Analysis of Consumer Preferences in Choosing Online Food Delivery Services in Indonesia
Aulia Salsabila Nurcahyani1*, AMA Suyanto1
1 Faculty of Economics and Business, Telkom University, Bandung, Indonesia
*Corresponding Author: [email protected], [email protected]
Accepted: 15 May 2022 | Published: 1 June 2022
DOI:https://doi.org/10.55057/ajrbm.2022.4.2.1
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Abstract: Current technological developments, followed by internet connectivity, are driving the emergence of online food delivery. Now, online food delivery has become a daily necessity.
To meet these needs, many companies offer online food delivery services with a variety of services. This shows that consumers are faced with various services or attributes provided by each online food delivery service, so consumers will be more selective in choosing according to their preferences. Therefore, companies must be able to know and understand the preferences and needs of consumers to win against the competition in this industry. This study aims to determine the combination of attributes that are most preferred by consumers in choosing online food delivery services, the most important attributes for consumers in choosing online food delivery services, and the level that is most useful for consumers in choosing online food delivery services. The data collection tool in this research uses online questionnaires to 390 respondents who are online food delivery consumers. The non-probability sampling method was used in this research. The analysis technique used is conjoint analysis. The results found that of the 8 cards, card number 1 was the most preferred combination card by the consumers, which consists of fast delivery, multi orders, COD payment methods, discounted prices, and many restaurant choices. Then the most important attribute to be considered is the time and delivery attribute with a value of 34,934, and the most useful level for consumers is the 24-hour service available with a usability value of 0.290. It is hoped that this research can be a reference for business people in online food delivery in Indonesia to be able to provide fast delivery services to consumers and provide promos in the form of discounted prices so that they can become consumers' choices.
Keywords: online food delivery, preferences, conjoint analysis
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1. Introduction
In the modern era like today, increasingly developing technology makes it easier to do everything. This technological development, followed by internet connectivity, has driven the emergence of online food ordering and delivery through marketplace applications (platforms that sell goods including food, such as Shopee) and food aggregator applications (platforms that offer access to several restaurants and handle logistics delivery, such as GoFood and GrabFood (Aprilianti & Amanta, 2020). The Head of Badan Pusat Statistik (BPS), Suhariyanto, stated that recent advances in technology and ease of communication have changed people's spending patterns, especially in terms of food. BPS sees that people now tend to buy food through applications digitally rather than cooking or going alone.
Based on the results of a survey conducted by Statista Global Consumer Survey which shows that restaurant and food delivery are the most ordered online services. This proves that currently food delivery services are increasingly in demand because they are considered more practical and faster. In addition, food delivery services are also a solution to meet food needs when people have busy activities, weather barriers, and other obstacles (cnbcindonesia.com, 2021).
Based on the results of research conducted by Nielsen in the Big 6 of Indonesia, it shows that the most widely used online platform for ordering food online is GrabFood or GoFood or similar applications.
The number of users of food delivery services through delivery platforms will experience a significant increase in 2021. This significant increase is due to the presence of the Covid-19 pandemic which has changed people's behavior and habits. Before the pandemic, people ordered delivery food only occasionally, but now it has become a routine that is done repeatedly. This is because during the pandemic that led to the emergence of social distancing rules, the PSBB and PPKM policies made consumers unable to eat outside the home and changed consumer behavior which made food ordering services something indispensable for their daily needs (Yuswohady, 2020).
Currently, there are many companies providing similar services, so the competition is getting tougher. GoJek and Grab, which are now developing in Indonesia, are the people's choice to order food from various desired restaurants. These two big startups also continue to innovate to make consumers more comfortable and satisfied with the services they provide, including food delivery. But now a newcomer has emerged, namely Shopee Food. Shopee Food is an online food delivery service owned by Shopee which started operating in April 2021. Based on the results of a survey conducted by the Katadata Insight Center (KIC) regarding Gen Z's preferences for digital services, it shows that GrabFood and GoFood are still the most popular online food delivery services frequently used (Katadata.co.id, 2021).
The three delivery services have its own advantages and services. Each of the advantages and services can become a stimulus for the community so that it can lead to someone's process in choosing to use online food delivery services. The increasing number of online food delivery service providers, it shows that consumers are increasingly faced with a variety of diverse services from each online food delivery service. The variety of services provided by all online food delivery services makes consumers are more selective in choosing. Consumers will feel satisfied if they can choose the desired service. The selection of online food delivery services will also provide several choices of attributes with the level or dimensions of each attribute to consumers.
Based on the description of the background above, the researcher wants toknowing the most important attributes for consumers in choosing online food delivery services, then knowing what level or level in each attribute is actually the most useful for consumers, and knowing what online food delivery stimuli are most liked by consumers. Because knowing what attributes, levels and stimuli are preferred by consumers, it will help business actors in the online transportation industry to be able to know exactly what consumers like and can fulfill consumer desires according to their preferences.
2. Literature Review
2.1 Marketing Management
According to Kotler & Keller (2016:27) Marketing management is the art and science of targeting and attracting markets and retaining and growing customers by creating, communicating, and delivering superior customer value.
2.2 Service
Rizal (2020:184) explains that services are activities or benefits that are intangible and do not lead to the ownership of anything that one party can provide to another. Services have four main characteristics, namely intangible (intangibility), can not be separated (inseparability), varied (variability), and not durable (perishability).
2.3 Consumer behaviour
Kotler & Keller (2016:179) explains that consumer behaviour is the science of how individuals, groups, and organizations meet their needs and wants by selecting, buying, using and disposing of goods, services, experiences or ideas.
2.3.1 Consumer Behavior Model
According to Kotler and Keller (2016: 187) the model of consumer behaviour is described as follows:
Figure 1: Consumer Behavior Model
2.3.2 Consumer Behaviour Factors
Simarmata et al (2021:6) explained that in making purchasing decisions, consumers are influenced by four factors. These factors come from internal and external. These factors are cultural factors, psychological factors, personal factors, and social factors.
2.4 Purchase Decision Process
According to Kotler and Keller (2016: 195) the stages of the purchasing decision process are described as follows:
Figure 2: Consumer Decision Making Process
2.5 Alternative Evaluation
Evaluation of alternatives is the third stage of the purchasing decision process, where at this stage consumer preferences are formed. As explained by Setiadi (2019:15) that alternative
evaluation is the stage where consumers form preferences using the information to evaluate brands contained in the choice set.
2.6 Attribute
Kotler & Armstrong (2018:249) explains that attributes are the delivery of the benefits to be offered in product or service development. This research uses five attributes adopted from research conducted by Rathore & Chaudhary (2018) and Das (2018), namely time and delivery, flexibility, ease of payment, price promotion, and restaurant choice.
2.7 Level
The definition of level according to Widayat (2018:71), Sudaryono (2017: 352), and Santoso (2017: 270) is part of the attribute of an object that indicates the value or level of each attribute.
This research uses 11 levels, which consist of:
1) Fast, slow, and available 24 hours, which explains the attributes of time and delivery.
2) Multi orders and being able to place orders in different locations explain the flexibility attribute.
3) Cash on Delivery (COD) and E-Wallet explain the ease of payment attributes.
4) Discounts and free shipping explain the attributes of price promotion.
5) Many choices of restaurants and a few choices of restaurants explain the attributes of choice of restaurants.
2.8 Consumer Preference
Consumer preferences appear in the evaluation of alternatives in the purchasing decision process, where consumers are faced with various choices for products and services with different attributes. The definition of consumer preference according to Sumarwan (2019: 234) is an assessment made by a person based on likes or dislikes of a service or product that is consumed.
2.9 Framework
The framework for this research was adopted from research by Rathore & Chaudhary (2018) and Das (2018) conducted in India. The reason for the two research journals in choosing attributes is because the two studies have similarities in this study, they both discuss consumer preferences for ordering food online. To facilitate understanding of the framework of thought in this research can be seen in the following figure:
Description: *Attributes used in research
Figure 3: Thinking Framework
3. Methodology
3.1 Population and Sample
The population in this study are users of online food delivery services spread throughout Indonesia. Determination of the number of sample members in this study used a non- probability sampling technique. The type of non-probability sampling technique used in this research is purposive sampling. The total population in this study is unknown, so the calculation of the number of samples using the Cochran formula produces a minimum sample size of 385 respondents.
3.2 Data Collection
In this study, the data collection technique used was a questionnaire in an online survey via a google form, which was distributed to users of online food delivery services through social media such as Instagram, Twitter, and WhatsApp.
3.3 Data Analysis Techniques 3.3.1 Descriptive Analysis
Djaali (2021:112) explains that descriptive analysis is a type of data analysis intended to present the state or characteristics of the sample data for each research variable singly. In this study, descriptive analysis was carried out using cross tabulation to determine the characteristics of the most dominant respondents.
Yamin (2021:36) also explains that cross tabulation is a descriptive analysis of the shared frequency distribution, namely crossing between row and column variables in tabular form.
Cross tabulation analysis or cross tabulation in this study was carried out on respondent data, namely gender, age, occupation, and income, using the layer cross tabulation method or the multiplication of three variables with the help of SPSS version 25 software.
3.3.2 Multivariate Analysis
Statistical analysis used in this study is multivariate analysis with conjoint analysis techniques with the aim of knowing consumer preferences in the combination of attributes and levels of online food delivery service users. In carrying out data processing, researchers used the help of IBM SPSS version 25 software. Wijaya & Budiman (2016:2) explain that multivariate analysis is one type of statistical analysis used to analyze data consisting of many variables, both independent variables and many dependent variables.
Multivariate analysis techniques are basically classified into two, namely dependent analysis techniques and interdependent analysis techniques. The dependent analysis technique is an analysis technique that has variable inequality, meaning that a variable or set of variables is identified as a dependent variable that is predicted or explained by other variables known as independent variables. While the interdependent analysis technique is an analytical technique that has variable equality, meaning that in this analysis technique there is no single variable or group of variables that are defined as independent or dependent. The interdependent technique involves simultaneous analysis of all variables (Hair et al, 2014:21).
3.3.3 Conjoint Analysis
Conjoint analysis according to Sudaryono (2017: 352) is one of the multivariate techniques devoted to understanding how respondents form their preferences for a product or service. The purpose of conjoint analysis is to find out how a person's perception of an object consists of one or more parts (Santoso, 2018: 299).
3.3.4 Conjoint Analysis Stages
According to Hair et al (2014:335) conjoint analysis consists of 5 stages, namely:
1) Purpose of Determining Conjoint Analysis
The purpose of the conjoint analysis in this study is to determine consumer preferences in choosing online food delivery services in Indonesia. The results of this study are expected to be used by online food delivery service companies to prioritize attributes that consumers think are important to increase sales.
2) Conjoint Analysis Design
This study uses 5 (five) attributes with 11 (eleven) levels. Based on this, the possible stimuli are 3x2x2x2x2 = 48 stimuli. According to Hair et al. (2014:370), if the number of stimuli that may appear is too much, it can be reduced by the formula:
Minimum number of stimuli = Number of levels - number of attributes + 1
= 11 - 5 + 1
= 7
Stimulus card making in this study used SPSS version 25 with an orthogonal design method and produced 8 stimuli as follows:
Table 1: Attribute and Level Card Combinations
Card Time and
delivery Flexibility Ease of payment
Price promotion
Choice of restaurants 1 Fast Multiple orders COD (Cash on
Delivery) Discounts Lots of restaurant choices 2 Fast Can order at other
locations E-Wallet Free shipping Few restaurant choices 3 Fast Can order at other
locations E-Wallet Discounts Lots of restaurant choices 4 Available 24
hours Multiple orders E-Wallet Free shipping Lots of restaurant choices 5 Slow Multiple orders E-Wallet Discounts Few restaurant
choices 6 Slow Can order at other
locations
COD (Cash on
Delivery) Free shipping Lots of restaurant choices 7 Fast Multiple orders COD (Cash on
Delivery) Free shipping Few restaurant choices 8 Available 24
hours
Can order at other locations
COD (Cash on
Delivery) Discounts Few restaurant choices 3) Assumptions of Conjoint Analysis
In this study, the conjoint analysis technique used is the traditional model and the full profile as the method of presentation. From the profile that has been provided, the respondent will provide a rating (rating metric) on the existing profile from the data. Then after getting the data, the researcher used SPSS version 25 software to find out consumer preferences based on the processed data.
4) Conjoint Model Estimation
The estimation technique used in this study is the traditional estimation method, which is an estimate of the order of preference using a form of analysis of variance designed
specifically for ordinal data (Hair et al., 2014:375). Meanwhile, the estimation model used is goodness of-fit. Lilik et al. (2021:101) explain that the goodness of fit is a test carried out by testing the correlation between the conjoint results and the respondent's assessment indicated by the Pearson and Kendall scores to determine the reliability of each attribute combination and measure predictive accuracy.
5) Interpretation of Results
The results of this study are to determine consumer preferences by looking at the number of importance scores and utility scores. Santoso (2017: 283) explains that the value of importance is a number that refers to the value of the respondent's interest in each of the existing attributes. The higher the value, the more important these attributes and levels are to consumers. Meanwhile, the utility score according to Santoso (2017:270) is the value of the opinion of each respondent which is expressed in numbers and becomes the basis for the calculation.
6) Conjoint Result Validation
After the conjoint analysis is completed and provides the results of the analysis, the next step is to validate and apply the results of the conjoint analysis if necessary. Validation is carried out if the predictive accuracy value shows a lack of reliability from the combination of attributes used.
3.3.5 Customer Value Index
According to Best (2013:147) the customer value index can be calculated by adding up each attribute level utility value in each attribute combination. The calculation of the customer value index in this study is as follows:
𝐶𝑉𝐼 = ∑𝑛𝑖=1𝑈𝑡𝑖𝑙𝑖𝑡𝑦 𝑉𝑎𝑙𝑢𝑒 𝐴𝑡𝑡𝑟𝑖𝑏𝑢𝑡𝑒 4. Discussion and Conclusion
4.1 Cross Tabulation Analysis of Respondent Data
Based on the results of the crosstabulation carried out using SPSS version 25 software, the results showed that the most dominant respondents in this study were students aged 18-21 years who were female and had an average monthly income of Rp. 1,000,000 – Rp. 2,000,000. This is in accordance with the data presented by katadata.co.id (2021) that the largest number of online food ordering users in Indonesia is Generation Z. So, it can be said that Generation Z is the target market for online food delivery. The results of the cross tabulation can be seen in the following table:
Table 2: Results of Cross Tabulation of Gender – Occupation – Income
Table 3: Cross Tabulation Results Gender - Occupation - Age
. 4.2 Conjoint Analysis Results
4.2.1 The Importance Value of Each Attribute
The value shows the value of the importance of each attribute according to consumers. In this study, the importance value shows the assessment given by consumers on each attribute of online food delivery services. The results of the importance value can be seen in the following graphic image:
Figure 5: Graph of the Importance of Each Attribute
Based on the results of survey data from 390 respondents who have been processed using SPSS version 25, it shows the results as shown in table 4.2 above. From the table, it can be seen that the time and delivery attribute is the attribute that has the highest importance value of the other four attributes, which is 34,934. Then followed by the ease of payment attribute with an importance value of 15,804, then the choice of restaurants attribute with an importance value of 17,020, then the flexibility attribute with an importance value of 15,804, and finally the price promotion attribute with an importance value of 15,104.
Based on this explanation, it can be concluded that of the 390 respondents who participated in filling out the questionnaire, they considered that the time and delivery attribute was the most important attribute and the price promotion attribute was the least important attribute.
4.2.2 Results of the Utility Value of Each Level
The utility value is the value possessed by each level of each attribute generated after processing survey data using SPSS version 25. In this study, the utility value can be used to show the value of consumer preference for an attribute level of online food delivery services.
The results of usability values can be seen in the following table:
Table 4: Results Utility Value
Utilities
Utility
Estimate Std. Error
Time_and_delivery Fast .257 .005
Slow -.547 .006
Available 24 hours .290 .006
Flexibility Multiple orders .176 .004
Can order at other locations
-176 .004
Ease_of_payment COD (Cash on
Delivery)
-.123 .004
E-Wallet .123 .004
Price_promotion Discounts .168 .004
Free shipping -.168 .004
0 5 10 15 20 25 30 35 40
Time and delivery
Flexibility Ease of payment
Price promotion
Choice of restaurants
The Importance Value of Each Attribute
Attribute
Choice_of_restaurants Lots of restaurant choices
.161 .004
Few restaurant choices -.161 .004
(Constant) 3.889 .004
The utility value can be seen in table 4.3. The utility value shows consumer preferences from each level on the online food delivery attribute. In the data obtained from 390 respondents, it can be seen that the utility value or the highest usability value is from the time and delivery attribute at the level of available 24 hours of service with a utility value of 0.290, followed by a fast level on the time and delivery attribute with a utility value of 0.257, then the multi-order level on the flexibility attribute with a utility value of 0.176, then the price discount level on the price promotion attribute with a utility value of 0.168, then the level of many choices of restaurants on the choice of restaurants attribute with a utility value of 0.161, and finally the E- Wallet level on the ease of payment attribute with a utility value of 0.123.
Based on this explanation, it can be concluded that the level of the attribute that has the highest utility value is the level of available 24 hours of service on the time and delivery attribute with a utility value of 0.290. Meanwhile, the level that has the lowest utility value is the slow level on the time and delivery attribute with a utility value of -0.547.
4.2.3 Customer Value Index Analysis
The customer value index analysis aims to determine the value of consumer preferences for each combination card which is indicated by the total utility value or usability value of each combination card. The utility value is obtained from data processing using SPSS version 25 with all the stimuli formed in as many as 8 combination cards. To get the value of the customer value index in choosing online food delivery services, calculations are carried out by adding up each utility value contained at each level in each combination card. The results of the customer value index can be seen in the following graphic image:
Figure 6: Graph of the Utility Value of Each Stimulus
Table 4.4 shows the results of the calculation of the customer value index (CVI) of each card combination of attributes for online food delivery services. The level of customer value index that has the highest value is card number 1 of 0.639. So it can be said that card number 1 is the most preferred stimulus by consumers in online food delivery services, and card number 6 is the card that is least liked by consumers in online food delivery services. The combination of card 1 is as follows:
-1 -0.5 0 0.5 1
Card 1 Card 2 Card 3 Card 4 Card 5 Card 6 Card 7 Card 8
The Utility Value of Each Stimuli
Stimuli Card
Table 5: Combination of Cards No. 1
Attribute Level Usability
Value
Time and delivery Fast 0.257
Flexibility Multiple orders 0.176 Ease of payment COD (Cash on
Delivery)
-0.123 Price promotion Discounts 0.168
Choice of
restaurants
Lots of restaurant choices
0.161 Total Value of CVI Stimuli 1 0.639
The most preferred level on the time and delivery attribute is the fast level. This shows that consumers like fast delivery. An example of the development of fast delivery services as has been carried out by freight forwarders starting from regular services with a duration of 3 days, then one-day service with a duration of 1 day, then same day service, namely delivery on the same day when the order was made, then service instant ie delivery made a few hours after the order is made. As mentioned by Rathore and Chaudhary (2018) in India that time is the most important factor in any form of business or service. On-time delivery is a key performance index that is often used to calculate delivery performance.
Based on the flexibility level attribute, the most preferred attribute is multi order. This shows that consumers like online food delivery services that provide multi-order services, meaning that consumers can make many orders at one time so they can save more time when ordering food.
In consumer choices related to the combination of attributes and levels in table 4.7 it is found that the cash on delivery level is not a useful level for respondents because it has a negative utility value. So it can be said that respondents like the payment method of using e-wallet.
Yulianti (2021) explained that the use of e-wallet in Indonesia is increasing, especially during the Covid-19 pandemic. The ease of making payment transactions using a digital wallet is the reason Indonesians prefer transactions using a digital wallet or e-wallet.
The most preferred level in the price promotion attribute is the discount level. This shows that consumers prefer promos in the form of discounts rather than free shipping. As mentioned by Rathore and Chaudhary (2018) in India that price is the main determinant of consumer choice.
Consumers prefer low prices in buying food online. Because everyone likes to save money and get the most out of what they pay for and hence these special offers in the form of discounts can attract consumers.
Based on the attribute of choice of restaurants, the most preferred attribute level is the level of many choices of restaurants. This shows that with many choices of restaurants, consumers can more easily choose the food they want. As stated by Das (2018) in India that online food delivery services that offer a large selection of restaurants will be preferred by consumers.
5. Conclusion and Recommendation 5.1 Conclusion
Based on the results of research on the analysis of consumer preferences in choosing online food delivery services in Indonesia, several conclusions can be drawn which are expected to provide answers to the problems formulated in this study, which are as follows:
1) The most dominant picture of respondents who like online food delivery are students aged 18-21 years who are female and have an average monthly income of IDR 1,000,000 - IDR 2,000,000.
2) The time and delivery attribute is the most important attribute of other attributes for consumers in online food delivery services in Indonesia with the highest importance value of 34,934. That is, in online food delivery services, consumers assess the importance and consider the attributes of time and delivery.
3) The level of service available 24 hours is the most useful level for consumers in online food delivery services in Indonesia with the highest usability value of 0.290. This means that in online food delivery services, consumers prefer online food delivery services where the waiters are available 24 hours, so that consumers can order food at any time without being limited by time.
4) The combination of attributes that are most favored by consumers are the attributes of time and delivery, flexibility, ease of payment, price promotion, and choice of restaurants with fast levels, multi orders, COD (cash on delivery), discounted prices, and many restaurant choices.
5.2 Recommendation 5.2.1 Practical Aspect
Suggestions for online service industry companies are to provide delivery services to consumers that are faster than what has been there so far. For example, food delivery services are available for at least 20 minutes in one city (depending on distance and road conditions) and at least one day for out of town. Then consumers also like promos in the form of discounts, so in order to become a consumer choice, companies can provide promos in the form of discounts on orders made by consumers.
5.2.2 Academic Aspect
This study has several limitations, including the use of conjoint analysis as an analytical technique and the limited number of objects of study. Therefore, it is recommended for further researchers in order to complete the perfection of this research to become a generally accepted science, and it is recommended as follows:
1) Can add research attributes to make it more varied, and similar research can be done regularly because consumer needs can change over time and with the progress of the times.
2) Conduct research on other online services such as online transportation services, goods delivery services, and online shopping services.
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