Abstract
Purpose
Although the sharing economy improves comfort and convenience, it is yet unclear how it affects subjective well-being. This study aims to offer a conceptual model for understanding the linkages between the antecedents and consequences of subjective well-being in ridehailing services.
Design/methodology/approach
Using a non-probabilistic sampling method and a pre-tested survey instrument, 450 responses were collected from January to March 2020. The data were analysed using structural equation modelling.
Findings
Experience quality and perceived convenience are correlated with subjective well-being. Perceived value and personal innovativeness were not correlated with subjective well-being, as the former does not contribute to the latter’s development. Continuous usage intention significantly correlated with subjective well-being, followed by customer relationship proneness and advocacy. Regarding gender and age differences, men place higher value on customer relationship proneness than women, while women place higher value on subjective well-being than men. Older users value perceived convenience and customer relationship proneness in ridehailing services more than younger users.
Practical implications
Understanding key factors contributing to user well-being in ridehailing would promote a more affordable mobility sector globally. This understanding would enable ridehailing businesses to create more effective business and marketing plans while prioritising user well-being, thus enhancing user happiness and reducing turnover rates.
Originality/value
This research demonstrates how crucial it is for users’ well-being to have a positive experience and find the service convenient. It also highlights the importance of building strong customer relationships and examines how gender and age influence people’s adoption and use of these services.
Propósito
Aunque la economía colaborativa mejora la comodidad y conveniencia, aún no está claro cómo afecta al bienestar subjetivo. Ofrecemos un modelo conceptual para comprender las conexiones entre los antecedentes y consecuencias del bienestar subjetivo en los servicios de transporte compartido.
Diseño/metodología/enfoque
Utilizando un método de muestreo no probabilístico y un instrumento de encuesta previamente probado, se recopilaron 450 respuestas entre enero y marzo de 2020. Los datos fueron analizados utilizando un modelo de ecuaciones estructurales.
Hallazgos
La calidad de la experiencia y la percepción de conveniencia están correlacionadas con el bienestar subjetivo. El valor percibido y la innovación personal no se correlacionaron con el bienestar subjetivo, ya que el primero no contribuye al desarrollo del último. La intención de uso continuo se correlacionó significativamente con el bienestar subjetivo, seguida por la propensión a las relaciones con los clientes y la defensa de estos servicios. En cuanto a las diferencias de género y edad, los hombres valoran más la propensión a las relaciones con los clientes que las mujeres, mientras que las mujeres valoran más el bienestar subjetivo que los hombres. Los usuarios mayores valoran más la percepción de conveniencia y la propensión a las relaciones con los clientes en los servicios de transporte compartido que los usuarios más jóvenes.
Originalidad
Esta investigación demuestra lo crucial que es para el bienestar de los usuarios tener una experiencia positiva y encontrar el servicio conveniente. También resalta la importancia de construir relaciones sólidas con los clientes y examina cómo el género y la edad influyen en la adopción y uso de estos servicios.
Implicaciones prácticas
Comprender los factores clave que contribuyen al bienestar de los usuarios en los servicios de transporte compartido promovería un sector de movilidad más asequible a nivel global. Esta comprensión permitiría a las empresas de transporte compartido crear planes de negocios y marketing más efectivos, priorizando el bienestar de los usuarios y mejorando así su felicidad y reduciendo las tasas de rotación.
目的
尽管共享经济提高了舒适度和便利性, 但它如何影响主观幸福感尚不清楚。我们提供了一个概念模型, 用于理解乘车服务中主观幸福感的前因后果之间的联系。
设计/方法/途径
采用非概率抽样方法和预先测试的调查工具, 在 2020 年 1 月至 3 月期间收集了 450 份回复。数据采用结构方程模型进行分析。
研究结果
体验质量和感知便利性与主观幸福感相关。感知价值和个人创新性与主观幸福感不相关, 因为前者无助于后者的发展。持续使用意愿与主观幸福感密切相关, 其次是客户关系倾向和拥护。在性别和年龄差异方面, 男性比女性更重视客户关系倾向, 而女性比男性更重视主观幸福感。老年用户比年轻用户更重视乘车服务中的便利感和客户关系代言。
独创性
这项研究表明, 用户获得积极的体验和便捷的服务对他们的福祉至关重要。研究还强调了建立牢固的客户关系的重要性, 并探讨了性别和年龄如何影响人们采用和使用这些服务。
实际意义
了解有助于提高乘车旅行用户幸福感的关键因素, 将在全球范围内推动建立一个更加经济实惠的移动出行行业。这种理解将使打车企业能够制定更有效的业务和营销计划, 同时优先考虑用户福祉, 从而提高用户幸福感并降低流失率。
Keywords
Citation
Shaikh, A.A., Liebana-Cabanillas, F., Alharthi, M., Alamoudi, H. and Karjaluoto, H. (2024), "Analysing user well-being in ridehailing services", Spanish Journal of Marketing - ESIC, Vol. 28 No. 2, pp. 207-227. https://doi.org/10.1108/SJME-12-2022-0253
Publisher
:Emerald Publishing Limited
Copyright © 2023, Aijaz A. Shaikh, Francisco Liebana-Cabanillas, Majed Alharthi, Hawazen Alamoudi and Heikki Karjaluoto.
License
Published in Spanish Journal of Marketing - ESIC. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
The sharing economy idea was initially put forth in 1978 (Felson and Spaeth, 1978). Nonetheless, the phenomenon of sharing or collaborative economy started appearing in the mainstream research since early 1980s and several empirical studies were contributed to the area of sharing, collaborative consumption, the mesh, commercial sharing systems, co-production, co-creation, presumption, product-service systems, access-based consumption and consumer participation (Alharthi et al., 2021). The contemporary research (cf. Prieto et al., 2022), has defined ridehailing service as peer-to-peer platforms, which consists of a triangle of actors: a service provider, a service enabler and a customer. In practice, the ridehailing services do not hire any person or staff to provide mobility services. Instead, it allows the registered and qualified drivers (also called captains) to deliver services at their convenience and when they are willing to serve. Unlike the traditional business models where the employer decides work responsibilities, schedule and salary, the “sharing” business models allow the registered drivers to decide when and how much to work independently.
Recently, the concept of subjective well-being has received considerable attention in marketing and consumer behaviour research (Shaikh et al., 2023), which seeks to understand how interacting with others either enhances or undermines one’s self-outlook, quality of life and satisfaction (Wang et al., 2023). Except a few studies (Alharthi et al., 2021; Ma et al., 2018) that have examined the relationship between the sharing services and subjective well-being, not much has been reported. Nonetheless, synthesising the prior literature on subjective well-being provided a holistic overview of the term “subjective well-being” such as well-being is a subjective evaluation of the degree of someone’s own happiness that also consider meaningfulness, positive emotions, engagement and global judgements of life satisfaction (Alharthi et al., 2021; Davlembayeva et al., 2020).
Despite the sharing economy’s significance, popularity and market share growth projections, mainstream research on it is still in its fledgling stage and has not yet peaked (Alharthi et al., 2021; Hamari et al., 2015). The literature examining the connections between sharing economy platforms and subjective well-being (or happiness/satisfaction/quality of life) remains scarce (Shaikh et al., 2019a). Over the past two decades, however, particularly since the advent of online businesses, digital platforms, mobile-based sharing services and social media, the concept of subjective well-being has gradually transformed, and the use of online and mobile-application-supported services have become a part of everyday life. While the advent of the sharing economy has made comfort and convenience more accessible (Shaikh et al., 2019a), it is not clear whether it has enhanced users’ subjective well-being. Nonetheless, the well-being research could help to better understand the relationship between the ridehailing users and their mental health. These deficiencies and assumptions led us to conclude that understanding user well-being in ridehailing services is an important area of research.
This study has two major objectives. Firstly, we contribute to the subjective well-being theory in the context of ridehailing services. Secondly, we examined the key antecedents and consequences of ride-hail user subjective well-being. For that matter, we identify the variables from the contemporary research (cf. Alamoudi et al., 2023; Wang et al., 2023; Shaikh et al., 2019a) and examine their relationships with the predicted variable of subjective well-being. Consequently, our research makes significant contributions to two streams of literature: research related to subjective well-being and research on the sharing economy. We have proposed following three research questions:
What key variables contribute to the overall well-being of ridehail users?
How does ridehail users’ increased well-being affect intention to use the service, develop better customer relationships and recommend it?
How do a ridehail user’s demographic traits, specifically gender and age, impact the relationships between the key variables?
Our study would increase the knowledge of the elements that affect the subjective well-being of ridehailing users. Moreover, our research would offer crucial industry insights for ridehailing companies. Ridehailing companies should create business and marketing plans to increase user satisfaction and happiness and lower turnover by knowing the variables that affect users’ subjective well-being.
Although ridehailing services consider three major players – ridehail driver, ridehail user and platforms such as Uber and Lyft (Cha and Lee, 2021) – we have concentrated on the antecedents and consequences of ridehail user well-being. Also, we have used the term “ridehailing”, which is considered different from “ridesharing services”. Recently, Pigalle and Aguiléra (2023) explained that ridesharing refers to shared automobile (such as car or van) journeys between people with comparable origin–destination pairings. Here, the profit is not the ultimate motto in the ridesharing services and the popular example include BlaBla. On the other side, ridehailing firms use smartphone applications to connect drivers and passengers at a profit such as Uber.
The rest of this paper is organised as follows. In Section 2, we provide the theoretical background. Conceptual model and hypothesis development are described in Section 3. The research method is presented and explained in Section 4. The results are presented in Section 5. Lastly, we conclude the paper with a discussion of the implications, limitations and future research directions in Section 6.
2. Literature review
2.1 Sharing economy – a non-ownership phenomenon
The term “sharing economy”, also referred to as “peer-to-peer economy”, “collaborative consumption/economy”, “platform economy”, “gig economy” and “lateral exchange market” (Rojanakit et al., 2022), was introduced by Lessig (2008). Sharing economy is defined as collaborative consumption made by the activities of sharing, exchanging and rental of resources without owning the goods (Lee et al., 2000, p. 1). Research (cf. Tunçel and Özkan Tektaş, 2020) has related the sharing economy with collaborative consumption and peer-to-peer markets. Here, sharing economy is considered an economic model that is based on traditional sharing, bartering, lending, trading, renting, gifting and swapping, but redefined through new digital technologies and peer communities. Other recent studies (Wei et al., 2020) stated that the sharing economy relies on the users’ willingness to share or use other people’s resources rather than own resources themselves, and that for such an exchange to be carried out, the users have to be trustworthy. Deloitte (2017) has classified carsharing into three major domains: free-floating, stationary and peer-to-peer. Free-floating facilitates micro- and macro-mobility and is considered the most flexible and convenient mobility concept introduced a few years ago in the global north. Popular free-floating examples include Tier, Bolt Helbiz and Lime. Free-floating allows the users to pick-up and return the vehicles or e-scooters at any place within the specified demographic location or area without any fixed stations. Using the mobile application, the free-floating is accessed and used for a short one-way trip, and the user is charged according to the time spend. On the other hand, stationary or station-based car-sharing allows round trips with the start and end point being the same. For example, Flinkster in Germany. The stationary mobility concept is less flexible and can be used for long distances. The peer-to-peer mobility concepts – the context of this study – is on the rise and will soon become mainstream equally in the developed and developing regions of the world. While the service provider arranges the cars and scooters for free-floating and stationary car sharing, peer-to-peer carsharing (or P2P car sharing) is a macro-mobility concept. Here vehicle belongs to a private individual and is identified and coordinated by a third-party company such as Uber (Barbour et al., 2020).
2.2 Subjective well-being theory, antecedents and consequences
According to Davlembayeva et al. (2020, p. 5), the term “well-being” is conceptualised as subjective well-being as it mirrors the subjective definition of the standard of living and subjective evaluation of the degree of someone’s own happiness and satisfaction. Loureiro et al. (2019) have defined consumer or subjective well-being as consumers’ perception of the extent to which a brand positively contributes to a quality-of-life enhancement. Positive impressions of a service such as ridehailing or brand may lead to an increase in consumers’ well‐being, which refers to an evaluation of one’s own life experiences consisting of happiness and life satisfaction (Loureiro et al., 2019). From the contemporary literature, various antecedents and consequences of subjective well-being were identified and examined from the users’ perspective. As subjective well-being represents the “quality of life” and has various philosophical and theoretical sources based on theories of hedonism and authentic happiness (Liang et al., 2020), we selected and included the antecedents into the theoretical model (Figure 1) that promote the end user well-being in a developed country context such as perceived value, personal innovativeness and perceived convenience. Furthermore, considering the significance of user welfare and quality of life in promoting subjective well-being, the variable “experience quality” (and not the service quality) was added to our model examining its relationship with the subjective well-being.
Research (Molinillo et al., 2020) has described experiences in terms of the thoughts and feelings that consumers have about what is happening when they are doing something. Experience quality or quality of experience is defined as the collection of subjective and objective human needs and experiences arising from the interaction of a person or customer with a specific technology, brand, service, system or business entity in a particular context (Laghari et al., 2020; Laghari and Connelly, 2012). The evaluation of experience quality tends to be holistic and affective rather than attribute based or cognitive, as in the case of service quality (Laghari et al., 2020).
The consumers’ concern for receiving more value for less and the service providers’ concern for providing greater value for less have always been the core of product and service development and deployment, increased consumer acceptance and sustained customer usage of the technology or service. Research (Zeithaml, 1988) has considered customer perceived value as customer benefit derived from his/her monetary and non-monetary payments, or sacrifice of risk, to the products and services offered by a company.
Rogers (1995) has defined personal innovativeness as the degree to which a person tends to adopt new technologies, products or services earlier than others. The quality of being innovative usually asks for the desire to take a risk and look for ways of experimenting on new technologies or services when available. This means that an individual with personal innovativeness is deemed more confident when using new and innovative services such as ridehailing.
Perceived convenience means how helpful a system, technology or application is in completing a certain task from the point of view of its users. Nonetheless, research (cf. Chang et al., 2012) has defined perceived convenience as a “level of convenience toward time, place, and execution that one feels during the participation in mobile learning” (p. 812).
Motivating consumers to use a technology or service for a prolonged period is one of the core elements of companies’ marketing and service innovation strategies. Bhattacherjee (2001), who introduced the information system continuance model, first identified the difference between the initial acceptance/adoption and post-adoption or continuous usage intention/behaviour and defined continuance usage intention as “an individual’s intention to continue using an information system” (p. 359).
Customer relationship proneness represents a customer’s relatively stable, conscious tendency and increased likelihood of developing relationships with service providers, including retailers or brands (De Wulf et al., 2001). Relationship proneness is considered an evolution of relational constructs such as commitment (Olavarría-Jaraba et al., 2018) and a personality trait (Bloemer et al., 2003) that motivates the consumer to develop a personal relationship with a specific product or service (e.g. ridehailing).
Customer advocacy has long been considered analogous to word of mouth and one of the important topics in the attitude literature (Bechler et al., 2020). Moreover, a number of studies (i.e. Wu and Chang, 2019) have defined advocacy as a willingness of a person to recommend a product, service or piece of information to others.
3. Conceptual model and hypotheses development
We used a theoretical model (see Figure 1) to examine the antecedents and consequences of subjective well-being in an emerging sharing-economy field.
3.1 Experience quality on subjective well-being
Prior research has examined the relationship between experience quality and ridehail user well-being as well as ridehail user happiness and satisfaction, which are considered very close to well-being. For example, Haji et al. (2021) discovered that the quality of the ridehailing experience significantly improved ridehail users’ pleasure and well-being. Wu et al. (2017) found that experience quality has a significant effect on happiness. In the tourist industry context, Laghari and Connelly (2012) found that the skydiving experience has a lasting influence on a tourist’s well-being, happiness and life. Thus:
Experience quality is positively related to subjective well-being.
3.2 Perceived value on subjective well-being
We intend to determine the extent to which the perceived value of sharing services influences ridehail user well-being. Silva et al. (2019) have found a positive relationship between these two variables. Specifically, in the context of bike-sharing services, Ma et al. (2018) found that perceived value (i.e. hedonic, utilitarian and social) has the greatest impact on subjective well-being. Of the three perceived-value dimensions, hedonic value has the strongest effect on subjective well-being, followed by social and utilitarian value. Similarly, in a recently published article, Liu et al. (2023), found that three perceived-value dimensions (functional value, emotional value and social value) significantly affect hedonic and eudaimonic well-being. In the sharing economy context, according to Hamari et al. (2015), enjoyment and economic reward are important intrinsic and extrinsic motivators that determine users’ intention to participate in the sharing economy. Thus, the hypothesis below was formulated.
Perceived value is positively related to subjective well-being.
3.3 Personal innovativeness on subjective well-being
Individuals that are innovative are more likely to be early users of technology (Marikyan et al., 2023). The relationship between personal or individual innovativeness and user well-being or happiness is established. Here, Honkaniemi et al. (2015) found that the higher the innovativeness, the higher the well-being, and vice versa. Marikyan et al. (2023) reported a positive relationship between individual innovativeness and well-being. In view of this relationship between personal innovativeness and well-being, the hypothesis below was formulated:
Personal innovativeness is positively related to subjective well-being.
3.4 Perceived convenience on subjective well-being
Perceived convenience or ease of use (Chang et al., 2012; Yoon and Kim, 2007) has been examined from various technology perspectives, and its role in increasing consumer well-being or quality of life has been established. El Hedhli et al. (2013) found that shopping mall convenience significantly and positively predicts shopping well-being. Xiao et al. (2023) found direct relationship between ease of use and well-being. The hypothesis below was thus formulated:
Perceived convenience is positively related to subjective well-being.
3.5 Subjective well-being on continuous usage intention
Continuous usage intention differs from the technology or service acceptance, entails the actual behaviour of the consumer since the initial adoption of a technology, system, application or service does not guarantee its continuous usage (Shaikh et al., 2019b). The relationship between subjective well-being and continuous use of a product or service has gradually become more prominent in the literature of various technology-related areas. For example, Azzahro et al. (2018) found that subjective well-being has a significant positive influence on users’ intention to continue using online dating applications. Thus, we hypothesised:
Subjective well-being is positively related to continuous usage intention.
3.6 Subjective well-being on customer relationship proneness and customer advocacy
Relationship proneness is considered an evolution of relational constructs such as commitment (Olavarría-Jaraba et al., 2018) and a personality trait (Bloemer et al., 2003) that motivates the consumer to develop a personal relationship with a specific product or service (e.g. ridehailing) that does not change for a long time. Olavarría-Jaraba et al. (2018) argue that customer relationship proneness is influenced by the quality of the relationship between a firm and a consumer, and that there is a relationship between relationship proneness and customer satisfaction and trust (Menidjel et al., 2019). As consumer satisfaction is considered the cognitive component of subjective well-being (Spruyt et al., 2020), the hypothesis below was formulated:
Subjective well-being is positively related to customer relationship proneness.
Rai and Nayak (2018) have found that customer well-being or happiness is one of the key indicators of advocacy and word of mouth. Customer satisfaction, which is strongly related to user well-being, is a primary driver of positive word of mouth communication (Hennig-Thurau et al., 2004). The study discovered that satisfied and happy customers are more inclined to advocate the company, product or service. Thus, the hypothesis below was formulated:
Subjective well-being is positively related to customer advocacy.
Vázquez-Carrasco and Foxall (2006) found a positive association between consumer relationship proneness and intention to remain in a business relationship, which develops a favourable usage behaviour. Kim et al. (2012) showed that consumer relationship proneness significantly affects resistance to change. Collectively, these findings led to our prediction that increased customer relationship proneness will lead to the continuous usage of a specific service or product and will improve customer advocacy for such service or product. Thus, the hypotheses below were formulated:
Customer relationship proneness is positively related to continuous usage intention.
Customer relationship proneness is positively related to customer advocacy.
3.7 Moderating effects of gender and age
Previous studies (cf. Herzallah et al., 2022) have examined the moderating effects of gender and age on digital services. Consequently, previous studies on the adoption and use of digital services have shown that men exhibit more traits related to productivity or task orientation compared to women (Venkatesh and Morris, 2000). Kalinić et al. (2019) showed that men are more likely to use peer-to-peer payments than women and are therefore less exposed to the potential risks involved. Glavee-Geo et al. (2017) examined the moderating effect of gender on the linkage between subjective norms and behavioural intention and found that the effect of subjective norms on mobile banking adoption is stronger for women than for men. In summary, the aforementioned researchers argue that gender plays a significant role and moderates the relationships between various variables. Thus, we conclude and suggest that gender may moderate some of the variables in the sharing economy:
The gender of ridehailing services users moderates all paths in the theoretical lens.
Many studies have also analysed the effect of age on consumer behaviour, particularly on mobile payments (Liébana-Cabanillas et al., 2014), online shopping (Tan and Ooi, 2018), review sites (Anaya-Sánchez et al., 2019) and the sharing economy (Hsiao et al., 2018), among others. Liebana-Cabanillas and Alonso-Dos-Santos (2017) discovered that age has a moderating effect on the intention to use e-commerce platforms; in their study, the younger subjects were more interested in the real value obtained from their purchases and in their immediate benefit and productivity. Hsiao et al. (2018) found a negative correlation between age and adoption of the sharing economy. Considering the significance of user age groups in moderating the relationships between various variables, we conclude and suggest that various age groups may moderate some of the variables in the sharing economy.
The age of ridehailing services users moderates all paths in the theoretical lens.
4. Research method
4.1 Research site and measurement method
In Spain, the context of this study, the collaborative economy has grown considerably in recent years. It is estimated that by 2025, in Europe, including Spain, companies in the five most important sectors of the collaborative economy (housing, transport, home services, professional services and collaborative finance) will have generated approximately €300,000m.
The model’s constructs were assessed using reflective measurement scales that had been previously validated in research and tailored to the specific context being considered. The questionnaire was then given to four experts (two professionals of the sector under research and two academicians), who commented on the content and phrasing of the questionnaire. A seven-point Likert scale ranging from “strongly disagree” to “strongly agree” was used to measure the study constructs (Table 2). The final version of the questionnaire was tested in December 2019 with a sample consisting of 50 college students. The reliability, acceptance, dimensionality and validity of the aforementioned measurement scales were also examined. Lastly, the proposed model was assessed after the scales and threshold values were deemed appropriate.
4.2 Data collection and analysis
The data for this research were obtained using a non-probabilistic sampling method defined by quotas based on the structure of the population. This study was contracted with the company Toluna Spain, a research company specialised in sampling services that chose the participants randomly. Data collection was carried out through an online survey based on a structured and pre-coded questionnaire developed on the Toluna Quick Surveys platform. During the initial phase of this research, the validity of the measurement scales used was tested. In addition, this study ensured that respondents understood and approached the survey correctly. The responses submitted within the period from January to March 2020 were collected. The sampling error from the number of ridehailing users in Spain was 4.62% for a confidence level of 95%.
A two-stage procedure adapted from a previous study by Anderson and Gerbing (1992) was used to examine the obtained data. The validity and reliability were first tested, and then the structural model was assessed. Confirmatory factor analysis (CFA) was conducted using AMOS 21.0.
4.3 Descriptive analysis of the sample
The demographic characteristics of the sample reflected by 450 valid responses are shown in Table 1. The sample was evenly distributed with regard to the gender of the participants, and the vast majority of them were aged 24–35 years, with an average income ranging from €1,100 to €1,800. Most of the participants had used ridehailing services for at least 12 months prior to the study, including Uber and Cabifay.
4.4 Reliability and validity of the measurement instruments
The validity and reliability values were assessed to test the adequacy of the measurements. Firstly, reliability analysis was performed using Cronbach’s alpha (CA) and composite reliability (CR) as internal consistency indicators. The obtained values were higher than the minimum recommended thresholds of 0.6 and 0.7, respectively. Secondly, the convergent validity was examined through the average variance extracted (AVE). In this sense, the AVE values were above the minimum recommended threshold of 0.50. The AVE estimations for each pair of variables were also greater than the correlation level between the two factors, confirming discriminant validity (Fornell and Larcker, 1981). In this light, following the recommendations by Hair et al. (2006), the measurements reached appropriate levels of reliability, discriminant validity and convergent validity, as can be seen in Table 2.
In addition, CFA was conducted to examine the convergent and discriminant validity of the scales. Convergent validity was assessed through the factorial loadings of the different indicators. The obtained values were far removed from 0, with factor loadings exceeding 0.7 throughout the analysis. In this light, UV1, PINN4, CADV3, SWB3, CEQ5, CEQ6 and CUI3 were excluded from the analysis as their results did not surpass the minimum threshold values.
As can be seen in Table 3, the values for discriminant validity were also significantly different from 0, with the correlation values for each pair of scales not exceeding 0.9, except in some relationships (Hair et al., 2006). In light of the aforementioned results, the adequacy of the measurements was confirmed for all the constructs involved.
5. Results
The hypotheses were tested using a structural equation model. As can be seen in Table 4, the results of the maximum likelihood analysis and bootstrapping techniques for 5,000 consecutive samples with a 95% significance level revealed reliable fit indicators for almost all them or very close according to previous research for the model (Bollen, 1989). The model was thus consequently used to validate the hypotheses. Figure 2 shows the p-values and standardised path coefficients. The second-order construct met the suggested requirements with regard to model identification, reliability and validity.
The aforementioned techniques revealed that two of the four antecedents (experience quality and perceived convenience) had a significant impact on subjective well-being. As such, H1 and H4 are supported. The expectations regarding perceived value (H2) and personal innovativeness (H3), however, were not met. In addition, the results for experience quality (β = 0.703; p < 0.05) were stronger than those for perceived convenience (β = 0.189; p < 0.05). With regard to the outcome variables, their results support H5 (β = 0.898; p < 0.001), confirming the impact of subjective well-being on continuous usage intention. H6, which posits that subjective well-being has a positive influence on customer relationship proneness, is also supported (β = 0.821; p < 0.001). H7, which establishes a positive relationship between subjective well-being and customer advocacy, is also supported (β = 0.791; p < 0.001). Lastly, the results for customer relationship proneness were mixed; as such, H8 (customer relationship proneness → continuous usage intention) is not supported. On the other hand, H9 (customer relationship proneness → customer advocacy) obtained empirical support and was verified (β = 0.159; p < 0.001).
To evaluate the moderating effects of gender and age, the sample was divided into two groups according to the study participants’ gender (male and female) and age (average of the data according to the proposed scale). We then carried out an invariance test through a chi square (χ2) value comparison (and the degrees of freedom) for the overall model and the constrained model. Significant differences were found, as can be seen in Table 5.
After checking for significant differences and to test the moderating effect of the proposed variables, we conducted a test to compare the regression coefficients or weights of the structural models considered in pairs, using a modified Student’s t-test for independent samples (Lee et al., 2000). The obtained results are listed in Table 6.
Differences were observed in three relationships with respect to gender and in two relationships with respect to age (H11a and H11b). With respect to gender, the relationship between customer experience quality and subjective well-being was found to be stronger in the men (β = 0.810) than in the women (β = 0.487). On the other hand, the relationships between perceived convenience and subjective well-being and between customer relationship proneness and customer advocacy were found to be stronger in the women (β = 0.361 and 0.259, respectively) than in the men (β = 0.051 and 0.069, respectively). With regard to age, the relationships between perceived convenience and subjective well-being and between customer relationship proneness and customer advocacy were shown to be stronger in the older users (β = 0.266 and 0.278, respectively) than in the younger users (β = 0.032 and 0.016, respectively).
6. Discussion
6.1 Theoretical implications
This study contributed to the literature on the antecedents and consequences of subjective well-being in the context of sharing-economy platforms in a developed country. The predictions were supported by empirical analysis based on a study sample consisting of 450 ridehailing users in Spain. The results supported most of the hypotheses and confirmed that among other constructs, the quality of the experience of using ridehailing services (H1) and the perceived convenience (H4) of such platforms are strongly correlated with the users’ subjective well-being. This is in line with the earlier findings on the sharing economy, such as those obtained by Laghari and Connelly (2012), El Hedhli et al. (2013) and Alharthi et al. (2021), which showed that the people consider experience quality and perceived convenience among the most significant factor related to ridehailing services that increases their well-being.
Unlike the results of previous studies (Ma et al., 2018), which showed positive relationships between perceived value and personal innovativeness on the one hand and subjective well-being on the other, the results of our empirical analysis do not support this association. In other words, the results of our study show that in a developed country, the perceived value of ridehailing services and consumer personal innovativeness do not play key roles in increasing the users’ subjective well-being. This is perhaps due to the fact that most of the people in developed countries have technophilia (i.e. an appetite for new technologies and innovations) and are thus very enthusiastic about technology, devices and innovative services. As such, the fact that perceived value and personal innovativeness did not show a connection with subjective well-being in such countries seems logical.
The relationship between the outcome variables and subjective well-being was also examined and found significant. Theoretically, this study identified customer relationship proneness as one of the key variables navigating the success and failure of ridehailing services via the predicted variable of subjective well-being. Earlier, Wei et al. (2015), while examining the effects of relationship benefits and relationship proneness on relationship outcomes, found that customer relationship proneness develops a sense of well-being among the customers, which leads to relationship commitment with the company.
Nonetheless, subjective well-being was found to be directly correlated with continuous usage intention, as was found in the study by Azzahro et al. (2018). This indicates that increased subjective well-being will further stabilise the customer relationship with the service providers, including that with ridehailing service providers. The effects of customer relationship proneness on continuous usage intention and customer advocacy were also examined, and the results suggest that relationship proneness, which refers to a user’s relatively stable and conscious tendency to engage in a relationship (De Wulf et al., 2001), increases the advocacy of ridehailing services.
We contributed to the literature on the role of gender and age differences in ridehailing service adoption and usage in developed countries. The key findings of our study enable us to draw the conclusion that women value subjective well-being more highly than men do, demonstrating that women have a hedonistic orientation while men have a utilitarian orientation. Men place a higher value on customer relationship propensity than women do. With regard to age, our study findings show that older users value perceived convenience and customer relationship proneness in ridehailing services more than younger users do.
6.2 Managerial implications
Our study findings provide several practical implications. Firstly, subjective well-being was shown in this study to be one of the core constructs that play a decisive role in consumer choices for any specific service or product. The study findings suggest that companies should focus their attention on increasing the well-being of the consumers. In addition, two important elements that affect consumers’ subjective well-being are the experience quality and perceived convenience. To improve ridehail user happiness and loyalty, service providers should put a higher priority on these factors in their business operations and marketing plans. The study findings also suggest that the industry leaders and marketing executives should devise policies and marketing and business strategies that support and promote consumer well-being for the consumers’ continuous usage of their service and for developing relationship proneness in the consumers, and for greater customer advocacy for the company and for ridehailing services on the whole.
Although consumers have not placed a greater emphasis on the perceived value of ridehailing services, from a global perspective, the sharing-economy industry should not ignore or undermine the perception of value. Price is widely considered a factor inevitably influencing perceived value or that ridehail user takes into account when ordering a ride. Other factors also play a decisive role, such as the usefulness, ease of use and quality of the mobile application, including its clear layout, transparent pricing policy, the cleanness and age of the cars and the attitudes of the captains or drivers. These will provide a superior value to the consumers and will increase their subjective well-being, which will in return promote their prolonged usage of the ridehailing service. In Spain and other western countries, where the high quality of the public transport or mass transit system can pose a serious challenge to and competition for ridehailing services, the high value, low price and service reliability of ridehailing services can play a decisive role in beating such competition.
The findings of this study also suggest that increasing the consumers/users’ subjective well-being will increase their advocacy of the services on the whole and of a service provider in particular. This advocacy by the happy and satisfied customers will gradually reduce the marketing and promotion overhead and increase the customer base of the company and should thus be considered an asset. The sharing-economy industry should also ensure the convenience of ordering as well as finding a ride close to consumers’ offices, homes or shopping centres. The “pick and drop” places should be clearly identified for consumer convenience, especially near public places such as shopping centres.
This study was among the very few studies so far that have identified the significance of micro-mobility and its growth due to social distancing and the pandemic. The macro-sharing industry should give greater emphasis to developing micro-mobility services to retain its market share, customer satisfaction and loyalty.
6.3 Limitations and future research directions
The present study has certain limitations. Firstly, this study was conducted from a cross-sectional perspective, which hindered any kind of further assessment of the evolution of user behaviour over time. In this sense, a longitudinal approach should enable such analysis to check the robustness of the relationships and constructs in the research mode while examining the evolution of the effects of the variables over time. Secondly, the scope of this study included macro-mobility services such as Uber. Future studies can expand the scope of this study and include other types of macro-mobility services, such as flying taxis, and micro-mobility devices such as e-scooters. Thirdly, previous studies have largely examined the consumer segmentation between users and non-users of sharing services (Lutz and Newlands (2018). However, segregating the consumers into different segments considering the nature of the services offered by Uber is worthy of examination and is thus recommended. Fourthly, several studies have considered the ridehailing service or a matching agency (e.g. Uber) and end user perspectives (e.g. Rider). Captain or driver who own assets plays a critical role and can navigate the success and failure of the sharing economy and its development or decline. The driver’s stake is thus paramount and should be examined. Fifthly, replicating the study with a bigger and more geographically diversified population is another important future research direction. This would enable a more thorough investigation of the phenomenon being studied and improve the generalisability of the results. The future study may shed light on potential differences or similarities in the interactions between the dimensions across various cultural and contextual settings by involving people from different areas or nations. Sixthly, majority of the studies on the sharing economy have been conducted in the context of developed countries (cf. Forno and Garibaldi, 2015); only a few have examined it in the context of developing countries (cf. Alharthi et al., 2021). Multi-country assessments – for example, comparing developed and developing countries – are rare and therefore recommended. Seventhly, in relation to the moderating effect of age analysed, we propose an extension of this effect by balancing the sample in a more exhaustive approach. Moreover, and as argued by Rojanakit et al. (2022), research conducted in sharing economy filed is dominated by the quantitative or survey methods, thereby overlooking the informative knowledge, which could be otherwise captured using qualitative approaches. Future research thus should conduct more thorough investigation of the filed using exploratory and explanatory approaches and therefore recommended and included in the analysis of new moderating effects such as experience (Azimi and Jin, 2022).
Last but not the least, the mobile applications facilitating ridehailing services are gradually evolved by way of adding new features, services and ideas, and have become popularly known as “super applications”. Future studies may consider all the features and services offered by super applications and may examine the consumers’ perspective on the usage of these applications for meeting their everyday needs.
7. Conclusion
This study examined the factors that contribute to and are affected by subjective well-being in the context of ridehailing services in Spain. We used a marketing firm in Spain to gather primary data using a non-probabilistic sampling technique and a survey instrument that had already been tested. The study constructs were measured using a seven-point Likert scale, and a 450-person survey sample was subjected to a structural equation model for data analysis.
According to the findings, experience quality and perceived convenience were highly connected with users’ subjective well-being. There was no discernible correlation between perceived value and personal innovativeness and subjective well-being. The tendency to form customer relationships was discovered to be a crucial factor that affected subjective well-being, encouraging continuous usage intention, customer relationship proneness and customer advocacy. Women valued subjective well-being more than men did, whereas men valued customer relationship proneness more than women did. Users over the age of 50 placed a higher emphasis on perceived convenience.
The practical ramifications of this study suggested that businesses should prioritise experience quality and perceived convenience while simultaneously providing greater value to improve consumers’ subjective well-being. To guarantee ongoing use of their services, foster relationships with customers and strengthen customer advocacy, businesses should also work to improve consumer well-being. Overall, this study offers insightful information that will help professionals in the field, legislators, new businesses and entrepreneurs create successful marketing plans and workable business ideas for the ride-hailing services sector.
Figures
Demographic profile
| Variables | Cases (%) | Variables | Cases (%) |
|---|---|---|---|
| Gender | Ridehailing usage duration | ||
| Male | 221 (49.00%) | 1–3 months | 52 (11.56% |
| Female | 229 (51.00%) | 4–6 months | 77 (17.11%) |
| 6–12 months | 109 (24.22%) | ||
| Age | 12–24 months | 126 (28.00%) | |
| Less than 18 | 06 (1.33%) | More than 24 months | 86 (19.11%) |
| 18–34 | 169 (37.56%) | ||
| 35–54 | 213 (47.33%) | Frequency of usage/month | |
| 55+ | 62 (13.78%) | 1 time | 322 (71.56%) |
| 2 times | 86 (19.11%) | ||
| Income | 3 times | 23 (5.11%) | |
| No income | 09 (2.00%) | 4 times | 16 (3.56%) |
| Less than €1,100 | 73 (16.22%) | 5 times | 03 (0.66%) |
| From €1,100 to €1,800 | 146 (32.44%) | ||
| From €1,800 to €2,700 | 129 (28.67%) | Ridehailing type | |
| More than €2,700 | 83 (18.44%) | Uber | 270 (60.00%) |
| Do not know/no answer | 10 (2.23%) | Cabifay | 163 (36.22%) |
| Others | 17 (3.78%) | ||
| Education | |||
| Junior high school | 07 (1.56%) | ||
| Senior high school | 63 (14.00%) | ||
| Polytechnic | 99 (22.00%) | ||
| Bachelor | 172 (38.22%) | ||
| Master | 89 (19.78%) | ||
| PhD | 20 (4.44%) | ||
Scales, descriptive statistics, convergent validity and internal consistency reliability
| Description | Mean | Skewness | Kurtosis | Factor loading |
|---|---|---|---|---|
| Subjective well-being. Adapted from Iii et al. (2015). α = 0.85; CR = 0.86; AVE = 0.75 | ||||
| My experience with ridehailing services was memorable and enriched my quality of life | 5.26 | –1.057 | 1.114 | 0.872 |
| After using the ridehailing services, I felt that my life was meaningful and fulfilling | 5.00 | –0.753 | 0.645 | 0.896 |
| Utilitarian value. Adapted from Sweeney and Soutar (2001) and Hwang and Griffiths (2017). α = 0.83; CR = 0.77; AVE = 0.63 | ||||
| Ridehailing services deliver expected economic benefits | 5.16 | –0.757 | 0.281 | 0.663 |
| Ridehailing services improve trip performance | 4.97 | –0.603 | 0.301 | 0.902 |
| Hedonic value HV. Adapted from Sweeney and Soutar (2001) and Hwang and Griffiths (2017). α = 0.90; CR = 0.91; AVE = 0.77 | ||||
| Ridehailing service is something I would enjoy | 4.86 | –0.478 | 0.096 | 0.887 |
| Ridehailing service appeal to me for using it | 4.86 | –0.629 | 0.270 | 0.875 |
| Ridehailing service make me feel relaxed | 4.70 | –0.536 | 0.131 | 0.864 |
| Social value SV. Adapted from Sweeney and Soutar (2001) and Hwang and Griffiths (2017). α = 0.89; CR = 0.90; AVE = 0.76 | ||||
| Ridehailing service gain me social recognition | 4.34 | –0.338 | −0.400 | 0.872 |
| Ridehailing service make me feel accepted by the society | 4.38 | –0.454 | −0.227 | 0.897 |
| Ridehailing service help me leave people with a positive impression | 4.74 | –0.686 | 0.470 | 0.838 |
| Personal innovativeness. Adapted from Lu et al. (2005). α = 0.85; CR = 0.85; AVE = 0.66 | ||||
| If I heard about a ridehailing service, I look for ways to experiment with it | 4.93 | –0.724 | 0.834 | 0.834 |
| Among my peers, I am usually the first to explore and use new innovative services such as ridehailing | 4.56 | –0.394 | −0.252 | 0.759 |
| I like to experiment with ridehailing service in my daily life | 4.70 | –0.478 | −0.148 | 0.847 |
| Perceived convenience. Adapted from Yoon and Kim (2007). α = 0.87; CR = 0.88; AVE = 0.70 | ||||
| I can book a ride at any time via cell mobile | 5.49 | –0.941 | 1.142 | 0.861 |
| I can book a ride at any place via cell mobile | 5.50 | –0.915 | 1.211 | 0.852 |
| Cell mobile is convenient to complete my ride booking process | 5.49 | –0.725 | 0.539 | 0.801 |
| Experience quality. Adapted from Chen and Chen (2010) and Otto and Ritchie (1996). α = 0.92; CR = 0.92; AVE = 0.75 | ||||
| It is a pleasure for me to use ridehailing service | 5.03 | –0.749 | 0.677 | 0.858 |
| I feel comfortable when I interact with ridehailing service | 5.10 | –0.679 | 0.365 | 0.879 |
| Ridehailing service meets my needs and covers my expectations | 5.21 | –0.819 | 0.663 | 0.860 |
| I like to interact with ridehailing service | 5.00 | –0.702 | 0.705 | 0.871 |
| Customer relationship proneness. Adapted from De Wulf et al. (2001). α = 0.88; CR = 0.89; AVE = 0.72 | ||||
| Generally, I like to be a regular customer of a ridehailing company | 4.81 | –0.604 | 0.326 | 0.903 |
| Generally, I want to be a regular customer of my ridehailing company | 4.79 | –0.717 | 0.600 | 0.885 |
| I am usually willing to make extra effort to use ridehailing services from the same company every time | 4.70 | –0.677 | 0.513 | 0.753 |
| Continuous Usage Intention. Adapted from Zhou (2013). α = 0.83; CR = 0.82; AVE = 0.71 | ||||
| I intend to continue using ridehailing service rather than discontinue its use | 5.43 | −0.988 | 1.074 | 0.850 |
| My intentions are to continue using ridehailing service rather than use any alternative means | 5.11 | −0.827 | 0.629 | 0.835 |
| Consumer advocacy. Adapted from Chelminski and Coulter (2011). α = 0.89; CR = 0.89; AVE = 0.67 | ||||
| By sharing my experience with a ridehailing service, I assist other people towards a similar experience | 5.13 | –0.539 | 0.493 | 0.770 |
| It makes me feel good to tell others about this ridehailing service | 5.04 | –0.453 | 0.084 | 0.741 |
| I suggest others about this ridehailing service | 4.73 | –0.523 | 0.045 | 0.867 |
| I give suggestion to other people about the quality of ridehailing service to help them have a similar experience | 5.11 | –0.690 | 0.875 | 0.886 |
Correlation matrix and Fornell–Larcker criterion
| PINN | PCONV | CEQ | SWB | CRP | CADV | CUI | SV | HV | UV | |
|---|---|---|---|---|---|---|---|---|---|---|
| PINN | 0.812 | |||||||||
| PCONV | 0.665 | 0.836 | ||||||||
| CEQ | 0.847 | 0.764 | 0.866 | |||||||
| SWB | 0.865 | 0.821 | 0.965 | 0.866 | ||||||
| CRP | 0.710 | 0.674 | 0.792 | 0.821 | 0.848 | |||||
| CADV | 0.798 | 0.757 | 0.890 | 0.922 | 0.809 | 0.818 | ||||
| CUI | 0.800 | 0.759 | 0.892 | 0.924 | 0.769 | 0.854 | 0.842 | |||
| SV | 0.734 | 0.496 | 0.701 | 0.687 | 0.564 | 0.633 | 0.635 | 0.871 | ||
| HV | 0.879 | 0.594 | 0.839 | 0.822 | 0.675 | 0.758 | 0.760 | 0.782 | 0.877 | |
| UV | 0.811 | 0.548 | 0.774 | 0.759 | 0.622 | 0.699 | 0.701 | 0.721 | 0.863 | 0.793 |
PINN = personal innovativeness; PCONV = perceived convenience; CEQ = experience quality; SWB = subjective well-being; CRP = customer relationship proneness; CADV = consumer advocacy; CUI = continuous usage intention; SV = social value; HV = hedonic value; UV = utilitarian value. Main diagonal in italic: square root of the AVE
Fit indices
| Fit indices | Recommended value* | Value in the model |
|---|---|---|
| Normal chi-square/degrees of freedom (CMIN/DF) | 2 < CMIN/DF < 5 | 4.144 |
| Goodness-of-fit index (GFI) | > 0.90 | 0.800 |
| Relative fix index (RFI) | > 0.90 | 0.840 |
| Normed fit index (NFI) | > 0.90 | 0.860 |
| Comparative goodness of fit (CFI) | > 0.90 | 0.900 |
| Tucker–Lewis Index (TLI) | > 0.90 | 0.890 |
| Incremental fit index (IFI) | > 0.90 | 0.900 |
| Root mean square error of approximation (RMSEA) | < 0.08 | 0.080 |
Invariance analysis
| Gender | Age | |||||||
|---|---|---|---|---|---|---|---|---|
| Overall model | Chi-square | df | p-value | Invariant? | Chi-square | df | p-value | Invariant? |
| Unconstrained | 2,150.627 | 477 | 2,150.627 | 477 | ||||
| Fully constrained | 2,970.793 | 954 | 2,994.078 | 956 | ||||
| Number of groups | 2 | 2 | ||||||
| Difference | 820.166 | 477 | 0.000 | NO | 843.451 | 479 | 0.000 | NO |
Results of multigroup analysis
| Male (n = 221) | Female (n = 229) | |||||
|---|---|---|---|---|---|---|
| Gender | β | SE | β | SE | t-testa | Significant differences |
| H1: EQ → SWB | 0.810 | 0.077 | 0.487 | 0.133 | 2.100 | Yes |
| H2: PEVA → SWB | –0.058 | 0.049 | 0.025 | 0.104 | –0.720 | No |
| H3: PINN → SWB | 0.233 | 1.223 | 0.162 | 0.519 | 0.050 | No |
| H4: PCONV → SWB | 0.051 | 0.049 | 0.361 | 0.088 | –3.080 | Yes |
| H5: SWB → CUI | 0.914 | 0.192 | 0.963 | 0.164 | –0.190 | No |
| H6: SWB → CRP | 0.841 | 0.140 | 0.804 | 0.129 | 0.190 | No |
| H7: SWB → CADV | 0.881 | 0.139 | 0.672 | 0.131 | 1.090 | No |
| H8: CRP → CUI | 0.008 | 0.090 | –0.017 | 0.088 | 0.200 | No |
| H9: CRP → CADV | 0.069 | 0.058 | 0.259 | 0.073 | –2.040 | Yes |
| < 35 (n = 175) | < 35 (n = 275) | |||||
| Age | β | SE | β | SE | t-testa | Significant differences |
| H1: EQ → SWB | 0.649 | 0.897 | 0.607 | 0.082 | 0.050 | No |
| H2: PEVA → SWB | –0.102 | –0.141 | 0.056 | 0.107 | –0.890 | No |
| H3: PINN → SWB | 0.591 | 0.228 | 0.107 | 1.570 | 0.310 | No |
| H4: PCONV → SWB | 0.032 | 0.031 | 0.266 | 0.056 | –3.660 | Yes |
| H5: SWB → CUI | 0.946 | 1.036 | 0.813 | 0.133 | 0.130 | No |
| H6: SWB → CRP | 0.924 | 0.791 | 0.836 | 0.098 | 0.110 | No |
| H7: SWB → CADV | 0.908 | 0.932 | 0.673 | 0.100 | 0.250 | No |
| H8: CRP → CUI | –0.123 | –0.133 | 0.128 | 0.081 | –1.610 | No |
| H9: CRP → CADV | 0.016 | 0.022 | 0.278 | 0.059 | –4.160 | Yes |
aThe evaluation was performed using the procedure suggested by Chin (2000) to develop a multi-group analysis based on Student’s t-test (using a parametric analysis through a t-test of m + n + 2 degrees of freedom) according to the following formulation:
Ho: B1 = B2
where Bi denotes path weights and SEi is the standard error of the path in the structural model
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