Next Article in Journal
A Comprehensive Virtual Testbed for Modeling Disinfection Byproduct Formation in Water Distribution Networks
Previous Article in Journal
Modelling Consumer Demand in Intermittent Water Supply (IWS) Networks: Evidence from Nepal
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Engineering Proceedings
Volume 74
Issue 1
10.3390/engproc2024074041
engproc-logo
Submit to this Journal
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Proceeding Paper

Impact of Food Delivery Platforms on Consumer Behavioral Intentions During COVID-19 Pandemic †

by
Chih-Wei Lin
1,2,
Yi-An Huang
1,
Wei Yeng Sia
2,
Kuan-Chuan Tao
1,* and
Yi-Chang Chen
2,*
1
Department of Leisure Services Management, Chaoyang University of Technology, Taichung 413310, Taiwan
2
Department of Business Administration, Chaoyang University of Technology, Taichung 413310, Taiwan
*
Authors to whom correspondence should be addressed.
†
Presented at the 2024 IEEE 4th International Conference on Electronic Communications, Internet of Things and Big Data, Taipei, Taiwan, 19–21 April 2024.
Eng. Proc. 2024, 74(1), 41; https://doi.org/10.3390/engproc2024074041
Published: 2 September 2024
Versions Notes

Abstract

:
The COVID-19 pandemic changed people’s dining habits and led to the rapid rise of food delivery platforms. Ordering food online and picking it up offline has become an essential dining habit. We applied the stimulus–organism–response model and constructed a model based on factors influencing consumers’ continuous use intention from three aspects: store product quality, delivery personnel quality, and delivery platform quality. Through snowball sampling, 321 questionnaires were distributed and collected from consumers who used food delivery platforms in Taiwan. All effective questionnaires were analyzed by structural equation modeling with SPSS 18 and Amos 21. The results showed that (1) the S-O-R theory explained consumer behavior effectively. (2) Store product quality influenced store repurchase intention. (3) Delivery personnel quality and platform quality influenced a platform’s continuous usage. (4) Product quality and delivery personnel quality were key factors that influenced store repurchase intention. (5) Platform quality was an important factor that affected platform usage. Finally, recommendations were proposed based on the research findings as a reference for relevant operators in the food delivery platform industry and future researchers.

1. Introduction

As of mid-March 2022, data from the World Health Organization (WHO) indicated that there were over 460 million confirmed cases of COVID-19 globally [1]. This led to an increase in the use of food delivery platforms by consumers to avoid direct or indirect social contact. With the rise of the internet generation and the impact of the 2020 pandemic, the food and beverage industry has been prompted to consider digital technologies to adapt to new business models, such as increasing online food delivery services and integrating online-to-offline services to meet consumer demands. Food delivery platforms helped food and beverage enterprises and restaurants survive and grow during the pandemic and fulfill consumer needs [2].
In terms of market value, the global food delivery business reached USD 248 billion in 2022, and it is expected to reach USD 449 billion by 2025 [3]. Taiwan’s food delivery industry is also experiencing rapid growth, with a market value that reached USD 707 million in 2022, and it is projected to increase to USD 1.034 billion by 2026 [3]. This makes online ordering and delivery services an integral part of the digital transformation in the food and beverage industry that cannot be ignored.
Service quality is a crucial factor for gaining a competitive advantage in the food and beverage industry [4,5,6]. With the continuous growth of the delivery industry, service quality has become a significant concern for many consumers. In the past, the service quality of food delivery was often overlooked by food suppliers, decreasing customer satisfaction and consumer willingness. For food delivery, the assessment of various aspects of service quality is necessary [7,8,9]. Given that service quality in food delivery positively influences customer satisfaction and loyalty [10,11,12], service quality and satisfaction are factors affecting consumers’ subsequent behavioral intentions on food delivery platforms. In this context, using the stimulus–organism–response (S-O-R) model [13], we assessed how environmental stimuli on food delivery platforms influenced consumers’ emotional and cognitive states, thereby leading to behavioral responses.
We proposed a structural model to examine the dimensions influencing customer satisfaction and the subsequent construction of behavioral intentions, including intentions for store repurchase and application. Secondly, we identified factors determining consumers’ continued intention to use food delivery platforms. Thirdly, we validated the use of promotions on repurchase intention behavior. The results of this study contribute to an understanding and reaffirmation of relationships between the quality of products offered by food delivery stores, the quality of delivery personnel and delivery platforms, satisfaction, and behavioral intentions.

2. Literature Review

2.1. Food Delivery Platforms

Delivery platform applications are an emerging form of online and offline mobile technology that provide a channel between food service businesses and customers. This enables consumers to complete the entire process, from ordering food to having meals delivered to their doorsteps, through the application [2,14]. Moreover, consumers can track their order status, communicate with delivery personnel, and enjoy benefits, such as avoiding waiting in lines, not needing to pick up, and accessing daily promotions or discounts, when ordering meals [15].

2.2. S-O-R Theory

The evolution of the S-O-R theory in psychology has been based on Thorndike’s [16] law of effect, or stimulus–response (S–R) theory, which posits that similar stimuli are likely to produce similar responses. Russell and Mehrabian [13] adopted a perspective from environmental psychology. They described a process in which external environmental factors (stimuli) influence the internal states of consumers (organisms), leading to approach or avoidance behaviors (responses). Jacoby [17] reconceptualized the S-O-R model as a Venn diagram rather than a continuous process, illustrating seven domains of psychological dynamics that occur over time. The S-O-R theory is frequently applied in the study of online environments [18], online purchase intentions [19], and consumer behavior [20]. This study invoked the S-O-R framework to describe causal relationships and provide an explanation for how consumers position food delivery platforms. Second, it offers a visualized framework for studying how stimuli influence users’ internal psychological responses, subsequently determining their behavioral reactions.
In this context, “stimulus” refers to the social or environmental cues capable of triggering psychological and behavioral responses or changes in individuals [21]. Stimuli can evoke positive or negative emotions in the consumer evaluation process [22], explaining the association between consumer satisfaction and prior expectations of service. Therefore, in this study, stimuli were defined as service quality. “Organism” refers to the intervening internal decision-making processes between stimuli and responses [23], representing the cognition that arises after receiving the service. Organism factors studied in consumer behavior research include attitudes, satisfaction, and perceived value. Thus, in this study, the organism was defined as satisfaction. “Response” refers to the intent, decision, or behavioral change caused by stimuli and organism factors, representing the individual’s ultimate reaction to specific stimuli. Therefore, in this study, responses were defined as behavioral intentions, further categorized into intentions to repurchase from a restaurant and intentions to use a platform.

2.3. Service Quality

The current food delivery industry is thriving, and service quality plays a crucial role [24]. Brady and Cronin [25] demonstrated that service quality is a multidimensional, hierarchical concept. Food delivery involves various service factors, including meal quality and hygiene, service convenience, functional attributes of the ordering system, tangible services, professional delivery personnel, and service efficiency [8,9]. The service process in the food delivery industry consists of placing orders through platforms, restaurants preparing meals, and delivery personnel providing delivery services to customers. Therefore, the major distinction in this study was categorizing service quality into three types: restaurant product quality, delivery personnel quality, and delivery platform quality.
Outcome quality is a measure of what consumers receive when a service is provided [25]. For food delivery platforms, customers are most concerned about the quality of the meals (taste, freshness, temperature, etc.), food packaging (hygiene, eco-friendly packaging, etc.), and the consistency of the meal’s content [24,25,26,27,28]. Therefore, we defined the quality of the meal, including its taste, freshness, hygiene, and correctness in quantity, as well as the completeness of packaging, as restaurant product quality. This aspect is crucial in assessing the overall quality of food delivery services.
In the food delivery industry, service delivery personnel are essential in providing meal delivery services to customers. Factors such as order consistency, delivery speed, the courtesy and friendliness of delivery personnel, the cleanliness of the meal box, the condition of the delivered food upon receipt, and affordable delivery fees can influence customer behavior [24,29,30]. Hence, in this study, we categorized delivery personnel quality based on the appropriateness of their behavior and communication, delivery speed, meal completeness, and the cleanliness of the delivery environment. Electronic service norms encompass a comprehensive evaluation of services provided by food delivery platforms [31]. When making purchases on a platform, consumers expect high-quality service, and good platform quality enhances consumers’ value and satisfaction, subsequently stimulating their behavioral intentions [32].
Platform quality encompasses the accuracy, relevance, and completeness of information provided by a food delivery platform [33]. It also includes the usability of a platform, customer service responsiveness, and the ability to provide diverse information [14,15,34]. For online shopping [35], Kim [36] used SERVQUAL to evaluate platform quality for food delivery, dividing the facets into platform service security, platform vendor professionalism, platform provider user interface, activities and interests, and customer service response time. Therefore, we considered ease of use, diverse meal choices, and prompt responsiveness as indicators of platform quality.

2.4. Satisfaction

Satisfaction has consistently been the central focus of marketing because it has a significant impact on the survival of any business. It is also one of the key constructs used to explain consumer behavior in research models [37]. In the literature on marketing and consumer behavior, customer satisfaction is a crucial predictive indicator for examining behavioral intentions [38,39]. In studies within the restaurant industry, satisfaction positively impacts behavioral intentions, and service quality positively affects both customer satisfaction and behavioral intentions [37,40].
Dash et al. [41] defined the measurement dimensions of customer satisfaction as service quality, professional competence, and frontline staff experience. Liang et al. [42] identified four indicators of satisfaction: dish indicators (taste, quality, hygiene, and freshness), service indicators (speed, attitude, and accuracy), price promotion indicators (price and discounts), and value-added indicators (delivery accessories and packaging). Therefore, in this study, satisfaction was defined as the overall evaluation made by consumers of their entire consumption experience on a food delivery platform after ordering a meal.
Research findings related to satisfaction indicate a positive relationship between store product quality and satisfaction [11,43,44]. When consumers perceive delivery personnel to have quality, satisfaction increases, thereby establishing a positive relationship between delivery personnel quality and satisfaction [26,45]. Additionally, the service quality of delivery platforms positively impacts customer satisfaction [3,34], indicating a positive relationship between delivery platform quality and satisfaction [24]. Based on the aforementioned literature, we proposed Hypotheses 1, 2, and 3.
 Hypothesis 1 (H1): 
Store product quality has a positive impact on consumer satisfaction.
 Hypothesis 2 (H2): 
Delivery personnel quality has a positive impact on consumer satisfaction.
 Hypothesis 3 (H3): 
Delivery platform quality has a positive impact on consumer satisfaction.

2.5. Behavioral Intention

Behavioral intention is an individual’s intention to perform specific actions or behaviors towards a product or service [46]. It involves anticipating and planning actions that are likely to occur [47], such as word-of-mouth and repurchase intentions [30]. In this study, behavioral intention was regarded as an outcome between behavior and commitment. Repeat usage, recommendations, and word-of-mouth were employed to explain consumers’ behavioral intentions as responses to service quality and satisfaction, distinguishing between store repurchase intention and platform continued usage.
Service quality and satisfaction significantly influence future purchase intentions [48]. When customers are satisfied with products or services they purchase, they tend to make repeat purchases from the same supplier [44]. This satisfaction also enhances their willingness to make online purchases [29,49]. Therefore, we proposed Hypotheses 4 and 5.
 Hypothesis 4 (H4): 
Satisfaction positively influences consumers’ store repurchase intention.
 Hypothesis 5 (H5): 
Satisfaction positively influences consumers’ platform usage.
Based on the hypotheses, we developed a research conceptual framework.

3. Research Method

The independent variable in this study was service quality, including three conceptions (store product quality, delivery personnel quality, and delivery platform quality). The mediating variable was satisfaction. The dependent variable was consumer behavior intention, including two conceptions (store repurchase intention and platform usage). These variables were explored using the following methods.

3.1. Data Collection

We distributed a questionnaire via Google Forms using a snowball sampling method. The questionnaire survey was conducted from February to March 2022. Participants were individuals in Taiwan who used food delivery platforms for ordering meals (excluding those who did not use contactless delivery). In total, 350 questionnaires were collected, and, after examination, 321 valid questionnaires were retained.

3.2. Research Tools

Questionnaire items were adapted from existing studies and modified to align with this research’s context. References for each construct were as follows: store product quality [24,26], delivery personnel quality [24], platform quality [32], satisfaction [26,29], repurchase intention [16,50], and platform usage [51]. A Likert five-point scale was used, in which 1 represented “strongly disagree” and 5 represented “strongly agree.” Descriptive statistics and reliability analyses were conducted using SPSS, and confirmatory factor analysis and structural equation modeling were performed using AMOS.

4. Results and Discussion

4.1. Demographics

A total of 61.4% of participants were female, while 38.6% were male. The majority of participants were 24 years old or younger, accounting for 41.7% of total participants, followed by participants aged 25–34 years (23.7%), 35–44 years (19.0%), 45–55 years (10.9%), and 55 years and above (4.7%). Regarding education levels, there were university graduates (62.3%), holders of master’s degrees or above (29.0%), and high school/vocational school graduates (8.7%). The majority of participants were students (39.3%), followed by those in the service industry (20.9%). The majority (69.2%) were not married, and 37.4% had a monthly income below NTD 20,000. Regarding ordering frequency, more than half of participants ordered 2–5 times (55.8%), and the most common spending range was between NTD 201 and 500 (54.5%). The most prevalent dining scenario was regular meals (79.5%), and Foodpanda was the most frequently used delivery platform (70.4%). Overall, the sample was collected to be as evenly distributed across age groups as possible, with a higher representation of young adults in their 20s and 30s, likely attributed to the online data collection method and the context of this study.

4.2. Confirmatory Factor Analysis

Results of the confirmatory factor analysis in this study showed that factor loadings for each factor exceeded 0.5 [52]. Composite reliability (CR) values were above 0.7, and average variance extracted (AVE) values surpassed the threshold of 0.5 [53]. Cronbach’s α for each dimension was between 0.904 and 0.943. Thus, the convergent validity of the measurement model was confirmed [54] (Table 1). The multivariate critical ratio (CR) in this study was 2153.47. However, for multivariate normality testing, the CR must be less than 10. To address the issue of non-multivariate normality in sample data, the bootstrap method was employed. The multivariate CR after using the bootstrap method decreased to 487.202, confirming compliance with multivariate normal distribution.

4.3. Test Hypothesis

The structural model examination in this study met the standards for various indicators (χ2/df = 1.042, CFI = 0.998, GFI = 0.956, AGFI = 0.948, NFI = 0.956, RMSEA = 0.011, and SRMR = 00.000) [55,56,57,58,59], indicating that the structural model was well fitted for further statistical analysis.
Statistical analysis results revealed that store product quality (H1: β = 0.54, t = 8.20, p < 0.000), delivery personnel quality (H2: β = 0.50, t = 7.95, p < 0.000), and delivery platform quality (H3: β = 0.42, t = 7.17, p < 0.000) positively influenced satisfaction. Additionally, satisfaction positively influenced repurchase intention (H4: β = 0.79, t = 9.79, p < 0.000) and continued intention to use a platform (H5: β = 0.71, t = 10.48, p < 0.000). Therefore, all hypotheses proposed in this study were supported.

5. Conclusions

Although food delivery platforms have been receiving academic attention, studies on store product quality, delivery personnel quality, and platform quality, along with satisfaction, repurchase intention, and continued use, are scarce. This study employed an S-O-R model to develop a framework explaining the relationships among three stimuli (quality of store products, quality of delivery personnel, and quality of a delivery platform), one emotional state (satisfaction), and two responses (intention to repurchase from a store and intention for continued use of a platform). It was confirmed that assessments of various qualities positively influenced satisfaction, and satisfaction positively influenced subsequent use. These findings align with previous research, indicating store product quality had a direct influence on satisfaction [3,43], delivery personnel quality had an influence on satisfaction [26,45], a positive relationship between delivery platform quality and satisfaction [24], and finally, satisfaction had an influence on both store repurchase intention and platform use intention [29,51]. The S-O-R model was used as a theoretical foundation, and its results can be used to enhance service quality, resource allocation, and marketing strategies for food delivery platforms.
Recently, Taiwan’s food delivery industry has been growing rapidly. However, the neglect of service quality in food delivery has decreased customer satisfaction and repurchase intention [24]. Service quality is a crucial factor in customer decision-making. Yet, the service quality of food delivery platforms has unique attributes, encompassing restaurant services, delivery personnel performance, and platform services. This underscores the importance of personnel training and the integration of platform resources. For instance, it is essential to ensure the quality, hygiene, freshness, and completeness of restaurant products. Delivery personnel must be well groomed, courteous, customer-oriented, and empathetic while delivering orders. A platform’s customer service must be prompt in response, and platform information needs to be comprehensive and easy to navigate. Failure to meet customer expectations or dissatisfaction may foster a negative attitude toward food delivery platforms, thereby influencing subsequent usage behavior. Customers may choose not to repurchase, or switch to alternative platforms. Therefore, platform managers must ensure that restaurants maintain a certain level of quality, that delivery personnel possess necessary qualities, and that their platform functions smoothly to meet customer expectations and provide excellent service. This ensures that the amount paid by customers corresponds to their expected service quality, potentially surpassing the perceived value of the payment. Ultimately, managers should establish consumer confidence and trust in their platforms.
Data for this study were collected in Taiwan, so its results cannot be generalized. Secondly, data were collected through an online questionnaire, which can be a limitation in using the results of this study. Thirdly, we focused only on users of Foodpanda and Uber Eats. Customer perceptions of each platform may vary. Therefore, it is essential to investigate the predictors influencing user behavior across various food delivery platforms. different food delivery platforms. Finally, as platform interfaces and usage evolve in response to varying temporal and spatial contexts, it will be essential to integrate data from different time periods to enhance the model’s validation.

Author Contributions

Conceptualization, C.-W.L., Y.-A.H., and K.-C.T.; methodology, C.-W.L.; writing—original draft preparation, Y.-C.C., Y.-A.H., and W.Y.S.; writing—review and editing, Y.-C.C. and W.Y.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article.

Acknowledgments

We sincerely acknowledge the enthusiastic participation of all subjects involved in this study. Their willingness to contribute their time and effort was essential to the success of our research. We are grateful for their invaluable support and cooperation.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. WHO Coronavirus Disease (COVID-19) Dashboard. Available online: https://covid19.who.int/?gclid=CjwKCAjwh7H7BRBBEiwAPXjadiKtb7aw1BDI7iFNjj4pF-b5J-k-VgJVmkC6MmqL8F34DBOxHnX6_BoC2rwQAvD_BwE (accessed on 15 March 2022).
  2. Zhao, Y.; Bacao, F. What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? Int. J. Hosp. Manag. 2020, 91, 102683. [Google Scholar] [CrossRef]
  3. Statistia eServices Report 2020—Online Food Delivery. Available online: https://www.statista.com/study/40457/food-delivery/ (accessed on 17 November 2021).
  4. Cheng, C.C.; Chen, C.T.; Hsu, F.S.; Hu, H.Y. Enhancing service quality improvement strategies of fine-dining restaurants: New insights from integrating a two-phase decision-making model of IPGA and DEMATEL analysis. Int. J. Hosp. Manag. 2012, 31, 1155–1166. [Google Scholar] [CrossRef]
  5. Cheng, C.C.; Chang, Y.Y.; Tsai, M.C.; Chen, C.T.; Tseng, Y.C. An evaluation instrument and strategy implications of service attributes in LOHAS restaurants. Int. J. Contemp. Hosp. Manag. 2019, 31, 194–216. [Google Scholar] [CrossRef]
  6. Kim, W.G.; Ng, C.Y.N.; Kim, Y.S. Influence of institutional DINESERV on customer satisfaction, return intention, and word-of-mouth. Int. J. Hosp. Manag. 2009, 28, 10–17. [Google Scholar] [CrossRef]
  7. Cho, M.; Bonn, M.A.; Li, J.J. Differences in perceptions about food delivery apps between single-person and multi-person households. Int. J. Hosp. Manag. 2019, 77, 108–116. [Google Scholar] [CrossRef]
  8. Fancello, G.; Paddeu, D.; Fadda, P. Investigating last food mile deliveries: A case study approach to identify needs of food delivery demand. Res. Transp. Econ. 2017, 65, 56–66. [Google Scholar] [CrossRef]
  9. Roh, M.; Park, K. Adoption of O2O food delivery services in South Korea: The moderating role of moral obligation in meal preparation. Int. J. Inf. Manag. 2019, 47, 262–273. [Google Scholar] [CrossRef]
  10. Annaraud, K.; Berezina, K. Predicting satisfaction and intentions to use online food delivery: What really makes a difference? J. Foodserv. Bus. Res. 2020, 23, 305–323. [Google Scholar] [CrossRef]
  11. Saad, A.T. Factors affecting online food delivery service in Bangladesh: An empirical study. Br. Food J. 2021, 123, 535–550. [Google Scholar] [CrossRef]
  12. Yusra, Y.; Eko, C.R.; Agus, A.; Azmi MA, A.; Ugiana, G.P.; Ching, C.R.; Lee, Y. An investigation of online food aggregator (OFA) service: Do online and offline service quality distinct? Serbian J. Manag. 2020, 15, 277–294. [Google Scholar] [CrossRef]
  13. Russell, J.A.; Mehrabian, A. Distinguishing anger and anxiety in terms of emotional response factors. J. Consult. Clin. Psychol. 1974, 42, 79. [Google Scholar] [CrossRef] [PubMed]
  14. Ray, A.; Dhir, A.; Bala, P.K.; Kaur, P. Why do people use food delivery apps (FDA)? A uses and gratification theory perspective. J. Retail. Consum. Serv. 2019, 51, 221–230. [Google Scholar] [CrossRef]
  15. Hong, C.; Choi, H.H.; Choi EK, C.; Joung HW, D. Factors affecting customer intention to use online food delivery services before and during the COVID-19 pandemic. J. Hosp. Tour. Manag. 2021, 48, 509–518. [Google Scholar] [CrossRef]
  16. Thorndike, E.L. Animal intelligence: An experimental study of the associative processes in animals. Psychol. Rev. Monogr. Suppl. 1898, 2, 4–160. [Google Scholar]
  17. Jacoby, J. Stimulus-organism-response reconsidered: An evolutionary step in modeling (consumer) behavior. J. Consum. Psychol. 2002, 12, 51–57. [Google Scholar] [CrossRef]
  18. Wang, Y.J.; Minor, M.S.; Wei, J. Aesthetics and the online shopping environment: Understanding consumer responses. J. Retail. 2011, 87, 46–58. [Google Scholar] [CrossRef]
  19. Brewer, P.; Sebby, A.G. The effect of online restaurant menus on consumers’ purchase intentions during the COVID-19 pandemic. Int. J. Hosp. Manag. 2021, 94, 102777. [Google Scholar] [CrossRef]
  20. Sultan, P.; Wong, H.Y.; Azam, M.S. How perceived communication source and food value stimulate purchase intention of organic food: An examination of the stimulus-organism-response (SOR) model. J. Clean. Prod. 2021, 312, 127807. [Google Scholar] [CrossRef]
  21. Eroglu, S.A.; Machleit, K.A.; Davis, L.M. Empirical testing of a model of online store atmospherics and shopper responses. Psychol. Mark. 2003, 20, 139–150. [Google Scholar] [CrossRef]
  22. Sezgen, E.; Mason, K.J.; Mayer, R. Voice of airline passenger: A text mining approach to understand customer satisfaction. J. Air Transp. Manag. 2019, 77, 65–74. [Google Scholar] [CrossRef]
  23. Loureiro, S.; Ribeiro, L. The effect of atmosphere on emotions and online shopping intention: Age differentiation. In Proceedings of the Australian and New Zealand Marketing Academy Conference, Perth, Australia, 28–30 November 2011. [Google Scholar]
  24. Cheng, C.C.; Chang, Y.Y.; Chen, C.T. Construction of a service quality scale for the online food delivery industry. Int. J. Hosp. Manag. 2021, 95, 102938. [Google Scholar] [CrossRef]
  25. Brady, M.K.; Cronin, J.J., Jr. Some new thoughts on conceptualizing perceived service quality: A hierarchical approach. J. Mark. 2001, 65, 34–49. [Google Scholar] [CrossRef]
  26. Ganapathi, P.; Abu-Shanab, E.A. Customer satisfaction with online food ordering portals in Qatar. Int. J. E-Serv. Mob. Appl. (IJESMA) 2020, 12, 57–79. [Google Scholar] [CrossRef]
  27. Yang, F.X.; Li, X.; Lau VM, C.; Zhu, V.Z. To survive or to thrive? China’s luxury hotel restaurants entering O2O food delivery platforms amid the COVID-19 crisis. Int. J. Hosp. Manag. 2021, 94, 102855. [Google Scholar] [CrossRef]
  28. Lu, C.Y.; Suhartanto, D.; Gunawan, A.I.; Chen, B.T. Customer satisfaction toward online purchasing services: Evidence from small & medium restaurants. Int. J. Appl. Bus. Res. 2020, 1–14. [Google Scholar] [CrossRef]
  29. Fakfare, P. Influence of service attributes of food delivery application on customers’ satisfaction and their behavioural responses: The IPMA approach. Int. J. Gastron. Food Sci. 2021, 25, 100392. [Google Scholar] [CrossRef]
  30. Chen, A.H.; Peng, N.; Hackley, C. Evaluating service marketing in airline industry and its influence on student passengers’ purchasing behavior using Taipei–London route as an example. J. Travel Tour. Mark. 2008, 25, 149–160. [Google Scholar] [CrossRef]
  31. Ramesh, R.; Prabhu, S.V.; Sasikumar, B.; Devi, B.K.; Prasath, P.; Kamala SP, R. An empirical study of online food delivery services from applications perspective. Mater. Today Proc. 2023, 80, 1751–1755. [Google Scholar] [CrossRef]
  32. Lin, C.W.; Tan, W.P.; Lin, Y.H.; Tseng, Y.C.; Lee, S.S. Exploring the consumer behavior of online platform by using information system success model. In Proceedings of the 2017 International Conference on E-Business and Internet, Taichung, Taiwan, 25–27 May 2017; pp. 12–16. [Google Scholar]
  33. Patrada, R.; Andajani, E. Effect and Consequence e-Customer Satisfaction for e-Commerce Users. IPTEK J. Proc. Ser. 2021, 219–227. [Google Scholar] [CrossRef]
  34. Dsouza, D.; Sharma, D. Online food delivery portals during COVID-19 times: An analysis of changing consumer behavior and expectations. Int. J. Innov. Sci. 2021, 13, 218–232. [Google Scholar] [CrossRef]
  35. Parasuraman, A.; Zeithaml, V.A.; Malhotra, A. ES-QUAL: A multiple-item scale for assessing electronic service quality. J. Serv. Res. 2005, 7, 213–233. [Google Scholar] [CrossRef]
  36. Kim, M. Conceptualization of e-servicescapes in the fitness applications and wearable devices context: Multi-dimensions, consumer satisfaction, and behavioral intention. J. Retail. Consum. Serv. 2021, 61, 102562. [Google Scholar] [CrossRef]
  37. Konuk, F.A. The influence of perceived food quality, price fairness, perceived value and satisfaction on customers’ revisit and word-of-mouth intentions towards organic food restaurants. J. Retail. Consum. Serv. 2019, 50, 103–110. [Google Scholar] [CrossRef]
  38. Oliver, R.L. A cognitive model of the antecedents and consequences of satisfaction decisions. J. Mark. Res. 1980, 17, 460–469. [Google Scholar] [CrossRef]
  39. Teeroovengadum, V. Service quality dimensions as predictors of customer satisfaction and loyalty in the banking industry: Moderating effects of gender. Eur. Bus. Rev. 2022, 34, 1–19. [Google Scholar] [CrossRef]
  40. Han, H.; Hyun, S.S. Impact of hotel-restaurant image and quality of physical-environment, service, and food on satisfaction and intention. Int. J. Hosp. Manag. 2017, 63, 82–92. [Google Scholar] [CrossRef]
  41. Dash, G.; Kiefer, K.; Paul, J. Marketing-to-Millennials: Marketing 4.0, customer satisfaction and purchase intention. J. Bus. Res. 2021, 122, 608–620. [Google Scholar] [CrossRef]
  42. Liang, D.; Dai, Z.; Wang, M. Assessing customer satisfaction of O2O takeaway based on online reviews by integrating fuzzy comprehensive evaluation with AHP and probabilistic linguistic term sets. Appl. Soft Comput. 2021, 98, 106847. [Google Scholar] [CrossRef]
  43. Belarmino, A.; Raab, C.; Tang, J.; Han, W. Exploring the motivations to use online meal delivery platforms: Before and during quarantine. Int. J. Hosp. Manag. 2021, 96, 102983. [Google Scholar] [CrossRef]
  44. Zibarzani, M.; Abumalloh, R.A.; Nilashi, M.; Samad, S.; Alghamdi, O.A.; Nayer, F.K.; Ismail, M.Y.; Mohd, S.; Akib, N.A.M. Customer satisfaction with Restaurants Service Quality during COVID-19 outbreak: A two-stage methodology. Technol. Soc. 2022, 70, 101977. [Google Scholar] [CrossRef]
  45. Uzir MU, H.; Al Halbusi, H.; Thurasamy, R.; Hock RL, T.; Aljaberi, M.A.; Hasan, N.; Hamid, M. The effects of service quality, perceived value and trust in home delivery service personnel on customer satisfaction: Evidence from a developing country. J. Retail. Consum. Serv. 2021, 63, 102721. [Google Scholar] [CrossRef]
  46. Ajzen, I. The theory of planned behavior. Organ. Behav. Hum. Decis. Process. 1991, 50, 179–211. [Google Scholar] [CrossRef]
  47. Lam, T.; Hsu, C.H. Predicting behavioral intention of choosing a travel destination. Tour. Manag. 2006, 27, 589–599. [Google Scholar] [CrossRef]
  48. McAlexander, J.H.; Kaldenberg, D.O.; Koenig, H.F. Service quality measurement. J. Health Care Mark. 1994, 14, 34–41. [Google Scholar]
  49. Abumalloh, R.A.; Ibrahim, O.; Nilashi, M. Loyalty of young female Arabic customers towards recommendation agents: A new model for B2C E-commerce. Technol. Soc. 2020, 61, 101253. [Google Scholar] [CrossRef]
  50. Allah Pitchay, A.; Ganesan, Y.; Zulkifli, N.S.; Khaliq, A. Determinants of customers’ intention to use online food delivery application through smartphone in Malaysia. Br. Food J. 2022, 124, 732–753. [Google Scholar] [CrossRef]
  51. Rita, P.; Oliveira, T.; Farisa, A. The impact of e-service quality and customer satisfaction on customer behavior in online shopping. Heliyon 2019, 5, e02690. [Google Scholar] [CrossRef]
  52. Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tatham, R.L. Multivariate Data Analysis, 6th ed.; Pearson Prentice Hal: Upper Saddle River, NJ, USA, 2006. [Google Scholar]
  53. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 1981, 18, 39–50. [Google Scholar] [CrossRef]
  54. Hair, J.F. Multivariate Data Analysis; Pearson: London, UK, 2009. [Google Scholar]
  55. Carmines, E.; Mclver, J. Analyzing models with unobserved models: Analysis of covariance structures. In Social Measurement; Sage Publications, Inc.: Beverly Hills, CA, USA, 1981; Current Issues; pp. 65–115. [Google Scholar]
  56. Bentler, P.M. Comparative fit indexes in structural models. Psychol. Bull. 1990, 107, 238. [Google Scholar] [CrossRef]
  57. Bagozzi, R.P.; Yi, Y. On the evaluation of structural equation models. J. Acad. Mark. Sci. 1988, 16, 74–94. [Google Scholar] [CrossRef]
  58. Hu, L.T.; Bentler, P.M. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct. Equ. Model. A Multidiscip. J. 1999, 6, 1–55. [Google Scholar] [CrossRef]
  59. Browne, M.W.; Cudeck, R. Alternative ways of assessing model fit. Sociol. Methods Res. 1992, 21, 230–258. [Google Scholar] [CrossRef]
Table 1. Results of confirmatory factor analysis.
Table 1. Results of confirmatory factor analysis.
Latent VariablesComposite ReliabilityAverage
Store Product Quality0.940.71
Delivery Personnel Quality0.930.69
Delivery Platform Quality0.910.62
Satisfaction0.860.59
Repurchase Intention0.860.61
Platform Usage0.890.67
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lin, C.-W.; Huang, Y.-A.; Sia, W.Y.; Tao, K.-C.; Chen, Y.-C. Impact of Food Delivery Platforms on Consumer Behavioral Intentions During COVID-19 Pandemic. Eng. Proc. 2024, 74, 41. https://doi.org/10.3390/engproc2024074041

AMA Style

Lin C-W, Huang Y-A, Sia WY, Tao K-C, Chen Y-C. Impact of Food Delivery Platforms on Consumer Behavioral Intentions During COVID-19 Pandemic. Engineering Proceedings. 2024; 74(1):41. https://doi.org/10.3390/engproc2024074041

Chicago/Turabian Style

Lin, Chih-Wei, Yi-An Huang, Wei Yeng Sia, Kuan-Chuan Tao, and Yi-Chang Chen. 2024. "Impact of Food Delivery Platforms on Consumer Behavioral Intentions During COVID-19 Pandemic" Engineering Proceedings 74, no. 1: 41. https://doi.org/10.3390/engproc2024074041

Article Metrics

Back to TopTop