literature review of zomato

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ETTIS 2021: Proceedings of Emerging Trends and Technologies on Intelligent Systems pp 235–242 Cite as

Zomato Review Analysis Using NLP

• Puppala Bala Bhavana 17 ,
• Hari Kishan Kondaveeti 17 &
• Asish Kumar Dalai 17  
• Conference paper
• First Online: 02 October 2021

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1371))

Natural language processing is generally used to understand the interactions between humans and machine, and it is also a subpart of artificial intelligence. Sentimental analysis is a subpart of AI and used to differentiate the words by the patterns that are given by people like good, bad, or average. Sentiment analysis is used to know about user’s likes and dislikes toward a product. Zomato is an app which is used for ordering foods from different restaurants and allows user to drop their reviews and ratings of respective food that they ordered. These reviews given by the customers will be taken for sentimental analysis. The buying decision of a customer is also depending on the reviews and ratings. So, reviews have a lot more important. It is estimated that 79% of unstructured data is producing every day. A lot of textual data are generating through social media, healthcare, e-commerce applications. We as humans cannot analyze these huge volumes of data. Sentiment analysis will help us to do this task very easily. In this project, we can train with millions of data and predict new data as positive, negative, and neutral. We use artificial neural networks (ANNs) to train the model and to predict the reviews as positive, negative, or neutral. Artificial neural networks use feedforward networks and work as biological neural networks.

• Natural language processing
• Artificial neural networks
• Sentimental analysis
• Feedforward network

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Munoz, R.: Evolution of communication. https://www.mobilecon2012.com/the-evolution-of-communication-through-the-centuries/

Loss Functions: https://ml-cheatsheet.readthedocs.io/en/latest/loss_functions.html

Patrikar, S.: Gradient descent (2019). https://towardsdatascience.com/batch-mini-batch-stochastic-gradient-descent-7a62ecba642a

Sentiment Analysis: https://monkeylearn.com/sentiment-analysis/

Sharma, A.V.: Activation functions (2017). https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0

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Puppala Bala Bhavana, Hari Kishan Kondaveeti & Asish Kumar Dalai

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Gaurav Trivedi

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Bhavana, P.B., Kondaveeti, H.K., Dalai, A.K. (2022). Zomato Review Analysis Using NLP. In: Noor, A., Sen, A., Trivedi, G. (eds) Proceedings of Emerging Trends and Technologies on Intelligent Systems . ETTIS 2021. Advances in Intelligent Systems and Computing, vol 1371. Springer, Singapore. https://doi.org/10.1007/978-981-16-3097-2_19

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Online food delivery companies' performance and consumers expectations during Covid-19: An investigation using machine learning approach

Purushottam meena.

a Department of Supply Chain & Information Management, School of Business, College of Charleston, Charleston, SC, 29424, USA

Gopal Kumar

b Department of Operations Management, Indian Institute of Management, Raipur, Chhattisgarh, 492015, India

Online food delivery (OFD) businesses flourished during COVID-19; however, OFD companies experienced different challenges and customers' expectations. This paper uses social media data to explore OFD companies' performance and customers' expectations during the COVID-19 pandemic. The most important topics in developed and developing countries are identified using machine learning. Results show that customers in India are more concerned about social responsibility, while financial aspects are more important in the US. Overall, customers in India are more satisfied with OFD companies during the COVID-19 pandemic than the US customers. We further find that factors such as OFD companies' brand, market size, country, and COVID-19 waves play a crucial role in moderating customer sentiment. The results of the study offer several managerial insights.

1. Introduction

The COVID-19 pandemic has caused a profound and severe impact on the global economy. As the COVID-19 pandemic started, many restaurants worldwide lost billions of dollars, and many even faced business closures ( National Restaurant Association, 2020 ). According to the National Restaurant Association (2020) report, the restaurant industry has already lost approximately $240 billion by 2020. Restaurants that remained in business found it essential to adapt to recent changes in the industry and offer online food delivery (OFD) services for their survival ( Brewer and Sebby, 2021 ). Many restaurants started using third-party food delivery providers' services during the pandemic. It resulted in a significant increase in online OFD businesses worldwide. For example, Uber Eats observed a substantial increase in OFD orders following the mandate of no dine-in service ( Raj et al., 2021 ).

The platform economy in the food sector is not more prominent in terms of labor participation in India ( Sundararajan, 2016 ; Pant and Shende, 2020 ). However, with entries of global OFD leading companies such as Uber Eats along with some Indian companies—Zomato and Swiggy—the OFD industry is proliferating with a 25–30 percent annually ( Boston Consulting Group, 2020 ). The overall platform economy in India is expected to be $30 billion by 2025 ( NASSCOM, 2018 ). In 2019, more than 48 million people used OFD platforms in the US, which is likely to be approximately 70 million in 2024. The OFD revenue in the US is expected to grow steadily at 7.26 percent annually and is estimated to be $41,504 million by 2025 ( Statista, 2021 ). As per the 2022 Statista report, the global OFD service market is expected to reach $223.7 billion in 2025 from $115.07 billion in 2020 (Statista, 2022).

In recent years, the number of laborers who work in the platform economy has increased significantly. According to the NITI Aayog (a policy think tank of the India government), the platform economy employs around 15 million workers, and approximately 0.44 million of them work in the food sector ( Tiwari et al., 2019 ). Food delivery workers are not hired as full-time employees like any other sharing economy sector. These workers are primarily considered interchangeable or “gig workers” by food delivery companies ( Sundararajan, 2016 ).

During the first wave of COVID-19, most restaurants in the US were forced to suspend dine-in services and were only allowed to operate via takeout, drive-through, or contactless deliveries. As the COVID-19 outbreak started, restaurants demand started plumbing globally and affected the financial performance of the restaurant industry. In US restaurants, customer demand decreased drastically with increasing COVID-19 cases ( Yang et al., 2020 ). However, more and more people started ordering foods online via food delivery platforms like DoorDash, Grubhub, Uber Eats (in the US), Zomato, and Swiggy (in India) ( Jain, 2020 ; Kim et al., 2021 ). Restaurants that provided OFD and curbside pickup services for contactless delivery experienced a lesser effect on financial performance. Most of these OFD services businesses were in operation during the COVID-19 outbreak as they are considered among the essential services. The OFD service platforms were instrumental for restaurants to remain in business during challenging times ( Puram et al., 2021 ). Compared to March 2019, customer spending on food delivery increased significantly (i.e., 70 percent)—during the first COVID-19 wave in March 2020 ( Chen McCain et al., 2021 ).

On the flip side, OFD service providers (i.e., riders or drivers) and consumers faced several challenges during the ongoing pandemic. For example, Indian OFD workers have faced personal and social risks, ranging from loss of income to exposure to COVID-19 and risking their lives ( Lalvani and Seetharaman, 2020 ). The OFD became crucial and popular as millions of people were quarantined and unquarantined required foods ( Chen McCain et al., 2021 ; Kim et al., 2021 ). While delivering food, OFD companies needed to follow strict COVID-19 appropriate behaviors; otherwise, riders and consumers would be exposed to life-threatening health risks.

Overall, during the COVID-19 pandemic, consumers prioritized expectations from OFD companies, and the challenges faced by OFD companies were different from those of the pre-COVID-19 pandemic ( Gavilan et al., 2021 ). The adaptability of food delivery companies to a situation of a global healthcare crisis, where customers' expectations are entirely different ( Gavilan et al., 2021 ; Nguyen and Vu, 2020 ). It may depend on several factors, such as fear ( Balakrishnan, 2020 ; Mehrolia et al., 2021 ; Ahorsu et al., 2020 ; Lo Coco et al., 2021 ), marketing strategies during COVID-19 waves ( Jia et al., 2021 ), brand perception ( Dsouza and Sharma, 2021 ; Prasetyo et al., 2021 ), market size, region (developing or developed countries) of their operations ( Keeble et al., 2020 ; Zanetta et al., 2021 ), public listing ( Bao and Zhu, 2022 ), and COVID-19 waves ( Kohút et al., 2021 ; Mohan et al., 2022 ), etc. For example, OFD companies with better brand perception, bigger market size, and listed in the stock market should do better in a crisis. They may also improve their performance in the second wave better than in the first wave of the COVID-19 pandemic. Therefore, to have a deeper understanding of the delivery operations of OFD companies and consumers' behaviors/expectations during a global healthcare crisis, it is crucial to understand how OFD companies performed, to what extent consumers' expectations were met, and factors that can explain OFD companies' performance or customers' satisfaction during COVID-19 pandemic.

Most of the erstwhile work in the literature on OFD is conducted predominantly using primary data with a limited sample size. To the best of our knowledge, no study exists in the literature that considers social media-based big data relating to OFD companies during the pandemic and uses qualitative (machine learning algorithms) as well as quantitative approaches to investigate issues faced by OFD companies, service providers, and customers during the first two waves of the COVID-19 in the US and Indian market. Specifically, this study investigates how do consumers' sentiments vary on OFD companies' services during COVID-19 across different dimensions of consumer expectations, market characteristics, and companies' characteristics?

The following research objectives (RO) are considered to address the research question:

RO1: To identify the broader issues/topics prominently discussed about OFD companies and service providers during the pandemic.

RO2: To investigate consumers' net sentiment and negative sentiment across identified dimensions, OFD companies, and different countries.

RO3: To test the impact of different market characteristics and OFD companies' characteristics on consumers' sentiments.

To address these research objectives, first, we collected data for four OFD companies (i.e., Uber Eats, Grubhub, Zomato, and Swiggy) from Twitter during the first two waves of the COVID-19 pandemic. Second, the text mining approach is used to identify different topics/issues discussed on Twitter about these OFD companies. Third, for each identified topic, positive and negative sentiments of people are computed. Fourth, different regression models are developed to investigate the relationship between the moderating variables and people's sentiments. Finally, the results of both countries' companies are compared to examine the differences among the topics and customers' sentiments. The results offer several important practical insights which are helpful for OFD companies.

The remainder of the paper is organized as follows. Section 2 discusses the analyses of relevant literature. Section 3 presents the research hypotheses. Data collection, research methodology, and results are provided in Section 4 . Section 5 presents discussion and managerial insights. Finally, section 6 concludes the research and offers the scope for future studies.

2. Literature review

There was not much research conducted on the OFD area before the COVID-19 pandemic. Previous studies have mainly focused on issues such as the OFD platform's performance and customer satisfaction ( Seghezzi and Mangiaracina, 2020 ; Gilitwala and Nag, 2019 ), consumers behavior and attitude ( Pigatto et al., 2017 ; Hwang and Choe, 2019 ; Yeo et al., 2021 ), vehicle routing problems ( Liu et al., 2020 ; Correa et al., 2019 ), adoption and acceptance of technology in OFD apps ( Pigatto et al., 2017 ; Gunden et al., 2020 ; Roh and Park, 2019 ), and riders issues related to wages and health ( Bates et al., 2020 ; Correa et al., 2019 ; Kougiannou and Mendonça, 2021 ). For a detailed discussion on these issues, interested readers can refer to Li et al. (2020) and Seghezzi et al. (2021) , who have provided an excellent review of OFD literature published before the COVID-19 period.

Research on the OFD has witnessed significant growth during the last two years. One of the reasons for this rapid growth was the rapid increase in OFD services demand during the COVID-19 pandemic. For safety reasons, more people started ordering their food from home via OFD apps ( Mehrolia et al., 2021 ; Zanetta et al., 2021 ; Hong et al., 2021 , Hong et al., 2021 ), such as Uber Eats, DoorDash, Zomato, and Swiggy. Several researchers ( Hong et al., 2021 , Hong et al., 2021 ; Gani et al., 2021 ) have studied how these new behaviors of people affect the food delivery business during the COVID-19 outbreak.

As per the world health organization (WHO), more than 6.28 million people have already died because of the COVID-19 pandemic. It has affected people's life emotionally, psychologically, and physically ( Ahorsu et al., 2020 ; Conte et al., 2021; Watson and Popescu, 2021 ). People have used several ways to deal with the stress, anxiety, and loneliness caused by the COVID-19 (Kumarand Shah, 2020; Ahorsu et al., 2020 ; Lo Coco et al., 2021 ). Consumers have extensively utilized different social media platforms to share their opinion and remain updated with the public policies and regulations during the COVID-19 ( Rydell and Kucera, 2021 ; Trivedi and Singh, 2021 ). Several researchers studied how the COVID-19 has changed consumers' behavior, habits, and tastes when buying food online ( Watson and Popescu, 2021 ; Birtus and Lăzăroiu, 2021 ; Rydell and Suler, 2021 ; Smith and Machova, 2021 ). Michalikova et al. (2022) have reviewed the literature on customers' judgment, behavior intentions, and purchase decision dynamics during the COVID-19 pandemic in the food delivery sector.

COVID-19 has considerably impacted the financial strength of restaurant firms; however, restaurant firms that used OFD and other contactless delivery services experienced relatively lesser financial glitches ( Kim et al., 2021 ). Before COVID-19, the primary marketing strategies were based on product imagery, links, and sponsorships. While combatting the COVID-19 pandemic, selling social distancing, appropriating frontline workers, and accelerating digitalization were the selling points ( Jia et al., 2021 ). We classify the research conducted on OFD during the COVID-19 outbreak into three categories—customer satisfaction/experience, intention to use OFD apps, and workers/drivers/riders' conditions during operations of OFD.

The first set of studies ( Kumar and Shah, 2021 ; Mehrolia et al., 2021 ; Prasetyo et al., 2021 : Sharma et al., 2021 ) focused on customer experience and OFD companies' performance. Mehrolia et al. (2020) and Sharma et al. (2021) investigated the characteristics of customers who used and did not use online food delivery services (OFDS) during the first wave of COVID-19 in India. Customers who purchased food via OFD platforms have experienced lesser perceived threats and a high purchase pattern, perceived benefits, and product involvement ( Al Amin et al., 2021 ; Mehrolia et al., 2021 ; Sharma et al., 2021 ; Uzir et al., 2021 ). Studies found that during the COVID-19 pandemic, hedonic motivation ( Prasetyo et al., 2021 ; Sharma et al., 2021 ; Shah et al., 2021 ), food quality, variety, and safety ( Dsouza and Sharma, 2021 ; Shah et al., 2021 ), and mobile app information design/features ( Pal et al., 2021 ) significantly affect customers satisfaction and loyalty. Hedonic motivation is also important for price, information quality, and promotion ( Prasetyo et al., 2021 ; Bao and Zhu, 2022 ; Shah et al., 2021 ; Sharma et al., 2021 ; Ramos, 2021 ; Pal et al., 2021 ). Unlike other studies, Zenetta et al. (2021) found that hedonic motivation was not the most crucial factor for the continuance intention.

India is one of the most populated countries and was severely affected by the COVID-19. Several studies ( Dsouza and Sharma, 2021 ; Mehrolia et al., 2021 ; Pal et al., 2021 ) have been conducted to understand OFD companies and customers' purchase behaviors in the Indian market. Trivedi and Singh (2021) found that Zomato received the most positive and less negative customer sentiments than other competitors, such as Swiggy in India. Dsouza and Sharma (2021) found that the food quality and safety measure of OFDAs positively affects customer satisfaction and loyalty in India. Pal et al. (2021) investigate student satisfaction and loyalty to OFDAs. Their findings suggest that satisfaction is the main predictor of loyalty, whereas mobile app information design has the highest impact on satisfaction and loyalty. Chen McCain et al. (2021) assessed customers' satisfaction with Uber Eats on different dimensions—OFD apps performance, food quality, and service quality during the first wave of COVID-19 in the US. They found that service quality was the most important dimension, followed by OFD app performance and food quality.

The second set of studies focused on the continuous usage intention of OFD apps during the COVID-19 outbreak period in different countries, such as India ( Mehrolia et al., 2021 ), the USA ( Hong et al., 2021 , Hong et al., 2021 ), China ( Zhao and Bacao, 2020 ), Brazil ( Zanetta et al., 2021 ), Mexico ( Ramos, 2021 ), Vietnam ( Tran, 2021 ). These and several other studies have found that performance expectancy ( Mehrolia et al., 2021 ; Zanetta et al., 2021 ; Pal et al., 2021 ; Ramos, 2021 ), habit ( Zanetta et al., 2021 ; Rydell and Kucera, 2021 ), effort expectancy ( Kumar and Shah, 2021 ; Ramos, 2021 ; Pal et al., 2021 ), price saving orientation ( van Doorn, 2020 ; Ramos, 2021 ; Pal et al., 2021 ), perceived usefulness, and employee trust ( Gavilan et al., 2021 ; Chakraborty et al., 2022 ; Trivedi and Singh, 2021 ; Uzir et al., 2021 ) affect consumers intention to use OFD services during the COVID-19 period.

Similarly, delivery, hygiene, subjective norms, attitudes, behavioral control, and social isolation ( Al Amin et al., 2021 ; Gani et al., 2021 ; Tran, 2021 ; Sharma et al., 2021 ; Yeo et al., 2021 ; Hopkins and Potcovaru, 2021 ) positively affect the consumers' continuance intention to use Mobile food delivery apps. Further, studies have also found that OFD apps features, ease of use, convenience, price saving, and food variety ( Pigatto et al., 2017 ; van Doorn, 2020 ; Pal et al., 2021 ; Dirsehan and Cankat, 2021 ; Shah et al., 2021 ; Kumar et al., 2021 ; Bao and Zhu, 2022 ) also affect the continued intention to use the FDA. Gavilan et al. (2021) found that customers preferred innovative solutions by OFD companies during COVID-19. Kumar and Shah (2021) observed that the app aesthetics were responsible for customers' pleasure, significantly affecting the customers' continued usage intent.

The third set of studies ( Huang, 2021 ; Parwez & Ranjan, 2021 ; Puram et al., 2021 ) attempted to understand food delivery drivers' conditions during COVID-19 in China and India. The precarity of work among the food delivery workers had aggravated during COVID-19—and it impacted workers' job loss, health risks, and occupational distress ( Huang, 2021 ; Parwez & Ranjan, 2021 ). Among drivers in China, work insecurity, financial distress, health risks, livelihood crisis, and inflamed racism were also observed ( Huang, 2021 ). Puram et al. (2021) analyzed the challenges faced by last-mile food delivery riders working for different OFD platforms in India during the COVID-19 pandemic. They categorize the riders' challenges under operational, customer-related, organizational, and technological categories.

Apart from the variables discussed above, several other variables moderate or affect customers' satisfaction ( Hu et al., 2009 ), sentiments about the food delivery service ( Oliver, 1977 ; Gavilan et al., 2021 ), and intent to use and reuse OFD services ( Mittal et al., 2001 ; Kim et al., 2021 ; Gani et al., 2021 ). A large number of studies show that a firm's brand image ( Fornell, 1992 ; O'Sullivan and McCallig, 2012 ; Peng et al., 2015 ; Chai and Yat, 2019 ; Hwang and Kim, 2020 ; Prasetyo et al., 2021 ) and market value/size (Daniel et al., 2015; Dai et al., 2021 ) positively affect customers satisfaction in different service industries. The customer's expectations about the OFD service performance may vary between different waves of the COVID-19 ( Sv et al., 2021 ; Mohan et al., 2022 ). The geographic location of customers may affect their expectations and purchase intention about the product and service ( Steyn et al., 2010 ; Ng, 2013 ; Leng et al., 2019 ; Punel et al., 2019 ). Rizou et al. (2020) found similar findings in the food delivery sector as well. Studies have explored the difference in people's sentiments during the first and second waves of the COVID-19 ( Lo Coco et al., 2021 ).

None of the above studies utilize secondary data from social media platforms like Twitter, where customers regularly express their concerns, experience, and advice to OFD companies and their businesses. This data can help companies comprehensively understand customers' expectations and measure their performance during the pandemic. This paper addresses this issue by collecting consumer data on the top two Indian and US OFD companies from Twitter and exploring the issues OFD companies face by employing the text mining approach and regression analyses.

3. Research hypotheses development

In addition to the traditional OFD business market factors, several other variables may affect or moderate customers' sentiments about OFD companies' performance and service during the COVID-19. The variables and related hypotheses are discussed in the following sub-sections.

3.1. The COVID-19 waves and people's sentiments

The COVID-19 pandemic brought havoc to everyone's life and affected people in different ways—financially, emotionally, psychologically, and physically ( Trivedi and Singh, 2021 ; Conte e t al., 2021). At the beginning of the COVID-19 outbreak (i.e., in the first wave), it was difficult for OFD companies and restaurants to run their business and serve customers efficiently and safely. In the first COVID-19 wave, most OFD companies were clueless about the customers' expectations of their delivery services and how to meet them. Due to safety reasons ( Zhao and Bacao, 2020 ), customers with self-protective behavior were hesitant to order food online during the first COVID-19 wave ( Ahorsu et al., 2020 ). The primary reasons for their safety concern were lack of information about hygiene, how and who is preparing food, ingredients used in foods, source of ingredients, and safety measures adopted at restaurants ( Gavilan et al., 2021 ).

However, to address these concerns, soon, OFD companies started adopting government regulations, offered contactless delivery, and implemented several measures for customers' safety ( Nguyen and Vu, 2020 ). During the first COVID-19 wave, OFD companies have learned to meet consumers' expectations in the best way possible. As COVID-19 time passed, OFD service providers started offering more information about safety measures adopted by them to consumers (Keeble et al., 2021). Therefore, in the second wave of COVID-19, companies' performance and consumers' sentiment about their performance should improve. Literature suggests a difference in people's sentiments between the first and second wave of the COVID-19 ( Kohút et al., 2021 ; Sv et al., 2021 ; Mohan et al., 2022 ), which also suggests people's psychological adoption of the new normal situation ( Koppehele-Gossel et al., 2022 ) as well satisfaction with improved services of OFD firms. We believe customers' sentiments on the operations of OFD companies ( Kim et al., 2021 ) should vary from COVID-19 first to the second wave. Therefore, we hypothesized:

COVID-19 waves moderate people's sentiments.

3.2. Market size and public listing impact on people's sentiments

From the expectancy theory perspective ( Oliver, 1977 ), customers will have specific expectations from a firm based on their previous experiences, influencing their behaviors to achieve specific goals. Before the COVID-19 outbreak, OFD companies with a bigger market size and listed publicly may have provided better customer experiences. Customers would be expecting similar services during the COVID-19 period as well. Some OFD companies (e.g., Zomato and Groubhub) are publicly listed and have a better brand value than others ( Savitri et al., 2020 ; Yao et al., 2021 ). OFD companies with more resources can invest liberally to bring innovation to delivery operations to handle the COVID-19 healthcare crisis and market them to the consumers. Different innovative OFD options are discussed in the literature ( Keeble et al., 2020 ; Richardson, 2020 ; Shah et al., 2021 ; Sharma et al., 2021 ; Bao and Zhu, 2022 ), which helps in reducing customers' fear of safety concerns and increases their experiential value ( Gavilan et al., 2021 ) during COVID-19. Such companies can be more successful in conveying their safety-related measures to consumers.

Numerous studies ( O'Sullivan and McCallig, 2012 ; Peng et al., 2015 ) have investigated how a firm's value affects customer satisfaction. Bolton et al. (2004) and Luo et al. (2010) found that customer satisfaction significantly affects the firm's value in the stock market and vice-versa. It is established in the literature that high firm value not only helps increase customer satisfaction ( Hu et al., 2009 ) but also enhances its brand image ( Prasetyo et al., 2021 ), purchase and repurchase intention ( Bao and Zhu, 2022 ), customer retention ( Mittal et al., 2001 ), and reduces complaints ( Fornell, 1992 ). Dai et al. (2021) studied the impact of the COVID-19 outbreak on small and medium-sized enterprises (SMEs) across different industries during the first and second waves. They found that during the second wave, companies were more prepared based on their learning from the first wave. Daniel et al. (2005) find a higher correlation between a firm's market values and customers' confidence in the firm. Based on the above studies, one can conclude that if a firm has a higher market size or value and is listed publicly, it leads to high customer confidence or sentiments in the firm.

The market size of OFD companies moderates people's sentiments .

Public listing of OFD companies moderates people's sentiments.

3.3. The brand image and people's sentiments

The brand image of a food company is crucial for customers to use their OFD service ( Prasetyo et al., 2021 ). In the restaurant industry, brand image is defined as “emotions, ideas or attitudes that customers associate with full-service dining restaurants” ( Jin et al., 2012 ). It is established in the literature that customers prefer to purchase products or services from well-known brands as they offer high-quality food ( Aaker and Equity, 1991 ). During the COVID-19 outbreak, several restaurants have increased their presence on OFD platforms to increase brand awareness ( Chai and Yat, 2019 ; Hwang and Kim, 2020 ). In general, a better brand image helps increasing consumer trust, purchase intention ( Erdem and Swait, 2004 ), and customer satisfaction with a better service and quality ( Baek et al., 2010 ). Aureliano-Silva et al. (2022) argued that brand love and service recovery are important for purchase intention and brand trust in food delivery platforms. Studies show that brand love is instrumental to customer satisfaction and intentions for future purchases ( Han et al., 2011 ; Erkmen and Hancer, 2019 ; Bao and Zhu, 2022 ).

Ibrahim et al. (2017) found a positive relationship between consumer sentiments on Twitter and the company's brand image. A positive brand image creates a good reputation for the company in customers' minds for a long time, and it increases customer loyalty toward a high brand image company ( Pitta and Katsanis, 1995 ). Customers heavily rely on a company's brand image when they are concerned about uncertain product or service quality ( Berry, 2000 ; Erdem and Swait, 2004 ), food safety, and quality under uncertain times like the COVID-19 outbreak ( Kim et al., 2021 ). A company's brand image positively affects customer loyalty and is a significant predictor of customer satisfaction in the restaurant ( Hwang and Kim, 2020 ) and other industries ( Jin et al., 2012 ; Ryu et al., 2012 ; Faullant et al., 2008 ). The high brand image of a food delivery firm positively affects the customer's behavioral intention to use OFD service ( Hwang and Choe, 2019 ; Gani et al., 2021 ). Therefore, we hypothesized:s.

The brand image of OFD companies moderates people's sentiments.

3.4. Operating regions and people's sentiments

The customers' expectations about the same products and services may vary between different regions based on different factors such as behaviors, loyalty, attitudes, and cultural influence ( Steyn et al., 2010 ). Punel et al. (2019) found that customers' experiences and expectations about the same airline service vary based on customers' geographical region. The authors find a difference in the Noth American and Asian passengers' expectations of the ticket price and in-flight service. Some studies show that cultural regions moderate online purchase intention ( Ng, 2013 ). Leng et al. (2019) observed differences in the US and Japanese people's choices and expectations about food products and services.

Twitter is a popular social media platform where people freely express their feelings. People's reactions on Twitter to the COVID-19 varied in different regions. For example, the US people's sentiment score was more negative than the UK people toward the COVD-19 ( Zou et al., 2020 ). Similarly, the fear and awareness of the COVID-19 safety measures vary from country to country Rizou et al. (2020) ; Lo Coco et al. (2021) . Consumers' expectations and sentiments about the OFD service providers may also vary according to their country ( Keeble et al., 2020 ), as consumers in developed countries would be more educated and aware and, hence, have more expectations. In India, the OFD services business is growing exponentially. However, it is still new compared to the US market. Thus, OFD companies may focus more on consumer-centric service delivery in developed countries than in developing countries. Variation in the above factors should change customer sentiment. Therefore, we hypothesize that:

Operating regions (i.e., countries) of OFD companies moderate people's sentiments .

4. Research methodology and results

We have collected consumer data for four popular OFD companies from India (e.g., Zomato and Swiggy) and from the US (e.g., Uber Eats and Grubhub). The consumer-level data were extracted from Twitter —a social networking site—on which billions of users express their ideas and experiences. Each post by a user is known as a “tweet”. The customer-level data were collected between February 01, 2020 to November 30, 2021 covering the first two COVID-19 waves.

As shown in Fig. 1 , in India, the first COVID-19 wave is considered from March 01, 2020 to February 28, 2021 and the second wave is considered from March 01, 2021 to November 30, 2021. For the US, the first wave is considered from February 01, 2020 to June 30, 2021 and the second wave is considered from July 01, 2021 to November 30, 2021. The second wave in India brought more negative impacts as the number of new daily cases and fatality rate were significantly higher than the first wave. The second wave inflicted more fear on people's minds as the number of people hospitalized, and the load on medical infrastructure were significantly higher than the first wave of COVID-19.

COVID-19 cases and deaths in India and US.

Fig. 2 summarizes the research methodology adopted in this paper. Primarily, a four steps method was utilized for: (i) data collection, (ii) topic extraction, (iii) sentiment analysis, and (iv) moderation variable analysis. We used the company name, their Twitter handle, and related hashtags as keywords to extract the data. We extracted a total sample of 11,134 for Zomato, 14,355 for Swiggy, 15,583 for Uber Eats, and 2030 for Grubhub. The data were preprocessed by employing language identification, cleaning, tokenization, lemmatization, and removing stopwords. We kept data in English for further analysis, leading to a useful sample of 9447 for Zomato, 13,160 for Swiggy, 12,536 for Uber Eats, and 1951 for Grubhub. The remaining three steps are described in subsections 4.1, 4.2, and 4.3.

The proposed research methodology framework.

4.1. Text analytics and topic extraction

We employed the latent Dirichlet allocation (LDA) tool ( Blei et al., 2003 ) to identify hidden topics discussed in the large volume of unstructured data, as it does not assume any structure of grammar properties. The model was trained to identify the number of topics in unstructured documents. The extant literature ( Nikolenko et al., 2017 ; Lee, 2022) suggests a coherence score as a more reliable measure to discover the most prominent number of topics in a large volume of documents. Fig. 3 shows the variation of coherence score over topics 2 to 40 for each OFD company.

Coherence score of each OFD company.

We found the highest coherence score for each OFD company when the number of topics extracted was 13 for Zomato, 6 for Swiggy, 2 for Uber Eats, and 11 for Grubhub. We believe that there must be more than two topics related to Uber Eats discussed by customers, so we preferred to extract topics that correspond to the next highest coherence score. It results in 7 topics for Uber Eats. Topics and their associated 10 most important keywords of Zomato, Swiggy, Uber Eats, and Grubhub are presented in Table 1 , Table 2 , Table 3 , Table 4 , respectively.

Keywords, topics, and dimensions of Zomato and their statistics.

Keywords, topics, and dimensions of Swiggy and their statistics.

Keywords, topics, and dimensions of Ubereats and their statistics.

Keywords, topics, and dimensions of Grubhub and their statistics.

Topics extraction results are analyzed in three steps: (i) an expert team reviewed keywords of each topic and gave an appropriate name, as shown in Table 1 , Table 2 , Table 3 , Table 4 , (ii) each topic was then reviewed and higher dimensions which were a collection of topics were formed (see Table 1 , Table 2 , Table 3 , Table 4 ), keeping literature in mind, and (iii) based on percent tweets related to the dimensions (see Table 5 ), most prominent dimensions of each food delivery company and regions were identified and discussed in Section 5 . To label topics, the keywords of each topic were discussed and reviewed carefully by experts from academics. Further, we observed that a set of topics represents a higher-level dimension. Thus, we identified sets of topics representing higher-level dimensions. These dimensions are also shown in Table 1 , Table 2 , Table 3 , Table 4 .

Performance of delivery companies across different dimensions.

Further, we also extracted topics based on the COVID-19 waves to check if there is any difference in topics. The results show that in India, people discussed more regarding help and support for drivers and restaurants, vaccination requests, customized orders, and food quality in the second COVID-19 wave. In the first wave, we observed more discussion about consumers' perceived responsibility, perceived solutions, and companies' social responsibility. On the other hand, US people discussed more about coupons and promocode during the first COVID-19 wave. Whereas in the second wave, people emphasize more about vaccination, money spending, and delivery operations in the US market.

4.2. Sentiment analysis

Sentiment analysis is a procedure to quantify customers' experiences or emotions based on the subjective text data expressed by the customers (Lee, 2022). The sentiment was labeled based on the python package, Gensim/VADER. We preferred the VADER-based model as they provide a parsimonious rule -or lexicon-based model specially designed for social media text sentiment analysis. Social media text, especially tweets data, is characterized as short sentences/descriptions with emoji, copious use of question marks and exclamation marks, and repetitive words. This technique of unsupervised sentiment computation does not require features and is widely used in the literature ( Ibrahim and Wang, 2019 ; Lee, 2022; Mehta et al., 2021 ).

Customer sentiment may include positive, negative, or neutral (Lee, 2022; Liu et al., 2021), depending upon VADER based compound score that varies from −1 to +1. If the compound score was ≤ −0.05, between −0.05 and +0.05, and ≥ +0.05, the sentiment was labeled as negative, neutral, and positive (Lee, 2022). Lexicon-based sentiment computation approach matches words with a dictionary of words. First, we assessed the sentiment score of each tweet. Subsequently, we assessed topic-level and dimension-level sentiments using dictionaries or a pre-defined list of words approach. The topic- and dimension-level sentiment was determined by averaging the tweet- and topic-level sentiments, respectively. Dimension-level positive and negative sentiments of delivery companies are shown in Table 5 . Based on the mean score of positive and negative sentiment, the results are further discussed in section 5 .

4.3. Moderating variables

To test hypotheses, we considered two types of sentiment: net sentiment and negative sentiment, as response variables. Five important variables—COVID-19 wave, brand, market size, country, and listed, may influence customers' both types of sentiment/satisfaction. We employ the ordinal least square (OLS) regressions to estimate response variables, net sentiment, and negative sentiment. The relationship between the response variable and explanatory variables takes the form of equations (1) , (2) .

The COVID-19 wave, country, and “listed” are binary variables. The COVID-19 wave has two labels —the first and the second wave. The country has two labels—India and US. Similarly, listed has labels yes and no, where yes implies a company is listed in the stock market, and no for otherwise. Zomato and Grubhub are publicly listed OFD companies, while Uber Eats and Swiggy are not. The brand and market size are categorical variables, where the brand has four labels—Zomato, Swiggy, Uber Eats, and Grubhub. The market size has three labels: small, medium, and large. With a nearly $80.53 billion market cap, Uber Eats is considered a large-market size company. Zomato, Swiggy, and Grubhub, with a market cap of nearly $13.82 billion, $5 billion, and $10.55 billion, are considered medium, small, and medium market sizes, respectively.

The ordinary least squares (OLS) regression results are presented in Table 6 . The results show that there is a significant relationship between net sentiment and explanatory variable, the COVID waves (p < 0.05), brand (p < 0.05), market size (p < 0.05), country (p < 0.05), and listed (p < 0.10). Thus, the hypotheses H1 , H2 , H3 , H4 , and H5 are supported for the net sentiment. For the negative sentiment, we found that all the explanatory variables are significant (p < 0.05) except the country (p > 0.10). Therefore, hypotheses H1 , H2 , H3 , and H4 are supported for the negative sentiment; hypothesis H5 is rejected.

OLS regression results for hypotheses.

To identify specific pairs of groups that differ from each other, we conducted the Post-Hoc test of multiple comparisons of means—Tukey Honestly Significant Difference (Tukey HSD). The results are shown in Table 7 and discussed in the Discussion section.

Multiple comparison test results.

5. Discussions and managerial implications

The topic modeling results (from Table 1 , Table 2 , Table 3 , Table 4 ) convey the prominence of 13 topics for Zomato, 6 topics for Swiggy, 7 for Uber Eats, and 11 for Grubhub. These topics are further used to form higher-level dimensions that resulted—6 dimensions for Zomato, 4 dimensions for Swiggy, 5 dimensions for Uber Eats, and 4 dimensions for Grubhub. We find a wide range of topics (and dimensions) relating to OFD companies were expressed and discussed during the COVID-19 pandemic. Delivery operations, social responsibility, perceived consumers responsibility, the financial impact of COVID-19 on consumers, consumers perceived behaviors, perceived solutions, perceived negative impact of OFD, promocode and food delivery, free delivery, adapting to pickup/takeout service, and appreciation for drivers/restaurants are dimensions that emerged from our analysis.

As count or percent contribution is shown in Table 5 , overall, perceived consumer responsibility, delivery operations, social responsibility, perceived negative impact, free delivery, etc., are the most prominent dimensions related to OFD companies across emerging and developed countries. These findings differ from the previous literature ( Chen McCain et al., 2021 ; Dsouza and Sharma, 2021 ; Shah et al., 2021 ; Trivedi and Singh, 2021 ) that observed food quality and safety measures, socialization, and marketing are the main themes for OFD companies during COVID-19. Our topics seem more enlightening and COVID-19 pandemic-oriented rather than topics related to the usual expectations from OFD companies. Reasons for the difference may be due to the fact that this study is more comprehensive in terms of the number of companies included, the sample size considered, and the time period during which the sample was collected.

Table 5 further conveys that the mean score of the positive sentiment of most dimensions is more than the mean score of a negative sentiment of most dimensions of all OFD companies. Exceptions to this are financial impact and consumers' perceived behaviors of Zomato; delivery operations of Swiggy; promocode and food delivery of Uber Eats. These findings are different from the findings of Chen McCain et al. (2021) in the sense that they found that negative sentiment is more than positive sentiment.

Most of the identified topics for Indian firms Zomato and Swiggy belong to delivery operations, social responsibility, and perceived consumer responsibility dimensions (see Table 1 ). However, most of the topics of Uber Eats operating in the US belong to promocode/coupon on food delivery orders and free delivery. In contrast, for Grubhub, most topics belong to the appreciation of drivers/restaurants for delivery service and food delivery operations. Interestingly, a closer look at the topics and dimensions of Zomato and Swiggy conveys that people in India are more concerned about society, such as the social responsibility of OFD companies and the responsibility of consumers to society in such a hard time. People are more inclined toward spreading awareness and highlighting the responsibility of both consumers and delivery companies. As expected for OFD companies, we also found people discussing delivery operations dimension concerning to responsiveness of OFD companies and customized orders by following COVID-19 appropriate behaviors. Results in Table 5 show that perceived customer responsibility, delivery operations, and social responsibility are the three most prominent dimensions in the Indian market. On the other hand, delivery operations, fee delivery, and perceived customer responsibility are the three most prominent dimensions in the US.

Table 8 shows our identified dimensions and compares them with dimensions discussed in the literature. We observe that most of the existing studies ( Kim et al., 2021 ; Pal et al., 2021 ) deal with the operational aspect of delivery, while a few studies ( Li et al., 2020 ; Yeo et al., 2021 ) highlight the sustainability aspect of OFD companies, discount and promotion ( Kim et al., 2021 ), and concerned for drivers ( Huang, 2021 ; Ramos, 2021 ). However, we find perceived customer responsibility (contributing 28.27 percent) is the most important dimension, followed by delivery operations (contributing 25.9 percent) during the COVID-19 (see Table 5 ). Interestingly, compared to the existing literature, this study finds perceived consumers' responsibility, COVID-19 impacts on delivery business, benefits of competition, free food delivery service, and pickup/takeout services as unique concerns expressed and discussed by customers during the COVID-19 pandemic. During the COVID-19 pandemic, customers in India want OFD companies to be more socially sensitive, while US customers are concerned about discounts, coupons, free food delivery, etc.

Identified dimensions in this study and comparison with dimensions in literature.

We further observed that the net sentiment (i.e., positive-negative ) differs across COVID-19 waves, brands, market sizes, countries, and listed (see Table 6 , Table 7 ). Net sentiment during the second COVID-19 wave is significantly higher than that of the first COVID-19 wave. However, we also found that negative sentiment in the second wave (0.0761) was higher than in the first wave (0.0696) and significantly different (p < 0.05). It conveys that even though negative sentiment increased in the second wave, OFD companies have used their learning and experiences from the first wave and therefore could improve net sentiment during the second wave of COVID-19. Our findings are different from the literature ( Hong et al., 2021 , Hong et al., 2021 ) that finds no moderation effect of COVID-19 on the relationship between explanatory variables and intention to use OFD apps. We find that COVID-19 waves significantly influence customer sentiment, which aligns with the literature ( Birtus and Lăzăroiu, 2021 ; Mehrolia et al., 2021 ; Watson and Popescu, 2021 ), that observed that COVID-19 reshaped customers behaviors, attitude, and expectations.

Analyzing net sentiment across brands, we find that except for Grubhub and Swiggy (p > 0.05), all other pairs of brands are significantly different based on the net sentiment. Grubhub's mean net sentiment (0.0398) is significantly higher (p < 0.05) than Uber Eats (0.0175) and Zomato (0.0274). It shows that Grubhub performed better than other OFD companies. Swiggy received the second-highest mean net sentiment score (0.035), which is significantly higher (p < 0.05) than the net sentiment of Uber Eats and Zomato (see Table 7 ). Finally, Zomato's net sentiment is significantly higher (p < 0.05) than Uber Eats. Overall, Grubhub received the highest net sentiment, which is significantly higher than all other OFD companies. In India, Swiggy performed better than Zomato, whereas Grubhub did better than Uber Eats in the US. Grubhub also received the highest negative sentiment (0.07489), followed by Zomato (0.0729), Uber Eats (0.0710), and Swiggy (0.0683).

Table 7 further conveys that Grubhub's and Zomato's negative sentiments are significantly (p < 0.05) higher than Swiggy's negative sentiments. The negative sentiment of the remaining pair of companies does not differ significantly. Our findings indicate that brands play an important role in driving customer sentiment. This finding is aligned with previous literature ( Gani et al., 2021 ) that observed that restaurant reputation influences OFD apps' usefulness during the COVID-19 pandemic. These findings are different from the literature ( Prasetyo et al., 2021 ) that discovers restaurant credibility does not impact the intention to use OFD apps. It also differs from Trivedi and Singh (2021) , who observed more positive and less negative sentiment for Zomato than Swiggy.

Observing the importance of market size, we found that OFD companies with low, medium, and large market sizes received a mean net sentiment score of 0.0350, 0.0295, and 0.0175, respectively. Further, Table 7 clearly shows that low and medium market size companies have significantly higher (p < 0.05) mean net sentiment scores than large market size companies. This interesting finding conveys that low or medium market size companies can do better and receive better customer appreciation if they perform better during a crisis or on special customer demands.

We further observed that the medium market size company received the highest negative sentiment (0.0732), followed by large (0.071) and small market share (0.068). The negative sentiment of medium and large companies is significantly (p < 0.05) higher than those with small market sizes. It conveys that smaller companies may satisfy customers more in odd times than bigger companies. It may be because smaller companies might be delivering more customer-friendly services as they may be determined to increase their market share by satisfying customers. Challenging times like the COVID-19 pandemic can be used as an opportunity to create more customer-based value. Our findings are different from the literature ( De Mendonca and Zhou, 2019 ) that says larger companies can generate higher profit and customer satisfaction. Our results are more contextual and complex than the literature that says companies with higher market value may positively influence customers' perception ( Hu et al., 2009 ) and repurchase intention ( Bao and Zhu, 2022 ).

Analyzing results at the country level, we noticed that the mean net customer sentiment scores in India and the US are 0.0318 and 0.0205, respectively. Table 7 further confirms that India's net sentiment score is significantly higher (p < 0.05) than the US. As the US is a developed country with excellent infrastructure and customer-based operations are more prevalent—one may expect better customer satisfaction and customer experience from OFD companies in the US. However, interestingly, we find customers are more satisfied with OFD companies in India than in the US. These findings are in line with previous studies ( Charm et al., 2020 ; Steyn et al., 2010 ) that found customer sentiments vary greatly across different countries. Consumers in India, China, and Indonesia display more optimism than in the US (and the rest of the world) ( Charm et al., 2020 ). As far as negative sentiment is concerned, we find no significant difference (p > 0.10). OFD companies in India and US attract similar negative sentiments. It may be because the challenges faced by the companies are almost the same, and the reasons for customers to get discontent are identical, especially in terms of sanitization and COVID-19 appropriate behaviors.

Further, we found mean net sentiment score of the publicly listed companies (0.0295) is significantly higher (p < 0.10) than those of not listed companies (0.0264). Listed companies may have better perceptions and brand names that may positively influence customers' net sentiment. It is also possible that listed companies may adopt a more stakeholder-oriented approach ( Hickman, 2019 ), bringing better customer experiences. These findings align with the literature ( De Mendonca and Zhou, 2019 ; Hickman, 2019 ), highlighting that customers may be more loyal to publicly traded companies as they are more likely to be responsible to the environment and society. The negative sentiment of listed OFD companies (0.07325) also differs significantly (p < 0.05) from unlisted companies (0.06964). Noticeably, the negative sentiment of listed companies is higher than that of unlisted companies. It is possibly because customers have more expectations from listed companies ( Hickman, 2019 ) during the COVID-19 pandemic. Service delivery below the expectation may generate more negative sentiment. Our observations in terms of negative sentiment are different from the literature ( Hickman, 2019 ).

6. Conclusions and future scopes

This study brings a deeper understanding of customers' experience of OFD companies operating in the COVID-19 pandemic, a worse health crisis globally. During the pandemic, thousands of people were quarantined, and the remaining people could not go out to have food in restaurants because of strict lockdowns. During this time, OFD companies delivered food to people who had different expectations from the companies than expectations during the normal time. Especially, sanitization, COVID-19 appropriate behaviors, and social responsibility were among the most important expectations of customers. The central theme of this paper is to explore how OFD companies performed during the COVID-19 pandemic and whether they performed up to the expectations. It also analyzed whether OFD companies' performance differs across emerging and developed countries and different COVID-19 waves. We collected consumer data from Twitter, conducted a qualitative and quantitative analysis to understand several intricacies of OFD companies' operations during the COVID-19 pandemic, and compared our findings with existing literature. This study throws several exciting findings.

The most talked-about topics were social responsibility, delivery operations, perceived consumer responsibility, financial impacts of COVID-19, etc. People in India and the US think differently regarding OFD service during such a hard time. People in India are more concerned about the responsibility of OFD and consumers towards society, but in the US, people discuss more about discounts, coupons, free food delivery, etc. Therefore, managers of OFD companies in India and the US should prioritize their services accordingly. Our findings convey to managers of OFD companies that customer expectations during a crisis are not usual expectations (such as prompt service, good taste, etc.); they expect something beyond. Therefore, managers must understand expectations first and deliver experience accordingly. A few previous studies demonstrated more generic expectations. Theoretically, we observe that dimensions of customer expectations during the COVID-19 crisis differ from normal times.

We further offer important theoretical contributions that the net sentiment of people differs across different COVID-19 waves, brands, market size, countries, and publicly listed (or unlisted) companies. OFD companies improved net sentiment in the second wave of COVID-19, perhaps by using their learnings from the first wave. Overall, Grubhub received the highest net sentiment as well as the negative sentiment. Hence, to minimize negative sentiment, managers of Grubhub should align their service operations as per customer expectations. In India, Swiggy did better than Zomato. Managers of OFD companies should see the events like COVID-19 as opportunities where OFD companies with smaller market sizes also can do better than medium and large-market size companies. Customers are more satisfied in India than in the US; however, negative sentiment is almost equal in the two countries. The publicly listed OFD companies generate higher net sentiment as well as negative sentiment than those of unlisted companies. Hence, managers of listed OFD companies should control negative sentiment as per the performance on different topics identified. Interactions of COVID-19 waves, brands, market size, countries, and publicly listed (or unlisted) companies with net and negative sentiment of customers of OFD companies and its intricacies are interesting theoretical implications of this research.

This paper compares and contrasts OFD companies' consumer service and operations across emerging and developed countries and during the first and second waves of the COVID-19. By studying OFD during the COVID-19 pandemic, this study significantly and uniquely contributes to the existing literature ( Huang, 2021 ; Kim et al., 2021 ; Yeo et al., 2021 ) on retailers and consumers. We identified new topics of importance during a hard time of the COVID-19 pandemic, which are entirely different from topics discussed before the COVID-19 outbreak. We further identified different variables which are significantly influencing customers' sentiment.

Like any other study, this paper also has some limitations. We mainly studied OFD companies operating in India and US by collecting customers' data from Twitter. Future studies can extend the scope of this study further. In addition to the US and Indian markets, it would be interesting to study people's sentiments and identify concerning dimensions about OFD companies' performance in the European and South American markets. Further, the inclusion of financial variables, such as revenue and profit for OFD companies' delivery performance, would be another exciting study. Nonetheless, this study documents the service and operations performance of OFD companies during the COVID-19 health crisis on a large scale which can be used for a future contingency plan. Finally, another promising problem for future studies is investigating how people's sentiment affects the OFD companies' performance in the stock market during the COVID-19 period.

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A case study on Zomato – The online Foodking of India

2020, IP innovative publication pvt ltd

Zomato started in 2008 underneath the name, ‘Foodiebay’ to begin with. Later in 2010, it had been renamed to ‘Zomato’. Constantly 2011, Zomato extended to increasingly urban regions the country over in Mumbai, Delhi NCR, Chennai, Bangalore, Kolkata and Pune. After that in the year 2012, the corporate extended working all around in various countries like the UAE, Qatar, Sri Lanka, UK, South Africa and Philippines. In the year 2013, Zomato had moved their organizations in Brazil, New Zealand, Turkey and Indonesia, with its applications and site open in various lingos isolated from English. After that in April 2014, Zomato impelled its organizations in Portugal Republic, trailed by Canada, Lebanon and Ireland around a similar time. The acquiring of Settled - based sustenance zone ‘Urban spoon’ signified the organization’s passageway into the United States, Canada and Australia, and conveyed it into direct test with ‘Wail’, ‘Zagat' and ‘Open Table’. With the introduction of .xxx zones in 2011, Zomato also impelled ‘zomato.xxx’, a site dedicated to finding spot to eat near to your territory. It later moved a print adjustment of the site substance named, ‘Citibank Zomato Restaurant Guide’, got together with Citibank in May 2012, at any rate later it was halted.

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TVISHA BHANOT

With digitization, the food industry has very efficiently utilized the e-commerce platform in the online food review and food ordering business. Through a vertical system of food delivery apps, the entire system has brought almost every restaurant in India under a single roof in the hands of the consumer. Now, finding a great place to eat, ordering food, reserving tables, and giving positive or negative feedback for a restaurant has become very easy and accessible for the consumer. This vertical has shown tremendous growth over the last 10 years signifying the untapped potential. The pioneer and leader of this segment in India has been Zomato. Zomato is a brand, which every consumer would relate to when s/he thinks of an e-food platform. This paper aims at critically examining the marketing and positioning aspect of the company by drawing on the works published in the form of surveys, journals, articles and reports by independent authors as well. This paper covers certain figures me...

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The country's GDP grew at a modest 4.5 per cent in the September quarter 2019, and the official data released showed a sixth straight fall in quarterly GDP growth and also the first time fall below the psychologically important 5 per cent mark in almost seven years. It is in this context that the festive sales hosted by the e-commerce sector ended first week of October 2019 where the e-tailers in India, mainly Amazon and Flipkart, achieved a record $3 billion (about Rs 19,000 crore) of Gross M erchandise Value (GM V) during the period as per a report by consulting firm RedSeer has to be evaluated. The success of business models, whether it be in e-tailing (amazon, flip-kart etc.), transportation (Uber, Ola Cabs etc..) or online ordering from eatery apps (Ubereats, Swiggy, Zomato etc.) despite the reverse trend in GDP growth and sustained recession, needs to be evaluated in the context of innovation applied and technology adoption. It is in the backdrop of above said upsurge of business model innovations that can combat the challenges in downfalls of an economy and/ or ever-increasing competition on a global platform, the effectiveness of business models assumes significance. A laggard manager clinging on to his age-old business model is now forced to look forward to articulate their existing business model, since the core enabler of a firm's performance is an effective business model. Understanding the possibilities for innovating through theoretical insight and practical guidelines needs identification of types and the development of a typology of business model innovations. The online eatery business of restaurants, with key partners such as payment processors, mapping data providers and delivery bike drivers through channels such as mobile apps and telephone ensures customer relations by providing convenience in the form of wide choice of sourcing and menu as well as easy payments has found its own way into urban and semi-urban centres of almost all the

IAEME PUBLICATION

Technology innovation has changed all the everyday exercises we do. Online food ordering service is among them. Online food ordering service was first begun by Zomato in the year 2018 pursued by Swiggy, Food Panda and Uber eats. Even though the physical sustenance administrations given by different Restaurants and lodging is as yet ruling the time at present, the online structure which has risen as of late is going to command the physical form. This pattern is very unmistakable in created developed cities in India. Subsequently, this article endeavors to discover the pattern in creating city like Bhubaneswar and different elements influencing that. This article talks about the inclinations of online food preferences that measurably and confirms the outcomes with statistics tools and techniques. Besides, it talks about the fate of offline food services in near future.

Rupkatha Journal on Interdisciplinary Studies in Humanities

Gitanjali Roy

Food habit articulates the local culture of a region. Tripura, a land-locked state of varied communities (the tribes and Bengalis of the soil) negotiates the countercultural exchange of cuisines. Traditional ethnic foods are markers of shared cultural values and identity. Preparation of traditional food involves the role of memory which involves passing down culinary skills, techniques, and ingredients from one generation to the next. The marketing industry and the restaurant culture have changed the taste of the consumers but again the ‘losses’ and the ‘need’ to preserve the traditional cuisines are archived in digital platforms. With the rise in YouTube food channels, Facebook pages, food delivery companies like Swiggy and Zomato; the local food met with the global consumer culture. On one hand, lost ethnic food habits are preserved by documenting the procedures of cooking traditional dishes. On the other, restaurants and bloggers are experimenting to prepare local food using glob...

Publisher ijmra.us UGC Approved

Online food delivery portals and applications have changed the way people select and order their food. The advancements in technology and easy availability of the internet and smartphones paved the way for online food ordering services. It was a concept unheard of in India, when the founders of Zomato decided to create a platform where people could conveniently browse through food menus of different restaurants, order online and get the orders delivered at their doorsteps. It was on a random afternoon when Mr Pankaj Chaddha and Mr Deepender Goyal noticed a long queue of people to glance through the menu card and order their lunch. This got them thinking and eventually resulted in the inception of Zomatothat time called Foodiebay. Since its inception in the year 2008, Zomato has grown exponentially. It started as a food directory which instantly gained popularity in Delhi NCR. And within 9 months the website gained popularity in other cities like Mumbai, Kolkata, Chennai, Bengaluru. In 2012, Zomato went international with its presence in many countries like UAE, UK, Sri Lanka, Philippines etc. Zomato steadily kept increasing its customer database and also ventured into online table reservation, finding nearby restaurants. However, Zomato has also undergone its share of crises. But be it because of its unconventional communication or other practices, Zomato does not shy away from communicating with different stakeholders. Zomato"s communication has never held back from responding and reaching out to their customers and community. Today Zomato has over 119 thousand restaurants against 43 thousand restaurants in 2019. Zomato has all the necessary elements that makes it a successful case to be explored. Apart from crisis management, it also has been able to strike the right chord with the sentiments of Indian customers by connecting with them at an emotional level. This case study delineates the journey of brand Zomatoan Indian entrepreneurial ventureand analyses the communication strategy adopted by the brand especially while managing any crisis situation. The paper also studies the measures adopted by Zomato to emerge as a stronger brand as the country grappled with lockdowns and other restrictions during the Pandemic. Based on the broad framework provided by the Situational Crisis Communication theory, the researchers have undertaken the evaluation using SWOT analysis to cover all aspects of the journey of Zomato so far.

International Journal for Research in Applied Science & Engineering Technology (IJRASET)

IJRASET Publication

Online food delivering system is the process where various restaurants and food outlets deliver their foods and drinks according to the demand of the customer and give the maximum customer satisfaction and quality service. Although, there are various factors that depends on demand and supply and what goes in the customer's mind. What motivates the customers, what benefits they will be having by ordering food online and what are the issues they face while adoption online food and ordering it. This paper will provide information of such factors. It is discussed what are the biggest reasons for same, how people achieve the benefits. How and where people face the issues and how can we solve it. In times of outbreak of Covid-19, technology helped in different ways. One of them was to deliver food to people in those tough times when people were sick, could not go out of their houses and protecting themselves and others as well. Hassle-free online payment made the things easier and still doing the same.

The Cornell Hotel and Restaurant Administration Quarterly

Edward Forrest

International Journal of English Language, Literature and Translation Studies

fahima faigee

In recent years, social media usage and growth in India have been equally significant. It has developed into a potent instrument for social networking, information exchange, and communication among India's vast population. Digital food culture has recently gained popularity and significantly altered India's culinary culture. Therefore, the purpose of this paper is to investigate the pervasive phenomena of digital food culture in Indian society today. The study also emphasizes on the transformation of capitalism into media capitalism. Furthermore, this paper also looks at how media capitalism makes customers into spectators and turns them into sheeple for monetary gain. Netizens, however, act in a sycophantic manner while denying the influence of media capitalism. Thus, the study intends to investigate how media capitalism affects customers and viewers in India through the lens of digital food culture and food bloggers. Therefore, the theory of media capitalism and spectacle is used to analyze these issues.

Claudio Nigro , Naomi Di Santo

Purpose-The paper aims to investigate the phenomenon of online food purchasing in Italy and, specifically, Italian consumers' future intentions to use these services even after the end of the COVID-19 emergency. To this purpose, to understand if these phenomena are robust enough to represent irreversible and well-established trends, in the present study consumers Behavioural Intention was investigated. Design/methodology/approach-A theoretical framework based on a preliminary literature analysis on the use of delivery services was developed. Then, an online survey was carried out in Italy during 2021. The data were then elaborated through an exploratory factor analysis, followed by confirmatory factor analysis. Following the verification of constructs reliability, a structural equation modelling using the statistical tools, SPSS and R (Lavaan) was performed. Findings-The results suggest that Behavioural Intention is influenced by items such as convenience, variety of food, and time savings. Moreover, the relevant role of social networks and interpersonal relationships in influencing consumers' purchasing intentions, has highlighted the importance of focusing on advertising campaigns and good "word of mouth." Originality/value-The present research represents the first attempt to investigate the Food Delivery and ghost kitchens phenomena, recently accepted thanks to the delivery applications use in Italy, a Country with strong gastronomic tradition and with an important digital gap. In this scenario, the research focused on the attention on the consumers' future intentions to use these services even after the end of the COVID-19 emergency.

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