What Drives Bangladeshi Consumers' Use of Online Food Delivery Applications? Investigating the Role of Trust in Repeat Purchase Intention

WHAT DRIVES BANGLADESHI CONSUMERS' USE OF ONLINE FOOD DELIVERY APPLICATIONS? INVESTIGATING THE ROLE OF TRUST IN REPEAT PURCHASE INTENTION Jyväskylä University School of Business and Economics Master’s Thesis 2024 Author: Shayala Yesmin Subject: Digital Marketing and Corporate Communication Supervisor: Outi NiiNinen Figure 1: A Diagram of the Research Structure ........................................................ 11 Figure 2: A Diagram of the Research Framework ................................................... 30 Figure 3: Structural Model (PLS-SEM Algorithm Result) ...................................... 44 Table 1: A Summary of the Studies Conducted on the Food Delivery Apps ...... 15 Table 2: Measurement Constructs Details with Related Source and Item Code . 35 Table 3: Profiles of the Respondents .......................................................................... 39 Table 4: Convergent Validity and Reliability ........................................................... 41 Table 5: Fornell-Larcker Criterion .............................................................................. 43 Table 6: Structural Relationship and Hypotheses Testing ...................................... 45 Table 7: Predictive Capability ..................................................................................... 46 3 ABSTRACT Author Shayala Yesmin Title What Drives Bangladeshi Consumers' Use of Online Food Delivery Applications? Investigating the Role of Trust in Repeat Purchase Intention Subject/Discipline Digital Marketing and Corporate Communication Type of work Master´s Thesis Date 12-11-2024 Number of pages 88 The rapid expansion of digital technology-based applications has altered the various aspects of our lives, with smartphones now perceived as multifunctional devices that serve purposes beyond mere communication. In 2024, more than 4.88 billion people are using smartphones globally, showing a sharp upward trend. Several scholarly findings suggest that consumers' motivations vary significantly when using various smartphone applications. However, despite these differences, the impact of trust on the continued use intention of these apps is not fully explored, especially in the field of online food delivery applications (OFDAs). Likewise, an emerging country like Bangladesh, with an over 174 million population and 77 million active internet users, along with growing smartphone penetration, presents promising opportunities for OFDAs, yet there is a significantly limited research focus in this area. Realizing the research gap and the importance of trust, this thesis intends to explore the factors more profoundly that affect consumer trust building in the online food delivery apps market and the influence of trust on the continuance use intention of Bangladeshi consumers. An extended Technology Accepted Model (TAM) is proposed as its theoretical foundation, and a quantitative research technique is adopted to measure the impact of trust on the continuance purchase intention of OFDAs. A combined total of 134 valid responses were collected via an online survey, and these collected data were analyzed using statistical techniques, employing both IBM SPSS 26.0 and PLS-SEM 4.0. The research findings revealed trust is a pivotal determinant of consumers' intentions to use OFDAs repeatedly. The findings further suggest that the two key factors of the TAM model, perceived usefulness and ease of use, are greatly influenced by operational efficiency and the user-friendliness of the app respectively. Furthermore, both factors exhibit a strong correlation with trust. However, menu visualization, personalization, and order management were not identified as significant predictors for Bangladeshi consumers. This thesis contributes to the academic understanding by bridging the gap of the foundational TAM model by including additional external variables and providing area-specific insights for a developing economy. The practical and managerial implications offered by this thesis will assist the OFDA service providers in formulating consumer trust-building and experience optimization strategies. Keywords: Digital Technology, Online Food Delivery App, Trust, Consumer Behaviour, Continuance Use Intention, Technology Acceptance Model Place of storage Jyväskylä University Library 4 TABLE OF CONTENTS 1 INTRODUCTION ................................................................................................ 5 1.1 Background .................................................................................................. 6 1.2 Research Gaps and Scope .......................................................................... 8 1.3 Research Objectives and Questions ....................................................... 10 1.4 Structure of the Thesis .............................................................................. 10 2 LITERATURE REVIEW AND THEORETICAL FRAMEWORK DEVELOPMENT ......................................................................................................... 12 2.1 Online Food Delivery Applications (OFDAs) ...................................... 12 2.2 International Online Food Delivery Apps Market .............................. 13 2.3 Online Food Delivery Apps Market in Bangladesh ............................ 17 2.4 Theoretical Framework ............................................................................ 20 2.4.1 Technology Acceptance Model (TAM) ...................................... 20 2.4.2 Hypothesis Development ............................................................ 22 3 METHODOLOGY .............................................................................................. 31 3.1 Research Type ........................................................................................... 31 3.2 Sampling Technique ................................................................................. 32 3.3 Data Collection .......................................................................................... 34 3.4 Data Analysis ............................................................................................ 37 4 FINDINGS AND ANALYSIS ........................................................................... 39 4.1 Demographic Analysis ............................................................................. 39 4.2 Measurement Model Assessment........................................................... 40 4.2.1 Convergent Validity and Reliability Analysis .......................... 40 4.2.2 Discriminant Validity of the Measurement Model .................. 42 4.3 Structural Equation Modeling ................................................................ 43 4.4 Predictive Capability Analysis ............................................................... 45 5 DISCUSSION AND CONCLUSION ............................................................... 47 5.1 Theoretical Implications .......................................................................... 48 5.2 Practical Implications ............................................................................... 49 5.3 Limitation and Future Research Direction ............................................ 50 5.4 Conclusion ................................................................................................. 51 REFERENCES ............................................................................................................... 53 APPENDICES ............................................................................................................... 79 5 The changing needs and tastes of consumers have fueled continuous advancement and innovation in the smartphone technology industry, providing mobile phone firms with a renewed sense of purpose. Smartphone gadgets are gaining popularity in households faster than any other technology breakthrough (Comer & Wikle, 2008). The unique characteristics of smartphones, which have been shown to have a significant impact on the purchasing decisions of mobile phone users around the world, make it possible to gain a competitive edge over competitors (Jamil et al., 2022). In today's smartphone-dominated world, mobile applications have evolved into vital instruments that meet a wide range of personal requirements while also contributing to substantial societal transformation. App usage empowers consumers to save time while shopping more efficiently and personalizing their purchases. At the same time, businesses gain valuable consumer insights that can lead to increased revenue (Taylor et al., 2016). The growing trend of online-to-offline commerce and smartphone apps has disrupted several industries (Xiao et al., 2019). In the hospitality industry, online food delivery applications (OFDAs) are a perfect example of an online-to- offline (O2O) platform. These platforms not only provide consumers with a larger variety of culinary selections, but they also boost sales prospects for caterers (Chen et al., 2020). The market for ordering and delivering food has experienced rapid growth since the start of the COVID-19 epidemic in 2020 (Hobbs, 2020). OFDAs have become the dominant players in the mobile commerce space (Pigatto et al., 2017). The food delivery sector has seen remarkable growth in the past decade, covering many regions around the world and encompassing both developing and developed countries (Devanesan, 2021). Over the past decade, Bangladesh underwent a period of intense urbanization with the widespread adoption of internet connectivity and smartphone usage. This integration of convenience and technology created fertile ground for the online food delivery industry and caused digital transformation to alter consumer purchasing behaviors (Pathao Food, 2024). Several factors, such 1 INTRODUCTION 6 as budget-friendly mobile devices, improved telecom infrastructure, raised disposable income, busy modern lifestyles, and youth-led fast-food culture, have driven the rapid expansion of Bangladesh’s OFDA market (Hasan, 2023). Consumers with hectic schedules are turning to OFDA services as a convenient solution to save time and effort (Hasan, 2023). This shift in shopping preferences has driven the rising appeal of OFDAs, providing an easy and trustworthy way to order food via smartphones (Ahmed, 2024; Ali et al., 2023). The OFDA companies in Bangladesh offer attractive discounts and food choices as well as deliver the food by charging a very reasonable delivery fee. Delivery fees are charged by balancing between the commissions imposed on restaurants, which range from 35%-40%, and rider remuneration, typically set between 30-45 Bangladeshi Taka (BDT) per delivery (Islam, 2019). Bangladesh witnessed the country's first app-based food delivery service, HungryNaki, debuted in the middle of 2013, and Foodpanda followed later in the same year (UddinAhmed & Ahmed, 2018; Muntasir, 2019). In the years that followed, the market for online food delivery boomed with fresh startups such as Pathao Food, Uber Eats, Shohoj Food, and e-Food, both locally and internationally (Akter & Disha, 2021). There are two main domains that collectively make up the OFDA landscape. First of all, there are outlets like Domino's, Pizza Hut, and KFC that manage their own food delivery app services. Contrary to this, intermediate entities, which are known as platform-to-customer food delivery applications, such as Deliveroo in the UK, Just Eat in the US, Foodpanda in Bangladesh, and Meituan Dianping in China, provide a multi-restaurant structure (Ray et al., 2019; Sjahroeddin, 2018). OFDA services in Bangladesh have expanded their businesses beyond just food delivery. Platform-to-customer apps such as Foodpanda and Pathao offer a range of other services, including grocery, medicine, and courier delivery, as well as ride-sharing options with both cars and bikes. Given the growing reliance on platform-to-customer OFDA services in Bangladesh, this study investigates the repeat purchase intention, specifically focusing on the role of trust as a determinant of sustained customer engagement within platform-to-customer online food delivery applications like Foodpanda and Pathao. 1.1 Background According to recent research, the percentage of people worldwide who own a smartphone has grown by 50% since 2017, and estimates show that by 2020, there will be over 6.1 billion smartphone users worldwide (Hsiao et al., 2017). On a global scale, consumers are becoming more and more prone to purchasing on e-commerce platforms using various apps because of the unique format's versatility and simplicity, which go beyond its traditional boundaries and offer a broad range of options and smooth transactions (Jiang et al., 2013; Rezaei et al., 7 2016). The Google Play Store and the iOS App Store are two of the main stores where users of mobile devices can download applications. In 2018, there were an astounding 103,5 billion app downloads globally, indicating the exceptional rise in digital app consumption. Google Play remained the most popular platform, processing about 75% of all downloads and raking in 76 billion dollars, a 13% rise over the previous year. Although at a slightly slower rate of 7%, the App Store witnessed growth in download numbers as well, with a total of 30 billion downloads in 2018. These numbers represent the first installations made via the two main channels of distribution (Iqbal, 2019). The coronavirus illness (COVID-19) epidemic quickened the global transition of food delivery models, as mass use of internet and smartphone technology drove OFDAs into a new phase of "new normalcy" (Mohammad et al., 2022; Ramos, 2022). On March 11, 2020, the World Health Organization (WHO) officially announced COVID-19 to be a pandemic, pointing out the disease's higher possibility for mortality as well as a tendency to spread from person to person (World Health Organization, 2020). Globally, the COVID-19 pandemic has brought about unprecedented challenges that drastically altered social and economic settings (Tang et al., 2020). In response, the WHO promotes protective behaviors like mask use, self-isolation, and social distancing to reduce exposure and stop the virus's transmission (Tang et al., 2020; Wilder-Smith & Freedman, 2020). Even with the substantial challenges the pandemic brought about, which have disrupted the supply and demand dynamics in the restaurant and catering businesses, it also caused a noticeable alteration in consumer behavior. Online offerings are being adopted with greater speed than traditional in-store ones as an effect of this change (Zhao & Bacao, 2020). OFDAs effectively comply with the needs of restaurant owners while also meeting consumer demands for hassle-free food delivery and heightened personal hygiene standards during the pandemic (Gani et al., 2021). This COVID- 19 crisis caused a flood of new clients to investigate online delivery services, including people who had never considered ordering food for home delivery or had previously overlooked the necessity for such services (Just Eat, 2020). Many consumers prefer OFDA platforms because they offer detailed and frequently updated menus, a broad selection of restaurants, and customized delivery tracking functions designed to meet individual needs (Gavilan et al., 2021). Furthermore, various advanced features of these platforms enable users to avoid prolonged waiting times, traffic jams, and communication breakdowns (Dsouza & Sharma, 2021). Different OFDAs have become well-known in different parts of the world. Throughout 2020, DoorDash, Uber Eats, and Grubhub emerged as the top rivals in the US online food delivery business, holding a combined market share of around 95% (Curry, 2023). As of 2020, Grubhub had hired over 65,000 people to handle delivery services, serving 31.4 million U.S. users across 4,000 cities. It partnered with a network of 265,000 restaurants and managed an average of 622,700 orders per day (Curry, 2023). The global income from online food delivery amounted to approximately $296.6 billion in 2021. Experts anticipate 8 that this figure will climb to $466.4 billion by 2026, signaling a fast-paced expansion of the OFDA market (Statista, 2020). These statistics illustrate the strong upward trend and stable consumer adoption of OFDAs (Sigurdsson et al., 2020). Considering the saturated state of the food market, a growing percentage of merchants are jumping into the online food service sector daily (Yeo et al., 2017). Thus, this implies that the growing trend of using OFDAs will remain prevalent for the foreseeable future in addition to being a consequence of the pandemic (Reply, 2021). Following the first wave of explosive growth, OFDAs globally have witnessed significant transformations, with some businesses closing, others being taken over by bigger companies, and still others experiencing dropping market share (Sigurdsson et al., 2020). Historical statistics indicate that sustaining OFDAs is more dependent on the continuing ordering behavior of existing consumers than acquiring new ones (Yuan et al., 2021). Thus, it is crucial to give priority to understanding the main factors that stimulate users' ongoing engagement with technology-based platforms like OFDAs (Goyal et al., 2023). Zhao and Bacao (2020) identified that consumers' persistent willingness to use OFDAs is shaped both directly and indirectly by perceived task-technology fit, social influences, trust, performance expectations, and confirmation. 1.2 Research Gaps and Scope Local food delivery entrepreneurs have been motivated to enter the OFDA industry by platform-to-customer service providers' market potential and profitability. Because of this, both established and prospective food delivery app providers want to strengthen their position in the market by transforming into all-encompassing platforms with cutting-edge capabilities like seamless ordering and smart delivery tracking systems (Daryna, 2020). The growing relevance of OFDAs as a subject to study is proven by an increasing number of recent investigations on its key factors (Cho et al., 2019; Pigatto et al., 2017). Researchers have not adequately explored and analyzed this area comprehensively, even though this sector is receiving significant attention worldwide (Raza et al., 2023). So far, several scholars have helped reveal the fundamental factors behind OFDA adopter decision-making processes and behavioral intentions (Yeo et al., 2017; Ray et al., 2019). Earlier research reveals that consumers may become discouraged by technology's sophistication if they feel the advantages compensate for the downsides (McCloskey, 2006; Gafni & Nissim, 2014; Bezovski, 2016). However, many investigations have explored how trust is built through these mechanisms to reduce uncertainty in digital platforms, which in turn alters consumer behavioral intentions; examples of these studies include social marketplaces (Kim & Park, 2013; Zhao et al., 2019). 9 A thorough review of existing literature identifies the gap in studies on sharing and repurchase intentions towards OFDAs, particularly regarding the role of trust, reputation, and commitment (Goyal et al., 2023). Researchers have paid limited focus on the attributes of OFDAs (Cho et al., 2019; Yeo et al., 2017). Although some authors have concentrated exclusively on information-related characteristics (Erkan & Evans, 2016), others have developed models that account for more general consumer behavior (Alagoz & Hekimoglu, 2012). Studies investigating the integrated impact of informational qualities and contextual elements on behavioral intentions toward OFDAs, featuring consumer perceived usefulness and consumer trust, are remarkably few (Troise et al., 2020). A prerequisite for adopting mobile commerce is the trust that consumers have in the primary merchant as well as the e-marketplace, as pointed out by Koksal (2016) and Sarkar et al. (2020). In this advanced stage of e-commerce evolution, it is more important than ever to investigate further into trust aspects since OFDAs mostly depend on ongoing consumer interaction for their sustainable success (Nguyen & Mai, 2022). According to Liébana-Cabanillas et al. (2017), the primary roadblock to consumer adoption of online services in mcommerce is an apparent absence of trust. As a result, researchers recommend looking into new options for increasing consumer trust and encouraging continued use of mobile commerce (B. Lu, Fan, & Zhou, 2016; Oliveira et al., 2017). Additionally, consumers who believe the OFDA platform may not immediately trust the food companies on it, and vice versa (Nguyen & Mai, 2022). Meanwhile, a key challenge for third-party service providers in the food delivery sector is to identify what specific features or benefits of an OFDA will cultivate client trust and ultimately earn their loyalty (Su et al., 2022). In their meta-analysis, Sarkar et al. (2020) found that researchers looked into techniques for increasing trust among consumers in m-commerce. It is crucial to understand that the conditions that inspire trust and the consequences that emerge may vary amongst different mobile-based platforms, including those for banking, shopping, booking travel, and food delivery (Sarkar et al., 2020; Tang, 2019). As Annaraud & Berezina (2020) highlighted, cultural and social norms mold consumer behavior, making it imperative to perform analyses of the environment of OFDA services. Moreover, Budiman et al. (2013) have observed that there are quite considerable variations in consumer behavior among various geographic regions and demographics. As mentioned by Yeo et al. (2017), there remains a scarcity of investigation on food delivery intermediaries, especially OFDAs, in developing countries. To fill this gap in the current research and advance academic understanding, this thesis study will be conducted in the context of Bangladesh to find out the effect of consumer trust on the reuse intention of online food delivery app services. Even with the growing trend of app-based delivery services in Bangladesh, the behavioral tendencies and engagement patterns of consumers with these apps remain unexplored (Akter & Disha, 2021). In recent years, Bangladesh, a developing economy, has seen tremendous growth in smart gadgets and internet subscribers, indicating a strong acceptance of digitization (Saad, 2021). There is still much to uncover by 16 Wang et al. (2019) Taiwan MCA IS Success Model Online Survey Independent Variables: Information quality, System quality, Service quality, Product quality, Perceived promotion Perceived price Dependent variables: Perceived value, User satisfaction, EWOM, Intention to reuse Muangmee et al. (2021) Thailand FDA UTAUT TTF Quantitative Independent Variables: Performance expectancy, Effort expectancy, Social influence, Timeliness, Tasktechnology fit, Perceived trust, Perceived Safety Dependent variable: Intention to reuse Lee et al. (2017) Korea FDA Extended TAM Quantitative Independent Variables: Usergenerated information, Firmgenerated information, System quality, Design quality, Perceived usefulness, Perceived Ease of use, Attitude Dependent variables: Intention to use Cho et al. (2019) China FDA Quality Attributes Survey Independent Variables: Convenience, Design, Trustworthiness, Price & various food choices Dependent variables: Perceived value, Attitudes & Intention to reuse Leung et al. (2023) Hong Kong OFDP S-O-R Semi-Structure Interview Independent Variables: Good app interface, Variety of food, Variety of payment methods, Ease of searching restaurants, Delivery time, Customer service, Discounts & Reviews Dependent variables: Satisfaction, expectation & Experience Prasetyo et al. (2021) Indonesia OFDS TPB Online Questionnaire Independent variables: Hedonic Motivation, Convenience, Perceived ease of use, Navigational design, Information quality, Privacy and safety, Restaurant credibility, Perceived severity, Price, Safe Packaging & Promotion Dependent variables: Satisfaction & Loyalty, Intention to use & Actual Use Brewer & Sebby (2021) United States Online Restaur ant S-O-R Survey Independent variables: Menu visual appeal, Menu informativeness, Perceived COVID-19 risk, Desire for food & Convenience Dependent variables: 17 Purchase intention Riaz et al. (2022) Pakistan FDA Cognitive and Affective Antecedents Survey Independent variables: Order tracking, Mapping, Information access, Customization, Call or chat support, Push notification Dependent variables: Cognitive experience, Affective experience, Satisfaction & Repurchase intention Kang & Namkung (2019) Korea Food O2O Privacy Calculus Theory TAM Online Survey Independent variable: Personalization Dependent variables: Perceived benefits, Perceived risk, Perceived ease of use, Perceived value, Trust & Continuance intention Abbasi et al. (2024) Saudi Arabia FDA S-O-R Survey Independent variables: Informativeness, Personalization, Interactivity, Trendiness & WOM Dependent variables: Customer engagement, Coproduction, Referral, Satisfaction & Purchase intention Fakfare (2021) Thailand FDA IPMA Online Survey Independent variables: Delivery experience, Social benefits, Ease of use, Reviews, Food hygiene, Time saving & Food Rider Dependent variables: Satisfaction, Advocacy & Intention to re-use Atulkar & Singh (2021) India FOA Psychological and Technological Attributes SEM Independent variables: Perceived ease of use, Perceived usefulness, perceived incentives, Perceived Price, Visual design, perceived Information, Customer relationship management & Order management Dependent variable: Customer conversion 2.3 Online Food Delivery Apps Market in Bangladesh Bangladesh's journey towards digital transformation has experienced an enormous spike in the use of digital technology, including a substantial spike in smartphone and internet users. Bangladesh’s drive to become a digital economy has boosted a surge in smartphone and internet users. It reported 18 around 110 million internet users in 2021, with the number doubling over five years (Bangladesh Telecommunication Regulatory Commission, 2022). BRTC's latest report reveals that internet consumption in Bangladesh jumped by 10.05% during the first six months of 2024, bringing the total to 142.17 million from 129.18 million. The number of mobile internet subscribers witnessed the sharpest growth, an 11.03% increase, from 116.30 million to 129.17 million (Rabbi, 2024). The inception of OFDA services took place in Bangladesh in 2013. HangryNaki was pioneered in this sector, followed by a wave of start-ups, including international ones, that tried to establish themselves, but a few succeeded (Tahmid, 2022). Before 2013, the food delivery market was virtually nonexistent in Bangladesh, as claimed by Ibrahim Bin Mohiuddin, deputy CEO of HungryNaki.com, but with a $10 million market worth, the industry has expanded tremendously in recent times (Kader, 2020). However, the growing use of smartphones has made home food delivery a standard practice in Bangladesh (Tahmid, 2022). Within six months, Foodpanda emerged as an early participant, quickly recognizing the potential of this evolving industry (The Daily Star, 2019). Meanwhile, ridesharing platforms soon seized the opportunity by utilizing their logistical knowledge to diversify into the OFDA market. Pathao, a leading ridesharing brand in Bangladesh, transitioned into the OFDA market in October 2015 and swiftly secured nearly 80% of the market share (The Daily Star, 2019). Another firm, Shohoz, started with a concentrated focus on the local neighbourhoods and entered the market in October 2018, preceded by a short two-month trial period. As time passed, it broadened its operation, securing a substantial presence in Dhaka’s competitive market (Kader, 2019). Uber Eats, a global competitor, launched its service in Bangladesh in April 2019 and became well-known for its reliable services during the pandemic crisis, when most businesses were forced to cease their operations (The Daily Star, 2019). In June 2024, Foodi, a new venture of US Bangla Airlines, entered the OFDA market, making a quick impact on Bangladeshi consumers by partnering with over 40,000 restaurants and running a team of more than 1,000 riders across 25 zones (The Daily Star, 2024). Long before the COVID-19 issue hit, in 2019, the fast-growing online food delivery business in Bangladesh—fueled by the emergence of food delivery apps—reached $10 million in sales and handled over 25,000 orders daily on average (Belanche et al., 2020; Kader, 2020). The market was estimated to reach $5 billion by 2025, but it experienced a major drop when the COVID-19 outbreak affected the country's normal operations (Kader, 2020; Muntasir, 2019). Over time, users adjusted to the new normal and turned more frequently to OFDA services. Uber Eats and Foodpanda responded to health and safety concerns by launching contactless deliveries, where food was left at the doorstep and payments were processed digitally (Akter & Disha, 2021). Prices of the offerings displayed on the OFDA platforms in Bangladeshi Taka (BDT), may vary in terms of VAT inclusion by the vendors. These rates may be different from the restaurant’s websites or physical stores because of the added commission. Additional charges, such as delivery or service fees, are clearly communicated on 19 the apps before the checkout (Foodpanda, n.d.). Promotional incentives given by these apps are consumer-centric, which include discount vouchers, exclusive deals with specific partner restaurants, and combo offers. These promotional offers are subject to specific conditions such as limited-time redemption, minimum order amount, and usage restriction (Foodpanda Bangladesh, n.d.; Pathao, n.d.). Recent advancements in Bangladesh's OFDA sector show remarkable shifts. Foodpanda stands as a market leader, processing around 100,000 orders per day, while other prominent players left the market, failing to withstand the high competition and financial challenges (Babu, 2024). In 2019, Uber Eats commenced its services in Bangladesh, but within just one year it was forced to cease operations by June 2020 due to the pandemic heightened market difficulties (The Daily Star, 2020). Shohoz Food, despite an initial promising growth with over 2,000 restaurant partners, ended its delivery operation in October 2021 as part of a strategic pivot (Kader, 2023). In the same way, HungryNaki, formerly a key player and later acquired by Alibabas´ Daraz Bangladesh, dealt with substantial operational issues and reduced its footprint significantly (Hasan, 2023). The current market of OFDA is led by Foodpanda and Pathao, which have adeptly addressed competitive challenges while maintaining a loyal consumer base in a sector where only a few can thrive (Pieal, 2023). In a short span, OFDA services have quickly gained attention with business researchers, practical managers, and the retail industry (Prasetyo et al., 2021). Sarkar et al. (2020) discovered consumer trust is a critical part of e-commerce penetration and sustainability. Since OFDAs fall in this category, enterprises must comprehend how trust is formed in these services to increase acceptance and assure business success (Wang et al., 2015; Wang, 2020). In their investigation, Sarkar et al. (2020) revealed that the possibility of danger, quality, and implementation of innovations collectively impact consumers' trust in online shopping. The latest features on electronic commerce platforms, in contrast with mainstream e-commerce platforms, are intended to facilitate user involvement (Sarkar et al., 2020; Wang et al., 2015). In order to encourage open innovation while sustaining e-delivery services, it is imperative that consumers actively participate (Pinheiro et al., 2022). Investigations currently underway into OFDAs reveal a preponderance of consumer purchase intentions and behaviors (Shah et al., 2022; Kaur et al., 2021; Muangmee et al., 2021; Tandon et al., 2021; Song et al., 2021). Some of these studies also investigated consistent purchasing tendencies and prolonged app usage (Ramos, 2021; Zanetta et al., 2021; Kumar & Shah, 2021). The continuous intention to use OFDAs has been the focal point of numerous studies, with the most significant variables identified as performance expectancy and self-image uniformity (Cho et al., 2019; Gunden et al., 2020; Suhartanto et al., 2019; Yeo et al., 2017). Significant determinants of the intent of consumers to use food delivery apps include system trust, accessibility, userfriendly design, and selection of foods (Cho et al., 2019); reliability, ease of use, and usefulness (Roh & Park, 2019); and pricing rewards, trust, and interaction with the apps (Ray & Bala, 2021). Even with these findings, the critical elements 20 impacting continuous intention to use are uniformly delineated among the studies conducted (Hong et al., 2021). The upsurge in app-based food delivery has yet to be matched by practical evidence of consumer behavior in Bangladesh (Akter & Disha, 2021). The empirical evidence on this subject matter exists insufficiently, with only a few studies investigating how consumers responded to app-based food delivery during and after the COVID-19 pandemic (Akter & Disha, 2021). At this point, no research has looked explicitly at the influence of consumer trust on purchases made repeatedly in Bangladesh's Online Food Delivery Apps (OFDAs). Because of Bangladesh's burgeoning OFDA sector, it is crucial to investigate this relationship to uncover on how trust affects consumer recurring purchases and the determinants impacting consumer trust in returning to online food delivery service apps. 2.4 Theoretical Framework 2.4.1 Technology Acceptance Model (TAM) Online food delivery applications have been the subject of adoption and usage intentions studies conducted in several distinctive regions, including India (Mehrolia et al., 2021), Jordan (Alalwan, 2020), Brazil (Pigatto et al., 2017), the USA (Okumus et al., 2018), China (Zhao & Bacao, 2020), Malaysia (Yeo et al., 2017), and Korea (Lee et al., 2019). These investigations used a multitude of theoretical models, including the Technology Acceptance Model, UTAUT, UTAUT2, the Theory of Planned Behavior, the Contingency Framework, the Theory of Technology Readiness, and the IT Continuance Model, and worked with quantitative, qualitative, and hybrid research designs (Raza et al., 2023). Researchers explored the usage trends of OFDAs and individuals' desires toward OFDAs from a variety of theoretical orientations. They have presented multiple theoretical models and frameworks to clarify the variables affecting the adoption of technology (Gani et al., 2021). Additionally, to validate core factors altering the way consumers perceive, their movies, intentions, and behavior in online food ordering, researchers have applied diverse frameworks (Alalwan, 2020). These include the IS Success model (Wang et al., 2019), IT Continuance Model and Contingency Framework (Yeo et al., 2017), TAM (Alagoz & Hekimoglu, 2012; Okumus & Bilgihan, 2014), quality parameters (Cho et al., 2019), UTAUT (Okumus et al., 2018), and app-specific attributes (Kapoor & Vij, 2018). Conceptual models such as TAM, TRA, UTAUT, and UTAUT 2 offer insights into explaining how individuals are likely to favor novel services and products, which improves the understanding of the dynamics of consumer behavior (Cho et al., 2019). Of these models, the Technology Readiness (TR) by Parasuraman (2000) and the Technology Acceptance Model (TAM) by Davis 21 (1989) are two prominent frameworks that are frequently applied to examine the acceptability characteristics of new technologies (Chen & Lin, 2018). Okumus and Bilgihan (2014) first used the TAM model and suggested that consumers' decision to adopt OFDAs is mainly influenced by perceived utility, enjoyment, selfefficacy, social norms, and convenience. But to the best of the author's knowledge, no research has yet fully studied the trust aspects, including the factors affecting consumer trust and studies that prioritize the impact of trust on the continuous use of OFDAs. Hence, the present investigation proposes a comprehensive framework that incorporates essential components derived from the TAM to evaluate consumer trust in the context of OFDAs. The Technology Acceptance Model will serve as the theoretical foundation for this research investigation in exploring the potential of OFDAs. The Technology Acceptance Model is generally acknowledged for its ability to explain and predict individual adoption of information technologies (Venkatesh & Davis, 2000). Multiple research investigations relied on TAM and its subsequent developments to investigate the acceptance mechanisms associated with information technology sectors, such as e-services, mobile apps, and internet-based systems (Abdullah et al., 2016; Ferreira et al., 2023). As the preferred starting point for exploring the acceptance of technology in different industries, the TAM model is playing an increasingly important role within existing research works. The building of a theoretical framework based on TAM will strengthen the research's methodological reliability and contribute to enhancing knowledge about technology diffusion and innovation adoption (Bandinell et al., 2023). Fishbein and Ajzen's (1975) Theory of Reasoned Action (TRA) served as the basis for the development of the TAM model. TRA explains the reasoning behind a person's decision to accept or reject a technology (Park, 2000). It advances the knowledge of how people perceive and embrace new technologies by focusing on critical characteristics such as usefulness and simplicity of use (Bandinell et al., 2023). Several academic studies have applied the TAM model as a theoretical framework for exploring potential users' behavioral intentions in adopting specific technologies. In this context, behavioral intention refers to how much a person has consciously developed plans for either participating in or refraining from a specified foreseeable action, which is compatible with the underlying concepts of the Theory of Reasoned Action (TRA) (Warshaw & Davis, 1985). Two elements or determinants of the classical TAM are perceived usefulness (PU) and ease of use (EOU) concerning technology use impacting the acceptance of users' behavior (Shrestha & Vassileva, 2019). TAM is routinely improved by researchers by integrating additional external constructs based on specific settings. The modification is necessary owing to the limitations of the traditional TAM, which does not contain certain crucial elements inside the model (Melas et al., 2011). This thesis will assess external factors to determine the impact of trust on consumers' repeat purchase intention toward OFDAs. This framework will discuss how the elements of perceived usefulness and ease of use affect consumer trust and eventually lead to positive behavioral outcomes. 22 2.4.2 Hypothesis Development Information Quality Information quality is delineated by its ability to deliver reliable, accurate, up-to-date and comprehensible information, which in turn controls the effectiveness of data created by an information system (Negash et al., 2003). Users distinguish high-quality information by how swiftly and precisely it delivers pertinent and useful data (Zhao et al., 2019). Scholars have highlighted the relevance of information quality in food delivery apps (Lee et al., 2019; Ray & Bala, 2021). A technology's effectiveness is directly related to the standard of information and services it offers, as well as the functionality of the system it runs on. These factors all have an essential effect on how useful consumers perceive a technology (Ahn et al., 2004; Lee et al., 2017). Relevance, comprehensiveness, accuracy, and timeliness are examples of information quality attributes that cater to the requirements of users (DeLone & McLean, 2003). Kim and Park (2013) uncovered an obvious connection between the dissemination of information and the creation of trust in social marketplace contexts. Consumers are more inclined to hold onto and receive information they deem beneficial in the digital world, which is rich in innovative thoughts and various perspectives (Cheung et al., 2008). Regarded as the bedrock of trust, information quality serves as the primary communication bridge that underpins the interaction between online consumers and businesses (Kim & Park, 2013). Prior studies have shown that information quality substantially impacts consumer perceptions of food delivery apps; despite this, comprehensive investigations that concentrate on the specific categories of information content that matter most remain limited (Lee et al., 2023). As noted by Kang & Namkung (2019), this factor is critical in evaluating the perceived usefulness, and consumers of online-to-offline services should be conscious of its significance. From this premise, the study hypothesizes the following: H1: Information quality has a positive impact on perceived usefulness. Menu Visualization The graphical appearance of an OFDA refers to its overall coherence, visual appeal, and attractiveness, which includes elements such as images, colors, fonts, shapes, graphics, and food item layout (Kapoor & Vij, 2018). Expanding the range of menu choices with engaging visualization motivates consumers to use OFDA services more often (Ray et al., 2019). Products displayed in various formats offer various levels of information richness. Visual elements such as, images convey more intricate details than textual descriptions (Wang et al., 2016) and using eyecatching graphics and appealing visuals easily draws users’ attention (Kumar et al., 2021). Through consumers' emotional and cognitive reactions to the online food ordering experience, well-designed and visually pleasing menus not only 23 constitute information simpler to comprehend but also boost perceived usefulness, eliciting favorable feelings toward online food distributors (Brewer & Shabby, 2021). The owners of restaurants may strengthen consumer trust in OFDAs by augmenting textual information with imagery of their establishments and food items, as well as using proper font sizes, visuals, colors, and other visual cues (Nguyen & Mai, 2022). Surprisingly, few research has been conducted concerning the impact of menu visualization and product presentation on consumers' perceived usefulness and buying inclinations (Truman, 2018). Given these considerations, the research presents the following hypothesis: H2: Menu visualization has a positive impact on perceived usefulness. Delivery Time The successful execution of an offering in saving time maximizes consumer convenience by lowering the time and effort costs linked to getting the service (Yeo et al., 2017). One significant variable driving consumers' positive behavioral intentions toward e-commerce is the promptness of delivery (Zulkarnain et al., 2015). Consumers continuously opt for online delivery services largely because of their effectiveness and rapidity (Blake et al., 2005). In addition to their hectic schedules and the high opportunity costs of physical shopping, financially secure individuals consider these services particularly tempting (Fancello et al., 2017; Punj, 2012). However, regardless of the circumstances, delivery delays that last longer than anticipated may drastically decrease consumer satisfaction (Roy & Zhao, 2010; Saad, 2021). In the online retail sector, consumers evaluate delivery time just as highly as the product itself, thus making it a critical factor that determines perceived usefulness and behavioral intention (Roy & Zhao, 2010; Lin et al., 2011; Saad, 2021). OFDA permits consumers fast access to their favorite cuisines and the freedom to order from wherever they are at any time, saving users time spent, also, food quality—which includes elements like taste and temperature—is greatly influenced by how swiftly it arrives at its destination (Chai & Yat, 2019). Therefore, this study hypothesizes the following- H3: Delivery time has a positive impact on perceived usefulness. Online Review Bhattacherjee (2002) draws attention to the fact that consumer trust in online markets is noticeably lower than in traditional markets. Hoffman et al. (1999) explain this disparity by pointing out that relatively little is known about the market value of products, services, and online vendors in online shopping. Because of this, feedback systems like online reviews and ratings largely influence consumer attitudes about online purchases, including trust (Wulff et al., 2015). The interactive aspect of OFDAs makes it possible for consumers to express their observations on restaurants and share their feedback with fellow 24 consumers of food ordering platforms (Bert et al., 2014). The impact of online reviews, whether favorable or adverse, on consumer behavior is significant (Phillips et al., 2017), as noted by Mathwick & Mosteller (2017), who claim that these reviews affect up to 50% of sales made online. Murphy (2018) further points out that a substantial proportion of consumers, more than 78%, exhibit the same level of trust in online reviews as they believe in recommendations from close relationships such as friends or family. For diners who order food online from restaurants, peer reviews are essential since they provide them with credibility regarding the quality of the food and the reliability of the service (Liu & Park, 2015). This reliance on reviews helps to mitigate the imbalance of information in the online space (Cheung et al., 2008). Elwalda et al, (2016) found a solid and positive connection between the intention of consumers to make online purchases and the qualities stated in online consumer reviews, such as perceived usefulness, and enjoyment. The trustworthy nature of online reviews is strengthened from the consumer's perspective because they are posted by actual past consumers who participated in orders on the same platform (Ehsaei, 2012). Drawing on the investigation, this study proposes the following hypothesis: H4: Online review has a positive impact on perceived usefulness. Personalization Researchers have explored how personalization affects online usage behavior and confirmed that personalization greatly enhances both consumers' affective and cognitive trust in digital platforms that require a referral (Komiak & Benbasat, 2006). Shao Yeh and Li (2009) also stated that businesses are required to provide an individualized web experience since it is a prerequisite for cultivating consumer trust in e-commerce businesses. Adapting or designing services that suit the specifications of consumers is what Ball et al. (2006) refer to as customization. This strategy entails co-creating value through the implementation of marketing tactics and possible options to improve personalization, frequently via technology (Blasco et al., 2014). In the face of intense competition, providing customized experiences vastly enhances the retention of consumers (Penney et al., 2016). Customization options, such as personalized menus, filtering searches for pricing, cuisine, and ratings, are critical elements that bring in and sustain users in the food delivery app market (Riaz et al., 2022). Data-driven insights from consumer behavior and history of orders are used to advertise favorite restaurants and food, along with ratings, which elevates the satisfaction of users (Metha, 2019). This individualized strategy not only attracts prospective consumers but also grows commitment among those who already use it (Metha, 2019), hence increasing the perceived value and usefulness of the food delivery applications (Kang & Namkung, 2019). Thus, the below hypotheses are put forward: H5: Personalization has a positive impact on perceived usefulness. 25 App Interface The design elements of an app such as user interface layout, color schemes, spatial arrangement, and navigation system trigger sensory responses which encourage users of an app to invest more effort in understanding its functionalities (Fang et al., 2017; Cyr et al., 2006). Zeithaml et al. (2000) interpreted platform interface as going beyond a visual appeal to include graphic design, color schemes, and animation (Hoehle et al., 2016), as well as the hierarchical foundation of online collections (Cai & Jun 2003). The theory of reasoned action clarifies perceived ease of use as the notion that a system is mentally relatively easy to use (Davis, 1989), yet research demonstrates that appearances such as color, form, and material strengthen consumers' functional and symbolic connections to the brand (Pantin-Sohier, 2009). Designing the layout of an app includes researching a range of aspects present in the interface's design, such as menus, images of food, marketing materials, and interactive aspects that encourage user interaction (Su et al., 2022). In an effort to meet the changing needs of young people who are proficient with technology, efforts in app design and product promotion have been spurred by the introduction of OFDAs (Cho et al., 2019). As pointed out by Sharma and Sharma (2019), users may begin to doubt the capability and integrity of online service providers to provide high-quality services if certain elements are missing. Likewise, consumers' trust in the OFDA platforms supports them to have reliance on the intermediary and eliminates the risks that come with adopting the platform (Nguyen & Mai, 2022). Considering how important app interface design is to the success of online apps, the following hypothesis has been developed: H6: App interface has a positive impact on perceived ease of use. Order Management Procedure Many older clients frequently had issues using online shopping platforms, therefore, implementing straightforward processes would significantly boost these consumers' perceived ease of use (McCloskey, 2006). Consumers' positive feelings and a desire to continue using online food ordering systems are severely impacted by their perception of those platforms as worthwhile and capable of simplifying daily operations, which is further reinforced when they find the experience pleasant and entertaining (Yeo et al., 2017). OFDAs require users to handle all the steps of ordering food independently, without any assistance from the restaurant’s staff (Alalwan, 2020). Tech-savvy consumers are drawn to OFDAs for their variety of meal options, detailed reviews, and prompt delivery (Ali et al., 2020). Through the simplification of placing orders, live tracking, restaurant selection, and food filtering OFDAs services further enhance consumer convenience by equipping users to cope with traffic-related hurdles while minimizing wait times at restaurants (Ganapathi & Abu-Shanab, 2020). The 32 and Technology. In particular, a quantitative research technique can be advantageous in expressing the importance of major strategic concerns and setting their priorities (Bell et al., 2022). Quantitative research centers on the systematic analysis of variable interaction and precise data measurement. This method leverages statistical tools and numerical analysis to uncover relationships (Bell et al., 2022). Numerical assessment serves as a fundamental task in linking empirical findings to mathematical modeling of quantitative associations (Singh, 2006; Goertz & Mahoney, 2012). The application of quantitative approaches is frequently associated with the testing of hypotheses. This research paradigm, which emphasizes hypothesis testing, is closely related to the deductive method. Furthermore, because of its ontological emphasis and positivist epistemological principles, quantitative research is commonly associated with objectivism (Bell et al., 2022). A deductive technique is used in quantitative research to investigate the relationships between theory and research. This approach guarantees the researcher's impartiality by pointing out a non-participatory role while also focusing on the role of a neutral observer (Bell et al., 2022). This investigation adopted a quantitative research approach to deepen the understanding of the factors behind consumer trust and repeat purchase decisions. The thesis objectives and research questions were addressed by testing hypotheses and validating the research theoretical model with a focus on presenting the findings that reflect the experiences of Bangladesh’s OFDA users. This technique is selected as it aligns perfectly with study goals. The efficacy of quantitative research is well supported by numerous previous investigations (Bhattacherjee, 2012). In this thesis, primary and secondary data sources were integrated to include both literature analysis as well as quantitative field studies to investigate (Saunders et al., 2009). In any research study, a comprehensive literature review is vital, since it assists in the discovery of useful insights within the subject topic. This extensive analysis of the relevant literature revealed gaps, which were then used to strengthen the current study (Jain et al., 2021). 3.2 Sampling Technique The selection of a sampling technique depends on the feasibility and effectiveness of gathering data relevant to the research questions (Saunders et al., 2009). The precision of study findings relies greatly on choosing the right sampling method that aligns with the research type, resources, budget, study aims, and time constraints (Sarstedt et al., 2021). Bhattacherjee (2012) emphasizes that in choosing a sampling technique, consideration should be given to the size and composition of the sample frame to ensure that the technique satisfies the specific requirements of the frame. The sample in the present investigation, according to Hair et al. (2017), is a smaller group recruited from a larger population for research objectives. This research sampling frame targets 33 individuals who own smartphones and are users of online food delivery applications and engage with these apps regularly. Therefore, by offering a narrowly focused and manageable sample for research, this pre-selected portion of the population represents the broader target group that the study is intended to inspect. The optimal way to control bias in a study is through probability sampling. However, since there is not a precise sampling frame that truly represents the target population, it is not attainable in this thesis study (Chew, 2023). Thus, the non-probability sampling strategy was used in this investigation. Moreover, to support the generalization of outcomes, researchers selected participants from diverse demographic backgrounds using snowball sampling (O`Donoghue et al., 2016; Zubair et al., 2019; Alexandrov et al., 2003). This method is commonly used in quantitative research data collection, as it taps into the interconnected nature of social networks and referrals to identify respondents (Parket et al., 2019). The procedure was started with a confined, chosen list of initial contacts who correspond with the research criteria and are invited to participate and recommend additional participants (Parker et al., 2019). Sampling is normally discontinued when the sample quantity target has been achieved or data saturation becomes apparent (Parker et al., 2019). The study involved participants who are members of the target group and who the researcher can contact via social media, personal connections, or academic networks due to the limited timeframe. These participants were requested to find more contacts that fit the research requirements. The study initiated collecting information by leveraging their social networks to build early contacts and develop traction for reaching an increased number of respondents (Parker et al., 2019). The top priority in the sampling method for this study is to protect the integrity of the analysis by properly managing the quantity of data. Simultaneously, the research emphasized the relevant studies that were most closely linked with our research intents to ensure that they were suitable for the analysis. As part of the research design, 134 valid responses were collected via a structured questionnaire, and the analysis was carried out using partial least squares (PLS), a technique grounded in structured equation modeling (SEM) that is particularly well suited for this type of analysis (Götz et al., 2009). In PLS-SEM, accurately estimating the minimum sample size for reliable analysis is critical. A frequently used method, the “10 times rule," specifies that the sample size mus be no less than 10 times the number of paths associated with the latent variable in the model (Hair et al., 2011). PLS-SEM is highly flexible and can deliver reliable results even with small sample sizes of around 100. Given the study's twelve latent variables, each with 4-5 indicators, the “10-times rules” require a minimum sample size of 120 (Afthanorhan, 2020). Thus, the collection of 134 valid responses exceeds the minimum requirement and comfortably meets the sample size requirement for PLS-SEM analysis. 34 3.3 Data Collection The data collection process entails methodically acquiring and measuring data on fundamental factors to answer inquiries into the study, assess hypotheses, and evaluate outcomes (Awang, 2012). There are many different types of quantitative research data, each with its own distinct features. Some examples of these types of research are survey research, correlational research, experimental research, and causal-comparative research (Sukamolson, 2007). The relevance of data-gathering techniques is further highlighted by Paradis et al. (2016), who claim that the researcher's methodology and analytical approach determine how the data is used and how much of it can be used to interpret. The challenge of carrying out empirical research involves choosing the most suitable techniques for data collection (Bhattacherjee, 2012; Creswell & Creswell, 2017; Fink, 2015). This thesis will implement an online survey approach to examine the key factors leading to consumer trust in the continuance use of OFDAs. To determine the causal relationships between the indicated constructs, a survey research methodology seems like the best strategy for this investigation. McMillan and Schumacher (2001) stated that survey research involves executing questionnaires or interviews to obtain data regarding the current circumstances, different points of view, convictions, and attitudes of a specified group. Surveys are an excellent means of swiftly and inexpensively gathering large quantities of data and identifying traits and insights about the population (Saunders et al., 2009; Cohen et al., 2002; Slavin, 2007). In self-completion surveys, predefined sets of scientifically formulated questions are used to gather accurate data from respondents. In survey research, designing well-crafted questions is key to ensuring data accuracy. While conducting a questionnaire-based study, key considerations to bear in mind are the general design of the questionnaire, validation through pretesting, and the chosen administration method. The main objective of questionnaire surveys is to gather a significant amount of numerical data (Hair et al., 2019). This thesis employed an online survey technique targeting respondents familiar with using online food delivery app services. The rationale for this decision is to obtain appropriate data consistent with the area of research interest. The survey data collection technique started with a self-administered, wellconstructed questionnaire design based on carefully selected constructs after thoroughly reviewing existing literature. The online survey platform Webropol 3.0 was used to design the questionnaire in English to get a well-rounded understanding of the participants. The survey questionnaire was organized into three sections. The initial section provided respondents with an overview of the study by outlining its title and objectives, clarified that participation is completely voluntary, and assured respondents about the confidentiality of the personal information that was collected anonymously. It was also mentioned that the survey was conducted only for academic research purposes. The second part focused on collecting 35 personal or demographic details to categorize respondents. The third section addressed twelve indicators: perceived usefulness, perceived ease of use, information quality, menu visualization, timeliness of delivery, online review, personalization, app interface, order management, easy payment, consumer trust, and continuance use intention of the proposed research model. The study followed the General Data Protection Regulation (GDPR) standards, and all collected data were managed with strict confidentiality, handled ethically, and used exclusively for academic purposes. Respondents' identities remained anonymous and no personally identifiable information was collected or saved. Before the distribution of the questionnaire, a pilot survey was conducted by five non-respondents who were regular users of online food delivery apps. Their feedback helped to address potential issues and clarity problems and made the questionnaire more respondent-friendly. The measurement items were chosen based on the research model, and slight wording adjustments were made to fit the study's framework. The questionnaire was designed by applying a fivepoint Likert scale ranging from “Strongly Agree” (1) to “Strongly Disagree” (5). Data collection took place between September 19 and September 25, 2024. Six responses were removed from the analysis due to errors, incompleteness, and inconsistencies, leaving a final valid response of 134 qualified respondents who answered “Yes” to the screening question, “Have you used any online food delivery app?” Table 2 provides a comprehensive overview of the measurement constructs examined in this thesis, including the related sources and item codes. The survey items for each construct are listed in Appendix 2, which contains the complete survey questionnaire used in this thesis study. Table 2: Measurement Constructs Details with Related Source and Item Code Constructs Code Source Information Quality The information provided by OFDAs about restaurants, and their offers is detailed The information provided by OFDAs is reliable The information provided by OFDAs is accurate The information provided by OFDAs is always up to date. The information provided by OFDAs is easy to understand The information provided by OFDAs is well-formatted. INQ1 INQ2 INQ3 INQ4 INQ5 INQ6 Yoo and Donthu (2001) Hanjaya et al. (2019) Lin (2008) Lin (2008) Eid (2011) Lin (2008) Menu visualization The way restaurants display their online menu in OFDAs is attractive The OFDA menu is visually appealing The way restaurants display their menu in OFDAs is informative The OFDAs display various menu options The OFDAs visualize potential diners with a comprehensive picture of the food being offered MV1 MV2 MV3 MV4 MV5 Brewer & Sebby (2021) Brewer & Sebby (2021) Brewer & Sebby (2021) Lee et al. (2023) Brewer & Sebby (2021) Delivery Time Using OFDAs allows me to know about the estimated time of delivery Using OFDAs allows me to order food at any time I am hungry. DT1 DT2 Fakfare (2021) Fakfare (2021) 36 Using OFDAs allows me for real-time tracking of the delivery person The OFDAs deliver the food in a fair amount of time. DT3 DT4 Fakfare (2021) Cao et al. (2018) Online Review Online reviews presented by OFDAs are credible Online reviews presented by OFDAs are relevant to my needs Online reviews presented by OFDAs are helpful for me in evaluating the product Online reviews in OFDAs help me with my purchasing decision OR1 OR2 OR3 OR4 Alalwan (2020) Alalwan (2020) Alalwan (2020) Lee et al. (2023) Personalization The OFDAs offer me personalized information based on my needs and requirements The OFDAs save my order details for my future order The OFDAs store my food preferences or habits and offer me suitable products/services The OFDAs offer me more relevant promotional information tailored to my preferences or personal interests. PE1 PE2 PE3 PE4 Abbasi et al. (2024) Wolfinbarger & Gilly (2003) Wolfinbarger & Gilly (2003) Xu et al. (2011) App Interface The OFDAs are visually appealing The user interface of the OFDAs has a well-organized appearance The OFDAs provide a friendly user interface The OFDAs show attractive promotional banners The OFDAs show good pictures of food/ beverage AI1 AI2 AI3 AI4 AI5 Su et al. (2022) Su et al. (2022) Wang et al. (2019) Su et al. (2022) Su et al. (2022) Order Management The OFDAs offer me an easy order placement process The OFDAs manage all the ordering processes seamlessly The OFDAs confirm the order immediately through mail and SMS The OFDAs facilitate order tracking facility The OFDAs handle multiple restaurants simultaneously OM1 OM2 OM3 OM4 OM5 Fakfare (2021) Atulkar & Singh (2021) Atulkar & Singh (2021) Atulkar & Singh (2021) Atulkar & Singh (2021) Easy Payment The OFDAs offer me a simple payment procedure The OFDAs offer me multiple payment methods (e.g. cash on delivery, credit card). The payment interface in OFDAs is easy for me to understand It is not complicated to purchase food using OFDA EP1 EP2 EP3 EP4 Lee et al. (2023) Fakfare (2021) Kapoor & Vij (2018) Lee et al. (2023) Perceived Usefulness The OFDAs are useful for meeting my demand for ordering food The OFDAs can make my food ordering more convenient The OFDAs can enhance my food purchasing efficiency The OFDAs give me more control over my food ordering The OFDAs save more time than ordering food at a restaurant PU1 PU2 PU3 PU4 PU5 Zhao & Bacao (2020) Zhao & Bacao (2020) Rehman et al. (2019) Rehman et al. (2019) Rehman et al. (2019) Ease of Use The OFDAs are easy for me to learn to operate It is easy to complete a food/beverage order on the OFDAs It is easy to complete a transaction quickly My interaction with the OFDAs is clear and understandable EOU1 EOU2 EOU3 EOU4 Su et al. (2022) Su et al. (2022) Suhartanto et al. (2019) Su et al. (2022) Trust The OFDAs are trustworthy The OFDAs fulfil the promises and commitments to customers The OFDAs are reliable The OFDAs are safe to use TR1 TR2 TR3 TR4 Su et al. (2022) Su et al. (2022) Mohammed & Rozsa (2024) Rehman et al. (2019) Continuance Intention I will continuously use the OFDAs in the future I intend to keep ordering food through OFDAs I will always try to use OFDAs in my daily life CI1 CI2 CI3 Su et al. (2022) Raza et al. (2023) Lee et al. (2019) 37 3.4 Data Analysis Data analysis allows researchers to extract fresh insights by systematically organizing and distilling survey materials while sustaining the integrity of the source material (Eskola & Suoranta, 2014). Although a researcher's bias cannot be completely eradicated, aiming for objectivity is extremely important. This entails not permitting personal thoughts and preconceptions to weigh on the findings of research (Eskola & Suoranta, 2014). Prior to performing thorough evaluations, it is imperative to review responses for consistency and completeness. Having a defined policy to address inconsistency and incompleteness in questionnaires is a key consideration (Kitchenham & Pfleeger, 2003). To make optimal use of the survey data, it is necessary to involve thorough editing, addressing inconsistencies, and rectifying errors. If the questions were lacking from pre-coding, a systematic coding mechanism would become imperative for the smooth integration of the data into the database. Dealing with missing data, which is normally referred to as blank responses, is fundamental for ensuring the validity of the data. Recognizing and resolving issues linked to missing data is an indispensable step often arising from difficulties encountered in data collection and data entry (Hair et al., 2019). In quantitative data analysis, researchers primarily use two data analysis strategies: descriptive statistics for summarising data patterns and statistical techniques to evaluate hypotheses in depth. These approaches enable researchers to verify whether the empirical evidence supports the hypotheses (Hair et al., 2019). In constructing the theoretical framework of consumer trust in online food delivery applications, this research project considers 'trust' and 'continuance use' as TAM elements. In this thesis study, IBM SPSS 26.0 was used for data cleaning and preprocessing the data which involved managing the missing data, data encoding, and generating the descriptive analysis like frequencies, means, and standard deviations. After pre-processing the data, it was transferred to PLSSEM 4.0 to perform structural equation modeling analysis. PLS-SEM 4.0 was instrumental in estimating the outer-inner model testing hypothesis and assessing the model performance. SEM is an effective means for researchers to verify whether their work meets accepted benchmarks for quality statistical analysis (Cook & Campbell, 1979). SEM facilitates the insertion of latent variables into the research model and the concurrent estimation of a range of causal links between variables that are both independent and dependent (Urbach & Ahlemann, 2010). The PLS-SEM method has advantages for achieving the stated objective, as it simplifies the development and evaluation of theories. Partial least squares structural equation modeling (PLS-SEM) is a versatile method that uses a reflective measurement model to investigate the links between observable variables and latent constructs (Pavlou & Fygenson, 2006). PLS-SEM is particularly useful for designing predictive models and for examining causal relationships between latent variables, frequently exceeding classic linear structural equation models, especially when performing 38 exploratory research (Pavlou & Fygenson, 2006; Melchor & Julián, 2008). This method of testing is advantageous to identify whether causal relationships are statistically significant and performs well at constructing theoretical frameworks (Henseler & Chin, 2010). It leverages the PLS algorithm combined with bootstrapping, where 5000 resamples are generated to derive path coefficients and determine their statistical significance (Henseler & Chin, 2010). In this thesis, PLS-SEM served as an effective tool due to its suitability for extended TAM theory that involves new constructs or relationships (Chowdhury, 2023). It is known for its statistical accuracy with limited sample sizes and proves effective for multi-variant analysis (Hair et al., 2019). PLS-SEM was used in this research to explore the correlation between variables, building on the methodologies from previous studies on continuous purchase intention (Hsu et al., 2015). Additionally, the extended TAM model was carefully evaluated by PLS-SEM 4.0 to confirm precision and reliability (Hair et al., 2019). 39 4.1 Demographic Analysis As reported in Table 3, the survey generated responses from 134 participants and shows a balanced gender distribution with 51.5% male and 48.5% female. Table 3: Profiles of the Respondents Sample- 134 Profile Categories Frequency Percentage (%) Gender Male Female 69 65 51.5 48.5 Age 18-24 25-31 32-38 39-45 46- above 26 68 34 5 1 19.4 50.7 25.4 3.7 0.7 Education High school or equivalent College Bachelor Postgraduate Other 2 1 55 75 1 1.5 0.7 41.0 56.0 0.7 Income (BDT) Below 10,000 10,001–30,000 30,001–50,000 50,001–70,000 70001 - above 37 29 37 17 14 27.6 21.6 27.6 12.7 10.4 Profession Student Teacher Doctor Engineer Businessman Employee Other 43 7 4 9 6 42 23 32.1 5.2 3.0 6.7 4.5 31.3 17.2 4 FINDINGS AND ANALYSIS 40 Frequency of Use Less than 1 time per week 1-3 times per week 3-5 times per week More than 5 times per week 79 47 7 1 59.0 35.1 5.2 0.7 Preferred app Foodpanda HungryNaki Pathao Food Foodi 119 4 8 3 88.8 3.0 6.0 2.2 Table 3 shows, the majority are between 25 and 31 years old (50.7%), with the next significant group between 32 and 38 years old (25.4%). The respondents are predominantly well-educated, with 56.0% holding postgraduate degrees and 41% bachelor’s degrees. Income level shows variation in which the largest segments earn either below 10000 BDT (27.6%) or between 30001–50000 BDT (27.6%). Additionally, 21.6% earn between 10001-30000 BDT, followed by 12.7% between 50001-70000 BDT and 10% earn over 70000 BDT. Students represent the largest occupational group 32.1% with employees close behind at 31.3%. Teachers represent 5.2% of the participants, subsequently followed by doctors (3.0%), engineers (6.7%), and businessmen (4.5%), with 17.2% classified under the "Other" category. Regarding the frequency of app usage, 59% of participants stated they used food delivery apps no more than once a week, while 35.1% used these 1-3 times per week. A smaller proportion of users use these applications more regularly, with 5.2% reporting 3-5 usage per week and 0.7% exceeding five times per week. These figures suggest that most participants consume these apps on an occasional basis. Foodpanda is the most popular choice, preferred by 88.8% of participants, followed by Pathao Food (6.0%), HungryNaki (3.0%), and Foodi (2.2%). 4.2 Measurement Model Assessment The measurement models were examined using two forms of validity: discriminant, and convergent, which is compatible with the method of analysis suggested by Ringle et al. (2020). The measuring model evaluation starts with indications of reliability and convergent validity and subsequently continues with an examination of discriminant validity adopting the Fornell and Larcker criteria (1981). 4.2.1 Convergent Validity and Reliability Analysis Reliability signifies the scale's measurement dependability and the evaluation indicators include the consistency of individual items and overall internal consistency (Huang, 2021). The reliability of each item on the scale is verified using factor loadings, and the convergent validity of the constructs is measured using the average variance extracted (AVE) (Hair et al., 2011). Convergent validity intends to measure the comparability between several 41 indicators of a given construct with factor loadings and average variance extracted serving as major methods for this examination (Hair et al., 2017). According to Ramayah et al. (2018), factor loading indicates the correlation between a concept and its indicators with a minimum acceptable loading value greater than 0.7 (Hair et al., 2017). The constructs' internal consistency is measured using Cronbach's alpha and composite reliability (CR). The composite reliability must be higher than 0.7, and the average variance extracted (AVE) needs to be greater than 0.5 (Hair et al., 2011). Table 4 presents the outer loadings, Cronbach's alpha, CR, and AVE for each construct in the measurement model. The results revealed that twelve indicators with outer loadings less than 0.60 were removed from the scales because their factor loadings fell below the allowed threshold of 0.7 (Hulland, 1999). The related constructs' internal consistency reliability was improved by this elimination (Sarstedt et al., 2021). All factor loading values presented in Table 4 are greater than the recommended threshold of 0.7, confirming that the measurement items strongly represent their respective construct. The results indicate that all dimensions have high reliability and internal consistency, as evidenced by Cronbach's alpha and CR values greater than 0.7 except for delivery time and order management. Each dimension's AVE is likewise more than 0.5, indicating that the convergent validity is adequate. Although item loadings range from 0.740 to 0.792, delivery time is characterized by low reliability, exhibited by a Cronbach's alpha of 0.653 and a CR of 0.651. Order management also falls below the suggested 0.7 level with a Cronbach's alpha of 0.555 and a CR of 0.601 (Table 4). Table 4: Convergent Validity and Reliability Measurement Item Outer Loadings Cronbach's alpha Composite reliability (rho_a) Composite reliability (rho_c) Average variance extracted (AVE) Information Quality 0,779 0,784 0,859 0,606 INQ1 0,720 INQ2 0.868 INQ3 0,812 INQ4 0,701 Menu Visualization 0,740 0,743 0,852 0,658 MV2 0,823 MV3 0,784 MV5 0,826 Delivery Time 0,653 0,651 0,808 0,585 DT1 0,792 DT2 0,740 DT3 0,761 Online Review 0,808 0,813 0,874 0,634 OR1 0,805 OR2 0,820 OR3 0,755 OR4 0,804 Personalization 0,765 0,778 0,864 0,680 48 neither personalization nor menu visualization as a meaningful predictor of perceived usefulness, though another study conducted in Bangladesh after COVID-19 by Gani et al. (2021) found that menu visualization has a significant impact on perceived usefulness. The lack of significance in this research may indicate the shifts in consumer behavior. Since the online food delivery market in Bangladesh is still maturing, consumers are still in the process of familiarizing themselves with the digital experience. Consequently, users may prioritize the fundamental functionality and efficiency of OFDAs over advanced features. 5.1 Theoretical Implications This thesis study adds to the current body of literature by offering several theoretical insights. First, this research is one of the earliest to employ an extended TAM model in the context of Bangladesh specifically to explore how trust affects repeated usage of online food delivery apps. Researchers have started to place greater attention on the factors driving consumer choices in the growing OFDA market. This research builds on the Extended TAM model, incorporating trust as a mediator among perceived usefulness, ease of use, and continuance use intention, addressing a gap in the traditional TAM framework that centers on perceived usefulness and ease of use (Legris et al., 2003; Joo et al., 2014; Lee et al., 2023). This investigation confirms that trust significantly meditates the relationship among perceived usefulness, ease of use, and continuous use intention, which aligns with the findings built on prior research that trust is a key role player in digital technology adoption (Gefen et al., 2003). Thirdly, in advancing theoretical knowledge, this thesis identifies external factors such as information quality, delivery time, online reviews, easy payment, and app interface and confirms their significant impact in shaping the perceived usefulness and ease of use of OFDA, which are limitedly used in the standard TAM model (Hong et al., 2021). The inclusion of these external variables demonstrates the importance of diverse factors driving consumer behavior in the digital context. Fourthly, the focus of the thesis study area is Bangladesh's online food delivery apps market, which provides a valuable context-specific contribution because a majority of the existing OFDA research is based on developed nations or more digitally mature markets, where consumer behavioral intention, expectation, and technological adoption patterns vary significantly. Therefore, the exploration of the Bangladesh's OFDA market in this study confirms TAM model applicability across diverse cultural contexts, showing the impact of trust on repeated behavior (Al-Azawei et al., 2017). Finally, this investigation uncovers a theoretical gap in the literature concerning the effect of menu visualization and personalization on perceived usefulness and order management on ease of use. These factors did not show any significant relationship. This non-significance indicates that further exploration is needed as to whether these variables become more relevant in varying cultures and 49 demographics. The understanding of how these variables discourage app usage can serve as a foundation for aligning recent findings on the increased adoption of OFDAs (Atulkar & Singh, 2021). 5.2 Practical Implications The outcome of this thesis study proposes some actionable strategies for food delivery service providers, app developers, marketing professionals, and restaurant owners in Bangladesh. Since trust has been identified as the most important factor driving repeat purchases, OFDA service providers must prioritize strategies designed to build consumer trust. According to earlier research, trust is critical in digital environments and has tremendous effects on consumer loyalty and repurchase intentions (Gefen et al., 2003; Pavlou & Gefen, 2004). This can be achieved by offering user-friendly secure multiple payment options, meeting delivery promises, and implementing a proactive approach to address consumer inquiries and feedback. It is confirmed by the analysis that the perceived usefulness of the OFDA is the key indicator of building consumer trust and individuals' choice to adopt OFDA (Chang et al., 2017; Park, 2009; Gani et al., 2021). Therefore, OFDA marketers should concentrate on how OFDA can optimize users’ daily lives by enhancing the satisfying user experience. Emphasizing the key differentiators like swift delivery, real-time tracking, and authentic and credible reviews is imperative for enhancing trust and reuse intention. Reliable and dependable delivery services are the cornerstone for strengthening consumer trust and loyalty in the OFDA sectors (Alalwan, 2020). Investing in consumer relationship management (CRM) strategies through open communication platforms via various popular social media in Bangladesh like Facebook, Instagram or TikTok and offering various incentives like discounts or loyalty points for honest detail reviews can enhance app credibility and install greater trust among the regular and potential users (Hong et al., 2021; Cheung & Thadani, 2012). Addressing and responding to consumer reviews, particularly negative feedback, is vital in building accountability and maintaining trust and transparency (Park et al., 2007). Online food delivery service providers need to resolve complaints and can promote close-ended review options in some cases to generate actionable objective feedback while retaining critical evaluations to improve app performance (Lee et al., 2023). According to the current analysis, when users perceive the information on an OFDA platform as accurate, detailed, reliable, and up-to-date, their trust in the platform increases, consequently motivating them to make re-purchases. OFDA providers must give preference to the credibility and accessibility of information, which can be attained through efficient data-handling techniques. Consumers are more inclined to trust online platforms for their food delivery requests when they can discover reliable information on restaurant options, offers, specials, and prices, which reduces the degree of uncertainty concerning 50 their selections (Filieri et al., 2018). Furthermore, implementing user-friendly visuals and navigation options improves the overall experience by allowing consumers to effortlessly browse between different connected pages. Including contact information additionally assures that consumers may easily get in touch with service providers for support and feedback (Gani et al., 2021). Delivery service providers also need to keep consumers informed of unanticipated occurrences that may impact food ordering and delivery, including the implementation of new policies, sudden adjustments, adverse conditions, and traffic disruptions (Gani et al., 2021). Ease of use, another significant factor of trust which are strongly influenced by the app interface and easy payment process, reveals the importance of a visually appealing and well-organized app interface and simplified payment process. Online food delivery app developers might want to concentrate their efforts on research and development that simplifies the consumer payment and transaction processes (Lee et al., 2023). The presentation of accurate and engaging content and information along with well-placed imagery allows app users to control their interaction with the app and ease of navigation, which significantly promotes their trust in OFDA (Sarkar et al., 2020). A well-structured, visually appealing interface promotes effortless navigation, allowing users to easily discover restaurants, place orders, and complete transactions. This simplified nature reduces cognitive demands while improving the overall user experience. When consumers find the app easy to use, their trust in the service grows, resulting in more regular usage. Additionally, the app will connect with Bangladeshi users with greater efficiency if local languages and culturally appropriate features are integrated into the navigation design. 5.3 Limitation and Future Research Direction In addition to the study's significant findings, it is essential to acknowledge several limitations for a more comprehensive evaluation. A key constraint in this research is the use of the online-based snowballing sampling technique which may have resulted in self-selection bias (Etikan et al., 2016). The study may favor participants with lower internet aversion and possibly did not capture those who are hesitant to engage with digital platforms. The study's applicability to older demographics is limited because nearly 90 percent of respondents are between the ages of 18 and 38, which underscores the buying habits of young consumers. Furthermore, the reliance of the study on Bangladesh's urban populations may fail to capture the behavior and viewpoints of rural consumers. Due to Bangladesh's fast urbanization, user experiences in places with less digitally developed areas could differ greatly. The study primarily examined specific online food delivery applications particularly platform-to-customer models like Foodpanda and Pathao, rather than addressing the entire OFD landscape. Subsequent research might investigate whether key factors affecting consumer 51 intention to use differ across various OFD platforms to find out whether consumers engage with these platforms differently. This limitation points out the relevance of examining consumer behavior in different OFD platforms (Hong et al., 2021). The user interaction with OFDAs in Bangladesh was the primary goal of the current research, which observed the challenge of projecting its findings to other cultural settings. The findings might not be applicable in areas with different societal norms considering the distinctive consumer behaviors influenced by various cultural contexts (Ali et al., 2020). Particularly for globally recognized platforms like OFDAs, it is highly important to examine how culture and national identity affect the adoption of technology (Leidner & Kayworth, 2006). Future research should pursue cross-national comparative analysis to evaluate how cultural variations affect OFDA user involvement in both developed and developing nations (Su et al., 2022). The use of a cross-sectional methodology in this thesis restricts the opportunity for measuring changes in consumer behavior and trust over time. It would be more efficient to use a longitudinal method to determine how trust might change or adapt in response to shifting consumer experiences, app improvements, variances in service quality, or broader social transformations (Alalwan, 2020). Future researchers might observe the shifting dynamics of consumer behavior in the OFDA market with this type of analysis (Wong et al., 2020; Ali et al., 2020). Lastly, the Technology Acceptance Model signifies how perceptions about usefulness and ease of use shape attitudes. But contrary to what other investigations have identified the current research did not investigate other influential factors, such as user perception, brand image, brand compatibility, and evaluative aspects (Ferraris et al., 2020; Makrides et al., 2022). Integrating the attitude variable could significantly improve the explanatory power of the model and offer deeper insights into TAM in subsequent research. 5.4 Conclusion The rapid evolution of technology and shifting consumer preferences have positioned the OFDA market as an integral part of urban lifestyles. The rapid expansion of OFDAs in Bangladesh has transformed consumers' dining experiences by offering easy accessibility and conveniences like never before. Therefore, maintaining growth and nurturing long-lasting consumer relationships require understanding the factors that influence consumer trust and repetitive purchasing behavior, especially when the competition in the marketplace is increasing. Although, the investigations focused on the specific determinants that impact consumer trust and continuous purchasing in Bangladesh are still limited. This research aimed to address this gap by identifying the interactions among various influential variables by leveraging the extended TAM model as its foundational theory and conducting in-depth analysis to understand the role of these variables in influencing the intention to 52 use OFDA services repeatedly. The data for conducting the analysis was collected through the snowballing sampling technique and analyzed using SPSS 26.0 and PLS-SEM 4.0. The results of the study imply that even as technology advances rapidly, the importance of consumer trust remains a key driver for the sustained performance of these apps. The data predominantly reflected the perception of urban young adults, which limits the applicability of older and rural demographics. In addition, limitations resulting from the culture-specific setting, cross-sectional approach, and reliance on self-reported data provide opportunities for further investigation. To sum up, in the fast-paced online food delivery app market, this research not only enriches the scholarly literature in the field of consumer behavior but also serves as a valuable reference for providing strategic insights to industry professionals. By examining the fundamental factors that contribute to consumer trust, the research creates ways for companies to optimize their operational efficiency and develop deeper emotional connections with their consumers, turning transactions into meaningful partnerships. 53 REFERENCES Aaker, J. L. (1997). Dimensions of brand personality. Journal of marketing research, 34(3), 347-356. Abbasi, A. Z., Qummar, H., Bashir, S., Aziz, S., & Ting, D. H. (2024). Customer engagement in Saudi food delivery apps through social media marketing: Examining the antecedents and consequences using PLS-SEM and NCA. Journal of Retailing and Consumer Services, 81, 104001. Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in human behavior, 63, 75-90. Aburumman, O. J., Omar, K., Al Shbail, M., & Aldoghan, M. (2022). How to deal with the results of PLS-SEM?. In International Conference on Business and Technology (pp. 1196-1206). Cham: Springer International Publishing. Afthanorhan, A. S. Y. R. A. F. (2020). General discussion on small sample size in consistent PLS. Advances in Mathematics: Scientific Journal, 9(3), 1053-1060. Ahmed, S. (2024). An empirical study of consumers’ attitudes toward using online food delivery services in Bangladesh (Doctoral dissertation, University of Northern British Columbia). Ahn, T., Ryu, S., & Han, I. (2004). The impact of the online and offline features on the user acceptance of Internet shopping malls. Electronic commerce research and applications, 3(4), 405-420. Aïmeur, E., Lawani, O., & Dalkir, K. (2016). When changing the look of privacy policies affects user trust: An experimental study. Computers in Human Behavior, 58, 368-379. Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological bulletin, 84(5), 888. Ajzen, I., and M. Fishbein. 1980. Understanding attitudes and predicting social behavior.Englewood Cliffs, NJ: Prentice-Hall. Akter, M., & Disha, N. A. (2021). Exploring consumer behavior for app-based food delivery in Bangladesh during COVID-19. Bangladesh Journal of Integrated Thoughts, 17(1). Al Amin, M., Arefin, M. S., Alam, M. R., Ahammad, T., & Hoque, M. R. (2021). Using mobile food delivery applications during COVID-19 pandemic: An extended model of planned behavior. Journal of Food Products Marketing, 27(2), 105-126. Al-Azawei, A., Parslow, P., & Lundqvist, K. (2017). Investigating the effect of learning styles in a blended e-learning system: An extension of the 54 technology acceptance model (TAM). Australasian Journal of Educational Technology, 33(2). Alagoz, S. M., & Hekimoglu, H. (2012). A study on tam: analysis of customer attitudes in online food ordering system. Procedia-Social and Behavioral Sciences, 62, 1138-1143. Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44. Alexandrov, A., Lilly, B., & Babakus, E. (2013). The effects of social-and selfmotives on the intentions to share positive and negative word of mouth. Journal of the Academy of Marketing Science, 41, 531-546. Ali, M. J., Rahaman, M. A., Latif, W. B., & Karim, M. M. (2023). Determinants of consumer motivation to use online food delivery apps: An empirical investigation of Bangladesh. Innovative Marketing, 19(2), 63. Ali, S., Khalid, N., Javed, H. M. U., & Islam, D. M. Z. (2020). Consumer adoption of online food delivery ordering (OFDO) services in Pakistan: The impact of the COVID-19 pandemic situation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 10. Allah Pitchay, A., Ganesan, Y., Zulkifli, N. S., & Khaliq, A. (2022). Determinants of customers' intention to use online food delivery application through smartphone in Malaysia. British Food Journal, 124(3), 732-753. Alonso-Dos-Santos, M., Soto-Fuentes, Y., & Valderrama-Palma, V. A. (2020). Determinants of mobile banking users’ loyalty. Journal of Promotion Management, 26(5), 615-633. Anbumathi, R., Dorai, S., & Palaniappan, U. (2023). Evaluating the role of technology and non-technology factors influencing brand love in Online Food Delivery services. Journal of Retailing and Consumer Services, 71, 103181. Annaraud, K., & Berezina, K. (2020). Predicting satisfaction and intentions to use online food delivery: what really makes a difference?. Journal of Foodservice Business Research, 23(4), 305-323. Asnakew, Z. S. (2020). Customers’ continuance intention to use mobile banking: development and testing of an integrated model. The Review of Socionetwork Strategies, 14(1), 123-146. Atulkar, S., & Singh, A. K. (2021). Role of psychological and technological attributes on customer conversion to use food ordering apps. International journal of retail & distribution management, 49(10), 1430-1446. Awang, Z. (2012). Research methodology and data analysis second edition. UiTM Press. 55 Babu, M. U. (2024). Foodpanda Bangladesh sees losses surpass Tk1,000cr by 2023- end. The Business Standard. https://www.tbsnews.net/economy/foodpanda-bangladesh-sees-losses- surpass-tk1000cr-2023-end-836776 . Accessed September 19, 2024. Ball, D., Coelho, P. S., & Vilares, M. J. (2006). Service personalization and loyalty. Journal of services marketing, 20(6), 391-403. Balnaves, M., & Caputi, P. (2001). Introduction to quantitative research methods: An investigative approach. Bandinelli, R., Scozzafava, G., Bindi, B., & Fani, V. (2023). Blockchain and consumer behaviour: Results of a Technology Acceptance Model in the ancient wheat sector. Cleaner Logistics and Supply Chain, 8, 100117. Bangladesh Telecommunication Regulatory Commission. (2022) Number of mobile phone subscribers, Government of Bangladesh, Dhaka. Available via: http://old.btrc.gov.bd/content/mobile-phone-subscribers- bangladesh-december-2021. Accessed 15 August, 2024. Bansal, G., Zahedi, F. M., & Gefen, D. (2015). The role of privacy assurance mechanisms in building trust and the moderating role of privacy concern. European Journal of Information Systems, 24, 624-644. Bao, Z., & Zhu, Y. (2022). Why customers have the intention to reuse food delivery apps: evidence from China. British Food Journal, 124(1), 179-196. Basias, N., & Pollalis, Y. (2018). Quantitative and qualitative research in business & technology: Justifying a suitable research methodology. Review of Integrative Business and Economics Research, 7, 91-105. Batra, B. 2020. 7 advantages of using a mobile ordering app for your restaurant [Online]. RestLabs. https://www.restolabs.com/blog/7-advantages-using-mobile- ordering-app-your-restaurant. Accessed 17 June 2024. Bejou, D., Ennew, C. T., & Palmer, A. (1998). Trust, ethics and relationship satisfaction. International Journal of Bank Marketing, 16(4), 170-175. Belanche, D., Flavián, M., & Pérez-Rueda, A. (2020). Mobile apps use and WOM in the food delivery sector: the role of planned behavior, perceived security and customer lifestyle compatibility. Sustainability, 12(10), 4275. Bell, E., Bryman, A., & Harley, B. (2022). Business research methods. Oxford University Press. Benner, C., Mason, S., Carré, F., & Tilly, C. (2020). Delivering insecurity: E- Commerce and the future of work in food retail. Berraies, S., Yahia, K. B., & Hannachi, M. (2017). Identifying the effects of perceived values of mobile banking applications on customers: Comparative study between baby boomers, generation X and generation Y. International Journal of Bank Marketing, 35(6), 1018-1038. 56 Bert, F., Giacometti, M., Gualano, M. R., & Siliquini, R. (2014). Smartphones and health promotion: a review of the evidence. Journal of medical systems, 38, 1- 11. Bezovski, Z. (2016). The future of the mobile payment as electronic payment system. European Journal of Business and Management, 8(8), 127-132. Bhattacherjee, A. (2002). Individual trust in online firms: Scale development and initial test. Journal of management information systems, 19(1), 211-241. Bhattacherjee, A. (2012). Social science research: Principles, methods, and practices. University of South Florida. Blake, B. F., Neuendorf, K. A., & Valdiserri, C. M. (2005). Tailoring new websites to appeal to those most likely to shop online. Technovation, 25(10), 1205-1214. Blasco-Arcas, L., Hernandez-Ortega, B., & Jimenez-Martinez, J. (2014). The online purchase as a context for co-creating experiences. Drivers of and consequences for customer behavior. Internet Research, 24(3), 393-412. Brewer, P., & Sebby, A. G. (2021). The effect of online restaurant menus on consumers’ purchase intentions during the COVID-19 pandemic. International Journal of Hospitality Management, 94, 102777. Bryman, A. (2006). Paradigm peace and the implications for quality. International journal of social research methodology, 9(2), 111-126. Bucko, J., Kakalejčík, L., & Ferencová, M. (2018). Online shopping: Factors that affect consumer purchasing behaviour. Cogent Business & Management, 5(1), 1535751. Budiman, A., Chhor, H., & Razdan, R. (2013). Understanding the diversity of Indonesia's consumers. McKinsey & Company.[Online][Accessed on 4th October 2016] www. mckinsey. com/global-themes/asia-pacific/understanding-the- diversity-of-indonesias-consumers. Cai, S., & Jun, M. (2003). Internet users' perceptions of online service quality: a comparison of online buyers and information searchers. Managing Service Quality: An International Journal, 13(6), 504-519. Cao, Y., Ajjan, H., & Hong, P. (2018). Post-purchase shipping and customer service experiences in online shopping and their impact on customer satisfaction: An empirical study with comparison. Asia Pacific Journal of Marketing and Logistics, 30(2), 400-416. Carlos Martins Rodrigues Pinho, J., & Soares, A. M. (2011). Examining the technology acceptance model in the adoption of social networks. Journal of research in Interactive Marketing, 5(2/3), 116-129. Carnevale, J. B., & Hatak, I. (2020). Employee adjustment and well-being in the era of COVID-19: Implications for human resource management. Journal of business research, 116, 183-187. 57 Chai, L. T., & Yat, D. N. C. (2019). Online food delivery services: Making food delivery the new normal. Journal of Marketing advances and Practices, 1(1), 62- 77. Chakraborty, D., Kayal, G., Mehta, P., Nunkoo, R., & Rana, N. P. (2022). Consumers’ usage of food delivery app: A theory of consumption values. Journal of Hospitality Marketing & Management, 31(5), 601-619. Chang, C. T., Hajiyev, J., & Su, C. R. (2017). Examining the students’ behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 111, 128- 143. Chen, H. S., Liang, C. H., Liao, S. Y., & Kuo, H. Y. (2020). Consumer attitudes and purchase intentions toward food delivery platform services. Sustainability, 12(23), 10177. Chen, H., & Hsieh, Y. (2017). The driving success factors of the online food ordering system–empirical evidence from the UTAUT model. Chen, J., & Dibb, S. (2010). Consumer trust in the online retail context: Exploring the antecedents and consequences. Psychology & Marketing, 27(4), 323-346. Chen, M. F., & Lin, N. P. (2018). Incorporation of health consciousness into the technology readiness and acceptance model to predict app download and usage intentions. Internet Research, 28(2), 351-373. Chen, S. J., & Chang, T. Z. (2003). A descriptive model of online shopping process: some empirical results. International Journal of Service Industry Management, 14(5), 556-569. Chen, X., Huang, Q., Davison, R. M., & Hua, Z. (2015). What drives trust transfer? The moderating roles of seller-specific and general institutional mechanisms. International Journal of Electronic Commerce, 20(2), 261-289. Cheung, C. M., & Lee, M. K. (2001). Trust in internet shopping: instrument development and validation through classical and modern approaches. Journal of Global Information Management (JGIM), 9(3), 23-35. Cheung, C. M., Lee, M. K., & Rabjohn, N. (2008). The impact of electronic word‐ of‐mouth: The adoption of online opinions in online customer communities. Internet research, 18(3), 229-247. Cheung, C. M., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision support systems, 54(1), 461-470. Chew, Y. W. (2023). Intention to use the deliver eat online food delivery app in Malaysia, a behavioral study among gen (Doctoral dissertation, UTAR). Chin, W. W. (1998). Commentary: Issues and opinion on structural equation modeling. MIS quarterly, vii-xvi. 64 https://futurestartup.com/2019/03/07/shohoz-food-growth-and- shohozstrategy/#:~:text=Shohoz%20launched%20Shohoz%20Food%20in,all%20o f%20Dhaka%20very%20rapidly. Accessed September 19, 2024 Kader, R. (2020, February 4). The state of online food delivery in Bangladesh at the beginning of 2020: Subsidies make true demand hard to gauge. FutureStartup. https://futurestartup.com/2020/02/04/online-food- delivery-in-bangladesh-subsidies-make-true-demand-hard-to-gauge/ . Accessed September 15, 2024. Kader, R. (2023, June 25). Hungry no more. Future Startup. https://futurestartup.com/2023/06/25/hungrynaki-is-reportedly- shuttingdown/#:~:text=However%2C%20in%20October%202021%2C%20Shohoz, Bangladesh%20market%20in%20June%202019 . Accessed September 19, 2024. Kang, J. W., & Namkung, Y. (2019). The information quality and source credibility matter in customers’ evaluation toward food O2O commerce. International Journal of Hospitality Management, 78, 189-198. Kapoor, A. P., & Vij, M. (2018). Technology at the dinner table: Ordering food online through mobile apps. Journal of retailing and consumer services, 43, 342- 351. Katawetawaraks, C., & Wang, C. (2011). Online shopper behavior: Influences of online shopping decision. Asian journal of business research, 1(2). Kaur, P., Dhir, A., Talwar, S., & Ghuman, K. (2021). The value proposition of food delivery apps from the perspective of theory of consumption value. International Journal of Contemporary Hospitality Management, 33(4), 1129-1159. Kaushik, A. K., Mohan, G., & Kumar, V. (2020). Examining the antecedents and consequences of customers’ trust toward mobile retail apps in India. Journal of Internet Commerce, 19(1), 1-31. Kim, K., & Prabhakar, B. (2000). Initial trust, perceived risk, and the adoption of internet banking. ICIS 2000 Proceedings, 55. Kim, S., & Park, H. (2013). Effects of various characteristics of social commerce (s-commerce) on consumers’ trust and trust performance. International journal of information management, 33(2), 318-332. Kitchenham, B., & Pfleeger, S. L. (2003). Principles of survey research part 6: data analysis. ACM SIGSOFT Software Engineering Notes, 28(2), 24-27. Koksal, M. H. (2016). The intentions of Lebanese consumers to adopt mobile banking. International Journal of bank marketing, 34(3), 327-346. 65 Komiak, S. Y., & Benbasat, I. (2006). The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS quarterly, 941-960. Kumar, P., Kumar, A., Palvia, S., & Verma, S. (2019). Online business education research: Systematic analysis and a conceptual model. The International Journal of Management Education, 17(1), 26-35. Kumar, S., & Shah, A. (2021). Revisiting food delivery apps during COVID-19 pandemic? Investigating the role of emotions. Journal of Retailing and Consumer Services, 62, 102595. Kumar, S., Jain, A., & Hsieh, J. K. (2021). Impact of apps aesthetics on revisit intentions of food delivery apps: The mediating role of pleasure and arousal. Journal of Retailing and Consumer Services, 63, 102686. Lawrence Neuman, W. (2014). Social research methods: Qualitative and quantitative approaches. Lee, E. Y., Lee, S. B., & Jeon, Y. J. J. (2017). Factors influencing the behavioral intention to use food delivery apps. Social Behavior and Personality: an international journal, 45(9), 1461-1473. Lee, M. K., & Turban, E. (2001). A trust model for consumer internet shopping. International Journal of electronic commerce, 6(1), 75-91. Lee, S. W., Sung, H. J., & Jeon, H. M. (2019). Determinants of continuous intention on food delivery apps: extending UTAUT2 with information quality. Sustainability, 11(11), 3141. Lee, W. S., Song, M., Moon, J., & Tang, R. (2023). Application of the technology acceptance model to food delivery apps. British Food Journal, 125(1), 49-64. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & management, 40(3), 191-204. Leidner, D. E., & Kayworth, T. (2006). A review of culture in information systems research: Toward a theory of information technology culture conflict. MIS quarterly, 357-399. Leung, Y. L., Chan, R. L., Chiu, D. K., & Ruwen, T. (2023). Investigating the consumption behavior of young adults using online food delivery platforms during the COVID-19 pandemic. Aslib Journal of Information Management. Li, C. Y., & Fang, Y. H. (2019). Predicting continuance intention toward mobile branded apps through satisfaction and attachment. Telematics and Informatics, 43, 101248. Li, C., Mirosa, M., & Bremer, P. (2020). Review of online food delivery platforms and their impacts on sustainability. Sustainability, 12(14), 5528. 66 Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z. (2017). A SEM-neural network approach for predicting antecedents of m-commerce acceptance. International Journal of Information Management, 37(2), 14-24. Lin, C. C., Wu, H. Y., & Chang, Y. F. (2011). The critical factors impact on online customer satisfaction. Procedia Computer Science, 3, 276-281. Lin, H. F. (2008). Determinants of successful virtual communities: Contributions from system characteristics and social factors. Information & Management, 45(8), 522-527. Lin, H. H., & Wang, Y. S. (2006). An examination of the determinants of customer loyalty in mobile commerce contexts. Information & management, 43(3), 271-282. Lippert, S. K., & Davis, M. (2006). A conceptual model integrating trust into planned change activities to enhance technology adoption behavior. Journal of information science, 32(5), 434-448. Liu, C., Bao, Z., & Zheng, C. (2019). Exploring consumers’ purchase intention in social commerce: An empirical study based on trust, argument quality, and social presence. Asia Pacific Journal of Marketing and Logistics, 31(2), 378- 397. Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism management, 47, 140-151. Lu, B., Fan, W., & Zhou, M. (2016). Social presence, trust, and social commerce purchase intention: An empirical research. Computers in human behavior, 56, 225-237. Makrides, A., Kvasova, O., Thrassou, A., Hadjielias, E., & Ferraris, A. (2022). Consumer cosmopolitanism in international marketing research: a systematic review and future research agenda. International Marketing Review, 39(5), 1151-1181. Malaquias, R. F., & Hwang, Y. (2019). Mobile banking use: A comparative study with Brazilian and US participants. International Journal of Information Management, 44, 132-140. Martin, W. E., & Bridgmon, K. D. (2012). Quantitative and statistical research methods: From hypothesis to results. John Wiley & Sons. Mathwick, C., & Mosteller, J. (2017). Online reviewer engagement: A typology based on reviewer motivations. Journal of Service Research, 20(2), 204-218. Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of management review, 20(3), 709-734. McCloskey, D. W. (2006). The importance of ease of use, usefulness, and trust to online consumers: An examination of the technology acceptance model 67 with older customers. Journal of Organizational and End User Computing (JOEUC), 18(3), 47-65. McKnight, D. H., & Chervany, N. L. (2001). What trust means in e-commerce customer relationships: An interdisciplinary conceptual typology. International journal of electronic commerce, 6(2), 35-59. McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information systems research, 13(3), 334-359. McMillan, J. H., & Schumacher, S. (2001). Research in education: A conceptual introduction. Longman. Mehrolia, S., Alagarsamy, S., & Solaikutty, V. M. (2021). Customers response to online food delivery services during COVID‐19 outbreak using binary logistic regression. International journal of consumer studies, 45(3), 396-408. Melas, C. D., Zampetakis, L. A., Dimopoulou, A., & Moustakis, V. (2011). Modeling the acceptance of clinical information systems among hospital medical staff: an extended TAM model. Journal of biomedical informatics, 44(4), 553-564. Melchor, M. Q., & Julián, C. P. (2008). The impact of the human element in the information systems quality for decision making and user satisfaction. Journal of Computer Information Systems, 48(2), 44-52. Metha, N. Hyper-Personalization in Food Delivery Business. 2019. Available online: https://nikhilmehta.me/hyperpersonalization-in-food-delivery- business/(accessed on 15th May 2024). Mohammad, A., Aldmour, R., & Al-Hawary, S. (2022). Drivers of online food delivery orientation. International Journal of Data and Network Science, 6(4), 1619-1624. Mohammed, A. A., & Rozsa, Z. (2024). Consumers’ intentions to utilize smartphone diet applications: an integration of the privacy calculus model with self-efficacy, trust and experience. British Food Journal, 126(6), 2416- 2437. Molinillo, S., Navarro-García, A., Anaya-Sánchez, R., & Japutra, A. (2020). The impact of affective and cognitive app experiences on loyalty towards retailers. Journal of Retailing and Consumer Services, 54, 101948. Muangmee, C., Kot, S., Meekaewkunchorn, N., Kassakorn, N., & Khalid, B. (2021). Factors determining the behavioral intention of using food delivery apps during COVID-19 pandemics. Journal of theoretical and applied electronic commerce research, 16(5), 1297-1310. Muntasir, B. (2019). Meteoric rise of online food business. Dhaka Tribune. 68 Murphy, R. (2018). Local consumer review survey: Online reviews statistics & trends. Bright Local. https://www.brightlocal.com/research/ localconsumer-review-survey/. Negash, S., Ryan, T., & Igbaria, M. (2003). Quality and effectiveness in web-based customer support systems. Information & management, 40(8), 757-768. Nguyen, T., & Mai, X. T. (2022). Exploring users’ trust transfer mechanism in food delivery app (Page Count: 7). Nguyen, T., Huang, E., & Nguyen, D. M. (2023). Food delivery app continuance: a dual model and segmentation approach. International Journal of Retail & Distribution Management, 51(5), 569-589. Nwaiwu, F., Kwarteng, M. A., Jibril, A. B., Buřita, L., & Pilik, M. (2020). Impact of security and trust as factors that influence the adoption and use of digital technologies that generate, collect and transmit user data. In ICCWS 2020 15th International Conference on Cyber Warfare and Security (Vol. 363). Norfolk, VA, USA: Academic Conferences and publishing limited. O’donoghue, G., Perchoux, C., Mensah, K., Lakerveld, J., van Der Ploeg, H., Bernaards, C., ... & DEDIPAC consortium. (2016). A systematic review of correlates of sedentary behaviour in adults aged 18–65 years: a socioecological approach. BMC public health, 16, 1-25. Okumus, B., & Bilgihan, A. (2014). Proposing a model to test smartphone users' intention to use smart applications when ordering food in restaurants. Journal of Hospitality and Tourism Technology, 5(1), 31-49. Okumus, B., Ali, F., Bilgihan, A., & Ozturk, A. B. (2018). Psychological factors influencing customers’ acceptance of smartphone diet apps when ordering food at restaurants. International Journal of Hospitality Management, 72, 67-77. Olaleye, S. A., Salo, J., Sanusi, I. T., & Okunoye, A. O. (2018). Retailing mobile app usefulness: Customer perception of performance, trust and tension free. International Journal of E-Services and Mobile Applications (IJESMA), 10(4), 1- 17. Oliveira, T., Alhinho, M., Rita, P., & Dhillon, G. (2017). Modelling and testing consumer trust dimensions in e-commerce. Computers in Human Behavior, 71, 153-164. Ozili, P. K., & Arun, T. (2023). Spillover of COVID-19: impact on the Global Economy. In Managing inflation and supply chain disruptions in the global economy (pp. 41-61). IGI Global. Pantin-Sohier, G. (2009). The influence of the product package on functional and symbolic associations of brand image. Recherche et Applications en Marketing (English Edition), 24(2), 53-71. 69 Pappas, N. (2016). Marketing strategies, perceived risks, and consumer trust in online buying behaviour. Journal of retailing and consumer services, 29, 92- 103 Paradis, E., O'Brien, B., Nimmon, L., Bandiera, G., & Martimianakis, M. A. (2016). Design: Selection of data collection methods. Journal of graduate medical education, 8(2), 263-264. Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307-320. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A conceptual model of service quality and its implications for future research. Journal of Marketing, 49(4), 41-50. Park, D. H., Lee, J., & Han, I. (2007). The effect of on-line consumer reviews on consumer purchasing intention: The moderating role of involvement. International journal of electronic commerce, 11(4), 125-148. Park, H. S. (2000). Relationships among attitudes and subjective norms: Testing the theory of reasoned action across cultures. Communication studies, 51(2), 162-175. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use elearning. Journal of educational technology & society, 12(3), 150-162. Parker, C., Scott, S., & Geddes, A. (2019). Snowball sampling. SAGE research methods foundations. Pathao. (n.d.). Pathao Food - Order food online. https://pathao.com/food/ . Accessed October 23, 2024. Pathao Food. (2024, July 15). Is this the future of food delivery in Bangladesh? Pathao. https://pathao.com/blog/future-of-food-delivery-in- bangladesh/ . Accessed September 19, 2024. Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7(3), 101-134. Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS quarterly, 115-143. Pavlou, P. A., & Gefen, D. (2004). Building effective online marketplaces with institution-based trust. Information systems research, 15(1), 37-59. Penney, T. L., Jones, N., Adams, J., Maguire, E., Burgoine, T., & Monsivais, P. (2016). Are sit-down restaurant, fast food and café usage independently 70 associated with diet and obesity? Tarra Penney. The European Journal of Public Health, 26(suppl_1), ckw170-022. Phillips, P., Barnes, S., Zigan, K., & Schegg, R. (2017). Understanding the impact of online reviews on hotel performance: an empirical analysis. Journal of Travel Research, 56(2), 235-249. Pieal, J. N. (2023, July 16). Where is Bangladesh’s online food delivery market headed? The Business Standard. Retrieved from https://www.tbsnews.net/features/panorama/where-bangladeshs- online-food-delivery-market-headed-666050 . Accessed September 19, 2024. Pigatto, G., Machado, J. G. D. C. F., dos Santos Negreti, A., & Machado, L. M. (2017). Have you chosen your request? Analysis of online food delivery companies in Brazil. British Food Journal, 119(3), 639-657. Pinheiro, J., Lages, L. F., Silva, G. M., Dias, A. L., & Preto, M. T. (2022). Effects of absorptive capacity and innovation spillover on manufacturing flexibility. International Journal of Productivity and Performance Management, 71(5), 1786- 1809. Prasetyo, Y. T., Tanto, H., Mariyanto, M., Hanjaya, C., Young, M. N., Persada, S. F., ... & Redi, A. A. N. P. (2021). Factors affecting customer satisfaction and loyalty in online food delivery service during the COVID-19 pandemic: Its relation with open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 76. Punj, G. (2012). Income effects on relative importance of two online purchase goals: Saving time versus saving money?. Journal of Business Research, 65(5), 634-640. Rabbi, A. R. (2024, September 1). Internet users surge by 10% in first half of 2024. The Business Post. https://businesspostbd.com/telecom/internet-users- surge-by-10-in-first-half-of- 2024#:~:text=A%20closer%20look%20at%20the,rate%20of%2011.06%20per %20cent . Accessed on September 21, 2024. Ramayah, T., & Ignatius, J. (2005). Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. ICFAI Journal of Systems Management (IJSM), 3(3), 36-51. Ramayah, T. J. F. H., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using smartPLS 3.0. An updated guide and practical guide to statistical analysis, 967-978. Ramos, K. (2022). Factors influencing customers' continuance usage intention of food delivery apps during COVID-19 quarantine in Mexico. British Food Journal, 124(3), 833-852. 71 Ray, A., & Bala, P. K. (2021). User generated content for exploring factors affecting intention to use travel and food delivery services. International Journal of Hospitality Management, 92, 102730. Ray, A., Dhir, A., Bala, P. K., & Kaur, P. (2019). Why do people use food delivery apps (FDA)? A uses and gratification theory perspective. Journal of retailing and consumer services, 51, 221-230 Raza, A., Asif, M., & Akram, M. (2023). Give your hunger a new option: Understanding consumers' continuous intention to use online food delivery apps using trust transfer theory. International Journal of Consumer Studies, 47(2), 474-495. Rehman, S. U., Bhatti, A., Mohamed, R., & Ayoup, H. (2019). The moderating role of trust and commitment between consumer purchase intention and online shopping behavior in the context of Pakistan. Journal of Global Entrepreneurship Research, 9(1), 1-25. Reply (2021), “Una nuova spinta verso la digitalizzazione del settore F&B (Food and Beverage)”, available at: https://www.reply.com/it/newsroom/news/una-nuova-spinta-verso-la- digitalizzazione-del-settore-fandb-food-and-beverage (accessed 20 July 2021). Rezaei, S., Shahijan, M. K., Amin, M., & Ismail, W. K. W. (2016). Determinants of app stores continuance behavior: a PLS path modelling approach. Journal of Internet Commerce, 15(4), 408-440. Riaz, H., Davidaviciene, V., Ahmed, H., & Meidute-Kavaliauskiene, I. (2022). Optimizing customer repurchase intention through cognitive and affective experience: an insight of food delivery applications. Sustainability, 14(19), 12936. Ringle, C. M., Sarstedt, M., Mitchell, R., & Gudergan, S. P. (2020). Partial least squares structural equation modeling in HRM research. The international journal of human resource management, 31(12), 1617-1643. Roh, M., & Park, K. (2019). Adoption of O2O food delivery services in South Korea: The moderating role of moral obligation in meal preparation. International Journal of Information Management, 47, 262-273. Rousseau, D. M., Sitkin, S. B., Burt, R. S., & Camerer, C. (1998). Not so different after all: A cross-discipline view of trust. Academy of management review, 23(3), 393-404. Roy Dholakia, R., & Zhao, M. (2010). Effects of online store attributes on customer satisfaction and repurchase intentions. International journal of retail & distribution management, 38(7), 482-496. 72 Saad, A. T. (2021). Factors affecting online food delivery service in Bangladesh: an empirical study. British Food Journal, 123(2), 535-550. Sanakulov, N. (2019). Mobile Technology Adoption and the Effects of Cultural Factors. JYU dissertations, (55). Santos, M. L. R. D., & Patiu, L. S. (2022). Determinants of Continuance Usage Intention and Customer Service: Evidence from Food Delivery Application Users in the Philippines. Sarkar, S., Chauhan, S., & Khare, A. (2020). A meta-analysis of antecedents and consequences of trust in mobile commerce. International Journal of Information Management, 50, 286-301. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587-632). Cham: Springer International Publishing. Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students. Pearson education. Shah, A. M., Yan, X., & Qayyum, A. (2022). Adoption of mobile food ordering apps for O2O food delivery services during the COVID-19 outbreak. British Food Journal, 124(11), 3368-3395. Shao Yeh, Y., & Li, Y. M. (2009). Building trust in m‐commerce: contributions from quality and satisfaction. Online Information Review, 33(6), 1066-1086. Sharma, S. K., & Sharma, M. (2019). Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation. International Journal of Information Management, 44, 65-75. Shi, X., & Liao, Z. (2017). Online consumer review and group-buying participation: The mediating effects of consumer beliefs. Telematics and Informatics, 34(5), 605-617. Shrestha, A. K., & Vassileva, J. (2019, December). User acceptance of usable blockchain based research data sharing system: an extended TAM-based study. In 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA) (pp. 203-208). IEEE. Siau, K., & Shen, Z. (2003). Building customer trust in mobile commerce. Communications of the ACM, 46(4), 91-94. Sigurdsson, V., Larsen, N. M., Alemu, M. H., Gallogly, J. K., Menon, R. V., & Fagerstrøm, A. (2020). Assisting sustainable food consumption: The effects of quality signals stemming from consumers and stores in online and physical grocery retailing. Journal of Business Research, 112, 458-471. 73 Singh, N., & Sinha, N. (2020). How perceived trust mediates merchant's intention to use a mobile wallet technology. Journal of retailing and consumer services, 52, 101894. Singh, S., & Srivastava, S. (2018). Moderating effect of product type on online shopping behaviour and purchase intention: An Indian perspective. Cogent Arts & Humanities, 5(1), 1495043. Singh, Y. K. (2006). Fundamental of research methodology and statistics. New Age International. Sjahroeddin, F. (2018, October). The role of ES-Qual and food quality on customer satisfaction in online food delivery service. In Prosiding Industrial Research Workshop and National Seminar (Vol. 9, pp. 551-558). Slavin, R. E. (2007). Educational research in an age of accountability. (No Title). Snell, S. A., & Dean Jr, J. W. (1992). Integrated manufacturing and human resource management: A human capital perspective. Academy of Management journal, 35(3), 467-504. Söllner, M., Hoffmann, A., & Leimeister, J. M. (2016). Why different trust relationships matter for information systems users. European Journal of Information Systems, 25(3), 274-287. Song, H., Ruan, W. J., & Jeon, Y. J. J. (2021). An integrated approach to the purchase decision making process of food-delivery apps: Focusing on the TAM and AIDA models. International Journal of Hospitality Management, 95, 102943. Statista. (2020). eServices Report-Online Food Delivery. https://www. statista.com/outlook/dmo/eservices/online-food-delivery/platform to consumer-delivery/asia Statista. (2024, July). Online Food Delivery - Bangladesh: Market Insights. https://www.statista.com/outlook/emo/online-food- delivery/bangladesh . Accessed on September 21, 2024. Su, D. N., Nguyen, N. A. N., Nguyen, L. N. T., Luu, T. T., & Nguyen-Phuoc, D. Q. (2022). Modeling consumers’ trust in mobile food delivery apps: Perspectives of technology acceptance model, mobile service quality and personalization-privacy theory. Journal of Hospitality Marketing & Management, 31(5), 535-569. Su, D. N., Nguyen-Phuoc, D. Q., Duong, T. H., Dinh, M. T. T., Luu, T. T., & Johnson, L. (2022). How does quality of mobile food delivery services influence customer loyalty? Gronroos’s service quality perspective. International Journal of Contemporary Hospitality Management, 34(11), 4178- 4205. 80 APPENDIX 2: “Survey Questionnaire” 81 82 83 84 85 86 87 APPENDIX 3: “List of Abbreviations” OFDA- Online food delivery application O2O- Online-to-offline WHO- World Health Organization M-commerce- Mobile commerce AI- Artificial Intelligence BRTC- Bangladesh Telecommunication Regulatory Commission E-delivery – Electronic delivery FDA- Food delivery app MFDA- Mobile food delivery app UTAUT- Unified theory of acceptance and use of technology TAM- Technology acceptance model 88 IAM- Information adoption model MFOA- Mobile food ordering app MCA- Mobile catering app TTF- Task-technology fit IS – Information system OFDP- Online food delivery platform S-O-R- Stimulusorganism-response OFDS- Online food delivery service TPB- Theory of planned behavior FOA- Food ordering app TRA- Theory of reasoned action PU- Perceived usefulness EOU- Ease of use E-service – Electronic service PLS- Partial least squares SEM- Structural equation modeling AVE- Average variance extracted CR- Composite Reliability BDT- Bangladeshi Taka