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Article

Understanding the Willingness of Students to Use Bicycles for Sustainable Commuting in a University Setting: A Structural Equation Modelling Approach

by
Ahmad Nazrul Hakimi Ibrahim
1,*,
Muhamad Nazri Borhan
1,2,*,
Nur Shaeza Darus
1,
Nor Aznirahani Mhd Yunin
3 and
Rozmi Ismail
4
1
Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
2
Sustainable Urban Transport Research Centre (SUTRA), Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
3
Road Traffic and Infrastructure Unit (RTI), Malaysian Institute of Road Safety Research (MIROS), Taman Kajang Sentral, Kajang 43000, Selangor, Malaysia
4
Psychology and Human Wellbeing Research Centre (PsiTra), Faculty of Social Sciences and Humanities, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
*
Authors to whom correspondence should be addressed.
Mathematics 2022, 10(6), 861; https://doi.org/10.3390/math10060861
Submission received: 3 February 2022 / Revised: 2 March 2022 / Accepted: 5 March 2022 / Published: 8 March 2022
(This article belongs to the Special Issue Quantitative Methods for Social Sciences)
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Versions Notes

Abstract

:
The bicycle is a forgotten and neglected mode of transport even though it offers numerous individual, social, and environmental benefits over motorised transport. This research seeks to determine the factors influencing students to use bicycles for commuting and focuses on the strategies that encourage bicycling in a university setting. This study proposes the novel model framework by adopting Ajzen’s theory of planned behaviour. We modified the theory of planned behaviour by adding two new constructs, perceived barriers and habit. The respondents in this study are 422 students from Universiti Kebangsaan Malaysia in Selangor, Malaysia. The structural equation model in this study showed that the main attitudinal constructs, namely attitudes, subjective norms and perceived behavioural control, have a significant positive and direct influence on the students’ willingness to cycle. In addition, the perceived barriers have a profound negative and indirect impact on behaviour intention through attitude, perceived behavioural control and habit. The perceived barriers did not have a significant influence on the subjective norms. However, habit has a profound positive and direct effect on three main attitudinal constructs (attitude, subjective norms and perceived behavioural control). This element could indirectly influence the behaviour intention of cycling in a university setting. Finally, this study has identified the physical, educational and economic policies for promoting bicycle use on university setting. It also contributes beneficial information that authorities, policymakers, planners and researchers could use to formulate effective strategies for increasing bicycle use on university setting and promote green and sustainable university settings.

1. Introduction

The university setting is a non-homogenous community, where individuals from different cultures, backgrounds, lifestyles, incomes, and attitudes come together for a living, education, career, and recreation [1]. According to Balsas [2], a university setting developed temporary societies with a complete human scale. This phenomenon led to the regard of the university setting as the ideal example of ‘people place’. While other areas, such as technology parks, ports, hospitals and industrial estates were created, the university campus involved a high concentration of human activity. Therefore, a high number of trips were made from the university area regularly [3]. Overall, this finding was in line with Shannon et al. [4], who mentioned that the university setting was a large educational and research institution consisting of community leaders and renowned trip organisers. University students belong to a social class, which often exhibits distinguished and complex travel attitudes [5]. Provided that the students are free in their campus environment, they had the autonomy of making decisions associated without being significantly controlled by university authorities and parents. This situation indicated that the university setting required special consideration of modal choice and travel demand model.
As a result of the car-oriented environment of universities, issues associated with high dependence of private cars on mobility in a university setting are present. Additionally, private cars are intensively used for small distance trips. To illustrate, Hooftman et al. [6] stated that approximately 30% of the trips by car was less distant than 3 km in European cities, while 50% of the trips were shorter than 5 km. This evidence indicated the crucial use of private transport in today’s society. Overall, the aforementioned literature recorded the impact of dependence on private transport. Ribeiro et al. [7] also stated that the use of motorized vehicles led to critical issues in spatial contexts, including energy consumption, global warming, air and noise pollution, inaccessibility, and deterioration of visual and natural conditions. To prove this point, statistics showed that the intensive use of cars contributed to CO2 emission by approximately 40%, while other populations occurred up to 70% [8]. In the case of social impact, the use of private motorised vehicles led to a reduction in personal safety and increase in accident risk, mental health problems, and excessive time spent during traffic congestion [5,9,10,11]. In summary, trips performed via motorised vehicles were identified as among the most negative causes of the university ecological footprint [12,13].
Following the aforementioned negative of dependence on motorised vehicles in the university setting, such as motorcycles and cars, most universities worldwide formulated the strategy of creating a sustainable environment in their respective university setting [14]. The use of active transport, particularly bicycle, was frequently suggested in the previous studies to address the abovementioned problems [1,15]. This suggestion was in line with the objective of “Healthy People 2020”, which aimed for the improvement in the percentage of the trips via active transport, such as cycling and walking [15]. Furthermore, although the bicycle is constantly identified as a ‘poor step-child’ of other alternative modes, this type of transport has developed from ‘forgotten modes’ to emblems of a high quality of life [2]. In this case, the bicycle is a sustainable mode of transport compared to other alternatives, such as motorcycle and cars for travelling in a university setting. This is because most students in university campuses reside on the campuses, while other students reside within a reasonable cycling distance [16].
Using a bicycle to commute has countless benefits for the individuals and community. Cycling brings people closer to nature and improves their physical and mental health [17]. It also significantly lowers the risk for non-communicable disease (NCDs) and fatality [15,18,19,20]. Similarly, Kelarestaghi et al. [1], Ribeiro et al. [7] and Shannon et al. [4] stated that consistent cycling activity diminishes the chances of developing health problems, such as obesity, asthma, hyperlipidemia, hypertension, atherosclerosis and type-two diabetes. Besides, cycling is an economical means of transport for everyone. Bicycles are a feasible and affordable mode of transportation for a reasonable distance for individuals who could not afford private cars or motorcycles. In the community and safer urban environment contexts, bicycles provide solutions for the lack of parking spaces and reduce the circulating vehicles, thus improving traffic flow and reducing environmental and noise pollution [10,15,21]. Bicycles are the most eco-friendly means of transportation; they do not emit air pollutants and thus are eco-friendly [1,8,17]. Cycling also promotes the principles of urban mobility [22].
Despite the advantages of cycling, most trips on university campuses, especially in Malaysian universities, were made using motorised vehicles [16,23,24]. According to Jalalkamali et al. [23], the rate of bicycle usage in Universiti Teknologi Mara (UiTM) is low due to weather constraints and the topography of the university campus. Zainal et al. [16] concurred that the weather and topography factors influence the low rate of bicycle use on the Universiti Kebangsaan Malaysia (UKM) main campus. The low rate of bicycle use is also due to the lack of cycling facilities, including bicycle lanes and parking [16].
This research was conducted to investigate the factors leading to the behavioural intention of students to encourage the use of sustainable modes of transport such as bicycle as an alternative mode to commute in a university setting. This research aimed to identify the answer to particular research questions, namely (i) what are the factors of the student’s willingness to cycle in a university setting? and (ii) what are the measures to promote the use of the bicycle in the context of the university setting? To answer these questions, the theory of planned behaviour was modified using additional perceived barrier and habit constructs. Following that, the structural equation model (SEM) was developed using the survey data from 422 individuals consisting of university students. The primary campus Universiti Kebangsaan Malaysia (UKM), Malaysia (Bangi Campus) was the study area selected to apply the proposed method. The results of this study facilitated the planners and advocates in designing and successfully adopting interventions or promotional campaigns for the enhancement of bicycle usage in the university setting.
This section of the article is followed by Section 2, which illustrates the literature review and hypothesis formulation. Section 3 presents a discussion of the methodology applied in this study. Next, Section 4 presents the findings, followed by discussion of the findings and policy implementation in Section 5. This article ends with a conclusion in Section 6.

2. Literature Review and Hypothesis Formulation

2.1. The Theory of Planned Behaviour

Theory of Planned Behaviour (TPB) is a model often used in numerous social attitude prediction research to predict and describe human behaviour [25,26,27,28]. Extended from the theory of reasoned action (TRA) [28], it demonstrates that individual’s attitudes, subjective norms, and perceived behavioural control to identify their behavioural intention [29,30].
Attitude towards behaviour is a favourable or adverse emotion of whether to perform an action or vice versa [31,32], while attitude is a psychological construct developed from other individuals, objects, and ideas in day-by-day routines. Furthermore, individuals with favourable attitudes frequently appear with positive principles, sentiments, and actions. In respect of transportation, attitudes could be described as a user’s enthusiasm towards a specific travel mode, which is cycling. In the implementation of TPB for transport mode selection, attitude is regarded as an essential element for positive or negative sentiments. Additionally, subjective norms indicate the social pressure caused by individuals who are significant to a person (parents, wife/husband, and associates) to achieve or reject a behaviour. Provided that a person would abide by their reference teams, any parties or teams with a role as a reference group could possibly have a significant impact on other individuals’ views, behaviour, and decisions [25,27].
Perceived behavioural control (PBC) is one of the elements of behavioural intention, which represents the supplies, predicted challenges, and events associated with an individual, including opportunity, profit, skill, and time, which are also crucial in accomplishing a behaviour [31,32]. In general, an individual with a higher perception of behavioural management would possess more supplies and chances, with fewer challenges [28,33]. Behavioural intention refers to the subjective probability for a person to be involved in the behaviour (for this case is use a bicycle). It was highlighted by TPB that a person’s intention to conduct a behaviour is the prompt factor of behaviour [31]. With the presence of probability to give a response and the accurate evaluation of the intention, the intention could be the determinant of behaviour with the highest efficacy [26,34].
Numerous previous studies regarding transportation recorded three constructs (attitude, subjective norms, and PBC), which impacted users’ behavioural intention. Hsiao and Yang [27] highlighted that attitude, subjective norms, and PBC had positive and significant impacts on undergraduate college students’ use of High-Speed Rail in Taiwan. Additionally, Nkurunziza et al. [35] stated in their case study from Dar-es-salaam that individual elements, such as attitude and PBC, and social elements including social norms had higher potential to influence the use of a bicycle. This finding was in line with the findings of De Sousa et al. [36], who drew a similar conclusion regarding bicycle use. In addition, Lois et al. [37] also proved that the proposed model based on TPB and social identity significantly predicted the car users’ intention to start commuting by bicycle in Spain. In addition, research by Borhan et al. [28] recorded that the behavioural intention of taking low-cost airlines for intercity travel among car drivers in Libya was a role of attitude, subjective norms, and perceived behavioural control. Similar findings were also recorded in other studies regarding transportation, which adopted TPB [25,26,34]. Applying this theory, the current research identified behavioural intention as the student’s disposition of using the bicycle in a university setting. Therefore, several hypotheses were developed according to a comprehensive literature review.
Hypothesis 1 (H1).
Attitude had a positive impact in a student’s intention of using the bicycle in a university setting.
Hypothesis 2 (H2).
Subjective norms had a positive impact in a student’s intention of using a bicycle in a university setting.
Hypothesis 3 (H3).
Perceived behavioural control had a positive impact in a student’s intention of using a bicycle in a university setting.

2.2. The Modification of the TPB

Borhan et al. [26], Hsiao and Yang [27], and Ibrahim et al. [25] reported that an effective way of improving the prediction performance of TPB was the modification of TPB framework structure by adding a new construct. Numerous studies in the transportation domain investigated the potential of new constructs, such as trust [25,27], novelty-seeking [27,28,34], and external influence [26,28] in their modified model. Subsequently, it was proven that the addition of new constructs into the modification of TPB could improve the prediction ability of the modified TPB model. Provided that the new constructs in the TPB for an improved ability to predict the factors influenced the bicycle use among students, this study adopted the perceived barrier and habit factors.

2.2.1. Perceived Barrier

A barrier is generally defined as a fence or other obstacle preventing movement or access. It is also known as one of the critical factors considered in the user’s choice to perform the action sought by people or vice versa [38,39]. However, there is no specific definition of the barrier in the context of transportation, specifically in the case of active transport, such as bicycles. In this study, the perceived barrier was defined as a physical or non-physical limitation factor, leading to the bicycle user’s intention to not use this mode of transport for commuting.
The discussions on bicycle use in the transportation literature often debate how the perceived barrier influence the decision to use sustainable transport, particularly bicycles [35,40,41,42,43]. According to Nkurunziza et al. [35], the perceived barriers for cycling comprise personal, social and physical environmental factors. Fishman et al. [40] introduced three elements related to bicycle use, accessibility/spontaneity, safety and weather/topography to categorise the perceived barriers. The perceived barriers that could influence cycling choice are personal factors, heavy traffic, long travel time, insecurity, stress, lack of physical fitness, topography, climate and the need to travel at night. Inadequate infrastructures, such as protected and connected bicycle lanes and access to infrastructures, could also influence the choice to cycle [36,42,43,44]. In addition, the safety concern due to the lack of appropriate infrastructure for safe cycling and the absence of proper road safety and education of riders, pedestrians and drivers are among the major barriers in promoting bicycle use [17]. Chatterjee et al. [45] reported that the relative impact of these perceived barriers on the decision to use bicycles varies depending on the individuals, locations and situations.
The perceived barriers to bicycle use had a significant direct and indirect impact on behavioural intention to cycle. This finding was also proven in De Sousa et al.’s [36] research on the perception of perceived barriers of bicycle use in Brazilian cities, which found that these barriers influenced individual’s attitude and social norms towards behavioural intention. The perceived barrier also showed an interconnection between behaviour management, in which the strongest perceived barrier led to the lowest behaviour management. This situation was caused by specific perceived barriers, such as the lack of dedicated cycling infrastructure [36]. Furthermore, a study by Nkurunziza et al. [35] recorded that the perceived barrier had a significant impact on cycling behaviour. This finding was in line with the result from the study predicting the behavioural attempt of using the Park & Ride service [46]. Meanwhile, the perceived barrier element was found to be connected to an individual’s motivation to use the bicycle, as reported in several previous studies on the same subject studies [35,42,43]. A higher perceived barrier would decrease an individual’s motivation to use the bicycle as an alternative mode of transport, contributing to the lack of willingness to cycle. This result was in agreement with the result from previous studies related to the perceived barriers to cycling in Montreal [47], Brussels [48] and The Netherlands [20]. Overall, the perceived barrier was predicted to have negative impacts on attitude, subjective norms, perceived behavioural control and habit, as well as the intention of applying a sustainable mode of transport (bicycle). Accordingly, several hypotheses were suggested based on the comprehensive literature review.
Hypothesis 4 (H4).
Perceived barrier had a negative impact on the attitude toward using the bicycle in a university setting.
Hypothesis 5 (H5).
Perceived barrier had a negative impact on subjective norms in using the bicycle in a university setting.
Hypothesis 6 (H6).
Perceived barrier had a negative impact on perceived behavioural control to use a bicycle in the university setting.
Hypothesis 7 (H7).
Perceived barrier had a negative impact on habit in the use of the bicycle in a university setting.

2.2.2. Habit

Habits play a crucial role in developing behaviours [49]. Habits are spontaneous situations that create a whim towards performing behaviours that had been perennially carried out in that setting [50], and this process controls the ‘habitual behaviours’. Lally et al. [51] pointed out that habitual behaviour is learned through a process of ‘context-dependent repetition’. According to Gardner [49], Lally et al. [51] and Thøgersen [52], habits are formed via repetition of behaviour in a specific circumstance. Recently, several research areas have explored the notion of habits to envisage human behaviour in specific contexts. According to Havlícková and Zámecník [53], habits (either by itself or together with other factors) are the critical factors influencing the travel mode choice. In a similar vein, a study conducted in Norway by Şimşekoğlu et al. [54] reported that the individual car user habit is the critical factor predicting the intention to use public transit. Furthermore, Ibrahim et al. [55], Cools et al. [56], Danner et al. [57], Gardner et al. [50] and Ouellette and Wood [58] reported the influence of an individual’s habits in predicting the investigated behaviours in their studies.
The initiative to employ the concept of habit to the domain of transportation planning has been examined extensively nowadays. Bruijn et al. [59] and Muñoz et al. [60] in their study in The Netherlands and Spain discovered that habits played a vital role in encouraging individuals to cycle for commuting. In addition, Lizana et al. [61] also revealed that habit has a significant influnce on predicting the bicycle use intention based on perception of students, faculty members and staff of two Chilean universities. Based on the comprehensive literature review, the investigation of the relationship between habit and attitudinal constructs, namely attitude, subjective norms, and perceived behavioural control could improve understanding regarding the influence of motivation on the students’ willingness to cycling in a university setting. Therefore, the following hypotheses were proposed:
Hypothesis 8 (H8).
Habit had a positive impact on the attitude in using the bicycle in a university setting.
Hypothesis 9 (H9).
Habit had a positive impact on the subjective norms in using the bicycle in a university setting.
Hypothesis 10 (H10).
Habit had a positive impact on the perceived behavioural control of using the bicycle in a university setting.

2.3. Theoritical Framework

Based on comprehensive literature review, the current research proposed a theoretical model to explore the relationship between six constructs, namely perceived barrier (PB), habit (HB), attitude (AT), subjective norms (SN), perceived behaviour control (PBC), and behavioural intention (BI), as illustrated in Figure 1 below.

3. Research Methodology

This section discussed the methodology implemented in this study to determine the factor of the student’s willingness to cycle in a university setting in order to formulate a compelling strategy of promoting the use of the bicycle in a university setting. The following subsection will present the discussion of the research area, questionnaire development, data collection, and data analysis.

3.1. Research Area

This study was conducted at the main campus of Universiti Kebangsaan Malaysia (UKM) located at Bandar Baru Bangi in Selangor (coordinate: 2°55′45.4″ N 101°46′37.6″ E), as displayed in Figure 2. This university was located approximately 25 km south of the Federal Territory of Kuala Lumpur and 30 km to the north of the main entryway to Malaysia, Kuala Lumpur International Airport. Furthermore, the area of UKM amounted to approximately 1100 hectares, and it featured hilly topography characteristics. Furthermore, the main campus of UKM consisted of ten residential colleges, nine faculties, and 16 research institutions encompassing 2460 academic staff, more than 9600 supporting staff, and approximately 18,688 students during data collection. In the case of UKM, no covered bicycle lane was provided to connect between residential colleges, faculties, and other facilities. As a result, the cyclist was exposed to extreme climate conditions, such as heavy rainfall and extreme hot temperatures. Besides, some of the faculties and residential colleges were located at the top of the hill, making bicycles the least popular transport alternative for commuting activities, such as travelling from residential colleges to the faculty or vice versa. However, the most common commuting modes used by students at this university campus were bus, cars, and motorcycles.

3.2. Measurement

Before the draft of the questionnaire was developed, a comprehensive literature review was made, while the item for measurement in this research was implemented from previous research, as shown in Table 1. Several procedures performed before this questionnaire could be used in the final data collection. The questionnaire was first evaluated by three researchers and a domain expert to examine the relevance of the questionnaire content. A pilot test was performed, in which a questionnaire was distributed to 50 UKM students chosen at random on 15 November 2018 to 30 November 2018. This research assessed the weakness present in the form of the instrument and improved in the weaknesses before presenting them in the real survey [62]. Based on the feedback from the pilot samples, several questions were excluded from the questionnaire due to the absence of a response from the participants or the presence of incorrect responses. The revision was performed on other questions for improvement in terms of coherence and dependability. Table 1 presents the findings from the reliability analysis of the pilot research.
The final questionnaire used in the actual survey was separated into two sections. Specifically, the first section presented five questions related to the respondents’ socio-demographic characteristics, including gender, age, level of the study, bicycle ownership, and cycling frequency. The second section measured the factor influencing the students and staff to use bicycles on university campus based on six constructs presented in Appendix A. All items in Section 2 in the questionnaire were evaluated using five-point Likert scale, which ranged from 1 (strongly disagree) to 5 (strongly agree). Essentially, higher scores indicated a stronger interest level in a particular measure.

3.3. Data Collection

Data collection for this study was performed by three enumerators, who distributed a questionnaire to the students and staff from the main campus of Universiti Kebangsaan Malaysia on 18 February 2019 to 5 April 2019. A total of 500 self-administrated questionnaires was distributed using the convenience sampling technique during this stage. Before the survey, the enumerators made a simple briefing regarding the purpose of this survey, with the potential respondents being asked about their agreement for participation in the survey. Notably, the questionnaire was distributed only to the respondents who were willing to participate in this survey to ensure that they would provide accurate and reliable responses to each item in the questionnaire. Besides, this approach could also increase the response rate, as highlighted by Borhan et al. [62]. When the questionnaires were returned, 78 questionnaires were eliminated due to invalid or/and incomplete responses, while 422 questionnaires with a response rate of 84.4% were used in further analysis. Accordingly, the summary of the socio-demographic characteristics of 422 respondents is presented in Table 2.

3.4. The Tools and Procedure for Data Analysis

The Statistical Package for Social Sciences Software (SPSS) 22.0 and Analysis of Moment Structure (AMOS) version 22 were used to analyse the data in this study. The socio-demographic profile of the respondent is presented in frequency and percentage, as shown in Table 2, was analysed using SPSS. The mean, standard deviation, skewness and kurtosis value for all items as displayed in Appendix A were also calculated using SPSS. The normality test results (see skewness and kurtosis value in Appendix A) proved that the data used in this study is significantly normal distributed [66]. Therefore, data normality estimates are observed, and the use of covariance-based SEM (CB-SEM)—specifically, maximum likelihood (ML) estimation is supported in this study. Cronbach’s alpha was calculated using SPSS to estimate the internal consistency of the items of TPB constructs, namely attitude, subjective norm, and PBC and the additional constructs, namely perceived barrier and habit. A confirmatory factor analysis was performed to determine the goodness-of-fit of the previous model (TPB) with the study cohort. A p-value of no more than 0.05 was considered significant in the analyses. AMOS version 22 was used for Structural Equation Modelling (SEM) to establish the ability of the perceived barrier, habit and the original TPB constructs to predict students’ willingness to cycle in the university setting. The maximum likelihood estimation was used to estimate the parameters of the model. Examination of the adequacy of the model fit was performed using the chi-square test statistic, the comparative fit index (CFI), and the root mean squared error of approximation (RMSEA).
Statistically, the SEM used in this study is an advanced version of the general linear model method (for example, multiple regression analysis), and it is used to assess whether a hypothesised model is consistent with the gathered data to reflect [the] theory [67].
SEM is a multivariate analytical approach for testing and estimating complex causal relationships among variables, even for a hypothesised relationship or when it is difficult to observe directly [68]. By combining factor analysis and linear regression models, researchers can use SEM to statistically examine the relationships between theory-based latent variables and their indicator variables by measuring directly observable indicator variables [69]. Even though the SEM approach is similar to the multiple regression techniques for testing the relationship between variables, it can simultaneously investigate multi-level dependent relationships, “where a dependent variable becomes an independent variable in subsequent relationships within the same analysis” [70]. It can also determine the relationship between the dependent variables [71].
When using SEM, the internal structural relationship between the constructs, known as structural models, is described by a set of linear regression equations [72,73]. The external relationship between each construct and its indicators, known as the measurement model, is described by the equation of factor analysis [74,75]. A latent variable is a variable that cannot be observed directly and is measured by the respective indicators. In summary, SEM, also known as a combination of factor analysis models, measures the relationship between the latent variable and the indicator and the regression or model of path analysis that describes the relationship between the latent variables.

4. Results

4.1. Measurement Model

To ensure a sufficient fit of the empirical data to the hypothesised measurement model, the evaluation of the fit criteria of the measurement model was performed. In this research, the ability of the model was evaluated via the most frequently used fit indices, including the ratio of chi-square to levels of freedom (χ2/df), goodness-of-fit index (GFI), adjusted goodness of fit index (AGFI), normed fit index (NFI), comparative fit index (CFI), and root mean square error of approximation (RMSEA) as suggested in the recent transportation studies [25,26,76,77]. According to Borhan et al. [26] and Fu et al. [76], the measurement model was regarded as a positive fit when the ratio of chi-square to degrees of freedom was lower than 3.00. Therefore, a positive fit was suggested from the measurement model of this research with the ratio of chi-square to degrees of freedom amounting to 2.543 (<3.00). Furthermore, the values of GFI, AGFI, NFI, and CFI for the measurement model in this study amounted to 0.951, 0.932, 0.954, and 0.964, respectively, which were accepted as stated by Ibrahim et al. [25] and Yilmaz and Ari [77]. The authors also stated that the acceptable values of fit indices were higher than 0.900 [25,77]. Hussain et al. [78] stated that the fit indices of higher than 0.950 indicated excellent fit, while the indices from 0.90 to 0.95 were acceptable. Additionally, the RMSEA value amounted to 0.070, which was lower than the cutoff criterion as suggested in previous studies [25,26,76,77]. The outcome of the evaluation of the fit criteria of this measurement model is summarised in Table 3.
The construct validity was assessed using the three stages recommended by Fu et al. [76] and Ibrahim et al. [25], namely construct reliability, convergent validity, and discriminant validity. The construct validity was measured using the Cronbach’s Alpha value and composite reliability [26,77]. The construct reliability was assessed to develop an internal constancy of the data coefficient measuring instrument for each construct [25]. Table 4 shows that the Cronbach alpha ranges from 0.713 to 0.938, and the composite reliability values range from 0.781 to 0.908. These values indicate that the reliability coefficient for all constructs is acceptable since it is higher than the cutoff criterion of 0.70 [26,77].
The second stage was convergent validity. According to the literature for transport engineering [25,26,28,77], convergent validity is dependent on two criteria. The standardised factor loading (also known as item loading) for all items must be equal to or higher than 0.6 and statistically significant, while the average variance extracted (AVE) value for all constructs should be 0.5 or higher [79]. Table 4 shows that the convergent validity values for all constructs are positive, thus fulfilling the criteria with the standardised factor loading for all items exceeding the 0.6 cutoff point, except for one item in the perceived barrier construct. However, this item is retained because the perceived barrier construct has Cronbach alpha and AVE values within the accepted value. All items were statistically significant (p < 0.01). Additionally, the AVE value is higher than 0.5, and the minimum AVE value for the perceived behavioural control construct is 0.546.
The discriminant validity is the low correlation between two constructs and is measured by comparing the square root value of AVE with the inter-construct correlation values [80]. Table 4 shows that the square root of the AVE of each construct exceeds the value of the interrelations between the construct and other model constructs. Therefore, appropriate discriminant validity was indicated [25,26,77].

4.2. Structural Model and Hypothesis Testing

In this section, the assessment of the structural coefficient of the model in this research was performed to identify the fundamentals in examining the suggested hypotheses. The overall model structure, which predicted the student’s willingness to cycle in the university setting as proposed in this study, is shown in Figure 3 and Appendix B. The result indicated that the proposed model was a sufficient fit for the purpose addressed in this study as the goodness-of-fit indices were within the acceptable ranges (χ2/df = 2.304, GFI = 0.931, AGFI = 0.905, NFI = 0.935, CFI = 0.924, RMSEA = 0.060).
As displayed in Figure 3, the attitudinal constructs could influence the intention of the students to use bicycle in university campus through attitude (β = 0.546, p < 0.001), subjective norms (β = 0.154, p < 0.05), and perceived behavioural control (β = 0.303, p < 0.001). Provided that these constructs had a significant positive and direct impact on student’s behaviour intention, Hypotheses 1–3 were accepted. From the three items, the attitude constructs showed the strongest influence towards the intention of students to cycle in university campus, while the subjective standards had the least influence on the student’s willingness to cycle around the university campus. Furthermore, the perceived barrier construct had a significant negative impact on attitude (β = −0.474, p < 0.001), perceived behavioural control (β = −0.317, p < 0.05), and habit (β = −0.532, p < 0.001). Provided that the relationship between the perceived barrier and subjective norms was not significant, Hypotheses 4, 6, and 7 were supported, while Hypothesis 5 was rejected. In addition, Figure 3 illustrates that habit had a significant and direct effect on three main attitudinal constructs, including attitude (β = 0.410, p < 0.001), subjective norms (β = 0.274, p < 0.01), and perceived behavioural control (β = 0.228, p < 0.001). Therefore, Hypotheses 8–10 were supported. The summary of the interrelationship between each construct for the proposed model in this study is presented in Table 5.

5. Discussions and Policy Implementations

The selection of travel mode is the most frequently debated issue in the transportation literature. The decision to choose the travel mode is subjective and mainly based on the individual, purpose of the trip, and type of transport among others. Several efforts were made by many researchers, engineers, and policymakers in this domain to examine the elements influencing the individuals’ decision regarding the preferred travel mode, especially public transport including buses and rail transit [26,27,28,81,82,83,84,85]. As a result of the emphasis on public transport, the study related to the most sustainable modes of transport, such as bicycle, gained less attention among the key players in this field (e.g., researchers, engineers, and policymakers). The bicycle was undoubtedly known as the “forgotten” mode of transport, as mentioned by Balsas [2]. The performance of the bicycle could not be compared to other modes of transport, which offer a comfortable, faster, and luxurious experience during the journey. However, the bicycle has significant individual, social, and environmental impacts, such as improving body health and saving journey time and cost for a reasonable cycling distance trip, and reducing congestion, parking demand, and air and noise pollution [17].
The current research was performed to investigate the student’s willingness to use a bicycle in a university setting by modifying Ajzen’s Theory of Planned Behaviour [32]. The authors of the current study modified the novel TPB model by incorporating two new constructs, namely perceived barrier and habit. This study formulated the strategy of promoting the use of the bicycle in a university setting to reduce the dependence on motorised transport for commuting as well as to create a green and sustainable campus environment.
Based on the results presented in the previous section, the model in this research received support and sufficiently fit in predicting the student’s intention for the subject matters. The current research recorded that the suggested model led to 73% of the variance elaborated on the case of behavioural intention to use bicycle in a university setting. Furthermore, almost all the hypotheses except Hypothesis 5 were supported. It was indicated from the results that the student’s intention to cycle in a university setting was influenced by three primary attitudinal constructs, such as individual attitude, subjective norms, and perceived behavioural control. Interestingly, this study recorded that attitude had the strongest influence on the willingness to use the bicycle in a university setting, while the subjective norms had the least influence. In this research, the low association between subjective norms and behavioural intention indicated that students were capable of independent decision making whether to use the bicycle to commute in a university setting. In this case, less advice was required from their families, lecturers, and friends. This result was in line with the result by Ajzen and Driver [64], who reported that the individuals’ intention towards recreation activities was mainly influenced by attitude, while the subjective norms had the minimum contribution to the development of individual intention. Additionally, the research conducted in Germany [86] and Canada [87] recorded that attitude construct had the highest impact on the reduction in dependence on car use, while subjective norms had the minimum to no impact. Hsiao and Yang [27] reported similar findings in their recent study, where the attitude was the major influential factor of students’ willingness to take the High-Speed Rail (HSR) for a long-distance trip, while the subjective norms had the least contribution to the subject matters. In recent studies, the role of subjective norms in motivating the individuals’ behaviour intention was found to have a low impact on their willingness to use the Park and Ride service [25]. Besides, the case study of low-cost airlines [28] and HSR [26] recorded that this construct did not have a significant role.
This study also found that perceived barriers could indirectly alter individual intentions, such as the intentions of cycling in a university setting through attitude, perceived behavioural control, and habit. As shown in Figure 3, the perceived barrier construct had a strong influence on habit and individual attitude. Similarly, Sousa et al. [36] recorded that these factors could alter the individuals’ attitude in their decision on the selection of travel mode. In this study, perceived barriers including lack of cycling facilities, extreme weather conditions (e.g., heavy rain, hot and humid environment), and the unsuitable types of topography among others weakened individuals’ attitude towards the bicycle as a transport mode [36,43,48]. This situation led to weaker intention to cycling in a university setting. Moreover, habit had an indirect significant impact on student’s willingness to use the bicycle through attitude, subjective norms, and perceived behavioural control. It was indicated that the low willingness to cycle in a university setting may be attributed from the negative attitude towards cycling activity, the impact of important parties (e.g., parents, lecturers, and friends), and perceptive chances to use the bicycle, which were affected by the habit of using the bicycle. The effect of habit was found to have mutual characteristics with the results in previous studies regarding the individual’s intention to use bicycle for commuting [59,60,61].
The findings of this study are beneficial in formulating effective measures and policies that promote cycling in a university setting. The critical factor in the effort to increase the intention of university students to use bicycles is identifying the perceived barrier for the student intention to cycle on the university campus and eliminating the perceived barriers. Weather is a critical factor for the low student intention to cycle on campus. Located in Southern Asia, Malaysia is hot, humid and receives rain throughout the year. Besides the topography of the university campus, the factors identified as the key barriers to cycling on campus are not owning a bicycle and inadequate cycling facilities that ensure safe cycling. These findings are similar to Nkurunziza et al. [35], who proposed that university administrators allocate a budget to provide cycling facilities on university setting and ensure a safe cycling environment for the students. Because of the hot weather and heavy rain, the university administrator should consider constructing bicycle lanes with a roof or modifying the existing road. It is essential to provide plenty of secure bicycle racks and bicycle parking, particularly at the faculty (classroom) and residential areas, to improve the habit and attitude towards cycling and the students’ intention to cycle.
The limitation of cycling in Southern Asia countries was the exposure to unpredictable and extreme weather, which led to discomfort among the students after cycling. This was a more crucial case when the students cycle to attend class. Therefore, the relevant should focus on providing facilities for showering and changing rooms for commuters. Meanwhile, providing rental or dockless bicycle-sharing service was another method of promoting the use of the bicycle, especially for individuals who do not own a bicycle. The on-campus bike repair service centre was one of the facilities, which should be provided by the university authority to encourage cycling among the campus committee. Overall, these suggested measures were also known as physical policies [26]. When the university authority took an initiative of offering the aforementioned measures and physical policies, the attitude of students to cycle would convince them regarding the benefits of cycling. Subsequently, the willingness of students to cycle in a university setting would be improved. These strategic measures were frequently suggested in the previous studies to promote the use of the bicycle in a university setting [1,15,16].
According to Parkin et al. [88] and Nkurunziza et al. [35], providing the cycling infrastructure is insufficient to create a modal development in bicycle use. Previous works have shown that personal barriers lead to the weak intention to cycle [35,89]. Specifically, individuals believe that they are not familiar with the use of the bicycle, while others regard it as a travel mode only for the poor. An effective way to change the attitude towards bicycle use is changing the bicycling culture and enhancing the image of cycling through educational policy. According to previous research, awareness campaigns are among the educational policies that could increase the intention to use various travel modes, such as low-cost airline service [28] and high-speed rail [26,27]. Universities should use communication channels, such as posters, television, social media (Facebook, Twitter, Instagram and others) and newspapers, to enhance the image of cycling and change student attitude and behaviour towards cycling. According to Borhan et al. [28], the awareness campaign through the mass media could improve comprehension of the message conveyed to the target audience. Stead et al. [90] reported a success story associated with a mass media campaign to reduce speeding on Scotland’s road. According to Linaki et al. [17], providing proper road safety and education to the riders and other road users, such as pedestrians and drivers, could improve safety during cycling and reduce the risk of accidents. Other examples of educational policies are courses on cycling safety, bicycle maintenance and confident cycling. These courses could increase the image of cycling and change people’s attitude and behaviour towards bicycling and increase the acceptance of the benefits of cycling. As the attitude towards cycling improves, there will be voluntary changes in travel behaviour towards bicycle use.
Instead of physical and educational policies, economic policy was another type of employed to promote the use of the bicycle in a university setting. In this type of policy, the ‘push and pull’ strategy was adopted, in which the ‘push’ strategy focused on the methods of controlling the use of cars in a university setting by introducing full-cost parking charge, park and ride policies, car-free zone, and forgoing with expansion of the car parking project in the campus area. Furthermore, the taxation of fuel and cars was considered a promising measure to reduce the dependence on motorised vehicles and promotion of bicycle use [84,91]. On the contrary, the ‘pull’ strategy, such as the omission of the bicycle import tax, was another method of ensuring the public own and choose the bicycle as a travel alternative for short (reasonable) distance trip. In respect of the bicycle import tax in Malaysia, the import duty rate for bicycles on 1 January 2019 was 15%. The bicycle import tax could become a burden for the public in buying the bicycle, which would also affect their perception towards the bicycle. Therefore, the strategy associated with the decrease or omission of bicycle import tax would enhance the possibility of commuting by bicycle among the general public and the university community. On the other hand, the financial incentives were offered to the individuals committed to commuting by bicycle around the university campus, including the ‘pull’ strategy to improve the perception of students towards cycling around the campus. In summary, the ‘push and pull’ strategies had a specific contribution towards the agenda to reduce the dependence on motorised transport and promote the use of the bicycle as a sustainable travel mode. However, adequate public support was a challenge in the implementation of the ‘push’ methods [92]. This finding was in line with the findings from the study of the suitability of diverse transport policy measures, which reported that individuals had a higher possibility to accept the positive (pull) strategies compared to the negative (push) strategies [93].
Concerning other measures implemented to gain the intention of the students regarding bicycle use in a university setting, the university authority should establish the bicycle advisory community on the campus. Subsequently, the student’s needs related to the cycling activity in the campus would be prioritised. This community could play a role as a channel to share opinions, complaints, and suggestions related to cycling in a university setting. Additionally, the university management team could easily prioritise and cater to their needs, which led to an improvement in the users’ attitude, and intention towards bicycle use in a university setting.
In summary, promoting the use of the bicycle in a university setting was not the sole responsibility of one party. Based on the discussion in this study, the university management team, students, important references (e.g., parents, spouses, lecturers and friends), the government, and the local authority should cooperate to change the culture and attitude towards bicycle use among the students in a university setting. These suggested physical, educational, and economic policies could improve the student’s habit, attitude, and perceived behavioural control related to the use of the bicycle in a university setting. These improvements would also change the perception regarding the choice of bicycle as a travel mode in a university setting. Overall, these initiatives contributed to the development of a green and sustainable environment of the university campus.

6. Conclusions

This study offered an in-depth understanding of students’ willingness to use bicycles to commute in a university setting using a modified theory of planned behaviour. The findings of this study indicated that this structural equation model is acceptable, and all hypotheses are supported to predict the students’ willingness to use bicycles on university setting when all goodness-of-fit indices criteria are fulfilled. Attitude has the most profound influence on the willingness to cycle in a university setting, while subjective norms are the least influential. Removing the perceived barrier for cycling increases the students’ habit, attitude, and perceived behavioural control to use bicycles on university setting. Furthermore, particular parties such as the university management team, students, important references (e.g., parents, spouses, lecturers and friends), the government, and the local authority plays a crucial role in influencing students’ intention to cycle. Based on the findings of this study, we recommend implementing physical, educational and economic policies to promote cycling as a way of commuting on university setting. However, it is worth noting that the results of this study apply to universities in the developing countries of Southern Asia, where cycling is not popular. The results of this study should be applied with caution to other countries because of the differences in socio-demographic and cultural characteristics between countries. In summary, this study provides beneficial information that could help planners and advocates effectively design and implement programmes or promotional campaigns to increase cycling in a university setting and promote green and sustainable university settings.

Author Contributions

Conceptualization, A.N.H.I. and M.N.B.; methodology, A.N.H.I. and M.N.B.; software, N.S.D. and N.A.M.Y.; validation, A.N.H.I., M.N.B. and R.I.; formal analysis, N.S.D. and N.A.M.Y.; data curation, A.N.H.I. and M.N.B.; writing—original draft preparation, A.N.H.I. and M.N.B.; writing—review and editing, A.N.H.I., M.N.B. and R.I.; supervision, M.N.B.; project administration, A.N.H.I. and M.N.B.; funding acquisition, M.N.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was sponsored by the Ministry of Higher Education, Malaysia via Project FRGS/1/2021/TK02/UKM/02/1.

Institutional Review Board Statement

Ethical review and approval were waived for this study, due to this study did not involve biological human experiment and patient data.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

All relevant data are within the paper.

Acknowledgments

The authors would like to thank the anonymous reviewers for their helpful suggestions and comments.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Descriptive Statistics

Construct/Item MeanStd. DeviationSkewnessKurtosis
Habit (HB)
I am a habitual bicycle user. I do it impulsivelyHB13.811.069−0.665−0.265
I use bicycle for commuting because I do it all the timeHB24.050.935−0.8450.141
I often use bicycle to commute in a university campusHB33.871.031−0.7100.011
Perceived Barrier (PB)
Extreme climate conditionPB14.140.984−1.0250.402
Long travel distancePB23.601.203−0.419−0.855
A limited number of bicycle racks/parkingPB33.771.058−0.434−0.652
No proper lighting facilities on the campusPB44.060.930−0.9050.532
No bicycle lanePB54.270.974−1.4161.654
Lack of cycling facilitiesPB62.971.3550.027−1.173
Unsuitable type of topographyPB72.951.273−0.036−1.022
No ownership over the bicyclePB83.731.120−0.614−0.332
Attitude (AT)
I believe the bicycle would be an ideal optionAT14.270.870−1.2211.345
Using the bicycle brings benefits to meAT23.991.033−0.9610.521
Cycling is a correct actionAT34.210.888−1.0971.042
I prefer to use a bicycleAT44.070.954−0.8640.275
Subjective Norms (SN)
People who are important to me would support me using the bicycle in the university campusSN13.361.011−0.314−0.074
People who influence me would want me to use the bicycle in university campus instead of other alternativesSN23.391.066−0.310−0.238
People whose opinions I value would prefer that I use the bicycleSN33.730.966−0.5320.084
Perceived Behavioural Control (PBC)
The decision to use the bicycle is within my controlPBC13.810.907−0.318−0.362
I think using the bicycle in the future would be very easyPBC22.971.142−0.112−0.673
Whether or not I use the bicycle is completely up to mePBC33.381.043−0.175−0.369
Behaviour Intention (BI)
I will try to use a bicycleBI13.660.997−0.444−0.213
I intend to use a bicycleBI23.640.993−0.325−0.329
I will make an effort to use the bicycleBI33.501.083−0.394−0.356

Appendix B. SEM Parameter Estimation Results

Mathematics 10 00861 g0a1 550
Figure A1. Final structural model.
Figure A1. Final structural model.
Mathematics 10 00861 g0a1

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Mathematics 10 00861 g001 550
Figure 1. Framework of the proposed model.
Figure 1. Framework of the proposed model.
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Figure 2. Map of the study area.
Figure 2. Map of the study area.
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Figure 3. Final model for prediction the willingness to cycle in a university setting.
Figure 3. Final model for prediction the willingness to cycle in a university setting.
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Table
Table 1. Construct items and reliability analysis of the instruments.
Table 1. Construct items and reliability analysis of the instruments.
ConstructNumber of ItemsCronbach Alpha (α)Adopted from:
Perceived Barrier (PB)80.876[43,44]
Habit (HB)30.924[60,63]
Attitude (AT)40.882[64,65]
Subjective norms (SN)30.915[64,65]
Perceived behaviour control (PBC)30.783[64,65]
Behaviour Intention (BI)30.89[64,65]
Table
Table 2. Socio-demographic profile of the respondent (n = 422).
Table 2. Socio-demographic profile of the respondent (n = 422).
Frequency (n)Percentage (%)
Gender
Male18543.8
Female23756.2
Age
19–2125660.66
22–249422.27
25–27337.82
28–30184.27
Above 30214.98
Level of the Study
Undergraduate students33779.86
Postgraduate students8520.14
Bicycle Ownership
Yes5913.98
No36386.02
Cycling Frequency
Daily133.08
Weekly429.95
Monthly6916.35
Occasionally29870.62
Table
Table 3. The summary of the fit indices value.
Table 3. The summary of the fit indices value.
Fit IndicesResultCut off CriterionStatus
χ2/df2.543<3.000Acceptable
GFI0.951>0.900Excellent *
AGFI0.932>0.900Acceptable
NFI0.954>0.900Excellent *
CFI0.964>0.900Excellent *
RMSEA0.070<0.100Acceptable
* Based on Hussain et al. [78].
Table
Table 4. The result of construct reliability, convergent validity, and discriminant validity.
Table 4. The result of construct reliability, convergent validity, and discriminant validity.
ConstructItem Loading (Range)Cronbach Alpha (α)Composite ReliabilityAVEFactor Correlation
PBHBATTSNPBCBI
PB0.520–0.9270.9150.9080.5580.747
HB0.762–0.8820.7130.8700.691−0.6330.769
AT0.686–0.8530.8810.8530.593−0.5850.6120.77
SN0.738–0.8140.9380.8310.622−0.4370.4540.4830.789
PBC0.642–0.7940.7810.7820.546−0.4560.5870.5440.5840.739
BI0.629–0.8310.8900.7810.547−0.6830.6250.6790.6130.7180.74
Note: The diagonals values (in bold) are the square root of the AVE; p < 0.05. Significantly at a level of 0.05, PB: Perceived Barrier, HB: Habit, AT: Attitudes, SN: Subjective Norms, PBC: Perceived behaviour controls, BI: Behaviour intention.
Table
Table 5. Summary of hypothesis testing model.
Table 5. Summary of hypothesis testing model.
HypothesisRelationshipEstimateStandard ErrorCritical Ratiop-Value
H1AttitudetowardsBehaviour intention0.5460.0206.045***
H2Subjective normstowardsBehaviour intention0.1540.1532.1350.033
H3PBCtowardsBehaviour intention0.3030.1343.561***
H4Perceived barriertowardsAttitude−0.4740.0223.352***
H5Perceived barriertowardsSubjective norms−0.0460.0790.0660.301
H6Perceived barriertowardsPBC−0.3170.0771.3640.045
H7Perceived barriertowardsHabit−0.5320.0292.594***
H8HabittowardsAttitude0.4100.0532.860***
H9HabittowardsSubjective norms0.2740.0252.1480.009
H10HabittowardsPBC0.2280.1343.561***
Note: *** p < 0.001.
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Ibrahim, A.N.H.; Borhan, M.N.; Darus, N.S.; Mhd Yunin, N.A.; Ismail, R. Understanding the Willingness of Students to Use Bicycles for Sustainable Commuting in a University Setting: A Structural Equation Modelling Approach. Mathematics 2022, 10, 861. https://doi.org/10.3390/math10060861

AMA Style

Ibrahim ANH, Borhan MN, Darus NS, Mhd Yunin NA, Ismail R. Understanding the Willingness of Students to Use Bicycles for Sustainable Commuting in a University Setting: A Structural Equation Modelling Approach. Mathematics. 2022; 10(6):861. https://doi.org/10.3390/math10060861

Chicago/Turabian Style

Ibrahim, Ahmad Nazrul Hakimi, Muhamad Nazri Borhan, Nur Shaeza Darus, Nor Aznirahani Mhd Yunin, and Rozmi Ismail. 2022. "Understanding the Willingness of Students to Use Bicycles for Sustainable Commuting in a University Setting: A Structural Equation Modelling Approach" Mathematics 10, no. 6: 861. https://doi.org/10.3390/math10060861

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