Online food delivery services: cross-sectional study of consumers’ attitude in Malaysia during and after the COVID-19 pandemic

Theoretical background

This study aims to examine the essential factors perceived by consumers as influential in their decision to persist in using OFDS following the COVID-19 outbreak. Previous studies have frequently combined the Theory of Planned Behaviour (TPB) and Technology Acceptance Model (TAM) to explain why people engage in a specific behaviour. For example, a study conducted in China used TPB, TAM and three patient-centered factors to examine the elements affecting patients’ acceptance of mobile medical platforms, ²³ while another study conducted in Italy combined TPB and TAM to analyse the main drivers of users’ intention to use foods delivery apps. ¹⁸ Other examples include the examination of continuance intention to utilise mobile banking in Jordan, achieved through the integration of UTAUT, TPB, TAM and service quality with machine learning methods, ²⁴ and understanding library user behavioural utilisation intention of physical book as compared to e-book format in Malaysia by combining TAM, TPB and Theory of Self-Regulation (TSR). ²⁵

This study proposes a similar approach to form an integrative theoretical research model adapted from TAM and TPB, expanding it to include three additional constructs: convenience motivation (CM), time-saving orientation (TSO), and price-saving orientation (PSO). TAM, conceptualised by Davis in 1989, put forward that users’ attitudes toward a technology are shaped by their perception of its ease of use and usefulness. These attitudes subsequently influence users’ behavioural intentions to adopt and continue using the technology. ²⁶ In the context of OFDS, perceived ease of use relates to how easy consumers believe ordering food online is. A user-friendly interface, straightforward navigation, and intuitive app design all contribute to a high level of perceived ease of use. Consumers who find the service easy to use are more likely to form positive attitudes and intentions toward its use.

TPB, developed by Ajzen in 1991 extends the understanding of user behaviour by incorporating attitudes, subjective norms and perceived behavioural control as determinants of behavioural intentions, which in turn impact actual behaviour. ²⁷ In the context of OFDS, consumers’ attitudes are primarily shaped by two key factors: their perceptions of the benefits, which encompass aspects such as convenience, cost-effectiveness, and time efficiency; and their perceptions of the ease of using the service. ²⁸ When consumers hold favourable beliefs regarding these factors, it leads to the development of positive attitudes, which in turn, significantly enhances the likelihood of consumers forming a positive intention to engage in OFDS. In essence, attitude acts as a critical determinant in the decision-making process regarding the adoption and utilisation of OFDS among consumers.

This study examines the continued use of OFDS among experienced users, a context where the typical drivers of the TPB may differ from those relevant during initial adoption. At this post-adoption stage, Perceived Behavioural Control (PBC) is less critical because users already have the skills, resources, and access needed to use the platform. Likewise, Subjective Norms (SN) play a limited role, as food ordering is usually a private, routine activity. Prior research suggests that once a behaviour becomes habitual and voluntary, consumers’ attitudes and practical evaluations tend to outweigh external pressures or perceived control.^29–32 Accordingly, the model retains the attitudinal and behavioural intention components of TPB while extending it to include factors that capture the real, practical value users derive from OFDS in their daily lives.

To reflect post-pandemic priorities, the model incorporates three additional constructs: convenience motivation, time-saving orientation, and price-saving orientation. These variables capture users’ desire to simplify daily routines, save time, and gain economic benefits. Including them allows the framework to account for the “post-pandemic shift,” showing how OFDS use has evolved from a temporary, pandemic-driven behaviour into a habitual, integrated part of everyday life.

Overall, the framework integrates TAM and TPB with these three additional variables, offering a comprehensive lens on consumer behaviour. This combination considers both the technological adoption aspects and the socio-psychological factors that influence attitudes and intentions, providing insight into how usability, convenience, efficiency, and economic considerations interact to shape continued engagement with OFDS.

Convenience motivation

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. ^{33 , 34}

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. ³⁵ Malaysians also prefer online shopping when they have a hectic schedule. ³⁶ 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. ³⁷

Perceived ease of use

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. ³⁸ 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. ^{17 , 26} It was discovered that if it is relatively effortless to use a system, consumers are more likely to order food online. ¹³

Time-saving orientation

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. ¹⁹ A study discovered that timesaving is the key determinant of consumers’ motivation to use technology-based self-service. ³⁹ When consumers are able to save time, their perception turns positive and as a result, their attitude towards OFDS also becomes favourable. ^{10 , 20 , 33}

Price-saving orientation

Price can be defined as the value (monetary or non-monetary) an individual must put forth in an exchange for a product or service. ^{40 , 41} One of the key factors influencing customer satisfaction is price-saving orientation (PSO), which includes offers and discounts provided by sellers. ¹¹ 82.9% of Malaysians purchased a product online in the past month.⁵ 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. ^{17 , 42}

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. ^{12 , 20}

Attitude, behavioural intention and continuance intention

Attitude (ATT) can be defined as a consumer’s overall reaction when using a specific device or technology. ²⁷ It refers to a person’s reaction, whether positive or negative, to a given object. ⁴³ 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. ^{10 , 18 , 44}

Behavioural intention (BI) is defined as a person’s proclivity to act in a certain way. ⁴⁵ The intent to use OFDS denotes a consumer’s desire to purchase food and beverages through online delivery platforms. ¹⁰ 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. ⁴⁶ Consumers who are pleased and content with their online purchase experience are expected to continue doing so. ¹²

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:

Figure 1. Research model.

Ethics

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

Questionnaire development

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 ^{10 , 12 , 14 , 18 , 22 , 47 – 49} and recorded in Table 1. All items were measured based on a five-point Likert-type ^{50 , 51} ranging from 1 (“Strongly Disagree”) to 5 (“Strongly Agree”).

Data collection

In this study, purposive sampling method was applied ^{52 – 54} because the selected samples are more representative of the population. It is commonly used by researchers for similar studies, such as a recent study on the intention to use OFDS among consumers in Malaysia, which gathered 224 samples for data analysis. ¹⁰ Questionnaire was sent to potential respondents who were close contacts (relatives, friends and students) of the authors of this study, and they were invited through email, Facebook and WhatsApp, between 22 March 2021 and 18 April 2021. A screening question was included to ensure that only participants with experience using OFDS were able to complete the survey.

A primary dataset of 256 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 minimum sample size of 191 is determined according to the guideline of Hair et al., ⁵⁵ with a maximum of 4 arrows pointing at a latent variable and minimum R² of 0.10.

Hypotheses approach

Demographic background of respondents is presented descriptively and graphically. Consistent Partial Least Square (PLSc) approach ^{55 – 57} 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. ⁵⁸

Profile of survey respondents

Table 2 shows the demographic profile of 256 respondents. ⁵⁹ All of them has experienced using OFDS and mostly are young adults between the age of 18 to 25 years old (40.63%). 68.75% preferred 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 (76.56%) and GrabFood (70.70%) are the most popular in Malaysia because consumers deemed that the platforms are user-friendly. ¹⁵ However, social media platforms such as Instagram are more suitable for promoting food rather than ordering. ⁶⁰

Figure 2. Distribution of online food delivery services platform.

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 the dining preference among the OFDS users based on age, gender, marital status and personal income level. Personal income levels were classified as B40 (less than RM4,850), M40 (RM4,851–RM10,970), and T20 (above RM10,971). As expected, the majority of OFDS users preferred to enjoy their food at home during pandemic especially the elderly or married adults prefer to enjoy their food at home (>80% for age group above 41 years old; married 73%). Although 71.88% of the users were earning a low income, they still preferred to use OFDS and dine at home (71%) compared to higher income respondents. This indicates COVID-19 pandemic has significantly changed people’s lifestyles and has became a new norm.

Reliability and validity

Table 6 shows Cronbach’s alpha ^{61 , 62} and composite reliability (CR) ^{55 , 63 , 64} 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. ⁵⁵ The average variance extracted (AVE) indices ⁶⁵ are greater than 0.5 for each variable, indicating no convergent validity problems.

In Table 7 Fornell-Larcker criterion, ^{65 , 66} 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_0.90 criterions. ⁶⁷

Consistent partial least square (PLSc) path modelling approach

Six hypotheses were tested using PLSc, ⁵⁷ 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.

Figure 3. Part coefficient and outer loading.

To test the model quality, effect size, f² and predictive relevance, Q² is measured. All the f² values are greater than 0.02 except the path of PEOU→ATT, which indicates no effect toward ATT. ^{68 , 69} The predictive relevance, Q² is used to determine the predictive power of dependent variables. All the Q² values are greater than 0.35. ⁷⁰ This means there is substantial predictive relevance in this model.

Limitations

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.

Underlying data

Figshare: Online Food Delivery Service.

DOI: http://doi.org/10.6084/m9.figshare.14772951. ⁵⁹

This project contains the following underlying data:

Extended data

Figshare: Online Food Delivery Service Questionnaire 2021

DOI: http://doi.org/10.6084/m9.figshare.16566414. ⁸¹

This project contains the following extended data:

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