Factors influencing bike share intentions of users in Indian cities: a structural equation modelling approach

Figures & data

Table 1. List of travel behaviour theories on NMT user behaviour.

Theory	Key Constructs	Main Focus/Assumption	Application to Travel Behaviour	Reference
Theory of Reasoned Action (TRA)	Attitudes, subjective norms	People make rational decisions based on their attitudes and subjective norms about the behaviour	Predicts the likelihood of a travel behaviour based on people’s attitudes and subjective norms towards that behaviour	Fishbein and Ajzen (1975)
Theory of Planned Behaviour (TPB)	Attitudes, subjective norms, perceived behavioural control	People’s intentions are determined by their attitudes, subjective norms, and perceived behavioural control, which in turn predict behaviour	Predicts the likelihood of a travel behaviour based on people’s attitudes, subjective norms, and perceived behavioural control towards that behaviour	Ajzen (1991)
Theory of Interpersonal Behaviour (TIB)	Attitudes, subjective norms, perceived behavioural control, social influence	People’s behaviour is influenced by social influence and the characteristics of their social environment	Predicts the likelihood of a travel behaviour based on people’s attitudes, subjective norms, perceived behavioural control, and social influence towards that behaviour	Triandis (1977)
Transtheoretical Model (TTM)	Stages of change, processes of change	People move through a series of stages of change and use different processes of change to reach a new behaviour	Describes the different stages of readiness for change in travel behaviour and suggests appropriate interventions	Prochaska and Velicer (1997)
Social Cognitive Theory (SCT)	Personal factors, environmental factors, behaviour	People’s behaviour is influenced by their personal factors, their environment, and their previous behaviour	Examines how personal and environmental factors influence travel behaviour and suggests interventions based on the individual’s unique circumstances	Bandura and National Inst of Mental Health (1986)
The Model of Goal-Directed Behaviour (MGB)	Attitudes, subjective norms, perceived behavioural control, habit	People’s behaviour is guided by their goals and the perceived behavioural control to achieve them, and the strength of habit	Examines how people’s goals and habits influence their travel behaviour, and how interventions can be designed to break negative habits and form new ones	Ajzen and Fishbein (2005)
Technology Acceptance Model (TAM)	Perceived usefulness, perceived ease of use	People’s intention to use technology is influenced by their perception of the technology’s usefulness and ease of use	Examines how people’s perception of technology’s usefulness and ease of use influence their intention to use technology for travel behaviour	Davis (1989)

Figure 1. Hypothesis model for intentions to use bike share.

Table 2. List of the hypotheses.

Hypothesis	Statement	Path
H1	Bike share users’ intents to use bike sharing are related to the perceived quality of bike share schemes	PE→BI
H2	Bike share users’ intents to use bike sharing are related to perceived bicycling conditions	PBC→BI
H3	Bike share users’ intents to use bike sharing are related to Personal Habits	H→BI
H4	Bike share users’ intents to use bike sharing are related to policy support/change	PC→BI
H5	Bike share users’ intents to use bike sharing are related to subjective norms	SN→BI
H6	Bike share users’ intents to use bike sharing are related to External factors	EF→BI
H7	Bike share users’ intents to use bike sharing are related to behavioural attitude	BA→BI
H8	Bike share users’ behavioural attitude is related to the perceived quality of bike share schemes	PE→BA
H9	Bike-sharing users’ frequency of use is related to perceived bicycling conditions	PBC→BA
H10	Bike share users’ behavioural attitude is related to Personal Habits	H→BA
H11	Bike share users’ behavioural attitude is related to policy support/change	PC→BA
H12	Bike share users’ behavioural attitude is related to subjective norms	SN→BA
H13	Bike share users’ behavioural attitude is related to External factors	EF→BA
H14	Bike share users’ intents to use bike share is related to bike share usage frequency	BI→BF

Figure 2. Research methodology.

Table

z is the z score	(z score for a 95% confidence level is 1.96) ε is the margin of error (considered as 5%)
N is population size	400000 residents enrolled through bike share apps considered as population size
ε is the margin of error	5%
$\hat{p}$ is the population proportion	Considered as 50% (It is assumed that at least half of the 400,000 registered residents used bike sharing.)

Table 3. Demographics and relevant frequency statistics of bike share user respondents.

Variable	Description	No of Respondents	Percentage (%)
Gender	Male	278	66.8
	Female	138	33.2
Age Group	Teenagers (<18)	34	8.1
	Adults (18–40)	278	66.9
	Middle-aged (41–65)	87	21
	Older (>65)	17	4
Educational level	Primary & Low	13	3.1
	Secondary	81	19.4
	Under Graduation	195	46.9
	Post-Graduation & above	127	30.6
Occupation	Student	154	36.9
	Employed	114	27.5
	Business	13	3.1
	Unemployed	79	19.1
	Others	56	13.4
Monthly Household Income	< Rs 20,000	75	18.1
	Rs 20,000 - Rs 50,000	139	33.5
	Rs 50,000 - Rs 1,00,000	147	35.4
	Rs 1,00,000 - Rs 2,00,000	37	9.0
	> Rs 2,00,000	13	3.1
Household Bicycle Ownership	0	193	46.3
	1	185	44.4
	2 & above	39	9.4
Household Motorcycle Ownership	0	91	21.9
	1	208	50
	2 & above	117	28.1
Household Car Ownership	0	312	75
	1	91	21.9
	2 & above	13	3.1

Table 4. Mean scores, standard Deviation values for behavioural attitude (BA) scale.

Observation Item	Label	Mean Score	Std. Deviation
Bike share is economical to use	BA1	3.79	1.257
Bike share is very interesting/exciting to use	BA2	3.62	1.334
Bike share helps to live a healthy life	BA3	3.42	1.326
Bike share is convenient to use in the city	BA4	3.13	1.371
Bike share is safe to use in the city	BA5	2.92	1.302
Bike share is environmentally friendly (Reduce carbon emissions)	BA6	3.51	1.214
Bike share is comfortable to use in the city	BA7	2.72	1.142

Table 5. Mean scores, standard Deviation values for perceived ease of use (PE) scale.

Observation Item	Label	Mean Score	Std. Deviation
It is easy for me to register to become a bike sharing member	PE1	2.76	1.098
It is easy for me to find the locations of bike sharing docking stations in the city	PE2	3.15	1.064
I am happy with the “Quality of bicycles & accessories”	PE3	3.04	1.122
I am happy with the “Bike sharing network coverage (availability of bike stations at desired places) in the city”	PE4	2.54	1.356
I am happy with the “Convenience of using bike sharing app”	PE5	3.62	1.137
I am happy with the “Payment Process/options”	PE6	3.24	1.234
I am happy with the “Bike sharing promotion & marketing in the city”	PE7	2.87	1.144
I am happy with the “Subscription fee/trip fare charges”	PE8	3.16	1.068

Table 6. Mean scores, standard Deviation values for habits scale.

Observation Item	Label	Mean Score	Std. Deviation
I like experiment with new ways of doing things.	H1	3.33	1.479
I like to perform physical activities (ex: exercise) to keep myself fit	H2	3.19	1.602
I often (the respondent) use Public Transport in my daily travel	H3	3.45	2.382
I often (the respondent) use a car/bike in my daily travel	H4	2.78	1.964

Table 7. Mean scores, standard Deviation values for subjective norms (SN) scale.

Observation Item	Label	Mean Score	Std. Deviation
My friends can influence my choice to use bike share	SN1	3. 57	1.039
My family members can influence my choice to use bikes share	SN2	3.12	1.260
My colleagues/workmates can influence my choice to use bikes share	SN3	3.42	1.458
Good bike-share promotional campaign can influence me to use bike share	SN4	3.58	1.316
Awareness information on bike share through social media can influence me to use bike share	SN5	3.72	1.213

Table 8. Mean scores, standard Deviation values for policy support (PC) scale.

Observation Item	Label	Mean Score	Std. Deviation
Government Investing more on bicycle infrastructure would encourage me to use bike share more	PC1	3.46	1.437
Government restrictions on private vehicles would encourage me to shift bike share	PC2	2.81	1.297
Government incentives for bike share trips would encourage me to use bike share more	PC3	4.01	1.145
Giving priority to cyclists at intersections & crossings would encourage me to use bike share more	PC4	3.11	1.428
Limiting vehicular speeds on roads would encourage me to use bike share more	PC5	3.52	1.027

Table 9. Mean scores, standard Deviation values for Perceived Bicycling Conditions (PBC) scale.

Observation Item	Label	Mean Score	Std. Deviation
I felt “Awareness shown by motorists while riding a (bike share) bicycle is poor in the city”	PBC1	3.54	1.275
I felt “Speeds of the motor vehicles are high with respect to cyclists’ safety in the city”	PBC2	3.26	0.918
I felt “Bicycle infrastructure (Bike lanes/tracks/treatment at crossings) coverage is poor in the city”	PBC3	3.97	1.044
I felt “Speeds of the motor vehicles are high with respect to cyclists’ comfort in the city	PBC3	3.64	1.4

Figure 3. Path diagram of Confirmatory factor Analysis.

Figure 4. Path diagram of Structural equation of bike share user intention.

Table 10. Results of confirmatory factor analysis.

Variables	Items	Factor Loading	Cronbach’s Alpha	AVE	CR
Behavioural Attitude (BA)	BA2	.801	0.896	0.725	0.854
	BA5	.678
	BA4	.763
	BA1	.723
	BA3	.863
Perceived Ease of Bike Share Service (PE)	PE5	.832	0.908	0.862	0.809
	PE4	.819
	PE2	.726
	PE3	.698
	PE1	.943
Subjective norms (SN)	SN5	.851	0.774	0.779	0.794
	SN3	.777
	SN4	.763
	SN2	.546
	SN1	.621
Policy Change (PC)	PC2	.965	0.824	0.745	0.834
	PC3	.906
	PC4	.891
	PC5	.607
	PC1	.791
Habits (H)	H2	.834	0.914	0.812	0.774
	H3	.706
	H5	.791
	H1	.917
External Factors (EF)	EF1	.768	0.961	0.858	0.912
	EF2	.755
	EF3	.934
	EF5	.912
	EF4	.739
Perceived Bicycling Conditions (PBC)	PBC 1	0.693	0.867	0.853	0.878
	PBC 2	0.789
	PBC 3	0.864
	PBC 4	0.908
Bike Share Intentions	BI 1	0.862	0.814	0.826	0.845
	BI 2	0.774
	BI 3	0.911

Table 11. Goodness of model fit statistics of Structural model.

Model Fitness Statastics	Ideal Value	Experimental Values
CMIN/DF	1–3	2.083
P	<0.05	0.01
RMSEA	<0.05	0.005
GFI	>0.90	0.912
CFI	>0.90	0.951
NFI	>0.90	0.935

Table 12. Path coefficients of the structural model.

Hypothesis	Constructs	Constructs	Structural Path	Standard Coefficient/Path Coefficient	Effect Direction	S.E	C.R	P Value	Result
H1	Perceived Ease/Quality of Bike share service	Behavioural Intention to Use	PE→BI	0.315	+ ve	0.029	2.561	***	Valid
H2	Perceived Bicycling Conditions	Behavioural Intention to Use	PBC→BI	−0.275	- ve	0.09	−4.299	***	Valid
H3	Habits	Behavioural Intention to Use	H→BI	0.082	+ ve	0.806	3.451	0.078	Not Valid
H4	Policy Support/Change	Behavioural Intention to Use	PC→BI	0.244	+ ve	0.03	7.303	**	Valid
H5	Subjective Norms	Behavioural Intention to Use	SN→BI	0.074	- ve	0.017	−9.096	0.128	Not Valid
H6	External Factors	Behavioural Intention to Use	EF→BI	−0.196	- ve	0.056	−1.593	**	Valid
H7	Behavioural Attitude	Behavioural Intention to Use	BA→BI	0.421	+ ve	0.034	1.493	***	Valid
H8	Perceived Quality of Bike share service	Behavioural Attitude	PE→BA	0.408	+ ve	0.07	4.02	***	Valid
H9	Perceived Bicycling Conditions	Behavioural Attitude	PBC→BA	−0.212	- ve	0.195	−2.999	**	Valid
H10	Habits	Behavioural Attitude	H→BA	0.082	-ve	0.671	1.862	0.063	Not Valid
H11	Policy Support/Change	Behavioural Attitude	PC→BA	−0.010	- ve	0.039	−0.196	0.144	Not Valid
H12	Subjective Norms	Behavioural Attitude	SN→BA	0.153	+ ve	0.036	2.828	**	Valid
H13	External Factors	Behavioural Attitude	EF→BA	− 0.388	- ve	0.107	−3.621	***	Valid
H14	Behavioural Intention to Use	Bike Share Behaviour	BI→BF	.0.622	+ ve	0.191	6.339	***	Valid

**p < 0.01; ***p < 0.001 (two-tailed).

Table 13. Effects of different variables on the intention to use bike share.

Variables	Standard Direct Effect	Standard Indirect Effect	Standard Total Effect
Habits	0.082	0.073	0.155
Perceived Ease/Quality of Bike share service	0.315	0.214	0.529
External Factors	−0.196	−0.128	−0.324
Subjective Norms	0.074	0.114	0.188
Policy Support/Change	0.244	0.096	0.340
Behavioural Attitude	0.421		0.421
Perceived Bicycling Conditions	−0.275	−0178	−0.453

Table A1. Description of latent variables used SEM model.

Variable	Observation Item	Label	Numerical Scale	Qualitative Scale
Perceived Ease of Use (PE)	It is easy for me to register to become a bike sharing member	PE1	1–5	Completely unsatisfied to completely satisfied
	It is easy for me to find the locations of bike sharing docking stations in the city	PE2	1–5	Completely unsatisfied to completely satisfied
	I am happy with the ‘Quality of bicycles & accessories’	PE3	1–5	Completely unsatisfied to completely satisfied
	I am happy with the ‘Bike sharing network coverage (availability of bike stations at desired places) in the city’	PE4	1–5	Completely unsatisfied to completely satisfied
	I am happy with the ‘Convenience of using bike sharing app’	PE5	1–5	Completely unsatisfied to completely satisfied
	I am happy with the ‘Payment Process/options’	PE6	1–5	Completely unsatisfied to completely satisfied
	I am happy with the ‘Bike sharing promotion & marketing in the city’	PE7	1–5	Completely unsatisfied to completely satisfied
	I am happy with the ‘Subscription fee/trip fare charges’	PE8	1–5	Completely unsatisfied to completely satisfied
Behavioural Attitude (BA)	Bike share is economical to use	BA1	1–5	Strongly disagree to strongly agree
	Bike share is very interesting/exciting to use	BA2	1–5	Strongly disagree to strongly agree
	Bike share helps to live a healthy life	BA3	1–5	Strongly disagree to strongly agree
	Bike share is convenient to use in the city	BA4	1–5	Strongly disagree to strongly agree
	Bike share is safe to use in the city	BA5	1–5	Strongly disagree to strongly agree
	Bike share is environmentally friendly (Reduce carbon emissions)	BA6	1–5	Strongly disagree to strongly agree
	Bike share is comfortable to use in the city	BA7	1–5	Strongly disagree to strongly agree
Subjective norms (SN)	If friends, family, and neighbours reduced driving their personal vehicles, I would follow their example.	SN1	1–5	Strongly disagree to strongly agree
	If my friends, family, and neighbours reduced their use of personal vehicles, I would follow suit.	SN2	1–5	Strongly disagree to strongly agree
	If my friends, relatives, and neighbours used public transit/bike share, I would do the same.	SN3	1–5	Strongly disagree to strongly agree
	Good bike-share scheme advertising campaign in the city influenced me to use bike share	SN4	1–5	Strongly disagree to strongly agree
Policy Support (PC)	Government Investing more on bicycle infrastructure would encourage me to use bike share more	PC1	1–5	Strongly disagree to strongly agree
	Government restrictions on private vehicles would encourage me to shift bike share	PC2	1–5	Strongly disagree to strongly agree
	Government incentives for bike share trips would encourage me to use bike share more	PC3	1–5	Strongly disagree to strongly agree
	Giving priority to cyclists at intersections & crossings would encourage me to use bike share more	PC4	1–5	Strongly disagree to strongly agree
	Limiting vehicular speeds on roads would encourage me to use bike share more	PC5	1–5	Strongly disagree to strongly agree
Habits	I like experiment with new ways of doing things.	H1	1–5	Strongly disagree to strongly agree
	I perform physical exercise more often (at least 20 minutes)	H2	1–5	Strongly disagree to strongly agree
	I often (the respondent) use Public Transport in my daily travel	H3	1–5	Strongly disagree to strongly agree
	I often (the respondent) use a car/bike in my daily travel	H4	1–5	Strongly disagree to strongly agree
External Factors	Weather conditions in the city affect my bike share usage	EF1	1–5	Strongly disagree to strongly agree
	Terrain conditions in the city affect my bike share usage	EF2	1–5	Strongly disagree to strongly agree
	Temporal factors such as (Morning/noon/evening) &; (weekday/weekend) affect my bike share usage	EF3	1–5	Strongly disagree to strongly agree
	Traffic conditions (Peak/Off Peak) in the city affect my bike share usage	EF4	1–5	Strongly disagree to strongly agree
	Availability of bicycle friendly infrastructure (bike paths/lanes/tracks) affect my bike share usage	EF5	1–5	Strongly disagree to strongly agree
Perceived Bicycling Conditions (PBC)	I felt ‘Awareness shown by motorists while riding a (bike share) bicycle is poor in the city’	PBC1	1–5	Strongly disagree to strongly agree
	I felt ‘Speeds of the motor vehicles are high with respect to cyclists’ safety in the city’	PBC2	1–5	Strongly disagree to strongly agree
	I felt ‘Bicycle infrastructure (Bike lanes/tracks/treatment at crossings) coverage is poor in the city’	PBC3	1–5	Strongly disagree to strongly agree
	I felt “Speeds of the motor vehicles are high with respect to cyclists’ comfort in the city	PBC4	1–5	Strongly disagree to strongly agree
Bike sharing Intentions	I intend to use bike-sharing systems	BI1	1–5	Strongly disagree to strongly agree
	In the future, I will choose to use shared e-bikes if needed.	BI2	1–5	Strongly disagree to strongly agree
	I will continue to use bike share	BI3	1–5	Strongly disagree to strongly agree

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