Mapping Online Food Delivery Servicevis-a- vis Customer Satisfaction and Behavioural Intention: A Bibliometric Analysis

Piyali Mullick1, Bikram Singh2*

1University of Calcutta, 9/A, Haladhar Bardhan Lane, Bowbazar, 700012 Kolkata, India

2University of Calcutta, 87/1, College Street, 700073 Kolkata, India

*Corresponding Author’s Email: bsbikramsingh@gmail.com

Abstract

Introduction: This study explores the progression of research on online food delivery (OFD) services and their impact on customer satisfaction and behavioral intentions, using bibliometric analysis. Methods: A total of 524 publications indexed in the Scopus database, published between 1996 and January 2025, were analyzed to identify research trends in this domain. The analysis examined various categories, including scientific productivity, total citations, most cited articles, authors, journals, leading institutions, country contributions, co-citations, and thematic mapping. Results: The year 2020 emerged as the most prolific in terms of publications. The most influential institutions were Boston College (US) and Florida State University (US). The United States was the most productive country, contributing 111 publications. The study also identified the top ten most prolific journals in the field, ranked by ABDC, SJR, H-index, and citation scores, along with five thematic clusters. Conclusion: The study visualizes the research patterns, thematic evolution, and influential contributions in the fields of OFD, customer satisfaction, and behavioral intention. It answers five research questions outlined at the outset and provides valuable insights for service providers, policymakers, and practitioners aiming to enhance customer satisfaction and overall experience in OFD services. Limitations: The analysis was limited to the Scopus database and employed only VOSviewer and Biblioshiny software. Future studies could expand the scope by including multiple databases, broader timeframes, and alternative analytical tools. Originality/Value: This paper provides an in-depth examination of OFD services in relation to customer satisfaction and behavioral intentions—an area that has received limited scholarly attention. It also offers practical and theoretical implications for advancing research in this domain.

Keywords: Behavioral Intention; Bibliometric Analysis; Customer Satisfaction; Online Food Delivery Service; Thematic Clustering

Introduction

Convenience, comfort, and time efficiency have triggered consumers to switch to online shopping (Jiang et al., 2013). A paradigm shift has also been recognized in the food delivery services from the traditional mode of food delivery to the online form of food delivery (Dai et al., 2022). Factors for adopting OFD service include ease and quick delivery of food at the doorstep of the consumers, simple access to various food options, easier payment, and lucrative price offers (Katoch & Sidhu, 2021). The food and beverage industry has been forced to adopt and provide new offerings to cater to the increasing demands of consumers based on several reasons, including cheaper smart devices, the fast development of telecommunication infrastructure, increasing capacity of purchase, lack of time, and convenience (Bezerra et al., 2013).

The present study offers a literature evaluation based on bibliometric analysis to answer a variety of research questions. The following are the prominent research questions (RQs) in the domain of OFD vis-à-vis customer satisfaction (CS) and behavioral intention (BI) that require further analysis:

RQ1: What is the publication trend?

RQ2: Who are the most influential authors, important institutions and leading countries? RQ3: Which are the most important sources, articles and references to the literature?

RQ4: What are the top co-citations in the study theme?

RQ5: What are the thematic clusters and the thematic trend over the study period?

To determine the answers to the above research questions, the ‘Scopus database’ search results have been analyzed using the VOSviewer and Biblioshiny applications for bibliometric analyses such as bibliographic coupling (BC), co-occurrences, and co-authorship analyses.

Literature Review

Over the last few years, online food delivery (OFD) services have become increasingly widespread (Alalwan, 2020). OFD allows consumers to meet both social and personal needs and provides a platform for purchasing a wide range of products and services, whether online or in physical stores (Cho et al., 2019). Troise et al. (2021) emphasized that convenience—through savings in both time and travel expenses—can lower the overall cost of food, thereby encouraging online purchasing behavior. Jun et al. (2021) found that perceived usefulness (PU) influences the use of OFD services both directly and indirectly via customer attitudes. Vinish et al. (2021) highlighted aggregator attractiveness as a key factor that can significantly affect customers’ intention to repurchase. Limantara et al. (2022) demonstrated that online ratings, online tracking, PU, and perceived ease of use (PEOU) significantly influence customer satisfaction (CS), which in turn drives the intention to reuse OFD services. Wijaya et al. (2024) examined the role of attitude and subjective norms in shaping CS and behavioral intention (BI) among OFD consumers in Indonesia. Sulaiman et al. (2024) explored drone-based food delivery in Malaysia, extending earlier research on the use of the UTAUT2 model by Mathew et al. (2023). Chiu et al. (2024) applied a meta-analytic structural equation modelling approach to identify the key factors influencing OFD adoption. Leung et al. (2025) investigated young adults’ consumption patterns on OFD platforms during the COVID-19 pandemic, while Kim and Hwang (2025) studied self-driving robot services for food delivery, finding that innovativeness significantly impacts consumer attitudes. Taheri et al. (2025) identified barriers to OFD adoption, and Mohamadabadi et al. (2025) applied TISM and Delphi techniques to determine the most critical factors for enhancing the OFD customer experience.

Online Food Delivery Service and Tourism: The Linkage

The impact of online food delivery (OFD) on the tourism sector is significant, encompassing offerings such as sustainable packaging, eco-friendly practices, diverse culinary options, and, above all, convenience for travelers. Rising demand for these services, coupled with a growing preference for personalized experiences, is reshaping the future landscape of customer engagement in tourism (India Brand Equity Foundation, 2024). In the aftermath of COVID-19, shifts in traveler behavior have heightened the need for digital and contactless food delivery (Kim et al., 2021). The expansion of digital tourism has also introduced new challenges, as travelers increasingly seek personalized and immersive experiences. OFD enables tourists to connect with hyper-local culture by pre-ordering regional cuisines before arrival or exploring locally recommended dishes during their trip. This not only enhances overall satisfaction but also positions OFD as an integral component of the modern travel ecosystem.

Methodology

The Terms Used for Data Extraction

In this research, three areas were examined: online food ordering, customer satisfaction, and behavioral intention (BI). Table 1 outlines the search procedure in the Scopus database, including the keywords used: online food ordering, e-food ordering, OFO, e-restaurant (with truncation), virtual food ordering, food delivery service, OFOD, customer satisfaction*, and behavioral intention. The search covered literature from the fields of business, management and accounting, social sciences, economics, and the arts, published between 1996 and January 2025. The retrieval of metadata is detailed in Table 1. Initially, approximately 563 publications were identified. After applying language and document-type filters, the final dataset comprised 524 records.

Table 1: Criteria for ‘Including’ and ‘Excluding’ an Article

Data Collection

Table 1 highlights the methodical technique employed to research the final figure of 524 articles and conference papers. The researcher used the ‘Scopus database’ for the bibliometric study. ‘Scopus database’ is often considered as one of the leading academic databases for social sciences for its comprehensive coverage, high-quality sources, global reach, user-friendly interface, advanced search features, regular updates, integration with other tools, etc. It has been documented that the ‘Scopus database’ offers about 20% more coverage than the ‘Web of Science database’, whereas the ‘Google Scholar database’ offers results of inconsistent accuracy. ‘PubMed database’ is good for biomedical research. ‘Scopus database’ covers a wider journal range, which is valuable both in keyword searching and citation analysis (Falagas et al., 2008). The period of study is between 1996 and January 2025. The search results revealed documents beginning in 1996; hence, it has been taken as the starting year. The keywords ‘online food ordering,’ ‘e-food ordering,’ ‘ofo,’ ‘e-restaurant*,’ ‘virtual food ordering,’ ‘food delivery service,’ ‘ofod,’ ‘customer* satisfaction,’ and ‘behavioral intention*’ were used. The search was further refined to include papers from the fields of business, management and accounting, social science, economics, and arts. Only articles and conference papers written in the English language were included. The researchers passed through various procedures of data cleaning to avoid duplication and other errors that are integral to such analyses.

Data recovered from the ‘Scopus database’ was pulled in CSV format; thereafter, the file was imported into VOSviewer and Biblioshiny (R Software), where multiple analyses were conducted as per the research questions formulated. In addition, some tests were done based on the ‘Scopus database’ using Excel. For better analysis and results, VOSviewer software provided a tool to clean various words presented in this article’s ‘titles, abstracts, and keywords’ by using ‘natural language processing.’ For example, the researchers used to include related searches (e- restaurant or e-restaurants to e-restaurants, virtual restaurant or virtual restaurants to virtual restaurant*, customer satisfaction or customers satisfaction to customer* satisfaction, and behavioral intention* or behavioral intention*). Finally, after many cleanups, the method aids the analyses that were done.

Selecting the Technique for Analysis

Bibliometric analysis is a prudent approach to finding out the evaluation of research domains, including topics and authors, based on various disciplines, namely social, intellectual, and conceptual structures (Donthu et al., 2020). Bibliometric analysis has grown in trend in the research community in recent years (Khan et al., 2021; Donthu et al., 2021). Due to this, the evolution, attainability, and advancement of bibliometric applications like ‘CiteSpace,’ Biblioshiny,’ VOSviewer,’ etc., with the electronic research databases in various disciplines have been used by researchers from information theory to business and management research (Sharma et al., 2022).

The present research provides a bibliometric overview of the research front on online food delivery (OFD), customer satisfaction (CS), and behavioral intention (BI) to identify emerging trends and concepts in the field. The descriptive analysis covers the total number of publications, citations, and keyword analysis (Hu et al., 2019). In contrast, the science mapping analysis (Baheti & Lenka, 2021) includes bibliographic coupling, co-occurrence, and co-authorship analyses.

Results

Analysis of Publication Trends

Source: Scopus database

Figure 1: Publication Trends

Figure 1 represents the publication trends in the area of OFD and answers RQ 1. It is observed that 2020 has the highest number of publications. It may be mentioned that the study in this domain was pioneered by Gupta and Stewart (1996). Initial research mainly focused on testing CS and post-purchase BI. According to figure 1, it is showing an increasing trend in the year- wise publications. It is observed that 2011 is the most important year in the publication trend. Authors have focused on the areas of service quality (SQ), service value (SV), CS, brand loyalty (BL), and BI. However, the maximum number of articles published on the domain of OFD is in the year 2020, and the rate of usage of OFD service is gradually rising every year. The study domain is going to grow in the future.

Table 2: Most Influential Authors, Institutions and Countries

Note(s): TC = total citations, TP = total number of publications (Source: Author’s own compilation)

Table 2 indicates the most influential authors, institutions, and countries in the study area, and it answers RQ 2. The minimum number of documents and citations of an author was set at 1 and 920, respectively, for selecting the top ten authors. The minimum number of documents and citations of an institution was set at 1 and 826, respectively, for selecting the top ten institutions.

Finally, the minimum number of documents and citations of a country was set at 1 and 1122, respectively, for selecting the top ten countries.Tomas Hult is the most influential author, with 4520 TCs and 3 TPs, followed by Michael K. Brady with 4295 TCs and 2 TPs. Similarly, the most important organizations are ‘Boston College, US’ and ‘Florida State University, US,’ with 4196 TCs each having 1 publication each, followed by ‘Dong-a University, SK,’ with 1045 TCs having published 3 documents. Likewise, the most influential country is the ‘United States’ with 18124 TCs and 111 TPs, followed by ‘South Korea’ with 5683 TCs with 61 TPs. Furthermore, Hwang, Jinsoo, is the most productive author, and the United States is the most productive country.

Table 3: Most Important Journals

(Source: Authors own compilation)

RQ 3 comprises three distinct analyses, namely, the most important sources, articles, and references to the literature. Table 3 represents the most important sources in the study area and answers the first component of RQ 3. The minimum number of documents and citations of a source was set at 1 and 837, respectively, for selecting the top ten sources. TCs indicate how often a research paper, journal, or author is cited by other research scholars. The most influential source in this category is ‘Journal of Retailing’ with 5282 citations and 3 documents, followed by ‘Journal of Services Marketing’ with 2523 citations and 23 documents. According to ABDC (2022), Journal of Retailing, International Journal of Hospitality Management, Tourism Management, and Journal of Service Research are A* journals.

The others, except Managing Service Quality, are all A-quality journals. SCImago Journal Rank (SJR) is a metric used to evaluate the scientific influence of scholarly journals and is based on information from the ‘Scopus database.’ It may be mentioned that a journal with a higher SJR indicates that it has received more citations from other reputable journals, suggesting a higher level of influence and impact within the scientific community. The SJR indicator is an open- access resource, while the Journal IF (JIF) requires a paid subscription. The SJR indicator based on the ‘Scopus database’ lists considerably more journal titles published in a wider variety of countries and languages than the JIF based on the ‘Web of Science database’ (Falagas et al., 2008).

According to SCImago Journal Rank (SJR) (2024), all the journals except ‘Managing Service Quality’ are in the ‘Q1’ category, with ‘Journal of Service Research’ having the highest SJR, i.e.,6.011. The ‘SCImago H index’ measures both productivity and citation impact. ‘Tourism Management’ has the highest ‘SCImago H index,’ followed by ‘Computers in Human Behavior.’ ‘Journal of Services Marketing’ is the most productive journal with 23 documents, followed by ‘International Journal of Hospitality Management’ with 16 documents. The most productive period is from 1996 to 2019.

Table 4: Most Important Articles

(Source: Authors own compilation)

Table 4 represents the most important articles in the study area and answers the second component of RQ 3. In order to get the top ten documents in table 4, the minimum number of citations of a document was set at 477. Normalized TCs are used to compare the citations' impact across different fields and publication years, and they help provide a fair assessment of research influence. With 4196 TCs, the article by Cronin et al. (2000) is the most influential. This study focuses on the effects of quality, satisfaction, and value on consumers' BIs and assesses the relationship between the above-mentioned constructs across multiple service industries. With 920 TCs, Dabholkar et al. (2000) conducted the second most influential study. This study identified CS's work as a strong mediator between SQ and BI.

Table 5: Most Important References

Note(s): LC = local citations, GC = global citations (Source: Author’s own compilation)

Table 5 presented the most important references of research publications based on LCs and their GCs, addressing the third component of RQ 3. A fair comparison for variations in citation behavior by accounting within the local context is known as normalized LCs. When normalization adjusts for differences in citation rates across disciplines, publication years, etc. in a global context, it is known as normalized GCs (Aria and Cuccurullo, 2017).

The paper authored by Ryu et al. (2012) is the top reference paper with 45 LCs and 825 GCs, a 5.45 LC to GC ratio. They observed that the important factors of restaurant image are the quality of the physical environment, food, and service. Restaurant image plays an important role for determination of customer perceived value (PV). Customer PV is both an expressive factor and an anticipator of BI. Similarly, Dabholkar et al. (2000) have the second-highest references, with 36 LCs, 920 GCs, and a 3.91 LCs-to-GCs ratio. According to their study, the factors of SQ are better considered as its causes rather than its elements. CS mediates between SQ and BIs.

Knowledge Foundations Through Co-Citation

Figure 2: Co-citation of References

Donthu et al. (2021) identified that for getting a field’s knowledge foundation, co-citation analysis is important. Figure 2 shows the co-citations map of references (Van Eck & Waltman, 2010). In the review compilation, the cited references were cited at least twenty times. Figure 2 answers RQ 4. Zeithaml et al. (1996), as depicted by blue nodes, have highly cited works on the behavioral consequences of SQ and CS. Oliver and Nunnally, depicted by red nodes, have highly cited the work on SQ and BI. Gronroos and Zeithaml, depicted by green nodes, have highly cited the work on consumer perception and SQ.

Table 6: Thematic Clusters Through Bibliographic Coupling (BC)

(Source: Authors owns compilation)

RQ 5 has two components, namely, the thematic clusters and the thematic trend over the study period. Table 6 shows the theme clusters of OFD services, CS, and BI, providing an answer to the first component of RQ 5. To get the clusters through BC, the minimum number of documents and citations per author was set at 1 and 126, respectively.

Table 6: Represents the Theme Clusters of OFD Service

Cluster 1 relates to the physical environment and restaurant image. Ali and Amin (2014), in the context of the resort hotel industry in China, examined the impact of the physical environment on emotions, customer satisfaction (CS), and behavioral intentions (BIs). They found that customers with higher perceptions of the physical environment experienced positive emotions, which in turn increased CS and BI. Therefore, to maintain strong CS, organizations should focus on enhancing their services to foster positive emotions. Han et al. (2008) investigated the quick- casual restaurant industry and identified links among overall restaurant image, perceived value (PV), CS, and BIs. Their findings indicated that overall restaurant image significantly influences PV; both restaurant image and PV influence CS; and, ultimately, all three variables significantly influence BIs.

Olorunniwo et al. (2006) assessed the relationship between service quality (SQ), CS, and BIs in a service factory context. They concluded that SQ directly influences BI, whereas CS, functioning as a mediator, plays a stronger role in shaping BI in the service industry. Similarly, Ryu et al. (2012) found that restaurant image, customer PV, CS, and BIs are positively influenced by the physical environment, food, and service quality. Their results also showed that PV is a significant predictor of CS, which in turn strongly impacts BIs. Bigné et al. (2008) observed that satisfaction influences both loyalty and BIs. They further noted that exceeding pre-purchase expectations strengthens on-the-spot consumer behaviors and that pleasure is positively associated with satisfaction and loyalty behaviors. Overall, the studies in this cluster have primarily focused on restaurant image, physical environment, and BIs, while largely overlooking issues relevant to non-physical service modes.

Cluster 2 is concerned with SQ and its measurement. Cronin et al. (2000) examined the effects of quality, satisfaction, and value on consumers' BIs. They identified that when all the variables are taken collectively, SQ, SV, and satisfaction may all be directly related to BIs. Moreover, the indirect effects of the SQ and value constructs enhanced their impact on BIs. Dabholkar et al. (2000) examined the constructs of SQ. They identified that the relationship between SQ and BIs is strongly mediated by CS. This paper elaborated on the utilization of the chronological framework in understanding and anticipating SQ and its consequences. The studies in this cluster were limited to only service quality and its measurements. The studies ignored PV, subjective norms, attitude, and customer loyalty and their importance in OFD.

Cluster 3 includes the research on e-services. Liao et al. (2007) revealed that CS is widely used to identify the BI of customers towards e-service continuance. Additionally, they said that customers’ BI was also affected by PU and subjective norms. Wang et al. (2009) investigated the attributes of m-learning acceptance and explored the impact of age and gender differences in the acceptance of m-learning. They identified that performance expectancy, effort expectancy, social influence, perceived playfulness, and self-management of learning are the remarkable factors of BI to use m-learning. The studies in this cluster focused primarily on e-services, a component of OFD; however, it is not directly related to the study domain. They focused more on the tools for engagement in the online system.

Cluster 4 includes the research of drone food delivery services (DFDs). Hwang et al. (2020) attempted to explain eco-friendly BI formation in the context of DFDs using the norm activation model (NAM) and the theory of planned behavior (TPB). The study investigated the moderating role of product knowledge about the pro-environmental role of the DFDs. This study was only focused on a particular geographical area and on DFDs. Kim et al. (2021) studied DFDs with an aim to identify the BIs before and after the outbreak of COVID-19. According to their findings, perceived innovativeness positively impacts attitude. The studies in this period have contributed greatly to the study domain.

Cluster 5 focuses on hedonic, utilitarian values and food quality. Ryu et al. (2010) investigated this theme in the fast-casual restaurant industry. They observed that hedonic and utilitarian values remarkably influence CS, and CS has a significant impact on BIs. On the other hand, utilitarian value represents a greater influence on CS as well as BI than hedonic value. The purpose of the study of Namkung and Jang (2007) was to investigate the impact of perception of food quality on CS and BIs in mid- to upscale restaurants. Their findings indicate CS is determined by overall food quality, and satisfaction mediates the relationship between food quality and BIs. The studies in this cluster have contributed to the hedonic and utilitarian values and food quality but are confined to the physical form of food delivery service.

Thematic Trends

Author keywords have been used for co-occurrence analysis. Keywords are specific terms which are used to highlight the central theme of the document (Zou et al., 2018). As keywords are assumed to be representative of the intent of the paper (Comerio and Strozzi, 2019), it helps to establish relationships and build a conceptual structure of the domain.

A chronological filter has been applied here through author keywords to identify the topical development of OFD, CS, and BI. Figures 4-6 depict this theme progression and answer RQ 5.

Figure 3: Influential Topics in the “period 1996-2019”

The network diagram shown in Figure 3 was generated using the VOS viewer. To eliminate noise and focus on the most relevant author keywords, a minimum occurrence threshold of five was applied. Out of 807 author keywords identified from publications between 1996 and 2019, only 33 met this criterion and are displayed in the network diagram. For each of these keywords, the total strength of co-occurrence links with other keywords was calculated, forming the basis of the diagram (Van Eck & Waltman, 2010).

The network comprises seven clusters, represented by colored nodes: red (Cluster 1), green (Cluster 2), blue (Cluster 3), yellow (Cluster 4), purple (Cluster 5), cyan (Cluster 6), and orange (Cluster 7). The clusters contain 8, 7, 5, 4, 4, 3, and 2 keywords, respectively. Larger nodes represent more frequently occurring keywords, with the largest—depicted in red—comprising customer satisfaction (CS), consumer behavior, customer retention, service quality (SQ), the internet, restaurants, retailing, and Taiwan. This indicates that these terms formed the dominant research themes during the study period.

Thicker lines between nodes signify higher co-occurrence frequency and interdisciplinary connections. Strong linkages were observed between red nodes (CS, consumer behavior, SQ), purple nodes (SQ, customer loyalty), and the blue node (behavioral intention, BI). Research during this period also highlighted BI, customer value, satisfaction, trust, and service recovery (green nodes), demonstrating how these factors shape BI and how behavioral models, such as TAM, influence online shopping and gender differences. Yellow nodes (BI, perceived value, corporate image, China) indicate the role of perceived value and corporate image in shaping BI. Purple nodes (SQ, customer loyalty) further reveal how SQ impacts both loyalty and satisfaction. In contrast, cyan (perceived service quality, gender, and behavioral intentions) and orange (structural equation modelling and India) nodes are relatively small, reflecting limited research focus on these themes.

Figure 4: Influential Topics during the “Period 2020-2022”

The network diagram, as shown in Figure 4, was obtained using VOSviewer. To focus on the most relevant author keywords, the researchers set the minimum number of occurrences for a keyword as 5. Out of the 400 author keywords extracted from all the papers published between 2020 and 2022, only 11 authors met this threshold, meaning they appeared five or more times. These 11 author keywords were displayed in the network diagram (Figure 5). Furthermore, for each of the eleven author keywords, the total strength of co-occurrence links with other keywords was calculated, and the network diagram was developed using VOSviewer. The diagram consisted of three clusters (1, 2, and 3), represented by red, green, and blue nodes, respectively. The eleven author keywords were distributed among the three clusters based on their relatedness. Clusters 1, 2, and 3 comprise 4, 4, and 3 author keywords, respectively.

It is observed that the largest node is green, comprising author keywords, namely, BI, CS, PV, and SQ. It means that during 2020-2022 most of the researchers used the keywords in the green node as their research theme. Thick lines linking green nodes and blue nodes (behavioral intention, loyalty, and satisfaction) indicate higher co-occurrences and strong linkage (Van Eck & Waltman, 2010). Research in the same time period highlights attitude, BI, DFDS, and TPB, as depicted by red nodes. This graph indicates how attitude and BI are affected by the adoption of innovative service delivery methods like DFDs. It may be mentioned that during the study period 2020-2022, the studies have examined the factors attitude, DFDs, and perceived value, which were previously ignored.

Figure 5: Influential Topics during the “Period 2023- January 2025”

The network diagram shown in Figure 5 was generated using VOSviewer. To focus on the most relevant author keywords, the minimum keyword occurrence threshold was set at five. Out of 304 author keywords identified from publications between 2023 and January 2025, only seven met this criterion and are displayed in the diagram. For each of these keywords, the total strength of their co-occurrence links with other author keywords was calculated, forming the basis for the network. The diagram consists of two clusters, represented by red (Cluster 1) and green (Cluster 2) nodes. Based on their relatedness, the seven keywords were distributed across these clusters, with Cluster 1 comprising four keywords and Cluster 2 comprising three.

It was observed that the largest node is green, comprising author keywords, namely, BI, CS, and PV. This indicates that during the period from 2023 to January 2025, most researchers focused on the keywords in the green node as their primary research themes. The thick lines between the green nodes indicate higher co-occurrences and stronger linkages (Van Eck & Waltman, 2010). Research conducted in the same period also highlights BI, satisfaction, and service quality, as represented by the red nodes. It may be noted that during the study period (2023–January 2025), little novelty was observed in the thematic trends compared to the period from 2020 to 2022.

Discussion

The researcher has found very few documented bibliometric studies in the study domain. Much of the work has been done in the allied areas of OFD service. Further, the existing bibliometric studies conducted in the field are limited to the use of only one application software or a brief time frame, thereby failing to address the underlying issues more precisely. Shroff et al. (2022) performed a systematic literature review with bibliometric analysis from 2015 to (March) 2021. Kumar et al. (2022) studied the trends for 11 years (2012–March 2022) using the free web app www.dimensions.ai. They concluded that the top three countries in terms of research publications are the United States of America, followed by India and the United Kingdom. This research trend has shown a shift in the current study, with the USA still holding the topmost position but followed by South Korea and then Taiwan. Nasir et al. (2022) used 893 papers published between 1995 and 2022 and highlighted that medicine accounted for most of the papers. Badenes-Rocha et al. (2022) reaffirmed the increasing trend of publications in OFD. Xu and Kim (2023) used the ‘Web of Science database’ and used ‘CiteSpace’ to conduct a bibliometric analysis on articles published from 2016 to 2023. The current paper is unique in many ways. The present study uses VOSviewer and Biblioshiny (R Software) to have a much more profound insight into the study domain, not observed in any of the earlier studies. Further, the current research has been done over a period of almost thirty years, making it unique. There is no documented study where researchers have taken such a long period for analyzing this field's trends. The research outcomes have been quite impressive and different from the previous studies, highlighting the dynamism in this field of research and the need for further research. However, there is an agreement with the previous research papers that the field of OFD is evolving very fast, especially after COVID-19, with increasing research publications in this area. The result validates the need for more research in the study domain.

Limitations

‘Scopus database’ has been used for data extraction, and the same has been analyzed using only VOSviewer and Biblioshiny. Future research can be done using a wider search result using different databases, time periods, and other application software. Moreover, while extracting from the ‘Scopus database,’ the publication type includes only articles and conference papers, and the language is English only. Future research can be done using a wider search result. This study has been conducted for the limited period between 1996 to (January) 2025. A larger time period can also provide a deeper understanding of the study domain. Finally, the constraint of the total number of words has limited the discussion to the most important ones only.

Conclusion

The present research visualizes the research pattern, thematic evolution, and influential research in the area of OFD. This present study provides answers to the five research questions outlined at the beginning of the study. All the research questions have been substantiated with explanations, tables, and references. This research offers suggestions for different stakeholders such as service providers, policymakers, and practitioners in improving the CS and overall experience in the area of OFD. Business owners and restaurateurs can understand market trends and shifts in customer preferences, thereby increasing their operational efficiency. They can discover niche markets or innovative models to boost profitability and enhance CS. This study will help online food delivery platforms to develop features through technology enhancement in app design, logistics, and order process. This study will help academic and industry researchers in knowledge expansion by knowing the gaps and opportunities for technological and business model innovation in OFD. On the whole, this study provides an exhaustive understanding of the research development, emerging areas, and future direction of OFD research.

Future Scope

While this bibliometric research provides an overview of the existing literature, there are other possible ways to explore this domain. Further research could have been conducted to investigate habit formation, trust, and risk perception that influence customer retention in food ordering and delivery services. Studies can be conducted on the sustainability practices, e.g., eco-friendly packaging, carbon-neutral delivery, etc., that affect customer loyalty.

In addition to the above, there may be a comparative study across different geographical regions or food delivery platforms. Through this kind of study, researchers can identify the determinants of consumers’ preference and satisfaction and examine how cultural and economic factors impact consumers’ behavior. In order to enhance customer engagement and encourage repeat orders, researchers can study the role of AI in analyzing past orders, browsing history, and dietary preferences that in turn will suggest dishes and restaurants aligning with a user’s tastes. A study can be made towards optimal pricing strategies using AI. Data extracted for the present study has been taken from the ‘Scopus database’ only. Future study can be done by combining bibliographic data from various databases. Additionally, future research can be conducted using bibliometric analysis on high-quality publications.

Conflict of Interest

The authors declare that they have no competing interests.

Acknowledgement

The researchers remain grateful to the library staff of the University of Calcutta, Kolkata, and IIM, Kolkata, for providing the necessary support to extract data required for the study. The article has not been funded by any institutions, agencies, or individuals.

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