The Impact of Information Quality, Swiftness, Food Quality, Experiences and Social impacts on the Dimensions of the Technology Acceptance Model

EXTENDED ABSTRACT
 
Introduction: Food delivery applications have become an integral part of people’s daily lives and respond to the needs of users by allowing them to order and receive food quickly, easily, and at any time. However, the growing competition in the food delivery market has forced companies to focus on user satisfaction and technology adoption to retain their customers and ensure business sustainability. Understanding the factors that influence users’ intention to use such platforms has therefore become a critical issue for both researchers and practitioners. The Technology Acceptance Model (TAM) provides a framework for analyzing user behavior toward technology adoption. Despite the extensive use of TAM in various domains, few empirical studies have examined its application in online food delivery systems, especially in developing countries such as Iran. Previous research has largely focused on service quality or customer satisfaction without exploring how technological, experiential, and social dimensions collectively affect users’ perceptions and attitudes toward these platforms. The present study aims to fill this gap by examining the effects of Information Quality, Swiftness, Food Quality, Experiences, and Social Impacts on the dimensions of the Technology Acceptance Model, using Snapp Food as the case study. The contribution of this research lies in simultaneously incorporating important variables and empirically testing five independent variables that have not been studied together before in this context. The study provides managerial insights for improving customer experience and satisfaction.
Methodology: This study is applied, quantitative, and descriptive–correlational. The statistical population consists of Snapp Food customers. Data were collected through a structured online questionnaire designed based on validated. A total of 384 respondents were selected using the convenience sampling method. To ensure the validity and reliability of the instrument, content validity was first confirmed by academic experts. Convergent validity and discriminant validity were assessed through factor loadings, Average Variance Extracted (AVE), and Fornell–Larcker criteria. All factor loadings exceeded 0.4, and AVE values were above 0.5, confirming acceptable convergent validity. The reliability of constructs was tested using Cronbach’s alpha and composite reliability, both exceeding the threshold of 0.7, which indicated strong internal consistency. Data were analyzed using the Structural Equation Modeling (SEM) approach via SmartPLS 3.0 software. The outer model tested the relationships between latent variables and their observed indicators; the inner model examined the hypothesized relationships among latent constructs. Finally, the overall model fit was assessed using the Standardized Root Mean Square Residual (SRMR) index, with a value of 0.076 indicating an acceptable fit.
Discussion and Results: The results revealed that Information Quality, Swiftness, Food Quality, and Social Impacts exerted positive and significant effects on Perceived Usefulness of the Snapp Food application. Among these, Food Quality had the strongest impact (β = 0.483, t = 9.804), followed by Swiftness (β = 0.251, t = 4.901) and Social Impacts (β = 0.183, t = 3.857). Information Quality also showed a positive but relatively weak influence (β = 0.087, t = 2.502). However, the path from Experiences to Perceived Usefulness was not statistically significant (β = 0.105, t = 1.515), suggesting that prior experience alone does not necessarily enhance users’ perception of usefulness unless accompanied by consistent service quality and performance. The findings further indicated that Perceived Usefulness significantly affected both Attitude (β = 0.640, t = 15.535) and Intention to Use (β = 0.243, t = 5.143). Perceived Ease of Use had significant positive effects on Perceived Usefulness (β = 0.190, t = 3.636) and Attitude (β = 0.126, t = 3.059). Finally, Attitude exerted a strong and positive influence on Intention to Use (β = 0.549, t = 11.688). The R² values indicated a satisfactory level of explanatory power for all endogenous constructs: 0.47 for Perceived Usefulness, 0.35 for Intention to Use, and 0.31 for Attitude, confirming the robustness of the structural model. These results support the core assumptions of the TAM framework and align with previous studies that emphasized the mediating role of perceived usefulness in linking service attributes to user attitudes. The insignificant effect of experiences suggests that in fast-paced digital contexts, current service quality and reliability outweigh past experiences in shaping user perceptions. Additionally, the strong impact of Social Impacts highlights the importance of peer recommendations and social proof in driving technology acceptance, especially within collectivist cultures such as Iran’s. From a managerial perspective, the findings suggest that enhancing Information Quality, including the accuracy, completeness, and timeliness of menu data, can strengthen users’ trust and satisfaction. Improving Swiftness through efficient order processing, optimized logistics, and real-time delivery tracking can further boost users’ perception of usefulness. Ensuring Food Quality by partnering with trusted restaurants, maintaining hygiene, and offering transparency in ingredient information contributes to higher user loyalty. Moreover, leveraging Social Impacts through online reviews, influencer marketing, and interactive feedback systems can enhance the app’s credibility and encourage repeated use.
Conclusion: The study provides empirical evidence that Information Quality, Swiftness, Food Quality, and Social Impacts significantly affect users’ Perceived Usefulness, which in turn shapes their Attitude and Intention to Use the Snapp Food application. Perceived Ease of Use also plays a vital mediating role, reinforcing the acceptance of the platform. The results underscore that technological, experiential, and social dimensions collectively determine the success of food delivery applications in digital markets. To enhance user experience and increase customer satisfaction, several recommendations are proposed: improving the accuracy and completeness of information, enhancing transparency and search functionality, optimizing delivery processes, utilizing Artificial Intelligence for prediction and personalization, ensuring consistent food quality, and providing efficient customer support. Continuous innovation in User Interface and User Experience design is also critical to maintaining competitiveness in a rapidly evolving market. Future research could apply longitudinal or comparative approaches across different service platforms to validate these relationships and explore additional mediating variables such as trust or perceived enjoyment. By addressing both technological and human-centered factors, this research contributes to advancing the understanding of technology acceptance in the food delivery industry and offers actionable insights for practitioners aiming to improve customer engagement and retention.