Self-Ordering Kiosk Usage and Purchase Behavior in Quick Service Restaurant: A Case in Alor Setar’s Quick Service Restaurants

Self-Ordering Kiosk Usage and Purchase Behavior in Quick Service Restaurant: A Case in Alor Setar’s Quick Service Restaurants

Muhd Affan Ayob, Noradzhar Baba^*

Department of Foodservice Management, Faculty of Hotel and Tourism Management, University Technology Mara

^*Corresponding author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.915EC0056

Received: 03 June 2025; Accepted: 07 June 2025; Published: 13 July 2025

ABSTRACT

This research investigates the connection between the use of self-ordering kiosks and the purchasing behavior of customers in quick service restaurants (QSRs) located in Alor Setar, Malaysia. The research uses the Unified Theory of Acceptance and Use of Technology (UTAUT2) to investigate important elements like performance expectancy, effort expectancy, social influence, enabling conditions, habit, and technology phobia. It examines the ways in which these characteristics affect customers’ willingness to accept and acquire products.

Keywords: Customers Acceptance and Usage, Technology Anxiety, Self-Ordering Kiosk Technology; Quick-Service Restaurants, Unified Theory of Acceptance and Use of Technology (UTAUT)

INTRODUCTION

The food sector is a major economic industry worldwide, with its importance increasing annually. This growth drives sector managers to seek sustainable strategies for effective business operations and long-term survival (Marques et al., 2018). International growth and expansion of the food chain have accelerated in recent decades due to national market saturation and the pursuit of more lucrative markets (Aruoma, 2019). By 2050, the global population is expected to exceed 9 billion, necessitating increased food production and distribution (FAO, 2019). Quick service restaurant, a significant segment of this industry, generates over USD 570 billion annually, surpassing the economic output of many countries. According to Statista (2021) the growth trajectory is highlighted and indicates that the global F&B market surpassed USD 8 trillion in revenue in 2020.

In Malaysia, Malaysia, the F&B industry is experiencing significant expansion, reflecting global trends. As depicted in figure 1.2, the Malaysian F&B sector’s revenue is projected to hit USD119 million in 2021, with a robust annual growth rate of 11.44%. This growth is anticipated to result in a market volume of USD184 million by 2025, accompanied by a rise in the number of users to 5.6 million. Such growth is underpinned by Malaysia’s proactive adoption of the Fourth Industrial Revolution (IR4.0) technologies. According to the Ministry of International Trade and Industry (2018), the integration of technologies like artificial intelligence and autonomous robots is enhancing productivity across the industry.

LITERATURE REVIEW

Quick Service Restaurant and Technology

The restaurant industry has grown rapidly due to changing lifestyles and increased dining out habits. In Malaysia, the sector has significantly expanded, driven by sophisticated technologies that improve efficiency through inventory control, payment systems, and decision-making tools (Tomar, 2024). Restaurants now leverage digital platforms for enhanced marketing, customer service, and operational management (Rashid et al., 2015; Vial, 2017). Technological integration, such as digital menus and sales forecasting, boosts customer satisfaction and business performance (Nguyen et al., 2018; Cavusoglu, 2019).

Self-Ordering Kiosks in Quick Service Restaurants

Consumers, notably tourists from abroad Self-ordering kiosks (SOKs) are widely adopted in QSRs to streamline service, reduce wait times, and enhance order accuracy (Kim et al., 2021). SOKs improve customer experiences by enabling personalization and reducing human error, while offering up-selling opportunities (Wang et al., 2020). However, implementation challenges include high setup costs and technology anxiety, especially among older users (Meuter et al., 2003). The reduction in human interaction also affects some customers’ dining satisfaction (Pantano et al., 2020). Despite these issues, SOKs have transformed customer behavior and operations by increasing order values and allowing staff reallocation (Beldona et al., 2020).

Performance Expectancy

Performance expectancy is foundational to several established theories, including the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and the Unified Theory of Acceptance and Use of Technology (UTAUT). In TAM, Davis (1989) introduced perceived usefulness, which closely aligns with performance expectancy, suggesting that users are more likely to adopt a technology if they believe it enhances their job performance. Similarly, in UTAUT, Venkatesh et al. (2003) identified performance expectancy as a direct determinant of user behavior to adopt a technology. Numerous empirical studies have validated the significance of performance expectancy in various contexts. For instance, Venkatesh et al. (2003) demonstrated that performance expectancy was the strongest predictor of behavioral behavior across different organizational settings, encompassing diverse technologies and industries. In consumer technology, research by Kim et al. (2008) on mobile internet services found that performance expectancy significantly influenced users’ behaviors to adopt these services, highlighting the importance of perceived benefits like efficiency and productivity gains.

Effort expectancy

Effort expectancy is a key determinant in shaping users’ attitudes and behaviors towards the adoption and utilization of self-ordering kiosks. Extensive research has shown that effort expectancy significantly influences user perceptions and behaviors across various technological contexts, particularly in the adoption of self-service technologies (SSTs) within quick-service restaurants (QSRs). As QSRs increasingly integrate SSTs—such as self-order kiosks, mobile ordering applications, and automated payment systems—understanding effort expectancy becomes essential. Effort expectancy also has long-term effects on behavior. Baba et al. (2023) demonstrated that perceived ease of use not only affects initial adoption but also encourages sustained usage overtime. Therefore, enhancing SST interface design is crucial. Dabholkar et al. (2003) recommended simplifying navigation, using readable fonts, and minimizing procedural steps to improve usability.

Social Influence

Baba et al. (2023) emphasized the positive impact of social influence on customers’ willingness to adopt SOKs in Malaysian QSRs. Their study revealed that social factors—including peer influence, societal norms, and perceived social expectations—substantially affect customer acceptance, trust, and perceived safety in using kiosk-based technology. This finding underscores the importance of leveraging social dynamics to increase user acceptance and engagement with self-service technologies. Beyond the foodservice industry, Ren et al. (2022) examined how social capital influences farmers’ adoption of green control technology. Their study illustrated that network of relationships, community norms, and trust within social groups can reduce uncertainty and promote cooperative behavior in adopting new technologies. This broader application reinforces the idea that social influence—through mechanisms such as community engagement and collective endorsement—can facilitate technology adoption across different sectors.

Facilitating Condition

Self-service technologies (SOKs) have significantly transformed the landscape of quick-service restaurants (QSRs), enhancing both operational efficiency and customer experience. Facilitating conditions—defined as the external factors that enable and support the use of new technology—are critical in ensuring the successful implementation and sustained adoption of SOKs in QSR environments. These conditions encompass the technological infrastructure, managerial support, customer readiness, and the regulatory environment. In the context of this study, facilitating conditions are described as the extent to which customers believe that the technical and organizational infrastructure is available to support the use of self-ordering kiosks. According to the Unified Theory of Acceptance and Use of Technology (UTAUT), facilitating conditions serve as a significant predictor of actual system usage behavior (Baba et al., 2020).

Habit

Habit plays a crucial role in technology adoption and continued usage, particularly in contexts involving repetitive actions and stable environments. In recent years, research in the fields of information systems and behavioral technology adoption has increasingly recognized habits as a vital determinant of user behavior. Habits are defined as automatic behaviors that are formed through repeated actions in consistent contexts (Limayem et al., 2007). Importantly, habit can exert a stronger influence on technology usage than behavioral intention alone. Venkatesh et al. (2020) suggest that, in some cases, habit predicts usage more effectively than intention, as it reduces cognitive load and enables users to perform tasks without conscious effort (Kaushik & Rahman, 2020). Limayem et al. (2018) posits that habit moderates the relationship between intention and behavior, whereby strong habits diminish the need for conscious behavioral deliberation. Consequently, any intervention aimed at modifying user behavior must take habit strength into account.

Technology Anxiety

Elliott, Meng, and Hall (2020) reinforce these concerns, reporting that consumers experiencing technology anxiety are more likely to avoid using SOKs and instead opt for traditional ordering methods that involve human interaction. This avoidance behavior is frequently driven by fear of the unknown and concerns about usability, especially in high-pressure environments like busy QSRs. As a result, anxious consumers may default to familiar or easily accessible menu options, limiting their exploration and customization of available choices (Meuter et al., 2003). This behavior can restrict customer satisfaction and reduce the potential for increased spending.

Conceptual Framework

Figure 1: Conceptual Framework

METHODOLOGY

This study adopts a quantitative research design to examine customer behavior toward self-ordering kiosks (SOKs) in quick-service restaurants (QSRs) in Alor Setar. The study uses a descriptive approach to capture customers’ behaviors, perceptions, and experiences with SOKs (Burns et al., 1993). Descriptive research helps to provide a clear understanding of user opinions and purchasing behavior based on their past interactions with technology. The study population includes customers aged 18 years and above who have used SOKs in QSRs such as McDonald’s, KFC, and Texas Chicken in Alor Setar. Due to time and resource constraints, the study employs non-probability sampling, specifically convenience sampling, targeting participants who are easily accessible (Hair et al., 2010). This method allows for faster data collection and is suitable when respondents meet specific criteria—namely, recent experience with SOKs.

Alor Setar was selected for its cultural diversity, urban growth, and increasing adoption of digital services in food establishments. These factors provide a dynamic environment to examine consumer behavior and technology acceptance. The appropriate sample size was calculated using G*Power software. Based on six predictors, an effect size (f²) of 0.15, power of 0.95, and a significance level (α) of 0.05, the recommended minimum sample size is 146 respondents. This aligns with Roscoe’s (1975) guideline that suggests sample sizes between 30 and 500 are suitable for most quantitative studies.

Pilot Study

The pilot data collection assessed the dependability and internal consistency of each variable to ensure that all components of the instrument were dependable and comprehensible. The Cronbach’s Alpha test served as a metric for internal consistency and demonstrated the degree of interrelatedness among a set of items. Discussions ensued on the most appropriate alpha value, ranging from 0.70 to 0.95

Table 3.1 Cronbach Alpha Co-efficient of Internal Reliability

Table 3.1 presents the findings of the Cronbach Alpha Coefficient for Internal Reliability. The overall alpha coefficient of the instrument, as indicated in Table 3.11, was reported to be 0.960. The analysis of the results indicated that Section A (Customer Acceptance and Usage) achieved a Cronbach’s alpha of 0.932. Section B (Technology Anxiety) had a Cronbach’s alpha of 0.927, whereas Section C (Purchase Behaviour) demonstrated a score of 0.795. In conclusion, all the numbers in the table above exceeded 0.70, rendering them acceptable for further examination.

FINDINGS

Demographic Profiles

Chapter four suggests that the demographic profile of the respondents is placed at the end of the questionnaire to increase their motivation to finish the survey. Dillman et al. (1998) contend that introducing personal inquiries in the preliminary section of the survey disconcerts and estranges respondents, even with the researcher’s guarantees of confidentiality and anonymity. This chapter employs the demographic profiles of the respondents to contribute to the discussion, utilizing a moderate frequency analysis of all variables included in the survey questionnaire.

Table 4.1Demographic Profile of the Respondents

This study involved a total of 160 respondents residing in Alor Setar, all of whom had experience using self-ordering kiosks in quick-service restaurants (QSRs). The demographic characteristics of the respondents were analyzed to provide a clearer context for interpreting the research findings.

In terms of gender, female respondents slightly outnumbered their male counterparts, with 57.5% identifying as female and 42.5% as male. This suggests a slightly higher engagement or willingness among women to participate in the survey, potentially reflecting greater interest or familiarity with self-service technology in dining environments.

The age distribution revealed that the majority of respondents were young adults. Specifically, 42.5% were aged between 18 and 25 years, followed closely by 40% in the 26 to 35 age group. Respondents aged 36 to 45 years made up 14.4%, while only 3.1% were aged 46 and above. This pattern highlights that younger individuals are the primary users of self-ordering kiosks, likely due to their higher digital literacy and comfort with technology. Employment status was also varied, with 33.1% employed in the private sector and 15% in the government sector.

Additionally, 20.6% of the respondents were self-employed, while students represented 21.3% of the sample. A smaller proportion, about 10%, were either unemployed or engaged in other forms of non-traditional work. This distribution indicates that working adults, particularly those in the private sector, are among the most active users of self-ordering systems in QSRs.

Marital status showed a fairly balanced distribution, with 53.1% of respondents being single and 41.3% married. A minority of 5.6% were either divorced or widowed. This suggests that both single and married individuals find self-ordering kiosks convenient, although single users might be more inclined to dine out frequently or adopt new technologies independently.

In terms of income levels, the majority of respondents earned between RM1,500 and RM2,999 monthly (35.6%), followed by 29.4% earning below RM1,500. Respondents with incomes between RM3,000 and RM4,999 accounted for 20.6%, while those earning RM5,000 and above made up 14.4%. These figures indicate a predominance of middle to lower-income groups among kiosk users, which reflects the affordability and accessibility of QSRs.

Finally, when analyzing the frequency of self-ordering kiosk usage, 26.3% of the respondents reported frequent usage, while 44.4% used the kiosks occasionally. The remaining 29.4% indicated that they rarely used them. These insights demonstrate that while the majority are occasional users, a considerable proportion are regular users, underscoring the growing acceptance and integration of self-service technologies in everyday dining experiences.

In conclusion, the demographic profile of the study’s respondents highlights that self-ordering kiosks are predominantly used by young, working adults with moderate income levels. These findings provide valuable context for understanding consumer behavior in relation to technology adoption in QSRs.

Table 4.2 Results of Descriptive Analysis

Table 4.2 presents the mean score and standard deviation for both the independent and dependent variables are presented. The independent variable with the highest score was Performance Expectancy (M=4.08; S.D=0.72), followed by Effort Expectancy (M=4.07; S.D=0.79). Facilitating Conditions recorded a mean of 3.98 (S.D=0.90), and Social Influence scored M=3.72 with S.D=0.83. Habit showed a moderate rating (M=3.38; S.D=1.11), while Technology Anxiety had the lowest score (M=2.33; S.D=1.27). The dependent variable, Purchase Behavior, recorded a mean score of M=3.99 and a standard deviation of S.D=0.73.

Hypotheses Testing

The evaluation of each hypothesis is conducted by examining the sign, magnitude, and statistical significance of the path coefficient (b) between the latent variable and its dependent variable. The comprehensive analysis in a hypothesis test accomplished via Pearson Correlation Analysis. This investigation aimed to examine the relationships between several characteristics and domestic visitors’ intention to revisit. Table 4.3 presents the summarized results of the hypotheses examined in this study.

Hypothesis 1: There is a significant relationship between performance expectancy and purchasing behavior.

The structural model revealed several key relationships between the variables. Performance Expectancy showed a significant positive effect on Purchase Behavior (β = 0.217, t = 2.469), supporting H1, indicating that customers are more likely to use self-ordering kiosks when they believe it enhances efficiency domestic tourists’ behavioral intention to revisit.

Hypothesis 2:There is no significant relationship between effort expectancy and purchasing behavior.

Contrary to expectations, H2 was not supported, with a non-significant path (β = -0.043, p = 0.458). This suggests that the ease of use of kiosks may no longer be a primary concern, possibly due to increasing user familiarity with digital interfaces in everyday transactions.

Hypothesis 3: There is a significant relationship between social influence and purchasing behavior

A significant positive relationship was observed (β = 0.273, t = 2.854), supporting H3. This underscores the role of peers, family, and social trends in shaping customer behavior, particularly in settings where social validation and recommendations can influence choices.

Hypothesis 4: There is no significant relationship between facilitating condition and purchasing behavior

H4 was not supported (β = -0.067, p = 0.635), indicating that having access to resources or support systems does not directly influence usage behavior. This may imply that external infrastructure is already sufficient, and customers are more influenced by internal motivations and experiences.

Hypothesis 5: There is a significant relationship between habit and purchasing behavior

Habit emerged as the strongest predictor (β = 0.493, t = 4.805), providing strong support for H5. This result highlights the impact of routine behavior—once customers get used to using kiosks, they are likely to continue doing so without conscious effort, reinforcing sustained usage patterns.

Hypothesis 6: There is a significant relationship between technological anxiety and purchasing behavior

Finally, a significant negative effect was found (β = -0.141, t = 3.299), supporting H6. This confirms that anxiety or discomfort with using technology can act as a barrier to kiosk adoption, particularly among customers who lack confidence in using digital systems or fear making mistakes.

DISCUSSION AND RECOMMENDATION

This study aimed to investigate the factors influencing customer purchase behavior toward self-ordering kiosks (SOKs) in quick-service restaurants (QSRs) by utilizing the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), with the inclusion of technology anxiety as an additional construct. The findings contribute valuable insights into how psychological and behavioral factors shape technology adoption in the food service context.

The analysis revealed that performance expectancy, social influence, and habits significantly and positively influenced customer purchase behavior. Customers perceived self-ordering kiosks as tools that improve ordering efficiency and accuracy, which, in turn, increased their likelihood of use. The role of habits was especially strong, suggesting that repeated usage reinforces customer confidence and familiarity with the system. Additionally, social influence emerged as a key determinant, indicating that peer behavior and societal expectations can drive the adoption of self-service technologies.

In contrast, technological anxiety has had a significant negative effect on purchase behavior. This highlights the psychological barriers that some customers experience when interacting with digital interfaces. Feelings of fear, discomfort, or uncertainty can reduce the likelihood of using kiosks, even if technology is beneficial. Interestingly, both effort expectancy and facilitating conditions were found to have no significant influence on purchasing behavior. This could suggest that modern kiosks are already perceived as easy to use and sufficiently supported, making these factors less critical in the decision-making process.

Based on these findings, several practical recommendations are proposed for QSR operators:

1. First, enhancing the functional performance of kiosks is essential. Features such as real-time order tracking, clear visual menus, and reliable payment systems can strengthen customers’ perceptions of the kiosks’ usefulness.
2. Second, QSRs should develop strategies to encourage habitual use. This may include loyalty programs, promotional offers, or personalized features that reward repeated kiosk usage, helping to reinforce routine customer behavior.
3. Third, leveraging social influence through strategic marketing can drive adoption. This might involve showcasing testimonials, social media endorsements, or highlighting popular usage trends to normalize kiosk usage among diverse customer segments.
4. Fourth, addressing technological anxiety should be a priority. Businesses can mitigate customer concerns by ensuring intuitive design, providing on-screen guidance, and assigning staff to assist first-time users. These efforts help build customer confidence and reduce hesitation in using the system.

Although effort expectancy and facilitating conditions were not significant in this study, maintaining user-friendly interfaces and continuous technical support remains important for long-term customer satisfaction.

From a theoretical perspective, the inclusion of technology anxiety in the UTAUT2 model offers a deeper understanding of barriers to technology adoption in service environments. It broadens the model’s applicability by capturing emotional and psychological dimensions that affect user behavior.

Future research should consider applying this framework to other types of dining establishments, such as fine dining or casual restaurants, where customer expectations may differ. In addition, adopting a longitudinal design could provide more dynamic insights into how user attitudes and behaviors evolve over time. A mixed-method approach combining quantitative surveys with qualitative interviews would also enrich the analysis by capturing deeper user experiences.

In conclusion, this study highlights the interplay between technological expectations, social dynamics, habitual behavior, and emotional responses in shaping the acceptance and use of self-ordering kiosks. By understanding and responding to these factors, QSR operators can enhance customer experience, increase operational efficiency, and remain competitive in an increasingly digital service landscape.

CONCLUSION

This study sheds light on the elements that influence customer purchasing behavior when using self-ordering kiosks in quick-service restaurants. Based on the UTAUT2 model with the addition of technology anxiety, the findings show that performance expectancy, social influence, and habit are important drivers of kiosk adoption. However, technology phobia surfaced as a major hurdle.

Customers are more likely to use kiosks when they believe they are efficient and become comfortable with them over time. Users are more likely to follow social norms and peer behavior; therefore, social influence is also important. On the other hand, technology anxiety reduces utilization, implying that enterprises should alleviate client concerns by providing simple interfaces and adequate assistance.

Interestingly, effort expectancy and facilitating conditions did not show significant effects, possibly because modern kiosks are already intuitive and widely accessible. As self-service technology becomes more common, ease of use may no longer be a strong factor in adoption decisions.

Overall, businesses should focus on improving the perceived benefits of kiosks, promoting regular use, leveraging social influence, and addressing technology-related fears. The inclusion of technology anxiety in the UTAUT2 model also strengthens the understanding of customer resistance, offering a more complete view of consumer behavior in digital service environments. This research contributes to existing literature and offers practical guidance for QSR operators aiming to enhance customer satisfaction, loyalty, and operational efficiency through self-service technologies

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APPENDIX

Items used in the questionnaire: