Convenience is defined as the perceived time, value and effort required to facilitate the use of OFDS. Consumers now have the freedom to choose from a wide range of food providers listed on the internet at any time and from anywhere. As a result of its convenience, consumers will be motivated to use OFDS on a regular basis. ^{6 , 7}

A total of 47% of e-commerce users in Southeast Asia shopped online to save time and energy, and 87% agreed on the usefulness of internet services during the COVID-19 outbreak. ⁸ Malaysians also prefer online shopping when they have a hectic schedule. ⁹ The ease of comparing prices across different online platforms and a wide variety of items are all motivating factors that drive consumers to shop online. Convenience was also cited as the top reason for shopping online in Q4 2020, and remained the top three reasons in Q1 2021. ¹⁰

Perceived ease of use (PEOU) refers to a person's perception of how hassle-free it is to use a system. The quality of a system is defined as the ease with which pages can be navigated, the presence of a clear and uncomplicated layout, and the system's dependability. ¹¹ It is critical for businesses to ensure that their online platform is simple to use because bad designs or a complicated process will deter consumers from continuing with the online purchase.

The amount of effort required to use a system will serve as a critical predictor of its adoption and subsequent usefulness. ^{12 , 13} It was discovered that if it is relatively effortless to use a system, consumers are more likely to order food online. ¹⁴

In today's fast-paced world, where consumers’ busy schedules mean time is in short supply, time-saving orientation (TSO) has become a critical factor in easing daily tasks while fully utilising time. Many office workers could not afford the time and trouble of going out to eat, including driving and queuing up to place order. Thus, using OFDS is the quickest way to get food and the time saved can be used to complete other tasks.

Higher-income consumers value time because of the opportunity costs. As such, they find online shopping appealing because it allows them to make better use of their time. ¹⁵ A study discovered that timesaving is the key determinant of consumers' motivation to use technology-based self-service. ¹⁶ When consumers are able to save time, their perception turns positive and as a result, their attitude towards OFDS also becomes favourable. ^{6 , 17 , 18}

Price can be defined as the value (monetary or non-monetary) an individual must put forth in an exchange for a product or service. ^{19 , 20} One of the key factors influencing customer satisfaction is price-saving orientation (PSO), which includes offers and discounts provided by sellers. ²¹ 82.9% of Malaysians purchased a product online in the past month. ¹ The internet makes it easier to compare prices among different online sellers, which has proven to be advantageous for consumers to purchase at a lower price, which in turn has a significant effect on their behavioural intention to shop online. ^{13 , 22}

OFDS provide additional perks such as not having to pay for service charge imposed by the restaurants, as well as getting free delivery and discount coupons. Additionally, consumers do not need to expend energy or effort to visit a physical store or restaurant. Thus, consumers will be more satisfied with their online food ordering experience and will be more likely to use these services in the future. ^{5 , 18}

Attitude (ATT) can be defined as a consumer's overall reaction when using a specific device or technology. ²³ It refers to a person's reaction, whether positive or negative, to a given object. ²⁴ When consumers believe that online food ordering is useful and capable of easing their daily lives, they are more likely to develop a positive attitude which will lead to continuance intention (CI) of using it. Thus, attitude is positively related to behavioural intention. ^{17 , 25 , 26}

Behavioural intention (BI) is defined as a person's proclivity to act in a certain way. ²⁷ The intent to use OFDS denotes a consumer's desire to purchase food and beverages through online delivery platforms. ¹⁷ Many studies have established that the factors used to measure BI include positive word-of-mouth, willingness to recommend a product or service to others and also repurchase intention. ²⁸ Consumers who are pleased and content with their online purchase experience are expected to continue doing so. ⁵

The main objective of this study is to identify the factors that may influence consumers’ attitude and behaviour towards continuance intention in using OFDS post pandemic, as illustrated in the proposed research model in Figure 1. The hypotheses are proposed as follows:

Research ethics approval was obtained from Multimedia University, Malaysia (EA1422021) and the respondents gave their written informed consent when filling out the Google Form.

An online survey with close-ended questions was designed using Google Form to examine the research hypotheses. It consisted of two parts: demographic information of respondents and 25 measurement items which indicated seven variables, namely, CM, PEOU, TSO, PSO, ATT, BI and CI towards OFDS, which were adopted from previous studies ^{5 , 17 , 25 , 29–33} and recorded in Table 1. All items were measured based on a five-point Likert-type. ^{34 , 35}

The Krejcie and Morgan sampling method was used as a guideline in estimating the sample size, and convenience sampling method was used to source participants. ³⁶ This sampling method is commonly used by researchers for similar studies, such as a recent study on the intention to use OFDS among university students in Malaysia, which gathered 290 samples for data analysis. ¹⁸

A primary dataset of 307 respondents was gathered, in order to examine consumers’ perception and attitude towards OFDS during the pandemic, which is critical to the future growth of the OFDS industry. The respondents were close contacts (relatives, friends and students) of the authors of this study, and were invited through email, Facebook and WhatsApp, between 22 ^nd March 2021 and 18 ^th April 2021.

Demographic background of respondents is presented descriptively and graphically. Consistent Partial Least Square (PLSc) approach ^37–39 was applied to study the reflective and formative factors in this study and SmartPLS.v3 software was the main tool used (a free version is available for 30 days). Reliability and validity were tested in factor analysis and bootstrapping of 5,000 subsamples was used to estimate PLSc path model.

Table 2 shows the demographic profile of 307 respondents. ⁵² Overall, 83.39% of respondents use OFDS and 70.68% prefer to eat at home, compared to at a restaurant. Figure 2 depicts the distribution of respondents who ordered food via third-party mobile apps, social media, or the company’s own website or mobile apps. Foodpanda (70.36%) and GrabFood (63.19%) are the most popular in Malaysia because it is user-friendly. ⁴⁰ However, social media platforms such as Instagram are more suitable for promoting food rather than ordering. ⁴¹

Table 3 recorded the feedback of the respondents whereby the mode for all measurement items is “Agree”, which contributes to the left-skewed distribution except PSO4. The average and standard deviation of variables are recorded in Table 4 and each average is close to “4” (Agree) except PSO.

Table 5 shows the ratio comparison of using OFDS and dining preference based on age, gender, marital status and personal income level. OFDS usage among single young adults (>85% each age group below 40 years old; single 86%) is higher compared to married adults (77%) during pandemic than before the pandemic. ¹¹ Elderly or married adults prefer to enjoy their food at home. Although 71.34% of the respondents were earning a low income, they still preferred to use OFDS (84%) and dine at home (71%) compared to higher income respondents. The statistics revealed a significant difference in online food ordering trends between age groups, but not between genders.

Table 6 shows Cronbach’s alpha ^{42 , 43} and composite reliability (CR) ^{37 , 44 , 45} for each variable as above 0.8, which indicates good internal consistency of the questionnaire’s questions scale in measuring a similar variable. * indicates CR>0.95 but there are no significant changes after its removal. ³⁷ The average variance extracted (AVE) indices ⁴⁶ are greater than 0.6 for each variable, indicating no convergent validity problems.

In Table 7 Fornell-Larcker criterion, ^{46 , 47} the diagonals represent the square root of AVE and off diagonals represent the coefficient of correlation. One tail t-test is conducted on the coefficient of correlation at 5% level of significance. The results revealed that there is a positive correlation between the variables with p-value of 0. There are no discriminant validity issues with the support of HTMT values, recorded in Table 8 based on HTMT _0.85 criterions.

Six hypotheses were tested using PLSc, ³⁹ a variance-based structural equation modelling technique, with no concerns about distribution or multicollinearity. In the past decade, the use of PLS modelling has gradually increased in order to handle more complex models.

Table 9 summarises the result of the hypotheses presented in Figure 3, which indicates the path coefficient and outer loading of the variable. PEOU is found to be insignificant in influencing consumers’ attitude towards OFDS ( p-value > 0.05). Consumers’ attitude towards using OFDS during and post the COVID-19 pandemic is, however, positively influenced by CM ( p-value < 0.05), TSO ( p-value < 0.05) and PSO ( p-value < 0.05). Furthermore, hypotheses of ATT positively influencing consumers’ BI ( p-value < 0.05) and also BI positively influencing consumers’ CI ( p-value < 0.05) towards OFDS are supported in this study. Thus, H1, H3, H4, H5 and H6 are validated while H2 is rejected.

This study did not take into account all of the possible factors that might influence the continuance intention of using OFDS after the pandemic. The model could be improved in the future by including more variables, such as, customer satisfaction and social influences. Furthermore, the findings cannot be generalised as a whole due to convenience sampling biasness. In the future, the study could be narrowed down to a specific group; perhaps looking at some larger cities with higher demand and supply for OFDS.

Figshare: Online Food Delivery Service.

DOI: http://doi.org/10.6084/m9.figshare.14772951. ⁵²

This project contains the following underlying data: •

Data file 1. (Survey results, CVS format)

Figshare: Online Food Delivery Service Questionnaire 2021

DOI: http://doi.org/10.6084/m9.figshare.16566414. ⁵³

This project contains the following extended data: •

Data file 1. (Survey questions, CVS format)

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).