Key Determinants of Continuance Usage Intention: An Empirical Study of Mobile Food Delivery Apps among Malaysians †
1
Labuan Faculty of International Finance, Universiti Malaysia Sabah, Labuan International Campus, Labuan 87000, Malaysia
2
Accenture Technology Solution Sdn. Bhd. Sunway, Kuala Lumpur 47500, Malaysia
*
Author to whom correspondence should be addressed.
†
Presented at the International Academic Symposium of Social Science 2022, Kota Bharu, Malaysia, 3 July 2022.
Proceedings 2022, 82(1), 15; https://doi.org/10.3390/proceedings2022082015
Published: 8 September 2022
(This article belongs to the Proceedings of International Academic Symposium of Social Science 2022)
Abstract
:Mobile food delivery apps have been widely used, especially among Malaysians during the COVID-19 pandemic. When the government allowed restaurants to reopen, mobile food delivery apps slightly declined as Malaysians rushed to dine in restaurants. What determinants Malaysian users to continue to use mobile food delivery apps services after the COVID-19 pandemic remain unknown. Underlying the Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) determinants, this study introduces two other relevant determinants: convenience and time saving. Partial least squares structural equation modeling techniques using SmartPLS were used to analyse the data.
1. Introduction
From 2018 to 2025, the Malaysian online meal delivery industry is expected to grow at 18.6% [1]. Mobile food delivery apps allow users to order their food fast and conveniently with real-time access, and users may either takeaway or obtain their food at their doorstep [2]. Users may need only a smartphone to download and visit restaurants, no physical contact is required to view menus and make orders and payments [3]. According to Ariel [4], food-related apps were users’ second most frequently installed application. By using mobile food delivery apps, users may plan their meals from a broad variety of outlets more simply and reliably at times and locations that are convenient for them. These apps often provide more thorough, updated, and reliable information regarding food places and the meals they serve. Since Malaysia banned dine-in on March 16, 2019 as part of a government Movement Control Order, food delivery has become a lifeline for restaurateurs. Mobile food delivery apps have progressively increased as people want to purchase meals in a contactless way by utilising food delivery apps. However, as the Malaysian government allows restaurants and cafés to reopen, will the usage of mobile food delivery apps decrease? It is questionable if Malaysians would continue actively to utilise mobile food delivery apps as before. As a result, this research focuses on identifying the factors that may influence users’ continued use of mobile food delivery apps through the lens of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) by Venkatesh et al. [5]. In addition, this research also offers two more crucial factors to the model which is time saving and convenience.
2. Literature and Hypothesis
Table 1 provides a selected study on food delivery apps that used the UTAUT theory over the past 3 years. Although researchers have mainly used the UTAUT theory as their underlining theory, researchers such as Zhao and Bacao [6] have also merged the task–technology fit model and expectancy confirmation theory into the UTAUT theory. These researchers have mainly focused on multi-dimensional attributes of food delivery apps and concentrated on users’ attitude and/or their behaviour. Although UTAU is the underlying theory, not every researcher retained the original independent variables [6,7] but also added new variables into their study [7,8,9]. In addition, given the importance of identifying what are the important aspects when evaluating food delivery apps, researchers such as Ray et al. [10]; Zhao and Bacao [6], and Fakfare [11] have urged for more research to be performed.
2.1. Performance Expectancy
Performance expectation was defined by Venkatesh et al. [5] as “how much the use of technology in specific activities will benefit consumers”. Individuals utilising mobile food delivery apps would help them accomplish a certain job [14]. According to Marinkovic, Dordevic, and Kalinic [15] performance expectation is equivalent to perceived usefulness. As a result, if the user considers mobile food delivery apps beneficial, they are more likely to continue to utilise the mobile food delivery services. Consequently, performance expectation is a major element in mobile food delivery apps continued use intention [5,6]. Hence, the following hypothesis was formulated:
H1.
Performance expectancy will positively influence the continuous usage intention of the mobile food delivery apps.
2.2. Effort Expectancy
Effort expectancy is “the extent of ease connected with the use of a system” [13]. Based on the literature, one of the most significant reasons customers use mobile food delivery apps is the expectation of effort [14] and not to mention particularly relevant in food delivery apps [16]. In meal delivery applications, users order their food without assistance from restaurant or café workers. Thus, a customer’s propensity to continue using mobile food delivery apps may be influenced by their perception of mobile food delivery apps simplicity and ease of use especially after they have adopted the apps for quite some time [15]. Therefore, effort expectations are anticipated to substantially impact customers’ continued use intentions of mobile food delivery applications. The following hypothesis was formulated:
H2.
The effort expectancy will positively influence the continuous usage intention of mobile food delivery applications.
2.3. Social Influence
One of the important factors in determining whether or not a user will continue to use an application is the opinions of their friends and family members [14]. For new technology, products, and services, social influence has been shown to affect customers’ behaviour [16] positively. Customers are more likely to consult their social network to learn more about mobile food delivery apps or to obtain social support for their choice to continue using them. In addition, social influence may impact how individuals think and act [17]. These applications continue to be used by customers if customers receive societal acceptance. However, consumers’ willingness to continue to use mobile food delivery apps decreases if they do not obtain social validation from their friends and family members. As a result, it was hypothesised that:
H3.
The social influence will positively influence the continuous usage intention of mobile food delivery applications.
2.4. Facilitating Condition
Facilitating conditions are the customers’ perceptions of available resources and support to perform the behaviour [5]. They also refer to consumers’ perception of the system’s access to instruction, guidance, and assistance [17]. As a result, if users are satisfied with the apps facilitating conditions, users are less likely to choose a new service and more likely to reuse food delivery applications. It may also be proposed that if consumers consider a sufficient degree of technological, operational, infrastructural, and human support by utilising food delivery applications, and more comfortable experience using them [18]. Therefore, it was hypothesised that:
H4.
The facilitating conditions will positively influence the continuous usage intention of mobile food delivery applications.
2.5. Hedonic Motivation
Hedonic motivation relates to multisensory, fantasy, and emotive aspects when using the products or services [5]. It is connected to users’ desire, entertainment, and pleasure after the experiences of using the products or services and, in this case, the mobile food delivery apps [19]. From a mobile food delivery app perspective, ordering food through an application enables consumers to enjoy their favourite food without going out of the house or workplace. As consumers find pleasure after using the food delivery application, this encourages them to continue using it. Therefore, it was hypothesised that:
H5.
The hedonic motivation will positively influence the continuous usage intention of mobile food delivery applications.
2.6. Price Value
Price value exerts a favourable impact on intention to use as the advantage of technology use is considered more than currency benefits. The findings showed that the concept of price is essential in attracting consumers and that certain quality value has a beneficial impact on intentions to reuse mobile food delivery apps [20]. Mobile food delivery apps can save the expense of buying food from restaurants. Not to mention mobile food delivery apps collaborate with the restaurants by giving more promotions such as promotions as well as other attractive incentives for users if they use food delivery apps instead of visiting restaurants. Hence, the following hypothesis is proposed:
H6.
The price value will positively influence the continuous usage intention of mobile food delivery applications.
2.7. Habit
Habit has been a crucial factor in the potential adoption of technologies. Research on habitual intentions has shown that the habit is a strong indicator of technology usage to facilitate behavioural improvements. Customer’s inclination to act spontaneously as a result of their collected learning experience can be defined as habit [21]. In addition, the influence of habit influences consumers’ continuous usage intention, especially when consumers are familiar with the food delivery apps [22]. This relationship has also been confirmed by research such as Hsu et al. [23]; and Zanetta et al. [24]. Thus, the following hypothesis proposes that:
H7.
Habit will positively influence the continuous usage intention of mobile food delivery applications.
2.8. Time Saving
Society is busy with daily routines and experiencing time constraints. As a result, products or services that can provide them time saving are chosen. In the mobile food delivery apps context, time saving is a perceived advantage to consumers [25]. Using the food delivery, users do not need to drive to the restaurant or café for food. Furthermore, time savings may boost a user’s feeling of self-control, which has been shown to increase the likelihood of continuing to use food delivery apps [26]. Thus, the following hypothesis proposes that:
H8.
Time saving will positively influence the continuous usage intention in mobile food delivery applications.
2.9. Convenience
In previous research on routes and mobile applications, Ozturk et al. [27], Xu, Hung, and Li [26] and Shah, Yan, and Qayyum [2] discovered that users’ continuing intention was substantially influenced by convenience. Convenience is known as perceived advantage associated with mobile apps [26]. In the mobile food delivery apps context, the application allows users to access the application anytime and anywhere if they want to order food. Not to mention, the food is then be delivered to their doorsteps. Due to these reasons, it encourages the user to reuse the application from time to time. As such, convenience positively influences continuance usage intention, as presented in the following hypothesis.
H9.
Convenience will positively influence continuance usage intention in mobile food delivery applications.
3. Research Method
This study concentrated exclusively on Malaysians. In this study, a non-probability sampling method, specifically purposive sampling, was used. To be eligible for this study, respondents had to be Malaysian and have use mobile food delivery applications. The measurement items for performance expectancy, effort efficacy, social influence, facilities conditions, hedonic motivation, price value, and habit were adopted from Alalwan [8]. Time saving and convenience were adopted from Yeo, Goh, and Rezaei [25]. The questionnaire was constructed using Google Forms and was distributed online on Whatsapp and Facebook. The G-Power software [28] suggested a sample size of 114 respondents (minimum). In total 301 respondents who participated in the research. Descriptive statistics for a demographic profile were analysed using SPSS 26 package and Partial least square analysis was analysed using SmartPLS version 3.3.9 [29].
4. Result
4.1. Respondents’ Profile
In total, there were 301 respondents, 165 are female, and 136 are male. A total of 53.5% are between 18 and 23 years of age, 35.5% between 24 and 39 years of age, 7.3% between 40 and 55 years of age, and 3.7% between 56 years of age and above. Malay (42.5%) comprise the majority of the respondents followed by the Indian (34.9%), Chinese (19.6%), and other ethnicities (3%).
4.2. Common Method Bias
The data for this research came from a single source (an experienced user), it is crucial to determine if common method bias has occurred [30]. To overcome this challenge, MacKenzie and Podsakoff [31] and Podsakoff, MacKenzie, Lee, and Podsakoff [32] suggested procedural control and statistical control strategies. Both procedural and statistical control were used since using a single control technique would not eliminate the possible influence of common method bias. For procedural control, a cover letter describing the foundation of the study was included in the surveys. A marker variable titled cognitive rigidity [33] was included as part of the questionnaire as statistical control. Cognitive rigidity is the cognitive process underpinning an individual’s inclination to resist or avoid making changes; nonetheless, this measure has no theoretical relationship with the other factors in this research. The approach of a partially out marker variable [33] was applied. The R2 changes are negligible (0.041). This suggests that methodological common method bias was not present in this research.
4.3. Measurement Model
Construct validity of the measuring model was evaluated using convergent and discriminant validity. Refer to the table below (Table 2), except for CIN4, HBT1, PVE4, FCO4, and PEY3, all indicator loadings were more than 0.50 [34]. For each latent variable, the average variance extracted (AVE) values were more than 0.50 [35]; and the composite reliability scores were greater than 0.70 [34]. These data demonstrate the achievement of convergent validity.
To ascertain the discriminant validity of the measurement model, the Fornell–Larcker Criterion was used. Table 3 demonstrates that each indication has a higher load on its constructs but a lower load on others. This implies that discriminant validity has been established.
4.4. Structural Model
Following the evaluation of the measurement model, the investigation moved on to the evaluation of the structural model. A bootstrapping approach with 5000 samples was utilised to generate the path coefficients and accompanying t-values. Table 4 reveals that all variables’ variance inflation factor (VIF) ratings were less than 5 [35]. To be considered sufficient, the R2 must be greater than the value of 0.02. The model explained 48.1% of the variation in continuance intention (R2 = 0.481) in this study. The model used in this study has strong explanatory power since the R is rather significant. Social influence (β = 0.119; t = 1.995); hedonic motivation (β = 0.111; t = 1.983); price value (β = 0.153; t = 2.619); habit (β = 0.140; t = 2.27); time saving (β = 0.177; t = 2.662) and convenience (β = 0.135; t = 1.968) are the factors that are significant and have positive influence on users’ continuance intention towards food delivery apps except for performance expectancy (β = 0.045; t = 0.671); effort expectancy(β = 0.032; t = 0.476), and facilities conditions (β = 0.028; t = 0.426). Except for effort expectancy, facilities conditions, and performance expectancy, none of the variables crossed the ‘0’ line at the lower and upper confidence levels. Furthermore, it is critical to determine if the exogenous constructions of convenience, habit, hedonic motivation, price value, social influence, and time saving have a significant influence on the endogenous constructs. Cohen’s [36] criteria for effect sizes (f2) were used to measure this, with 0.02 being a small impact, 0.15 representing an average effect, and 0.35 representing a large effect. According to this research, habit (f2 = 0.020), price value (f2 = 0.026) and time saving (f2 = 0.023) have little effect. The Q2 value for continuous intention (0.222) was more than 0, indicating that the model has sufficient predictive relevance.
5. Discussion
Data analysis results demonstrated that convenience, habit, hedonic motivation, price value, social influence, and time saving are the factors that are a positive influence on users’ continuance intention towards food delivery applications. As a result, users’ continued intention of using food delivery apps are based on their convenience, habit, hedonic motivation, price value, social influence, and time saving. However, users’ continuous usage intention is not influenced by users’ effort expectancy, facilities conditions, and performance expectancy. The results for facilities conditions and performance expectancy are consistent with Lee, Sung, and Jeon [16]. Thus, the result for effort expectancy is inconsistent with Lee, Sung, and Jeon [16]. The research respondents are mainly 18–39 years of age, which are the generations familiar with the technology and mobile applications. As a result, they do not need much effort expectancy and facilities conditions when they use the food delivery applications. As for performance expectancy, respondents do not perceive that mobile food delivery apps are helpful post-pandemic because users do not rely only on food delivery apps for food as dine-in is allowed.
6. Conclusions
This study contributes both to theoretical and practical research. From a theoretical perspective, this study added two additional determinants: time saving and convenience, into the UTAUT2 model. Furthermore, by adding these two determinants, it is also confirmed that both time saving and convenience contribute to continued usage intention. In addition, this study also contributes to mobile food delivery and the framework given in this model can be used as a foundation for future research on mobile food delivery apps. From a practical perspective, organisations that offer mobile food delivery apps services need to understand the importance of convenience, habit, hedonic motivation, price value, social influence, and time saving to users, especially if they want to encourage users to continue to use mobile food delivery app services. The focus of this project is constrained in certain aspects: data were solely obtained among Malaysians; therefore, the findings’ generalizability is constrained. As a result, this study may not be appropriate for other countries. Secondly, most of the respondents in this study are between 18 and 39 years of age, favouring the younger generation, particularly Millennials. Therefore, future research needs to focus or consider older generations and identify their determinant of continuance usage of mobile food delivery applications.
Author Contributions
Conceptualization, methodology, formal analysis, supervision, writing—review and editing, E.H.T.Y.; Writing—original draft, investigation, S.K. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Not applicable.
Acknowledgments
Paper presented during a panel—organized by Faculty of Information Management, Universiti Technologi Mara, Kelantan Branch, Malaysia—in the context of the International Academic Symposium of Social Science 2022 conference, organized and hosted by Universiti Technologi Mara, Kelantan Branch, during the IASSC 2022.
Conflicts of Interest
The authors declare no conflict of interest.
References
- Adroit Market Research. Available online: https://www.adroitmarketresearch.com/industry-reports/malaysia-online-food-delivery-market (accessed on 2 February 2022).
- Shah, A.; Yan, X.; Quayyum, A. Adoption of mobile food ordering apps for O2O food delivery services during the COVID-19 outbreak. Br. Food J. 2021, 28. ahead-of-print. [Google Scholar] [CrossRef]
- Okumus, B.; Ali, F.; Bilgihan, A.; Ozturk, A. Psychological factors influencing customers acceptance of smartphone diet apps when ordering food at restaurants. Int. J. Hosp. Manag. 2018, 72, 67–77. [Google Scholar] [CrossRef]
- Ariel, M. App Stores Growth Acceleratesin 2014. Available online: https://www.developernation.net/blog/app-stores-growth-accelerates-2014 (accessed on 2 February 2022).
- Venkatesh, V.; Thong, J.Y.; Xu, X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Q. 2012, 36, 157–178. [Google Scholar] [CrossRef]
- Zhao, Y.; Bacao, F. What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? Int. J. Hosp. Manag. 2020, 92, 102683. [Google Scholar] [CrossRef] [PubMed]
- Gunden, N.; Morosan, C.; DeFranco, A. Consumers’ intentions to use online food delivery systems in the USA. Int. J. Contemp. Hosp. Manag. 2020, 32, 1325–1345. [Google Scholar] [CrossRef]
- Alalwan, A. Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. Int. J. Inf. Manag. 2020, 50, 28–44. [Google Scholar] [CrossRef]
- Tam, C.; Santos, D.; Oliveira, T. Exploring the influential factors of continuance intention to use mobile apps: Extending the expectation confirmation model. Inf. Syst. Front. 2020, 22, 243–257. [Google Scholar] [CrossRef]
- Ray, A.; Dhir, A.; Bala, P.; Kaur, P. Why do people use food delivery apps (FDA)? A use of gratification theory perspective. J. Retail. Consum. Serv. 2019, 51, 221–230. [Google Scholar] [CrossRef]
- Fakfare, P. Influence of service Attributes of food delivery application on customers satisfaction and their behavioural responses: The IPMA. Int. J. Gastron. Food Sci. 2021, 25, 100392. [Google Scholar] [CrossRef]
- Surya, A.; Sukresna, I.; Mardiyona, A. Fctors affecting intention to use food order-delivery feature of ride-hailing applications: The UTAUT approach. Int. J. Bus. Soc. 2021, 22, 1363–1383. [Google Scholar] [CrossRef]
- Agarwal, V.; Sahu, R. Predicting repeat usage intention towards O2O food delivery: Extending UTAUT2 with user gratifications and bandwagoning. J. Foodserv. Bus. Res. 2022, 25, 434–474. [Google Scholar] [CrossRef]
- Venkatesh, V.; Morris, G.B.; Davis, F.D. User acceptance of information technology: Toward a unified view. MIS Q. 2013, 27, 425–478. [Google Scholar] [CrossRef]
- Marinković, V.; Đorđević, A.; Kalinić, Z. The moderating effects of gender on customer satisfaction and continuance intention in mobile commerce: A UTAUT-based perspective. Technol. Anal. Strateg. Manag. 2019, 32, 306–318. [Google Scholar] [CrossRef]
- Lee, S.W.; Sung, H.J.; Jeon, H.M. Determinants of continuous intention on food delivery apps: Extending UTAUT2 with information Quality. Sustainability 2019, 11, 3141. [Google Scholar] [CrossRef]
- Chao, C.M. Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model. Front. Psychol. 2019, 10, 1652. [Google Scholar] [CrossRef]
- Al-Saedi, K.; Al-Emran, M.; Ramayah, T.; Abusham, E. Developing a general extended UTAUT model for M-payment adoption. Technol. Soc. 2020, 6, 101293. [Google Scholar] [CrossRef]
- Akdim, K.; Casalo, L.V.; Flavian, C. The role of utilitarian and hedonic aspects in the continuance intention to use social mobile apps. J. Retail. Consum. Serv. 2022, 66, 102888. [Google Scholar] [CrossRef]
- Dodds, W.B.; Monroe, K.B.; Grewal, D. Effects of price, brand, and store information on buyers. J. Mark. Res. 1991, 28, 307–319. [Google Scholar]
- Limayem, M.; Hirt, S.G.; Cheung, M.K. How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance. MIS Q. 2007, 31, 705–737. [Google Scholar] [CrossRef]
- Nascimento, B.; Oliveira, T.; Tam, C. Wearable technology: What explains continuance intention in smartwatches? J. Retail. Consum. Serv. 2018, 43, 157–169. [Google Scholar] [CrossRef]
- Hsu, M.-H.; Chang, C.M.; Chuang, L.W. Understanding the determinants of online repeat purchase intention and moderating role of habit: The case of online group-buying in Taiwan. Int. J. Inf. Manag. 2015, 35, 45–56. [Google Scholar] [CrossRef]
- Zanetta, L.D.A.; Hakim, M.P.; Gastaldi, G.B.; Seabra, L.M.A.J.; Rolim, P.M.; Nascimento, L.G.P.; Medeiros, C.O.; Cunha, D.T.D. The use of food delivery apps during the COVID-19 pandemic in Brazil: The role of solidarity, perceived risk, and regional aspects. Food Res. Int. 2021, 149, 110671. [Google Scholar] [CrossRef] [PubMed]
- Yeo, V.; Goh, S.K.; Rezaei, S. Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. J. Retail. Consum. Serv. 2017, 35, 150–162. [Google Scholar] [CrossRef]
- Xu, F.; Huang, S.; Li, S. Time, money, or convenience: What determines Chinese consumers’ continuance usage intention and behavior of using tourism mobile apps? Int. J. Cult. Tour. Hosp. Res. 2019, 13, 299–322. [Google Scholar] [CrossRef]
- Ozturk, A.B.; Nusair, K.; Okumus, F.; Hua, N. The role of utilitarian and hedonic values on users’ continued usage intention in a mobile hotel booking environment. Int. J. Hosp. Manag. 2016, 57, 106–115. [Google Scholar] [CrossRef]
- Faul, F.; Erdfelder, E.; Buchner, A.; Lang, A.G. Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behav. Res. Methods 2009, 41, 1149–1160. [Google Scholar] [CrossRef]
- Ringle, C.M.; Smith, W.R.; Becker, J. SmartPLS 3; SmartPLS GmbH: Boennigstedt, Germany, January 2015. [Google Scholar]
- Tehseen, S.; Ramayah, T.; Sajilan, S. Testing and Controlling for Common Method Variance: A Review of Available Methods. J. Manag. Sci. 2017, 4, 142–168. [Google Scholar] [CrossRef]
- MacKenzie, S.B.; Podsakoff, P.M. Common Method Bias: Nature, Causes, and Procedural Remedies. J. Retail. 2012, 88, 556–562. [Google Scholar] [CrossRef]
- Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
- Oreg, S. Resistance to change: Developing an individual differences measure. J. Appl. Psychol. 2003, 88, 680–693. [Google Scholar] [CrossRef] [Green Version]
- Byrne, B.M. Structural Equation Modeling with AMOS: Basic Concepts, Appsand Programming, 3rd ed.; Routledge: New York, NY, USA, 2016. [Google Scholar]
- Hair, J.; Hult, G.T.; Ringle, C.; Sarstedt, M. A Primer On Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed.; SAGE Publications: Thousand Oaks, CA, USA, 2017. [Google Scholar]
- Cohen, J. Statistical Power Analysis for the Behavioural Science, 2nd ed.; Lawrance Elrbaum: Mahwah, NJ, USA, 1988. [Google Scholar]
Table 1.
Studies on food delivery apps using UTAUT theory.
| Authors | Variables |
|---|---|
| Alalwan [8] | Performance expectancy, effort expectancy, social influences, facilitating conditions, hedonic motivation, price value, habit, online review, continued intention, online rating online tracking, and satisfaction |
| Gunden et al. [7] | Performance expectancy, congruity with self-image, habit, impulse buying tendency, mindfulness, and intention |
| Tam, Santos, and Oliveira [9] | Social influence, performance expectancy, effort expectancy, perceived fear, facilitating conditions, and continuous intention |
| Zhao and Bacao [6] | Performance expectancy, effort expectance, social influence, trust, perceived task–technology fit, confirmation, satisfaction, continuance intention |
| Surya, Sukresna, and Mardiyano [12] | Performance expectancy, effort expectancy, social influence, facilitating condition, behavioural intention |
| Agarwal and Sahu [13] | Delivery Experience, time saving orientation, societal pressure, search for restaurants, listing, performance expectancy, effort expectancy, facilitating conditions, price saving orientation, hedonic motivations, habit, e-satisfaction, usage intention, and repeat use intention bandwagon effect |
Table 2.
Measurement model.
| Construct | Items | Loadings | CR | AVE |
|---|---|---|---|---|
| Continuance Intention | CIN1 | 0.786 | 0.754 | 0.507 |
| CIN2 | 0.622 | |||
| CIN3 | 0.719 | |||
| Convenience | CON1 | 0.779 | 0.819 | 0.602 |
| CON2 | 0.737 | |||
| CON3 | 0.809 | |||
| Effort Expectancy | EEY1 | 0.676 | 0.811 | 0.519 |
| EEY2 | 0.697 | |||
| EEY3 | 0.722 | |||
| EEY4 | 0.782 | |||
| Facilitating Conditions | FCO1 | 0.762 | 0.766 | 0.522 |
| FCO2 | 0.698 | |||
| FCO3 | 0.705 | |||
| Habit | HBT2 | 0.754 | 0.768 | 0.525 |
| HBT3 | 0.685 | |||
| HBT4 | 0.734 | |||
| Hedonic Motivation | HMN1 | 0.749 | 0.780 | 0.542 |
| HMN2 | 0.746 | |||
| HMN3 | 0.714 | |||
| Performance Expectancy | PEY1 | 0.759 | 0.796 | 0.567 |
| PEY2 | 0.800 | |||
| PEY4 | 0.696 | |||
| Price Value | PVE1 | 0.714 | 0.784 | 0.548 |
| PVE2 | 0.722 | |||
| PVE3 | 0.783 | |||
| Social Influence | SIF1 | 0.714 | 0.758 | 0.511 |
| SIF2 | 0.698 | |||
| SI3 | 0.732 | |||
| Time Saving | TS1 | 0.773 | 0.851 | 0.588 |
| TS2 | 0.793 | |||
| TS3 | 0.717 | |||
| TS4 | 0.781 |
Note: CON: convenience; EEY: effort expectancy; FCO: facilitating conditions; HBT: habit; HMN: hedonic motivation; PEY: performance expectancy; PVE: price value; SIF: social influence; TSG: time saving; CIN: continuance intention.
Table 3.
Discriminant validity.
| CIN | CON | EEY | FCO | HBT | HMN | PEY | PVE | SIF | TSG | |
|---|---|---|---|---|---|---|---|---|---|---|
| CIN | 0.712 | |||||||||
| CON | 0.528 | 0.776 | ||||||||
| EEY | 0.474 | 0.460 | 0.720 | |||||||
| FCO | 0.471 | 0.464 | 0.562 | 0.722 | ||||||
| HBT | 0.522 | 0.394 | 0.417 | 0.493 | 0.725 | |||||
| HMN | 0.492 | 0.437 | 0.482 | 0.480 | 0.518 | 0.736 | ||||
| PEY | 0.447 | 0.392 | 0.671 | 0.503 | 0.472 | 0.412 | 0.753 | |||
| PVE | 0.513 | 0.440 | 0.487 | 0.482 | 0.558 | 0.463 | 0.400 | 0.740 | ||
| SIF | 0.476 | 0.400 | 0.436 | 0.450 | 0.502 | 0.427 | 0.445 | 0.421 | 0.715 | |
| TSG | 0.554 | 0.732 | 0.542 | 0.506 | 0.441 | 0.450 | 0.491 | 0.414 | 0.416 | 0.767 |
Note: CON: convenience; EEY: effort expectancy; FCO: facilitating conditions; HBT: habit; HMN: hedonic motivation; PEY: performance expectancy; PVE: price value; SIF: social influence; TSG: time saving; CIN: continuance intention. The black background show that CIN and CON do not have value and so on for other variables.
Table 4.
Structural model.
| Std. Beta | Std. Error | T-Value | p Values | Decision | LL | UL | f2 | VIF | R2 | Q2 | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| H1: PEY->CIN | 0.045 | 0.067 | 0.671 | 0.251 | Not supported | −0.071 | 0.15 | 0.002 | 2.068 | 0.481 | 0.222 |
| H2: EEY->CIN | 0.032 | 0.067 | 0.476 | 0.317 | Not supported | −0.078 | 0.144 | 0.001 | 2.354 | ||
| H3: SIF->CIN | 0.119 | 0.06 | 1.995 | 0.023 | Supported | 0.031 | 0.222 | 0.017 | 1.568 | ||
| H4: FCO->CIN | 0.028 | 0.067 | 0.426 | 0.335 | Not supported | −0.075 | 0.138 | 0.001 | 1.849 | ||
| H5: HMN->CIN | 0.111 | 0.056 | 1.983 | 0.024 | Supported | 0.025 | 0.21 | 0.014 | 1.676 | ||
| H6: PVE->CIN | 0.153 | 0.058 | 2.619 | 0.005 | Supported | 0.054 | 0.247 | 0.026 | 1.759 | ||
| H7: HBT->CIN | 0.140 | 0.062 | 2.27 | 0.012 | Supported | 0.027 | 0.232 | 0.020 | 1.932 | ||
| H8: TSG->CIN | 0.177 | 0.066 | 2.662 | 0.004 | Supported | 0.066 | 0.281 | 0.023 | 2.577 | ||
| H9: CON->CIN | 0.135 | 0.068 | 1.968 | 0.025 | Supported | 0.023 | 0.248 | 0.015 | 2.325 |
Note: CON: convenience; EEY: effort expectancy; FCO: facilitating conditions; HBT: habit; HMN: hedonic motivation; PEY: performance expectancy; PVE: price value; SIF: social influence; TSG: time saving; CIN: continuance intention.
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Yapp, E.H.T.; Kataraian, S. Key Determinants of Continuance Usage Intention: An Empirical Study of Mobile Food Delivery Apps among Malaysians. Proceedings 2022, 82, 15. https://doi.org/10.3390/proceedings2022082015
AMA Style
Yapp EHT, Kataraian S. Key Determinants of Continuance Usage Intention: An Empirical Study of Mobile Food Delivery Apps among Malaysians. Proceedings. 2022; 82(1):15. https://doi.org/10.3390/proceedings2022082015
Chicago/Turabian StyleYapp, Emily H. T., and Saraniya Kataraian. 2022. "Key Determinants of Continuance Usage Intention: An Empirical Study of Mobile Food Delivery Apps among Malaysians" Proceedings 82, no. 1: 15. https://doi.org/10.3390/proceedings2022082015
APA StyleYapp, E. H. T., & Kataraian, S. (2022). Key Determinants of Continuance Usage Intention: An Empirical Study of Mobile Food Delivery Apps among Malaysians. Proceedings, 82(1), 15. https://doi.org/10.3390/proceedings2022082015
