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“AN ANALYSIS ON FACTORS AFFECTING THE ONLINE FOOD PURCHASING DECISION”

ABSTRACT

Food is considered to be the basic necessity of every living being. Human Beings have evolved from consuming raw food and after invention of fire we have cooked and consumed food after civilization hotels were born and human beings purchased from them and now because of the evolution in technology we order food online. Our work has been carried out to investigate the "An Analysis of factors affecting online food purchases”. Questionnaire was designed and circulated among the consumers in order to collect the relevant information. The research shows that social influence, audio and visual advertisement, convenience, price value, reviews and ratings, and online tracking positively influences the intentions towards online food ordering.

1. INTRODUCTION

Food delivery in recent days is supported by digital apps which has emerged as one of the fastest- growing developments in the e-commerce space. Consumers have the privilege to choose from a variety of cuisines, anywhere, anytime from a range of food providers listed in the e-commerce space. Added attractions like no minimum order value and the multitude of payment options like net banking, digital wallets, and cash on delivery all have increased the consumer convenience. Companies have remodeled their business strategies on a modern-day digital platform to keep pace with the customer’s changing needs and preferences.

2. PROBLEM STATEMENT

The research aims to identification and analysis of the factors that influences the purchasing decision of a consumer in the online food delivery applications. We identified eight attributes that are likely to affect the customer’s attitude towards online food ordering namely social influence, audio and visual advertisement, convenience, price value, reviews and ratings, and online tracking food ordering. Questionnaire was designed and circulated in order to identify the factors that affect the food purchasing decision of the consumer.

3. LITERATURE REVIEW

The explosive growth of the Internet has influenced online retailing and Ecommerce development in general. With the internet and technology becoming more complicated, the marketing, accounting, advertising, educating habits and methods are also changing simultaneously. To safeguard their existence in the face of harsh competition, food retailers have shifting their attention from goods to service. In this development, mobile services have emerged as suitable venues for intensifying companies' service orientation (Saarijärvi et al., 2014).

Online food delivery market in India, comprising of aggregators and internet kitchens, showed a spectacular growth in recent years. The market in India is of the tune of 15 billion USD and is all set for an exponential growth phase (Kanteti, 2018).

The sector is very competitive, and growth of online food ordering via digital platform made the businessmen and entrepreneurs awake and took notice. Some popular ‘food aggregators’ like Zomato, Swiggy, Food Panda, and UberEats are feeding the Indian cities online and making decent profits. Various food delivery web portals and mobile apps offer the rational Indian customer a direct comparison between the prices and rating of different food outlets and restaurants serving the same dishes and to choose among the various options (Thamaraiselvan et al., 2019)

TAM puts forward the perceived ease of use and perceived usefulness as two main factors while trying to explain the attitude directly and behavioral intention indirectly towards using a technology. Besides it is referred that trust and external influences are some of the key factors that influence customers' attitude towards online shopping. (Alagoz & Hekimoglu, 2012)

The TAM, model of IT Continuation and Contingency Framework when used to investigate the relationship between external factors and intention towards OFD, the results demonstrated the relationship between latent variables towards attitude and behavioral intentions. This means that with a better perception of post-usage usefulness and convenience motivation, a person's attitude towards OFD services will improve significantly, thereby increasing intentions to use OFD services.

There is a positive relationship between hedonic motivation, prior purchase and convenience motivation. Furthermore, customers are attracted to technology that can provide them convenience through saving time and effort. (Yeo et al., 2017)

The business of delivering restaurant meals to the home is undergoing rapid change as new online platforms race to capture markets and customers across most of the metropolitan cities in India. The paper aims to investigate online food aggregators (OFA’s) by proposing and empirically testing mobile app attribute-conversion model, to examine how mobile app attributes of online food aggregators inﬂuence the purchase decision of a consumer and subsequently lead to conversion. (Anuj Pal Kapoor, Madhu Vij - 2018)

The results from three experimental studies found that restaurant-generated information cues in mobile apps (i.e., message sidedness, information load, and image cues) influenced diners’ expectations differently depending on the levels of need for cognition. Findings of this study provide important implications for the restaurant industry to design marketing messages more effectively via O2O food service apps. (Xianying Xua, Yinghua Huang - 2019)

Prior research has mostly examined consumer attitudes toward online services/retailing in general and a few researchers have addressed consumer experiences with online food delivery (OFD) services. The purpose of this study is to examine the structural relationship between convenience motivation, post-usage usefulness, and hedonic motivation, price saving orientation, time saving orientation, prior online purchase experience, consumer attitude and behavioural intention towards OFD services. (Vincent Cheow Sern Yeo, See-Kwong Goh, Sajad Rezaei - 2019)

The objective of this research is to explore the motivated consumer innovativeness in the context of drone food delivery services. More specifically, it was hypothesized that four dimensions of motivated consumer innovativeness, which include functionally, hedonically, cognitively, and socially motivated consumer innovativeness, play an important role in the formation of attitude and behavioral intentions. In addition, this study proposed that the attitude positively affects desire and behavioral intentions. (Jinsoo Hwang, Hyun Kim, Woohyoung Kim- 2018)

Indian food industry is composed for huge growth, increasing its contribution to world food trade

every year. In India, the food sector has emerged as a high-growth and high-profit sector due to its immense potential for value addition, particularly within the food processing industry. The online food sector which has been written off not too long has started witnessing revival over the past one year in Indian market. This new format of online ordering and home delivery has gained a lot more customers. The orders started to grow after every quarter every year and importantly companies like Swiggy and Zomato are entering into tier-2 cities of the country and still this model of business has to yet to semi-urban areas and rural areas. Thus, there is a massive growth opportunity for the online food retailers the only challenge is to Adapt to various tastes and preferences of the consumers. (Syamalarao, 2021)

The recent development of the net has boosted the extension of on-line food services by facultative individuals to go looking, compare costs and handily access these services. On-line ordering has been a growing as a requirement have factor for the eating place business. On-line ordering has taken the food business by a storm. Technology puts a buried impact on the business industry, technology

has changed the entire frame of restaurant industry, and it will continue doing a great job. A technically developed online food ordering system has changed the restaurant’s culture drastically and gives a new amazing comfort zone to the people across the globe. (Gupta, 2019)

Organizations in today's time especially in the e-commerce business have either a website, mobile app or both, to give its customers accessibility to their products and services. There are plenty of attributes for a mobile app or website, which inﬂuences a consumer's purchase intention. (Kapoor & Vij, 2018)

The feature that attracts consumers the most is Doorstep Delivery at any place at any time. Consumers are mostly motivated when they receive any Rewards & Cashbacks followed by loyalty points or benefits. The factors that block customers to try the online food delivery apps are Bad Past Experience, reviews, and word of mouth. (Saxena, 2019)

5. HYPOTHESIS

H1: Social Influence

• H0: Social Influence does not positively affect the customer’s intention towards online food ordering

• HA: Social Influence positively affects the customer’s intention towards online food ordering

H2: Audio and visual advertisement

• H0: Audio and visual advertisement does not positively affect the customer’s intention towards online food ordering

• HA: Audio and visual advertisement positively affects the customer’s intention towards online food ordering

H3: Convenience

• H0: Convenience does not positively affect the customer’s intention towards online food ordering

• HA: Convenience positively affects the customer’s intention towards online food ordering

H4: Price value

• H0: Price value does not positively affect the customer’s intention towards online food ordering

• HA: Price value positively affects the customer’s intention towards online food ordering

H5: Reviews and ratings

• H0: Reviews and ratings do not positively affect the customer’s intention towards online food ordering

• HA: Reviews and ratings positively affect the customer’s intention towards online food ordering

�� H6: Online tracking

• H0: Online tracking does not positively affect the customer’s intention towards online food ordering

• HA: Online tracking positively affects the customer’s intention towards online food ordering

6. RESEARCH METHODOLOGY

6.1 Research Objective

The research aims to identify the factors influencing the purchase decision of a consumer over the online food delivery applications and subsequently lead to conversion.

Social influence

SI1 People who are important to me think that I should use mobile food order apps.

SI2 People who influence my behavior think that I should use mobile food order apps.

SI3 People whose opinions that I value prefer that I use mobile food order apps.

(Alalwa n, 2020)

Audio and video advertisement

AD1 Advertisements influence my buying behavior

AD2 I read the comments after I am impressed by the advertisement AD3 I think that advertisement directed to my mobile phone has some advantages compared to the regular marketing channels (TV,

Radio, and Mail)

(Xu & Huang, 2019)

Convenience

CN1 Using the food delivery app would be convenient for me CN2 Food delivery app would allow me to order food anytime

CN3The food delivery app would allow me to order food to any place

(Cho et al., 2019)

Price

PR1 I can save money by using food delivery apps for purchasing foods by comparing the prices offered at different online stores.

PR2 I like to search for cheap deals at different online stores when I purchase foods through food delivery apps.

PR3 When I order food through the delivery app, the food is reasonably priced

(Alalwa n, 2020)

Reviews and ratings

RR1 Customer ratings provided in mobile food order apps have improved my understanding of the quality of the product

RR2 The information provided in online reviews of mobile food order apps was helpful to evaluate the product.

RR3 The information from online reviews provided in mobile food apps

were relevant to my needs.

(Alalwa n, 2020)

Online Tracking

OT1 I like the food delivery app’s provision to know about the estimated time of delivery

OT2 I like the food delivery app’s provision for locating the delivery address on the map

OT3 I like the food delivery app’s provision for tracking the delivery person on real-

time

(Alalwa n, 2020)

Intention towards online food delivery

IN1 I desire to use the delivery app when i purchase food IN2 I intend to continuously use food delivery apps

IN3 I am strongly in favour of ordering food through the food delivery app

(Cho et al., 2019)

7. Hypothesis Testing: (Regression table)

Input

Output

Variance

p-value

Accept/Reject

SI

IN

0.420

0.000

Accept

AD

IN

0.340

0.000

Accept

CN

IN

0.462

0.000

Accept

PR

IN

0.416

0.000

Accept

RR

IN

0.372

0.000

Accept

OT

IN

0.380

0.000

Accept

7.1 Hypothesis testing Interpretation:

● Hypothesis 1: From the above table it can be inferred that P value of the input variable 'SI’ is 0.00 which is less than the significance level, 0.05. Therefore, we reject the null hypothesis and it also confirms that the variable is statistically significant.

● Hypothesis 2 : From the above table it can be inferred that P value of the input variable 'AD’ is 0.00 which is less than the significance level, 0.05. Therefore, we reject the null hypothesis and it also confirms that the variable is statistically significant

● Hypothesis 3: The data showed us that the P value of the input variable ‘CN’ is 0.00 which is lesser than 0.05. So, we reject the null hypothesis and it also confirms that variable pre purchase information is statistically significant

● Hypothesis 4: From the above table it can be inferred that the P value of the input variable 'PR’ is 0.00 which is less than the significance level, 0.05. Therefore, we reject the null hypothesis and it also confirms that the variable is statistically significant

● Hypothesis 5: The data showed us that the P value of the input variable ‘RR’ is 0.00 which is lesser than 0.05. So, we reject the null hypothesis and it also confirms that variable pre purchase information is statistically significant

● Hypothesis 6: The data showed us that the P value of the input variable ‘OT’ is 0.00 which is lesser than 0.05. So, we reject the null hypothesis and it also confirms that various

purchase information is statistically significant

7.2 Interpretation

After the following observation, the P all input variables are less than 0.05, meaning we accept all these cases. Hence, we can conclude that there is a relation between input variables Social influence, trust, advertisements, convenience, price, ratings and reviews, user interface, online tracking and the output variable Intention to purchase.

Factor analysis

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling

Adequacy.

.956

Bartlett's Test of Sphericity

Approx. Chi-Square

6715.074

df

276

Sig.

.000

Intepretation: Validity test

The Kaiser–Meyer–Oklin (KMO) measure of sampling adequacy is a statistic calculated for individual and multiple variables and represents the ratio of the squared correlation between variables to the squared partial correlation between variables. Kaiser suggests that values greater than 0.5 should be accepted.The initial solution of our factor analysis revealed a KMO value of 0.956, which is an ideal condition met in our case

Rotated Component matrix

Rotated Component Matrix

Component

1

2

3

SI1

.856

SI2

.776

SI3

.846

AD1

.736

AD2

.687

AD3

.616

CN1

.704

CN2

.688

CN3

.693

PR1

.893

.754

PR2

.738

PR3

.743

.697

RR1

.738

RR2

.697

RR3

.754

OT1

.747

OT2

.800

OT3

.798

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 6 iterations.

Interpretation:

· The first component is highly correlated with OT2.

· The second component is highly correlated with PR1 and PR3.

· The third component is highly correlated with SI1 and SI3.

This shows that in the further analysis it is much better to focus on OT2, PR1, PR3, SI1 and SI3.

8. Conclusion

Our study focuses on the six attributes that are likely to affect the customer’s attitude towards online food ordering namely social influence, trust, audio and visual advertisement, convenience, price value, reviews and ratings, user interface and online tracking. We conclude that the above factors have a positive impact on the intentions towards the online food ordering

REFERENCES

Alagoz, S. M., & Hekimoglu, H. (2012). A Study on Tam: Analysis of Customer Attitudes in Online Food Ordering System. Procedia - Social and Behavioral Sciences, 62, 1138– 1143. https://doi.org/10.1016/j.sbspro.2012.09.195

Gupta, M. (2019). A Study on Impact of Online Food delivery app on Restaurant Business special reference to zomato and swiggy. 6(1), 889–893.

Kapoor, A. P., & Vij, M. (2018). Technology at the dinner table: Ordering food online through mobile apps. Journal of Retailing and Consumer Services, 43(September 2017), 342–351. https://doi.org/10.1016/j.jretconser.2018.04.001

Saarijärvi, H., Mitronen, L., & Yrjölä, M. (2014). From selling to supporting - Leveraging mobile services in the context of food retailing. Journal of Retailing and Consumer Services, 21(1), 26–36. https://doi.org/10.1016/j.jretconser.2013.06.00

Saxena, A. (2019). An Analysis of Online Food Ordering Applications in India : Zomato and Swiggy. 9(April), 13–21.

Syamalarao, G. (2021). INTERNATIONAL JOURNAL OF BUSINESS, MANAGEMENT AND ALLIED SCIENCES ( IJBMAS ). 2017–2019.

Thamaraiselvan, N., Jayadevan, G. R., & Chandrasekar, K. S. (2019). Digital food delivery apps revolutionizing food products marketing in India. International Journal of Recent Technology and Engineering, 8(2 Special Issue 6), 662–665. https://doi.org/10.35940/ijrte.B1126.0782S619

Yeo, V. C. S., Goh, S. K., & Rezaei, S. (2017). Consumer experiences, attitude and behavioral intention toward online food delivery (OFD) services. Journal of Retailing and Consumer Services, 35(July 2016), 150–162. https://doi.org/10.1016/j.jretconser.2016.12.013

This Assignment is done by Girish C G as a part of the DARP Assignment

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