International Journal of Applied Business & International Management (IJABIM) Vol.
10 No.
3, pp.
December, 2025
E-ISSN: 2621-2862 P-ISSN: 2614-7432
https://w.
com/index.
php/IJABIM Consumer Attitude and Intention Toward AI-Based Smart Ordering and Delivery Systems in the Fast-Food Industry Azura Abdullah Effendi1.
Tek Yew Lew2.
Wei Ning Ooi1.
Soak Suan PAong1.
Mei Shan Poh1.
Puteri Balqis Binti Hamizan1.
Arun Singh Tomar1.
Daisy Mui Hung Kee1* Universiti Sains Malaysia.
Curtin University Malaysia Malaysia.
Jalan Sg Dua, 11800 Minden.
Puala Pinang.
Malaysia *Corresponding Email: daisy@usm.
ARTICLE INFORMATION
ABSTRACT
Publication information The increasing use of artificial intelligence (AI) in fast-food ordering and delivery Research article systems has transformed service processes, yet consumer adoption HOW TO CITE remains uneven.
This study examines the Effendi.
Lew.
Ooi.
PAong, factors influencing consumer attitude and .
Poh.
Balqis.
A, & Kee, behavioral intention toward AI-based .
Consumer attitude and smart ordering and delivery systems in intention toward AI-based smart ordering the fast-food industry, using McDonaldAos and delivery systems in the fast-food AI-based Smart Ordering and Delivery International Journal of Applied System in Malaysia as the empirical Business & International Management, context.
Using a quantitative approach, 10.
, 464-480.
data were collected from 150 consumers in Malaysia and analyzed using multiple DOI:
regression analysis.
The results show that https://doi.
org/10.
32535/ijabim.
4325 social influence has a significant positive effect on consumer attitude ( = 0.
313, p Copyright @ 2025 owned by Author.
< 0.
, while ease of use, enjoyment.
Published by IJABIM perceived convenience, and trust do not significantly affect attitude.
For behavioral intention, perceived convenience ( = 218, p < 0.
, social influence ( = 291, p < 0.
, and trust ( = 0.
248, p < 0.
are significant predictors, whereas consumer attitude is not ( = 0.
066, p > This is an open-access article.
The models explain 29.
8% of the License:
Attribution-Noncommercial-Share Alike (CC variance in consumer attitude and 44.
of the variance in behavioral intention.
BY-NC-SA)
These findings suggest that adoption of AI-based ordering systems in fast-food Received: 13 October 2025 contexts is driven more by social Accepted: 15 November 2025 Published: 20 December 2025 Keywords:
Artificial Intelligence.
Behavioral Intention.
Consumer Attitude.
Fast-Food Industry.
Smart Ordering Systems International Journal of Applied Business & International Management (IJABIM) Vol.
10 No.
3, pp.
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E-ISSN: 2621-2862 P-ISSN: 2614-7432
https://w.
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php/IJABIM INTRODUCTION Over the past decade, the fast-food industry has undergone a substantial transformation driven by shifting consumer preferences, accelerated technological advancement, and increasing demand for convenience, speed, and service personalization.
As one of the worldAos largest quick service restaurant (QSR) chains.
McDonaldAos has consistently demonstrated strategic adaptability in responding to these dynamics through operational innovation, marketing realignment, and digital integration (Chia et al.
, 2.
Prior research has examined McDonaldAos from multiple perspectives, including corporate social responsibility and consumer attitudes (Kee et al.
, 2.
, regional marketing effectiveness in Southeast Asia (Dilip et al.
, 2.
, and the role of social media in shaping consumer behavior (Lok et al.
, 2.
Collectively, this body of literature highlights McDonaldAos capacity to align organizational strategies with evolving external pressures and generational consumption trends across diverse markets.
Beyond marketing and branding considerations.
McDonaldAos has increasingly embedded sustainability and corporate citizenship into its operational framework, strengthening brand legitimacy and stakeholder trust (Kee et al.
, 2.
Empirical evidence further suggests that such initiatives influence purchasing intention and customer loyalty, particularly when supported by transparent organizational practices and responsiveness during periods of crisis, such as the COVID-19 pandemic.
These findings reinforce the view that McDonaldAos competitive advantage is closely linked to its ability to integrate technological, social, and organizational elements in addressing consumer expectations at a global scale (Kee et al.
, 2.
Despite the breadth of research on McDonaldAos strategic and digital initiatives, scholarly attention to its adoption of artificial intelligence (AI), particularly in the context of smart ordering and delivery systems, remains limited.
Recent advancements in AI, including machine learning, natural language processing, and real-time analytics, have expanded opportunities for service automation, personalized interaction, and logistics optimization within the food service industry (Adeoye et al.
, 2025.
Khan et al.
, 2.
AI-enabled applications such as chatbots, virtual assistants, and intelligent delivery systems are increasingly deployed to enhance service efficiency, reduce operational costs, and improve customer experience.
However, empirical evidence explaining how these technologies influence consumer attitudes and behavioral intention within established fast-food brands is still scarce, especially in emerging and digitally evolving markets such as Malaysia.
Existing technology adoption research often focuses on isolated determinants, such as ease of use, enjoyment, trust, or social influence, without sufficiently considering how these factors jointly operate within specific service contexts.
Moreover, the relative importance of these determinants may vary depending on technological maturity, service routinization, and user expectations.
In highly digitalized and time-sensitive service environments like fast-food consumption, certain functional attributes may be perceived as baseline requirements rather than salient drivers of adoption.
As a result, consumersAo behavioral decisions may rely less on reflective attitudinal evaluation and more on pragmatic and socially embedded considerations.
Therefore, empirical investigation is needed to clarify how technological and social factors shape consumer attitudes toward AI-based service systems and to reassess the extent to which these attitudes translate into behavioral intention.
Addressing this gap, the present study empirically examines consumer attitudes and behavioral intentions toward McDonaldAos AI-based Smart Ordering and Delivery System, using McDonaldAos as a representative context within the fast-food industry.
Specifically.
International Journal of Applied Business & International Management (IJABIM) Vol.
10 No.
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E-ISSN: 2621-2862 P-ISSN: 2614-7432
https://w.
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php/IJABIM the study investigates the effects of ease of use, perceived convenience, enjoyment, trust, and social influence on consumer attitude, as well as the effect of consumer attitude on behavioral intention.
Rather than presuming an automatic attitudinal pathway, this study empirically reassesses the role of consumer attitude within AI-based fast-food services, acknowledging the possibility that adoption decisions may be shaped by factors beyond attitudinal evaluation.
The significance of this study lies in its contribution to the technology adoption literature by extending existing models to AI-enabled food service systems, a context that remains underexplored (Kee et al.
, 2.
The novelty of the research is reflected in its integrated examination of multiple adoption determinants within a single empirical framework, while critically re-evaluating the role of consumer attitude in explaining behavioral intention.
Theoretically, the study refines the understanding of consumer behavior in advanced, routine service technologies.
Practically, it offers actionable insights for fast-food operators in designing AI-based ordering systems that emphasize social acceptance, perceived convenience, and trust to encourage adoption and sustained usage in competitive markets.
LITERATURE REVIEW
Technology Acceptance Model (TAM) The Technology Acceptance Model (TAM) explains how individuals develop attitudes and intentions toward adopting new technologies (Davis, 1.
The model proposes that users form evaluations of a technological system based on their perceptions of its characteristics, which subsequently shape their attitude toward use and influence behavioral intention (An et al.
, 2023.
Troise et al.
, 2.
TAM has been widely applied to examine consumer acceptance of digital and AI-enabled services across various service industries.
Within TAM, attitude toward use represents an individualAos overall evaluative response to a technology, while behavioral intention reflects the likelihood of adopting or continuing to use the system.
Attitude is positioned as a key mediating construct that translates perceptions of technology attributes into usage-related decisions.
To adapt TAM to AI-based smart ordering and delivery systems, this study incorporates several external variables that reflect cognitive, experiential, social, and relational evaluations of technology use.
Ease of use represents usersAo perception of the effort required to interact with the system.
Enjoyment captures the extent to which using the system is perceived as pleasurable beyond functional outcomes.
Perceived convenience reflects usersAo assessment of time efficiency and process simplicity.
Social influence accounts for the impact of peers and social norms on individual evaluations of the Trust represents usersAo confidence in the systemAos reliability, security, and Behavioral Intention Behavioral intention is important in determining an individual's likelihood of using a new service or technology.
Consumer behavior, which involves the psychological, physical, and social factors that influence the way individuals select, utilize, and dispose of products and services, plays a vital role in determining these intentions.
When it comes to how McDonald's shapes its AI experiences to match what users want, factors such as efficiency, speed, and ease of use play a big role.
By grasping these key influences on behavior, marketers can craft strategies that align with how consumers make decisions, ultimately boosting their satisfaction with the service (Bhasin et al.
, 2.
International Journal of Applied Business & International Management (IJABIM) Vol.
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php/IJABIM Similar to online food outlets such as GrabFood, usefulness, convenience, and service perceptions will most probably affect customers' intentions to use McDonald's AI system (Liou et al.
, 2.
Research also indicates that if the user perceives technology as making them perform better on tasks and simplifying procedures, the intention to adopt rises significantly (Nagy et al.
, 2.
For example, if the system can effectively predict orders, reduce waiting time, and enable smooth delivery tracking, consumers will reuse the service.
Furthermore, it has been proven through research that perceived convenience, quality of service, and security significantly influence consumers' attitudes and behavioral intentions toward online food delivery services (Chowdhury, 2.
Besides, food neophobia was also discovered to serve as a mediator of the relationship between restaurant satisfaction and post-meal behavior intentions among foreign tourists who consumed local meals (Tarinc et al.
, 2.
The findings indicate how the nature of digital services should align with consumer demands in a way that ensures there is a positive behavioral intention.
Ease of Use McDonald's Smart Ordering and Delivery System, an AI-powered system, is a driving factor in buying behavior among customers (Wolfe, 2.
A user-friendly system ensures a seamless and effortless experience, encouraging repeated adoption and heightened engagement.
The chances of customers adopting and maintaining use improve when the platform is easy to utilize and efficient.
This is consistent with a study by Yo et al.
, which found that customer satisfaction with Shopee in Malaysia was strongly influenced by perceived ease of use and convenience.
In the example of McDonald's, an easy system enhances user satisfaction by minimizing errors, decreasing ordering time, and lowering the cognitive effort required for the processing of a transaction (Wolfe, 2.
This is also confirmed by Widiar et al.
, through a study that had already determined that perceived usefulness and perceived ease of use directly influence the intentions of the behavior of the individual, where the element of trust comes in.
Literature also attests that where the system is transparent, customer enthusiasm for applying and using the system is encouraged.
The other characteristics, including voice control, suggestions, and live order status, also ease user interaction, which ultimately initiates positive behavioral intention and loyalty.
Enjoyment Enjoyment is a wonderful, hassle-free, joyful, good, and pleasant feeling that customers gain through McDonald's Smart Ordering and Delivery System with AI.
Customers are attracted to technologies that enhance the purchasing experience and allow them to enjoy and treat themselves better.
McDonald's Smart Ordering and Delivery System enhances customer enjoyment over time.
Personalized aspects, such as instant feedback, create customer happiness and delight (Alter Solutions, 2.
Interactive aspects such as chatbots also attract customers, giving a better sense of reality.
Personalization also increases customer satisfaction through easy ordering, making it easier and more effective (Alter Solutions, 2.
Perceived convenience Consumers usually perceive how seamlessly and effortlessly the AI system can assist them in the ordering and delivery process (Ravi et al.
, 2.
McDonaldAos Smart Ordering and Delivery System, powered by AI, influences consumer behavior and enhances service experiences.
Making the ordering process simpler by automating repetitive tasks and providing real-time updates helps customers save time and effort, ultimately improving their efficiency (Khoso & Hussain, 2.
Personalized menus based on historical data contribute to a smoother, more convenient ordering experience, which in International Journal of Applied Business & International Management (IJABIM) Vol.
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When customers feel that the experience is tailored to their preferences, they are more likely to find the process both enjoyable and efficient.
Personalized experiences lead consumers to perceive the system as more convenient, reinforcing positive behavioral intentions (Bradley, 2.
Social influence Social influence, expressed as peer belief and external pressure, is most significant in shaping consumer attitudes and behavior towards adopting new technology.
Word of mouth by close relatives, friends, or colleagues concerning the convenience of AI systems tends to build user confidence and trust in the technologies (Alshakhsi et al.
When individuals see others successfully utilizing AI, they find the system more dependable and less difficult to use.
This effect is typically more powerful in resistant users, i.
, older individuals, who are more likely to apply AI-driven services like smart ordering platforms when motivated by social norms or information encouragement (Jang et al.
, 2.
Apart from consumer engagement and trust.
AI systems should be designed in terms of responsiveness and fairness so that they maintain their pace with evolving social needs and cultivate an inclusive experience among various users (Afroogh et al.
Trust Trust is really the factor that influences how consumers engage with McDonald's Smart Ordering and Delivery System, which is driven by AI.
When it comes to placing their trust in this system, consumers tend to focus on a few key aspects, which they want to feel assured of its credibility, having confidence in its reliability, and being certain that their personal information is protected and secure.
Some recent research records that trust would have a particularly prominent role as a determinant of users' attitudes and behavioral beliefs toward AI-driven technologies.
For instance.
Choung et al.
proved that trust in AI-powered voice assistants has positive effects on users' attitudes and intentions to use AI-enabled technologies and is mediated through perceived ease of use and usefulness.
Similarly.
Chi and Vu .
depicted how anthropomorphism and empathetic feedback during human-AI interactions increase customers' trust and, consequently, increase acceptance of AI solutions.
AI-based personalization in ecommerce settings increases consumer trust and satisfaction, thereby influencing purchase intentions.
For McDonald's, trust establishment in their AI system will translate into increased customer satisfaction, repeat use, and loyalty to the brand.
Consumer Attitudes Consumer attitude is a consumer's overall judgment, impression, or feeling about a product, service, or technology, and is a critical determinant of behavioral intention.
For McDonald's Smart Ordering and Delivery System with AI, a positive consumer attitude can play a major role in improving the system adoption and future use probability.
explained by Yi and Choi .
, favorable attitudes towards AI services enhance customers' intentions to utilize them, especially if they perceive the technology to be convenient and helpful.
Nasir et al.
also found that consumers holding a positive attitude towards the employment of AI within digital platforms will most likely repurchase and express greater brand commitment.
In e-retailing.
He et al.
observed that positive attitudes of customers towards AI-supported services increase their purchase intention greatly, and attitudes also proved to be a good predictor of future behavior.
For McDonald's, creating positive attitudes by using plain communication, ease of use, and customization options will win customers' trust, increase satisfaction, and result in repeat usage of the AI ordering system.
International Journal of Applied Business & International Management (IJABIM) Vol.
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php/IJABIM Hypotheses Development Based on the TAM and prior empirical studies, several hypotheses are formulated to examine the relationships among the study variables.
Accordingly, the following hypotheses are proposed:
H1: Ease of use has a positive effect on consumer attitude.
H2: Enjoyment has a positive effect on consumer attitude.
H3: Perceived convenience has a positive effect on consumer attitude.
H4: Social influence has a positive effect on consumer attitude.
H5: Trust has a positive effect on consumer attitude.
H6: Consumer attitude has a positive effect on behavioral intention.
Figure 1 illustrates the proposed research framework, depicting the hypothesized relationships among ease of use, enjoyment, perceived convenience, social influence, trust, consumer attitude, and behavioral intention toward AI-based smart ordering and delivery systems.
Figure 1.
Theoretical Framework
RESEARCH METHOD
This study adopts a quantitative research approach to examine the factors influencing consumer attitude and behavioral intention toward McDonaldAos AI-based Smart Ordering and Delivery System in the fast-food industry.
A cross-sectional research design was employed to capture consumersAo perceptions and behavioral tendencies at a single point in time, which is appropriate for assessing technology adoption behavior in routine service contexts.
The target population of this study consists of consumers in Malaysia who have prior experience using McDonaldAos Smart Ordering and Delivery System, including its mobile application and AI-assisted ordering and delivery features.
A purposive sampling technique was used to ensure that respondents had relevant exposure to McDonaldAos AI-enabled ordering platform and were therefore capable of providing informed Data were collected through an online questionnaire distributed using Google Forms, resulting in 150 valid responses for analysis.
This sample size exceeds the minimum requirement for multiple regression analysis and is considered adequate for exploratory technology adoption research in applied business studies.
International Journal of Applied Business & International Management (IJABIM) Vol.
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https://w.
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php/IJABIM All measurement items were adapted from previously validated studies to ensure content validity and contextual relevance.
The questionnaire comprised two sections: the first captured respondentsAo demographic characteristics, including frequency of using the McDonaldAos mobile application, while the second measured the study variables, namely ease of use, enjoyment, perceived convenience, trust, social influence, consumer attitude, and behavioral intention toward McDonaldAos Smart Ordering and Delivery System.
All constructs were measured using a five-point Likert scale ranging from 1 .
trongly disagre.
to 5 .
trongly agre.
Data analysis was conducted using IBM SPSS Statistics version 26.
Descriptive analysis was used to summarize respondentsAo demographic profiles and usage characteristics related to the McDonaldAos application.
Reliability analysis using CronbachAos alpha was performed to assess the internal consistency of the measurement scales.
Correlation analysis was conducted to examine the bivariate relationships among the study variables and to assess the suitability of the data for regression analysis.
To test the proposed hypotheses, multiple regression analysis was conducted in two In the first regression model, ease of use, enjoyment, perceived convenience, trust, and social influence were entered as independent variables to examine their effects on consumer attitude toward McDonaldAos Smart Ordering and Delivery System.
In the second regression model, behavioral intention was regressed on consumer attitude, perceived convenience, trust, and social influence to evaluate the determinants of consumersAo intention to use the system.
This analytical approach allows for the examination of the relative influence of each predictor while controlling for the effects of other variables.
Multiple regression analysis was selected due to its suitability for examining direct relationships among variables in applied behavioral research and its ability to assess explanatory power without imposing complex structural assumptions.
Mediation analysis was not conducted, as the study aimed to empirically reassess the role of consumer attitude rather than to assume its mediating function a priori.
Statistical significance was evaluated at the 0.
05 level.
RESULTS
Table 1.
Respondent ProfileAos Summary (N=.
Response Gender Female Male Age Above 27 Below 18 Race Chinese Indian Malay Education Level Degree or equivalent Diploma or equivalent High School Frequency Percentage (%) International Journal of Applied Business & International Management (IJABIM) Vol.
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150 people responded to the survey.
The sample was dominated by young women, with the majority being female .
7%) and between the ages of 18 and 22 .
3%).
The largest group of responders .
3%) was Chinese, followed by Malay .
0%) and Indian .
7%).
The group appears to be highly educated, as the majority of respondents .
0%) had a degree or its equivalent.
Only a small percentage were working either full-time .
3%) or part-time .
0%), with the majority being students .
0%).
Regarding monthly allowance and income, 41.
3% made less than RM500 per month, while nearly half .
7%) reported an allowance between RM500 and RM1500.
Just 8.
0 percent made more than RM2,500.
When analyzing McDonald's app usage, 41.
3% of respondents used the app a few times a month, 35.
3% used it once a month or less, and 22.
0% used it once a week.
Daily users were rare .
3%).
Table 2.
Descriptive Analysis.
Cronbach's Correlations for All Study Variables Variables 1 Ease Use 2 Enjoyment 380** 0.
3 Perceived Convenienc 0.
258** 0.
483** 0.
4 Social 338** 0.
364** 0.
Influence 5 Trust 282** 0.
420** 0.
6 Consumer 309** 0.
374** 0.
Attitudes 7 Behavioral 313** 0.
388** 0.
Intention Number Items Mean Standard Deviation Coefficient Alpha, and Zero-Order Note: N = 150.
*p < 0.
05, **p < 0.
The diagonal entries represent Cronbach's coefficient alpha International Journal of Applied Business & International Management (IJABIM) Vol.
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The mean values of all variables range from 4.
52 to 4.
indicating generally positive perceptions toward AI-based smart ordering and delivery systems among respondents.
The standard deviation values are relatively low, suggesting consistent responses across participants.
The reliability analysis demonstrates satisfactory internal consistency for all constructs.
CronbachAos alpha values range from 0.
707 to 0.
812, exceeding the recommended threshold of 0.
70, thereby confirming the reliability of the measurement scales used in this study.
The correlation analysis reveals that all variables are positively and significantly correlated with each other at the 0.
01 significance level.
Consumer attitude shows moderate positive correlations with ease of use .
= 0.
, enjoyment .
= 0.
perceived convenience .
= 0.
, social influence .
= 0.
, and trust .
= 0.
Behavioral intention is also positively correlated with all antecedent variables, indicating that more favorable perceptions toward AI-based systems are associated with stronger adoption intention.
However, as correlation analysis does not imply causality, further regression analysis is required to examine the predictive relationships among the Table 3.
Regression Analysis Variables 1 Ease to Use 2 Enjoyment
3 Perceived Convenience 4 Social Influence 5 Trust
6 Consumer Attitudes RA
F Value Durbin-Watson Statistic Consumer Attitudes 313*** Behavior Intention
291***
Note: N = 150: *p < 0.
05, **p < 0.
01, ***p< 0.
Table 3 reports the results of multiple regression analyses examining the determinants of consumer attitude and behavioral intention toward AI-based smart ordering and delivery systems.
With respect to consumer attitude, the results indicate that social influence has a statistically significant positive effect ( = 0.
313, p < 0.
, supporting H4.
This finding suggests that peer recommendations, social norms, and perceived social endorsement play a decisive role in shaping consumersAo attitudes toward adopting AI-based systems.
In contrast, ease of use ( = 0.
, enjoyment ( = 0.
, perceived convenience ( = .
, and trust ( = 0.
do not exhibit statistically significant effects on consumer Accordingly.
H1.
H2.
H3, and H5 are not supported.
The regression model 8% of the variance in consumer attitude (RA = 0.
, indicating a moderate level of explanatory power.
Regarding behavioral intention, the regression results show that consumer attitude does not have a statistically significant effect on behavioral intention ( = 0.
066, p > 0.
Therefore.
H6 is not supported, indicating that a favorable attitude alone is insufficient to directly translate into intention to adopt AI-based smart ordering and delivery systems in this context.
Nevertheless, the model explains 44.
5% of the variance in behavioral International Journal of Applied Business & International Management (IJABIM) Vol.
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, reflecting strong overall predictive capability.
The F-statistics for both regression models are statistically significant, confirming the adequacy of the models.
Additionally, the DurbinAeWatson statistics of 2.
006 for the consumer attitude model and 1.
632 for the behavioral intention model indicate no serious autocorrelation issues, supporting the reliability of the regression estimates.
Overall, the findings demonstrate that social influence is the only antecedent that significantly shapes consumer attitude, while consumer attitude itself does not exert a direct influence on behavioral intention, suggesting that adoption of AI-based smart ordering and delivery systems is driven more by external social factors than by internal attitudinal evaluation.
Figure 2 presents an overview of the empirical results, summarizing the supported and unsupported hypotheses based on the regression analysis findings.
Figure 2.
Overview Result
DISCUSSION
H1: Ease of Use and Consumer Attitude The findings indicate that ease of use does not have a statistically significant effect on consumer attitude, leading to the rejection of H1.
This result contrasts with a substantial body of prior research that positions ease of use as a central determinant of favorable attitudes toward digital and AI-enabled services (Widiar et al.
, 2022.
Wolfe, 2025.
Yo et , 2.
In earlier technology adoption contexts, usability often reduced cognitive effort and uncertainty, thereby strengthening attitudinal acceptance.
However, the present findings suggest that this mechanism may weaken in technologically mature One plausible explanation is that ease of use has evolved into a baseline expectation rather than an attitudinal driver.
Among digitally experienced consumers, particularly younger cohorts, functional usability is assumed rather than evaluated.
As a result, ease of use no longer differentiates AI-based systems at the attitudinal level, even though it remains necessary for system acceptance.
This interpretation aligns with emerging perspectives that suggest traditional TAM constructs may lose explanatory power in settings where digital interfaces are already normalized.
Consequently, ease of use may International Journal of Applied Business & International Management (IJABIM) Vol.
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its absence causes dissatisfaction, but its presence does not actively generate positive attitudes.
H2: Enjoyment and Consumer Attitude The results show that enjoyment does not significantly influence consumer attitude, resulting in the rejection of H2.
This finding diverges from studies emphasizing hedonic motivation as a key driver of engagement with AI-based and interactive technologies (Alter Solutions, 2.
In entertainment-oriented or experiential platforms, enjoyment enhances emotional attachment and positive evaluation.
However, the present findings indicate that such hedonic considerations play a limited role in shaping attitudes toward AI-based smart ordering systems in fast-food services.
This discrepancy can be attributed to the task-oriented nature of fast-food consumption, where speed, efficiency, and convenience outweigh experiential pleasure.
AI-based ordering systems in this context are evaluated primarily as functional tools rather than sources of enjoyment.
While interactive features such as chatbots and personalized recommendations may enhance momentary satisfaction, they appear insufficient to shape overall attitudes toward adoption.
This suggests that enjoyment may function as a complementary factor rather than a core attitudinal determinant in routine, efficiencydriven service environments.
H3: Perceived Convenience and Consumer Attitude Contrary to expectations, perceived convenience does not have a statistically significant effect on consumer attitude, leading to the rejection of H3.
This result contrasts with prior studies that identify convenience as a critical determinant of attitudes and intentions in online food delivery and digital service platforms (Chowdhury, 2023.
Ravi et al.
, 2.
However, the present findings suggest that convenience may be implicitly embedded in consumersAo expectations of AI-based systems.
In highly competitive fast-food markets, convenience is often assumed rather than Consumers may view AI-based ordering as inherently designed to reduce effort and save time, thereby diminishing its salience in attitudinal formation.
Importantly, this does not negate the importance of convenience.
rather, it indicates a shift in its role from an attitudinal antecedent to a direct behavioral motivator.
This distinction underscores the need to differentiate between factors that shape evaluative attitudes and those that directly trigger usage decisions.
H4: Social Influence and Consumer Attitude Consistent with H4, social influence emerges as the only variable with a statistically significant positive effect on consumer attitude.
This finding strongly aligns with prior research highlighting the role of social norms, peer influence, and observational learning in technology adoption (Alshakhsi et al.
, 2025.
Jang et al.
, 2.
In socially embedded consumption contexts, such as fast-food services, individuals often rely on external cues to evaluate the legitimacy and desirability of new technologies.
The strong influence of social factors suggests that attitudinal acceptance of AI-based ordering systems is not solely a result of individual cognitive evaluation but is socially When consumers observe widespread usage or receive positive recommendations, uncertainty surrounding AI adoption diminishes, leading to more favorable attitudes.
This effect may be particularly pronounced among users who are initially skeptical or less technologically confident.
The findings reinforce the importance of social validation mechanisms in shaping attitudes toward AI-enabled services.
International Journal of Applied Business & International Management (IJABIM) Vol.
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php/IJABIM H5: Trust and Consumer Attitude The findings indicate that trust does not significantly influence consumer attitude, leading to the rejection of H5.
This result partially contradicts prior studies that identify trust as a fundamental antecedent of attitudes toward AI-driven technologies (Chi & Vu, 2022.
Choung et al.
, 2.
However, this divergence can be explained by contextual factors specific to the service environment examined.
In the case of a globally recognized fast-food brand, organizational credibility and brand reputation may significantly reduce perceived risk.
As a result, consumers may not actively evaluate trust when forming attitudes toward AI-based systems, as trust is already implicitly granted.
In such contexts, trust becomes more salient at later stages of the decision-making process, particularly when consumers assess whether to rely on the system for actual usage.
This finding suggests that trust operates differently across attitudinal and behavioral stages, depending on brand strength and perceived H6: Consumer Attitude and Behavioral Intention The results reveal that consumer attitude does not have a statistically significant effect on behavioral intention, leading to the rejection of H6.
This finding challenges traditional technology adoption models that posit a strong attitudeAeintention relationship (Nasir et , 2023.
Yi & Choi, 2.
Instead, the findings suggest that, in AI-based fast-food services, behavioral intention is shaped more directly by external and instrumental considerations than by internal evaluative attitudes.
This outcome implies a behavior-first adoption logic, where consumers prioritize efficiency, reliability, and social endorsement when deciding whether to use AI-based ordering systems.
Attitude may still reflect general favorability toward AI, but it does not directly translate into intention.
Such a pattern is consistent with highly routinized consumption contexts, where decisions are made quickly and pragmatically.
The weakening of the attitudeAeintention link underscores the importance of contextualizing technology adoption models within specific service domains.
Integrative Discussion Taken together, the findings indicate a context-dependent adoption mechanism for AIbased smart ordering and delivery systems in the fast-food industry.
Social influence plays a central role in shaping consumer attitudes, while behavioral intention is driven primarily by perceived convenience, trust, and social endorsement.
The absence of a significant attitudeAeintention relationship suggests that traditional attitudinal pathways may be less influential in standardized, efficiency-oriented service environments.
This study contributes to the literature by demonstrating how the relative importance of adoption determinants shifts as digital technologies become embedded in everyday consumption practices.
CONCLUSION
This study examined consumer behavioral intention toward AI-based smart ordering and delivery systems in the fast-food industry.
The findings reveal that social influence is the only factor that significantly shapes consumer attitude, indicating that attitudes toward AI-based systems are primarily formed through peer recommendations, social norms, and perceived social acceptance.
In contrast, ease of use, enjoyment, perceived convenience, and trust do not exert significant effects on consumer attitude, suggesting that these attributes are largely perceived as expected or standardized features rather International Journal of Applied Business & International Management (IJABIM) Vol.
10 No.
3, pp.
December, 2025
E-ISSN: 2621-2862 P-ISSN: 2614-7432
https://w.
com/index.
php/IJABIM than evaluative criteria that actively shape attitudinal judgments in fast-food service With regard to behavioral intention, the results demonstrate that perceived convenience, social influence, and trust significantly influence consumersAo intention to use AI-based smart ordering and delivery systems.
These findings indicate that adoption decisions are driven mainly by practical considerations related to efficiency, reliability, and system dependability, as well as by social endorsement.
Consumer attitude does not show a significant effect on behavioral intention, implying that favorable evaluations of AI systems do not automatically translate into usage intention in routine, efficiency-oriented service contexts.
Instead, consumers appear to rely more directly on functional and socially embedded cues when deciding whether to adopt AI-based ordering services.
From a theoretical perspective, this study contributes to the technology acceptance literature by demonstrating that attitude formation and adoption intention may follow distinct explanatory pathways in AI-enabled fast-food services.
While social influence plays a dominant role in shaping consumer attitudes, behavioral intention is influenced more directly by convenience, trust, and social validation.
These findings challenge the assumption of a universal attitudeAeintention linkage and underscore the importance of contextualizing technology adoption models within standardized, time-sensitive consumption environments.
From a managerial standpoint, the findings suggest that fast-food firms should prioritize strategies that strengthen social endorsement mechanisms, such as peer visibility, positive word-of-mouth, and normative acceptance, while simultaneously enhancing perceived convenience and system reliability to encourage actual system usage.
Although ease of use and enjoyment do not emerge as significant predictors of either attitude or intention, they remain essential baseline conditions that support smooth system operation and help prevent user dissatisfaction.
LIMITATION
This study is subject to several limitations that should be considered when interpreting the findings.
First, the sample is dominated by relatively young consumers, which may limit the generalizability of the results to older or less digitally familiar populations who may evaluate AI-based systems differently.
Second, the study is conducted within a single-country context, which may restrict the applicability of the findings to markets with different cultural, economic, or technological characteristics.
Additionally, the cross-sectional research design captures consumer perceptions at a single point in time and does not account for changes in attitudes or behavioral intentions as users gain more experience with AI-based ordering systems.
Future research is therefore encouraged to employ longitudinal designs to examine how consumer responses evolve over time.
Further studies may also explore cross-national comparisons, alternative service contexts, and additional outcome variables, such as customer loyalty, spending behavior, and long-term brand attachment, to extend understanding of AI adoption in fast-food and other service industries.
ACKNOWLEDGMENT
The authors express gratitude to those who have had the pleasure of cooperating during this study or research.
International Journal of Applied Business & International Management (IJABIM) Vol.
10 No.
3, pp.
December, 2025
E-ISSN: 2621-2862 P-ISSN: 2614-7432
https://w.
com/index.
php/IJABIM DECLARATION OF CONFLICTING INTERESTS The authors have declared no potential conflicts of interest concerning the study, authorship, and/or publication of this article.
REFERENCES