1. Introduction
Online food delivery has grown substantially in recent years [1]. Food companies have increasingly implemented these services based primarily on third-party food-delivery platforms through which consumers can order and receive food products [2]. These third-party intermediary platforms can operate as gig economy services (GES) or professional service companies. In the case of online food delivery, GES has spread through platforms such as Deliveroo, Uber Eats, and Glovo.
Choosing between gig economy (GE) or professional service companies to deliver food can affect the quality of service because the two types of companies use different business models. GE companies neither hire employees nor own the main resources required for online food delivery. These firms rely on self-employed workers [3]. By contrast, professional food-delivery companies hire couriers and own most of the assets involved in the delivery service (e.g., scooters). Studies comparing GES to traditional service companies have not addressed consumers’ perceptions of the same service quality factors, regardless of who provides the service.
Professional service companies face higher costs than GE companies do because they need to hire employees [3]. Nevertheless, the importance of service employees for the success of services based on workers is a generally accepted premise [4]. Furthermore, firms should invest in human resource management practices to improve service employees’ performance [5]. These are long-standing assumptions [6]. However, currently, the context is very different. There has been a proliferation of personal services that avoid hiring staff because of digital platforms and the Internet [7]. Testing whether food-delivery GES is associated with worse service quality will reinforce the belief that employee management in the service industry is important. However, if the results indicate otherwise, studies on employees in service industries should reconsider some of their premises.
Food-delivery services are on-demand services. This means that “upon experiencing a need for service, a customer desires service immediately and is sensitive to delay” [8]: 704). Under these circumstances it is possible for GES to obtain worse results in terms of service quality and customer satisfaction compared to traditional service companies because GE food couriers are not employees. This implies that they are beyond the scope of common practices that employers use to manage their staff and on which they base their business activities [8,9]. Thus, some food-delivery service quality dimensions, such as timeliness and availability, could be negatively affected [10]. In addition, there is a high turnover among workers of food-delivery GE platforms, which therefore need to retain workers and offer enough providers to guarantee deliveries [11]. By contrast, because food-delivery professional service companies hire their employees, they fulfill the conditions that allow them to manage their workforce [12]. Therefore, based on the two types of third-party food-delivery platforms, this study seeks to answer the following research questions:
RQ1
Are consumers less satisfied with food-delivery from GES than from professional service companies?
RQ2
Do consumers perceive poorer service quality from GES than from professional service companies?
RQ3
Is the difference in service quality perceptions between food-delivery GES and service companies because of GES′ reliance on independent providers?
GE companies, particularly those offering food-delivery services, have been criticized from both legal and social perspectives because of the type of employment they promote [13,14]. If food-delivery GES achieves worse service quality results than those of equivalent traditional firms, they would have an additional weakness. By contrast, if the service quality of both types of firms is similar, traditional service companies would be disadvantaged in terms of cost. Furthermore, it would imply that the greater investment in human resources inherent in hiring employees does not always mean better service quality compared to a low-cost approach.
To answer the research questions, this study performs a content analysis of electronic word-of-mouth (eWOM) from clients of three food-delivery GE platforms and a professional food-delivery platform. Since delivering food through self-employed workers may influence some dimensions of food-delivery service quality, there may be differences between the eWOM of food-delivery GE platforms and that of professional food-delivery platforms.
The rest of the article is structured as follows. The first section describes the theoretical basis for the study and presents the research objective. The methodology is explained in the following section. Next, the results are described and discussed. Finally, the conclusions and limitations are presented.
2. Literature review
2.1. Food-delivery services
Twenty years ago, a rising demand for food-delivery systems was observed [15], which included telephone ordering with home delivery and pick-up services. Internet-based ordering was considered a novelty. Nowadays, the state of the food-delivery industry is completely different because of the platform economy and the spread of GE [1].
Certain statistics highlight the importance of the food-delivery industry; for example, Keeble et al. [16] estimate that 15% of the population uses online food-delivery services, which contribute to 30% of the restaurant meals eaten at home. This service represents approximately 7% of total restaurant sales, and growth exceeds 10% annually [17]. Furthermore, this industry has been attracting investors. In 2018, venture-capital firms invested more than four times the amount of money they invested in 2017 in online food-delivery ventures [17]. The industry's future is also promising because the online food-delivery market is expected to grow significantly. IMARC [18] expects revenue in this market to grow approximately 11% a year until 2026. Statista [19] states that, in 2022, revenue in this industry is expected to increase by 12.2% in the US and 15.7% in the UK. Furthermore, owing to COVID-19 restrictions on both the onsite activity of restaurants and people's mobility, the demand for food delivery has suddenly increased [20,21]. Thus, some restaurants have started to use online food-delivery services and others have modified their activities to increase sales through this channel [22].
According to Kumar and Shah [20]; online food delivery is performed through restaurants' self-owned platforms (e.g., Domino's and KFC) or third-party intermediary platforms (e.g., Uber Eats and Just Eat). Li et al. [2] distinguished between GE and professional food-delivery platforms. GE food delivery is the most common type of third-party food-delivery platform [23]. Mahmuda et al. [1] find that food-delivery GES is among the fastest-growing GES. In fact, Belanche et al. [14] predict that the revenues of food-delivery GE companies will grow by 9.3% annually for several years to come.
Customers who use third-party food-delivery platforms place orders that are forwarded to restaurants. These restaurants prepare the orders, and once they are ready, couriers deliver the food to the consumers [16]. A professional food-delivery platform behaves as an intermediary between two markets: restaurants and consumers. Food-delivery GE platforms are multi-sided, as they have three markets: restaurants, consumers, and couriers. Food-delivery GE platforms consider couriers a market of their own because these platforms do not hire them. Unlike professional food-delivery platforms, food-delivery GE platforms rely on independent workers who temporarily offer their own resources and skills [3,24].
2.2. Work in GE food-delivery platforms versus work in professional food-delivery platforms
A professional food-delivery platform is a business-to-consumer firm. This type of firm owns its own resources and hires employees to deliver its services. Therefore, it is able to control and organize its workers [24]. Li et al. [2] explain that professional food-delivery platforms recruit and train couriers and guarantee a minimum fixed salary.
In the case of food-delivery GE platforms, couriers are independent providers who offer their services through the platform. GE platforms claim that couriers are people who want to make money (e.g., students) or self-employed individuals who freely decide how and when to provide their services. Goods et al. [25]: 516) find that couriers perceive this job “as a short-term source of income for a particular point in time.” These authors state that food delivery demands relatively limited skills and assets (e.g., mobile phones or bicycles). Urzi Brancati et al. [26] confirm that, in the case of GE platforms, workers who provide food-delivery services are those who have the lowest educational levels. Thus, food-delivery GES has low barriers to entry, which allows many individuals to perform the service.
In most countries, self-employment is characterized by workers’ independence and autonomy [27]. Therefore, in the case of food-delivery GE platforms, couriers should enjoy autonomy and freedom, whereby they determine how and when to work. Furthermore, GE work is temporary and short-term [28]. Since food-delivery GE platforms require a sufficient number of workers to meet client demands, autonomy and temporary work can pose challenges [29]. Lin et al. [11]: 2) explain that food-delivery GE platforms use “algorithmic management to automate human-related duties and functions, which are traditionally performed by human resource managers.” According to Wood et al. [30]; this practice allows these platforms to exercise control over workers after the performance process, rather than during it. To accomplish this, food-delivery platforms such as Deliveroo consider the following factors: work attendance, late deregistration, and participation in peak periods [31].
Since they use self-employed couriers, food-delivery GES is considered an efficient and low-cost approach to food delivery [32]. In Spain, where Just Eat operates as a professional food-delivery platform, the company's general manager stated that the professional model is more expensive and demanding because the company hires its own couriers and its number of employees does not vary when the number of orders is low.1 It should be noted that in Europe, food-delivery GES faces challenges related to the provider's work status. Companies consider these workers self-employed, but the workers demand to be treated as employees and the unions agree [33,34].
According to several authors [8,10,29] food delivery by independent workers can negatively affect service performance. In the following sections, this possibility is analyzed based on the service quality construct.
2.3. Service quality in food-delivery services
As with other services, SERVQUAL [35] has been used to measure service quality in food-delivery services. According to SERVQUAL, service quality is reflected in consumers' perceptions of five dimensions of the service received: tangibles, reliability, responsiveness, assurance, and empathy. Eresia-Eke et al. [36] found that the dimensions of tangibles (i.e., the appearance of physical facilities, equipment, and personnel), assurance (i.e., the knowledge, courtesy, and ability of employees), and empathy (i.e., the level of caring and individualized attention) significantly influenced clients' satisfaction with food-delivery services. Parasuraman et al. [37] proposed E-S-QUAL to assess the quality of online sales interactions. Belanche et al. [14] used this scale to measure the service quality of food-delivery GES. E-S-QUAL includes the following four dimensions: efficiency (i.e., ease and speed of the app), system availability (i.e., correct technical functioning of the system), fulfillment (i.e., company reliability in its functioning and product presentation), and privacy (i.e., security and protection of customer information). They found that service quality influenced both the intention to use the food-delivery service and the intention to recommend them. Nevertheless, the global dimensions of these models (e.g., assurance or fulfillment) do not accurately reflect the specificities of food-delivery services. For example, Hirschberg et al. [38]: 4) state that speed of delivery is one of the most important attributes of the food-delivery service, “with an average of 60% of consumers across markets citing it as a key factor.” However, in E-S-QUAL, the two items that assess delivery timeliness are combined with items regarding order errors and companies’ selection of products.
Physical distribution service quality is often measured through dimensions such as availability, timeliness, reliability, and product condition [39]. Chou and Lu [40] used the following dimensions to evaluate the quality of home-delivery services: reliability, price, staff professionalism, range of service, tracking, claims, and invoicing. Ko [41] proposed the following dimensions of service quality in the food-delivery industry: food quality, economics, ease of ordering, employee quality, sanitation, and order quality.
Other studies have analyzed the factors that explain client satisfaction with food-delivery services. Park and Bae [42] found that consumer satisfaction with online food-delivery services was influenced by the quality of the delivery platform (e.g., minimum order price, price, and menu description), quality of the delivery service (e.g., courier friendliness, and delivery speed and accuracy), convenience and variety (e.g., payment methods, app convenience, and menu variety), food quality (e.g., food taste and temperature, and hygiene control), and health and safety (food origin).
In addition to the service quality framework, other studies have analyzed factors that predict the usage intention of food-delivery services. Syazana [43] found that perceived usefulness, a construct that involves convenience and time saving, was the strongest predictor of usage intention for services provided by companies such as Just Eat and Deliveroo. Annaraud and Berezina [44] reviewed studies that sought to explain the intentions to use online food delivery. They found that time saving, cost, convenience, and usefulness were the most common explanatory factors. Williams et al. (2020) analyzed consumers' tweets and app reviews of food-delivery GES. Although these authors sought to develop software to analyze this type of content, they performed an inductive categorization of consumers' concerns. They found consumers were most concerned about drivers’ behaviors, customer services, refunds, service outages, promotional codes, communication issues, security, routing issues, and order allocations; however, they did not provide detailed statistics on the categories.
2.4. Service quality and types of third-party food-delivery platforms
Food-delivery services must deal with large variations in demands for food in a single day and during the week [45]. Demand changes can also be less cyclical, such as surges in demand associated with bad weather [46]. If third-party food-delivery platforms do not have sufficient couriers to meet these increased demands, the delays in order response times will be unacceptable to clients. A lack of sufficient providers has been cited as a reason for GES failure [47]. As mentioned in the previous section, food-delivery GES relies on self-employed workers. Taylor [8] explains that this can unfavorably affect the facets of delivery services that consumers appreciate. In theory, this type of worker freely decides how and when to work, which can negatively affect the factors and dimensions of food-delivery service quality, such as those mentioned in the previous section. The reasons include not having enough providers, providers not working when the GE platform needs them, and providers following their own rules [29,48]. Reyes et al. [10] noted that the GE model in food delivery can cause uncertainty in service scheduling (e.g., couriers are free to choose when to work), dispatching (e.g., couriers can reject some assignments), and routing (e.g., couriers can disregard the suggested sequence of deliveries). Veen et al. [29] mentioned what they named food-delivery GE platforms' obfuscating practices. These reflect the effort that platforms make to ensure that their self-employed couriers are available in times of heightened demand. Heiland [46] mentions the challenges that food-delivery GES face in trying to influence their couriers’ behaviors. This is difficult because legal action can be taken against GE platforms if they disrespect the characteristics of self-employment. In fact, this is a common occurrence among the most popular food-delivery GE platforms. There have been sixteen court decisions in Spain on whether couriers were self-employed workers across three food-delivery GE platforms. In ten cases, the rulings were that couriers were managed as if they were employees [49].
By contrast, professional food-delivery platforms hire couriers as permanent staff. This allows them to ensure available staff and to legally exercise managerial prerogative and functional flexibility in the use of the workforce [12]. Thus, they are legally allowed to implement practices (e.g., performance assessments and shift work) that seek to positively influence service facets that are usually associated with service quality (e.g., timeliness). This legitimate managerial power represents an advantage compared to the legal constraints faced by food-delivery GE companies [12].
3. Methodology
This research draws on consumers’ eWOM on three food-delivery GE companies (Deliveroo, Glovo, and Uber Eats) and one food-delivery firm that operates as a professional service company in Spain (Just Eat España). These firms were chosen because they are the most important firms in the Spanish food-delivery industry.
The eWOM data considered in this study consist of reviews uploaded to consumer-opinion platforms [50]. Since eWOM has become a widespread consumer behavior tool, it is frequently used as a source of client information in research [51]. The positive relationship between service quality and favorable eWOM has also been tested [52].
The eWOM data were obtained from Trustpilot, a website that collects consumer reviews of different types of services and products.
On November 16, 2020, all the eWOM data of the aforementioned companies were downloaded using web scraping. In some countries, Just Eat operates as GES. Thus, to ensure that the eWOM data were from Spain, where Just Eat does not operate as a GE company, only reviews from Just Eat España were considered. In the case of Deliveroo, the Spanish version (Deliveroo.es) did not have sufficient reviews; therefore, reviews uploaded by consumers from Spain to the most important Deliveroo profile in Trustpilot (deliveroo.co.uk) were included. Uber Eats only had one profile in Trustpilot; therefore, only reviews from Spanish consumers were analyzed. Glovo is a Spanish firm with only one profile (glovoapp.com). Given that it operates in various countries, only Spanish reviews were included.
Trustpilot calculates a TrustScore based on several inputs, such as the number of reviews, age of the reviews, and star rating on a five-point scale. The TrustScore ranges from one to five. Trustpilot explains that the TrustScore is calculated as follows: recent reviews have a greater weight than older ones, the number of recent reviews influences the final score, and the Bayesian average is calculated in order to ensure that firms with few ratings have a balanced TrustScore. The latter means that seven ratings of 3.5 stars are automatically included. The more ratings a firm receives, the less weight these ratings have. Table 1 presents information on this study's sample.
Table 1. Sample information and eWOM data.
| Company | Type of food- delivery service in Spain | Reviews | TrustScore | Years* | Mean | STD | Reviews with only one reviewer | ||
|---|---|---|---|---|---|---|---|---|---|
| % | Mean | STD | |||||||
| Just Eat | Professional service company | 1685 | 1.8 | 2014–2020 | 3.99 | 1.50 | 79.94% | 4.00 | 1.50 |
| Glovo | GES | 1259 | 1.2 | 2016–2020 | 1.18 | 0.78 | 82.59% | 1.31 | 0.70 |
| Uber Eats | GES | 685 | 1.1 | 2019–2020 | 1.13 | 067 | 75.77% | 1.10 | 0.60 |
| Deliveroo | GES | 111 | 1.3 | 2018–2020 | 113 | 056 | 72.97% | 1.10 | 0.44 |
*Data from 2020 are from January to November 16.
GES: gig economy service. TrustScore is the score allocated by Trustpilot (max. 5). STD: standard deviation.
Source: Trustpilot.
Thematic analysis was used to analyze the consumer reviews. A deductive approach [53], drawing on the dimensions of food-delivery service quality, was applied. Based on the dimensions mentioned in Section 2.2, the following categories were considered: price, reliability, worker performance, availability, food condition, order quality, timeliness, order system, claims, and invoicing. These categories include the food-delivery service quality dimensions [41], other dimensions related to physical distribution, and factors most frequently mentioned in studies on usage and satisfaction with food-delivery services.
Due to the large number of reviews (3740), text analytic software, KH Coder [54,55], was used to support the thematic analysis. Four files with reviews were created on each company. For each file, the text corpus was first processed using the Stanford Log-linear Part-Of-Speech tagger [56]. This task consists of four sequential steps: tokenization (i.e., decomposing text into words, phrases, and other meaningful parts), elimination of stop words (i.e., definite or indefinite articles and auxiliary verbs such as “a,” “the,” and “are”), part-of-speech (POS) tagging (i.e., categorizing words in correspondence to a particular part of speech, depending on the definition of the word and its context, such as noun, verb, and adjective), and lemmatization (i.e., returning different forms of a single word to its root form, such as “waiting” and “waited” into “wait”).
Based on the list of frequent words that the software produced, those that could be related to the dimensions of food-delivery service quality (e.g., bad, to wait, to cancel, to arrive, charge, to claim, scammer, good) were chosen through consensus by the author and another researcher. This resulted in a list of 148 words. Next, a sample of 100 reviews that included one or more of these words was selected. Both researchers inspected these reviews and realized the following: First, although most of the reviews were negative, some included positive comments; therefore, it was decided that the assessment of each service quality dimension would include a positive and negative valence. Second, because some reviews consisted of several statements, they provided information that fit different categories of food-delivery service quality. For example, the following review “It is unfortunate that orders arrive one to 2 h late and mostly, the food is cold” was classified in the categories of timeliness and food condition. Finally, many reviews included consumers’ overall assessments of the service received. They consisted of positive impressions, such as good or excellent, or negative assessments, such as bad or disastrous. Thus, in addition to the categories considered to represent food-delivery service quality (Table 2), two others—positive and negative—were included to assess the overall impression of the service.
Table 2. Categories of food-delivery service quality and examples of review content.
| Category | Valence | Content examples |
|---|---|---|
| Overall assessment | – | Bad, disaster, the worst, lamentable, disappointing, shameful, terrible, awful, unacceptable, botched, nefarious. |
| + | Good, excellent, effective, positive, fantastic, great, professional, impeccable. | |
| Price | – | Expensive, excessive pricing. |
| + | Cheap, inexpensive. | |
| Availability | – | Restaurants are never available, lacked variety, few menus, low supply, always closed, there are no offers, saturated, they said there were no couriers available. |
| + | Great offer, good selection of restaurants, variety of food, many restaurants to choose from. | |
| Reliability | – | Unreliable, fraud, irresponsible, flippant, hoax, thieves, misleading, scam, shameless. |
| + | Reliable, highly recommended, responsible, never fails, trustworthy. | |
| Worker performance | – | Rude, liar, badly treated, courier struggled to find the address, pizza boxed turned vertically, worker took the food, courier stole food, food left hanging on the door. |
| + | Nice, kind, polite, always smiling. | |
| Food condition | – | Cold, hot beverages, inedible, bad condition, food was scattered, food was bad, food was hard, frozen, melted. |
| + | Hot, tasty, nicely presented, food was perfect, good condition, good quality, healthy food. | |
| Order quality | – | Order canceled, never arrived, some products are missing, incorrect order, wrong address, only half of the food. |
| Order system | – | Improper fees charged, difficulties getting refund, issues with credit card, impossible to cancel, difficult to use, charged before confirming the order, double charged, the app has many problems. |
| + | Easy to use, very intuitive, good website, secure site, great app, everything is easily found, easy to place orders, very convenient. | |
| Timeliness | – | We waited more than an hour, 90 min late, 2 h late, 3 h late, arrived late, they are late to arrive, not punctual, late delivery, tired of waiting. |
| + | Always on time, fast delivery, met scheduled time, reasonable waiting time, delivered on time, food arrives within a few minutes, more than punctual. | |
| Claims | – | I submitted a complaint, I complained, I filed a complaint, I complained several times, when you complain they do not offer any solution, we will report them to the consumer watchdog. |
| + | They solved my problem, they quickly solved the issue, my complaint was immediately resolved, they fix any incident quickly. | |
| Invoicing | – | Invoice was wrong, they sent me a fake invoice, invoice was unclear, why do they take so long to send the invoice? |
Thereafter, both researchers independently assessed all the reviews of each company that included any of the previously mentioned 148 words. If the phrases contained those words, the essential parts of the phrases (e.g., order canceled, never arrived, some products are missing, incorrect order, only half the order, food was missing) were coded into the specific delivery service quality categories, including the valence (e.g., negative order quality). KH Coder allows easy access to text that contains a particular word and makes it possible to code words (e.g., disappointing), combinations of words (e.g., not recommended), and phrases (e.g., I will never order anything again) into categories (e.g., negative overall assessment). For each company, researchers independently established essential parts of the phrases that reflected each service-quality dimension. Both researchers met and compared their work. There were no significant issues regarding matching phrases to the service quality dimensions or their valence. Subsequently, based on both researchers’ results and agreement, a definitive list of the phrases, text, and service quality categories was established. Based on this list, the software provided the total number of reviews for each company that fit each of the service quality categories.
Table 2 presents information about the categories of food-delivery service quality and examples of words and phrases found in each category. Most reviews contained both favorable and unfavorable consumer comments; thus, most of the categories had positive and negative valences.
4. Results
Table 1 provides eWOM data for the analyzed companies. The means of consumer ratings, which range from one to five, coincide with Trustpilot's TrustScore, with the exception of Just Eat. In this company, the mean consumer scores reflect a more positive perception (3.99) than the TrustScore (1.8). This difference is not related to the number of users who rated their experience more than once, because their means (4.00) and standard deviations (1.50) are quite similar.
Since the difference between Just Eat's TrustScore and ratings' mean was so large, this issue was examined in depth. Trustpilot gives more weight to recent reviews when calculating their TrustScores. Thus, the rating means were calculated for each year in the analysis, and the following results were obtained: 2014 (4.74), 2015 (4.70), 2016 (4.47), 2017 (4.56), 2018 (3.85), and 2019 (1.15). Therefore, in 2019, this company's customers were clearly less satisfied. Thus, two content analyses were performed for Just Eat, one based on all the eWOM data and the other focused only on 2019. Table 3 presents the results of the review categorization.
Table 3. Food-delivery service quality.
| Category | Professional service company | GE companies | ||||
|---|---|---|---|---|---|---|
| Valence | 2014–2019 | 2019 | Glovo | Uber Eats | Deliveroo | |
| Overall assessment | – | 10.47% | 61.79% | 55.79% | 63.38% | 56.25% |
| + | 48.59% | 8.94% | 2.73% | 3.17% | 1.56% | |
| Price | – | 0.88% | 4.88% | 6.76% | 3.87% | 7.81% |
| + | 0.63% | 0% | 0% | 0% | 0% | |
| Availability | – | 1.07% | 1.63% | 1.04% | 1.06% | 1.56% |
| + | 2.32% | 0.81% | 0% | 1.76% | 0% | |
| Reliability | – | 2.63% | 21.95% | 20.81% | 27.82% | 20.31% |
| + | 5.77% | 0% | 0% | 0% | 0% | |
| Worker performance | – | 1.13% | 8.13% | 2.21% | 3.87% | 12.50% |
| + | 0.38% | 0.81% | 1.04% | 1.06% | 0% | |
| Food condition | – | 1.07% | 10.57% | 3.90% | 1.41% | 9.38% |
| + | 4.39% | 0.81% | 0.52% | 0.35% | 1.56% | |
| Order quality | – | 5.96% | 34.15% | 22.63% | 57.04% | 28.13% |
| Order system | – | 7.96% | 38.21% | 37.19% | 38.73% | 34.38% |
| + | 3.32% | 0% | 0% | 0% | 0% | |
| Timeliness | – | 10.47% | 45.53% | 62.03% | 34.86% | 53.13% |
| + | 16.99% | 4.88% | 1.69% | 0.70% | 1.56% | |
| Claims | – | 2.07% | 13.01% | 26.92% | 32.75% | 18.75% |
| + | 1.76% | 0% | 0% | 0% | 0% | |
| Invoicing | – | 0% | 0% | 1.43% | 17.61% | 0% |
| Uncategorized | 13.82% | 6.50% | 5.32% | 4.67% | 5.41% | |
| Reviews | 1452 | 123 | 1192 | 653 | 105 | |
4.1. Delivery service quality in the professional service company
Food-delivery service quality was clearly worse in 2019. In the 2014–2019 period, almost half of the reviews (48.59%) were overall positive about the delivery experience, whereas 10.47% of the reviews were overall negative. The valences of these assessments were inverted in 2019 (61.79% negative and 8.94% positive).
The same scenario applied to the delivery-service-quality categories. Throughout the sample period, reviews referred to these categories in both positive and negative ways, but in 2019, negative perceptions prevailed. Overall, 16.99% of the reviews recognized that services were delivered on time, whereas 10.47% of the reviews criticized this aspect. In 2019, 45.53% contained comments about tardiness and 4.88% about food delivered on time. Considering all the reviews, 7.96% mentioned mistakes in the ordering system (e.g., improper fees charged, problems receiving refunds), and 3.32% mentioned positive experiences (e.g., easy to use). In 2019, the negative aspect of this category rose to 38.21%, and the positive aspect was not mentioned. Overall, 5.96% of the reviews referred to mistakes in the order (e.g., never arrived, some products were missing, incorrect order), whereas this category increased to 34.15% in 2019. Moreover, 5.77% of the reviews directly stated that Just Eat was reliable or suggested this characteristic (e.g., never fails, reliable, interface is secure, highly recommended), which did not occur in 2019. By contrast, 2.63% described it as unreliable or used words that conveyed this condition (e.g., misleading, fraud). This result was found in 21.95% of the review content in 2019. In addition, 4.39% of the reviews mentioned that the food was in good condition (e.g., hot, tasty, nicely presented), and 1.07% mentioned the opposite (e.g., cold, inedible). These figures were 0.81% and 10.57%, respectively, in 2019. Furthermore, 2.32% stated that availability was great (e.g., variety of food, good choice of restaurants), and 1.07% had negative comments about it (e.g., lack of supply, low food variety). In 2019, positive and negative perceptions of availability represented 0.81% and 1.63% of the review content, respectively. Overall, 2.07% of the reviews described claims to the company, and this percentage rose to 13.01% in 2019. Additionally, 1.76% mentioned that the company resolved consumers’ claims, but this characteristic did not appear in 2019. Regarding cost, 0.88% of the reviews revolved around the high cost (4.88% in 2019), and 0.63% considered the price to be cheap (0% in 2019).
Finally, overall, 1.13% of the reviews described worker's poor performance (e.g., being rude, courier struggled to find the address) and 0.38% identified good performance (e.g., being nice). These figures were 8.13% and 0.81%, respectively, in 2019.
Based on all the categories used to portray food-delivery service quality, positive reviews accounted for 51.69%, and negative reviews represented 48.31%. In 2019, the percentages were 3.95% and 96.05%, respectively. Furthermore, the categorization captured most of the review content because only 13.82% and 6.50%, respectively, were not included in the categorization process.
4.2. Delivery service quality in GES
For Glovo, slightly more than half of the reviews (55.79%) showed overall negative opinions about the delivery experience, while 2.73% of the reviews contained good overall opinions. The most frequent comments in the reviews revolved around tardiness (62.03%). Only 1.69% of the reviews recognized that orders arrived on time, and 37.19% of the reviews contained criticisms about mistakes in the ordering system (e.g., improper fees charged, difficulties getting the money back, issues with credit cards). Claims were described in 26.92% of the reviews, 22.63% of them showed that orders had failed (e.g., order canceled, never arrived, incorrect order, wrong address), and 20.81% included statements indicating that Glovo was unreliable (e.g., scam, flippant, not trustworthy, shameless, hoax, thieves). In addition, 6.76% of the reviews complained about price and 3.90% about the food's condition (e.g., cold, hot beverages, inedible, bad condition). Only 0.52% of the reviews stated that the food was in good condition (e.g., hot). Moreover, 1.43% claimed that the invoice was wrong, and 2.21% stated that the workers did not perform well (e.g., being rude, courier struggled to find the address). By contrast, 1.04% detected good worker performance (e.g., friendliness). Finally, criticisms about low availability were present in 1.04% of the reviews (e.g., there are no offers, closed, saturated).
The negative delivery service quality categories represented 97.58% of all the identified categories, whereas positive categories represented 2.42%. Only 5.32% of the content of the reviews was not covered by the categories listed in Table 2.
In the case of Uber Eats, 63.38% of the reviews showed overall negative opinions about the delivery experience, whereas 3.17% of the reviews contained good overall opinions. The most frequent delivery-service-quality category revolved around ordering mistakes (57.04%) (e.g., order canceled, never arrived, some products are missing, incorrect order, wrong address). The ordering system (38.73%) was also negatively assessed (e.g., charged before confirming the order, impossible to cancel, improper fees charged, difficulties getting refund, difficult to use). In addition, 34.86% of the reviews referred to tardiness, and 0.70% stated that deliveries were on time; furthermore, 27.82% suggested that Uber Eats was unreliable (e.g., unreliable, scam, dishonest, flippant, not trustworthy, shameless, hoax, thieves) and 32.75% mentioned claims made to the firm. Moreover, 3.87% of the content of the reviews included examples of bad worker performance (e.g., workers took the food, turned pizza boxes vertically, struggled to find the address), and 1.06% acknowledged the contrary (e.g., very friendly). Furthermore, 3.87% considered the price expensive and 1.06% stated that the service was unavailable (e.g., never available). Finally, 1.41% mentioned that food quality was poor and 0.35% said it was good.
Positive categories made up 3.37% of all the delivery service quality categories identified, whereas 96.63% were negative perceptions. Only 4.67% of the content of the reviews was not covered by the categories listed in Table 2.
In addition, 56.25% of the reviews on Deliveroo showed negative overall opinions about the delivery experience, whereas 1.56% of the reviews contained good overall impressions. Tardiness was the most frequent service delivery category (53.13%), whereas 1.56% of the reviews described a service that was on time. Mistakes in the ordering system made up the second most important category (34.38%) (e.g., improper fees charged, difficulty receiving money), and 28.13% of the reviews mentioned order failures (e.g., order canceled, never arrived, some products are missing, incorrect order). In addition, 20.31% included statements suggesting that Deliveroo was unreliable (e.g., flippant, scam, shameless, hoax, thieves), and 18.75% referred to claims made to the company. Moreover, 12.50% noted that workers’ performance was poor (e.g., being rude, lying), and 7.81% stated that the service was expensive. Furthermore, 9.38% of the reviews stated that the food was in poor condition (e.g., cold, scattered), and 1.56% said it was good. Finally, low availability was cited in 1.56% of the content of the reviews.
Positive categories made up 1.90% of all the delivery service quality categories identified, whereas 98.10% were negative perceptions. Only 5.41% of the content of the reviews was not included in the categorization process.
5. Discussion
Consumers’ general opinions clearly reflect their perceptions of service quality. In the case of Just Eat, almost half of the reviews contained overall positive opinions. Favorable perceptions in the categories of food-delivery service quality represented 50.85% of the reviews. For Just Eat in 2019 and in GES, most of the global opinions were negative (above 50%), and unfavorable perceptions in the categories of food-delivery service quality were clearly emphasized (above 93%). The low ratings consumers gave to these firms are in line with these negative perceptions.
If we consider the data in all the sample years for Just Eat, it appears that a professional service company achieves better client satisfaction and food-delivery service quality compared to GES. For example, the latter failed in one of the key aspects of food-delivery services, being on time. Tardiness was the most frequent criticism in Glovo and Deliveroo's reviews and was mentioned in more than 60% and 50% of the content of the reviews, respectively. In Uber Eats, this was the third highest category (34.86%). By contrast, tardiness in Just Eat appeared in 10.47% of the reviews' content, and comments on timeliness were higher (16.99%). Nevertheless, if only 2019 is considered, Just Eat does not present the best service quality results for tardiness (45.53%).
The same scenario applies to other service-quality categories, which, in theory, could also be influenced by the type of workforce used, ordering quality, and reliability dimensions. Many of the problems with order quality were related to order cancelations and orders that never arrived. Some reliability criticisms may stem from tardiness or mistakes. Overall, important differences between GE and professional service companies arose in these dimensions. The three GES showed errors in ordering quality (the most prominent was Uber Eats, with 57.04% of the reviews complaining about this issue) and negative reliability (more than 20% of the content of the reviews). In the case of Just Eat, these issues were clearly less frequent, but in 2019, the results in these dimensions were at the same level as those of GE companies.
Two other service quality dimensions, which might be affected by the type of workforce used, are worker performance and claims. Although the results are not as relevant as those described above, the differences between Just Eat and GES in poor perceptions of workers’ performances are greater when the whole period is considered. In the case of claims, Just Eat always obtained better results.
Overall, GES also stood out negatively in the service quality category that did not depend on whether couriers were employees or self-employed workers. An example is mistakes in the ordering system, which represent more than 20% of GES reviews. Once again, if only 2019 is considered for Just Eat, it has the same poor results as GE companies in this category.
Based on 2019, the sample reviews used in this study appear to convey that, regardless of how food-delivery services are carried out (i.e., GES versus professional delivery services), they are characterized by poor service quality. Naturally, both the sample size and composition must be considered when contradicting the generalizations in the abundant literature, and evidence on the importance of service employees. Nevertheless, the case of food-delivery GES may be a particular type of service where traditional service employee management can be substituted by other factors or conditions. One of these factors is algorithmic management, which is associated with GE [57]. This type of management simplifies and automates performance management practices, which can be considered equivalent to human resource management in traditional companies. Since food-delivery service quality does not seem to be clearly worse in GE companies, this type of automated management may affect worker behaviors similarly to those found in companies that hire and manage their employees. Therefore, in some businesses, the GE model achieves similar service quality results without incurring the labor costs of traditional firms, which would give GES an advantage and require traditional firms to differentiate their services in other ways. Nevertheless, food-delivery GES has been accused of controlling and constraining their workforce through algorithmic management [25,29]. This type of management limits the autonomy that should be a feature of self-employed workers. For example, if couriers do not accept a delivery request, there may be serious consequences. Furthermore, algorithmic management has been associated with long working hours, feeling stressed, and low worker involvement [29,32].
A condition that possibly favors the GE model is the supply of workers because delivering food does not demand complex skills and resources. Therefore, the theoretically high worker turnover that characterizes GE does not seem to be an issue for food-delivery GES in a country that had an unemployment rate of 14.10% in 2019. In this regard, Veen et al. [29] explains that the high level of underemployment in Australia allows food-delivery GE companies to have a considerably large reserve of workers who can perform deliveries. Nevertheless, other GES, which rely on the availability of more complex skills (e.g., those related to information technologies) might be more affected by high worker turnover. Recent reviews on GE note that this field includes very different services and contexts [58], which alerts us to the risk of proposing general explanations on the workings of GE.
Just Eat's reviews from 2014 to 2018 show that a professional service company can distinguish itself from GES through service quality. Why did Just Eat perform poorly in 2019? A possible reason is that it grew very quickly to make the most of the market. The winner-take-all strategy is characteristic of some digital platform markets [59]. This desire for quantity could have been at the cost of service quality. In Spain, Just Eat started in 2010, based on restaurants that had their own delivery services. As the company grew, it opened its platform to restaurants that did not have their own delivery services, offering them delivery services through its couriers or specialized delivery firms. It is possible that Just Eat did not monitor the performance of these services while trying to capture most of the market. However, accurate data are not available to support this proposition. According to Statista [60]; Just Eat had 3000 restaurants in Spain in 2014, and the company itself stated that it had more than 11,000 restaurants in the country in 2019 (Just Eat, 2020). Additional public data on the evolution of these figures, and how Just Eat supervises the food-delivery operations, are unavailable.
Professional food-delivery companies cannot compete with GE platforms based on cost. These companies should offer better services (e.g., worker courtesy, order quality, reliability, and timeliness), and may find algorithmic management useful for this. There are two reasons why these companies are in much better position, compared GE platforms, to use this type of performance management: they are entitled to influence their couriers' performance based on human resource practices and they can combine algorithmic management with other human resource practices to counterbalance the effects of the tight control that this generates. Supportive human resource practices (e.g., training, supervisor support, rewards, and employee benefits) increase employees’ perceptions that the organization supports them. This perception is positively associated with employee attitudes and performance [61]. Food couriers appreciate these types of practices [29]. Thus, grocery delivery service companies such as Gorilla use them (e.g., fixed employment contracts, payment for waiting time, health insurance, and employer-financed accident insurance). In fact, this company states that it wants to be a countermodel for GE companies. By contrast, food-delivery GE platforms must not use these countervailing human resource practices because doing so would indicate that the couriers are, in fact, employees.
The methodology used by this study has several limitations. It is not possible to ensure that the sample analyzed is representative of consumers’ perceptions of these services. This is one of the weaknesses of eWOM data [62]. Nevertheless, in Trustpilot, other digital platforms related to food delivery (i.e., digital supermarkets) mostly receive positive reviews. Furthermore, thematic analysis has a subjective component that can lead to errors. Although text analytic software was used, the researchers assessed the contexts in which the words appeared. They compared their analyses, but they could have made mistakes when they individually read those words in the reviews.
6. Conclusion
This study aimed to determine whether the service quality of food delivery is affected by the use of independent workers, which is a feature of GES. A definitive answer to this question has not been provided. If the study relied on the scores that Trustpilot publishes, a clearer conclusion would have been reached (i.e., the food-delivery service quality of GES is worse). Nevertheless, the deterioration of Just Eat's ratings in 2019 was remarkable and a rigorous study would not ignore it.
This study confirms that food-delivery services based on GE platforms are characterized by problems that, in theory, can be attributed to the use of self-employed individuals. Nevertheless, a professional service provider, which does not depend on this type of workforce, has the same issues. Thus, there is insufficient evidence to state that, in the case of food-delivery services, relying on independent workers to provide on-demand services produce worse results than a professional service company in terms of service quality and customer satisfaction. Further research is required to confirm the results of this study.
Food-delivery GE companies have been accused of promoting precariousness in employment. These results provide a complementary view of these platforms. According to the results, these firms perform as well as their counterparts in traditional business management, but with the caveat that GE platforms face lower labor costs. Thus, the cost savings involved in employing freelance workers, compared to hiring employees, does not affect service quality negatively. A possible explanation is that, based on automated management, food-delivery GE platforms manage independent workers as if they are employees and certain court decisions supported this criticism.
Therefore, certain limitations of this study pave the way for future research. This study only considered one professional service company's eWOM, and one of the three GE companies analyzed had a relatively low number of reviews. Furthermore, we only used data from Spain and including other countries would contribute to the generalization of the results. Finally, in this study reviews such as “disappointing” and “first and last time I use this platform” are coded in the same category; therefore, a sentiment analysis could improve the objectivity and produce more accurate results.