Exploring Consumers’ Intention to Use Bikes and E-Scooters during the COVID-19 Pandemic in the Philippines: An Extended Theory of Planned Behavior Approach with a Consideration of Pro-Environmental Identity

Abstract

The onset of the COVID-19 pandemic has prompted nations globally to adopt lockdown policies, resulting in a substantial shift in people’s travel behavior. This pandemic has influenced micro-mobility, an emerging urban transportation mode, including in the Philippines. However, a limited quantifiable study is dedicated to understanding the evolving micro-mobility use in response to the COVID-19 pandemic. This study aims to determine the impacts of the COVID-19 pandemic on our future intention to use micro-mobility, particularly e-scooters/bikes, and to explore how hedonic and environmental motivations influence the decision-making process of its adoption. An extended Theory of Planned Behavior (TPB) was employed, with 152 Filipinos answering an online questionnaire that was distributed using a convenience sampling approach. Structural Equation Modeling (SEM) showed that the COVID-19 pandemic emphasized the awareness of environmental issues and the negative impact of cars, thereby significantly influencing the usage intention of e-scooters/bikes. In addition, social norms and perceived behavioral control partially mediated the relationship between the COVID-19 pandemic and the intention to use micro-mobility. Meanwhile, the magnitude and significance of motivation variables were inconclusive, although they confirmed a positive relationship with attitude. The findings of this study could help provider firms and policymakers devise evidence-based policies concerning micro-mobility and promote its usage, particularly in developing countries.

1. Introduction

In Metro Manila, Philippines, public transport accounts for approximately 49 percent. Several public transports such as jeepneys, buses, and other utility vehicles account for most land transportation trips, while private modes and walking account for approximately 20 percent and 30 percent [1,2,3]. Due to COVID-19’s limitations imposed on public transportation and the resultant reduction in capacity, many non-essential trips were either avoided or suspended. Even for long-distance travel, walking or cycling was frequently the only option for traveling. Moreover, healthcare professionals who had to travel interminable distances and hours to work were greatly affected [4]. Indeed, the high risk of exposure to the virus on public transit has led to a substantial decline in its use [5,6], particularly among those without access to private vehicles, which accounts for 88.5 percent of all households [2].

Even with the relaxation in public transportation, the worry of the “risk of infection” has affected people’s choice of transportation mode. Passengers regard public transportation as unsafe and a potential source of infection, which negatively impacts the transportation system [7]. Consequently, it ushered in a new mobility paradigm and revolutionized how people travel, work, and shop. Moreover, it switched attention to micro-mobility transportation [8]. Micro-mobility generally refers to shorter-distance travel encompassing small, lightweight vehicles (bicycles, electric bikes (e-bikes), e-scooters, and mopeds) designed for personal transport. They are primarily two-wheelers, either powered or manually operated. Potential advantages include a lower carbon footprint, less traffic congestion [9], and a workable solution to the first-last mile problems [10].

Micro-mobility has caused substantial changes in mobility patterns in many places [11], along with complicated and dynamic adjustments in commuting and mode of transportation selection habits. As a result, policymakers are pushed to accommodate the said development. On the other hand, the changes in travel behavior gradually facilitate an environmentally beneficial transportation system. The utilization may result in healthier and more sustainable cities, with more excellent room for active modes of transportation [12], making walking, scooting, and cycling popular [13]. Moreover, recent studies show changes in travel behaviors as a result of COVID-19, including how people tend to shift from shared travel modes to individual ones as a precautionary measure, even in developing countries [14,15,16,17]. However, adopting or continuing the use of these vehicles relies on the individual’s perception of them. To successfully encourage its use, it is necessary to further investigate this development to appreciate its benefits in terms of environmental concerns and context-specific factors in an individual’s decision-making process.

Despite several papers published on adopting micro-mobility vehicles (MMV) in different countries, there is limited quantifiable evidence that studies the behavioral intention behind its usage in the Philippines, particularly following the COVID-19 pandemic crisis. The research on consumers’ preferences toward the usage intention of MMV in the future is underdeveloped. Thus, it is necessary to measure users’ perceived behavior toward MMV, the benefits associated with these modes of transportation, and whether the pandemic affected their behavioral intention.

This study aims to determine the impacts of the COVID-19 pandemic on our future intention to use micro-mobility, particularly e-scooters/bikes, and to explore how hedonic and environmental motivations influence the decision-making process of its adoption. A Theory of Planned Behavior (TPB) was utilized by extending the COVID-19 effects, including environmental consciousness, into the model. Moreover, the current research investigates e-scooters used primarily for hedonistic activities connected to recreation and leisure [18].

The current study might contribute to numerous theoretical and practical implications. First, it contributes to understanding the relationship between environmental and hedonic motivation in deploying MMV or other green transport innovations. Although several scholars have researched hedonic motivation, attitudes and intentions have received greater attention than actual behavior [19,20,21,22]. Rezvani et al. [23] state that embracing new transportation options requires emotional acceptance and rational consideration. Second, this research contributes to identifying the impact of consumer identity and personal traits on innovative quality attributes. Moreover, this paper offers insight into how far consumers who identify as environmentally conscious may see the benefits of green transportation solutions.

2. Conceptual Framework and Hypotheses Development

2.1. Theory of Planned Behavior (TPB)

TPB has been used for exploring consumer behavior in a variety of disciplines and is seen as a powerful model for explaining pro-environmental behavior [24,25,26]. The model primarily incorporates three factors: attitude toward behavior (ATT), which indicates what a person believes and observes; perceived behavioral control (PBC), which means the perceived ease of use or difficulty of engaging in behavior; and social norms (SN), which indicates the social pressure to perform the behavioral intention (BI). The application of TPB has also been used in transportation and COVID-19-related studies. A study conducted by Baig et al. [27] revealed that the perceived threats of COVID-19 influenced the attitudes and subjective norms of the people toward their intention to use public transportation in Greece. A similar scenario was discovered by Lee et al. [21], in which they stated that an individual’s attitude had a positive relationship with their intention to use public transportation during the COVID-19 pandemic. Both studies have indicated a clear relationship between the perceived effect of COVID-19 and the intention to use public transportation. Overall, the studies show that TPB can facilitate the prediction of user intentions toward a form of transportation. Moreover, Neto et al. [28] mentioned that TPB approach is recommended for understanding the selection of different transportation options.

Ajzen [29] stated that models may be extended to incorporate more variables, allowing for a more exact description of the desired behavior. Similarly, the TPB’s ability to predict behavior has been enhanced by the addition of new factors. To better understand the intent to use e-scooters and e-bikes, this research considered the important aforementioned aspects and thus, added the COVID-19 effect (COV) into the TPB model. With that, the following hypotheses are proposed.

H1: 

ATT positively affects BI.

H2: 

SN positively affects BI.

H3: 

PBC positively affects BI.

H4: 

COV has significant positive relationship to BI.

H5: 

COV has significant positive relationship to ATT.

H6: 

COV has significant positive relationship to SN.

H7: 

COV has significant positive relationship to PBC.

2.2. Hedonic Motivations (POS)

According to Venkatesh et al. [30], the degree of pleasure and delight people acquire from new products can impact their intention to use them. This POS encourages people to spend a longer time with the product, thus increasing their knowledge of its benefits. In terms of green transportation innovation, the novelty of micro-mobility, particularly e-scooters, has been demonstrated to exhibit hedonic experiences associated with leisure and pleasure for short distance movement. Kopplin et al. [31] verified the influence of POS on BI and found that most people view e-scooters as a fun object, which increases people’s willingness to use them. Similarly, Fitzmaurice [32] recommended exemplifying the positive emotions that individuals may experience to encourage the adoption of this new behavior. Given the findings, the current study examined the effect of POS on the usage intention of e-scooter and e-bikes, and hypothesized that POS impacts BI and may influence BI indirectly through attitude.

H8: 

POS positively affects BI.

H9: 

POS positively affects ATT.

2.3. Environmental Motivation (ENV) and Pro-Environmental Identity (PID)

Aside from hedonic and cognition factors, research has demonstrated that consumer identity and personal characteristics influence decision-making. In the context of green innovation, it is worthwhile to investigate consumer innovativeness and green identity [33]. Moreover, consumer researchers have long recognized that individuals consume products in ways that are consistent with their self-concept [34]. In fact, there is a vast interest in studies concerning the consumption of products to preserve and promote one’s sense of self [35]. Consequently, Whitmarsh and O’Neill [36] proposed that consumers may be dissuaded from using or purchasing certain products that do not correspond with their self-perception.

In studies regarding sustainable behavior, Dermpdy et al. [37] elucidated pro-environmental or green identity as a strong indicator for the actual adoption or intent to adopt and use sustainable products. This means that individuals who exhibit or identify themselves as pro-environmental have favorable attitudes toward products that have less of a negative impact on the environment. In the context of alternative transport vehicles, viz., micro-mobility and electric cars, pro-environmental identity positively impacts the green impressions of vehicles [20,38]. Similarly, Griskevicius et al. [39] concluded that the environmental and social innovative value of electric vehicles reflects the environmentalist identities of consumers in a substantial manner. Indeed, these findings support the notion that consumers’ perception of green products hinged on their sense of self, and because micro-mobility is considered a sustainable mode of transport, individuals who ascertain themselves as “green” should have a higher possibility of perceiving micro-vehicles as green. Therefore, this study hypothesizes that:

H10: 

PID has positive relationship with ENV in e-scooters/bikes.

2.4. Consumer Innovativeness (INN)

Consumer innovativeness refers to the propensity of an individual to adopt unconventional lifestyles more frequently than the average member of his or her society [19,40], which is considered an essential personality feature. As a core view of diffusion theory [41], consumer innovativeness involves the need for stimulation and uniqueness, and hence can be regarded as a motivational factor for predicting the intention to adopt innovations. Moreover, innovativeness consists of three stages: knowledge, persuasion, and decision, which result in a change in the psychological process driven by motivation factors among individuals. This means that people with higher innovativeness have a greater desire to interact with innovation, especially innovations that fall within their specific domain of interest [19], and are correspondingly more inclined to seek information in an effort to gain an understanding of the product or services [42].

Past research on innovation adoption (Rogers and Shoemaker [40]) states that consumers have different levels of innovativeness (innovators, early adopters, early majority, late majority, and laggards), which exerts an asymmetrical psychological disposition on the behavioral adoption of innovations (e.g., Ong et al. [43] and Heidenreich and Handrich [44] revealed that innovators and early adopters show higher consumer innovativeness), indicating a positive effect on the intention to adopt.

Furthermore, innovativeness can also be correlated to hedonic motivations. From the perspective of stimulation, sustainable products contain hedonic attributes, such that consumers require additional emotional experience to purchase and use those products. Because such emotional experiences improve the connection between consumers and other individuals, consumers with greater hedonist innovativeness may view the purchasing of sustainable products as a highly socially desirable activity. Similarly, innovators can focus a disproportionate amount of emphasis on the emotional benefits of the innovation, so examining their degree of innovativeness could help explain their likelihood of feeling positive emotions during product use [45,46]. Based on these findings, this paper hypothesizes that:

H11: 

INN has positive relationship with POS on the use of e-scooters/bikes.

Aside from the effect of consumer innovativeness on emotions, pro-environmental identity also affects POS, albeit indirectly through environmental motivations. This assertion is consistent with prior research, indicating that consumers of sustainable products have a favorable effect due to their understanding of the products’ environmental advantages and the products’ compatibility with their personal beliefs [47]. Therefore, this study hypothesizes that:

H12: 

ENV has positive relationship with POS.

2.5. Model Route

Figure 1 depicts a conceptual model that synthesizes the theoretical foundations of the variables mentioned in the study. The framework is inspired by Lucarelli et al. [48], which utilizes the factors derived from TPB as mediators for the COVID-19 effect and usage intention of e-scooters/bikes. The model also incorporates POS and ENV, which provides a unique viewpoint on the antecedents of TPB-related variables and an in-depth understanding of the decision-making mechanisms underlying the uptake of green innovation.

3. Methodology

3.1. Data Collection

The present study deployed an online survey designed to test the model and collect data to assess the factors influencing their usage intention. This was accomplished using various online forums, viz., Instagram, LinkedIn, and Facebook. A chain sampling technique was also utilized to generate a pool of participants. To ensure each response’s validity, continuous checking was performed. In total, 152 respondents completed the questionnaire.

The questionnaire comprised four parts. The first part included a brief description of what micro-mobility is. This was trailed by a test question to gauge the understanding of the participant, and at the same time, to ensure the validity of the response and an optional equation for rating the current mood. The second part aimed to identify the socio-demographic characteristics of the participant, which includes gender, age, region, professional status, income, and driver’s license (

Table 1. Construct questionnaires.

Construct	Questions	References
Perceived Behavioral Control (PBC)	During the pandemic…	[27,49]
PBC1	Using MMV is entirely up to me
PBC2	I can use MMV to fulfill my transportation needs
PBC3	I believe the use of MMV would be easy for me
PBC4	My freedom using MMV is high
PBC5	I am confident in the reliability of MMV
Subjective Norms (SN)	During the pandemic…	[49]
SN1	most people who are important to me think that I should use MMV
SN2	most people who are important to me approve my usage of MMV
SN3	my colleagues support me using MMV
SN4	public opinions affect my choice to use MMV
Attitude (ATT)	During the pandemic, I feel using MMV…	[50]
ATT1	is a great idea
ATT2	is safe
ATT3	is convenient
ATT4	will benefit our society
COVID-19 effects (COV)	For me COVID-19 has changed my…	[50]
COV1	Perception of MMV use
COV2	Awareness of negative impact caused by motorized vehicles use
COV3	Awareness of environmental issues
COV4	Awareness of climate change issues
COV5	Travel behavior especially related to MMV use
Behavioral Intention (BI)	After the pandemic…	[51]
BI1	I intend to use MMV
BI2	I recommend others to use MMV
BI3	I predict that our society will predominantly support the use of MMV
	After the pandemic, how interested are you to use for…
BI4	Sport
BI5	Time-constrained activities: going to work, campus, or meeting
BI6	Time-unconstrained activities: leisure, social, and shopping activities
BI7	First-last mile problem: going to/from train/bus station
Hedonic Motivation (POS)	Using MMV is…	[51]
POS1	Entertaining
POS2	Fun
POS3	Makes me enjoy life
Environmental Motivations (ENV)	Renting or using MMV, in general would…	[51]
ENV1	help reduce environmental problems caused by motorized vehicle traffic
ENV2	reduce climate problems caused by car motorized vehicle traffic
ENV3	help reduce air pollution caused by motorized vehicle traffic
ENV4	reduce society’s dependence on fossil fuel
Pro-environmental Identity (PID)	It is important for me to…	[51]
PID1	protect the environment
PID2	prevent environmental pollution
PID3	respect nature
PID4	live in harmony with nature

). The breakdown of the socio-demographics of the participants are reflected in

Table 2. Breakdown of sociodemographic.

	Sub-Category	Percentage (%)
Gender	Male	59.9
	Female	40.1
Age	below 18	0.70
	18–24	25.4
	25–34	35.9
	35–44	19.7
	45–54	11.3
	above 54	7.0
Region	NCR	23.9
	Cagayan Valley	21.1
	CALABARZON	22.5
	Central Luzon	19.0
	Others	13.5
Professional Status	Student	12.0
	Employed	84.5
	Unemployed	3.50
Income (PHP/mo.)	below 12,000	10.6
	12,000–24,000	24.6
	24,000–48,000	26.1
	48,000–84,000	6.30
	84,000–144,000	16.2
	144,000–240,000	13.4
	Above 240,000	2.80
Driver’s license	Yes	68.3
	No	31.7

. The third part involved constructing items linked to assess the behavioral intention to use e-scooters/bikes.

The distribution of the sample in terms of gender indicated that most respondents were male (59.9%). About 81% of participants were below 44 years old, and 68% claimed to have a driver’s license. This suggests that most respondents may have driven at least once, although additional information is required for the claim. Moreover, the majority of the respondents are employed (84.5%), and approximately half have an income between 12,000 to 48,000 (in Philippine peso). Meanwhile, over 80% of the participants reside in Metro Manila, Calabarzon, Cagayan Valley, and Central Luzon.

3.2. Measure

The study is interested in four constructs: environmental motivation (ENV), hedonic motivation (POS), pro-environmental identity (PID), and COVID-19 effect (COV), apart from the original TPB constructs. The questionnaires for HED, PID, and ENV were adopted in the research of Bouman et al. [51] and Noppers et al. [42], while COV was based on the study of [50]. Each set of items was evaluated using a five-point Likert scale ranging from one (strongly disagree) to five (strongly agree). Items reflecting the constructs’ attributes were summarized in Table 1. The last part assessed the degree of innovativeness of participants, similar to those used by Noppers et al. [42]. Each participant was asked to choose a characterization from the five adopter segments: innovators, early adopters, early majority, late majority, and traditionalists, which he/she thought they were most aligned with.

Table 3. The results of innovativeness segmentation.

Adopter Segment	Percentage
Innovator	16.1
Early adopter	18.9
Early majority	44.8
Late majority	15.4
Traditionalist	4.8

summarized the segmentation of the participants.

In the study, approximately 80% of participants tended to view themselves as innovators, early adopters, or early majority. This descriptive result follows the trend [41] presented, stating that only 2.5%, 13.5%, and 34% must be part of innovators, early adopters, and early majority, respectively. It depicts that self-classified adopters are either early users or have a higher chance to use innovative products in the transport domain.

To test the hypotheses mentioned, this study implemented the Confirmatory Factor Analysis and Partial Least Square Structural Equation Modelling (PLS-SEM) technique using SmartPLS 3 software. The Confirmatory Factor Analysis (CFA) was initially conducted to verify the validity of the observed variables (items) and their underlying latent constructs [52]. Items with factor loading (FL) below 0.5 must be omitted from the latent construct. Likewise, a Composite reliability (CR) value over 0.7 was observed to further confirm the internal consistency of the model’s construct. In management research, PLS-SEM is a well-supported technique meant for estimating the causal relationship among latent variables [53].

4. Results

4.1. Descriptive Analysis

The mean and standard deviations of each item of the eight construct variables are shown in

Table 4. Descriptive statistics, item loadings, reliability, and validity results.

Constructs	Items	Mean (μ)	St.Dev (o)	FL	CR	AVE
Behavioral Intention (BI)	BI1	3.741	0.676	0.806	0.879	0.517
	BI2	3.909	0.721	0.787
	BI3	3.748	0.755	0.733
	BI4	3.860	0.736	0.423
	BI5	3.678	0.680	0.757
	BI6	3.762	0.636	0.738
	BI7	3.629	0.699	0.718
COVID-19 Effect (COV)	COV1	3.881	0.525	0.849	0.936	0.747
	COV2	4.035	0.514	0.854
	COV3	4.154	0.518	0.885
	COV4	4.154	0.545	0.887
	COV5	4.035	0.743	0.846
Perceived behavioral control (PBC)	PBC1	4.147	0.996	0.679	0.898	0.640
	PBC2	3.860	0.753	0.794
	PBC3	3.790	0.720	0.874
	PBC4	3.790	0.760	0.808
	PBC5	3.727	0.877	0.830
Social norms (SN)	SN1	3.329	0.948	0.914	0.910	0.718
	SN2	3.448	0.941	0.888
	SN3	3.476	0.882	0.852
	SN4	3.329	0.910	0.722
Attitude (ATT)	ATT1	4.273	0.845	0.851	0.882	0.652
	ATT2	3.573	0.795	0.726
	ATT3	4.063	0.963	0.801
	ATT4	4.210	0.870	0.846
Hedonic motivation (POS)	POS1	3.979	0.731	0.925	0.938	0.834
	POS2	4.126	0.818	0.901
	POS3	4.007	0.869	0.914
Environmental motivation (ENV)	ENV1	4.427	0.969	0.876	0.951	0.829
	ENV2	4.490	0.903	0.942
	ENV3	4.545	0.893	0.924
	ENV4	4.455	0.707	0.898
Pro-environmental Identity (PID)	PID1	4.678	0.716	0.957	0.972	0.898
	PID2	4.671	0.735	0.963
	PID3	4.699	0.772	0.926
	PID4	4.657	0.706	0.943

. The responses revealed several significant patterns. The intention to use e-scooters/bikes after the pandemic was dominated by sports (μBI4 = 3.860) and time-unconstrained activities (μBI6 = 3.762), such as leisure and shopping. Moreover, most respondents agreed to recommend using MMV (μBI2 = 3.909) after the pandemic. Most also tended to agree or strongly agree that using e-scooters/bikes during a pandemic is a great idea (μATT1 = 4.273) and benefits society (μATT2 = 4.210), but tended to stay neutral in terms of safety (μATT2 = 3.573). Meanwhile, most participants thought COVID-19 effects had changed their perception of MMV (μCOV1 = 3.881) and travel behavior (μCOV5 = 4.035), their awareness of environmental and climate change issues (μCOV3,4 = 4.154), and the negative impact caused by motorized vehicles (μCOV2 = 4.35). Regarding the hedonic motivation, they likewise consider using MMV as entertaining (μPOS1 = 3.979), fun (μPOS2 = 4.126), and enjoyable (μPOS3 = 4.007). As for pro-environmental identity, most respondents consider themselves green based on the mean values.

4.2. Measurement Model

Table 4 also shows the factor loadings for all items within each latent construct. All factor loadings, except for two factors (i.e., the fourth discriminant for behavioral intention and the first determinant for perceived behavioral control), are higher than the suggested value of 0.7 [54]. Internal consistency metrics are examined to determine whether to retain the two factors with loadings of 0.423 and 0.679 in the model. Based on Table 4, the Composite Reliability for the overall constructs obtained values above the acceptable threshold of 0.70. Moreover, the Average Variance Extracted (AVE) and CRs are all higher than 0.50 and 0.70 [54], respectively, for all items, which confirms convergent validity. For these reasons, the two factors were retained in the model.

The discriminant validity was also investigated through cross-loadings. Discriminant validity measures the distinction of latent constructs from each other. To validate this distinction, correlations between the same determinant evaluated with different constructs must be sufficiently substantial and more prominent than those between different determinants evaluated with either different or similar constructs [55]. The discriminant validity was also assessed through Fornell and Larcker. The results are reported in

Table 5. Discriminant validity using Fornell and Larcker.

	ATT	BI	COV	ENV	PBC	PID	POS	SN
ATT	0.880
BI	0.670	0.719
COV	0.568	0.691	0.864
ENV	0.587	0.435	0.475	0.910
PBC	0.700	0.699	0.489	0.343	0.800
PID	0.441	0.350	0.355	0.615	0.310	0.948
POS	0.525	0.580	0.391	0.450	0.468	0.387	0.913
SN	0.450	0.688	0.436	0.224	0.539	0.165	0.356	0.847

Note: Bold face indicates square root of AVE.

. From the table, discriminant validity has been established since the square root of AVE of each construct is higher than its correlation with another construct, signifying that each item loads better in its designated construct. Overall, the measurement model fits the data well.

4.3. Structural Model

The next step in the analysis was to investigate the hypothesized relationships presented in Section 2. The hypotheses’ statistical significance was assessed using a bootstrapping technique with 4999 subsamples. Figure 2 depicts the findings of the standard estimate (β), while

Table 6. Bootstraps validation results.

Hypothesis	Causal Relationship	β	SE	p-Value	Results
H1	ATT -> BI	−0.023	0.021	0.752	Rejected
H2	SN -> BI	0.337	0.017	0.000	Accepted
H3	PBC -> BI	0.298	0.020	0.000	Accepted
H4	COV -> BI	0.366	0.016	0.000	Accepted
H5	COV -> ATT	0.428	0.024	0.000	Accepted
H6	COV -> SN	0.437	0.019	0.000	Accepted
H7	COV -> PBC	0.489	0.021	0.000	Accepted
H8	POS -> BI	0.121	0.018	0.066	Rejected
H9	POS -> ATT	0.356	0.024	0.000	Accepted
H10	PID -> ENV	0.615	0.021	0.000	Accepted
H11	INN -> POS	0.088	0.022	0.272	Rejected
H12	ENV -> POS	0.326	0.021	0.000	Accepted

Note: β: Original path coefficient, SE: Standard error.

summarizes in detail the bootstrap results of the structural model. Meanwhile, the mediation analysis results are presented in

Table 7. Mediation effect analysis.

IV	MV	DV	Estimate	p-Values	Supported
COV	ATT	BI	−0.010	0.766	No
COV	SN	BI	0.147	0.000	Yes
COV	PBC	BI	0.145	0.000	Yes
COV	POS	BI	0.026	0.153	No
COV	POS, ATT	BI	−0.002	0.785	No
ENV	POS	BI	0.039	0.104	No
ENV	POS, ATT	BI	−0.003	0.766	No
PID	ENV, POS	BI	0.024	0.118	No
PID	ENV, POS, ATT	BI	−0.002	0.771	No
INN	POS	BI	0.011	0.343	No
INN	POS, ATT	BI	−0.001	0.825	No
POS	ATT	BI	−0.008	0.757	No

Note: IV: Independent variable, MV: Mediating variable, DV: Dependent variable.

and the model fit is presented in

Table 8. Model fit.

Goodness of Fit Measures the SEM	Parameter Estimates	Minimum Cut-Off	Recommended by
SRMR	0.072	<0.08	[53,54]
(Adjusted) Chi-square/Df	3.723	<5.0	[53,54]
Normal Fit Index (NFI)	0.843	>0.80	[53,54]

, respectively.

The three constructs that directly influenced the intention to use e-scooters/bikes are COVID-19 effects (β = 0.366, p < 0.001), social norms (β = 0.337, p < 0.001), and perceived behavioral control (β = 0.298, p < 0.001), in decreasing order of precedents. This suggests that H2, H3, and H4 are supported. Meanwhile, in H1 and H8, attitude (β = −0.023, p = 0.752) and positive emotions (β = 0.121, p < 0.066) are unsuccessful in establishing a significant effect on the intention to use e-scooters/bikes.

5. Discussion

In the wake of the pandemic, it is increasingly crucial to understand people’s inclination to switch to micro-mobility, and the strategies to increase their acceptance. This study aimed to assess the psychosocial factors influencing the intention to use e-scooters/bikes by integrating COVID-19′s effects on TPB. Consequently, this paper examined how hedonic and environmental motivations affect people’s decision to use e-scooters/bikes in the future.

The proposed framework’s endogenous variable obtained an explanatory power of 0.741. It means that the five latent constructs—perceived behavioral control, social norms, attitude, hedonic, and environmental motivations—accounting for 23.9%, 19.1%, 42.9%, 25%, and 37.8%, respectively, can explain 74.1% of the variance of intention or likelihood to use e-scooters/bikes. Furthermore, Table 5 indicates that while participants perceived e-scooters/bikes as enjoyable and fun, this did not seem to influence their intention to use e-scooters/bikes, which is inconsistent with the study of Kopplin et al. (2021) [24].

Despite the fact that attitude and usage intention were not correlated, COVID-19 appeared to be related to people’s attitude toward the use of e-scooters and bikes (H5: β = 0.428, p = 0.001). It significantly and positively influenced both perceived behavioral control and subjective norms (H6: β = 0.437, p = 0.001). This indicates that COVID-19 increased people’s knowledge of environmental problems and the negative effects of motorized vehicles, which led to a favorable perception of e-scooters and e-bikes. This result supports a study from Germany which found that attitudes changed before and after the initiation of COVID-19 [56]. Similarly, various research shows that those who switched from using motorized vehicles to cycling during the pandemic experienced the advantages of riding [57]. According to a different study, PBC significantly influenced the number of participants who chose cycling as their mode of active transportation.

Findings from Table 6 also reveal that environmental motivations drive positive emotions brought about by the perceived usage of e-scooters/bikes (H12: β = 0.326, p < 0.001), indicating that respondents consider the environment when choosing a method of transportation. Perhaps one of the main reasons to use e-scooters/bikes is the environmental benefits [58]. Another latent variable, pro-environmental identity, also significantly affects environmental motivation (H10: β = 0.615, p = 0.021), as shown by other behaviors such as the continued usage of e-bikes [33] and the adoption of alternative fuels [38]. This result also verifies prior studies indicating that the higher a person’s degree of environmental concern, the greater the possibility that they will perceive the environmental advantages of the items they use. Customers also find it easy to justify their use of or intention to use the product in terms of the eco-friendliness of e-scooters based on their green identity.

Contrary to the conclusions of related studies [31,33], ENV did not significantly influence consumers’ intention to use e-scooters or e-bikes after the pandemic (see Table 7, indirect effects). It may be attributed to the strong effects of COVID-19, subjective norms, and perceived behavioral control. Likewise, hedonic motivations, assessed through positive emotions, have a weak mathematical relationship with intention to use, yet failed to demonstrate a statistically good correlation. Thus, perceiving e-scooters/bikes as entertaining and fun is not substantial enough to drive their decision to use the products.

Table 7 shows that SN (β = 0.147, p < 0.001) and PBC (β = 0.145, p < 0.001) partially mediate the effect of COV on BI. Nevertheless, the effect is minimal compared to COV’s direct influence on BI. Surprisingly, COVID-19 seemed to have the most influence compared to all the factors that affect people’s usage intentions for e-scooters and e-bikes, followed by subjective norms and perceived behavioral control. This finding implies that the pandemic and subjective norms had a more significant behavioral influence than desired on people’s intention to ride bicycles or e-scooters in the future. The outcome is also consistent with recent research showing that COVID-19 will probably affect long-term travel behaviors, leading to a beneficial shift toward more environmentally friendly means of transportation, such as walking and e-scooters [8,59]. It also explains why views toward using two-wheeled vehicles after the pandemic ended did not correlate with using e-scooters or e-bikes.

The factors impacting views toward MMV use were another noteworthy assumption from the results. Through POS, green identity and ENV indirectly positively impacted attitudes. This indicates that consumers use the cognitive-environmental approach rather than only relying on hedonic effects to justify their view of e-scooters and e-bikes by understanding how these modes affect the environment. This research makes a significant theoretical contribution by explaining how an individual’s unique innovativeness affects their use of green transportation. The results show that even though most respondents believe they are in the early majority, this does not necessarily indicate a positive relationship with the intention to use. The adoption of green products cannot be entirely predicted by a person’s innovativeness, contrary to suggestion [41].

5.1. Practical Implications

The research presented in this paper may have a practical application in helping green transportation providers improve their marketing plans and boost demand for e-scooters and e-bikes after COVID-19. Policymakers may use this pandemic as a motivation to persuade people to consider using an alternative form of transportation if they can use the positive correlation between COVID-19 and usage intention. Additionally, as COVID-19 successfully raised public awareness of environmental issues, climate change, and the negative effects of motorized vehicles, promotion strategies that highlight the environmentally friendly features of e-scooters and e-bikes may be suggested. Compared to other means of transportation, e-scooter and e-bike manufacturers might include features on their products that alert users to their carbon footprint.

Another important aspect that strongly affected the intention to use e-scooters and e-bikes was social norms. Therefore, by investing in publicity to boost the favorable image of e-scooters/bikes through commercials in which public personalities utilize the product, policymakers and businesses might concentrate on boosting social acceptance. Additionally, given that the factor loadings relating to the first-last mile difficulty and time-constrained activities received high values, the government may support a work-to-bike policy or offer a suitable intermodal transit policy. On the other hand, more study is needed to address the surprising association between the attitude and impact of hedonic and environmental motives on usage intention. With these findings, a provision for segregated bike lanes may improve the public’s perception of the safety of e-scooters and bikes. Finally, the results might be useful to government agencies and private companies regarding the feasibility to provide the availability of public e-scooter services in the Philippines.

5.2. Limitations

Despite the contribution of this study, several limitations need to be addressed—primarily the limitations of the data employed in the study. First, the sample size used in this research is relatively small and random, which may lead to biases in social desirability, and thus should not be expected to be representative of the demographics in socio-economic terms. Second, the sampling was conducted in the Philippines. As such, results may be applicable in other areas with different transportation systems or cultural contexts. Lastly, this paper solely focuses on the intention to use e-scooters/bikes, and therefore may entail a gap between behavior-actual use. In addition, e-scooters and bikes are two different transportations. These limitations provide opportunities for future research, especially the multi-group analysis. Moreover, although this paper proves substantial in using the TPB framework, other variables, i.e., perceived risks, may also be incorporated [60]. Consequently, due to the failed results of accumulating knowledge on the decision-making involved in green innovations, future research may conduct separate analyses on the matter.

6. Conclusions

This study emphasizes evaluating the effect of COVID-19 on the future intended use of e-scooters/bikes in a framework representing an extended TPB. In addition, this study examined the complexity of the motivations involved in e-scooter/bike adoption by incorporating hedonic and environmental motivations. The research proposes 12 hypotheses with the model’s constructs based on the existing literature. Running the dataset through PLS-SEM suggests that both the measurement and structural model have strong consistency and moderate explanatory power, and are thus reliable. The findings confirmed that nine out of the original hypotheses were statistically and empirically significant. Although, only three latent variables (i.e., COV, SN, and PBC) had a good effect on the usage intention of e-scooters/bikes. According to the order of precedence, the COVID-19 effect appeared to be the most determinant factor influencing the usage intention in the post-pandemic era. On the other hand, the magnitude and significance of motivation variables concerning using e-scooters/bikes were lacking, meaning that having positive emotions and identifying oneself as green do not correlate to using an e-scooter/bike, a micro-mobility vehicle often considered a green product.