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Article

Examining the Impact of Marketing Motives and Concerns on User Satisfaction and Re-Purchase Intentions in a Sharing Economy

by
Abdullah F. Alnaim
1 and
Nadia Abdelhamid Abdelmegeed Abdelwahed
2,*
1
Department of Business Administration, College of Business, King Faisal University, P.O. Box 400, Al Hofuf 31982, Saudi Arabia
2
Department of Management, College of Business, King Faisal University, P.O. Box 400, Al Hofuf 31982, Saudi Arabia
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(5), 4498; https://doi.org/10.3390/su15054498
Submission received: 29 January 2023 / Revised: 21 February 2023 / Accepted: 23 February 2023 / Published: 2 March 2023
(This article belongs to the Special Issue Marketing and Sustainable Development: A Predictive Empirical Insight)
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Abstract

:
In a fast-growing global economy, there is much debate in the socio-economic models about the sharing economy, which is a digital platform that benefits society and improves people’s quality of life. A significant benchmark of the sharing economy is that it enables individuals to monetize their assets that need to be used fully. This highlights an individual’s ability and perhaps their preference to either rent or borrow goods rather than own them. This study investigated Saudi Arabian students’ User Satisfaction (US) and their Re-Purchase Intentions (RPI) in the context of the sharing economy. We employed a deductive approach that utilized cross-sectional data collected through online sampling. The results were derived from 324 acceptable completed questionnaires. We used a Structural Equation Model (SEM) to confirm the positive and significant predictive power of Trust, Economic Benefits (EBs), Sharing Economy Philosophy (SEP), Service Quality and Net Benefits (NBs) on US and RPI. The results also demonstrated a positive and significant effect of concerns such as Lack of Trust (LoT) and Expected Effort (EE) on US. Finally, among Saudi Arabian students, US is a positive and significant predictor of RPI. In the context of a developing country such as Saudi Arabia, this study’s insights to the practical and theoretical spheres contribute to operational management and the literature about online digital learning.

1. Introduction

The new technologies or digital tools support the reshaping of green products and their markets and have a profound effect on business and work practices. With the widespread use of digital technologies, new forms of sharing have emerged. These forms are called ‘the sharing economy’. Digitalization has become the enabler that covers a wide range of sharable resources (a technical aspect of sharing) and operations beyond the limits of small groups and personal relationships (a social aspect of sharing). The digital sharing economy is a socio-economic phenomenon that makes no reference to normative presuppositions such as pro-social motivations for sharing. From a marketing perspective, the digital economy enhances businesses’ productivity as they use digital technology to automate their operations and processes. In addition, it increases competitiveness because companies can use the internet to reach new markets and consumers.
The sharing economy has piqued the interest of stakeholders, practitioners and policymakers who are eager to see the digital economy is extended [1]. Due to the extensive application of information technology, this trend is continuously increasing [2]. The popularity of the sharing economy has also been furthered in many sectors and among all ages, including those younger and older people who are digitally literate. Therefore, the sharing economy enables individuals to collaboratively use fee-based sharing activities on online platforms [3] and to boost societal, technological, and economic drivers through eagerly participating in sharing economy practices [4]. These drivers also affect the usage intentions and the diverse blends of sharing economy components such as network externalities, enjoyment, cost savings, perceived quality, and efficiency. The sharing economy happens in structured networks that carry out sharing practices such as facilities, transportation services, renting and lending [5]. In the fast-growing competitive market, the sharing economy’s success depends on customers’ RPIs [6]. In recent years, the shift towards a modest business model of a sharing economy has accelerated due to consumers’ greater awareness of shiftless resources [5]. Although changing existing markets, it establishes, also pertinent socio-economic dynamics. For instance, the ride-sharing market, led by Uber, has transformed the taxi market [7]. The sharing economy is a highly sustainable development model [8] that positively impacts the environment [9]. Therefore, the sharing economy improves resource utilization and reduces environmental damage. Consumers, who use sharing economy services, believe they contribute to sustainable development by avoiding excessive waste production [10]. Moreover, convenience is the consumers’ fundamental motive for sharing. In the case of Uber, it provides enjoyment, trust, and network externalities. Enjoyment and network externalities affect both platforms and these mean that sharing brings consumer happiness to the platform. Furthermore, their relatives and friends encourage people to use platforms such as Uber [11].
User satisfaction also confronts the severe problems and concerns of the LoT and EE [12,13,14,15,16,17]. Trust is an individuals’ firm belief towards attaining and fulfilling their responsibilities [18]. Net benefits (NBs) offer practical decision-making methods that improve market proficiency and user benefits [9]. Likewise, due to a considerable motive [19], the Sharing Economy Philosophy (SEP) plays a vital role in sustainability and cataloguing. It robustly develops the consumers’ intentions to share in a favorable environment [10]. Further, Service Quality (SQ) is an active appliance that fulfils consumer expectations due to consistency and functional ability [20]. In a similar mode, Economic Benefits (EBs) help to develop attitudes and intentions towards satisfaction with sharing services [21,22]. Turning to concerns, LoT and EE are major concerns of consumers about sharing services [12,14]. Individuals, who receive sustenance from online services, have a higher RPI [23]. The association between US and RPI is supported in both contexts of developing economies including Albania, Saudi Arabia, Ghana, India, Japan, Hong Kong, and China. [24,25,26,27,28,29,30] and in developed economies that include USA, Germany, and Australia [31,32,33]. However, a need remains to investigate the association between US and RPI among Saudi Arabia’s university students. The graduates and postgraduate students always try to purchase environmentally friendly or green products [25].
Moreover, the existing literature supports the associations between US and RPI and the sharing economy [34,35,36]. However, its existence is limited among students in general and, more particularly, among Saudi Arabian students [3,6,7,37,38]. Against this background, we conducted this study among university students at King Faisal University, located in Al Ahsa in the Eastern Province of Saudi Arabia. King Faisal University is one of Saudi Arabia’s leading higher education institutions, and it attracts students from across the country and from countries worldwide. Therefore, given the gaps in the existing literature, we developed the following questions:
What are the motives behind the development of User Satisfaction?
What are the concerns that resist User Satisfaction?
Is User Satisfaction responsible for developing the RPI among Saudi Arabian students?
In conducting this study, we aimed to provide findings that would offer novel contributions to policy and theory. These findings would enable interested parties to participate in collaborative consumption activities across various industries and to share their understanding of the necessary constructs to maintain US and RPI. These findings would also be helpful for executives in creating a reliable system that delivers quick and efficient service with good user-experience consistency and provides US. To this end, we offer recommendations with warnings to sharing economy agents on the need to resolve problems to strengthen US and use trust as a competitive tactical advantage among sharing economy service providers. The sharing economy should offer more valuable and relevant services to address the consumers’ wants and needs. Finally, we aimed to provide theoretical and practical understandings that would contribute to the literature on operation management and online digital learning in the context of a developing country.

2. Literature Review and Development of Hypotheses

In the age of globalization, there is a need to investigate US and RPI in terms of sharing services [39]. Similarly, the findings of Kwon et al.’s [40] comprehensive overview demonstrate that, through physical and psychological satisfaction and comfort, US is a positive predictor. To inspect the robustness of customer return and trust towards investment, Boateng et al. [41] quantitatively investigated rider contributions to the usage of the Uber service. The survey outcomes claim no role of social connection and consumers’ need for prestige in developing consumers’ (riders’) usage of Uber services. In Izogo’s [42] study, satisfaction affected RPI and willingness to recommend in both ways, directly and indirectly. Trust is a vigorous analyst of RPI but is not associated with willingness to recommend rather than in the mediation of RPI and satisfaction. Among the credit consumers of Banks, a survey-based study was conducted by Indriastuti and Hidayat [43] that posited a significant influence of trust and service experience on customer satisfaction. Besides, customer satisfaction appears to mediate between trust towards loyalty and service experience. For investigating the RPI from the online platform, the trust factor plays a significant and mediating role in formatting the association between privacy and security concerns and RPI among university students. However, e-satisfaction mediates between ease of use and security [15]. Both modes (online and offline) are employed to gather the data among fast-food consumers. As a result, restaurant stimuli, such as service quality, atmosphere, food quality, location, food variety, and price, have powerful significant effects on customer satisfaction. Further, customer satisfaction is forecasted through social trust through brand loyalty [44]. Among Indonesian high school students, repeat visits, and purchases contributed to US. Productivity, task performance, decision time, learning and decision quality positively envisage the net benefits. On the other hand, system quality does not stimulate net benefits and US. Moreover, US has a significant positive impact on net benefits [45].
The content and process gratification are meaningfully correlated with citizen satisfaction with the government [17]. In the same viewpoint, customer satisfaction and loyalty affect privacy and fulfilment issues in the different positions such as instructors, academic staff, head of the faculty and department in British, Arabic, Kurdish, Uzbek, Indian, Turkmen, Nigerian, Turkish, and Pakistani individuals [46]. In South Africa, the calculative commitment had an enormous influence on customer RPI [47]. The data of USA public utility firms do not show the effect of customer satisfaction on future revenues. Still, it does undoubtedly expect future profitability by dipping utility firm operating costs. Higher satisfaction decreases the costs of utility firm delivery, administration expenses, sales, and customer service [48]. Kusumawati et al. [16] postulated a considerable influence of partial confidence benefits on satisfaction, with partial social benefits and moderately significant convenience benefits.
Nyagowaa et al. [49] tried to determine the impact of service quality, US, and net benefits on e-School success. They acknowledged that service quality and net benefits donate towards e-School’s success; however, US certainly did not contribute to the success of e-School. Similarly, Lee and Jeon [50] conducted a cross-sectional study among students in cyber universities in Korea. The findings specify that service quality, system quality and information quality have a significant effect on US, and the consequent US has, also a positive influence on net benefits. The constructs such as satisfaction, net benefits and trust are momentous positive predictors of customers’ RPI and US toward online shopping. In addition, the development of online trust is possible through interactional procedural and distributive justice [51]. According to Geissinger et al. [52], the sharing economy is conceptually separate from community-based economies, platforms, and access. The user perceptions in social media underline the community-based economies, platforms, and access are unglued. The local website has a striking presence in nearly all design classes. Moreover, the local site imparted greater satisfaction, loyalty, and trust [53]. More recently, the study of Gelashvili et al. [54] did not support the positive association between stress–trust and anxiety.
Consequently, the literature offers diverse factors and motives, i.e., service quality, information quality, system quality, partial confidence benefits, costs of utility firm delivery, gratification, trust, privacy, and security concerns etc. [15,16,17,48,49], which positively predict US, customer satisfaction and RPI in the different regions. Likewise, the scholars tested the concerns (LoT and EE) towards US [12,13,14]. Moreover, in different contexts and countries, from time to time, the RPI also confirms US [35,36,55].
However, an integrated model has yet to test the suggested aspects (motives, concerns, US and RPI) [7,38]. Context-wise, researchers have yet to conduct complete empirical investigations among Saudi Arabian students. Based on the above-prevailing gaps and inadequacies in the existing literature about functional relationships, we developed a model (see Figure 1) to investigate the different motives and concerns that affect US. Furthermore, these factors must be investigated separately due to their significant roles in developing US and RPI among the students [52] and the trustworthiness and probabilities of user engagement in the sharing economy. Accordingly, we aimed to demonstrate that this research would provide better sharing solutions for consumer intentions within a gentler and more conducive environment. In addition, the results would also show the US’ predictive power in respect of the Saudi Arabian students’ RPI.

2.1. Trust and User Satisfaction (US)

From the consumer’s perception, trust is part of the inner feelings that provide their firm belief to achieve transactional accountability [18]. It delivers the transaction support of great altruism and morality [56]. Kim et al. [18] suggested that trust plays a substantial and robust role in affecting the consumer’s intentions in undefined and risky circumstances. The theory of Morgan and Hunt [57] “commitment-trust theory of relationship marketing” highlights the importance of trust as the protagonist predictor of any shared activity. User satisfaction depends mainly on the trustworthiness of selecting a sharing service [9]. From Leonard’s [58] and Hawlitschek et al.’s [59] perceptions, there are valuable transactions when the users perceive the providers as truthful in a sharing economy. In Indonesia’s Bank Rakyat, Trust, service experience and satisfaction affect consumer loyalty [43]. Social trust has a positive and significant influence on brand loyalty and US [44]. Due to its significant role, trust needs further investigation in a sharing economy [60]. Therefore, we formulated the following hypothesis:
Hypothesis 1 (H1).
Trust is a positive and significant predictor of US.

2.2. Economic Benefits (EBs) and User Satisfaction (US)

Generally, Economic Benefits (EBs) play a crucial role when initially using sharing economy services [61]. Economic Benefits have a positive and significant effect on consumer attitudes and intentions [10]. Böcker and Meelen’s [21] and Mahadevan’s [19] studies showed that cost savings have a substantial effect on the users’ existing motives to be satisfied with sharing services. Economic Benefits are a particular motive for consumers when goods or services, such as cars or accommodation, are more expensive [21]. According to Tussyadiah and Pesonen [62], consumers of accommodation-sharing services have surplus benefits since they can stay longer due to cost savings. In a similar mode, Sung et al.’s [22] findings underline that EBs have no significant effects on attitudes towards sharing economy services. However, other constructs, such as networking, affect their enjoyment of such services. Frank and Enkawa’s [63] study underlined that in the USA, South Korea, Sweden, and Germany, macroeconomic growth has a substantial and unidirectional impact on US. The factors, such as EBs and enjoyment, are the determinants of US that mediate such constructs toward future intentions [37]. Consequently, the existing literature has focused on the investigation of the impact of EBs on US in countries [22,62,63] other than Saudi Arabia. In view of the need to investigate in the context of Saudi Arabia, we formulated the following hypothesis:
Hypothesis 2 (H2).
Economic benefits are a positive and significant predictor of US.

2.3. Sharing Economy Philosophy (SEP) and User Satisfaction (US)

The Sharing Economy Philosophy (SEP) is the substantial motive of US. This represents for the consumer the feeling of being at home and sustainability [19,21,61]. On the one hand, scholars like [10] consider that using sharing solutions is a crucial determining factor of consumers’ intentions to share within a gentler and more conducive environment. On the other hand, some other studies’ findings show a positive and significant association between sustainability and the consumer [9]. The SEP encompasses the factors such as home benefits and the feeling of being home. The existing literature underlines the positive relationship between the SEP and US [61]. In the sharing economy, the findings of Elmeguid et al.’s [7] quantitative study highlighted that rideshare services, such as taxis, Careem, and Uber, affect US. However, student users of any service or product in Saudi’s sharing economy need serious concentration. Therefore, we formulated the following hypothesis:
Hypothesis 3 (H3).
The Sharing Economy Philosophy is a positive and significant predictor of User Satisfaction.

2.4. Service Quality (SQ) and User Satisfaction (US)

Service quality (SQ) is a well-known tool for US and offers services to fulfil the consumers’ expectations with great precision, receptiveness, sympathy, practical ability, and consistency [20,64,65]. On the one hand, in bank services, US has a significant effect on customer loyalty. On the other hand, SQ is not a considerable analyst of customer loyalty through US [66]. In Vietnamese supermarkets, SQ impacts customer loyalty and brand image [67]. In the Philippines’ automotive industry, SQ is used extensively in the after-sales service. The SERVQUAL dimensions, such as empathy and reliability, have a positive effect on US. In contrast, the SERVQUAL model’s other constructs, such as assurance, tangibles, and responsiveness, have no significant association with US [68]. Consequently, in a sharing economy, the SQ experience is essential to preserve US. Accordingly, it needs to be considered further among Saudi Arabian students. Therefore, we formulated the following hypothesis:
Hypothesis 4 (H4).
Service Quality is a positive and significant predictor of US.

2.5. Net Benefits (NBs) and User Satisfaction (US)

Net Benefits (NBs) transform into more significant decision-making, improved output, promoted sales, cost reductions, amplified returns, and marketability. An individual, who gladly accomplishes a sharing economy operation, merely does so if it benefits both parties. In the sharing economy, providers and users may attain expressive practice and physical benefits, such as saving and earning costs through communication and on an acquaintances’ community basis [9]. The most common models of sharing are the sharing of cars (i.e., RelayRides, Careem, Uber, and Zipcar) and accommodation. These offer consumers enjoyment of goods and services without owning them [69]. Kusumawati et al.’s [16] empirical investigation showed that partial confidence benefits have a significant impact on US. In Indonesia’s Bina Nusantara University, system and information quality have positive and substantial effects on US. In addition, this study’s findings show a positive relationship between NBs and US [70]. Aspects such as perceived usefulness, ease of use, and user confirmation, have a robust effect on US [71].
Consequently, in different contexts, NBs are significant predictors of US [16,69,70]. However, in Saudi Arabia, such exploration remains in its infantry. Therefore, in recognition of the lack of evidence, we formulated the following hypothesis:
Hypothesis 5 (H5).
Net Benefits are positive and significant predictors of User Satisfaction.

2.6. Lack of Trust (LoT) and User Satisfaction (US)

Lack of trust (LoT) is the most significant concern of consumers of P2P accommodation-sharing services [9,10,14]. In the accommodation business model, trust is essential for sharing services. According to Guttentag [13] and Mahadevan [19], in a stranger’s home, individuals may initially feel uncomfortable sleeping. Trust is the factor required in the manner of trustworthiness of the depiction of the place. It is the only path consumers can use beforehand to assess if it meets their desires [19]. However, on the one hand, several scholars have concentrated only on trust as a major construct contributing to P2P sharing services [10,14]. On the other hand, it is evident that trust LoT has a negative association with US [9,19]. Several investigations have dealt only with trust as a vital element for participating in sharing services. It affects US as consumers only contribute again if satisfied with it [9]. Homans [72] advanced the social exchange theory that LoT is not greater than the benefits of an exchange association whereby the consumer is unwilling to uphold an affiliation. Different scholars, such as [10,13], have suggested that the review system is used to ensure trust in sharing economy mechanisms, systems, and mechanisms as it is regarded as the most common trust system. In this system, guests and hosts can write a review about the experience with each other [14,73]. The findings of the existing studies show that trust is an inevitable concern for consumers involved in sharing services [9,19]; the existing literature shows that in a sharing economy service, trust is a negative predictor of US [9,19]. Therefore, to further its negative validation, we formulated the following hypothesis:
Hypothesis 6 (H6).
Lack of Trust is a negative and significant predictor of User Satisfaction.

2.7. Expected Efforts (EE) and User Satisfaction (US)

Expected Efforts (EE) relate to the consumers’ efforts when contributing to lodging-sharing services [12,13,14]. According to Neunhoeffer and Teubner [14], EE is the most significant concern. Several consumers make greater efforts when availing themselves of P2P-sharing services in reserving a hotel room [14]. Consequently, they choose traditional offerings over sharing services. The EE relates not only to the time and effort employed when watching for an appropriate offering and messaging with the masses [13,74] but also to technology use [12]. Moreover, it is recognized that acquaintance with the sharing platforms has a major effect and assists in overcoming the challenges of EE [9]. The P2P platforms make it easy to connect to mobile or web apps to control such concerns. This has been demonstrated to have a negative impact on US [74]. The perceived consumers’ EE predicts satisfaction with the transaction irrespective of EE’s influence on the outcome. Consequently, it does not help to develop US. However, in a sharing economy, EE may offer a different scenario. Therefore, we formulated the following hypothesis:
Hypothesis 7 (H7).
Expected effort is a positive and significant predictor of US.

2.8. User Satisfaction (US) and Re-Purchase Intentions (RPI)

User Satisfaction is the consumer’s passionate response to completing a transaction regarding the online sharing economy [75]. The sharing economy’s US imitates the consumer’s passionate response to successfully purchasing either green products or services. Therefore, it is known as a vital feature that persuades RPI. Likewise, scholars like Bhattacherjee [76] have stated that satisfied consumers uphold RPI, whereas dissatisfied consumers doubt their successful future use. User satisfaction provokes loyalty and trust inspires consumer RPI behaviors [34,77]. Growing US results in consumers having greater user intent than would have been achieved later by paybacks [78]. A positive association exists between Vietnamese online shoppers and US and RPI [36]. Similarly, Mansourimoayyed et al.’s [35] and Kazancoglu and Demir’s [55] studies indicated that user e-satisfaction substantially impacts RPI. Adekunle and Ejechi [79] perceived that usefulness and US significantly affect RPI. Contrary to a consumer who has no complaints, a consumer, who has a negative experience and complains about the purchase, is not interested in repurchasing. In summary, the existing literature shows that US plays a positive and significant role in developing RPI [34,35,36]. However, in either a collaborative or sharing economy, the predictions of both US and RPI are limited in the context of students and the antecedents of their future intentions [37]. Against this background, we used the following methods in this study.
Hypothesis 8 (H8).
User satisfaction significantly and positively predicts RPI.

3. Methods

3.1. Approach and Survey Tools

If a good study [80] is to answer the research questions, we need to develop a clear and effective plan. Accordingly, we aimed to answer how the students’ motives and concerns affected their US and, in turn, their RPI in a sharing economy. With a view to answering such questions, we used a quantitative approach due to its suitability and practical nature [80]. This process is also crucial in systematically capturing universal facts [81]. Previously, many investigations preferred quantitative methods to predict attitudes, intentions, and US in normal situations and during the urgent period of the COVID-19 pandemic [7,35,36,55,82]. The survey strategy was based on cross-sectional data [71] collected using a questionnaire. This allowed for the collection of standardized data from a chosen population. It was quick and reduced the study’s expenses [80]. We based the questionnaire items on the existing literature [9,12,19,74,83,84,85].

3.2. Reliability and Validity

To achieve credible results, we ensured the reliability and validity of the questionnaire [80,86]. The collected data is reliable when there is great consistency among the questionnaire items [80,87]. In this way, validity undertakes the face validity and the uniformity of the items and aligns with the study’s aim and objectives [87]. To ensure these essential assumptions, we conducted a pilot study by gathering 36 usable answers to obtain some ideas and understanding about the questionnaire’s structure and content. Having acquired some comments from respondents, we made some changes to ensure the reliability of the questionnaire. Moreover, the findings show that the overall Cronbach’s Alpha was 0.818; this is greater than 0.60 and, therefore, was satisfactory [88]. More specifically, the reliability of individual factors was confirmed by excellent scores: Re-Purchase Intention = 0.809, (User Satisfaction = 0.789), (economic benefits = 0.829), (Sharing economy philosophy = 0.862), Service quality = 0.699, Net benefits = 0.768, Lack of trust = 0.790, Expected effort = 0.833.
Turning to validity, we sent the questionnaire to university professors who knew about the new research trends [80]. We recommended some minor changes to the format of the questionnaire. Consequently, we distributed the questionnaire to collect large-scale data to ensure reliability and validity.

3.3. Data Gathering Process and Sample Size

We collected the data during the different waves of the COVID-19 pandemic [89]. Considering the Saudi Arabian Government’s social distancing policy, we distributed the questionnaire online [90,91]. We preferred the questionnaire to examine human behaviors. The questionnaire collected data from students who used either any service or product. We chose students as a significant group in sharing economy services because they represent early adopters of limited resources. Due to the COVID-19 pandemic, previous studies supported such trends [92,93] as these made better use of the limited financial resources and available time [80]. We selected students from Saudi Arabia’s King Faisal University because it was easily accessible from other major cities in the region and it was well-connected by roads, airports, and other transportation hubs [94]. The university has a large and diverse student body, with thousands of students enrolled in various academic programs. It makes it an ideal location for students from all over the country and from countries worldwide who are looking for high-quality education in a dynamic and vibrant environment. The students at King Faisal University use Uber as a convenient mode of transportation [95]. Overall, the popularity of Uber among King Faisal University students reflects the dynamic and forward-thinking spirit of modern-day Saudi Arabia.
We used English language in the questionnaire. We sent the online questionnaire link to Facebook timelines and WhatsApp groups and put a hyperlink on the King Faisal University database. In addition, we sent them emails [93]. We employed convenience sampling due to students’ movement restrictions and the Saudi Arabian Government’s instructions for people to stay at home [93]. During the COVID-19 pandemic, researchers frequently used this method [93,96]. Consequently, we collected 326 completed questionnaires. After data cleaning and screening, we had 324 usable samples for further analysis.

3.4. Insurance of Data Bias

We conducted an online survey due to social distancing restrictions during the COVID-19 pandemic. Therefore, this may have created some variations in the respondents’ thinking and behaviors [92,93]. Consequently, we reduced the data bias to ensure the collection of information from the respondents as, frequently, their uncertain refusal (ineligibility) lessens the pattern dimension and misprint the validity of the pattern when applied to the overall populace [97]. The outcomes are considered biased when the arrangement no longer depicts the whole data [80]. According to [97], a non-response bias occurs when the respondents’ responses vary significantly from those of non-respondents. To reduce such apprehension, we applied the Mann–Whitney U test to distinguish between early and late respondents in terms of intensifying the means of the overall constructs [98,99]. Accordingly, the first fifty completed questionnaires were regarded as early responses, and the last fifty were regarded as late responses. Consequently, insignificant (>0.05) results reflected the non-existence of non-response bias (see Table 1).

3.5. The Respondents’ Ethical Values

We considered the respondents’ ethical values to reduce the deficiency of knowledgeable consent, invasion of privacy and harm to the participants. These are significant challenges for researchers to maintain [86]. We used the Association of Business Schools’ ethics guidelines. First, we obtained consent from the students who were this study’s potential participants. Second, we minimized their risks of harm. Third, we properly ensured the students’ confidentiality, anonymity, and assured them that the data would be used only for research purposes with no harm or invasion [86]. Fourth, we gave the students the right to withdraw without any hesitancy from the study. In addition, the students’ responses were unlikely to be altered as their answers were anonymous. Therefore, the respondents did not feel stressed about the reply in compliance with social norms [80,87]. Finally, we used an internet-mediated questionnaire to overcome any geographical distance restrictions and within a definite timeframe to provide the respondents with the convenience of completing the questionnaire anywhere and whenever they wanted [86].

3.6. Measurement Scales

We adopted all the factors from the existing literature. In terms of the motive factors, we measured Trust on three items adopted from [85]. The sample item of the scale is “The user/service provider is trustworthy”. The EB were derived from [83] and measured by three items such as “My participation can improve my economic situation”. Likewise, they used five items, adopted from [19] to evaluate the SEP factor. The sample item of the scale is “The accommodation helps ordinary people earn some money”. They measured the SQ predictor on three items adopted from [84]. The mockup item of the scale is “The experience at the sharing economy transaction is excellent”. Further, they measured the NBs construct on four items adopted from [100]. The sample item is “The sharing economy system saves me time”.
Turning to the concerns predictors, LoT and EE, we used three items each, adopted from [9,12,19,74], to evaluate them. The sample items of the scale were “I trust that I am safe when using accommodation sharing services” and “I am familiar with using accommodation sharing platforms”. Similarly, regarding dependent variables, we adopted User Satisfaction from [84,101]. The sample item was “I am satisfied with the sharing economy.” Finally, concerning RPI, we used three items adopted from [102] to measure them. The sample item was “If I could, I would like to continue using the sharing economy to purchase green products/services”. We used a five-point Likert scale (strongly agree to strongly disagree) to measure all the items.

4. Data Analysis and Results

4.1. Demography

As regards the respondents’ demographic details, 67.90% (n = 220) were male, and 32.10% (n = 104) were. Likewise, most of the respondents were between 26 to 35 years of age (59.88% or n = 194); 33.33% (n = 108) were less than 25 years of age; and only 0.62% were more than 45 years of age (see Table 2). Further, the demography shows that many respondents have a Master degree (61.11% or n = 198), 28.40% have a Bachelor degree and only 01.23% (n = 4) are PhD holders. Likewise, turning to the frequent use of the services, most respondents (27.16% or n = 88) used the online service once a month; 17.90% used the service once a week; and only a small number of respondents (4.32 or n = 14) utilized the services daily (see Table 2).

4.2. Measurement Model

Turning to the measurement model, we evaluated the convergent validity based on the items’ loadings. These were, namely, Composite Reliability (CR); and Average Extracted Variance (AVE) [103]. The factor loadings showed that, as indicated by [87], most items had acceptable and meaningful loading scores >0.70. However, as some items, such as ebs2, sep1, and nbs4, did not load above the required values (>0.70), we excluded them, while the remaining items were carried to the next steps (see Table 3). Furthermore, the CR values of the rest of the constructs achieved 0.70 or above (CR = 0.834 to 0.897) (see Table 3), and the AVE values (0.793–0.896) for the rest of the variables ensured the identity of the constructs (>0.50) [87] (see Table 3). Generally, CR was >0.6 and values of AVE > 0.5 demonstrated the model’s excellent reliability [104]. In addition, Cronbach’s alpha (α) of all variables fulfilled the acceptable reliability coefficient (>0.70) [87]. The Alpha of all factors (independent and dependent) remained satisfactory with minimum (α = 0.780 = EE) and maximum (α = 0.892 = trust) reliability values. These ensured the existence of internally consistent and reliable items. Finally, we assessed the Discriminant Validity (DV) (see Table 4) to estimate how the items discriminated among the constructs. According to [103], DV should be examined by noting the AVE values higher than the inter-construct correlations. We also compared the correlations to the square root of the AVE. The existence of the square root of the AVE diagonally appeared to have > values in the rows and columns of concrete variables (Table 3). In this way, we ensured adequate scores of DV.

4.3. Structural Model

We tested the Chi-square statistics and the other model fit indices. The non-significant (CMIN/df = 2.867; p > 0.005) values of χ2 provided the initial signal of model fitness with the data [87] (see Figure 2 and Table 5). In addition, several model fit indicators, such as GFI = 0.929; AGFI = 0.930; NFI = 0.929; CFI = 0.948; and RMSEA = 0.046, ensured the well-fitness of the model (cut-off value is 0.08) [87] (see Figure 2 and Table 5).
We assessed the hypotheses paths based on standard error (ER) and the critical ratio (CR) through the maximum likelihood estimates (MLE) path at the level of <0.01 ***. The SEM analysis highlighted that the motives (Trust, EBs, SEP, SQ and NBs) had a positive and significant predictive power on US (H1 = SE = 0.041; CR = 5.836 ***; H2 = SE0.078; CR = 4.668 ***; H3 = SE = 0.040; CR = 6.449 ***; H4 = SE = 0.069; CR = 6.809 *** and H5 = SE = 0.057; CR = 7.333 ***; p =< 0.01) (see Figure 2 and Table 6). These mean that hypotheses H1, H2, H3, H4, and H5 were accepted. Contrary to expectations, the scores for hypothesis H6 (SE = 0.051; CR = 0.6.271; p =< 0.01) (see Figure 2 and Table 6) showed that LoT had a positive and significant impact on US. Therefore, hypothesis H6 was rejected. The findings show, also that there was a positive and significant effect of EE on US (SE = 0.062; CR = 6.999; p =< 0.01) (see Figure 2 and Table 6); thus, H7 was accepted. Finally, the findings show that US positively and significantly impacted RPI (SE = 0.061; CR = 7.789 ***; p =< 0.01); therefore, the last hypothesis (H8) was accepted.

5. Discussion and Conclusions

This study aimed to investigate US and RPI through the different motives and concerns in a sharing economy. We based our study on a robust conceptual framework developed from the existing literature. The outcomes of the SEM analysis confirmed that Trust had a positive and significant impact on US (therefore, hypothesis H1 was accepted). These results are consistent with numerous studies, such as [9,43]. Our results reflect that the students find the sharing economy trustworthy, reliable, and value based. Together, these develop trust. In addition, the service providers deal honestly and are committed to adequately satisfying their consumers’ requirements.
Similarly, these results show that EBs significantly affect US (therefore, hypothesis H2 was accepted). Several studies have supported these effects in the literature, such as [10,37,61]. From the respondents’ perceptions, they can save their money and resources through participation in sharing services. They expect an improvement in the economic situation. They are also ambitious to save time as EB is the most significant developer of attitudes and RPI. Ultimately, these attitudes form positive user intentions and US [37].
On the same path, the results confirm that SEP substantially impacts US (therefore, hypothesis H3 was accepted). These findings are consistent with the conclusions of previous studies and provide valuable insights into the resources and EB in developing the RPI and US in developed and developing economies [21,24,25,26,27,28,29,61]. As was suggested by Hamari et al. [10], the respondents to this study think SEP is the crucial aspect of consumer intentions to share in a favorable environment. The services give them appliances and conveniences. They consider it an efficient approach to empowering people to earn some resources.
Furthermore, our results support SQ’s impact on US (therefore, hypothesis H4 was accepted). Likewise, these findings are consistent with earlier studies conducted in various contexts [20,66,68]. According to this study, three is convenience attached to online services in a sharing economy. The respondents think the online sharing economy is meaningful and provides excellent transaction modes. Such perceptions ensure that good SQ preserves US. Moreover, the final motives support the positive effect of net benefits on US (therefore, hypothesis H5 was accepted). By demonstrating NBs’ positive effect on US, these findings were consistent with those of several studies [9,16,69]. In Saudi Arabia, the sharing economy system saves costs and time [24]. Simultaneously, it responds quickly to retorts and proceeds complaints and opinions into contemplation. The sharing economy system is seen to provide US. Turning to the concerns, our results show that LoT and EE had a positive and significant effect on US. These findings are consistent with studies such as [10,14,19], that found a positive and significant association between LoT, EE, and US. However, the positive association between LoT and US appears opposite to the proposed hypothesis/association. The respondents have trust in offering space on the platforms. In using sharing services and online sharing services. They believe in their hosts’ credibility.
Moreover, these results show that US has a robust effect on RPI (hypothesis H8 was accepted). These findings are consistent with those of previous studies by various researchers such as [23,35,36,55]. In developing and developed economies, these findings are supported by research like [8,9,10,11,24,25,26,27,28,29]. These provide evidence of the positive and significant effect of US on RPI. However, our results are constructive as these are confirmed in sharing economy, predominantly in Saudi Arab and in student contexts.
Consumers feel immense pleasure in purchasing within a sharing economy and believe they can meet their wants. They are ready to continue with such purchase modes for products and services which are pro-environmental or green products. In addition, they have shown their willingness to continue such trends in the future.
In conclusion, our overall findings indicate that motives, such as Trust, EBs, SEP, SQ, and NBs, are significant predictors of US; however, concerns such as LoT and EE positively impact US. Finally, this research demonstrates US has predictive power on RPI in a sharing economy. Once users become satisfied and trust SQ, EBs, and SEP, US encourages RPI. Our results add to the knowledge in operations management on how, in the sharing economy, consumers or students cope with the effects of the COVID-19 pandemic [105]. Globally, the COVID-19 pandemic has reduced countries’ economies and has had massive consequences on the sharing economy [106]. Therefore, these results further strengthen the operations in sharing economy platforms. Our study offers a novel contribution by presenting Figure 1, which is integrated into two aspects, motives, and concerns towards US, and how US enhances RPI, particularly among the university students of Saudi Arabia.

Implications, Limitations and Future Research Avenues

The study’s findings fill a gap observed in the domain literature, as no model appears in which the investigation of two aspects, such as motives and concerns, have been integrated and tested towards US, specifically with RPI as an outcome variable. Hence, the present study offers theoretical implications based on filling these gaps. Our results provide a theoretical contribution to assessing US and RPI in a sharing economy with different motives and concerns. The assessment of such a scenario will assist in developing the theory from the perspective of the sharing economy. In this study, the concerns had a significant impact in reducing the students’ US which provides a positive trend for developing RPI. Therefore, these trends may encourage researchers to further test the same hypotheses in different regions. This study’s findings provide insights to the practical and theoretical sphere of the sharing economy. We achieved this by scrutinizing the existing literature and empirical studies and conducting our own quantitative empirical online analysis. Therefore, this study’s findings benefit both scientific and non-scientific readers. Moreover, based on empirical evidence in a developing context, these findings contribute to operations management and the literature about online digital learning.
Based on our findings, we recommend that executives develop a robust system that delivers a rapid and swift service with a good user experience that results in US. By providing insights into the constructs to continue US and RPI, our findings enable concerned authorities to engage in collaborative consumption services in several fields. These findings highlight that the LoT and EE are significant resistors to developing US. Therefore, the results provide a signal to the sharing economy agent to overcome such issues to boost US and for sharing economy service providers to use Trust to secure a strategic competitive advantage [107]. These findings establish that if US and RPI are to be realized, more is needed to shape a sharing economy through motives and concerns. Therefore, to meet consumers’ needs and desires, the sharing economy should provide more valuable and pertinent services [108]. Moreover, the results deliver solid insights into the Saudi Arabian students’ US and RPI. The findings also assist in developing a coordinated approach to online services [7].
As we employed an online approach for this study during the COVID-19 pandemic, it was not free from certain limitations. We collected the data through an online questionnaire, therefore, the limited survey modes affected the findings [86]. In conducting this study, we did not apply any concerned theory to underpin the study’s conceptual model. We used a convenient sampling technique, which may have created some issues in targeting the respondents. The results were based on the completed questionnaires from 324 respondents. Based on the sharing economy’s specific products and services, which are pro-environmental or green products, this study utilized a few predictors to investigate US and RPI.
We used a model which provided a more profound study of the pro-environmental products or services of the sharing economy. The results were based on cross-sectional data. A longitudinal investigation would increase insights about the users, and the relationships among contrasts will vary from time to time in the future. Therefore, there is a continuing need to investigate individuals’ technical characteristics that affect the sharing economy’s usage intentions/behaviors. We recommend that future studies investigate the relevant theories in different phenomena, such as the gift economy, entrepreneurship, commercial economy, and digital transformation. Moreover, our findings open the door to investigate further innovative and entrepreneurial approaches to bring valuable insights and findings of the concerns. While this study surveyed Saudi Arabian students, we recommend that future studies investigate SMEs and the sharing economy’s service sectors. Finally, we suggest that future studies consider the relevant environmental effects of the sharing economy.

Author Contributions

Conceptualization; A.F.A. and N.A.A.A.; software, N.A.A.A.; validation, N.A.A.A. and A.F.A.; formal analysis; investigation, A.F.A. and N.A.A.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia [Project No. GRANT2834].

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethical Committee of the Deanship of Scientific Research, King Faisal University (project number: GRANT2834, date of approval: 1 February 2023).

Informed Consent Statement

The researchers conducted this study in accordance with the guidelines of the Declaration of Helsinki and approved by the Ethical Committee of the Deanship of the Scientific Research, King Faisal University (project number: GRANT2834, date of approval: 1 February 2023).

Data Availability Statement

Data are available upon request from the researchers who have met the eligibility criteria. Please kindly contact the first author privately through e-mail.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 04498 g001 550
Figure 1. This Study’s Conceptual Model. Source: Developed by the authors.
Figure 1. This Study’s Conceptual Model. Source: Developed by the authors.
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Figure 2. Structural Equation Model. Source: Authors own estimations. *** p < 0.05.
Figure 2. Structural Equation Model. Source: Authors own estimations. *** p < 0.05.
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Table
Table 1. Mann–Whitney U test detecting Non-Response Bias.
Table 1. Mann–Whitney U test detecting Non-Response Bias.
Test Statistics
TrustEBsSEPSQNBsLoTEEUSRPI
Mann–Whitney U1385.5001231.0001136.0001007.5001029.0001089.000906.500989.5001159.000
Wilcoxon W2611.5002059.0002264.0002223.5002379.0002426.0002178.5002269.5002447.000
Z−0.093−1.208−1.162−1.719−0.725−0.173−2.349−1.816−0.568
Asymp. Sig. (2-tailed)0.9300.2290.2560.0840.4770.8390.0390.0730.578
Notes: EBs = Economic Benefits; SEP = Sharing Economy Philosophy; SQ = Service Quality; NBs = Net Benefits; LoT = Lack of Trust; EE = Expected Effort; US = User Satisfaction; RPI = Re-Purchase Intentions.
Table
Table 2. Demography of the Respondents.
Table 2. Demography of the Respondents.
CategoryFrequencyPercent
GenderMale22067.90
Female10432.10
Total324100.0
Age<25 years10833.33
26–35 years19459.88
36–45 years206.17
>45 years20.62
Total324100.0
EducationHigh school/diploma309.26
Bachelor Degree9228.40
Master Degree19861.11
Doctorate Degree41.23
Total324100.0
Usage frequencyOnce a year3310.18
Once every six months226.80
Once every three months5416.67
Once a month8827.16
Once a week5817.90
Once every 4 to 5 days268.02
Once every 2 to 3 days298.95
Almost every day144.32
Total324100.0
Table
Table 3. Measurement Model.
Table 3. Measurement Model.
ConstructItem CodeFactor LoadingsCRAVEα
Trustt10.8680.8700.8680.892
t20.860
t30.848
Economic benefits (EBs)ebs10.8800.8890.8960.861
ebs30.878
ebs40.859
Sharing economy philosophy (SEP)sep20.8580.8340.7930.849
sep30.849
sep50.826
sep40.809
Service quality (SQ)sq10.8790.8770.7990.836
sq30.868
sq20.840
Net benefits (NBs)nbs10.8720.8970.8820.808
nbs20.796
nbs30.780
Lack of trust (LoT)lot10.8970.8810.8560.792
lot30.888
lot20.825
Expected effort (EE)ee10.8720.8670.8830.780
ee20.861
ee30.839
User satisfaction (US)us10.8950.8590.8590.829
us40.879
us20.862
us30.829
Re-Purchase Intention (RPI)rpi10.8700.8400.8260.863
rpi20.854
rpi30.819
Notes: AVE = summation of the square of the factor loadings. CR = square of the summation of the factor loadings. α = Cronbach’s alpha.
Table
Table 4. Discriminant Validity.
Table 4. Discriminant Validity.
Constructs123456789
1Trust0.889
2EBs0.2870.873
3SEP0.3080.1400.900
4SQ0.3280.1690.1390.879
5NBs0.3890.2610.2960.3260.853
6LoT0.3000.3020.2900.3880.4320.870
7EE0.3620.2560.3670.3220.2220.3490.850
8US0.3090.2400.3700.1490.3020.2240.3660.868
9RPI0.3220.2720.2170.3280.3800.2990.2990.3760.839
Note: Diagonals represent the square root of the AVE while the other entries represent the correlations.
Table
Table 5. Model Fit Indices.
Table 5. Model Fit Indices.
Model fit indicatorsCMIN/dfGFIAGFINFICFIRMSEA
2.8670.9290.9300.9290.9480.046
Suggested values<3>0.90>0.90>0.90>0.90<0.05
Note: CMIN = χ2/Chi-square/df; df = degree of freedom; GFI = goodness of fit index; AGFI = adjusted goodness of fit index; NFI = normed fit index; CFI = comparative fit index; RMSEA = root mean square error of approximation.
Table
Table 6. SEM Analysis.
Table 6. SEM Analysis.
H.No.Independent VariablesPathDependent VariablesEstimateSECRpDecision
H1Trust→US0.3480.0415.836***Accepted
H2EBs→US0.2480.0784.668***Accepted
H3SEP→US0.2390.0406.449***Accepted
H4SQ→US0.3980.0696.809***Accepted
H5NBs→US0.2990.0577.333***Accepted
H6LoT→US0.3820.0516.271***Rejected
H7EE→US0.2900.0626.999***Accepted
H8US→RPI0.3020.0617.789***Accepted
Note: SE = standard error; CR = critical ratio; p = significance level *** p < 0.05. EBs = economic benefits; SEP = sharing economy philosophy; SQ = service quality; NBs = net benefits; LoT = lack of trust; EE = expected effort; US = User Satisfaction; RPI = repurchase intention.
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MDPI and ACS Style

Alnaim, A.F.; Abdelwahed, N.A.A. Examining the Impact of Marketing Motives and Concerns on User Satisfaction and Re-Purchase Intentions in a Sharing Economy. Sustainability 2023, 15, 4498. https://doi.org/10.3390/su15054498

AMA Style

Alnaim AF, Abdelwahed NAA. Examining the Impact of Marketing Motives and Concerns on User Satisfaction and Re-Purchase Intentions in a Sharing Economy. Sustainability. 2023; 15(5):4498. https://doi.org/10.3390/su15054498

Chicago/Turabian Style

Alnaim, Abdullah F., and Nadia Abdelhamid Abdelmegeed Abdelwahed. 2023. "Examining the Impact of Marketing Motives and Concerns on User Satisfaction and Re-Purchase Intentions in a Sharing Economy" Sustainability 15, no. 5: 4498. https://doi.org/10.3390/su15054498

APA Style

Alnaim, A. F., & Abdelwahed, N. A. A. (2023). Examining the Impact of Marketing Motives and Concerns on User Satisfaction and Re-Purchase Intentions in a Sharing Economy. Sustainability, 15(5), 4498. https://doi.org/10.3390/su15054498

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