Examining customers’ continuous intention to use self-service kiosks: An extended approach in the context of fast food restaurants using the technology readiness index and technology acceptance model

Abstract

In response to the increasing demand for faster checkouts, shorter wait times, and enhanced customer experiences in fast-food establishments, the use of self-service kiosks is on the rise. This study employs an integrative framework combining the Technology Acceptance Model, the Technology Readiness Index and the subjective norms component of the Theory of Planned Behavior to explore the factors influencing customers’ continued intentions to use self-service kiosks. Data collected from 412 customers across fast-food restaurants in Jordan were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that perceived usefulness and ease of use significantly influence ongoing intentions to use self-service kiosks, while subjective norms do not exert any notable influence. Additionally, the Technology Readiness Index plays a crucial role in shaping subjective norms, perceived usefulness, perceived ease of use, and continuous intention. These findings underscore the significance of individual technology readiness and perceived benefits in encouraging sustained kiosk usage. The study offers actionable insights for fast-food marketers aiming to enhance kiosk adoption and usage rates strategically.

Keywords

Continuous intention self-service technology self-service kiosk technology acceptance model technology readiness index subjective norms

Introduction

Information and communication technology (ICT) has profoundly reshaped the service industry, leading to a transition from conventional, in-person services to self-service technologies (SST) (Guan et al., 2021). This transformation has particularly impacted fast-food restaurants, where the escalating demands for faster checkouts and an enhanced customer experience have driven the widespread adoption of electronic fast-food kiosk machines. These self-service kiosks enable customers to browse menus, select items, make payments, and generate invoices independently. Their potential to enhance service experiences has garnered support from industry leaders (Law et al., 2020). In early adopter countries such as South Korea, kiosk sales doubled from 2018 to 2021, underscoring rapid market growth (Korea JoongAng Daily, 2021). The market for self-service kiosks in the United States is projected to expand to $38.52 billion by 2028, demonstrating a compound annual growth rate of 4.8% (Brand Essence, 2022).

In Jordan, the shift towards digitalization in sectors like telecommunications and banking is evident, driven by government initiatives outlined in the Kingdom’s Economic Modernization Vision (2023–2025) (Ministry of Digital Economy and Entrepreneurship, 2021). This shift is also prominent in the fast-food industry, where self-service kiosks are becoming increasingly common. For instance, McDonald’s Jordan (n.d) has installed two to three kiosks in each of its 46 branches, enhancing customer service and operational efficiency. These kiosks significantly reduce wait times and increase transaction speeds (WaveTec., n.d). Additionally, the integration of these technologies helps restaurants to manage peak hours more effectively and gather data on consumer preferences, which can inform menu adjustments and marketing strategies (Oracle, n.d). These developments reflect a broader trend of adapting to digital transformations that align with evolving consumer preferences and the demands of a youthful demographic (Xmap.ai, 2024).

Even though SST are becoming more popular in many industries, consumers in Jordan still prefer services delivered by humans (Hassan and Farmanesh, 2022). This preference is not limited to Jordan but is also evident in other countries (Lian, 2021). Acknowledging this preference is crucial as it underscores the importance of understanding how consumers adapt to and utilize smart technologies, especially within the restaurant sector. Introduced by Parasuraman (2000), the Technology Readiness Index (TRI) serves as a pivotal framework for understanding individual predispositions towards embracing innovations and new technologies. This framework explores characteristics—such as optimism, innovation, unease of use, and insecurity—alongside the features of the technology itself to determine how these factors influence motivations and intention, particularly in the realm of smart purchasing experiences (Park et al., 2021).

However, existing research on self-service kiosks has largely focused on perceived usefulness and ease of use, the core constructs of the Technology Acceptance Model (TAM), with limited attention given to the role of personal readiness and societal norms in influencing users’ continuous intention to adopt these technologies (Abdul Rahim et al., 2023; Na et al., 2021). This study addresses this gap by integrating the TAM with the TRI to explore the interplay between technology readiness and user acceptance of self-service kiosks in the fast-food sector, providing robust theoretical underpinnings for both models within this specific service context.

The current study aims to investigate the subsequent research questions:

1. What are the primary elements that affect the continuous intention to use self-service kiosks?

2. In what ways does the TRI impact these factors within the fast-food industry?

By examining these questions, the study provides insights for managers and policymakers to enhance customer satisfaction, operational efficiency, and competitiveness in an increasingly digitalized environment. The findings contribute to both theoretical knowledge and practical strategies for sustaining the usage of SST.

Literature review and hypothesis development

Self-service technology and self-service kiosks

Self-service technology (SST) includes a variety of customer service methodologies that leverage advanced information and communication technologies to replace traditional human service interactions (Hassan and Farmanesh, 2022). The hospitality industry has particularly embraced this technology to enhance service delivery, aiming to boost efficiency and improve service quality (Law et al., 2020). This shift towards SST allows customers to engage in service delivery, facilitating a transition from conventional human-led services to automated solutions. This evolution enables restaurants to optimize operations with fewer staff members, mitigating the traditionally labor-intensive nature of the industry and offering substantial cost-saving opportunities (Law et al., 2020).

The decision to implement SST in restaurants often arises from the perceived operational efficiency and potential labor savings (Yoon, 2023). However, the impact of SST on service delivery attributes and consumer experiences remains relatively understudied within the hospitality sector (Sujood et al., 2024). Unlike industrial technologies primarily aimed at boosting retail revenue, SST in service-oriented sectors is designed to significantly affect the financial performance of service providers and customer experiences (Pollak, 2018). Despite their potential benefits, SST systems are vulnerable to failures from technological glitches or user errors. SST promises to transform service delivery in restaurants, yet its success depends on overcoming technical issues and meeting customer expectations (Rahimi, 2020). Understanding the varied acceptance of SST among customers and enhancing its functionalities are crucial for optimizing its use and maximizing its impact.

Studies have shown that SST implementation in restaurants boosts check averages and marginal profits beyond traditional human service (Rahimi, 2020; Yoon, 2023). A report by Lightspeed (2022) indicates customers often spend 20–30% more using SSTs due to the ease of adding extras. Ahn and Seo (2018) report a notable increase in the number of diners using SST, indicating a rising adoption trend. Additionally, Datos Insights (2024) notes a 43% rise in global restaurant kiosks over 2 years up to June 2023, totaling nearly 350,000 installations, highlighting their widespread acceptance and use in the sector.

Recently, the adoption of fast-food kiosks has become a key focus in the industry. These kiosks allow customers to place orders independently, reducing the errors often associated with verbal orders and minimizing customer-employee contact, a substantial advantage throughout the COVID-19 pandemic (Park and Zhang, 2022). Driven by a desire to reduce reliance on human staff and enhance operational efficiency, the industry is integrating these systems extensively (Law et al., 2020). Furthermore, developing user-friendly interfaces is essential, as complex systems may not perform well in the competitive market (Na et al., 2021).

Fast-food kiosks, known for enhancing order accuracy and customer service, are valuable tools for restaurants in a competitive market (Datos Insights, 2024). However, research on user characteristics related to these kiosks remains limited (Kim and Park, 2024; Peng and Yan, 2022). This study seeks to address this gap by examining how individuals’ technology readiness influences their engagement with fast-food kiosks.

Theoretical foundations: The technology acceptance model and technology readiness index

Technology acceptance model

The TAM offers a foundational framework for analyzing user behaviors towards specific technologies (Davis, 1989). It delineates the relations between ease of use, usefulness, and intention to use (Venkatesh and Davis, 2000). Several studies have utilized TAM to gain insights into user behavior, including mobile payments, hotel applications and kiosks, and online travel reviews (Huang et al., 2019; Min et al., 2021). These investigations collectively demonstrate TAM as a solid framework for comprehending various patterns of technology use. Despite its widespread validation in business and hospitality sectors (Law et al., 2020; Sujood et al.,, 2024), TAM’s application to fast-food kiosk services remains under-explored, justifying its selection as the theoretical basis for this research.

TAM identifies perceived usefulness and ease of use as key factors influencing technology adoption and continued use. Perceived usefulness is defined as the extent to which people consider that utilizing a technology will improve the way they perform their jobs (Abdul Rahim et al., 2023; Davis, 1989). Perceived ease of use denotes the user’s perception of the simplicity of the technology (Davis, 1989; Ferreira et al., 2023). This framework helps to explain why some technologies are more readily accepted and integrated into daily operations than others.

Technology readiness index

The TRI is characterized by individuals’ tendency to engage in and employ modern technologies for achieving personal or professional goals (Parasuraman and Colby, 2015). Contrary to TAM, TRI acknowledges that individuals might feel apprehension or insecurity alongside positive sentiments towards new technologies. Introduced by Parasuraman (2000), TRI assesses a person’s willingness to engage with high-tech products and services, acknowledging that while they can be beneficial, they can also provoke frustration and disillusionment. These mixed emotions toward technology—positive (favorable) and negative (unfavorable)—highlight the nuanced response to new technologies (Chang and Chen, 2021).

TRI encompasses four inner dimensions: optimism, innovativeness, discomfort, and insecurity (Parasuraman, 2000). Optimism entails a belief that technology can positively influence control, accessibility, and efficacy in daily usage (Parasuraman and Colby, 2015). Innovativeness denotes a tendency to embrace new technologies early in their adoption cycle. Discomfort is characterized by feelings of being overpowered by technology and a perceived absence of control. Insecurity is characterized by skepticism and concerns over safety (Parasuraman and Colby, 2015).

In this study, we integrate the four dimensions of the TRI into a single construct to capture the complex interplay of these factors in influencing technology adoption. This unified approach, rooted in the model proposed by Parasuraman (2000) and supported by empirical research (Chang and Chen, 2021; Damerji and Salimi, 2021; Nguyen et al., 2023; Peng and Yan, 2022), offers a comprehensive perspective on individual technology readiness and its impact on decision-making processes.

Hypothesis development

The technology readiness index influences subjective norms

As described above, TRI is a pivotal factor in comprehending how individuals perceive and interact with new technologies. It reflects an individual’s propensity to adopt and utilize technological innovations, which is largely influenced by their levels of optimism and innovativeness. These traits reveal personal attitudes toward technology and significantly shape how individuals engage with their social environments (Parasuraman, 2000).

Subjective norms, defined as perceived social pressures to perform or abstain from specific behaviors (Fishbein and Ajzen, 2011), are critical within the technology adoption framework. These norms encapsulate the expectations of important social influencers, such as peers, family, and colleagues, regarding an individual’s behavior (Shishan et al., 2021). This social influence is integral to understanding the dynamics of technology usage. Recent findings from Rahmat et al. (2022) further support the connection between TRI and subjective norms, indicating that individuals with high technology readiness are more likely to adopt positive attitudes towards new technologies and value the opinions of significant others more favorably. These insights underscore the complex interplay between an individual’s readiness for technology and the social pressures that influence their technology adoption decisions.

H1: TRI positively and significantly influences subjective norms.

Technology readiness index influences perceived usefulness and ease of use

Several studies have consistently confirmed the significance of TRI in technology approval (Chiu and Cho, 2021; Naruetharadhol et al., 2021). Optimism significantly influences individuals’ perceptions of technology’s attributes, impacting their views on pragmatic qualities such as usefulness and ease of use (Chen and Lin, 2018). Likewise, innovativeness is crucial in technology acceptance. Individuals with high innovativeness are more certain and open to novel insights (Shahzad et al., 2021). Further, highly innovative users of dietary and fitness mobile apps tend to find new technologies more user-friendly and useful (Chen and Lin, 2018). Consequently, consumers with high levels of optimism and innovativeness are expected to view SSTs in the fast-food sector as both user-friendly and useful.

However, discomfort and insecurity can act as inhibitors within the TRI (Parasuraman, 2000). The existing literature emphasizes the negative impact of those barriers on both the cognitive and emotional dimensions of adopting technology (Park et al., 2021). People with high levels of discomfort and anxiety often perceive new technologies as complex and unsafe (Park and Zhang, 2022). This perception decreases their willingness to interact with these technologies and limits their exploration of new technology-based products and services, ultimately also limiting their understanding of the ease of use and usefulness of technology (Park and Zhang, 2022).

TRI positively and significantly influences perceived usefulness.

TRI positively and significantly influences perceived ease of use.

Technology readiness index influences continuous intention

The TRI is a key framework for understanding how individual traits influence their continuous intention to use innovations such as self-service kiosks. The four dimensions—optimism, innovativeness, discomfort, and insecurity—are critical not only for predicting initial adoption but also for determining the continuous intention to use technology (Parasuraman and Colby, 2015). Research has demonstrated that while optimism and innovativeness encourage continuous usage, discomfort and insecurity can significantly impede it, ultimately affecting the success of technology implementation in fast-food settings (Kim and Park, 2024).

Scholars acknowledge the significant role of TRI in technology usage, though opinions on its precise impact vary (Blut and Wang, 2020). The introduction of innovative technologies, while beneficial, can lead to confusion and insecurity among users, reducing their willingness to adopt and engage with these systems (Rahman et al., 2023). This poses challenges for businesses, as they must balance technological implementation with human factors, such as addressing user resistance through targeted training and resource allocation (Rahman et al., 2023). Furthermore, discomfort and insecurity amplify these challenges, making it essential for marketers to assess both consumer readiness for and resistance to new technologies (Parasuraman, 2000; Parasuraman and Colby, 2015).

Despite these obstacles, some studies confirm a significant relationship between TRI dimensions and the continuous intention to use SST (Blut et al., 2016; Ferreira et al., 2023). Thus, understanding the interplay between these traits and user behaviors is critical for the effective integration of self-service kiosks in consumer settings.

TRI positively and significantly influences the continuous intention to use self-service kiosks.

Subjective norms influence continuous intention

Subjective norms describe how users perceive endorsement from important individuals for the adoption of new information systems (Venkatesh et al., 2012). This concept aligns with rational behavior frameworks, suggesting that perceived importance from close associates boosts technology adoption likelihood (Kumar et al., 2020; Nimri et al., 2024). Subjective norms have repeatedly been identified as key predictors of the intention to use various technologies (Kumar et al., 2020; Rahmat et al., 2022). For instance, Buabeng-Andoh (2021) reported a significant correlation between social norms and the intention to utilize mobile learning. Similarly, Na et al. (2021) reported that social impact significantly affects customers’ acceptance intentions of SST in restaurants and the behavioral intention to use self-order kiosks in fast-food settings, respectively.

Subjective norms positively and significantly influence the continuous intention to use self-service kiosks.

Perceived usefulness and perceived ease of use influence continuous intention

TAM highlights perceived usefulness and perceived ease of use as critical factors affecting technology usage and continuous intention (Davis, 1989; Naruetharadhol et al., 2021). Research demonstrates that perceived usefulness substantially impacts the intention to use new technologies (Abdul Rahim et al., 2023; Chang and Chen, 2021; Park et al., 2021). A considerable body of research has consistently reported a substantial effect of perceived ease of use on continuance intention (Abdul Rahim et al., 2023), underscoring its critical role in users’ ongoing commitment to technology.

Conversely, some studies challenge this consensus, revealing that perceived ease of use may not impact continuous intention in some contexts (Ferreira et al., 2023; Jo, 2022). Given these contrasting perspectives, it is imperative to investigate further how perceived ease of use influences continuous intent to utilize self-service kiosks. Additionally, while much of the existing literature explores the initial intention to engage with self-service kiosks, there has been insufficient investigation into the sustained use of these systems (Blut et al., 2016). Consequently, the current study aims to delve into this relationship, contributing to the ongoing debate and broadening our comprehension of the determinants that drive the ongoing intention to engage with innovative technologies, rather than just the initial intention (Manis and Choi, 2019; Min et al., 2021).

Perceived usefulness positively and significantly influences the continuous intention to use self-service kiosks.

Perceived ease of use positively and significantly influences the continuous intention to use self-service kiosks.

Building on the preceding literature, the research framework is presented in Figure 1.

Figure 1.

Proposed research framework.

Methodology

Research design, sample, and data collection

This research employs a quantitative design to develop and empirically test a structural model grounded on the TAM and TRI frameworks. A purposive sampling strategy was utilized, focusing specifically on customers who have experience using self-service kiosks at various fast-food restaurants across Jordan. This approach ensured that participants were well acquainted with the subject matter, thereby enriching the robustness and diversity of the study’s findings.

The survey was developed using Google Forms and administered online from January to April 2023. To collect data effectively, the questionnaire was disseminated through social media channels such as Facebook and LinkedIn. This approach facilitated widespread survey distribution, enhancing the precision and representativeness of the results. A preliminary filter question was incorporated to confirm that respondents had previous experience with self-service kiosks, ensuring the relevance of the data collected. Additionally, before soliciting demographic details, respondents were provided with a clear explanation of self-service kiosks at the beginning of the questionnaire to ensure understanding. Full ethical approval was obtained from the researchers’ institution. Participants were informed about the study’s objectives, benefits, confidentiality, and their right to withdraw at any time, with details provided in the survey.

A multiple regression power analysis was performed using G*Power software to ascertain the minimum sample size required for structural equation modeling (Faul et al., 2009). The power analysis revealed that a minimum of 89 participants was necessary, considering seven predictors, a two-tailed test, a medium effect size of 15%, an alpha level of 5%, and a power level of 95%. Several quality control measures were implemented during data collection. First, customers who engaged with self-service kiosks at fast food outlets in Jordan were targeted. Participants were screened with two questions: (1) Have you used a self-service kiosk to order your meal at any restaurant in Jordan in the past 3 months? and (2) Do you remember any specific details about your experience with using these self-service kiosks? Second, to confirm the reliability of the responses, four attention-check questions were distributed throughout the survey. Third, any survey responses where more than 15% of the fields were incomplete were excluded. After these quality checks, 412 respondents remained in the sample.

The sample had an equal gender distribution, with males and females each comprising 50%. The largest age group was 25–31 years (37.4%), followed by 18–24 years (26.5%), while individuals below 18 years (0.7%) and above 52 years (2.2%) were under-represented. In terms of income, 40.8% of participants earned between 260 and 500 JD, 30.7% earned between 501 and 1000 JD, and 28.5% earned above 1000 JD, indicating economic diversity. Education levels were notably high, with 49% holding a bachelor’s degree and 34.9% having higher qualifications. These demographics reflect a predominantly young, educated, and economically varied sample, shaping the study’s findings. Table 1 presents the sample profile.

Table 1.

Sample profile.

Variables	Items	Frequency	%
Gender	Female	223	54.1
Gender	Male	189	45.9
Age group	Below 18	3	0.7
	18–24	107	26.5
	25–31	151	37.4
	32–38	71	17.6
	39–45	25	6.2
	46–52	38	9.4
	Above 52	9	2.2
Monthly income (JD)	260–500	165	40.8
	501–1000	124	30.7
	Above 1000	115	28.5
Education	High school or below	35	8.1
	Diploma	30	7.4
	Bachelor	198	49.0
	Higher education	141	34.9

One Jordanian Dinar (JD) equals 1.41 US dollars.

Survey instruments and measurement items

The constructs’ measurement items were derived from the existing literature. Perceived usefulness and perceived ease of use were assessed through three and six items, respectively, both modified from Davis (1989), Venkatesh and Davis (2000) and Huang et al. (2019). Subjective norms were assessed using three items also modified from Fishbein and Ajzen (2011) and Venkatesh and Davis (2000). The TRI was classified as a second-order factor comprising four lower-order constructs—discomfort, insecurity, innovativeness, and optimism—all adapted from Parasuraman (2000). Finally, continuous intention to use was assessed with four items, modified from Lim et al. (2019) and Zaidan et al. (2024).

A team of four experts in consumer psychology and behavior from the business school rigorously reviewed, refined, and validated each research instrument, ensuring the appropriateness of the survey and scales. The questionnaire, originally developed in English, was translated into Arabic by a professional translator to ensure linguistic accuracy and maintain the semantic integrity of the survey content. This translation was further reviewed by bilingual academics from the department, who provided their feedback to refine the translation. The questionnaire was presented in both English and Arabic, allowing participants to choose the language with which they were most comfortable for their responses.

Data analysis

Structural equation modeling with partial least squares (PLS-SEM) was used to analyze the data and assess the model, providing a method for simultaneous analysis of both structural and measurement data, recognized for its robustness in various fields including marketing and information management (Sarstedt et al., 2022; Sarstedt and Liu, 2023). PLS-SEM is particularly advantageous for studies focused on achievement motivators and continuous intention (Ghasemy et al., 2020) and in models involving higher-order constructs. PLS-SEM was preferred over covariance-based SEM to assess the predictive theory (Becker et al., 2023). This method is well suited to evaluating hierarchical component models (i.e., higher-order constructs consisting of lower-order factors) (Sarstedt et al., 2019). The TRI conceptual framework used in this study is hierarchical, incorporating an extended repeated indicator approach of higher-order constructs. The methods outlined by Hair et al. (2021) were used to assess the measurement models and the structural model. The analysis was run using SmartPLS V4.1.0.2 (Ringle et al., 2022).

Results

Measurement model

In accordance with Sarstedt et al.’s (2019) protocols, the second-order construct (TRI) was treated by a repeated indicator approach. For this approach, Sarstedt et al.’s (2019) technique was followed to analyze the second-order construct based on a repeated indicator approach conducted manually. First, the model specification requires assigning all indicators from the lower-order components (optimism, innovation, discomfort, insecurity) to the higher-order component (TRI) simultaneously (using Mode A). All item loadings surpassed the cut-off of 0.708, except for one item (DISC3), which measured discomfort. Second, the measurement model assessment begins with evaluating the lower-order components through multiple reliability metrics. Internal consistency reliability is assessed using composite reliability (ρC), which examines the relationship between indicator loadings and measurement errors. Cronbach’s alpha provides another reliability measure, considering the number of components and their average correlations. The ρA metric offers an additional reliability check, incorporating weight vectors and covariance matrices. Convergent validity is established through Average Variance Extracted (AVE), calculated as the mean of squared loadings divided by the number of components. The findings confirmed that the reliability 0.70 cut-off value was met by all constructs (Henseler and Chin, 2010). The AVE was measured to calculate convergent validity. For all constructs, the AVE surpassed the cut-off value of 0.50, implying that over half of the variance in the constructs was accounted for.

The HTMT criterion involves a sophisticated comparison of heterotrait-heteromethod and monotrait-heteromethod correlations. The heterotrait-heteromethod correlations are derived from average cross-loadings between constructs, while monotrait-heteromethod correlations vary depending on whether the construct uses single or multiple items. The HTMT ratio should remain below 0.90 to establish discriminant validity (Henseler et al., 2015). The full measurement model reliability and validity results are illustrated in Table 2.

Table 2.

Reliability and validity results.

Construct	Items code	Measurement items	Item loading	α	ρA	CR	AVE
TRI*				0.8377	0.8824	0.8645	0.6589
Discomfort	DISC1	I Think technical support lines are not helpful because they don’t explain things in terms I understand	0.8781	0.7835	0.8260	0.8717	0.6951
	DISC2	I Think there is no such thing as a manual for a high-tech product or service that’s written in plain language	0.8808
	DISC4	I Believe that if you provide information to a technology-based system, you can never be sure it really gets to the right place	0.7337
Optimism	OPTM1	Technology gives people more control over their daily lives	0.8488	0.8733	0.8757	0.9135	0.7254
	OPTM2	Technology makes me more productive in my personal life	0.8874
	OPTM3	Technology gives people more freedom to live and work where they please	0.8037
	OPTM4	I Like technologies that allow me to tailor things to fit my own needs	0.8648
Innovation	INOV1	Other people come to me for advice on new technologies	0.7597	0.8526	0.8576	0.9008	0.6947
	INOV2	In general, I am among the first in my circle of friends to acquire new technology when it appears	0.8684
	INOV3	I Can usually figure out new high-tech products and services without help from others	0.8486
	INOV4	I Enjoy the challenge of figuring out high-tech gadgets	0.8528
Insecurity	INSC1	I Believe that if you provide information to a technology-based system, you can never be sure it really gets to the right place	0.8018	0.7388	0.7548	0.8346	0.5591
	INSC2	I Think that too much technology distracts people to a harmful point	0.7992
	INSC3	I Believe technology lowers the quality of relationships by reducing personal interaction	0.7262
	INSC4	I Do not feel confident doing business with a place that can only be reached online	0.7125
Perceived ease of use	PEUS1	I Believe I exerted less mental effort using the self-service kiosk	0.7401	0.9242	0.9292	0.9411	0.7278
	PEUS2	I Feel that the self-service kiosk does not require high effort to use	0.8283
	PEUS3	I Believe that the user interface of the self-service kiosk does not require high mental effort to use	0.8711
	PEUS4	I Feel that the self-service kiosk is easy to use	0.9081
	PEUS5	I Believe it is straightforward to use the self-service kiosk	0.9031
	PEUS6	I Think the self-service kiosk is a simple system to use	0.8667
Perceived usefulness	PUSE1	I Think that a self-service kiosk can enhance my ordering experience	0.8620	0.8660	0.8661	0.9183	0.7895
	PUSE2	Most of the time, I can use the self-service kiosk alone to complete the entire ordering process	0.8727
	PUSE3	I Believe the self-servicing kiosk is an effective method to complete the ordering process	0.9294
Subjective norm	SUBN1	People who are important to me would recommend using self-service kiosks	0.8341	0.7478	0.7907	0.8551	0.6646
	SUBN2	People who are important to me would find using self-service kiosks beneficial	0.8843
	SUBN3	People who are important to me would find using self-service kiosk a good idea	0.7184
Continuous intention	INTU1	I Intend to use the self-service kiosk at fast-food restaurants in the future	0.9669	0.9563	0.9573	0.9718	0.9198
	INTU2	I Will always try to use self-service kiosks at fast-food restaurants	0.9726
	INTU3	I Will keep using self-service kiosks as regularly as I do now for my purchases at fast-food restaurants	0.9374

*second-order construct.

Finally, discriminant validity was tested to ensure the uniqueness of the measures used, in addition to reflecting other factors. This involved evaluating heterotrait-monotrait (HTMT) ratios based on HTMT criteria. All lower-order constructs demonstrated discriminant validity as their HTMT ratios remained under the recommended level of 0.90 (Henseler et al., 2015). The HTMT ratios of the higher-order construct was manually calculated following Sarstedt et al.’s (2019) methodology. The outcomes in Table 3 validate that each indicator has a high degree of loading on the concept it is measuring, thereby establishing discriminant validity.

Table 3.

Discriminant validity results (HTMT ratio).

	Discomfort	Ease of use	TRI	Innovation	Insecurity	Intention	Optimism	Subjective norm
Discomfort
Ease of use	0.2674
TRI*	0.7431	0.4932
Innovation	0.2274	0.4888	0.8170
Insecurity	0.6449	0.0998	0.6606	0.0927
Intention	0.2861	0.6161	0.5989	0.5172	0.1257
Optimism	0.2150	0.4211	0.8003	0.7411	0.1596	0.5952
Subjective norm	0.4605	0.8311	0.6217	0.4854	0.1942	0.6314	0.4953
Usefulness	0.2608	0.7114	0.5069	0.4661	0.0897	0.7434	0.4921	0.7083

*Second-order construct.

Common method bias

The dataset went through a statistical analysis to identify potential bias, specifically addressing the issue of common method bias. The Harman’s single-factor test was conducted using the SPSS factor analysis method. The correlation matrix was examined for indications of common method bias. The use of a common technique is not an issue, as it has been demonstrated that no single factor contributed more than 50% of the covariance among the measurements (Podsakoff et al., 2003). The relationships among the constructs were relatively weak, consistent with the discriminant validity analysis presented in Table 3. Table 4 presents the findings of the Variance Inflation Factor (VIF) analysis, indicating that all VIF values are below 5. The model lacks a unified bias approach. The assessments of convergent validity, construct reliability, indicator reliability, discriminant validity, and common method bias yield high-quality findings. This prompted the recognition that the elements of this research contribute to a more comprehensive evaluation of the structural model.

Table 4.

Hypotheses testing.

H. No	Path	Β	p-value	t-value	VIF value	99% BCa confidence intervals	Supported?
1	TRI -> subjective norm	0.4911	0.000	9.6793	1.000	[0.3821:0.5823]	Yes
2	TRI -> usefulness	0.4704	0.000	12.4324	1.000	[0.3919:0.5404]	Yes
3	TRI -> ease of use	0.4650	0.000	6.3062	1.000	[0.3630:0.5478]	Yes
4	TRI -> continuous intention to use	0.2780	0.000	5.7968	1.425	[0.1784:0.3696]	Yes
5	Subjective norm -> continuous intention to use	0.0752	0.082	1.7385	2.199	[-0.0096:0.1575]	No
6	Usefulness -> continuous intention to use	0.4144	0.000	6.3789	1.913	[0.2833:0.5371]	Yes
7	Ease of use -> continuous intention to use	0.1353	0.018	2.3543	2.308	[0.0272:0.2525]	Yes

Structural model

Upon verifying the validity and reliability of the measurement models, the structural model (Figure 2) was evaluated. The results of the structural model collinearity (VIF) check, which was performed first, showed that all VIF values were lower than the cut-off value (5), demonstrating no issues with collinearity (Hair et al., 2021). The next stage involved testing the hypotheses at a significance level of 0.05, using 5000 subsamples in a bootstrapping procedure. The outcomes of this path assessment are also shown in Table 4. The analysis revealed that the model accounted for 56% of the variance in the intention to use, 24.1% of subjective norms, 22.1% of perceived usefulness, and 21.6% of ease of use perception. Figure 2 illustrates the model results. The findings indicated that TRI positively and significantly affects subjective norms (B = 0.4911, t-value: 9.67936), perceived usefulness (B = 0.4704, t-value: 12.4324), perceived ease of use (B = 0.4649, t-value = 9.8175), and intention to use (B = 0.2777, t-value: 5.7968). Perceptions of usefulness and ease of use impact intention to use positively and significantly (B = 0.1353, t-value: 2.3543; B = 4144, t-value: 6.3789; B = 0.4144, t-value: 6.3789). However, subjective norm did not substantially impact intention to use. Therefore, all hypotheses are accepted except for H5.

Figure 2.

Model results.

Following the model assessment, the out-sample prediction model was evaluated based on Sharma et al. (2023). All indicators showed that the prediction errors in the linear modeling (LM) method were greater than those in PLS-SEM when comparing the root mean squared error (RMSE) values of both methods. Subsequently, the cross-validated predictive ability test showed that the PLS model demonstrated superior performance compared to the indicator-average (IA) method, exhibiting an average loss difference of −0.2834. However, PLS did not surpass the linear model (LM), which recorded an average loss difference of 0.4602. Demonstrating medium predictive power. The prediction outcomes are detailed in Table 5.

Table 5.

Prediction assessment.

Item	PLS-SEM		LM	Average loss difference
Item	RMSE	Q ² _predict	RMSE	Average loss difference
INT1	0.7441	0.3079	0.8155
INT2	0.7401	0.3003	0.8158
INT3	0.8131	0.2811	0.8833
Overall PLS-IA				−0.2834
Overall PLS-LM				0.4602

Discussion

This study addresses a crucial gap in existing research by investigating how TRI influences perceptions of usefulness and ease of use, and subjective norms, in the context of self-service kiosks. Defined as a second-order construct, TRI encompasses optimism, innovativeness, discomfort, and insecurity. Our investigation into how these components influence continuous intention to use self-service kiosks offers robust empirical evidence that emphasizes the crucial role of TRI in modeling these key determinants.

The results show that TRI significantly affects perceived usefulness, supporting the arguments made by Ferreira et al. (2023) and Chiu and Cho (2021). Those with elevated optimism and innovativeness perceive modern technologies favorably, acknowledging their benefits and thereby enhancing perceived usefulness (Kim and Chiu, 2019). This suggests that an individual’s readiness for technology shapes their evaluation of its potential advantages (Chen and Lin, 2018). For instance, users open to new technologies are more likely to appreciate the efficiency and performance improvements offered by self-service kiosks. Conversely, those experiencing discomfort or insecurity may see these technologies as less advantageous, potentially hindering their intent to use such systems.

The findings also show a significant impact of TRI on ease of use perceptions, aligning with recent studies (Chiu and Cho, 2021; Ferreira et al., 2023). Technology readiness shapes individuals’ perceptions of a technology’s complexity or simplicity. Optimistic and innovative users often view technology as less complex and more user-friendly, enhancing their ease of use perceptions (Chen and Lin, 2018). Conversely, those experiencing discomfort or insecurity might find technology more challenging, negatively impacting their perceived ease of use (Jeng et al., 2022). These variations in ease of use perceptions, influenced by TRI, can affect the technology’s continuous usage intention.

The findings demonstrate that TRI significantly influences subjective norms, with highly technology-ready individuals more likely to adopt a positive stance towards new technologies and value the opinions of “important others” more favorably (Rahmat et al., 2022). This proactive attitude, fueled by optimism and innovativeness, shapes their perception of subjective norms and influences their ongoing intention to engage with new technologies.

The findings underscore that usefulness and ease of use perceptions, along with TRI, are pivotal in shaping the ongoing intention to engage with self-service kiosks, aligning with prior research (Chang and Chen, 2021). In particular, usefulness perceptions exerted the greatest impact on the ongoing intention to engage with self-service kiosks as supported in previous studies (Chang and Chen, 2021; Lim et al., 2019). Those studies highlight that the decision to continue to utilize technology is heavily motivated by the individual’s recognition of its practical benefits. For instance, in the context of self-service kiosks in fast-food restaurants, users may perceive the technology as useful because it can significantly reduce waiting times and minimize order errors. Moreover, TRI exhibited a moderately positive effect on continuous intention, indicating that a consumer’s readiness for new technologies has a pivotal role in influencing their ongoing intent to engage with these systems aligning with previous studies (Abdul Rahim et al., 2023; Naruetharadhol et al., 2021). Consequently, continuous intention to use self-service kiosks depends on the extent to which individuals are prepared to embrace new technologies, especially their levels of optimism and innovativeness. These findings suggest that marketing strategies should not only highlight these positive attributes but also address any associated discomfort and insecurity. Effective strategies might include enhancing kiosk design, ensuring employees are well trained to communicate the benefits of kiosk usage, and creating promotional materials that emphasize the ease of use and security features of self-service kiosks to boost customer acceptance.

The findings also reveal that ease of use perceptions have a weak significant impact on continued intention, aligning with a substantial body of research that supports this relationship (Chang and Chen, 2021; Chung et al., 2017). This suggests that users who perceive technology as easy to use are more willing to sustain their ongoing intent to engage with it, aligning with TAM, which suggests that ease of use encourages persistent usage (Venkatesh and Davis, 2000). However, these findings contrast with other studies that downplay the significance of perceived ease of use in continuous intention (Ferreira et al., 2023; Jo, 2022). By highlighting this alignment and divergence, this study contributes to the debate on the impact of ease of use perceptions in affecting continuous intent to use SST. Although this impact is less significant in our study, it should still be considered in developing advanced self-service solutions to enhance the experiences of the customers and operational efficiency.

Within this integrated model, subjective norms may have a less pronounced impact than previously reported (Yang et al., 2022). This discrepancy could stem from several contextual factors, such as the stage of technological adoption, limited awareness, or the presence of alternative influences. For instance, if self-service kiosks are relatively new in Jordan, individuals may base their decisions more on personal experiences than on the opinions of others, leading to a diminished influence of subjective norms. Additionally, limited awareness or exposure to self-service kiosks among Jordanians might indicate that societal influences are less likely to significantly affect usage. Furthermore, factors like personal convenience or ease of use could have a more significant impact in driving the ongoing intention to engage with self-service kiosks, potentially overshadowing the effect of the subjective norm.

Conclusion and implications

This study enhances our understanding of the elements that drive continuous intentions to engage with SST in the fast-food industry. By merging TAM, TRI, and subjective norms, it offers a comprehensive framework for examining continuous usage intentions. This approach enriches our perspective on the psychological and social influences shaping these intentions, addressing a research gap that typically emphasizes initial adoption over sustained usage. Notably, it underscores the significance of TRI in impacting consumer decisions and emphasizes its theoretical prominence, especially from the perspective of developing countries such as Jordan. In the fast-food sector, prior research on consumers’ continuous intention to utilize SST has been limited (Rahimi, 2020; Yoon, 2023). Addressing this gap, our study enhances understanding of consumer behavior regarding emerging technologies. The findings provided substantial support for all hypotheses regarding consumers’ continuous intent to engage with in-store SST, with the exception of the impact of subjective norms.

This study enriches the literature on technology usage and offers practical insights for managers and decision-makers in the fast-food industry. Specifically, it highlights the critical roles of ease of use perceptions and TRI in shaping consumer intent toward such kiosks. Innovation adoption in restaurants can significantly enhance performance by streamlining operations, improving customer satisfaction, and increasing competitiveness. For instance, innovations like self-service kiosks and digital ordering systems can reduce wait times, enhance order accuracy, and personalize customer experiences. These technologies optimize resource allocation by reducing reliance on manual processes while also enabling restaurants to gather valuable customer data for tailored marketing strategies (Abdulmawla et al., 2024). Moreover, fostering an innovative culture encourages continuous improvement and ensures adaptability to dynamic consumer demands and market conditions. These practices are particularly impactful in competitive hospitality markets, where innovative capabilities can drive higher financial returns and strengthen non-financial aspects such as customer loyalty, creating a holistic improvement in performance (Abdulmawla et al., 2024).

In terms of fast-food establishments, emphasizing the kiosks’ ease of use is crucial for influencing customer intention. Clear instructions and an intuitive interface can enhance the customer experience, encourage repeat use and reduce potential frustration. Placing easy-to-understand visual aids, step-by-step guides, or interactive tutorials directly on the kiosks can further simplify usage for first-time users. Further, managers can initiate onboarding sessions where customers can familiarize themselves with the kiosks in a guided, stress-free environment. Such sessions could include interactive tutorials and FAQs that address common user concerns and demonstrate the convenience of kiosk use, ideally implemented during non-peak hours or through digital displays that do not require staff intervention. Moreover, assigning a specific employee to encourage customers to use the kiosks and provide immediate assistance to those who find the kiosks challenging could ensure a smoother and more confident customer experience.

Furthermore, the findings highlight the perceived usefulness of kiosks as a crucial driver of customer continuous intention. Managers should emphasize both ease of use and practical benefits, ensuring the kiosks offer valuable features that improve the customer experience. Features such as reduced wait times, contactless payment options, and enhanced order accuracy directly address customer needs for efficiency and convenience. Self-service kiosks expedite the ordering process and offer a preferred method for payment and customization, allowing customers to personalize their orders without the risk of miscommunication. Providing incentives, such as discounts for kiosk users or loyalty points for frequent usage, can further motivate customers to choose kiosks over traditional ordering methods.

This study further illustrates the significance of addressing the major components of TRI—optimism, innovativeness, discomfort, and insecurity—to enhance the continuous intent to engage with self-service kiosks. Simultaneously, it examines the effect of TRI on the customer’s perceived ease of use and perceived usefulness. Optimism about the technology’s efficiency can be enhanced by creating user-friendly kiosks that emphasize convenience and speed. Innovativeness can be fostered by developing early adopter programs, continuously updating kiosks with new features, and creating a community for tech-savvy users to exchange experiences. Effective upselling techniques implemented via kiosks can suggest add-ons and special offers in a non-intrusive manner, enhancing the customer’s purchasing experience and potentially increasing the average order size, significantly contributing to the restaurant’s performance.

Promoting technology optimism and innovation, addressing user discomfort and insecurity, and educating about the technology’s benefits are vital. These strategies can boost perceived usefulness, driving greater adoption and sustained use. Employing a targeted communication strategy that highlights real-life success stories of customers who have efficiently used the kiosks can also promote optimism and reduce hesitation among new users. Digital signage or app-based notifications can be used to disseminate these stories effectively, reinforcing the ease and convenience of kiosk use. Moreover, showcasing these success stories near the kiosks, through screens or posters, can serve as immediate motivators for hesitant customers by creating a sense of familiarity and trust in the technology. Displaying testimonials or user reviews would further assist in normalizing kiosk usage, making it more appealing.

To alleviate discomfort, which often arises from feeling overwhelmed by technology, fast-food establishments should provide clear instructions, visual guides, and on-site assistance, enabling confident and effortless navigation of the kiosks. Additionally, addressing insecurity, related to concerns over data safety, necessitates implementing and effectively communicating robust security measures to reassure users about the protection of their data and payment transactions. These security protocols must be made transparent through both digital and physical communications at the point of interaction. By adopting these strategies, managers can significantly enhance customer readiness and positively influence their continuous intention to use self-service kiosks. This proactive engagement promises higher adoption rates and improves overall customer satisfaction.

Limitations and future research

This study focused solely on fast food restaurants in Jordan. While this provided a robust dataset, this exclusive focus restricts the applicability of the results to other dining venues or geographical regions. The insights gained are context-specific and may not fully represent other restaurant environments or cultural settings. Future research should broaden its scope to include various dining formats and extend geographically to cover diverse cultural and operational contexts. This expansion would enhance the reliability of the results and advance our interpretation of the global dynamics influencing the usage of SST.

Another limitation involves reliance on self-reported data, which can introduce response distortions or impacts on social desirability. The survey was exclusively conducted among customers who had utilized the self-service kiosks post-purchase, potentially biasing the results towards those with positive perceptions of the technology. Future studies may utilize observational techniques or behavioral experiments to supplement self-reported data and reduce bias. Additionally, exploring the views of non-users or those with neutral or negative attitudes towards self-service kiosks would offer a more comprehensive view of consumer behavior and intentions. The study also did not consider external influences such as marketing campaigns or the availability of alternative ordering options, which could affect continuous usage intentions. Examining these elements in future studies would provide a more nuanced understanding of the factors influencing technology adoption in fast-food restaurants.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Farah Shishan

Rawan Nimri

Author Biographies

Fadi Amiri is an experienced business development professional with a strong background in sales and market expansion. Over the past decade, he has successfully led teams and developed strategic initiatives that have significantly increased revenue and market share across various industries, including building materials, homecare, and construction. Fadi’s expertise includes securing high-value contracts, revitalizing dormant markets, and expanding customer bases. He holds an MBA in Marketing and is a certified Project Management Professional (PMP), showcasing his comprehensive skill set in both leadership and technical domains. E-mail: Fadi.j.amiri@gmail.com

Dr. Farah Shishan is a Marketing Assistant Professor at the University of Jordan. Since September 2020, she has also served as the Assistant Dean for Development and Quality Assurance and in 2022 she has been appointed as the Director of Alumni Relations office. Dr. Shishan has published some papers in prestigious journals (SCOPUS Q1, Q2 and ISI) and given presentations at national and international conferences. Her research focuses on service innovation, tourism marketing, services marketing, green marketing, sustainability, and resource utilization for small to medium-sized independent firms. Email: f.shishan@ju.edu.jo

Dr. Saleh Bazi is an Associate Professor of Digital Marketing at Yarmouk University, Jordan. His research interests include social commerce, luxury brand, and social media marketing. His research has appeared in Information Technology and People Journal and Journal of Business Research. Saleh has presented his research at European Marketing Academy (EMAC) and American Marketing Association (AMA) conferences. He is an invited and an ad hoc reviewer at Journal of Business Research, Computers in Human Behavior, Information Technology & People, Technological forecast & Social Change, Journal of Strategic Marketing, and International Journal of Information Management. Email: saleh.bazi@yu.edu.jo

Dr. Rawan Nimri is a Lecturer in the Department of Tourism, Sport and Hotel Management and an emerging researcher in the area of green practices in hospitality and low-carbon travel. She was granted her Doctor of Philosophy from Griffith University, Australia in April 2019. Her research interest is the area of consumerism and behaviour change, particularly focusing on how different queuing could affect sustainable behaviour. Before joining Griffith, Dr. Nimri was working in both academia and industry in Jordan. Email: r.nimri@griffith.edu.au

Dr. Zaid Obeidat holds a PhD in Marketing from Durham University. Zaid is currently a full professor in marketing at the University of Jordan. His work focuses on consumer rights, consumer behavior and misbehavior. His work has been published in Many well respected Journals such as Computers in Human Behavior, Psychology and Marketing, Industrial Management and Data Systems, Journal of consumer affairs and Journal of Retailing and Consumer services. E-mail: z.obeidat@ju.edu.jo.

References

Abdul Rahim

Abbasi

Iranmanesh

, et al. (2023) Determinants of continuous intention to use e-government services: an extension of technology continuance theory. Journal of Systems and Information Technology 25(3): 245–267. DOI: 10.1108/JSIT-09-2020-0166.

Abdulmawla

Imperiale

Fasiello

(2024) Fetching the ‘exterior’ in’: effects of an alliance’s collaborative governance and collaborative accountability on hotel innovation performance. International Journal of Hospitality Management 122: 103848. DOI: 10.1016/j.ijhm.2024.103848.

Ahn

Seo

(2018) Consumer responses to interactive restaurant self-service technology (IRSST): the role of gadget-loving propensity. International Journal of Hospitality Management 74: 109–121. DOI: 10.1016/j.ijhm.2018.02.020.

Becker

Cheah

Gholamzade

, et al. (2023) PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management 35(1): 321–346. DOI: 10.1108/Ijchm-04-2022-0474.

Blut

Wang

(2020) Technology readiness: a meta-analysis of conceptualizations of the construct and its impact on technology usage. Journal of the Academy of Marketing Science 48(4): 649–669. DOI: 10.1007/s11747-019-00680-8.

Blut

Wang

Schoefer

(2016) Factors influencing the acceptance of self-service technologies: a meta-analysis. Journal of Service Research 19(4): 396–416. DOI: 10.1177/1094670516662352.

Brand Essence (2022) Interactive kiosk market. Available at: https://brandessenceresearch.com/electronics-and-telecommunication/interactive-kiosk-market (Accessed 9 December 2024).

Buabeng-Andoh

(2021) Exploring university students’ intention to use mobile learning: a research model approach. Education and Information Technologies 26(1): 241–256. DOI: 10.1007/s10639-020-10267-4.

Chang

Chen

(2021) What motivates customers to shop in smart shops? The impacts of smart technology and technology readiness. Journal of Retailing and Consumer Services 58: 102325. DOI: 10.1016/j.jretconser.2020.102325.

10.

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. DOI: 10.1108/IntR-03-2017-0099.

11.

Chiu

Cho

(2021) The role of technology readiness in individuals’ intention to use health and fitness applications: a comparison between users and non-users. Asia Pacific Journal of Marketing and Logistics 33(3): 807–825. DOI: 10.1108/APJML-09-2019-0534.

12.

Chung

Tyan

Han

(2017) Enhancing the smart tourism experience through geotag. Information Systems Frontiers 19(4): 731–742. DOI: 10.1007/s10796-016-9710-6.

13.

Damerji

Salimi

(2021) Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accounting Education 30(2): 107–130. DOI: 10.1080/09639284.2021.1872035.

14.

Datos Insights (2024) Global demand for self-ordering kiosks continues to soar. Available at: https://datos-insights.com/press-release/global-demand-for-self-ordering-kiosks-continues-to-soar (Accessed 9 December 2024).

15.

Davis

(1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13(3): 319–340. DOI: 10.2307/249008.

16.

Faul

Erdfelder

Buchner

, et al. (2009) Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behavior Research Methods 41(4): 1149–1160. DOI: 10.3758/BRM.41.4.1149.

17.

Ferreira

Silva

Dias

ÁL

(2023) Determinants of continuance intention to use mobile self-scanning applications in retail. International Journal of Quality & Reliability Management 40(2): 455–477. DOI: 10.1108/IJQRM-02-2021-0032.

18.

Fishbein

Ajzen

(2011) Predicting and Changing Behavior. Hove, UK: Psychology Press.

19.

Ghasemy

Teeroovengadum

Becker

, et al. (2020) This fast car can move faster: a review of PLS-SEM application in higher education research. Higher Education 80(6): 1121–1152. DOI: 10.1007/s10734-020-00534-1.

20.

Guan

Xie

Shen

, et al. (2021) Are you a tech-savvy person? Exploring factors influencing customers using self-service technology. Technology in Society 65: 101564. DOI: 10.1016/j.techsoc.2021.101564.

21.

Hair

Joseph

Hult

GTM

, et al. (2021) A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). 3rd edition. Newcastle, UK: Sage.

22.

Hassan

Farmanesh

(2022) Customer adoption of self-service technologies in Jordan: factors influencing the use of internet banking, mobile banking, and telebanking. Management Science Letters 12(3): 193–206. DOI: 10.5267/j.msl.2022.1.002.

23.

Henseler

Chin

(2010) A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling: A Multidisciplinary Journal 17(1): 82–109. DOI: 10.1080/10705510903439003.

24.

Henseler

Ringle

Sarstedt

(2015) A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science 43(1): 115–135. DOI: 10.1007/s11747-014-0403-8.

25.

Huang

Y-C

Chang

L-L

C-P

, et al. (2019) Examining an extended technology acceptance model with experience construct on hotel consumers’ adoption of mobile applications. Journal of Hospitality Marketing & Management 28(8): 957–980. DOI: 10.1080/19368623.2019.1580172.

26.

Jeng

Pai

Yeh

(2022) Antecedents for older adults’ intention to use smart health wearable devices: technology anxiety as a moderator. Behavioral Sciences 12(4): 114. DOI: 10.3390/bs12040114.

27.

(2022) Determinants of continuance intention towards e-learning during COVID-19: an extended expectation-confirmation model. Asia Pacific Journal of Education: 1–21. DOI: 10.1080/02188791.2022.2140645.

28.

Kim

Chiu

(2019) Consumer acceptance of sports wearable technology: the role of technology readiness. International Journal of Sports Marketing & Sponsorship 20(1): 109–126. DOI: 10.1108/IJSMS-06-2017-0050.

29.

Kim

Park

(2024) Understanding restaurant users’ attitudes towards self-service ordering via kiosks during the COVID-19 pandemic: an application of the theory of anxiety. Tourism and Hospitality Research 24(1): 80–94. DOI: 10.1177/146735842211336.

30.

Korea JoongAng Daily (2021) Electronic kiosks are all the rage in contactless Korea. Available at: https://koreajoongangdaily.joins.com/2021/02/23/business/industry/kiosk/20210223182200375.html (Accessed 5 December 2024).

31.

Kumar

Bervell

Annamalai

, et al. (2020) Behavioral intention to use mobile learning: evaluating the role of self-efficacy, subjective norm, and WhatsApp use habit. IEEE Access 8: 208058–208074. DOI: 10.1109/ACCESS.2020.3037925.

32.

Law

Leung

Chan

(2020) Progression and development of information and communication technology research in hospitality and tourism. International Journal of Contemporary Hospitality Management 32(2): 511–534. DOI: 10.1108/IJCHM-07-2018-0586.

33.

Lian

(2021) Why is self-service technology (SST) unpopular? Extending the IS success model. Library Hi Tech 39(4): 1154–1173. DOI: 10.1108/LHT-01-2018-0015.

34.

Lightspeed (2022) Global state of the retail industry report: omnichannel is ‘do or die. Available at: https://www.lightspeedhq.com/resources/retail/state-of-retail-industry-report (Accessed 9 December 2024).

35.

Lim

Kim

Hur

, et al. (2019) An empirical study of the impacts of perceived security and knowledge on continuous intention to use mobile Fintech payment services. International Journal of Human-Computer Interaction 35(10): 886–898. DOI: 10.1080/10447318.2018.1507132.

36.

Manis

Choi

(2019) The virtual reality hardware acceptance model (VR-HAM): extending and individuating the Technology Acceptance Model (TAM) for virtual reality hardware. Journal of Business Research 100: 503–513. DOI: 10.1016/j.jbusres.2018.10.021.

37.

McDonald’s Jordan (nd) Locations. Available at: https://www.mcdonalds.com.jo/EN/CustomPages/Locations (Accessed 10 December 2024).

38.

Min

KKF

Jeong

(2021) Consumer adoption of the Uber mobile application: insights from diffusion of innovation theory and technology acceptance model. In: Kim

Wang

(eds) Future of Tourism Marketing. England, UK: Routledge, 2–15.

39.

Ministry of Digital Economy and Entrepreneurship (2021) National digital transformation strategy and implementation plan 2021–2025. Available at: https://www.modee.gov.jo/ebv4.0/root_storage/en/eb_list_page/dts-2021-eng.pdf (Accessed 10 December 2024).

40.

Yang

Lee

(2021) Determinants of behavioral intention of the use of self-order kiosks in fast-food restaurants: focus on the moderating effect of difference age. Sage Open 11(3): 21582440211031907. DOI: 10.1177/21582440211031907.

41.

Naruetharadhol

Ketkaew

Hongkanchanapong

, et al. (2021) Factors affecting sustainable intention to use mobile banking services. Sage Open 11(3): 21582440211029925. DOI: 10.1177/21582440211029925.

42.

Nguyen

Tran

, et al. (2023) The relationship between service quality of banking kiosk and customer satisfaction: the moderating role of technology readiness. International Journal of Quality and Service Sciences 15(3/4): 273–290. DOI: 10.1108/IJQSS-01-2023-0004.

43.

Nimri

Kralj

Shishan

, et al. (2024) To ‘green dine’ or not to ‘green dine’? Assessing the impact of beliefs and altruism. Journal of Retailing and Consumer Services 77: 103680. DOI: 10.1016/j.jretconser.2023.103680.

44.

Oracle (nd) Self-service restaurant kiosks. Available at: https://www.oracle.com/jo/food-beverage/restaurant-pos-systems/pos-hardware/self-service-kiosks (Accessed 9 December 2024).

45.

Parasuraman

(2000) Technology Readiness Index (TRI): a multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research 2(4): 307–320. DOI: 10.1177/109467050024001.

46.

Parasuraman

Colby

(2015) An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research 18(1): 59–74. DOI: 10.1177/1094670514539730.

47.

Park

Zhang

(2022) Technology readiness and technology paradox of unmanned convenience store users. Journal of Retailing and Consumer Services 65: 102523. DOI: 10.1016/j.jretconser.2021.102523.

48.

Park

Lehto

(2021) Self-service technology kiosk design for restaurants: an QFD application. International Journal of Hospitality Management 92: 102757. DOI: 10.1016/j.ijhm.2020.102757.

49.

Peng

MY-P

Yan

(2022) Exploring the influence of determinants on behavior intention to use of multiple media kiosks through technology readiness and acceptance model. Frontiers in Psychology 13: 852394. DOI: 10.3389/fpsyg.2022.852394.

50.

Podsakoff

MacKenzie

Lee

, et al. (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology 88(5): 879–903. https://psycnet.apa.org/record/2003-08045-010

51.

Pollak

(2018) Why restaurants are finding self-serve kiosks attractive. Available at: https://www.gocanopy.com/news-insights/why-restaurants-are-finding-self-serve-kiosks-attractive (Accessed 11 December 2024).

52.

Rahimi

(2020) The service delivery process. In: Kenyon

Robinson

Musgrave

(eds) Managing Hospitality Experiences. Wallingford, UK: CABI, 115–140.

53.

Rahman

Bag

Gupta

, et al. (2023) Technology readiness of B2B firms and AI-based customer relationship management capability for enhancing social sustainability performance. Journal of Business Research 156: 113525. DOI: 10.1016/j.jbusres.2022.113525.

54.

Rahmat

Raza

Zahid

, et al. (2022) Nexus between integrating technology readiness 2.0 index and students’ e-library services adoption amid the COVID-19 challenges: implications based on the Theory of Planned Behavior. Journal of Education and Health Promotion 11(1): 50. DOI: 10.4103/jehp.jehp_508_21.

55.

Ringle

Wende

Becker

J-M

(2022) SmartPLS 4. SmartPLS GmbH. Available at: https://www.smartpls.com (Accessed 10 December 2024).

56.

Sarstedt

Liu

(2023) Advanced marketing analytics using partial least squares structural equation modeling (PLS-SEM). Journal of Marketing Analytics 12: 1–5. DOI: 10.1057/s41270-023-00279-7.

57.

Sarstedt

Hair

Cheah

, et al. (2019) How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal 27(3): 197–211. DOI: 10.1016/j.ausmj.2019.05.003.

58.

Sarstedt

Hair

Pick

, et al. (2022) Progress in partial least squares structural equation modeling use in marketing research in the last decade. Psychology and Marketing 39(5): 1035–1064. DOI: 10.1002/mar.21640.

59.

Shahzad

Khan

Saleem

, et al. (2021) What factors affect the entrepreneurial intention to start-ups? The role of entrepreneurial skills, propensity to take risks, and innovativeness in open business models. Journal of Open Innovation: Technology, Market, and Complexity 7(3): 173. DOI: 10.3390/joitmc7030173.

60.

Sharma

Liengaard

BDD

Hair

, et al. (2023) Predictive model assessment and selection in composite-based modeling using PLS-SEM: extensions and guidelines for using CVPAT. European Journal of Marketing 57(6): 1662–1677. DOI: 10.1108/Ejm-08-2020-0636.

61.

Shishan

Mahshi

Al Kurdi

, et al. (2021) Does the past affect the future? An analysis of consumers’ dining intentions towards green restaurants in the UK. Sustainability 14(1): 276. DOI: 10.3390/su14010276.

62.

Sujood

Bano

Siddiqui

(2024) Consumers’ intention towards the use of smart technologies in tourism and hospitality (T&H) industry: a deeper insight into the integration of TAM, TPB and trust. Journal of Hospitality and Tourism Insights 7(3): 1412–1434. DOI: 10.1108/JHTI-06-2022-0267.

63.

Venkatesh

Davis

(2000) A theoretical extension of the Technology Acceptance Model: four longitudinal field studies. Management Science 46(2): 186–204. DOI: 10.1287/mnsc.46.2.186.11926.

64.

Venkatesh

Thong

JYL

(2012) Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly 36(1): 157–178. DOI: 10.2307/41410412.

65.

WaveTec (n.d.) McDonald’s leveraging self-service technologies. Available at: https://www.wavetec.com/blog/mcdonalds-leveraging-self-service-technologies (Accessed 11 December 2024).

66.

Xmap.ai (2024) Jordan restaurants, cafés and bakeries in 2024: insights for F&B and FMCG industries. Available at: https://www.xmap.ai/blog/jordan-restaurants-cafes-bakeries-in-2024-insights-for-f-b-and-fmcg-industries (Accessed 11 December 2024).

67.

Yang

Yan

Wang

, et al. (2022) Flow experiences and virtual tourism: the role of technological acceptance and technological readiness. Sustainability 14(9): 5361. DOI: 10.3390/su14095361.

68.

Yoon

(2023) Technology adoption and jobs: the effects of self-service kiosks in restaurants on labor outcomes. Technology in Society 74: 102336. DOI: 10.1016/j.techsoc.2023.102336.

69.

Zaidan

Shishan

Al-Hasan

, et al. (2024) Cash or cash-less? Exploring the determinants of continuous intention to use e-wallets: the moderating role of environmental knowledge. Competitiveness Review: An International Business Journal 35: 319–346. DOI: 10.1108/CR-11-2023-0279.