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Understanding the Customers’ Acceptance of Pick -up Apps Over the COVID-19 Pandemic Period, Stay-at-Home Orders: An Integrated Model of UTAUT

Current technological developments allow online sellers to request pick-ups via mobile apps. During the COVID-19 outbreak and with the stay-at-home orders, a survey in Indonesia showed that online transactions are increased, which led to the rise of pick-up and delivery activities. However, the pick-up order cancellation possibly happens, bad reviews appear, and apps abandonment cannot be avoided. The purpose of this study is to examine user acceptance of the pick-up apps and observe its correlation with the COVID-19 stay-at-home orders using the Unified Theory of Acceptance and Usage of Technology (UTAUT) model with subjective norm to stay at home from the Theory of Planned Behavior (TPB) model as an additional variable. The sample was obtained from 205 users of one of the pick-up apps in Indonesia through a structured questionnaire based on a five-point Likert scale. The results showed that the stay-at-home order due to the impact of the COVID- 19 pandemic affects customers’ intention to use pick -up apps and performance expectancy and social influence. Then, the non-linear UTAUT variables influence each other, which is the effect of effort expectancy and social influence on performance expectancy. Finally, the main factor influencing the use behavior of pick-up apps is behavioral intention. This study can contribute to IS research on technology acceptance and provides an overview of customer behavior using the pick-up apps from a pick-up service provider viewpoint.

Indonesia has agreed to introduce "stay-at-home" orders or known as Large-Scale Social Restrictions (LSSR). The law defined some restrictions, including schools closure and working from home, suspension of social-cultural activities, and public gatherings and activities (Andriani, 2020).
The COVID-19 outbreak has adversely affected human lives and many sectors of the economy like manufacturing operations, logistics and supply chains (Singh, Kumar, Panchal, & Tiwari, 2020) and introduced an unprecedented amount of uncertainty into the global economy (Stošić-Mihajlović & Trajković, 2020). E-commerce firms in Indonesia reported that online sales volumes increased as consumers practiced social distancing during the outbreak (Oxford Business Group, 2020). It certainly affects the logistics and pick-up or delivery services (Hohtoulas, Phan, Mheen, Kwan, & Kwan, 2020). A survey from CommerceHub reported that 69% of the customers were willing to subscribe to a delivery service following the pandemic (Berthiaume, 2020).
However, users or customers sometimes feel dissatisfied with filing a complaint or canceling orders apart from this expansion of the application and pick-up service. Pick-up order cancellation wastes drivers' efforts and reduces the availability of supplies on the transportation sector (Wang et al., 2018;Wang, Zhang, and Zhang, 2019). Adam et al. (2020) stated that reliability, price allocation, and ride comfort are the factors of customer satisfaction on Grab as one of the transportation providers. Besides, the performance of the predicting Behavioral Intentions to use technology especially in the organizational context called Use Behavior (Venkatesh, Thong, & Xu, 2016). The model found that social influence, effort expectancy, and performance expectancy affect behavioral intention to use technology.
In contrast, technology usage is impacted by behavioral intention and facilitating conditions.

Non-linear Relationship Between UTAUT Variables
Previous studies in user acceptance models such as TAM-1, 2, and 3 (Lai, 2017)  The effect of perceived efficiency of use which is similar to effort expectancy in UTAUT; on perceived usefulness has long been debated in the TAM study (Bervell & Umar, 2017).
Supporters of this association argue that experience with new technologies engenders perceptions of how easy or not the technology will be for potential users. Therefore, individuals who experience an acceptance stage bind the relationship between using technology and performance results (Bervell & Umar, 2017).
The influence of social customs on perceived usefulness which has similarities with social influences well as performance expectancy in UTAUT, is another relationship projected in the TAM3 model (Bervell & Umar, 2017). Positive or negative impacts from one person to another can influence how valuable or significant the new technology is. This study will investigate the non-linear relationship between social influence and effort expectancy on performance expectancy to complement the linear UTAUT variable synergy (Bervell & Umar, 2017). It is based on research conducted by Bervell and Umar (2017), which investigated the relationship between linear and non-linear variables of the UTAUT model in evaluating the Learning Management System (LMS).

Theory of Planned Behavior (TPB)
In 1991, Ajzen developed the TBP theoretical model to assess a person's behavior. The model was developed from TRA theory that which in itself was developed in 1977 by Ajzen and Fishbein (Tommasetti, Singer, Troisi, & Maione, 2018). TPB states that a person's intention to behave will be more significant when that person's attitude towards the behavior is considered beneficial, when other people who are essential to that person agree on the behavior, and when the person believes that he can perform the behavior (Callow, Callow, and Smith, 2020).

COVID-19 Stay-at-Home Orders as a TPB Variable
WHO conveyed several appeals to the public to reduce the spread of the SARS-CoV2 virus, including maintaining distance, wearing masks, maintaining well-ventilated rooms, avoiding crowds, cleaning hands, and covering nose and mouth coughing with tissue or inner elbow (WHO, 2020). LSSR is limiting individual citizens' activities in areas that are perceived to be high risk with disease contamination to preventing the spread of the virus. This rule is in line with WHO's appeal to maintain physical distancing. Physical distancing calls have also been issued in other countries with similar terms, such as shelter-in-place in the United States, which has since changed to stay-at-home and safer-at-home (Jacobsen & Jacobsen, 2020). Some of these different terms have in common, namely the limitation of individual mobility. As part of limiting economic activity, staying at home can play a significant role in reducing the daily COVID-19 infection curve (Camba & Camba, 2020). The LSSR regulation itself was relaxed to become a Transitional LSSR on October 12, 2020. The start of allowing restaurants and eating places to operate and limiting 25 to 50 percent visitor capacity for recreation areas markets and malls (Enrico, 2020).

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As stated in the research background in the Introduction, the increasing number of online sales transactions led to the rise of pick-up and delivery activities. Naturally, the use of pickup apps has coincided with the COVID-19 phenomenon and the enactment of LSSR or stayat-home orders. To date, although extensive research has been carried out on factors thought to influence use behavior, no study has thoroughly investigated the use behavior on pick-up apps of the users with their new habitual patterns caused by the pandemic. This paper intends to bridge the gap in understanding the relationship between increased pick-up requests and stay-at-home calls using the subjective norm variable from the TPB model. people's behavior. In overall, subjective norms play an essential role in forming people's behavioral intentions with positive or negative influences that encourage each individual to take and avoid specific actions (Tommasetti et al., 2018). The proposed conceptual model is shown in Figure 1 with the subjective norm to stay at home variable from the TPB model as an extra variable in UTAUT model.

Study Design
This study used an evaluative research method with a quantitative approach method.
Evaluative research is a systematic process in which the activity collects and analyzes data on the quality as well as effectiveness, benefits of programs, practices or products. Unlike other types of research that aim to establish novel knowledge or comprehension, evaluative research emphases on formulating decisions about products, programs, and procedures (Gay, Mills, & Airasian, 2012 (1998), this method also facilitates measurement errors in the observed variables (Hair, Hult, Ringle, & Sarstedt, 2016).

Survey Participants
An online survey of 20 questions was applied to gather data. Bina Nusantara University approved the survey. The participants were users of one of the pick-up apps in Indonesia. The survey included questions about: social influence, performance expectancy, effort expectancy, facilitating conditions, the subjective norm to stay at home, use behavior, and behavioral intention. The participants were also asked about demographic information, such as age, sex, educational level, employment status, and application usage history. The participation in this survey was completely voluntary.
Two hundred and five individuals finished the survey which was a structured questionnaire designed on a five-point Likert scale. The analysis was carried out on the survey results collected from 205 respondents, which showed that most respondents were women (67%).
Almost all respondents have used the app more than once (92.2%).

Reliability Test
The coefficient of Composite Reliability (CR) and Cronbach's Alpha were applied to evaluate reliability of the outcomes. Reliability measures the reliability of a study, namely the extent to which the measurement of a test remains consistent after repeated tests on research subjects. To evaluate discriminant validity used the square root value of AVE measurement, which value must be greater than other correlations (Khechine & Augier, 2019).

Path Coefficient
After the assessment of the measurement model has been completed, the next step in evaluating the results of the SEM-PLS is to assess the structural model. In the standard assessment criteria, the measurement uses statistical significance and relevance from the path coefficient (Hair et al., 2019). The path coefficient is used in assessing the relationship between variables from the designed model. In the testing in this study, the bootstrapping method was used with 5,000 sub samples.

Coefficient of Determination
The coefficient of determination or R 2 is used to show how far the model can explain the variance of the dependent variable. Hair et al. (2019) recommends an R 2 of 0.5 or more.

Hypotheses Test
Hypotheses testing was conducted to test the relationship between the independent variable and the dependent variable. In this test, the bootstrapping method was carried out. The significance level (α) used is 0.05, where this value is used as the p-value limit. So in this study, a hypothesis is said to be accepted if the p-value has a value less than or equal to 0.05. The relationship between variables is said to be significant if the t-statistic is more than or equal to 1.96.

Reliability, Convergent and Discriminant Validity
Reliability and validity tests were carried out using the Smart-PLS software. In testing the reliability, this study made use of Cronbach's Alpha and CR coefficients. Hair et al. (2019) suggested that Cronbach's Alpha and CR are better at scores above 0.7 and not more than 0.95. As visualized in Table 1 the value of two coefficients for all measurement instruments was satisfying because they exceed 0.7.

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For the convergent validity test, the AVE measurement was used. Table 2 showed the AVE results for each variable more than 0.5 as Hair et al. (2019) recommended. Discriminant validity is measured by the value of the square root of AVE. Discriminant validity assesses the extent to which different concept measures differ statistically (Tarhini et al., 2016). The square root value of AVE must be greater than other correlations (Khechine & Augier, 2019) and can be confirmed in Table 2.   Another assessment used the coefficient of determination or R 2 to show how far the model can explain the variance of the dependent variable. Table 4 shows the variance of performance expectancy, behavioral intention, and use behavior with values exceeding 0.5, which were quite satisfying (Hair et al., 2019).
The effect of effort expectancy was quite significant on performance expectancy (β = 0.442, t = 5.069, p < 0.01). The findings regarding non-linear relationships between UTAUT variables align with research conducted by Bervell and Umar (2017). It means that customers rely on the amount of effort they expected to put in before finally assessing the utility of pick-up apps.
Another finding on the non-linear variable relationship is that social influence affected performance expectancy (β = 0.354, t = 4.394, p < 0.01). It turned out to be inconsistent with research conducted by Bervell and Umar (2017). This interesting finding suggests that social influence influenced customers before finally assessing the usability of pick-up apps.
Subjective norms to stay at home was found to have a positive influence on behavioral intention (β = 0.265, t = 4.047, p < 0.01). Callow, Callow, and Smith (2020) Tarhini et al., 2016;Venkatesh et al., 2003). These findings can be interpreted that when a customer used pick-up apps, intention to use the apps was very influential, followed by facilitating conditions (such as information and guidance provided by customer service or drivers).

Conclusion
In this study regarding customer acceptance of pick-up apps, the following findings can be concluded. First, customers' intention to use pick-up apps is influenced by performance expectancy, social influence, and subjective norms to stay at home. The interesting finding in this study confirms that the stay-at-home order due to the impact of the COVID-19 pandemic has affected customer's intention to use pick-up apps. Then, the non-linear UTAUT variables influence each other, which is the effect of effort expectancy and social influence on performance expectancy. Finally, the main factor influencing the use behavior of pick-up apps is behavioral intention. Overall, the results showed that pick-up apps are well accepted, especially in Indonesia.
This study provides an overview of customer behavior using the pick-up apps from a pick-up service provider viewpoint. This insight is essential considering the increasing demand for this pick-up service. Another perspective is the subjective norms to stay at home variable as a UTAUT extension can contribute to IS research on technology acceptance. The limitation of this study is the limited scope of the survey, so that the sample data size is relatively small. In addition, data were obtained through questionnaires filled out by respondents. Retrieval of data using the questionnaire method caused the respondents' views on the pick-up apps to be limited to the statements given only. For further research, the scope of the study can be widened by enlarging the sample. In addition, this study has been carried out according to quantitative research approach, however, similar studies in the future can use a mixed-method approach to reinforce the findings.