The unified theory of use and acceptance of technology (UTAUT) predicts users’ behavioral intentions toward new technologies. Various determinants can be used to determine the technology usage intentions and behavior; for instance, gender, age, voluntariness, and experience are some of the determinants of user behavior [26
]. The theory highlights key constructs towards user behavior and acceptance, including performance expectancy, effort expectancy, social influence, and facilitating conditions. The UTAUT model can therefore be applied together with other variables to determine the factors influencing the FDA users’ behavior and acceptance.
Performance expectancy under the UTAUT model involves the user’s level of belief that the technology will improve performance in particular activities. The performance expectancy can therefore be used to determine the user’s likeliness to adopt new technology [32
]. Thus, using the performance expectancy concept to determine users’ acceptance intention to FDAs indicates greater intentions [33
]. The aim of using FDAs by many consumers includes the convenience of food delivery to their doorsteps at any time and the increased transparency in pricing, among other benefits. The performance expectancy of the FDAs affected the continuation of their use due to the closing of the satisfaction gap during the COVID-19 pandemic.
Hypothesis 1 (H1).
Performance expectancy (PE) positively affects behavioral intention to use (BIU) FDAs during the COVID-19 pandemic.
The effort expectancy is another determinant variable of the UTAUT theory, which involves the level of association of use of particular technology with the users. The effort expectancy can be used to determine the continued use of technology based on its perceived easiness of use [34
]. FDAs are often easy to use as they involve few steps and offer multiple choices of payments. Effort expectancy positively affects the use of particular new technologies and, in this case, indicates a continued intention of use of FDAs by the users. However, in the long term, increasing the familiarization of the users with the new technologies does not directly affect its continued use.
Hypothesis 2 (H2).
Effort expectancy (EE) positively affects behavioral intention to use (BIU) FDAs during the COVID-19 pandemic.
Social influence is another determinant of behavior and intention of use of new technology. Social influence involves users gaining willingness to try new technologies from others, including friends, colleagues, and families. The social influence has a positive effect on the user’s intentions to use new technologies [34
]. Thus, this determinant can determine the user’s intention of continued use of the FDA. According to [37
], the growing number of mobile social networks has increased the social influence on the use of new mobile technologies, including FDAs. The increased social influence is, however, pegged on the positive effects on the user’s satisfaction.
Hypothesis 3 (H3).
Social influence (SI) positively affects behavioral intention to use (BIU) FDAs during the COVID-19 pandemic.
Timeliness is another important determinant of behavior and intention of use of new technology by the users. According to [28
], timely diffusion of technology influences the willingness and the intention to use the technology. The mobile industry has been growing at an increasing pace, with the business industries integrating mobile apps into their business operations. The timeliness of diffusion of FDAs during the COVID-19 pandemic had a positive effect on the intention of continued use and influences user behavior towards continued use.
Hypothesis 4 (H4).
Timeliness (TM) positively affects behavioral intention to use (BIU) FDAs during the COVID-19 pandemic.
The task-technology fit is a technology effectiveness theory that articulates that the effectiveness of new technology can be determined through the assessment of the relationship between the technology and the tasks supported. Based on the task-technology fit (TTF) model, new users are likely to have a positive impact with new technologies if the technology’s performance matches the tasks that the user hopes to perform [38
]. Various factors can be used to measure the level of task-technology fit, including systems reliability, compatibility, ease of use, quality, and the relationship with the user.
The perceived task-technology fit is an important measure of the user’s behavior intentions towards new technologies. Inferring from [39
], high fitness of the technology’s performance with the user’s tasks leads to a positive adoption of the new technologies. Thus, determining the adoption of the users with FDAs can also be determined by the performance of customer’s tasks by the FDA. During the COVID-19 pandemic, many consumers had limited mobility due to lockdown regulations and limited contacts to limit the spread of the disease. The need for new food delivery technology thus could perform tasks which the users had limited ability to perform, such as easy access to food, reduced queuing to order food in restaurants, and the ability to access the online delivery services at any time. The perceived task-technology fit had a positive impact on the continued intention of users to use FDAs during the pandemic, as it successfully performed the user’s intended tasks.
Hypothesis 5 (H5).
Task-technology fit (TT) positively affects behavioral intention to use (BIU) FDAs during the COVID-19 pandemic.
Perceived trust can be used as a measure of determining the behavioral intention of using new technology as it involves the state of individuals’ faith regarding a particular technology. The individuals’ faith intentions are often followed by other behavior, such as integrity [28
]. In determining the behavior leading to the use of FDAs during the COVID-19 pandemic, the level of integrity accorded by FDAs could have led to the development of trust by the users and indication of a continued behavior to use the applications. Indeed, the operation of FDAs is simple and includes not only the convenience of placing an online food order for delivery to the doorstep but also the increased transparency in pricing with no hidden charges and the provision of multiple ways for payment. The increased integrity has a positive effect on the user’s trust and influences the continued intention of use of the FDAs.
Hypothesis 6 (H6).
Perceived trust (PT) positively affects behavioral intention to use (BIU) FDAs during the COVID-19 pandemic.
Perceived safety was the other factor determining the customer’s continued use of FDAs during the COVID-19 pandemic. Safety concerns during the COVID-19 pandemic include reducing the risk of transmission of the COVID-19 disease. Studies of Teo et al. [41
] and Rashid et al. [42
] highlight the importance of safety as a consideration when adopting new technologies. The food delivery services performed excellently, promoting user safety through encouraging contactless deliveries in a bid to observe the social distancing rules. Other safety measures promoted by FDAs involved cashless transactions and the practice of high hygiene through regular sanitization. The increased perceived safety has a positive impact on the user’s behavioral intentions for continued use of the FDA’s.
Hypothesis 7 (H7).
Perceived safety (PS) positively affects behavioral intention to use (BIU) FDAs during the COVID-19 pandemic.
Behavioral intention involves individual attitudes towards particular technology. The individual’s intention to accept new technology is based on the technology’s abilities to be useful. The degree to which a user considers a particular technology as being useful involves the belief of being free of effort. Thus, the positive behavioral intention towards FDAs depends on the belief of users for the apps to be useful. FDAs come with various benefits to the users, including reduced hustle of queuing for food in restaurants and the convenience of ordering food at any time [43
]. The COVID-19 pandemic period is coupled with various limitations, including lockdowns and the need for social distancing regulations. The FDAs thus have a positive effect on the behavioral intentions of the users as the apps offer them effortless services to their convenience.
Based on the research conducted in the literature review and the proposed hypothesis developed above, the research model was developed and is graphically illustrated in Figure 1
Based on the findings of the previous studies, and as shown in the above conceptual framework, a total of eight factors were used in this study. The items for performance expectancy (PE), effort expectancy (EE), social influence, and behavioral intention to use (BIU) were adopted from the works of Venkatesh et al. [45
], San Martín and Herrero [46
], and Escobar-Rodriguez and Carvajal-Trujillo [47
]. Other items for factors such as task-technology fit (TT), perceived safety (PS), perceived risk (PR), and timeliness (TM) were developed with reference to previous studies from Shahbaz et al. [48
] and Ponte [49
The research used the questionnaire to collect the data used to validate the applied conceptual model and examine the proposed research hypothesis. The questionnaire was made of two sections. The first section consisted of the demographic statistics, which included gender, age, education levels, and occupation concerning the use of FDAs during the COVID-19 pandemic. The second section of the questionnaire composed a collection of data from the study constructs developed from literature review. The variables used included PE, EE, SI, TM, TT, PT, PS, and BIU. To collect the data, five-point Likert scale was applied, which included the measurement ranging from 1 = strongly disagree to 5 = strongly agree.
The specific targets of this research were the people who adopted the use of FDAs during the period of COVID-19 in Bangkok, Thailand. The questionnaire was developed in a native Thailand language to make sure there were no language barriers and to guarantee quality data. The data were collected using the convenience sampling technique from different districts in Bangkok where FDAs were used. In the process of data collection, a total of 550 questionnaires were distributed to the sample respondents. From the sample, 434 questionnaires were filled and returned. The data were collected between 12 December 2020 and 1 February 2021. After evaluating, the data were collected, removing the missing values and the outliers, and a total of 402 valid responses were considered satisfactory for research. The data were primarily collected from Bangkok city, the capital city of Thailand. The data analysis involved carrying out the descriptive statistics, evaluating the reliability and the validity of the data and the analysis using structural equation modeling (SEM) using AMOS Version 26.2.2. Reliability and Validity
Before conducting the actual analysis of evaluating the hypothesis, this study conducted an evaluation of the model through reliability and validity analyses. The validity analysis was conducted using average variance extracted (AVE), while the reliability analysis was conducted through the construct reliability (CR). AMOS 26 was applied to conduct the analysis. The applied CR and AVE formulas are presented below.
below shows the variables used for the study, their factor loadings, the estimates indicators, the CR, and the AVE. The threshold used for the analysis was that the AVE should be greater than 0.5 [50
], the factor loadings should be greater than 0.5, while the CR should be greater than 0.6 [51
]. From the results obtained, all three thresholds were achieved, which implied that reliability and validity levels of the model and the constructs used were satisfactory.