gap, we further extended [12,13,14,15,16] the model by introducing antecedents of cognitive and affective experiences and by adding a situational factor as a moderating variable between application satisfaction and repurchase intention. Food delivery application users were surveyed through questionnaires to note their responses about the proposed model which may influence their decision to repurchase. The remainder of the paper is discussed in the following manner: The next section contains a thorough review of the literature along with a conceptual framework and hypothesis development, while the research methodology section presents details about the research design and data collection instrument; the last section presents a detailed analysis of the study findings and a discussion of results.
2. Literature Review
2.1. Cognitive Experience
Customer experience is the result of customer interaction with the organization which instigates maximum value, nurtures satisfaction, and motivates repurchase intention [17]. Modern retailers have emphasized augmenting customer experience in terms of both cognitive and affective perspectives not only in brick and mortar settings but also in all customer touch points, particularly in mobile shopping applications [15]. It has been observed from previous studies that the customer experience construct can consist of multiple dimensions [18]. It has also been noted that few researchers have added cognitive and affective approaches as an antecedent of customer experience in their conceptual framework. Reference [19] uncovered that the experience that customers undergo during navigation is concerned with cognition. Reference [12] considered “cognitive as the ideal experience” felt by customers. Taking the same stance, [5] described that cognitive experience is based on user rational experience during all points of interaction with food delivery applications in terms of navigation, information access, and order tracking. Furthermore, consumer behavior studies in the context of online food ordering systems instill that cognitive experience originates from the cognitive aspects for instance application effectiveness, information access, utilitarian value, and perceived usefulness. Cognitive customer motivation stood as a prime standard in reinforcing loyalty towards food delivery networks. While using online food delivery applications, customers show more adaptation towards cognitive cues which act as a predecessor in building application satisfaction [20].
2.2. Antecedents of Cognitive Experience
The findings of the above studies and the theoretical framework developed by [5,9,16] give us a hand in identifying and setting the following antecedent for cognitive customer experience concerning food delivery applications.
2.2.1. Order Tracking
Order tracking is one of the key functions in food delivery applications pertinent to the utilitarian value of the ordering process. This feature permits customers to track their desired orders in real-time settings. Order tracking is defined as “the extent to which user can track their food order and delivery riders in real-time setting” [5]. From order booking to delivery checks, customers can trace their food orders with a GPS technique. To create a distinguished place in the mind of customers and to give them a better experience, food delivery apps are introducing innovative strategies such as delivery status tracking and scheduling time to attract more customers [21]. In a recent study, ref. [22] developed multiple scales to rank the performance of food delivery applications in building brand equity. The results interpret the factors that drive the choice of preferring a specific food delivery app. Apart from convenience and ease of use, order tracking has proven to be the most desirable function by customers to install food delivery applications. All leading food delivery apps are adding the options of tracing the progress and movement of meal orders and delivery riders to provide an enhanced cognitive experience to customers [23]. Order tracking systems are associated with customer mental response or cognition perspective; therefore, the following hypothesis is developed.
Hypothesis 1 (H1).Order tracking will positively influence customer cognitive experience with food delivery applications.
2.2.2. Mapping and Location
In the food delivery industry, increasing customer experience and reducing perceived risk are the key factors that food delivery applications can exploit to achieve differentiation in the market. A fully integrated app equipped with great APIs, such as Google maps, Waze’s Navigation, or Apple or Windows maps, offer a balanced overview of nearest situated restaurants [24]. Mapping and location are defined as “the extent to which users can save/identify their exact location on a map before log-out. It also includes user ability to efficiently map available restaurants based upon their current location” [5]. The online food delivery system can only be successful if it is incorporated with accurate and active mapping which gives customers the choice to easily browse abundant restaurant options [25]. Customers opt for those food delivery applications which employ active GPS technology to track more restaurants encompassed by customer location. Users can register their exact drop-off spot on their profile account, which accelerates the entire delivery process with greater precision [6]. The overall efficiency of the ordering and delivery system will be in the interest of both customers and restaurants. In a few food delivery apps, customers faced difficulty tracing restaurant lists due to their poor map functionality. To streamline business operations and to offer a smart customer experience, food delivery applications are synchronizing location technology to have real-time information and interactive maps to design effective restaurant lists with optimum route options [26]. The above literature justifies mapping and location as an essential factor in building customer cognitive experience. Thus, accordingly, we develop the following hypothesis.
Hypothesis 2 (H2).Mapping and Location will positively influence customer cognitive experience with food delivery applications.
2.2.3. Information Access
The proliferation of online food delivery applications seems to have made phenomenal disruptions in the food industry. These apps are not only a good opportunity for users but are also advantageous for food retailers to earn customer satisfaction, a strong online presence, and cost-saving benefits [27]. The excellence of food ordering applications depends on the extent to which users can access complete information about restaurant listing, menus, prices, current deals, and special discounts. Applications that are fully equipped with information access tools such as custom search, build tracker, navigation menu, order history and analytics allow users to gauge their food ordering experience based on rationality and usefulness [27]. Customers are looking for those food delivery applications which possess operational efficiency in terms of real-time dynamics and detailed information. It is very important to build the right information features to engage the wide base of customers and to create a unique selling proposition. The more particularities (i.e., geolocation, address book, search filter) an app can adopt, the more it will be able to offer a cohesive experience to its users [28]. Besides, user ratings and reviews are significant features that will help customers to make wise decisions. A comprehensive guide regarding restaurant reviews and rating status assures customers about the quality and type of food they wish to order. Such ratings and reviews have a huge influence on user restaurant/menu selection and perceived service experience [29]. Applications’ star ratings and reviews substantially reflect user-perceived usefulness over time. Food delivery applications should frequently manage ratings and reviews to improve their app to anticipate customer expectations. Online ratings and reviews also facilitate food retailers to augment user perception about the performance and efficiency of their application [30]. Based on the above pieces of evidence, the following hypothesis is suggested.
Hypothesis 3 (H3).Information access will positively influence customer cognitive experience with food delivery applications.
2.3. Affective Experience
Customers can have both affective and cognitive experiences in their buying journey. The affective side of experience refers to customer feeling responses. The affective experience is the fusion of specific moods, emotions, and attitudes. Affective aspects represent what people are and what they feel. Users seek pleasure during their interaction with food delivery applications and develop positive emotions [1]. Though both affective and cognitive perspectives are significantly different, they are closely linked together and every dimension controls the other. Reference [12] Found in empirical research that in food delivery applications, the affective dimension is the real key in creating a substantial customer experience. The impact of affective experience on fostering loyalty and app satisfaction is much higher than cognitive experience. According to [31], affective trust is a key mediator between customer satisfaction and loyalty intention. The resilience of operating food delivery applications helps to build a strong base of customers with minimum effort. As customer affective mode is proactive in every channel, thus utilizing it efficiently will positively influence customer and food retailer relationships. Reference [15] noted several affective factors that impact the adoption of online food delivery systems. Customer inclination to use food delivery apps is drawn upon the extent of personal customization. At the same time, ref. [18] inferred that affective experience in terms of immersion has a bigger role in cultivating emotional customer relationships (i.e., affection, passion) as against their cognitive experience.
2.4. Antecedents of Affective Experience
The empirical evidence of previous studies, prevailing industry trends, and the conceptual framework proposed by [5,7,9,16] helped us in determining and setting the following antecedent for affective customer experience concerning food delivery applications.
2.4.1. Order Customization
Order customization refers to the extent to which a user can customize their food order and set filters according to their cuisine preferences and favorite restaurants. Personalization of food delivery apps strongly influence their perceived benefit and usefulness [32]. Offering personalized experience has a huge potential to retain the customer in this rampant competition. Customers are ravenous for such features and there are many ways to go the customization route [33]. Customizing your menu is one of the most popular gimmicks to engage customers who will stick with your food ordering app regardless of the number of new entrants in the market. Users can also set search filters for price range, food type, and top-rated restaurants. This personalization option has become the must-have feature and the new tactic to procure more food orders from users [34]. Food delivery apps are redesigning their restaurant menu card by putting frequently ordered dishes at the top of the home page so that users can save time rather than scrolling the whole list to get what they want. By observing customer behavior patterns and ordering history, placing a favorite restaurant tab or recommendation check based on user favorite meal options with corresponding ratings and reviews will optimize the entire experience. Executing a customization platform aids in attracting new customers and in retaining the old ones with minimal effort. These nifty personalization hallmarks have a great positive influence on user affective experience with food delivery apps [35]. We thus hypothesize the relation between order customization and affective experience as follows:
Hypothesis 4 (H4).Order Customization will positively impact customer affective experience with food delivery applications.
2.4.2. Push Notifications
As the online food delivery market shows a wide potential for growth, it is obvious to have a fully integrated app with all trendy features. Reference [36] defined push notifications as a communication tool that is used to get customers informed about their order status, current offers special discounts, and trending search. Sending the right notification at the right time with the right channel would help food delivery applications to improve the customer service experience. Ingenious push notification management is a great way to seize customer attention and to stay connected with them. These alerts pop up on user mobile devices or are saved in the app notification box and contain information about special deals, discounts, promotions codes, and order confirmation and dropped off status [37]. Customers check notifications impulsively and stored these messages in their subconscious minds. Consequently, these alerts play a big role in shaping buying patterns and overall experience with food delivery applications. This inherent function has greater visibility and click-through rate and eventually results in higher online food orders. A clever push notification strategy provides a seamless user experience throughout the buying journey and keeps customers informed about the latest trends. Today’s aggressive competition triggers the need to offer more tailored experiences and customized features to elicit positive affective customer responses [38]. Thus, the following hypothesis has been developed to test the influence of push notifications on affective customer experience.
Hypothesis 5 (H5).Push notifications are expected to have a positive impact on customer affective experience with food delivery applications.
2.4.3. Call or Chat Support
An in-app chat or call option is a guiding light in the efficient working of food delivery applications. It is a customer service tool that enables users to share their feedback and interact with customer support teams for any order-related queries [36]. For positive customer experience and long-term engagement, live chat integration and feedback support are the must-have features for any food delivery app [39]. Users might have some suggestions, queries or even complaints that they need to settle down before any further action. The absence of these features will lead to confusion or even dissatisfied customers. No matter how attractive the app interface is, if the app lacks basic customer service functions, it will never cultivate a good user experience. The queries or requests of customers should be answered in the shortest possible time with great concern. Users will start preferring food delivery apps if they get a timely response to their issues [40]. Hungry customers can be the angriest customers. Customers demand fast feedback and responses. Live chat options or call support will fulfill customer needs and preferences and improve customer experience. In addition, customers voted chat support as the most convenient mode of communication and an important element in cohesive experience. Moreover, food delivery apps can also utilize chatbots to track user affective experience and to redefine their strategies as per changing customer expectations in the food delivery market [41]. We thus hypothesize the relation between call or chat options and affective experience as follows:
Hypothesis 6 (H6).Call or chat support is expected to have a positive impact on customer affective experience with food delivery applications.
2.5. Cognitive and Affective Experience and Application Satisfaction
Application satisfaction can be defined as “the extent to which an application’s perceived performance matches or exceeds user expectations”. It indicates customer aggregate experience, which is derived when their expectations meet or surpass perceived performance or conversely when customer experiences and expectations are equivalent [12]. Numerous studies [8,9] break down experience into affective and cognitive perspectives that are observed to hold a favorable influence on customer satisfaction. In the online retail environment, [42] found that affective and cognitive experience dimensions tend to improve customer satisfaction and convert them into loyal customers. In the context of mobile commerce, [43] stressed the significance of augmenting customer satisfaction by employing cognitive and affective tools. In this competitive business environment, where products and services possess little differentiation, the key to creating satisfied customers has transformed towards the experience. Reference [12] concluded that when a customer garnered satisfaction with the application they tended to increase their loyalty and trust with the food retailer. However, creating a satisfying application experience is a daunting task because a food delivery app has multiple touch points, unlike other retail segments. Reference [44] believes that, despite the thriving demand of online food delivery systems, loyalty towards food delivery services cannot be achieved by ignoring the satisfaction benchmarks. Application functionality and the quality of food are the center point in creating a loyal customer base and app service. However, the loyalty towards food delivery apps is strongly channeled through app satisfaction and user-perceived value. It is also noted by [45] that the high perceived quality of the food delivery application will improve customer satisfaction with the application. This notion triggers the need for food delivery services to adopt customer satisfaction indicators before pursuing the race of customer loyalty. According to [25], to increase consumption level and app satisfaction, the features of food delivery applications should be of high quality and perfectly designed. In a recent study, [10] also emphasized the indispensable role of cognitive and affective approaches in creating application satisfaction. Taking into consideration all of the above evidence, the following hypotheses have been developed:
Hypothesis 7 (H7).Cognitive experience will positively influence application satisfaction with food delivery applications.
Hypothesis 8 (H8).Affective experience will positively influence application satisfaction with food delivery applications.
2.6. Application Satisfaction and Repurchase Intention
In online food delivery services, app satisfaction is considered a key factor in building customer loyalty and it is also deemed a strong motivating force in customer repurchase intention [46]. A repurchase intention refers to the probability of a customer’s future intention to buy a specific product or service from the same producer or company. Reference [47] verified customer satisfaction as a decisive factor in enhancing repurchase intention and in building long-term customer trust towards food delivery applications. The perks of having a loyal customer base largely depend on the ability of food delivery brands to provide a seamless shopping experience. Customers with positive experiences intended to spend more and develop an emotional relationship with the application [48]. Food delivery apps with high quality and interactive features increase customer traffic and their predicted lifetime value. Users of online food delivery platforms have a distinct set of needs and preferences. Once a customer installs a particular app, they hardly use another platform as long as their needs are satisfied [49]. Online food delivery solutions increase customer likelihood to place repeat orders. The quality of food and the convenience of ordering online are the best ways to retain old customers and to capture the new market options [44]. In an empirical study, [50] found eight contributing variables behind the continuous usage of food delivery applications. Out of these identified gratification factors, ease of use, customer experience, and satisfaction were spotted as the main reasons behind repeat purchase intention. Reference [5] suggests that perceived usefulness, order tracking, and online reviews are significant predictors in establishing satisfaction and buying intention. A food delivery app must possess essential features to gain a large number of users and to keep them coming back. Reference [41] further inferred that well-designed and user-friendly food delivery apps deliver a better customer experience and help to retain value-conscious customers. Based on the relevant literature, the following hypothesis is suggested:
Hypothesis 9 (H9).Application satisfaction will positively influence customer repurchase intention with food delivery applications.
2.7. Moderating Effect
Finally, this study also hypothesized the moderating role of situational factors (distance to restaurant and availability of time to visit restaurant) on the relationship between application satisfaction and customer intention to reuse food delivery applications (Hypothesis 10).
Situational Factors
In an empirical study, ref. [51] defined situational factors as the “temporary external factors that influence individual buying behavior at the time of placing an order”. According to [52], situational factors can alter customer choices, attitudes, and preferences in the short term and impose a lasting effect on customers’ regular brand choices and eventually on their buying pattern. Customers who face time constraints may prefer impulse buying or online shopping to those who have sufficient time to complete their purchase. Earlier studies have identified location convenience, distance to a physical store, personal situation, and store environment as primary situational indicators that influence customer buying journey in online, offline, and in BOPS context [51,52,53]. Online food delivery models are quite different from offline food delivery networks. In the former case, customers do not need to put any additional physical and emotional cost of their time. However, it is difficult to capture the favorable or unfavorable impact of situational factors in mobile food delivery applications. While using online food delivery apps, situational factors are one of the most important considerations in shaping customer final action as it can smooth, prolong or even end the buying procedures. In traditional dine-in restaurants, customers must need to cover a specific distance to reach the service provider (restaurants) [54]. Additionally, ref. [55] discovered that distance between the customer location and the store (restaurant) has a negative effect on the use of restaurant reservation booking. During bad weather conditions, customers show less willingness to eat outside and prefer to stay indoors. However, the use of online ordering also depends on customer mood, as they may want to cherish the weather or have ample time to cover the distance of the particular restaurant. Moreover, time plays a critical role in customer purchase and the overall decision-making process. Time of the day, day of the week, and days of the month significantly affect customer usage and stickiness towards online food delivery platforms. Busy and hectic schedules motivate many customers to order food online but on the contrary, we tend to see swarms of people waiting outside the restaurants on weekends [56]. In the current study, we examined situational factors as categorical moderator variables. Prior empirical research [54] used location convenience and time as situational factor categories as a theoretical basis for their study. The findings revealed location convenience as a significant situational factor for the intention to use BOPS function. Furthermore, ref. [52] also considered situational factors as a direct predictor of impulse buying intention across different gender groups. As a general review, we observed varied literature support for situational factors to be considered as a categorical moderator variable. Based on the previous research findings, we derived a categorical moderating role of situational factor in terms of distance to restaurant and availability of time to visit restaurant between application satisfactions and repurchase intention. In summary, we hypothesize the following:
Hypothesis 10 (H10).Situational factors moderate the relationship between application satisfaction and customer repurchase intention to use food delivery applications.
2.8. Conceptual Framework of the Study
Figure 1 illustrates the conceptual framework of the study that has been developed to ascertain the role of cognitive and affective customer experience in user repurchase intention about food delivery applications. The proposed model is a synthesis of three types of constructs. The first type is composed of six antecedents of cognitive and affective customer experience endorsed within the literature. The second type comprises customer experience component constructs and the later one elucidates outcome constructs. In the current study, we further extended the [12,16] model by introducing antecedents of cognitive and affective customer experience and by adding situational factors as a moderating variable.
3. Methodology
3.1. Description of Research Instrument
On the basis of an extensive literature review, the study constructs were measured by adopting relevant items with appropriate modification to synchronize them with the study context. The inclusion and exclusion of items were decided based on factor loading and data reliability values. In the first part of the questionnaire, the key objective of the study was explained and the definitions of study constructs were provided for the better understanding of respondents. Subsequently, a filtering question was added to scan only those respondents who have ordered food from any of the selected mobile food ordering apps. After qualifying with the desired criteria, the respondents were asked to fill out their demographic details (e.g., gender, age, qualification). Questions were also included to assess respondents’ background of using mobile food ordering apps in terms of purchasing history and frequency of usage. In the second section, the constructs proposed in the conceptual framework were measured by using 50 items (apart from the demographic and filtering questions). All items were designed on 5-point Likert scale ratings, where 1 denoted “strongly disagree” and 5 denoted “strongly agree” level of agreement. Cognitive customer experience antecedents, namely order tracking, mapping and location, and information access were measured through four items each, and the items were adopted from [5,28]. Antecedents for affective experience, including order customization and call and chat support, were assessed by using four items each and were taken from [12,36]. Items for measuring cognitive and affective experience were adopted from the study of [12,42]. Application satisfaction and repurchase intention constructs were operationalized through using six and five items respectively and were adopted from [5,12,16,20]. Measurement items for the construct of push notification and situational factors were self-developed. The questionnaire was initially sent to research experts, mobile food delivery managers and marketing professionals in the pursuit of valuable feedback, specifically for the authenticity of measures that were self-designed. Based on their comments and evaluations, the items were further amended and revised for relevance and clarity.
3.2. Sample Design and Data Collection Procedure
In a seminal study, ref. [57,58] advocated that a sample between 200–400 respondents should be acquired to reduce bias and to generate accurate and reliable results. Furthermore, as we need structural equation modeling to test the proposed model, the selection of sample size should be a minimum of 200 respondents. After thoroughly studying the dynamics of the population and prior research [15,28], the respondents should fall under the following criteria to gain the probability of sample selection:
The respondent must have ordered/purchased food from any of the selected food ordering apps;
The respondent must have ordered food in the last three months to understand their recent app experience and how it influenced their intention towards repurchasing;
The respondent must have installed at least one mobile food ordering app on their mobile devices and have used it a minimum of twice.
A judgmental cum convenient sampling technique was used to distribute questionnaires as the sampling frame of the complete food delivery app users was hard to construct. Another factor for employing this technique is to disseminate questionnaires through online platforms. In this study, 500 survey questionnaires were distributed for data collection purpose; however, only 350 questionnaires were completed and valid for further analysis. A total of 350 food delivery app users were assessed and analyzed based on their responses after removing outliers and missing values. Moreover, to ensure the unbiasedness of responses, we used Herman’s single factor test to estimate whether the instrument produces any biases in the data. The results found 18.90% variance explained by the first factor after transforming all the measurement items in a single variable. As the resultant variance of 18.90% is less than the cut-off value of 50%, a common method bias was not present.
4. Result and Discussion
4.1. Demographic Characteristics of Respondents
Table 1 represents the demographic characteristics of the survey participants. The results showed that the majority of the respondents (60.4%) were male, while females accounted for (39.6%) of the current sample size. As for the age section, more than half of the participants were within the age group of 21–25 years (41.9%), followed by the 26–30 years group (31.3%) and the 41–above (10.5%) age categories. The educational profile of the participants was dominated by (69.5%) university graduates, (29.6%) of the respondents held a master’s degree or above. Regarding profession, employees (48.4%) and students (43.6%) constituted the largest portion of the total sample size.
According to the survey results, the most widely used mobile food order app was Foodpanda (95%), followed by Careem Now (40%) and Cheetay (11%). In addition, users of Eat Mubarak, Tossdown and super meal accounted for (24.6%) of the sample size. The order frequency across eight major mobile food order apps was divided into four categories. More than half of the participants (47.9%) endorsed a purchasing frequency of four to five times a year. Of the respondents, 21.4% purchased food one to three times or more in a month, while the rest of the participants (29.1%) responded that they do not have any specified purchasing pattern. More than half of the respondents (59.3%) in the study sample have been using mobile food order apps for more than 1–2 years.
4.2. Measurement/Outer Model Evaluation
4.2.1. Convergent Validity and Reliability Analysis
The evaluation of the measurement model will begin by presenting indicators of reliability and convergent validity values. Subsequently, discriminant validity will be assessed by using Fornell and Larcker criteria (1981) [57]. The reliability of each item in the measurement scale was examined by individual factor loading whilst average variance extracted (AVE) was applied to evaluate the convergent validity of study constructs. The indicators reliability/factor loading should be greater than 0.7, composite reliability (CR) should be higher than 0.7 and the values for average variance extracted (AVE) must be above 0.5 [58].
Table 2 presents values of the factor loading, composite reliability (CR), and average variance extracted (AVE) of study constructs. As indicated in Table 2, factor loading values of all measurement items are greater than 0.7 and illustrate sufficient variance that is explained by the variable on that specific construct. The results also revealed eight items with outer loadings of between 0.6–0.7. These items were retained for further analysis as the removal leads to a decrease in values of average variance extracted and composite reliability as mentioned in the above-discussed threshold. The internal consistency of the study constructs was measured by composite reliability (CR) and Cronbach’s alpha. As shown in Table 2, all values of Cronbach’s alpha and composite reliability exceed the minimum recommended level (CR > 0.70, CB alpha > 0.70). The convergent validity of the measurement model was examined by the average variance extracted (AVE). All the constructs in the study model indicate AVE values above the suggested threshold value of AVE > 0.50 and guaranteed that indicators positively correlate with other indicators of the same construct [58]. In addition, eight items were observed with a factor loading of less than 0.5, so they were all removed from the measurement model to maintain AVE and CR values. Finally, to test the problem of multicollinearity among constructs, the variance inflation factor (VIF) was used.
4.2.2. Discriminant Validity
To measure discriminant validity, the Fornell–Larcker criteria (Fornell and Larcker, 1981) was applied to ensure the square root of each construct’s AVE is greater than its inter factor correlation [57].
As seen in Table 3, the measurement model expresses sufficient discriminant validity. Since the square root values of AVE (the diagonal values of each construct column) were greater than the correlation among any other construct. Thus, the discriminant validity of the study model is confirmed.
4.2.3. Evaluation of Model Fit
Normed fit indices (NFI) and Standardized Root Mean Square Residual (SRMR) were used to evaluate the fit of the structural equation model. Reference [59] examined the fit between the structured model and the estimated model of the study. Reference [60] suggested a range between 0–1 as a good indicator of model fit. Standardized Root Mean Square Residual (SRMR) was used to measure the discrepancies between predicted correlation and observed correlation. As Standardized Root Mean Square Residual is a perfect measure of fit, a cut-off value < 0.1 is recommended to meet the minimum threshold.
Table 4 lists the values of the goodness of fit for the study model. The results for NFI and SRMR fit indices were recorded at 0.94 and 0.08, respectively. Both fit indices meet the minimum required threshold and confirm model fitness. The results show sufficient fitness and validate that the study model is reasonably consistent with the data and thus may not oblige any further specification.
4.3. Structural Model Assessment
4.3.1. Path Analysis
The structural model was evaluated by considering the values of the hypnotized path, standardized beta, and their corresponding T-statistics values for latent endogenous constructs. To test established hypotheses, a bootstrapping method with randomly drawn 5000 subsamples was performed. Furthermore, the effect size (F2) of each hypnotized relationship was also examined as suggested by [58] to validate the structural model. Table 5 lists the findings of the structural model with hypothesis testing results to infer statistical conclusions.
In the first step, we evaluated all structural paths in the model. As depicted in Figure 2 and Table 5, all constructs had a positive impact on customer repurchase intention towards food delivery application except three constructs (contrast, mapping and location, Push notification, and call and chat support) that did not show any association with their respective endogenous constructs. All the supported hypotheses are noticed to have significance at 99.9% (p < 0.001), except for H9, which is significant at 99% (p < 0.01). According to [60] the value of standardized estimates should be higher than 0.2. In this study, all designed structural relationships show standardized weights greater than 0.2 (H1, H3, H4, H7, H8, H9), excluding (H2, H5, and H6) that recorded coefficient values of less than 0.2 (see Figure 2). As our model consists of both reflective and formative indicators, bootstrapping procedure is used to calculate t-values of the respective structural paths in the model [61,62]. According to [62], the absolute value t-statistics should be higher than its cut-off value (0.96) to accept the alternative hypothesis. The results of the structural analysis exhibit that all t-values of the hypothesized relationships (excluding H2, H5, and H6) are greater than (2.5) and support the acceptance of the hypotheses. Moreover, the inner VIF values also detect no issue of multicollinearity, as all the inter-construct values are less than 3. Taken together, hypotheses (H1, H3, H4, H7, H8, H9) of the structural equation model were accepted; however, three of them (mapping and location push notification and call & chat support) reported a reduction in effect size (see Figure 2).
4.3.2. Multigroup Analysis for the Moderating Effect
To check the role of the moderating variable, multigroup analysis was performed to assess the difference of path coefficient between groups and finally for testing the hypothesis. According to [58], the multigroup analysis technique is suitable for categorical moderator that effect the relationship between all the exogenous and endogenous constructs. However, in the current study we hypothesized the moderating role between application satisfaction and repurchase intention. Prior to performing multigroup analysis, the measurement invariance of composite (MICOM) test was evaluated to measure the invariance of the composite model. The results MICOM endorse the invariance and equivalence of the measurement. Subsequently, a multigroup analysis using Smart-PLS was conducted to evaluate the difference the between groups and to check the statistical significance the proposed relationship.
As presented in Table 6, it can be inferred that situational factors including the distance to the restaurant and the availability of time to visit a restaurant increase the magnitude of the positive relationship between application satisfaction and repurchase intention. The path coefficient difference between the groups (distance to restaurant and availability of time to visit restaurant) is positive (0.371) and significant (p-value = 0.001). Therefore, H10 is accepted.
4.3.3. Evaluation of Path Model for Predictive Relevance (Blindfolding) and Coefficient of Determination
The predictive relevance (Q2) of latent constructs obtained with the blindfolding (sample re-use technique) procedure and the results of the coefficient of determination (R2) are listed in Table 7. R2 indicates the amount of shared variance in latent endogenous constructs explained by other latent exogenous constructs. On the other side, Q2 values greater than zero > 0 express that the path model has predictive relevance, and values are finely reconstructed [58].
4.4. Discussion of Results
To investigate the role of cognitive and affective experience in application satisfaction and repurchase intention, the current research designed and empirically examined a structural model to explore how antecedents of cognitive and affective experience lead customers towards application satisfaction which subsequently impact their repurchase intention. Particularly, we incorporated literature support and empirical evidence that order tracking, information access, and mapping and location have a significant influence on customer cognitive experience, while order customization, push notification and call and chat support are strong predictors of customer affective experience. Situational factors (distance to restaurant and time available to visit restaurant) were also hypothesized as a moderating variable between application satisfaction and repurchase intention. The conceptual framework of the study was tested using PLS-SEM as the main objective of the study was to identify key antecedes of cognitive and affective experience. The study also aimed to explore variation in several endogenous constructs rather than only predicting goodness of fit or coefficient estimates [63,64,65]. When taken together, the results showed that the hypothesized antecedents predicted 36% variance in cognitive experience and 31% variance in affective experience. Furthermore, the measurement model also explains 30% and 36% variance in application satisfaction and repurchase intention. The results encapsulate that the cognitive and affective experience significantly contributes to customer repurchase intention towards food delivery applications by increasing their application satisfaction. Food delivery applications are not only another touch point in retailers’ selling strategy, but rather it nurtures a complete holistic experience that drives customer purchase intention. As the number of food delivery applications is growing, along with the number of users utilizing this service, a real cognitive and affective experience can yield a huge difference in a throng of competitors. The results also found cognitive experience (β = 0.362, p < 0.000) as a stronger predictor of application satisfaction than affective experience (β = 0.263, p < 0.000). Thus, it could be inferred that customers who had better cognitive experience are likely to hold more positive perceptions towards food delivery applications and also found themselves motivated for repurchase. These findings are consistent with the research of [12,16] in relation to mobile food delivery applications and online shopping. Based on the study model, we hypnotized that order tracking, mapping and location, and information access positively influences customer cognitive experience. The results of the current study demonstrate that order tracing (β = 0.389, p < 0.000) and the availability of sufficient information (β = 0.250, p < 0.000) are significant antecedents of user cognitive experience (see Figure 2). Apart from convenience and ease of use, order tracking has proven to be the most desirable function by customers to install food delivery applications. Live tracking of food orders from pick up to delivery point helps users to know their order status and accelerate their cognitive experience. In addition, the excellence of the food ordering application depends on the extent to which users can access complete information about restaurant listing, menu, prices, current deals, and special discounts. These results are concurred with the findings of [5] and infer that these two antecedents cumulatively enhance customer cognitive experience for food delivery applications. However, mapping and location are not found significant in our research and might indicate that the need to map available restaurants within food delivery applications is not as important in cultivating cognitive experience as the other two antecedents. Among the antecedents resulting in affective experience, order customization (β = 0.548, p < 0.000) had the greatest and positive effect on user affective experience and subsequently their application satisfaction. Order customization refers to the user’s ability to customize their food orders and set filters according to their cuisine preferences and favorite restaurant. In Pakistan, users deeply appreciate this distinctive feature of the food delivery application. Order customization empowers users to craft their meal orders and optimize the overall experience. These findings are in line with the studies of [12,42] that found a positive effect of customization on user affective experience in the context of online retailing. On the other side, the empirical findings of the current research could not find the influence of either push notification or call and chat support in predicting customer’s affective experience with food delivery applications. The findings demonstrate that users seem to be less influenced by these two antecedents regarding their affective experience. A few applications do not offer live chat options while others are not very responsive when it comes to accuracy and speed of customer support. However, [36] in their study endorsed the positive effect of live chat support in the online shopping context. The results also revealed the negative impact of push notification (β = −0.057, p < 0.436) towards affective experience. Customers do not consider push notifications as an important feature in food delivery applications. Pakistani users do not like to check updates on the latest offers, promotions, and trending searches sent through push notifications.
The current research connects application satisfaction with customer repurchase intention towards food delivery application and highlights the significance of satisfaction for reinforcing continued buying intention. So far, limited studies about food delivery applications have dwelled upon factors that encourage customers to frequent purchase [5,6]. A recent study [12] found application satisfaction to be a major direct and indirect predictor of customer loyalty towards food delivery applications. The current study also asserts users’ satisfaction with pre-purchase (e.g., awareness, information quality menu search) during purchase (e.g., navigation, order tracking, payment options) and post-purchase (e.g., customer support, delivery care, after-sales support) experience with food delivery application working in Pakistan. A similar concept was tested in India by [65], who also found value preposition as the main driver of repurchase intention. As shown in Figure 2, users are satisfied with the quality of performance provided by food delivery applications and also found themselves motivated for repurchase, accounting for 36% variance in repurchase intention. These findings concur with those of [12] in relation to user app satisfaction in driving loyalty. Finally, our results also confirm the moderating effect of situational factors between app satisfactions and repurchase intention. Participants of the recent study endorsed that distance to the restaurant, location convenience, and social influence might influence their buying intention at the time of placing an order. Additionally, user preference to use food delivery applications is also significantly influenced by the distance to restaurant and availability of time to visit restaurant, hence confirming the moderating role of situational factors in their regular buying choices. This evidence is close to the findings of [51], which examined situational factors as moderators within a (BOPS) Buy-Online, Pickup In-Store context.
5. Conclusions
A rigorous study of mobile application purchase literature exemplifies that only a few studies have considered cognitive and affective dimensions of customer experience and repurchase intention. Besides, the significance of this relation was neglected by many food delivery aggregators in Pakistan. So, the current study was attempted to identify key antecedents of cognitive and affective experience and elucidate the relationship between experience components, application satisfaction, and buying intention to reuse food delivery applications. In this highly competitive market with the increased diversity in the target population, the only way to retain a strong customer base is to deliver a remarkable customer experience. In this work, we conceptualized and empirically tested a complex model that examined different antecedents of cognitive and affective experience and dwelled to explore how it might influence user satisfaction and continuous buying intention. Moreover, one construct of situational factors was also added as a moderator between application satisfaction and repurchase intention. The research methodology used a mono-method research design with a quantitative primary data collection technique. Subsequently, the data were analyzed using PLS-structural equation modeling. Our results thus indicate order tracking, information access, and order customization as the key antecedents of cognitive and affective experience, which significantly contribute to customer application satisfaction and repurchase intention towards food delivery application in Pakistan.
5.1. Managerial Implications
This research provides salient insights for researchers and retail professionals in the online food delivery business to design and implement effective approaches and strategies to offer customers a consistent experience. Though several managerial implications have already been addressed in the previous section, our findings emphasize that food delivery applications need to work on the functional characteristics of their ordering apps (e.g., order tracking, mapping and location, live chat support, and information design) to motivate customers for frequent purchase. Regarding order tracking, food delivery applications should keep a record of the entire food delivery process to evaluate the performance of delivery riders, order status, record dispatch time, and to assess the total time required to deliver the order to the final destination. This practice will help food delivery aggregators to classify customers based on their order data and optimize routes. Food delivery applications must ensure the availability of sufficient information in terms of restaurant listing, digital menus, pricing, current deals, promo codes, and online ratings and reviews. Featuring a concise online menu with a clear call-to-action detail will enable users to easily navigate their orders. Moreover, it is essential to embed a stellar landing page for online order booking to optimize the seamless experience and higher conversion rate. Food retailers must also pay attention to order customization features, as it is found to be the strongest predictor in user affective experience. The use of sorting algorithms will enable food aggregators to closely study user behavior and buying patterns on the food delivery application. Moreover, recommended tabs based on user frequently ordered food and previous order history may lead to higher customer engagement and satisfaction. It was found from the results that customers have tuned out from frequent push notifications. However, timely messages can trigger instant purchases and stimulate user engagement at certain touchpoints.
Most importantly, food delivery applications need to add relevant and useful content (e.g., information about current deals and latest offers) to enhance user experience and reduce the chances of intrusion. In addition, efficient online chat support in multiple languages can drive higher satisfaction and a great user experience. Food delivery apps should also work on the technical and functional characteristics of the application, such as easy navigation, search filters, ubiquitous connectivity, user dashboard, active control, payment options, and other features that contribute to user cognitive experience. Furthermore, considerable attention should be paid to offering a positive affective experience. Creating fun and pleasure through interactive elements easy order management and a simple user interface will surely add value to the overall customer experience. Finally, food delivery providers should always seek to ensure user satisfaction because repurchase intention is heavily reliant on user satisfaction level. Few selected applications were found with poor usability and insufficient functionalities. Thus, food retailers just need to work on integrated features that can enhance both cognitive and affective user experience. In particular, scheduled delivery, administrative dashboard, personalized tags, barcode scanner, cash back and rewards points are must-have features in a food ordering app to offer seamless experiences and promote repurchase intention.
5.2. Theoretical Contribution
This research has intended to offer a greater understanding of the factors that could improve the cognitive and affective experience and subsequently lead to repurchase intention. As addressed in the literature, only a few types of research have focused on customer interaction with food delivery applications [5,6,12,28]. Furthermore, the cognitive and affective dimensions of customer experience are less understood and require further attention particularly due to the absence of such studies in Pakistan in relation to food delivery applications. Thus, this research will add a significant contribution by providing essential insights regarding the key factors that can enhance customer experience and purchase intention, either in Pakistan or in the Asia Pacific region. Previous studies have mainly examined cognitive and affective perspectives in online shopping environments [7,8]. Therefore, the recent study added value to the literature by investigating the role of cognitive and affective dimensions in mobile applications. Additionally, this study highlights user app satisfaction and repurchase intention than other retail outcomes such as customer loyalty and trust as it is frequently considered in previous studies [12,42]. This study also made a significant contribution by testing the antecedents of cognitive and affective dimensions in mobile applications. This in turn provides a firm basis for exploring current and relevant antecedents for predicting user experience. Finally, to the best of the author’s knowledge, only a few researchers have conceptualized situational factors as moderating variables. Recently, ref. [51] considered situational factors in an Omni channel context. In this connection, this research will be the first to examine situational factors as a moderator between application satisfactions and repurchase intention in the mobile application context.
5.3. Limitations and Directions for Future Research
This study has noted several limitations, which may provide directions for future research. The current study has employed a convenience sampling technique to collect data from Pakistani customers. Future studies can adopt probability sampling techniques to mitigate the chances of common method bias and should extend in other cultural settings and geographic locations for a substantial representation of the target population. This study follows a cross-sectional time horizon; thus, it might not be capable of analyzing user preferences and the change in buying intention over some time. Accordingly, future research can be performed in a longitudinal time frame to fully capture how users’ satisfaction and experiential states vary over time. There is also a concern about methodological perspective; the current study has proposed a survey method for data collection. Though a screening question was added to filter users’ current experiential state with food delivery applications. However, participants’ responses were majorly supported by their memory about food delivery application experience; this may lead to imprecision due to short-term memory potential. Future researchers may look at other research designs such as post hoc interviews and field experiments and may record user experience immediately after the purchase through a survey link embedded on the food delivery application for more authentic results. Furthermore, our criteria to select survey respondents was based on users’ current purchasing experience, regardless of the frequency of purchase. Thus, future studies could perform a comparison between frequent and infrequent users to study the difference between experiences. The present study was limited to only Pakistani customers. The results revealed that the age profile of the survey respondents was dominated by the 20–30 age group (73.2%). This might be attributable to the fact as the data were collected using a single online data collection tool (Google forms). Follow-up research should consider multiple data collection tools and diverse samples for greater generalizability of research findings. In addition, the present study has selected antecedents adopted from previous studies. We will recommend future studies to obtain current qualitative insights to establish relevant and unequivocal antecedents. Furthermore, future research should replicate this model with other moderating variables such as promo codes, age, and gender to investigate any change in buying intention as suggested by [12]. Finally, future studies can include control variables in the model to eliminate selection bias and increase the certainty of the model.
Conceptualization, H.R. and V.D.; methodology, H.A. and I.M.-K.; software, H.A.; validation, I.M.-K. and V.D.; formal analysis, I.M.-K.; investigation, H.R. and V.D.; resources, H.R.; writing—original draft preparation, H.R. and H.A.; writing—review and editing, I.M.-K. and V.D.; visualization, H.A. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
The data of this study is available from the authors upon request.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. Conceptual Framework of the study.
Enlarge this image.Figure 2. Structural Modeling Results.
Respondents Demographic Profiles.
| Items | Category | Frequency | Percentage |
|---|---|---|---|
| Gender | Male | 212 | 60.4% |
| Female | 139 | 39.6% | |
| Age | 16–20 | 13 | 3.7% |
| 21–25 | 147 | 41.9% | |
| 26–30 | 110 | 31.3% | |
| 31–35 | 18 | 5.1% | |
| 36–40 | 26 | 7.4% | |
| 41 or above | 37 | 10.5% | |
| Education level | Matric/O-Levels | 23 | 0.9% |
| Undergraduate/Graduate | 244 | 69.5% | |
| Master or Above | 104 | 29.6% | |
| Profession | Employee/Professionals | 170 | 48.4% |
| Business Person | 9 | 2.6% | |
| Student | 153 | 43.6% | |
| House wife | 18 | 5.1% | |
| Most Frequently used Mobile food order apps (in last three months) | Foodpanda | 95% | |
| Careem Now | 40% | ||
| Eat Mubarak | 8.3% | ||
| Cheetay | 11.7% | ||
| Super Meal | 8.3% | ||
| Toss Down | 7.4% | ||
| Hi Food | 0% | ||
| Fikifoo | 0% | ||
| Mobile food order apps purchase frequency | 1–3 times or more in a month | 75 | 21.4% |
| 4–5 times a year | 168 | 47.9% | |
| 1–3 times a year | 6 | 1.7% | |
| I don’t have a specific purchasing pattern | 102 | 29.1% | |
| Mobile food order apps usage History | Less than 12 months | 69 | 19.7% |
| 1–2 Years | 208 | 59.3% | |
| 3–5 Years | 61 | 17.4% | |
| Above 5 Years | 13 | 3.7% |
Construct Validity and Reliability Analysis.
| Construct | Item | Factor Loading | Cronbach’s Alpha | CR | AVE |
|---|---|---|---|---|---|
| Order Tracking (OT) | OT1 | 0.710 | 0.710 | 0.795 | 0.556 |
| OT2 | 0.822 | ||||
| OT3 | 0.765 | ||||
| Mapping and Location (ML) | ML1 | 0.686 | 0.709 | 0.804 | 0.513 |
| ML2 | 0.710 | ||||
| ML3 | 0.778 | ||||
| Information Access (INFA) | INFA1 | 0.740 | 0.703 | 0.748 | 0.503 |
| INFA2 | 0.810 | ||||
| INFA3 | 0.662 | ||||
| Cognitive Experience | COGE1 | 0.709 | 0.707 | 0.816 | 0.528 |
| COGE2 | 0.701 | ||||
| COGE3 | 0.824 | ||||
| COGE4 | 0.679 | ||||
| COGE5 | 0.701 | ||||
| Order Customization (OC) | OC1 | 0.738 | 0.756 | 0.842 | 0.572 |
| OC2 | 0.727 | ||||
| OC3 | 0.810 | ||||
| Call and Chat Support (CCS) | CCS1 | 0.663 | 0.758 | 0.802 | 0.582 |
| CCS2 | 0.849 | ||||
| CCS3 | 0.841 | ||||
| Push Notification (PN) | PN1 | 0.701 | 0.754 | 0.825 | 0.546 |
| PN2 | 0.762 | ||||
| PN3 | 0.866 | ||||
| Affective Experience | AFFE1 | 0.680 | 0.771 | 0.798 | 0.543 |
| AFFE2 | 0.701 | ||||
| AFFE3 | 0.757 | ||||
| AFFE4 | 0.750 | ||||
| AFFE5 | 0.680 | ||||
| Application Satisfaction | APPS1 | 0.725 | 0.706 | 0.819 | 0.531 |
| APPS2 | 0.736 | ||||
| APPS3 | 0.758 | ||||
| APPS4 | 0.710 | ||||
| APPS5 | 0.676 | ||||
| Situational Factors (SF) | SF1 | 0.788 | 0.772 | 0.842 | 0.516 |
| SF2 | 0.707 | ||||
| SF3 | 0.637 | ||||
| SF4 | 0.701 | ||||
| Repurchase Intention (RPI) | RPI1 | 0.713 | 0.754 | 0.836 | 0.508 |
| RPI2 | 0.640 | ||||
| RPI3 | 0.820 | ||||
| RPI4 | 0.782 | ||||
| RPI5 | 0.742 |
Discriminant Validity (Fornell–Larcker Criterion).
| AFE | APS | CCS | COGE | INFA | ML | OC | OT | PN | RPI | SF | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Affective Experience | 0.706 | ||||||||||
| Application Satisfaction | 0.511 | 0.729 | |||||||||
| Call & Chat Support | 0.169 | 0.208 | 0.763 | ||||||||
| Cognitive Experience | 0.566 | 0.468 | 0.134 | 0.727 | |||||||
| Information Access | 0.627 | 0.391 | 0.220 | 0.460 | 0.709 | ||||||
| Mapping & Location | 0.688 | 0.415 | 0.206 | 0.407 | 0.474 | 0.694 | |||||
| Order Customization | 0.551 | 0.461 | 0.168 | 0.602 | 0.491 | 0.409 | 0.756 | ||||
| Order Tracking | 0.603 | 0.664 | 0.173 | 0.542 | 0.416 | 0.482 | 0.520 | 0.752 | |||
| Push Notification | 0.110 | 0.258 | 0.354 | 0.180 | 0.166 | 0.204 | 0.242 | 0.176 | 0.739 | ||
| Repurchase intention | 0.384 | 0.552 | 0.228 | 0.440 | 0.347 | 0.270 | 0.471 | 0.478 | 0.446 | 0.713 | |
| Situational Factors | 0.491 | 0.503 | 0.193 | 0.497 | 0.434 | 0.355 | 0.519 | 0.623 | 0.267 | 0.574 | 0.691 |
Model Fit.
| Estimated Model | Cut-Off Criteria | |
|---|---|---|
| NFI | 0.94 | >0.9 |
| SRMR | 0.08 | <0.1 |
Results of Hypothesis Testing.
| Hypothesized Path | Estimate | T-Value | F2 | Inner VIF | p-Value | Decision | |
|---|---|---|---|---|---|---|---|
| H1 | Order Tracking → Cognitive Experience | 0.389 | 6.922 | 0.172 | 1.385 | 0.000 | Supported |
| H2 | Mapping & Location → Cognitive Experience | 0.100 | 1.888 | 0.011 | 1.477 | 0.060 | Not Supported |
| H3 | Information Access → Cognitive Experience | 0.250 | 4.517 | 0.072 | 1.371 | 0.000 | Supported |
| H4 | Order Customization → Affective Experience | 0.548 | 10.261 | 0.408 | 1.071 | 0.000 | Supported |
| H5 | Push Notification → Affective Experience | -0.057 | 0.779 | 0.004 | 1.190 | 0.436 | Not Supported |
| H6 | Call & Chat Support → Affective Experience | 0.097 | 1.404 | 0.012 | 1.153 | 0.161 | Not Supported |
| H7 | Cognitive Experience → Application Satisfaction | 0.362 | 5.013 | 0.129 | 1.472 | 0.000 | Supported |
| H8 | Affective Experience → Application Satisfaction | 0.263 | 4.535 | 0.068 | 1.472 | 0.000 | Supported |
| H9 | Application Satisfaction → Repurchase Intention | 0.314 | 2.713 | 0.049 | 2.160 | 0.007 | Supported |
Multigroup Analysis.
| Relationship | Path Coefficient—Diff, Situational Factors (Distance to Restaurant-Availability of Time to Visit |
p-Value Original 1-Tailed (Distance to Restaurant vs. Availability of Time to Visit Restaurant) | p-Value New (Distance to Restaurant vs. Availability of Time to Visit Restaurant) | Decision |
|---|---|---|---|---|
| Application Satisfaction → Repurchase Intention | 0.371 | 0.001 | 0.001 | Supported |
Predictive Relevance and Coefficient of Determination.
| R2 | Q2 | |
|---|---|---|
| Cognitive Experience | 0.367 | 0.174 |
| Affective Experience | 0.312 | 0.141 |
| Application Satisfaction | 0.309 | 0.152 |
| Repurchase Intention | 0.363 | 0.166 |
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; Davidaviciene, Vida 2
; Hassaan, Ahmed 3
; Meidute-Kavaliauskiene, Ieva 2
1 Department of Business Studies, Bahria University, Karachi 75420, Pakistan
2 Department of Business Technologies and Entrepreneurship, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, Lithuania
3 Department of Business Administration, Salim Habib University, Karachi 74400, Pakistan