Service quality, satisfaction and loyalty among sharing economy vehicle users

Abstract

Sharing economy has emerged as an influential concept, shaping the way and manner businesses are transacted. Of particular interest is the transport industry, where a combination of GPS and the development of software have resulted in the creation of an e-hailing transportation system. Issues such as the provision of quality services persist; giving rise to further investigations. The study, therefore, delved into service quality, satisfaction and loyalty of users of e-hailing transport services. Using a cross-sectional descriptive survey, a convenience sample was employed to intercept 436 users of e-hailing vehicles at pick-up and drop-off points in Accra, Ghana. A structural equation model was used to test the relationships between service quality, satisfaction and loyalty among users of e-hailing vehicles. Results show assurance (β = 0.149; p = .022); tangibles (β = 0.140; p = .045), responsiveness (β = 0.040; p = .008); reliability (β = 0.014; p = .015); empathy (β = 0.062; p = .000); system information (β = 0.013; p = .005) and price (β = 0.001; p = .008) significantly influence user satisfaction. Similarly, customer satisfaction was found to influence the loyalty of users of e-hailing vehicles. The study concludes that both individual service quality dimensions and the composite service quality model influence users’ satisfaction which in turn influences users’ willingness to re-use and recommend e-hailing services to others. Implications for theory and practice are discussed. 

Keywords:

• Service quality
• customer satisfaction
• loyalty
• sharing economy vehicles
• Ghana

Reviewing Editor:

• Pier Luigi Sacco, Humanities, IULM University, Milano, Italy

Subjects:

• Hospitality marketing
• Tourism

Introduction

In recent years, sharing economy has emerged as an influential concept (Bremser & Alonso-Almeida, 2017), shaping the way and manner businesses are transacted. Of particular interest is the transport industry, where a combination of the Global Position System (GPS) and the development of software have resulted in the creation of e-hailing transportation system (Azudin et al., 2018). This has further been enhanced by the advancement and increasing ownership of handheld devices including smart phones and tablets (Phuong & Trang, 2018), making it easier for people to access online sharing economy transport services. From its inception, sharing economy was conceptualised as giving access to people to make use of unused or underused assets with the aim of making financial gains (Botsman & Rogers, 2011; PwC, 2015; Schor, 2014).

However, recent conceptualisation of the concept has moved from just accessing unused or underutilised assets to the application of business models such as business-to-person and peer-to-peer to mobility such as ridesharing, car sharing or bike sharing (Bremser & Alonso-Almeida, 2017). Sharing economy is defined by Dalberg (2016, p. 2) as ‘sharing assets – physical, financial and/or human capital, between many without transferring ownership, via a digital platform to create value for at least two parties’. This has enabled people to travel or commute more cheaply than using the traditional forms of transport (Barnes & Mattsson, 2016) leading to a reform in the traditional consumption patterns of transport services (Olmstead & Smith, 2016).

Sharing economy businesses provide numerous benefits to all stakeholders in the sharing economy value chain. For instance, PwC (2015) observed that sharing economy ‘allows individuals and groups to make money from underused assets by sharing physical assets as services’ (p. 5); thereby giving opportunities for riders to supplement their income (Azudin et al., 2018). It has also been found that e-hailing transport services have the potential of bringing to the barest minimum, the incidence of drunk driving accidents and fatalities (Dills & Mulholland, 2018; Greenwood & Wattal, 2017; Lim, 2016; Peck, 2017). Due to this, countries such as Malaysia have taken keen interest in supporting sharing economy transport companies (Sammy, 2016). Aside this, research on sharing economy has increased in recent years. Majority of these studies (Chen & Sheldon, 2015; Greenwood & Wattal, 2017; Hall et al., 2018; Stalmašeková et al., 2017) have delved into the service quality-satisfaction nexus of sharing economy companies/vehicles.

However, Azudin et al. (2018) intimated that several issues such as the provision of quality services by e-hailing vehicles still persist. Further to this, Bismo et al. (2018) opined that e-hailing transport services should pay critical attention to service quality issues; giving rise for further investigation into service quality and customer satisfaction of e-hailing transportation services.

Theoretically, studies on e-hailing service quality have used the SERVQUAL model (Parasuraman et al., 1985), to the neglect of using other models in measuring service quality of e-hailing transport services. Another observation from the literature relates to the fact that little conceptual and empirical studies have been done on e-hailing transport service quality, satisfaction and loyalty in Africa in general and in Ghana, specifically. Much of the focus has been on safety and security, impacts of ride-sharing/hailing services on traditional taxi services, profiling of users of e-hailing transport services and lived experiences of ‘disabled’ users of Uber (see Acheampong, 2020; Acheampong & Siiba, 2019; Acheampong et al., 2020; Agyeman et al., 2019; Agyemang, 2020; Odame et al., 2023).

However, e-hailing transport services have been in operation in Ghana for six years now. Uber and Bolt (formerly Taxify) entered the Ghanaian market on 11 June 2016 and 23 December 2017, respectively. Both companies operate e-hailing transport services in the capital city of Accra and Kumasi, the second largest city after Accra. Other e-hailing transport services such as Yango and Lyft also provide services in Accra.

In view of the forgoing, the current study is set out to investigate service quality, satisfaction and loyalty among users of sharing economy vehicles in Ghana. The study is significant in a number of ways. First, insights from the study will serve as a baseline for further investigations into the services of these sharing economy companies. Furthermore, findings from the study will provide valuable insights into the dimensions of service quality that are important to users, thereby providing management of these companies the opportunity to provide services that will meet the needs of their customers. Finally, the findings can aid policy makers, particularly within the transport sector in developing strategic policies regarding the operations of sharing economy businesses.

The remaining section of the paper is structured as follows: a literature review on service quality, satisfaction and the relationship between these two concepts. Literature on customer loyalty and influence of satisfaction on loyalty was reviewed. This is followed by details on materials and methods, results and discussions on the key findings. Conclusion and implications of the study is presented in the last section.

Sharing economy vehicle service quality

Service quality has received considerable attention in both traditional and sharing economy transport literature (see Bismo et al., 2018; Mensah & Ankamah, 2018; Nutsugbodo, 2013; Pasharibu et al., 2018; Phuong & Trang, 2018; Sharma & Das, 2017; Suhaimi et al., 2018). Service quality is an expression of customers’ overall perception of the service provided (Culiberg, 2010; DeMoranville & Bienstock, 2003). It can also be conceptualised as the difference between expected service and experienced service. If the services provided falls below the expectations of consumers, they will be dissatisfied. However, if the services provided are the same as expected or exceeds expectation, consumer will be satisfied or delighted respectively (Parasuraman et al., 1985). For this reason, service providers aim at either meeting or exceeding the expectations of customers.

A plethora of dimensions have been used in assessing service quality. One of such dimensions is the one proposed by Parasuraman et al. (1985); which has become one of the most influential measurements for evaluating service quality. According to Parasuraman et al. (1985), service quality is evaluated with the following dimensions: tangibles, reliability, responsiveness, assurance and empathy. From the perspective of Fitzsimmons and Fitzsimmons (2011), customers’ evaluation of the quality of services provided will be based on availability, convenience, dependability, personalisation, price, quality, reputation, safety and speed. Other dimensions such as website design, information quality and interface interactivity have been found to be critical to the evaluation of service quality (Hahn et al., 2017; Ku & Chen, 2015; Xu et al., 2013). Notwithstanding, Silvestro (2005) observed that, the best criteria for assessing service quality are the ones defined by the customer. In essence, these dimensions only serve as a guide, whilst the customer defines what constitute service quality based on their expectations and experiences.

A study by Sharma and Das (2017) on service quality of online cab in India found tangibles, responsiveness and empathy as the three most important dimensions in relation to service quality ratings of online cabs by consumers. Specifically, the study revealed that physical facilities, equipment, personnel, printed and visual materials play a critical role in customers’ assessment of service quality when travelling or commuting with online cabs. The study further identified cabs driver’s willingness to help customers, provide prompt service as well as their physical appearance as issues that need to be addressed by management of online cab services. Suhaimi et al. (2018) assessed service attributes of sharing economy vehicles that contribute to customers’ assessment of service quality. They found that safety, price, convenience and the use of the internet as customers’ perception of service quality. Pasharibu et al. (2018) observed that issues such paying attention to cleanliness and tidiness of sharing economy vehicles, provision of responsive services as well as improvement of driver capabilities were important dimensions of service quality that sharing economy vehicle owners should pay attention to. Theses, according to them, go a long way in ensuring customers’ comfort and maintain customers’ trust in the service providers.

Customer satisfaction with sharing economy vehicles

Satisfaction relates to instant assessment of service after consumption (Culiberg, 2010) and has been studied in almost every sector of the hospitality and tourism industry (Adam et al., 2017; Amissah, 2013; van Lierop et al., 2018). According to Oliver (1997), satisfaction is the completion and fulfilment of needs. In essence, customers compare their expectations prior to patronising the service with their experience in determining their satisfaction (Morfoulaki et al., 2010). Satisfaction is very critical as it gives service providers an understanding as to whether the needs and wants of customers are fulfilled or otherwise (Phuong & Trang, 2018).

Empirical research has revealed that users of sharing economy vehicles are mostly satisfied with the convenience, price, app, opportunity of knowing the driver ahead of time and the ability to know how much the trip will cost them to utilising the service (Chen & Sheldon, 2015; Greenwood & Wattal, 2017; Hall et al., 2018; Stalmašeková et al., 2017). For instance, Uber services have been found to charge about 20–30% lower than what traditional taxis charge. This has been seen as a disincentive to operators of traditional or regular taxis and even buses. In Malaysia, this created a brawl between sharing economy vehicles (Uber and GrabCar) when people switched from using traditional taxis to sharing economy vehicles due to their relatively cheaper prices (Kaur et al., 2016; Lee, 2016).

Azmi et al. (2019) reported that user satisfaction from using sharing economy vehicles emanates from users’ ability to use several modes of payments either before or after the trip. Specifically, users’ can make payment by using their credit cards or through direct transfer from bank accounts. The implication is that, the user can avoid making cash payments, hence, ease and safety of payment (Hughes & MacKenzie, 2016). Additionally, reduction in waiting time when using sharing economy vehicles has been found to influence users’ satisfaction (Caranza et al., 2016; Hughes & MacKenzie, 2016; Rayle et al., 2016). Compared to traditional taxis and other forms of transport; sharing economy vehicles offer flexible itinerary as the customer has the flexibility of deciding when to use the vehicle. This is different from taxis, buses and trains which have fixed itinerary (Azmi et al., 2019)

Service quality and customer satisfaction nexus

The relationship between service quality and customer satisfaction has been widely discussed in the literature. The literate on service quality and customer service posit three types of relationship between these two constructs. Whilst service quality has been described as an antecedent to customer satisfaction, some others propose that either customer service causes service quality or none of the concepts is antecedent to the other (Bitner, 1990; Brady et al., 2002; Dabholkar et al., 2000; Naik et al., 2010).

A number of studies have found that service quality influences customer satisfaction (Bismo et al., 2018; Pasharibu et al., 2018; Soleh et al., 2018; Susilo et al., 2018). Empirically, Bismo et al. (2018) found that all the five attributes of the SERVQUAL model positively affect customer satisfaction for using Grabcar services in Jakarta, Indonesia. Other studies (Chao & Kao, 2009; Deneke et al., 2016; Yao & Ding, 2011) allude to the influence of all the dimensions of service quality on customer satisfaction. Further to this, Hamenda (2018) found that all attributes of service quality significantly influence the satisfaction derived by users of ride-sharing services in Indonesia. A study by Horsu and Yeboah (2015) among taxi cab users in Cape Coast, Ghana found that service quality attributes used in the study – continuous service, reasonable price, comfort, constituency and driver’s conduct – positively influences the satisfaction derived by users.

Moreover, specific service quality attributes have also been found to influence customer satisfaction. In a study to examine the influence of service quality attributes on satisfaction of online cab users in India, Sharma and Das (2017) observed that empathy, responsiveness and tangible significantly influence customer satisfaction. Balachandran and Hamzah (2017) as well as Alemi et al. (2018) also found attributes related to empathy and tangibles as having significant influence on the satisfaction of ride sharing users in Malaysia and Uber users in California respectively. Whilst the former identified drivers’ attitude, the later indicated that Uber cabs comfort contributed to users’ satisfaction. Inbalakshmi and Krishnamoorthy (2015) as well Horsu and Yeboah (2015) confirmed the relationship between service quality and customer satisfaction, though not all the SERVQUAL dimensions were found to influence customer satisfaction. Tangibles, reliability and assurance were found to be antecedent of customer satisfaction.

In an earlier study, Noor et al. (2014) intimated that service quality attributes of tangibility, reliability, responsiveness and assurance were responsible for the satisfaction of users of express bus services in Malaysia. Reliability, assurance and tangibility were found to be significant factors influencing customer satisfaction (Morton et al., 2016). Whilst Man et al. (2019) observed that tangibles, empathy and reliability had significant relationship with satisfaction of ride-hailing service users in Kuala Lumpur, Malaysia, Ziyad et al. (2020) study on users Uber and Careem in Pakistan found assurance, empathy and responsiveness as attributes of service quality that contributes to customer satisfaction. Further to this, Mensah and Ankamah (2018) opined that service quality has a significant influence on customer satisfaction. Specifically, their study on taxi service quality and customer satisfaction among commuters in Accra found that, service quality attributes of assurance, reliability and tangibles contribute significantly to satisfaction derived from by taxi users in the Accra Metropolis, Ghana. In terms of other shared mobility services in Ghana, Ojo et al. (2014) revealed that service quality of public transport on Cape-Coast route influences satisfaction of customers who patronised these public transports. Accordingly, they concluded that whilst empathy, responsiveness and assurance have a direct positive influence, tangibles and reliability had a negative influence on customer satisfaction. In another study, Birago et al. (2017) found that passengers using the Metro Mass Transit in Accra bemoaned that over-crowding, delays, poor/unavailability of seats and long waiting times as the source of poor service delivery, hence, their non-preference or dissatisfaction for using Metro Mass Transit buses in Accra.

The forgoing indicates that a bundle of service quality attributes can combine to create the necessary satisfaction derived by customers. Despite this trend, some studies (Jun, 2012; Murad et al., 2019) have found single attributes of service quality contributing to customer satisfaction. For instance, Jun (2012) identified tangibility as the only service quality attribute that influences customer satisfaction. In their study of Uber and Careem users in Jordan, Murad et al. (2019) found reliability as the most influencing service quality dimension in terms of satisfaction derived from using Uber and Careem services. In a recent study, Kumar et al. (2022) confirmed the finding of Murad et al. (2019) that reliability significantly influences customer satisfaction. Kumar et al. (2022) further indicated that specific reliability items such as availability of ride-hailing services, picking of passengers at the right location and timely arrival ride-hailing cabs. Based on the following, it is expected that specific service quality attributes or dimensions will influence customer satisfaction. The study therefore proposes the following:

Hypothesis 1 (H1): Tangibles positively and significantly influences customer satisfaction

Hypothesis 2 (H2): Assurance positively and significantly influences customer satisfaction

Hypothesis 3 (H3): Reliability positively and significantly influences customer satisfaction

Hypothesis 4 (H4): System and Information Quality positively and significantly influences customer satisfaction

Hypothesis 5 (H5): Empathy positively and significantly influences customer satisfaction

Hypothesis 6 (H6): Responsiveness positively and significantly influences customer satisfaction

Customer loyalty

Customer loyalty has been studied extensively, especially, in the service industry and has been viewed as a resultant of customer satisfaction (Khuong & Dai, 2016). The concept has been defined from many perspectives. Oliver (1997) conceptualise customer loyalty as ‘a deeply held guarantee to re-buy or re-patronise a favoured product or service consistently in the future, despite issue influences and marketing efficiency having the potential to cause changing behaviours’. In essence, loyalty connotes the intention of a customer to reuse or purchase a service/product in the future to the actual use or purchase (Hume et al., 2007). Authors such as Gede and Sumaedi (2013) believe that customers willingness to repurchase a product or service emanates from the customers’ psychological bond built during the initial purchase. Such customers mostly exhibit favourable attitudes for the products or services, hence, the decision to make purchase the product or service again. Literature posits that customer loyalty has two main dimensions, that is, attitudinal and behavioural loyalty (Phuong & Trang, 2018). Whilst attitudinal loyalty is concern with customers’ willingness to recommend or engage in positive word-of-mouth promotion of the service or product, behavioural loyalty deals with the customer making regularly purchases after the initial purchase (Gronholdt et al., 2000; Khuong & Dai, 2016; Phuong & Trang, 2018).

Influence of satisfaction on customer loyalty

The relationship between customer satisfaction and customer loyalty has received considerable attention in the literature (see Huang et al., 2019; Khuong & Dai, 2016; Kuswanto et al., 2019; Slack et al., 2022). In fact, a customer becoming loyal to a service or product is preceded by the level of satisfaction such customer derives from using the said service or product. In essence, the antecedent of customer loyalty is customer satisfaction (Khuong & Dai, 2016). The conclusion is that customer satisfaction drives customer loyalty (Casidy & Wymer, 2015; Srivastava & Kaul, 2016) both in terms of attitude and behaviour (Schijns et al., 2016).

A study by Slack et al. (2022) which examined the influence of customer satisfaction on customer loyalty and disloyalty dimensions revealed that customer satisfaction has a direct and positive influence on the repurchase intention of customers and customer positive word-of-mouth. By extension, when the customer is satisfied, the customer becomes repeat customer and also speaks well of the services or products to other, hence, becoming a loyal customer. Kuswanto et al. (2019) investigations into the influence of online and offline services of ride-sharing motorbikes in Indonesia found that satisfaction has a significant relationship with customer loyalty. The more customers were satisfied with the services provided by ride-sharing motorbikes, the more customers were inclined to be loyal by recommending the services to others. In an earlier study, Huang et al. (2019) concluded that satisfaction derived from local taxi services in Vietnam significantly influences customer loyalty as well as the two sub-dimensions of loyalty – attitudinal and behavioural. In view of this, the study proposes that:

Hypothesis 7 (H7): Customer satisfaction positively and significantly influences customer loyalty.

Flowing from the literature review, the following conceptual model is proposed (Figure 1):

Figure 1. Conceptual model and hypotheses.

Theoretical underpinning

The service performance (SERVPERF) model developed by Cronin and Taylor (1992, 1994) was adapted for the study. The model evaluates quality service by assessing the actual service instead of comparing expectation and experiences of customers. Accordingly, customers mostly evaluate the quality of service by examining the performance of the services they receive. Since the study measured service quality from the performance of sharing economy vehicles, the SERVPERF model was deemed appropriate. However, since the current study was on the sharing economy (online) transport services, two additional dimensions, system and information quality and price were introduced to the existing measures of the SERVPERF model, that is, tangibles, assurance, responsiveness, reliability and empathy. This was done in order to capture keys issues on the online component of the sharing economy vehicles and pricing of sharing economy vehicles.

Materials and methods

Study setting

The study was set out to investigate service quality, satisfaction and loyalty among users of sharing economy vehicles in Ghana. Specifically, the study sought to influence of service quality attributes (tangibles, assurance, responsiveness, reliability, empathy, system information quality and price) on customer satisfaction as well as determine the relationship between customer satisfaction and loyalty among users of sharing economy vehicles in Ghana.

The study was conducted Accra, Ghana. Accra is both the capital city of Greater Accra Region and Ghana. The city is the most densely populated and commercial city in Ghana. Due to increasing population, the city is faces massive vehicular traffic, especially during the peak hours of the day. It is estimated that about 70% of the city’s inhabitants rely on informal minibuses (often called ‘trotro’) and taxis on a daily basis. In terms of long haul journeys, residents of the city utilise the private-owned buses and the government-private owned Metro Mass Transit (MMT)and State Transport Corporation (STC) buses. Whilst trotros and taxis can be patronised almost everywhere in the city, transport companies operating buses are mostly found in the city’s largest bus terminal at ‘Kwame Nkrumah Circle’, popularly known as ‘Circle’. The MMT, in addition to the bus terminal at ‘Circle’, has three other terminals at Achimota, Madina and Keneshi, all in Accra.

However, the advent of e-hailing transport services has shifted attention from the use of trotros and taxis for daily commutation among residents of Accra. This trend could possibly be blamed on the poor turn around transport system in the city (Amoako et al., 2021). Studies indicate patrons of e-hailing services are mostly young males with at least a tertiary education (eg Acheampong, 2021). Accra has a composite of all the e-hailing transport services operating in the country. In fact, the first two e-hailing transport services that entered the country started their operations in Accra before commencing business in other cities such as Kumasi, Takoradi and recently, Sunyani. After the initial entry of Uber and Bolt (formerly Taxify) in Accra in 11 June 2016 and 23 December 2017 respectively; other e-hailing transport services (eg Yango, Accra Cab, Yenko, Fameko, Enshika, First Class and Uru) later joined the e-hailing transport market in Accra, though some could not survive. Currently, Uber, Bolt and Yango are considered as the ‘big three’ e-hailing transport services in Ghana (Pasquali et al., 2022). Accra is the only city in Ghana having all three in operations. This makes Accra the preferred choice for the study.

Sampling procedure and participants

Due to the absence a sampling frame, the study adopted a convenience sampling method (Reynolds et al., 2003). The adoption of the convenience sampling affirms of the use of this sampling method in similar studies (see Birago et al., 2017; Horsu & Yeboah, 2015; Mensah & Ankamah, 2018). Specifically, a public intercept survey was used (see Slack et al., 2022). Respondents were intercepted at the premises of the University of Ghana, Accra Mall, West Hills Mall, Ministries and Ghana Institute of Management and Public Administration. Anecdotal evidence suggests that these facilities served a ‘dual purpose’ of being both a pick-up and drop-off points for users of e-hailing vehicles. In essence, users of e-hailing transport services served as the participants of the study. Out of 750 users of e-hailing transport service users intercepted, 436 agreed and participated in the study, representing 58.1% response rate. The consent of respondents was sought before the questionnaires were administered on them. Again, respondents were assured of anonymity and confidentiality.

Questionnaire design

The study questionnaire consisted of four (4) sections. The first session focused on eliciting information on participants’ socio-demographics and travel characteristics of participants. Items from the SERVPERF model (Cronin & Taylor, 1992) were adapted for the second section which measured service quality. However, the items were reworded to suit the current study setting (Table 1). Again, two additional dimensions – price and system and information quality – were added to the existing five dimensions (tangibles, assurance, responsiveness, reliability and empathy). Two questions on price were adapted from Zhou and Zhang (2019) whilst items on system information and quality were sourced from Anderson et al. (2013). The third and fourth sections examined customer satisfaction and loyalty respectively. Whilst items on the former were taken from Rust and Oliver (1994), items on the later were adopted from Slack et al. (2022). With the exception of items measuring socio-demographics and travel characteristics, the remaining sections of the study instrument were measured using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Table 1. Questionnaire items.

Construct	No. of items	Statements	Source
Service quality
Tangibility	4	Vehicles look modern Vehicles are visually appealing Drivers appear neat and tidy Seats in vehicles are comfortable	Cronin and Taylor (1992)
Assurance	3	Behaviour of drivers instil confidence I felt safe whilst in the vehicle Driver was consistently courteous
Reliability	3	Driver picks you up at the time he promised to pick you Driver satisfies your requests the first time Driver drives safely
Empathy	3	Driver gives personal attention Driver has your best interest at heart Driver understands your specific needs
Responsiveness	3	Driver provides timely and efficient service Driver is always willing to help out Driver is never too busy to respond to your request
System information	3	The app is easy to use The app is upgraded regularly to improve service Online platform assures privacy	Anderson et al. (2013)
Price	2	The reasonableness of price I had value for money	Zhou and Zhang (2019)
Customer satisfaction	3	The performance of vehicles matched my expectation It is a pleasure travelling with these vehicles Overall, I was satisfied	Rust and Oliver (1994)
Customer loyalty	2	I intend to continue using e-hailing transport services I will recommend to my family and friends to use e-hailing transport services	Slack et al. (2022)

Results

Background of participants

Majority of the participants (Table 2) were males (60.1%) and were mostly single (65.4%). In terms of age, participants aged below 26 years (34.2%) slightly dominated participants aged between 26 and 30 years (33.5%) and those above 30 years (32.3%). Participants were well educated with majority of them (45%) having obtained a bachelor degree and were largely self-employed (37.2%).

Table 2. Socio-demographic characteristics.

Characteristic	Frequency (N = 436)	Percentage (%)
Sex
Male	262	60.1
Female	174	39.9
Age (years)
<26	149	34.2
26–30	146	33.5
>30	141	32.3
Marital status
Single	285	65.4
Married	151	34.6
Level of education
Basic/junior high	89	20.4
High school	117	26.8
Degree	196	45.0
Post-graduate	34	7.8
Employment status
Students	92	21.1
Unemployed	45	10.3
Formal employment	137	31.4
Self-employed	162	37.2

Descriptive statistics

As seen from Table 3, users of e-hailing transport services had high scores for the following statements: REL 1 (m = 4.11), EMP 2 (m = 4.08), ASS 2 (m = 4.06), SIQ 1 (m = 4.06), REL 3 (m = 4.06), TAN 3 (m = 4.05), SIQ 2 (m = 4.04), SIQ 3 (m = 4.03) and REL 2 (m = 4.01). However, the mean ratings by users of e-hailing transport services of the following were the lowest: LOYAL 1 (m = 2.04), LOYAL 2 (m = 2.21), SATIS 1 (m = 3.03), SATIS 3 (m = 3.050) and SATIS 2 (m = 3.08). It can also be observed from Table 3 that the standard deviation scores for the statements used in assessing the various constructs ranged from 0.420 to 0.960.

Table 3. Descriptive statistics.

Construct, dimension & items	Mean	SD	Cronbach’s alpha	Cronbach’s alpha if item is deleted
Service quality
Tangibility	3.98	0.856	0.881
TAN 1	3.99	0.870		0.849
TAN 2	3.97	0.882		0.844
TAN 3	4.05	0.830		0.853
TAN 4	3.92	0.842		0.841
Assurance	4.00	0.904	0.787
ASS 1	3.95	0.960		0.736
ASS 2	4.06	0.874		0.738
ASS 3	3.98	0.879		0.747
Reliability	4.06	0.862	0.810
REL 1	4.11	0.815		0.732
REL 2	4.01	0.837		0.703
REL 3	4.06	0.934		0.786
Responsiveness	3.81	0.888	0.772
RES 1	3.83	0.838		0.653
RES 2	3.82	0.953		0.657
RES 3	3.78	0.874		0.758
Empathy	3.97	0.878	0.845
EMP1	3.86	0.908		0.766
EMP 2	4.08	0.851		0.762
EMP 3	3.98	0.875		0.820
System information	4.04	0.766	0.866
SIQ 1	4.06	0.786		0.824
SIQ 2	4.04	0.742		0.820
SIQ 3	4.03	0.771		0.790
Price	3.94	0.840	0.971
PRI 1	3.95	0.839		–
PRI 2	3.93	0.840		–
Customer satisfaction	3.05	0.536	0.768
SATIS 1	3.03	0.530		0.612
SATIS 2	3.08	0.542		0.733
SATIS 3	3.05	0.537		0.676
Customer loyalty	2.13	0.443	0.733
LOYAL 1	2.04	0.420		–
LOYAL 2	2.21	0.467		–

Reliability analysis

To ensure that the study questionnaire measured what it intended to measure, the Cronbach’s alpha was computed for the items measuring service quality, customer satisfaction and customer loyalty. As seen from Table 3, the Cronbach’s alpha coefficients for these constructs from 0.733 to 0.971. This indicates that the study questionnaire was internally consistent as suggested by Hair et al. (2006). AMOS Graphics (version 24) was employed for the confirmatory factor analysis (CFA) (Table 4) and subsequently for the Structural Equation Modelling (SEM). The composite reliability (CR) values of between 0.756 and 0.829 as well as the average variance extracted (AVE) values of 0.510 and 0.601 indicates the data was adequately reliable (see Fornell & Larcker, 1981; Hair et al., 2006) for the analysis (Table 5).

Table 4. Confirmatory factor analysis.

Variable	FL	CR	AVE
Tangibles		0.829	0.549
TAN 1	0.777
TAN 2	0.735
TAN 3	0.727
TAN 4	0.723
Assurance		0.769	0.526
ASS 1	0.737
ASS 2	0.720
ASS 3	0.720
Reliability		0.792	0.560
REL 1	0.794
REL 2	0.725
REL 3	0.723
System info		0.783	0.546
SIQ 1	0.764
SIQ 2	0.734
SIQ 3	0.718
Price		0.819	0.601
PRI 1	0.785
PRI 2	0.778
Empathy		0.776	0.537
EMP 1	0.783
EMP 2	0.709
EMP 3	0.703
Responsiveness		0.786	0.551
RES 1	0.794
RES 2	0.747
RES 3	0.681
Customer satisfaction		0.777	0.539
SATIS 1	0.763
SATIS 2	0.770
SATIS 3	0.665
Loyalty		0.756	0.510
LOYAL 1	0.768
LOYAL 2	0.736

Table 5. Model fit indices for the model.

Fix indices	Recommended value	Model value	Interpretation
Probability value (p)	≤.050	.000	Good
CMIN/DF	≤ 5.00	2.929	Allowable
Adjusted goodness of fit (AGFI)	≥ 0.800	0.855	Good
Incremental fit index (IFI)	≥ 0.900	0.925	Good
Tucker-Lewis index (TLI)	≥ 0.900	0.907	Good
Comparative fit index (CFI)	≥ 0.900	0.924	Good
Root mean square error of approximation (RMSEA)	Between 0.040 and 0.080	0.067	Good

Structural equation model fit indices

Further to the reliability analysis, the proposed model was checked for adequacy by using the fit indices recommended by Byrne (2001) and Hu and Bentler (1998). The model fit indices (IFI = 0.925; TLI = 0.907; CFI = 0.924; and RMSEA = 0.067) met the cut-off values and thus, satisfactory (Table 4). Furthermore, the square root of AVE (Table 6) was higher than the squared correlation of factors under consideration, confirming the assumption of discriminant validity (Fornell & Larcker, 1981).

Table 6. Inter-construct correlation and square root of AVE.

	PRI	SIQ	EMP	REL	RES	TAN	ASS	SATIS	LOYAL
PRI	0.775
SIQ	0.163	0.738
EMP	0.146	0.286	0.733
REL	0.096	0.213	0.277	0.748
RES	0.097	0.242	0.289	0.337	0.742
TAN	0.181	0.298	0.315	0.236	0.258	0.741
ASS	0.136	0.268	0.264	0.216	0.224	0.390	0.725
SATIS	0.005	0.018	0.033	0.021	0.027	0.016	0.019	0.734
LOYAL	0.004	0.012	0.022	0.014	0.018	0.011	0.012	0.009	0.714

Note. Square root of AVE in bold; correlation coefficients are significant at p ≤ .05.

Hypotheses testing

In all, the study tested eight (8) hypotheses (see Table 7 and Figures 2 and 3). As presented in Table 6, all the tested hypotheses were supported. All the seven (7) dimensions used in measuring service quality were significantly related to customer satisfaction. Specifically, Assurance (β = 0.149; p = .022); tangibles (β = 0.140; p = .045), responsiveness (β = 0.040; p = .008); reliability (β = 0.014; p = .015); empathy (β = 0.062; p = .000); system information (β = 0.013; p = .005) and price (β = 0.001; p = .008) were significantly related to customer satisfaction. Further to this, customer satisfaction was found to significantly influence customer loyalty (β = 0.663; p = .000).

Figure 2. Validated research model.

Figure 3. Simplified validated research model.

Table 7. Path coefficient of the structural equation model.

Hypothesis	β	S.E	p	Conclusion
H1: TAN→ CUST_SATIS	0.140	0.084	.045	Supported
H2: ASS →CUST_SATIS	0.149	0.096	.022	Supported
H3: RES →CUST_SATIS	0.040	0.024	.008	Supported
H4: REL →CUST_SATIS	0.014	0.022	.015	Supported
H5: EMP →CUST_SATIS	0.062	0.018	.000	Supported
H6: SIQ →CUST_SATIS	0.013	0.019	.005	Supported
H7: PRI →CUST_SATIS	0.001	0.009	.008	Supported
H8:CUST_SATIS→ CUSTO_LOYAL	0.663	0.113	.000	Supported

Discussion

In achieving the objectives of the study, the study employed the SERVPERF model and used the structural equation modelling technique to test the influence of service quality dimensions on customer satisfaction and the influence of customer satisfaction on customer loyalty among users of e-hailing transport services in a developing country context. All seven dimensions used in measuring service quality were significantly related to customer satisfaction, an indication of how important these dimensions are when it come the measurement customer satisfaction through the lens of service quality. This phenomenon alludes to previous research (Bismo et al., 2018; Pasharibu et al., 2018; Soleh et al., 2018; Susilo et al., 2018) on the practicality of users’ satisfaction of sharing economy transport in relation to service quality. It is an established fact that customers are most likely to rate their satisfaction from using sharing economy transport services when they perceive the service quality attributes to be positive (Bismo et al., 2018; Deneke et al., 2016). Whilst affirming the findings of Hamenda (2018) that all attributes of service quality significantly influence satisfaction, the findings of the current study is at variance with studies that indicate that either a single construct of service quality (Murad et al., 2019; Jun, 2012) or a bundle of service quality attributes (Alemi et al., 2019; Balachandran & Hamzah, 2017; Sharma & Das, 2017) rather influence customer satisfaction.

In relation to specific attributes of service quality influencing satisfaction, the study found that ‘tangibles’ was directly related to the satisfaction users derive from using sharing economy transportation. In fact, ‘tangibles’ was found to be the most significant and dominant service quality dimension influencing users of Uber and Careem in Jordan (Jun, 2012). By extension, users of sharing economy transport services use ‘tangibles’ such as how clean, comfortable, modern vehicles are as well as how well dressed drivers are as a measure of their satisfaction in relation to ‘tangibles’. Users also measure their level of satisfaction from using sharing economy vehicles depending on whether they are able to see vehicle information displayed on the ‘app’ during the trip. Alemi et al.’s (2018) study finding that ‘comfort’ is a determinant of users’ satisfaction of Uber cabs in California is a clear indication of the role ‘tangibles’ contribute to customer satisfaction for using sharing economy vehicles. However, the finding contradicts other studies (Kumar et al., 2022; Murad et al., 2019) that suggest that reliability is the most significant attribute that influences customer satisfaction of sharing economy vehicles.

Aside ‘tangibles’, assurance, responsiveness, reliability, empathy, system information quality and price also influence customer satisfaction in descending order. This finding collaborates findings from other studies (Alemi et al., 2018; Balachandran & Hamzah, 2017; Horsu & Yeboah, 2015; Inbalakshmi & Krishnamoorthy, 2015; Sharma & Das, 2017). For instance, users of sharing economy vehicles may be satisfied when they believe that drivers have knowledge of route, responds to complaints promptly, detour route on their own, have good attitude and prices are moderate. The implication is that; these service quality attributes contribute significantly to users’ satisfaction with sharing economy vehicles. As noted by Balachandran and Hamzah (2017), cab drivers’ attitude towards Uber users significantly relates to users’ satisfaction.

As already alluded to, the study finding confirms previous studies. However, the order in which service quality attributes influence sharing economy vehicles users’ satisfaction as found in the current study contradicts previous research. In the current study, tangibles, assurance and responsiveness were found to be the first three service quality dimensions that influence customer satisfaction of users of sharing economy vehicles. However, empathy, responsiveness and tangibles (Sharma & Das, 2017), empathy and tangibles (Alemi et al., 2018; Balachandran & Hamzah, 2017) were the antecedents of customer satisfaction. These variations could be contextual. Findings from these studies may not necessarily apply to the context of this study. The domain within which sharing economy vehicles operate may have an influence on which service quality attributes users perceive to influence their satisfaction from sharing economy vehicles. The socio-cultural as well as technological differences between these two economies are likely to account for the varied perception of the influence of service quality attributes on the satisfaction they derived from using e-hailing vehicles. Interestingly, whilst empathy was cited as the most important service quality attribute influencing satisfaction in studies in developed economies (eg Alemi et al., 2019), tangibles (physical features of vehicles) was cited in the current study (developing economy). This is further confirmed by the study of Horsu and Yeboah (2015) in Ghana in which tangibles was cited as the most important service quality attribute influencing customer satisfaction.

The study further established the influence of customer satisfaction on customer loyalty; affirming previous findings (Huang et al., 2019; Khuong & Dai, 2016; Kuswanto et al., 2019; Slack et al., 2022) that customer satisfaction is a precursor to customer loyalty. The implication is that, users’ will be inclined to use the services of sharing economy vehicles again, whilst recommending same to others if they are satisfied. Such customers exhibit both ‘attitudinal’ and ‘behaviour’ elements of loyal customers (Phuong & Trang, 2018).

Conclusion

The study examined the influence of service quality attributes on the satisfaction of using sharing economy vehicles and how satisfaction determines whether users will be loyal in terms of reuse and recommendation of sharing economy transport services to others. This is of greater importance from a developing economy perspective where the influx of sharing economy vehicles is on the ascendancy. The study employed the ‘performance only measure’ – SERVPERF Model developed by Cronin and Taylor (1992) to measure perceived service quality of sharing economy vehicles. Findings from the study suggest that individual service quality dimensions as well as the composite service quality model both influence customer satisfaction in relation to usage of the services of sharing economy transport vehicles. Findings from the study further suggest that users of sharing economy vehicles are more inclined to reuse the services again and will recommend others to do same when they are perceived sharing economy transport services provide quality service. This draws attention to the important role of service quality in evaluating customer satisfaction and loyalty.

Implications

Theoretical implications

The study extends the applicability of the SERVPERF Model in measuring service quality of sharing economy vehicles in a developing economy’s context. Previous studies (see Azudin et al., 2018; Khuong & Dai, 2016; Kuswanto et al., 2019; Man et al., 2019; Murad et al., 2019; Sharma & Das, 2017; Siagian, 2019) explored sharing economy vehicle’s service quality from the SERVQUAL Model perspective to the neglect of SERVPERF Model. However, SERVPERF and SERVQUAL scale have been found to be adequate and can be used to predict overall service quality (Carrillat et al., 2007). Findings from the current study show the utility of the SERVPERF Model as a measure of sharing economy vehicles service quality. The study also contributes to the existing literature by validating the view that service quality of sharing economy vehicles could be measured either by unpacking the dimensions of service quality or by using the composite service quality construct, especially from the perspectives of users from a developing economy.

Furthermore, findings from the study could serve as baseline data for further investigations into service quality, customer satisfaction and loyalty within the sharing economy transportation domain. This is imperative considering the fact that these concepts are largely unexplored with Ghana and the sub-region. A cursory look at current literature points to a growing interest in governance, factors influencing the adoption of as well as the travel characteristics of users of ride-hailing services in Ghana and sub-Saharan Africa, safety and security of users of ride-hailing services and unionisation of ride-hailing drivers (Acheampong, 2021; Acheampong & Cugurullo, 2019; Acheampong & Siiba, 2019; Agyemang, 2020; Akorsu et al., 2022; Dzisi et al., 2020; Giddy, 2019).

Practical implications

Findings from the study have implications for management and drivers of sharing economy vehicles. Service providers strive to provide services that are consistent in order to enhance the satisfaction levels of their customers. To be able to do this, management of sharing economy vehicles should have a fair idea of who their clientele is and what attributes of service quality brings satisfaction to them. The study highlights the fact that every dimension of service quality contributes to the satisfaction of sharing economy users. Nonetheless, the study shows that the most critical dimension of service quality that produces significant satisfaction and users’ loyalty tendencies is ‘tangibles’. It is therefore imperative for operators of sharing economy transport services to ensure that the vehicles used for e-hailing services are in good condition, clean and comfortable as well as having their vehicles driven by drivers who are neat and clean (Nguyen-Phuoc et al., 2020; Sharma & Das, 2017; Ziyad et al., 2020). Combined with other dimensions, the physical attributes of sharing economy vehicles and physical appearance of drivers will continue to serve as the basis of users’ satisfaction which has implication for users becoming loyal. In essence, operators of sharing economy vehicles could use ‘tangibles’ as a bait to attract customers and leverage on professionalism of drivers, picking passengers at the appropriate time and location, avoiding detours during trips, adoption of empathetic behaviours by drivers towards passengers, continuously providing timely and up-to-date information on ‘Apps’ and charging relatively cheaper prices (Acheampong et al., 2020; Bahadur et al., 2018; Islam et al., 2019; Sharma & Das, 2017). These will ensure that users’ get the necessary satisfaction from patronising sharing economy vehicles which eventually lead to users becoming loyal by way re-using the services and recommending same to friends and family members.

Limitations

The study was conducted in the capital city of Ghana, to the neglect of other cities (such as Kumasi, Takoradi and Sunyani) which have sharing economy vehicles in operation. In view of this, generalisation of the study findings to cover the entire country should be done with caution. Future research should consider users of sharing economy vehicles in Takoradi, Kumasi and Sunyani. Results from such studies could give a clear picture of what dimensions of service quality contributes to users’ satisfaction and loyalty tendencies within the country at large.

Acknowledgement

The authors are grateful to all who participated in the study by providing valuation information during the data collection.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data used to support the findings of this study are available from the corresponding author upon request.

Additional information

Notes on contributors

Sampson Wireko-Gyebi

Sampson Wireko-Gyebi (PhD) is a full time lecturer at the Department of Ecotourism, Recreation and Hospitality at University of Energy and Natural Resources, Sunyani, Ghana. His research interests include hospitality and tourism education, customer satisfaction, waste management at tourism sites and work-related stress among hotel employees.

Nancy Grace Baah

Nancy Grace Baah is a full time lecturer at the Department of Ecotourism, Recreation and Hospitality at University of Energy and Natural Resources, Sunyani, Ghana. Currently, she is pursuing her PhD at the Hong Kong Polytechnic University. She specialises in food and entrepreneurship in the hospitality and tourism industry.

Ricky Yao Nustugbodo

Ricky Yao Nustugbodo (PhD) is a senior lecturer in the Department of Ecotourism, Recreation and Hospitality at the University of Energy and Natural Resources, Sunyani Ghana. His research focuses on hospitality and tourism education, and women participation in ecotourism destinations, and tourism transportation, waste management in tourism sites and water, sanitation and hygiene practices among tourists.

Perpetual Zaazie

Perpetual Zaazie, a full time lecturer at the Department of Ecotourism, Recreation and Hospitality at the University of Energy and Natural Resources, Sunyani, Ghana. She is interested in research related to hospitality human resources, food production and product development.

Elizabeth Kafui Senya

Elizabeth Kafui Senya is a lecturer at Accra Technical University and currently a PhD student with research interest in employee safety in the hospitality industry.

Vida Bosomefi

Vida Bosomefi holds a Bachelor of Science in Hospitality Management from the University of Energy and Natural Resources, Sunyani, Ghana. Her research interest is related to e-hailing transport service quality and events management in the hospitality industry.

Sarah Amankona

Sarah Amankona holds a Bachelor of Science in Hospitality Management from the University of Energy and Natural Resources, Sunyani, Ghana. She is interested in service quality in the hospitality industry and food related research.