Shared Mobility and India’s Generation Z: Environmental Consciousness, Risks, and Attitudes

Abstract

Shared mobility platforms have built scalable digital marketplaces that facilitate the allocation and sharing of transportation and promote sustainable urban travel. Generation Z’s attitude toward shared consumption is closely linked to their perceptions of the importance of sustainability. This study identifies Generation Z’s awareness of shared mobility platforms in India and the factors that influence their use. Data were collected from 318 respondents from Generation Z in India and analyzed using partial least squares structural equation modeling. Findings indicate that Generation Z’s intention to use shared mobility is influenced by environmental consciousness, social aspects, economic benefits, and perceived risks. Results also show that perceived risks have an indirect effect on intention, which is mediated by attitude. The novel conceptual model developed and tested in this research can be used to inform policies and business models for the adoption of shared mobility services for Generation Z, ultimately promoting more sustainable transportation systems and improved urban mobility.

1. Introduction

Resource depletion and other critical environmental issues are detrimental repercussions of intensified consumption and a wasteful consumer culture [1]. As the sharing economy has grown, its advocates have claimed that it can ameliorate unsustainable consumption patterns [2]. Despite the growth of consumer markets in the sharing economy, there is little consensus on the exact nature of the phenomenon or its boundaries due to its wide impact across many sectors, as well as the fragmented nature of academic research into these [3]. Although sharing economy business models are diverse, they are typically characterized as innovative and frequently provide platforms for rapid internationalization [4], which has been the case in shared mobility, which is the focus of this study.

Sharing economy businesses involve sharing access to underutilized goods and services, promoting their optimal utilization, and the shared accessibility of resources rather than ownership [5]. This type of economy creates opportunities for redundant assets to be made more productive in terms of their location and their temporal availability and for their trade (for example, an empty seat in a drive between two cities) on online platforms [5,6,7].

Recent developments in technology across all consumer sectors have been accompanied by novel transport innovations and a shift in consumer mobility behavior [8]. Shared mobility, and especially carsharing, has become associated with sustainable travel behavior as well as the greater provision of sustainable urban infrastructure [9].

Carsharing has become an effective means of lowering car-related expenditure for consumers, allowing them to maintain mobility without incurring many of the costs associated with car operation and maintenance, especially since these costs have been steadily rising [10,11]. From a customer perspective, trip cost, ease of payment, avoiding driving after consumption of alcohol, as well as reduced travel and waiting times have been found to be some of the most highly valued aspects associated with these services by users [12].

The cognitive decision-making process of users for adopting shared mobility can be attributed to their perception of the sustainability of modes of transportation, taking into consideration collective and personal motives, environmental consciousness and social aspects, and economic benefits derived from using shared mobility [13,14]. Ref. [15] also argued that ease of usage, heightened sustainable and social awareness, and a growing preference for temporary access instead of ownership are the major motivating factors for collaborative consumption in carsharing contexts.

Individuals belonging to Generation Z, particularly those residing in densely populated urban areas with high ICT usage, take a higher-than-average number of shared mobility trips compared to other generations [16], and this study focuses on the factors that impact their intention to use these platforms. Generation Z is generally regarded as the cohort born from 1997 to 2012, although there is no clear consensus in previous studies on the boundaries of this generational cohort [17,18]. Young consumers, particularly in Generation Z, have been identified as a cohort with an inherent tendency to take part in the sharing economy due to their native proficiency with technology and digital platforms, concern for social and environmental problems, and their relationship-based identities, making them ideal adopters of sharing economy behavior [19,20,21,22,23]. Moreover, Generation Z—dubbed the “Green Generation” for their environmental consciousness—frequently factors sustainability into their purchasing decisions [24]. In contrast to Gen Z, Millennials are more likely to embrace ride-hailing services like Uber and Lyft as alternatives to car ownership, especially in densely populated urban areas where parking is scarce and expensive [25]. Similarly, while Generation X and Baby Boomers may initially show reluctance towards shared mobility due to ingrained car-centric lifestyles and concerns about safety and convenience, there is growing evidence of adoption within these cohorts [26].

Generation Z is characterized by a decrease in their personal ownership of vehicles and an increase in their dependence on alternative modes of transportation [27,28]. This trend is a departure from previous generations, who have traditionally valued car ownership as a symbol of independence and mobility. Generation Z, on the other hand, appears to prioritize convenience, cost-effectiveness, and sustainability, leading them to opt for ridesharing services, public transportation, and active transportation such as walking or cycling. This shift in behavior may also be linked to the increasing costs of car purchase and ongoing ownership, including insurance, maintenance, and fuel expenses, as well as an increased awareness of the environmental impact of excessive car usage [29].

Therefore, this research aims to provide an analysis of the motivations and preferences of Generation Z when choosing shared mobility platforms. Through this study, we have identified important factors affecting the intentions of India’s Generation Z to use shared mobility for the first time. The new model that we develop and test in this study helps to show the relationships between these factors and can be used in future studies in other sharing economy contexts, as well as in other national markets where Generation Z is emerging as a significant consumer segment. This study can help to improve the user experience and drive the adoption of shared mobility services in established and emerging markets. The beneficiaries of this research include, but are not limited to, shared mobility service providers, as well as those involved in the approval and regulation of these platforms in local and national government agencies and researchers in transport and urban planning.

The following section presents a review of the relevant literature, which is then used to develop the hypotheses for this study. Our methodology is then explained, and data are analyzed using the PLS-SEM Algorithm and Bootstrapping techniques. Finally, the results of this analysis are discussed, along with the significance of the research.

2. Hypothesis Development and Research Framework

2.1. Environmental Consciousness

Green consumerism has gained increasing momentum among successive generations of young people, and in particular now in Generation Z [30], as they bear witness to rising pollution, as well as the multiple adverse impacts of global climate change, prompting the pressing need to conserve the environment and adopt sustainable business models [31]. Environmental consciousness has been identified as a driver for the development of green consumerism. It refers to the psychological traits that determine an individual’s inclination towards eco-friendly actions [32]. These traits can include one’s beliefs, attitudes, values, knowledge, and understanding of the environment and their role in preserving it.

Ref. [33], through their study in Greater Melbourne, found that a major portion of users of carsharing services held deep-seated values regarding the environment, through which they perceived minimizing car ownership and limiting car usage as a moral obligation and that they considered carsharing as an effective means of reducing their dependency on cars, showing the value of the environmental consciousness concept. Because most cars are idle for 95% of the time and support just one individual for a large portion of the remaining time, cars remain underutilized assets in the economy. However, if a car was used more intensively and carried multiple passengers, the costs incurred per rider would significantly decrease for each journey, leading to more optimal car usage [34]. Viewed optimistically as these services continue to grow, ride-hailing services can contribute to the reduction of greenhouse gas emissions and energy consumption resulting from the production and disposal of cars, as well as gradually reducing the number of cars owned by households [35].

Results of a study in China by [36] showed that ridesharing, shared bike services, and fast ride pooling had contributed to the reduction of CO₂ emissions and overall energy savings. Ref. [12] support the propositions of previous research that shared mobility, which increases the rate of occupancy of trips, reduces, or at least does not lead to an increase in, urban traffic [34,37]. Similarly, the findings of the study by [38] conducted in Chengdu, China, indicated that ride-splitting could reduce trip hours by a substantial amount (22%), thereby lowering traffic congestion.

2.2. Social Aspects of Shared Mobility Platforms

The social aspects of shared mobility platforms refer to the ways in which these services interact with their social context. This section will explore the impact of social factors such as security, booking of seats, and waiting times on users’ willingness to adopt shared mobility services. By examining these social aspects, we can gain a better understanding of how shared mobility platforms can be made more accessible and appealing to a wider range of users.

For example, [10] demonstrated that the demand for shared mobility in poorer neighborhoods in New York City did not vary significantly from more mainstream carsharing localities, but affordability did, meaning that developing new shared mobility services or expanding the territorial coverage of existing services’ territorial coverage to these areas would not be effective. This analysis of social context leads to the conclusion that rental prices for carsharing can be fixed at competitive rates or even subsidized and that different neighborhoods can be charged different rental prices to promote equitable access [10,39]. Developing this idea further, [40] have argued that incorporating shared mobility platforms into broader transport networks and making these accessible easily to users in deprived areas could be used as a tool for promoting spatial justice in cities.

In [41] found that 67% of respondents using ridesourcing said they generally experienced wait times of less than five minutes, and 90% said they always waited ten minutes or less. If the waiting time for accessing the services of a shared mobility platform is lower than that of public transportation, it can serve as a motivating factor for consumers to adopt shared mobility.

The Uber model in South Africa is valued for its safety and security benefits, as it removes the use of cash and reduces the likelihood of criminal activity against its users. Due to this, it is often preferred over public transportation in the country [42]. In Mexico City ‘booking of seat’ was picked by respondents as the primary reason for choosing Jetty, a shared-mobility platform, indicating that having an assured, safe seat for the journey improved perceptions of the overall quality of transport and emphasizing the fact that in many countries, using a shared mobility vehicle is considered to be more secure, especially at night [13]. Their study revealed that the reasons for this perception of relative safety when compared to public transportation were the tracking facilities that are inherent to many shared mobility transport apps, as well as the greater and more convenient availability of journeys.

2.3. Economic Benefits of Shared Mobility

Eliminating the costs of car ownership, coupled with the convenience of payment systems, can motivate consumers to use shared mobility platforms [13]. Many participants in [33] research highlighted carsharing as an economical way to gain access to a car when compared to outright purchasing and further dealing with the rising costs of operating a vehicle. Residents of downtown San Francisco indicated that the ease of payment on ridesourcing platforms was the number one reason to opt for it [41]. In a study of the comparative benefits of carsharing versus traditional taxi transport in Beijing, China, [43] found that for short and medium-length trips, carsharing was seen as offering cost advantages, and this was particularly the case for segmented journeys, with multiple stops, and for journeys under one hour in duration.

Another perceived benefit of carsharing is the variety of cars on offer, which goes beyond the affordability of options available for private ownership for many people. Ref. [33] found that some consumers found the provision of using different cars or borrowing cars appropriate for specific tasks appealing, with some carsharing users being attracted to hiring luxury vehicles, specifically. This suggests that car sharing can appeal not only to the cost-conscious consumer but also to those who view it as a status symbol.

Avoiding the inconvenience of finding and paying for parking spaces is one of the major advantages that comes with using shared mobility platforms. Ref. [42] show that 90% of respondents agreed that one of the reasons for them to use app-based taxis was to avoid costs associated with parking. This also removes the need to pay for parking spaces, which is an added economic benefit for users.

There are other forms of personal mobility vehicles [44] discussed in the literature, such as scooters and bikes, which can offer further economic benefits in the case of short trips taken in crowded city centers, which are typically time-sensitive. These alternative modalities have been referred to as ‘micro-mobility’ [45]. The competitive pricing of bike-sharing systems, along with the growth in the provision of the required infrastructure, will increase their relative usage over time when compared to other forms of shared mobility through economies of scale [46], notwithstanding concerns over the sustainability over e-scooters in particular, because of their short product lifetime [47].

2.4. Perceived Risks of Shared Mobility Platforms

Consumers perceive product scarcity risk in shared mobility, that is, possible non-availability of vehicles, as the number one reason against adopting carsharing [14]. In Greater Manchester, a study of carsharing by [48] revealed that 30% of respondents chose ‘uncertainty over availability at the location I need it’ as a reason for limiting their usage, and this proportion rose to 47% amongst users of other forms of shared mobility, thus indicating that availability and accessibility of options such as bikes and scooters had an impact on the utility of other shared mobility services.

Scarcity is also linked to the territorial coverage of carsharing services, which can have a significant impact on the value proposition of these services for users, hence the need to expand the coverage beyond just central city areas to also include peripheral areas [13]. The study by [49] showed that sharing propensity is determined by both the technical costs involving learning the car’s actual controls and functionalities as well as the mobility utility pertaining to territorial coverage provided by the application.

An additional risk identified by [50] concerns the future development of shared mobility services, as these services become increasingly autonomous, initially through the automation of commercial processes but moving further towards the integration of autonomous vehicles in shared mobility. Consumers express concerns about the likelihood of driving errors and accidents, suggesting that work will be needed to maintain the current situation of trust in shared mobility platforms in the future.

2.5. Intention to Use Shared Mobility Platforms

Numerous reasons affect consumers’ intention to use shared mobility platforms, including but not limited to ‘not having to search or pay for parking’, ‘ease of payment’, ‘general comfort and security/safety’, ‘variety of vehicle types’, and ‘perception of security against crime’ [51]. Specifically investigating young people’s intentions to use shared mobility in Seville, Spain, [52] found that the likely environmental benefits of this for the city and for climate change generally were found to be significant predictors of intention. Therefore, in this study, a number of such indicators were used to measure Generation Z’s intention to use shared mobility platforms in India.

However, when it comes to consumers’ intention to substitute ownership of vehicles by making use of shared mobility solutions, [41] research should be noted, wherein ninety percent of vehicle owners responded that there was no change in their vehicle ownership following their use of shared mobility. Additionally, for those who did report changes in this regard, they were as likely to now own more cars rather than fewer, therefore indicating that the existence of shared mobility alone does not necessarily lead to a decrease in car ownership. In the study conducted by [53], it was found that users with a higher perceived need for a car were relatively less willing to adopt carsharing.

2.6. Attitude towards Shared Mobility Platforms

Attitude is a significant factor contributing to the development of an intention. This is evident in the study by [54], wherein usage intention was found to be influenced by positive attitudes toward electric scooter sharing, and the study by [14], where carsharing adoption was found to be directly influenced by attitude.

Research by [55] showed that concern for the environment, social influence, and perceived financial benefits play a role in the creation of consumers’ attitudes towards shared ride-hailing services. Similarly, ref. [13] also argued that attitude towards carsharing is influenced by environmental, social, and economic factors.

Therefore, following this review of the literature, the following hypotheses were developed:

H1: 

Gen Z’s attitude (GA) towards shared mobility platforms affects their intention (IN) to use them.

H2: 

Perceived risks (PR) affect Gen Z’s attitude (GA) towards shared mobility platforms.

H3: 

Perceived risks (PR) affect Gen Z’s intention (IN) to use shared mobility platforms.

H4: 

Gen Z’s attitude (GA) towards shared mobility platforms mediates the relationship between perceived risks (PR) and intention (IN) to use.

The relationship between these hypotheses is set out in the proposed conceptual framework in Figure 1.

3. Methods

3.1. Sampling and Data Collection

Because this research focused on Generation Z consumers with high levels of mobility using shared mobility platforms, it was necessary to investigate perceptions of shared mobility use from respondents with a good knowledge of these platforms. To achieve this, a minimum of, on average, five travel sessions in a month was decided to be the threshold criteria for the selection of respondents. Since it was practically impossible to create an exhaustive list of the population, this study adopted the convenience sampling method while selecting the respondents.

The data collection for this study involved two stages to ensure the selection of valid samples. In the first stage, a detailed list of 700 potential samples was created by frequently visiting prominent colleges with undergraduate courses during August 2022. Undergraduate courses were selected as they were very likely to be attended by members of Generation Z. Potential respondents were chosen after checking their age at their college or other valid ID to confirm their membership of Generation Z and upon confirming the minimum threshold criteria. In the second stage, the study questionnaire was forwarded to these potential respondents from September to October 2022 using Google Forms. On a few occasions, reminder calls were made to ensure the timely filling of the questionnaire. Finally, 318 valid responses (response rate—45.4%) were retained for further analysis. As argued by [56], the sample size ought to exceed ten times the highest count of inner or outer model links, i.e., the structural paths directed towards any latent variable within the entire model. Therefore, a sample size of 318 meets this condition, as there are 24 indicators discussed in the model. The demographic details of the respondents are shown below in

Table 1. Demographic profile of the respondents.

		Count	Column N %
Gender	Male	131	41.2%
	Female	184	57.9%
	Other	3	0.9%
Educational qualification	Class X/XII	84	26.4%
	Undergraduate (UG)	199	62.6%
	Postgraduate (PG)	35	11.0%
Area of stay	Rural	18	5.7%
	Suburb	35	11.0%
	Urban	265	83.3%
Monthly expenditure level (in ₹)	Below 10,000	180	56.6%
	10,000–20,000	93	29.2%
	20,000–30,000	27	8.5%
	Above 30,000	18	5.7%
Monthly travel expenditure level (in ₹)	Below 2500	195	61.3%
	2500–5000	86	27.0%
	5000–7500	17	5.3%
	7500–10,000	10	3.1%
	Above 10,000	10	3.1%

.

In Figure 2, we see a stark contrast in familiarity among respondents regarding various transportation platforms. It is evident that the majority, constituting over 90% of the surveyed population, were well-acquainted with significant ride-hailing services like Uber and Ola, indicating widespread recognition and adoption of these services. However, the landscape shifts drastically when considering lesser-known alternatives such as BlaBlaCar and BluSmart Mobility, with awareness levels falling below 10%. This discrepancy underscores the dominance of established players in the market and highlights the challenge faced by emerging platforms in gaining traction and visibility among consumers. Despite offering innovative solutions for shared mobility, these lesser-known services struggle to penetrate the market and capture mindshare, pointing to the need for targeted marketing efforts and strategic partnerships to enhance brand awareness and attract users.

Similarly,

Table 2. Respondents’ preferred mode of traveling and source of awareness about shared mobility platforms.

		Frequency	Column N %
What is your most preferred way of commuting in case of short distances?	Walking/cycling	182	57.2%
	Public transportation	50	15.7%
	Own vehicle	51	16.0%
	Ride-hailing services (cab, auto, two-wheeler, etc.)	28	8.8%
	Travel arrangements with a friend/colleague (e.g., carpooling)	7	2.2%
What is your most preferred way of commuting in case of long distances?	Walking/cycling	9	2.8%
	Public transportation	100	31.4%
	Own vehicle	89	28.0%
	Ride-hailing services (cab, auto, two-wheeler, etc.)	105	33.0%
	Travel arrangements with a friend/colleague (e.g., carpooling)	15	4.7%
Where do you hear most about Shared Mobility Platforms?	Social media sites	153	48.1%
	Newspapers and magazines	16	5.0%
	TV commercials	50	15.7%
	Billboards and hoardings	11	3.5%
	YouTube advertisements	88	27.7%

illustrates preferences and awareness regarding commuting modes and shared mobility platforms among respondents. For short distances, walking/cycling emerges as the overwhelmingly preferred mode (57.2%), followed by public transportation (15.7%) and own vehicles (16.0%), while ride-hailing services and carpooling are less favored options. Conversely, for long distances, ride-hailing services are most preferred (33.0%), with public transportation (31.4%) and own vehicles (28.0%) close behind, indicating a shift towards on-demand services for extended travel. Social media sites (48.1%) and YouTube advertisements (27.7%) are the primary sources of information about shared mobility platforms, followed by TV commercials (15.7%), whereas newspapers and magazines (5.0%) and billboards/hoardings (3.5%) have relatively lower reach, emphasizing the dominance of digital channels in promoting such services. Furthermore, Table 2 shows that for short distances, the majority (57.2%) of the respondents prefer to walk/cycle, and for long distances, a majority (33%) of the respondents prefer to use ride-hailing services. When it comes to the sources from which they hear about shared mobility platforms, social media sites are most prominent (48.1%), but this may be attributed to the fact that the respondents belong to Generation Z, a generation of digital natives. This information can be used by existing platforms and new entrants to lay out marketing strategies and to determine their market positioning.

3.2. Measures

Twenty-four measurement items from existing literature were used in the survey for this study. To ensure that the indicators were appropriate for a study on shared mobility in this context, A few minor phrasal changes were made. A five-point Likert scale was utilized for the survey, ranging from “Strongly Agree” (1) to “Strongly Disagree” (5). The constructs shown in

Table 3. Construct, construct items, and references.

Construct	Construct Items		Reference
Environmental Consciousness (EC)	EC1	Shared mobility contributes to reduce the level of pollution.	[13,57]
	EC2	Shared mobility contributes to reduce the level of traffic in my city.	[13,57]
	EC3	Shared mobility makes me feel like a responsible traveller from an environmental viewpoint.	[13]
	EC4	Using a shared vehicle is a sustainable mode of consumption.	[15,58]
	EC5	Using a shared vehicle reduces the consumption of natural resources.	[15,49]
Social Aspects (SA)	SA1	Shared mobility allows those who do not own a private vehicle to always have one at hand.	[10,13]
	SA2	Shared mobility provides greater security than public transport.	[13]
	SA3	Shared mobility improves the quality of travel compared to public transport (waiting times, availability, crowding).	[13]
	SA4	I feel good when I share resources and avoid overconsumption.	[15,59]
Economic Benefits (EB)	EB1	Shared mobility allows for saving money compared to the ownership of a private vehicle.	[13,60,61,62]
	EB2	Shared mobility allows for access to vehicles that otherwise could not be driven.	[10,13]
	EB3	Shared mobility allows for saving time with easy payment.	[13]
	EB4	I use vehicle sharing because I only pay for the usage time.	[15]
	EB5	I appreciate not having to worry about filling the tank in the car.	[15]
	EB6	I appreciate using the shared vehicle and not having to worry about parking spaces or parking.	[15,49,63]
Perceived Risks (PR)	PR1	Using a shared mobility service can be complicated.	[13]
	PR2	Vehicles from a shared mobility platform are not always easily available.	[13]
	PR3	The territorial coverage of a shared mobility service is not widespread.	[13]
	PR4	There’s a risk that I will not be able to get the vehicle that I want at the time I want to use it.	[49]
	PR5	I’m afraid of not being able to familiarize myself with the controls of different vehicles every time I use them. (Reversed)	[15,49]
Intention (IN)	IN1	I plan to use shared mobility platforms in the future.	[39,64]
	IN2	I intend to continue using shared mobility platforms in the future.	[39,64]
	IN3	I would be likely to choose a shared mobility platform instead of buying a vehicle myself.	[49]
	IN4	I would prefer a sharing option to owning my own vehicle.	[49]

—environmental consciousness, social aspects, and economic benefits—together form Generation Z’s attitude towards shared mobility platforms.

Table 4. Construct items and corresponding responses.

Construct Item	Mean	Percentage (%)
		Strongly Agree	Agree	Neutral	Disagree	Strongly Disagree
		1	2	3	4	5
EC1	1.80	42.1	39.6	14.8	2.8	0.6
EC2	1.92	39.3	37.1	16.4	6.9	0.3
EC3	2.02	33.0	40.9	18.2	6.9	0.9
EC4	1.89	38.1	41.5	15.1	4.4	0.9
EC5	1.90	40.9	35.5	17.3	5.0	1.3
SA1	1.97	32.7	45.3	15.4	6.0	0.6
SA2	2.46	19.5	32.7	32.7	12.9	2.2
SA3	2.07	26.1	45.9	23.0	4.7	0.3
SA4	2.06	28.0	44.0	23.0	4.4	0.6
EB1	2.02	33.3	41.8	15.7	7.9	1.3
EB2	2.25	19.5	44.3	29.2	6.0	0.9
EB3	2.12	27.0	41.5	25.2	5.3	0.9
EB4	2.33	18.2	41.2	31.1	8.5	0.9
EB5	2.10	27.7	42.8	23.0	5.3	1.3
EB6	1.93	34.6	43.7	17.3	2.5	1.9
PR1	2.54	17.3	34.0	29.6	15.7	3.5
PR2	2.31	20.4	44.3	22.0	9.7	3.5
PR3	2.16	25.5	43.1	22.3	8.2	0.9
PR4	2.18	27.7	42.1	20.1	5.0	5.0
PR5	2.28	23.3	38.7	27.7	7.9	2.5
IN1	2.09	25.2	45.3	25.5	3.5	0.6
IN2	2.30	18.6	41.5	32.1	7.2	0.6
IN3	2.58	18.2	31.1	28.9	17.3	4.4
IN4	2.64	14.5	33.0	31.8	15.4	5.3

indicates the distribution of respondents’ agreement with the construct items on five-point Likert scales. The mean value is also shown to determine the range in which majority of the responses are concentrated.

3.3. Procedure

To perform the model assessment, Smart PLS 4.0 [65,66] was used. The analysis was conducted using partial least square structural equation modeling (PLS-SEM). This has gained popularity because of its value in the analysis of data sets with non-normal distribution that are comparatively small [67]. In the fields of travel and tourism, a number of influential works have used this technique, and it has been frequently used to analyze data regarding intention to purchase [68,69].

A two-phase analysis is performed in PLS-SEM. This involves measurement model specification and also structural model assessment [70]. The measurement model is used to analyze the loading of its constructs, composite reliability (CR), convergent, and discriminant validity, and then, the structural model assessment evaluates the significance of the relationships along with their path coefficients.

4. Results

4.1. Measurement Model Assessment

To assess the reliability and validity of the measurement model in this study, the guidelines provided by [71,72] were followed. All 24 indicators were retained without any deletion based on the factor loadings of the latent variables being above 0.60.

Table 5. Reliability and validity of the measurement model.

	Λ	α	CR	AVE	VIF
Generation Z Attitude
Economic Benefits		0.843	0.884	0.561
EB1	0.746				1.629
EB2	0.759				1.764
EB3	0.754				1.691
EB4	0.721				1.575
EB5	0.78				1.915
EB6	0.731				1.768
Environmental Consciousness		0.873	0.908	0.664
EC1	0.785				1.857
EC2	0.856				2.338
EC3	0.813				1.984
EC4	0.749				1.85
EC5	0.866
Social Aspects		0.737	0.835	0.558
SA1	0.716				1.341
SA2	0.763				1.45
SA3	0.757				1.55
SA4	0.751				1.385
Perceived Risks		0.776	0.844	0.519
PR1	0.72				1.63
PR2	0.726				1.574
PR3	0.785				1.628
PR4	0.679				1.595
PR5	0.688				1.227
Intention		0.821	0.881	0.649
IN1	0.816				1.769
IN2	0.818				1.788
IN3	0.805				1.972
IN4	0.782				1.98

highlights the factor loadings, Cronbach’s Alpha (reliability statistics), CR (composite reliability), AVE (Average Variance Extracted) and VIF (Variance Inflation Factor) of the corresponding indicators.

The factor loadings and α of all indicators are more than the threshold values of 0.5 and 0.7, respectively. Additionally, the corresponding AVE and CR values of the latent variables are above 0.5 and 0.7, respectively. These values indicate both reliability and convergent validity. Further, the establishment of discriminant validity, shown in

Table 6. Discriminant validity (Fornell and Larcker criterion).

	Mean	EB	EC	IN	PR	SA
EB	2.123	0.749
EC	1.906	0.426	0.815
IN	2.404	0.475	0.378	0.805
PR	2.294	0.271	0.199	0.255	0.721
SA	2.138	0.576	0.398	0.436	0.156	0.747

EB—economic benefits; EC—environmental consciousness; SA—social aspects; PR—perceived risks; IN—intention.

, was based on the approach of [73]. In the reflective model, such as the model conceptualized in this study, it is believed that higher values for VIF (greater than 1) are deemed to be good since they ensure that indicators correlate adequately [74]. Based on the same argument, all indicators were found to be highly correlated.

4.2. Structural Model Assessment

The results revealed an R² value of 0.305 for IN. This provides support for the model’s in-sample predictive power [75] because it exceeds the required level of 0.10. However, the R² value for GA was only 0.065, suggesting a weak explanation of variance in GA by PR, as suggested by [76]. However, [77] justifies this low value by noting that “if you are in consumer behavior and want to explain some real behavior by some specific intervention (e.g., treatment vs. no treatment) it is rather common to expect R² in the range of 10–20% or even less”. In this study, GA was predicted by PR, and IN was predicted by GA and PR. The relative effect sizes (f²) of the predicting (exogenous) constructs were calculated and show that the exogenous variable has a medium-large effect on the endogenous variable (0.15 < f² > 0.35) [76] (see

Table 7. Results of structural model path coefficient (direct relationships).

Hypotheses	Relationship	β	SD	t-Value	p Values	Decision
H1	GA → IN	0.505	0.053	9.449	0	Supported
H2	PR → GA	0.255	0.066	3.887	0	Supported
H3	PR → IN	0.131	0.064	2.06	0.039	Supported
GA	R2 = 0.065	GA → IN	f2 = 0.343
IN	R2 = 0.305	PR → GA	f2 = 0.070
		PR → IN	f2 = 0.023

Note: PR—perceived risks; IN—intention; GA—Generation Z’s attitude. p < 0.05.

).

The Bootstrap method was used for re-sampling, using 5000 re-samples to determine the significance of direct paths, as well as to estimate standard errors (Becker et al., 2023). Table 7 shows that there is a significant positive effect of GA on IN (β = 0.505, t = 9.449, p < 0.05). Therefore, H1 is supported. Similarly, there is a significant positive and direct effect of PR on GA (β = 0.255, t = 3.887, p < 0.05) and PR on IN (β = 0.131, t = 2.06, p < 0.05). These results support H2 and H3.

4.3. Mediation Analysis

H4 hypothesizes that GA mediates the relationship between PR and IN. We see a significant effect of PR on IN (β = 0.26, t = 4.014, p < 0.05). When the mediator was introduced, the direct effect remained both positive and significant (β = 0.131, t = 2.06, p < 0.05), while the indirect effect with the inclusion of the mediator into the analysis was also found significant (β = 0.129, t = 3.555, p < 0.05). In line with this analysis regarding H4, the results demonstrate a partial mediating role of GA in the linkage between PR and IN. Therefore, H4 is accepted. This mediation analysis is shown in

Table 8. Mediation analysis.

	Total Effects			Direct Effects			Indirect Effects
	β	t-Value	p Value	β	t-Value	p Value	Hypotheses	β	t-Value	p Value
PR → IN	0.26	4.014	0.00	0.131	2.06	0.039	H4: PR → GA → IN	0.129	3.555	0.00

.

5. Discussion and Implications

Using a sample of young Indian consumers, this research analyzed the impact of environmental consciousness, economic benefits, social aspects, and perceived risks on Generation Z’s intention to use shared mobility platforms and the indirect effect of perceived risks on intention with the mediation effect of attitude towards shared mobility platforms. In this section, the results of this analysis are discussed and placed into the context of previous studies on the factors affecting share mobility consumption, which were reviewed above.

First, results indicated that environmental consciousness, economic benefits, social aspects, and perceived risks have a significant impact on Generation Z’s intention to use shared mobility platforms in India. This is consistent with the results of the existing literature [12,13,15,41,49,62,78,79].

Second, it was found that Generation Z’s attitude towards shared mobility platforms (which is formed by combining the constructs of environmental consciousness, social aspects, and economic benefits) has a significant effect on their intention to use such platforms. This is in line with the results of the study by [13], which showed that attitude does affect respondents’ future intention to re-use free-floating carsharing services.

Third, analysis of the primary data also revealed that perceived risks have a significant impact on attitude, indicating that Generation Z, despite their familiarity with platform-based shared mobility services, share similar concerns to the wider shared mobility customer base [14]. Fourth, results show that perceived risks have a significant effect on intention. Lastly, this study shows the existence of an indirect effect of perceived risks on the intention to use shared mobility platforms, which is mediated by Generation Z’s attitude towards them. Thus, all the four proposed hypotheses hold.

5.1. Theoretical Implications

It is evident from previous research that Generation Z is well-suited to participate in the sharing economy, given their extensive experience with technology and heightened awareness of sustainability issues [19,20,22]. However, their involvement with shared mobility platforms has not been extensively studied. Similarly, existing works predominantly focus on analyzing the factors that influence consumers’ intention to adopt shared mobility but have not focused on specific age groups.

Therefore, the current study has attempted to bridge these gaps by analyzing the factors that affect Generation Z’s intention to use shared mobility platforms in India. A noteworthy finding here is the partial mediation effect of attitude between perceived risks and intention. A key contribution of this research is the novel conceptual model that was presented in Figure 1 and tested in this study. Developing a new model and testing it was PLS-SEM means that new insights have been uncovered that would not have been apparent when applying previous models, where the relationship between their concepts is already established. The results of this study can be used by researchers to further research into shared mobility using this novel model.

Results also indicate that Generation Z is willing to substitute the ownership of a vehicle through the adoption of shared mobility, with 49.3% and 47.5% of respondents agreeing that they would prefer shared mobility over buying or owning their own vehicle, respectively. This suggests that Generation Z places a higher value on flexibility and convenience and is willing to trade off the traditional benefits of vehicle ownership (e.g., control and convenience) for the advantages of shared mobility (e.g., cost-effectiveness and sustainability). The willingness of Generation Z to embrace shared mobility is also in line with wider trends towards sustainable and environmentally conscious consumption, which may indicate that shared mobility is becoming increasingly mainstream and culturally acceptable.

5.2. Practical Implications

Based on the findings of this study, shared mobility businesses can implement the following strategies to increase Generation Z’s intention to use shared mobility platforms:

• Reduce perceived risks—Shared mobility businesses should focus on addressing the concerns and fears of Generation Z related to using shared mobility platforms. This can be undertaken by expanding territorial coverage and ensuring the availability of services at all times;
• Promote environmental consciousness—Shared mobility businesses should communicate the environmental benefits of their platforms and further the expansion of their electrical fleets to strengthen their commitment to reducing emissions;
• Highlight economic benefits—Shared mobility businesses can highlight the cost savings associated with using shared mobility platforms, such as fuel, parking, and maintenance charges;
• Emphasize positive social aspects—Shared mobility businesses should communicate the social benefits of using shared mobility platforms, such as increased flexibility and convenience.

As of 2021, ten of the world’s fifteen most polluted cities in terms of air quality are in India [80]. A major reason behind this is the sheer volume of vehicle emissions, which, when controlled, can decrease the severity of air pollution. Shared mobility, particularly carsharing, can result in a more optimal utilization of resources and thereby reduce congestion and emissions. Government officials, economists, policymakers, and other concerned authorities can use this study’s findings to promote the adoption of shared mobility by the public (particularly Generation Z) through financial incentives and subsidies, dedicated infrastructure, and regulatory policies. These could include tax breaks and dedicated lanes for carsharing, subsidies for shared mobility services, charging stations for autonomous sharing vehicles, and congestion charges on single-occupancy vehicles.

6. Conclusions and Limitations

The research analyzed the impact of environmental consciousness, economic benefits, social aspects, and perceived risks on Generation Z’s intention to use shared mobility platforms in India. The results showed that all four factors, along with Generation Z’s attitude towards the platforms (formed by environmental consciousness, social aspects, and economic benefits), have a significant impact on their intention. The analysis also found that perceived risks have a significant impact on both attitude and intention and that there is an indirect effect of perceived risks on intention, which is mediated by attitude.

Despite its merits, this study has certain limitations. First, convenience sampling (which is a non-probability sampling technique) was used for data collection. Although it is less time-consuming, it does not produce a sample that is fully representative of the population, unlike probability sampling techniques. Because of this, it is recommended that future researchers conduct studies across multiple cities to increase the available and achievable sample size. Increasing the sample size in this way will also allow for further statistical analysis to be carried out that can move beyond this exploratory testing of a new model using SEM.

As well as using multiple sites, it is important to acknowledge the impact of settlement size on the availability and impacts of shared mobility, along with the perceptions associated with these by Generation Z. In larger cities, where short trips by taxi and public transport are already more common, shared mobility use may be viewed as more practical and impactful by consumers. Further research is needed to evaluate the attitudes and intentions of Generation Z consumers in smaller settlements and in non-urban areas where these services are less common.

This study was focused on Generation Z in India. This is an important consumer market, but research into Generation Z suggests that there may be contextual differences between national cohort groups within this generation despite the apparent similarities between them. Future research can be carried out with Generation Z consumers in other markets in developing and developed country contexts in order to provide a more comprehensive understanding of the intentions of this vital segment regarding shared mobility.

Additional insights can also be gained into Generation Z’s attitudes and intentions using qualitative methods. These can provide additional insights, especially regarding cultural and social issues, making use of innovative methods, including ethnographies and netnographies of shared mobility use.