Promoting Sustainable Mobility on Campus: Uncovering the Behavioral Mechanisms Behind Non-Compliant E-Bike Use Among University Students

Abstract

Electric bikes (e-bikes) offer a low-carbon, space-efficient solution for campus mobility, yet their sustainable potential is increasingly challenged by patterns of non-compliant use, including speeding, informal parking, and unauthorized charging. This study integrates the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to examine the cognitive and contextual factors that shape such behaviors among university students. Drawing on a survey of 408 e-bike users and structural equation modeling, the results show that non-compliance is primarily driven by perceived usefulness, ease of action, and behavioral feasibility, with affective and normative factors playing indirect, reinforcing roles. Importantly, actual behavior is influenced not only by intention but also by students’ perceived capacity to act within low-enforcement environments. These findings highlight the need to align behavioral perceptions with sustainability goals. The study contributes to sustainable mobility governance by clarifying key psychological pathways and offering targeted insights for designing perception-sensitive interventions in campus transport systems. Furthermore, by promoting compliance-oriented campus mobility, this research highlights a pathway toward enhancing the resilience of transport systems through behavioral adaptation within semi-regulated environments.

1. Introduction

The electric bike (e-bike) has emerged as a popular and sustainable transportation option, particularly among university students [1]. Their affordability, convenience, and eco-friendly nature make them a preferred choice for commuting between dormitories, lecture halls, and recreational facilities. As a low-carbon alternative to traditional motor vehicles, e-bikes align with broader sustainability goals by reducing greenhouse gas emissions and promoting greener mobility practices [2]. However, alongside their widespread adoption on university campuses, concerns about the non-compliant use of e-bikes have risen, posing significant challenges to campus safety, management, and the long-term potential of sustainable transportation systems [3].

Non-compliant behaviors, such as speeding, riding against traffic, improper parking, unauthorized modifications, and unsafe charging practices, are frequently observed among students. These actions bring significant risks to both individuals and the broader campus community [4]. For example, speeding and riding against traffic increases the chances of accidents, putting pedestrians and riders in danger. Improper parking clogs pathways, making it difficult for others to use shared spaces. Unauthorized modifications, such as tampering with speed controls or batteries, create safety hazards and increase energy use, which contradicts the environmental benefits of the e-bike [5]. Moreover, unsafe charging practices, such as using unauthorized charging points or overloading electrical outlets in dormitories, can lead to fire hazards and damaging campus infrastructure. Together, these behaviors harm campus safety, reduce the efficiency of transportation systems, and weaken the potential of e-bikes to contribute to sustainable development.

Addressing these challenges has become an important topic in sustainable campus transportation governance. Studies on this issue often emphasize infrastructure-based influence factors and solutions, such as creating dedicated parking spaces, implementing speed limits, and installing safe charging stations [6]. Such measures have proven effective in mitigating some non-compliant behaviors by providing students with clearer guidance and better facilities [7]. Policy-based approaches, including stricter enforcement of traffic rules and the introduction of penalties, have also been explored as methods to deter unsafe practices. In addition, educational campaigns and peer advocacy programs have shown potential in promoting compliance by fostering awareness and community engagement. Despite these efforts, however, existing studies often overlook the psychological and social drivers of non-compliant behaviors, focusing instead on technical or regulatory solutions [8]. Understanding the motivations behind students’ decisions to engage in these behaviors requires a deeper investigation of the behavioral mechanisms at play, particularly within the unique context of university campuses.

Unlike general urban traffic environments, university campuses represent semi-regulated, socially dense micro-communities where behavioral norms, enforcement intensity, and spatial constraints differ markedly from those in public domains. Students often operate within loosely monitored mobility systems, where peer behavior, informal social expectations, and limited infrastructural capacity play a disproportionately large role in shaping transportation choices [9]. Moreover, the temporal flexibility and lifestyle autonomy associated with campus life create conditions in which convenience and personal discretion may override formal rules. These unique environmental and social dynamics make university campuses both fertile grounds for the diffusion of non-compliant behaviors and strategic intervention points for cultivating sustainable mobility habits.

This study adopts an integrated framework combining the Theory of Planned Behavior (TPB) and the Technology Acceptance Model (TAM) to examine the psychological and contextual factors influencing non-compliant e-bike use among university students [10]. TPB emphasizes the roles of attitudes, subjective norms, and perceived behavioral control in shaping behavioral intention [11], while TAM complements this by introducing perceived usefulness and perceived ease of use as additional cognitive determinants of behavior [12]. By integrating these two models, the study aims to uncover how these psychological constructs interact to influence both behavioral intention and actual non-compliant behaviors in the context of sustainable campus transportation.

The objectives of this study are twofold. First, it seeks to identify the key psychological and contextual factors and paths driving non-compliant e-bike use among university students. Second, it aims to provide evidence-based recommendations for campus administrators and policymakers to promote safer and more sustainable e-bike use. This study holds substantial theoretical and practical significance in the context of sustainable urban mobility. As e-bikes emerge as a low-carbon alternative in university settings, their contribution to sustainability goals depends not only on adoption rates but also on patterns of compliant use. By shifting the focus from infrastructural and regulatory measures to psychological and contextual determinants of non-compliant behavior, this research underscores the role of human factors in shaping the long-term sustainability performance of micro-mobility systems. The insights generated can inform the development of targeted, cognition-sensitive strategies that enhance compliance, reduce safety risks, and ultimately promote safer and greener mobility practices within and beyond campus environments. In doing so, promoting sustainable e-bike use on campus also contributes to the resilience of transport systems by fostering adaptive behaviors and reducing systemic vulnerabilities in semi-regulated environments.

2. Literature Review and Research Hypotheses

2.1. Theoretical Background

This study integrates the TPB and the TAM to construct a comprehensive framework for explaining non-compliant e-bike use among university students [13]. TPB posits that behavioral intention is the proximal determinant of behavior, shaped by three psychological constructs: attitude, subjective norm, and perceived behavioral control [14]. TAM complements this by introducing perceived usefulness and perceived ease of use as key predictors of technology-related behavioral intention [15]. Given the semi-regulated environment of university campuses, combining these models enables a dual-perspective analysis [16]: TPB captures the normative and motivational forces underlying rule-violating behavior, while TAM emphasizes cognitive evaluations shaped by functional considerations. This integrated approach enables a more nuanced understanding of how functional considerations and social influence interact to shape students’ non-compliant use of electric bicycles within semi-regulated campus environments.

2.2. Attitude Toward Non-Compliant Behavior

In the TPB, attitude refers to an individual’s overall evaluation of a behavior as favorable or unfavorable. The prior research has further differentiated attitude into two distinct components [17]; cognitive attitude, which reflects instrumental appraisals based on expected outcomes such as efficiency, convenience, or safety, and affective attitude, which captures affective responses such as enjoyment, autonomy, or exhilaration.

With regard to non-compliant e-bike use, cognitive attitude reflects students’ rational assessment of rule-violating behaviors as utilitarian strategies. Behaviors such as speeding, unauthorized parking, or informal charging may be perceived as effective in reducing travel time or physical effort. When perceived benefits outweigh perceived risks, especially under conditions of lax enforcement, students are more likely to develop favorable intentions toward non-compliance [18]. Therefore, we propose the following hypothesis:

H1. 

Cognitive attitude toward non-compliant e-bike use has a significant positive effect on behavioral intention.

While cognitive evaluations provide functional justification, affective attitude represents the affective motivations underlying behavior [19]. In semi-regulated contexts like university campuses, students may experience intrinsic affective rewards, such as excitement, pleasure, or a sense of freedom, from violating rules, particularly when compliance is associated with inconvenience or loss of autonomy. These affective responses can exert an independent influence on intention formation. Accordingly, the following hypothesis is posited:

H2. 

Affective attitude toward non-compliant e-bike use has a significant positive effect on behavioral intention.

Moreover, dual-process models of decision-making suggest a sequential mechanism in which cognitive appraisals shape affective responses. Individuals are more likely to experience positive emotions when they have already evaluated a behavior as effective or instrumental [20]. In this context, students who perceive non-compliant behaviors as beneficial are more inclined to associate them with pleasurable or relieving affective states. This suggests a directional influence from cognitive to affective attitude. Although some models treat cognitive and affective attitudes as separate but equal predictors of behavioral intention, doing so may overlook how closely the two are connected. Modeling affective attitude as a result of cognitive attitude helps reflect this relationship more accurately [21]. It also avoids overlapping roles in the model and provides a clearer explanation of how students form their intentions in this context. This modeling approach is also consistent with previous TPB-based studies that have explored the interaction between cognitive and affective components of attitude. Thus, the study advances the following hypothesis:

H3. 

Cognitive attitude toward non-compliant e-bike use has a significant positive effect on affective attitude.

2.3. Subjective Norms

Subjective norms refer to an individual’s perceived social pressure to perform or avoid a behavior, shaped by expectations from peers, family, and institutional figures [22]. In collective and relatively closed environments like university campuses, students’ behavioral decisions are often shaped by both injunctive norms (what others approve of) and descriptive norms (what others actually do), which jointly guide behavior through mechanisms of social conformity, observational learning, and fear of social punishments.

In the context of non-compliant e-bike use, observing peers frequently engaging in behaviors such as speeding or unauthorized parking without facing noticeable consequences can foster the perception that such behaviors are socially acceptable or even typical [23]. This perceived normalization reduces psychological resistance and increases behavioral intention. Hence, we formulate the following hypothesis:

H4. 

Subjective norms have a significant positive effect on behavioral intention.

In addition to directly influencing intention, subjective norms may also reshape cognitive evaluations. When rule-violating behaviors are widely practiced and implicitly endorsed, individuals may rationalize them as efficient, justifiable, or even appropriate, leading to more favorable instrumental assessments [24]. Consequently, we derive the following hypothesis:

H5. 

Subjective norms have a significant positive effect on cognitive attitude toward non-compliant e-bike use.

Subjective norms may further affect perceived usefulness, a core construct in TAM, by framing non-compliant behaviors as pragmatic responses to structural inefficiencies [25]. When peers frequently employ such strategies to bypass constraints, these behaviors may come to be viewed as functional and utility-maximizing, as a result of the following:

H6. 

Subjective norms have a significant positive effect on perceived usefulness.

Finally, normative environments can enhance perceived behavioral control by reducing perceived risks or institutional constraints [26]. When peers engage in non-compliance without repercussions, individuals may feel more capable of doing the same. This socially reinforced perception of feasibility increases behavioral confidence. Based on the above, the following hypothesis is proposed:

H7. 

Subjective norms have a significant positive impact on perceived behavioral control.

2.4. Perceived Behavioral Control

Perceived behavioral control (PBC) refers to individuals’ perceived capability to perform a behavior, given both internal resources (e.g., knowledge, skills, confidence) and external conditions (e.g., institutional constraints, environmental support) [27]. In the university context, students’ perceived control over non-compliant e-bike use may be shaped by their familiarity with campus routes, perceived ability to evade monitoring, and the overall weakness of enforcement mechanisms or infrastructure limitations.

According to TPB, higher perceived behavioral control is associated with stronger behavioral intention, as individuals are more likely to intend a behavior when they believe they can execute it with ease [28]. In this context, students who perceive minimal institutional or situational barriers to non-compliant e-bike use are more inclined to engage in such behaviors, due to the following hypothesis:

H8. 

Perceived behavioral control has a significant positive impact on behavioral intention.

Moreover, a strong sense of behavioral control may reinforce cognitive appraisals of non-compliant behavior as rational and effective [29]. When students perceive they can easily violate rules without repercussions, they are more likely to evaluate these behaviors as efficient or advantageous, thereby fostering favorable cognitive attitudes such as the following:

H9. 

Perceived behavioral control has a significant positive impact on cognitive attitude toward non-compliant e-bike use.

Perceived behavioral control may also enhance perceived usefulness by reducing perceived effort and increasing perceived instrumental value [30]. If a student feels capable of bypassing rules to achieve desired outcomes, the behavior may be seen as functionally useful. On this basis, we advance the following hypothesis:

H10. 

Perceived behavioral control has a significant positive impact on perceived usefulness.

Finally, beyond intention, TPB suggests that PBC may directly predict actual behavior, especially when perceived facilitators (e.g., low risk, high autonomy) are present [23]. In semi-regulated environments such as university campuses, students may engage in non-compliant e-bike behavior not necessarily due to strong intention, but because they perceive few barriers and high personal capability. When individuals believe they can act freely and without consequence, behavior may follow independently of deliberate planning. As a result, we propose the following hypothesis:

H11. 

Perceived behavioral control has a significant positive impact on actual non-compliant behavior.

2.5. Perceived Ease of Use and Perceived Usefulness

The TAM identifies perceived ease of use and perceived usefulness as critical cognitive determinants of behavioral intention [31].

Perceived ease of use refers to the extent to which a behavior is seen as requiring minimal cognitive or physical effort. In contrast, perceived behavioral control (PBC) reflects an individual’s overall assessment of their ability to perform the behavior, taking into account both internal resources (e.g., skills, knowledge, confidence) and external constraints (e.g., rules, environment). Psychologically, PEU emphasizes the inherent simplicity of the behavior itself, while PBC emphasizes the feasibility of execution under real-world conditions [32,33]. For instance, using an e-bike on campus may be perceived as easy because it requires little effort to operate (high PEU), but a student may still feel low behavioral control if parking is limited or unauthorized use is penalized (low PBC). Thus, PEU is task-centered, whereas PBC is actor- and context-centered.

In the context of non-compliant e-bike use, students may regard actions such as unauthorized charging or riding on restricted paths as low-effort strategies that circumvent institutional barriers. When a behavior is perceived as effortless, it is more likely to be seen as less risky, more manageable, and therefore more cognitively justifiable. Given the theoretical framework, the following hypothesis is formulated:

H12. 

Perceived ease of use has a significant positive impact on cognitive attitude toward non-compliant e-bike use.

Non-compliant behaviors may be viewed by students as instrumental means to save time, reduce physical exertion, or access convenient locations. Such functional advantages promote a favorable cognitive evaluation, even in the face of formal prohibitions. The preceding analysis leads to the following hypothesis:

H13. 

Perceived usefulness has a significant positive impact on cognitive attitude toward non-compliant e-bike use.

Beyond their direct effects, TAM indicates a structural linkage between the two constructs; behaviors perceived as easier to perform are often judged as more useful, as the reduction in effort enhances perceived efficiency and goal alignment [25]. In this context, when students find it easy to engage in non-compliant behaviors, they may concurrently view such behaviors as more beneficial. From the above rationale, the following hypothesis emerges:

H14. 

Perceived ease of use has a significant positive impact on perceived usefulness.

Moreover, perceived usefulness is theorized to exert a direct influence on behavioral intention [34]. Individuals are more likely to form intentions to act when a behavior is evaluated as functionally effective in achieving desirable outcomes. On university campuses, where students may prioritize expediency and autonomy, the instrumental value of non-compliant behaviors can outweigh normative concerns. Based on this reasoning, we hypothesize the following:

H15. 

Perceived usefulness has a significant positive impact on behavioral intention.

2.6. Behavioral Intention

Behavioral intention is regarded as the most immediate and proximal determinant of actual behavior, reflecting an individual’s conscious commitment to act. According to the TPB, when individuals form strong intentions and encounter minimal situational constraints, these intentions are likely to manifest in observable actions [25]. In this study, students with a higher intention to engage in non-compliant e-bike use are correspondingly more likely to translate that intention into actual behavior. We therefore infer the following hypothesis:

H16. 

Behavioral intention to engage in non-compliant e-bike use is positively associated with non-compliant e-bike use.

Based on the sixteen hypotheses proposed above, a theoretical model is constructed to illustrate the cognitive mechanisms underlying university students’ non-compliant e-bike use, as shown in Figure 1.

3. Research Methods

3.1. Questionnaire Survey

This study aims to explore the mechanisms that shape university students’ non-compliant behaviors in the use of e-bikes by improving the TPB, which requires specific indicators to measure [24]. To ensure the scientific validity and accuracy of the questionnaire, the design process followed several key steps: (1) established and validated measurement instruments were used whenever possible; (2) when established and validated instruments were unavailable, new items were developed by synthesizing constructs from prior TPB- and TAM-based studies, contextualizing them to the specific domain of campus e-bike use, and refining their phrasing through expert review to ensure conceptual clarity and content validity; and (3) the questionnaire needed to undergo a rigorous testing process and pre-tested before use. The questionnaire design for this study was divided into eight sections: (1) cognitive attitude; (2) affective attitude; (3) perceived behavioral control; (4) subjective norms; (5) perceived usefulness; (6) perceived ease of use; (7) behavioral intentions; and (8) actual behavior. All questions were assessed using a five-point Likert scale ranging from 1 Strongly Disagree, 2 Disagree, 3 Neutral, 4 Agree, and 5 Strongly Agree. Finally, the questionnaire was sent to experienced industry experts to verify its reasonableness and accuracy, and a small number of students were invited for a pilot survey, which was answered anonymously to avoid subjective tendencies. Based on the feedback from the pre-test and the experts’ comments, the vague and inaccurate questions were modified, and the final full version was used for data collection. The questionnaire reflects a multidimensional framework for assessing university students’ non-compliant e-bike use, aiming to comprehensively reveal the intrinsic psychological mechanisms and social-environmental factors affecting university students’ e-bike use (see

Table 1. Variables and measures.

Factors	Measurement Items	References
Cognitive Attitude (CA)	AT1. Riding an electric bike at excessive speeds on campus is an effective way to save time. AT2. Parking my electric bike in non-designated areas makes my daily activities more convenient. AT3. If the campus charging facilities are insufficient, using unauthorized charging points is a reasonable choice. AT4. Modifying my electric bike (e.g., increasing speed or battery capacity) can greatly improve its performance. AT5. Overall, I believe that violating the use of electric bikes will not cause serious harm to me or others.	[35]
Affective Attitude (AA)	EA1. I feel angry when I see others’ violations that may pose a danger to myself or others. EA2. I feel anxious if violations may implicate me or put me in danger. EA3. I feel disgusted by violations that clearly violate moral standards or public order. EA4. I may feel disappointed when people around me, especially those I am familiar with (such as classmates, friends), frequently violate regulations.	[20]
Subjective Norms (SN)	SN1. My friends or classmates often ride electric bikes at excessive speeds on campus. SN2. Most students believe that parking electric bikes in non-designated areas is acceptable. SN3. My friends will not criticize me for using unauthorized charging points. SN4. Many people on campus do not take the rules for electric bikes seriously. SN5. If I engage in violations involving electric bike, I don’t think others will consider it irresponsible.	[26]
Perceived Behavioral Control (PBC)	PBC1. It is difficult to avoid speeding while riding an electric bike on campus, especially when I am in a hurry. PBC2. Due to limited space, finding designated parking spots for electric bikes on campus is often challenging. PBC3. If campus charging facilities are insufficient, it is hard to avoid using unauthorized charging points. PBC4. Even with clear rules and penalties, I find it difficult to fully comply with campus electric bike regulations in some situations. PBC5. To avoid violating campus electric bike rules, I feel I need to spend extra time and effort, which can be stressful.	[11,30]
Perceived Ease of Use (PEU)	PEU1. The campus electric bike management measures are clear and easy to understand and follow. PEU2. Adhering to campus electric bike rules does not significantly affect my daily travel plans. PEU3. Following campus electric bike rules makes my travel on campus easier, without worrying about the trouble caused by violations. PEU4. The campus traffic facilities (such as roads, signs, etc.) help me ride an electric bike safely and conveniently.	[16,36]
Perceived Usefulness (PU)	PU1. Using an electric bike in compliance allows me to better integrate into campus culture, showing my respect for campus rules and regulations. PU2. Following electric bike rules helps cultivate good behavioral habits and improve my self-management skills. PU3. Adhering to campus electric bike rules makes my travel on campus more in line with public order requirements, enhancing my civic quality. PU4. Using an electric bike in compliance helps me establish a good personal image, making me more popular and respected on campus.	[13,32]
Behavioral Intention (BI)	BI1. When I need to quickly reach my destination, I plan to ride an electric bike at excessive speeds. BI2. If parking spaces are full, I plan to park my electric bike in non-designated areas. BI3. If campus charging points are inconvenient to use, I might choose unauthorized charging options. BI4. I might consider modifying my electric bike to improve its performance. BI5. Overall, I plan to engage in behaviors that do not comply with campus electric bike regulations.	[28]
Actual Behavior (AB)	AB1. In the past month, I have ridden an electric bike at excessive speeds on campus more than once. AB2. In the past month, I have parked my electric bike in non-designated areas at least once. AB3. I have used unauthorized charging points to charge my electric bike instead of using campus charging facilities. AB4. I have modified my electric bike (e.g., increased its speed or replaced the battery) to meet my needs. AB5. In the past month, I have engaged in behaviors that violated campus electric bike regulations at least once and received a warning or penalty.	[33]

).

3.2. Sample

This study employed purposive sampling, targeting undergraduate and graduate students from various universities across China. The questionnaire was distributed online via several WeChat groups commonly used by university students. These groups include members from diverse institutions and regions, allowing for broad coverage across different geographic and institutional contexts. Participants had to be individuals who own or frequently use e-bikes. The study established several inclusion criteria to select suitable respondents, including the following: (1) demographic characteristics such as age and gender; (2) ownership of an e-bike; (3) the ability to operate an e-bike; and (4) riding experience. The questionnaire was primarily distributed to university students via the internet. Following purposive sampling, eligible participants were identified and informed of the study’s objectives, instructions for completing the survey, and the confidentiality agreement. A total of 500 responses were collected, of which 408 were deemed valid after data screening, yielding a valid response rate of 81.6%.

Table 2. Demographic information and proportion of the respondents.

Demographic Variable	Category	Number of People	Percentage
Gender	Male	297	72.79%
	Female	111	27.21%
Grade Group	Freshman	35	8.58%
	Sophomore	64	15.69%
	Junior	76	18.63%
	Senior	98	24.02%
	Graduate	135	33.09%
Walking Time from Academic Buildings to Dormitories	<10 min	155	37.99%
	10–15 min	164	40.20%
	>15 min	89	21.81%

presents the demographic composition of the 408 valid respondents. Among them, 72.79% were male and 27.21% were female. In terms of academic standing, the largest proportion of respondents were senior undergraduates (24.02%) and graduate students (33.09%), while only 8.58% were freshmen. This distribution shows that the majority of people who use e-bikes are senior students.

3.3. Overview of Statistical Analyses

To empirically test the proposed model and hypotheses, a multi-stage statistical analysis was conducted using SPSS 26.0 and AMOS 26.0. The analytical process included data screening, measurement validation, and structural modeling.

First, descriptive statistics were applied to examine the distributional characteristics of the data, such as means, standard deviations, skewness, and kurtosis. This step ensured that the data satisfied the normality assumptions required for subsequent parametric analyses.

Second, Confirmatory Factor Analysis (CFA) was conducted to evaluate the validity and reliability of the measurement model. CFA is used to confirm whether observed variables adequately represent the underlying latent constructs as theoretically specified. It is particularly appropriate in theory-driven studies like ours, where measurement items are adapted from established frameworks such as the TPB and the TAM. In our analysis, standardized factor loadings, Cronbach’s α, composite reliability (CR), and average variance extracted (AVE) were calculated to assess internal consistency and convergent validity. Discriminant validity was established by comparing the square root of AVE with inter-construct correlations.

Third, SEM was used to examine the hypothesized relationships among latent constructs. SEM is a comprehensive statistical technique that integrates path analysis and factor analysis, enabling the simultaneous estimation of multiple dependent relationships while accounting for measurement error. This method is particularly suitable for our study, which involves a complex model with mediating and interacting variables. The SEM model was estimated using the maximum likelihood method. Model fit was evaluated using multiple fit indices: the chi-square to degrees of freedom ratio (χ²/df ≤ 3), Comparative Fit Index (CFI ≥ 0.90), Goodness-of-Fit Index (GFI ≥ 0.90), and Root Mean Square Error of Approximation (RMSEA ≤ 0.08).

Fourth, to assess indirect effects and mediation mechanisms, we employed bias-corrected bootstrapping with 5000 resamples. Bootstrapping is a non-parametric resampling technique that does not rely on the assumption of normal distribution and is therefore well-suited for detecting indirect effects in complex models. This method estimates the confidence intervals of indirect effects by repeatedly sampling from the observed dataset with replacement. If the 95% confidence interval of an indirect path does not include zero, the mediation effect is considered statistically significant. This approach provides a robust test of the multi-path mediating effects hypothesized in our extended TPB-TAM framework.

In summary, the combination of CFA, SEM, and bootstrapping offers a rigorous and comprehensive analytical approach for testing theoretical models in behavioral research. These methods collectively ensure the validity of the measurement instruments, the accuracy of the model structure, and the reliability of the inferred causal pathways.

4. Results

4.1. Data Distribution Check

Descriptive statistical analysis confirmed that the data followed a normal distribution, with skewness values ranging from −1.018 to 0.152 and kurtosis values between −1.239 and 1.794. All values fell within acceptable thresholds, validating the suitability of the dataset for subsequent statistical analyses.

4.2. Reliability and Validity Tests

Table 3. Results of validity and reliability of the scales.

Constructs	Dimensions	Factor Loadings	Cronbach’s α Coefficients	Composite Reliabilities
Subjective Norm (SN)	SN1	0.794	0.884	0.887
	SN2	0.723
	SN3	0.82
	SN4	0.78
	SN5	0.791
Perceived Ease of Use (PEU)	PEU1	0.817	0.867	0.867
	PEU2	0.775
	PEU3	0.783
	PEU4	0.775
Perceived Behavioral Control (PBC)	PBC1	0.766	0.883	0.883
	PBC2	0.782
	PBC3	0.761
	PBC4	0.766
	PBC5	0.799
Perceived Usefulness (PU)	PU1	0.801	0.871	0.871
	PU2	0.797
	PU3	0.821
	PU4	0.749
Cognitive Attitude (CA)	CA1	0.785	0.892	0.892
	CA2	0.78
	CA3	0.811
	CA4	0.784
	CA5	0.782
Affective Attitude (AA)	AA1	0.766	0.84	0.84
	AA2	0.762
	AA3	0.752
	AA4	0.733
Behavioral Intention (BI)	BI1	0.785	0.874	0.874
	BI2	0.784
	BI3	0.776
	BI4	0.75
	BI5	0.714
Actual Behavior (AB)	AB1	0.766	0.886	0.886
	AB2	0.742
	AB3	0.869
	AB4	0.7
	AB5	0.835

and

Table 4. Correlation between factors.

Factor	AVE	CR	SN	PEU	PBC	PU	CA	AA	BI	AB
SN	0.612	0.887	0.782
PEU	0.62	0.867	0.167	0.788
PBC	0.601	0.883	0.432	0.141	0.775
PU	0.628	0.871	0.186	0.361	0.274	0.792
CA	0.622	0.892	0.349	0.358	0.387	0.347	0.789
AA	0.568	0.84	0.178	0.098	0.149	0.145	0.407	0.754
BI	0.581	0.874	0.315	0.234	0.445	0.387	0.528	0.42	0.762
AB	0.616	0.889	0.164	0.253	0.541	0.347	0.425	0.075	0.552	0.785

present the results of the reliability and validity assessments. Cronbach’s alpha coefficients for all constructs met the acceptable and satisfactory threshold (standardized Cronbach’s $\mathsf{\alpha }$ > 0.7). Composite reliability (CR) values also fell within the recommended range (CR > 0.7), and standardized factor loadings exceeded the minimum acceptable level (all > 0.5), confirming good convergent validity.

Discriminant validity was also established, as the square root of the average variance extracted (AVE) for each construct was greater than its highest correlation with any other construct. The confirmatory factor analysis (CFA) further supported the adequacy of the measurement model, with goodness-of-fit indices as follows: ${\mathsf{\lambda }}^2$/$df$ = 1.319 (≤3), p < 0.001, CFI = 0.913 (≥0.9), and RMSEA = 0.024 (≤0.08); these indicate a satisfactory model fit. As shown in Table 4, all CR values exceeded the recommended threshold of 0.70, indicating strong internal consistency, and all AVE values were above 0.50, suggesting that each construct explains a sufficient proportion of variance in its indicators. These results provide evidence of acceptable convergent validity, confirming that the measurement items effectively represent their corresponding latent constructs.

4.3. Structural Equation Modeling

Following the completion of the reliability and validity assessments, the SEM analysis was conducted to explore the underlying mechanisms of non-compliant e-bike use. Figure 2 illustrates the structure of the SEM, including the standardized path coefficients representing the hypothesized relationships.

Table 5. Model fit criteria of SEM.

	${\mathsf{\lambda }}^2$$/\mathit{d}\mathit{f}$	CFI	RMSEA	GFI
Index	1.319	0.913	0.024	0.906
Criteria	≤3	≥0.9	≤0.08	≥0.9

presents the model fit indices, all of which meet established criteria, indicating a strong correspondence between the model and the observed data.

4.4. Hypothesized Model Test

The hypothesized model tested both the direct and indirect effects among key variables, including cognitive attitude, affective attitude, subjective norms, perceived behavioral control, behavioral intention, and actual behavior. Additionally, the model incorporated perceived usefulness and perceived ease of use to evaluate their interactions with constructs. The structural model results revealed the following:

Cognitive attitude was significantly and positively influenced by perceived ease of use (H12, $\mathsf{\beta }$ = 0.266, p < 0.001), subjective norms (H5, $\mathsf{\beta }$ = 0.184, p = 0.001), perceived usefulness (H13, $\mathsf{\beta }$ = 0.159, p = 0.005), and perceived behavioral control (H9, $\mathsf{\beta }$ = 0.262, p < 0.001).

Affective attitude was significantly and positively affected by cognitive attitude (H3, $\mathsf{\beta }$ = 0.465, p < 0.001).

Perceived behavioral control was significantly and positively influenced by subjective norms (H7, $\mathsf{\beta }$ = 0.479, p < 0.001).

Perceived usefulness was positively affected by perceived ease of use (H14, $\mathsf{\beta }$ = 0.375, p < 0.001) and perceived behavioral control (H10, $\mathsf{\beta }$ = 0.255, p < 0.001), while the effect of subjective norms was non-significant (H6, $\mathsf{\beta }$ = 0.014, p = 0.814).

Behavioral intention was significantly and positively influenced by perceived usefulness (H15, $\mathsf{\beta }$ = 0.216, p < 0.001), perceived behavioral control (H8, $\mathsf{\beta }$ = 0.264, p < 0.001), cognitive attitude (H1, $\mathsf{\beta }$ = 0.272, p < 0.001), and affective attitude (H2, $\mathsf{\beta }$ = 0.260, p < 0.001), while the effect of subjective norms was non-significant (H4, $\mathsf{\beta }$ = 0.012, p = 0.811).

Actual behavior was significantly and positively predicted by both perceived behavioral control (H11, $\mathsf{\beta }$ = 0.387, p < 0.001) and behavioral intention (H16, $\mathsf{\beta }$ = 0.399, p < 0.001).

Consequently, hypotheses H4 and H6 were not supported, while the remaining 14 hypotheses were confirmed. The non-significance of these two paths may be attributed to the nature of the university context, which functions as a semi-regulated environment. In such settings, formal rule enforcement tends to be inconsistent or lenient, reducing students’ sensitivity to normative expectations. Furthermore, university students often exhibit high levels of behavioral autonomy and instrumental rationality, prioritizing personal convenience and utility over social approval. As a result, subjective norms may exert limited influence on their behavioral intentions or evaluations of usefulness. Additionally, the complexity and ambiguity of normative referents, such as peers, teachers, or campus authorities, could weaken the clarity and salience of normative pressure, further attenuating its predictive power. Taken together, these contextual and psychological factors offer a plausible explanation for the weakened role of subjective norms in the current study.

Building on the above analysis, the mediating effects between key variables were further examined. Specifically, the mediating effects of subjective norms on actual behavior and perceived ease of use on actual behavior were calculated. The results are presented in

Table 6. Mediating effects in the extended Theory of Planned Behavior model.

No.	Pathway	Mediating Effect	Variance Accounted for (%)	SE	Bias-Corrected Percentile 95% CI		Sig (Two-Tailed)
					Lower	Upper
1	SN→PBC→AB	0.202	63.13%	0.037	0.145	0.269	***
2	SN→PBC→BI→AB	0.055	17.19%	0.017	0.033	0.088	***
3	SN→PBC→CA→BI→AB	0.015	4.69%	0.006	0.008	0.027	***
4	SN→PBC→CA→AA→BI→AB	0.002	0.63%	0.001	0.001	0.004	***
5	SN→PBC→PU→BI→AB	0.011	3.44%	0.005	0.006	0.022	***
6	SN→PBC→PU→CA→BI→AB	0.002	0.63%	0.001	0.001	0.006	**
7	SN→PBC→PU→CA→AA→BI→AB	0.001	0.31%	0.001	0.001	0.002	**
8	SN→CA→BI→AB	0.022	6.88%	0.009	0.001	0.042	***
9	SN→CA→AA→BI→AB	0.010	3.13%	0.004	0.005	0.017	***
10	PEU→PU→BI→AB	0.030	38.46%	0.010	0.017	0.049	***
11	PEU→PU→CA→BI→AB	0.006	7.69%	0.003	0.002	0.013	**
12	PEU→PU→CA→AA→BI→AB	0.003	3.85%	0.001	0.001	0.005	**
13	PEU→CA→BI→AB	0.027	34.62%	0.010	0.014	0.048	***
14	PEU→CA→AA→BI→AB	0.012	15.38%	0.004	0.007	0.021	***

Note: Standardized estimation of 5000 bootstrap samples, **: p < 0.01, ***: p < 0.001. ** and ***: significant.

.

The effect of subjective norms on actual behavior is mediated by a total of nine mediating pathways. As shown in Table 6, all nine mediating effects were significant, indicating that subjective norms can significantly influence actual behavior through these nine pathways. Among these, Path 1 plays a dominant role, accounting for 63.13% of the total effect. The effect of perceived ease of use on actual behavior is mediated by five mediating pathways. Table 6 indicates that, across all five pathways, perceived ease of use exerts a significant mediating effect on actual behavior. Among these, Path 10 and Path 13 exhibit similar effect sizes (38.46% and 34.62%, respectively), jointly occupying a dominant position.

To further examine the influence of campus size on non-compliant e-bike use, we conducted a one-way ANOVA using walking time between dormitories and classrooms as a proxy for spatial scale. The results show no significant difference in violation scores across distance groups (F = 1.763, p = 0.173 > 0.05), suggesting that campus size does not substantially affect students’ non-compliant behaviors. A likely explanation is that most university campuses, even at their largest, do not involve commuting distances long enough to generate meaningful differences in mobility burden. Furthermore, with the assistance of electric bikes which effectively eliminate the physical effort of travel, the impact of distance becomes even less salient. In this context, campus size may influence the initial decision to purchase an e-bike, but it has limited relevance to whether students choose to use it in a compliant or non-compliant manner. Once students have access to e-bikes, the convenience advantage remains consistent across different spatial scales, and compliance decisions are more strongly shaped by perceived utility, enforcement expectations, and peer norms than by travel distance itself.

In summary, the results support the validity of the integrated TPB-TAM framework in explaining university students’ non-compliant e-bike use. Cognitive attitude emerged as the most influential factor shaping behavioral intention, followed by affective attitude, perceived behavioral control, and perceived usefulness. Actual behavior was driven by both behavioral intention and perceived behavioral control, highlighting the joint role of motivational commitment and situational feasibility. While subjective norms did not directly influence intention or perceived usefulness, they played an important indirect role by shaping students’ perceptions of control and cognitive evaluations. Similarly, perceived ease of use indirectly reinforced intention by enhancing students’ perceptions of usefulness and the rationality of non-compliance. These findings indicate that students’ decisions are primarily driven by perceived efficiency and feasibility, rather than by normative expectations alone. The model demonstrates strong overall explanatory power and confirms that cognition-related mechanisms are central to understanding and addressing non-compliant mobility behavior in campus settings.

5. Discussion

5.1. Understanding the Cognitive Mechanisms Behind Non-Compliant E-Bike Use

The mechanisms behind non-compliant e-bike use on university campuses differ significantly from those in typical public or urban settings. In public spaces, rule-following behavior is often shaped by strong external factors such as visible enforcement, legal penalties, and formal monitoring systems [37]. In contrast, university campuses are relatively closed and loosely regulated environments. Enforcement is often inconsistent, consequences for rule violations are minimal, and peer behavior plays a larger role in shaping perceived norms. This allows students to rely more on personal reasoning and social observation than on institutional authority when making mobility decisions. Non-compliant behavior such as speeding, improper parking, unauthorized charging, or e-bike modifications are often viewed not as risky or deviant, but as convenient solutions to everyday problems such as long distances, lack of charging facilities, or tight schedules.

Within this context, our findings suggest that non-compliant behaviors are not simply the result of impulsiveness or disregard for rules. Instead, they emerge from a complex process of cognitive evaluation and contextual adaptation [38]. Students weigh perceived benefits, required effort, likelihood of detection, and the behavior of peers before acting. This highlights the importance of internal psychological factors, such as perceived usefulness, ease of action, and behavioral feasibility, in shaping decision-making. The integrated TPB-TAM framework adopted in this study captures these dynamics by linking cognitive evaluations with behavioral intention and actual behaviors, offering a more comprehensive understanding of why non-compliance persists in semi-regulated environments like university campuses.

At the core of this decision-making process is cognitive attitude, which reflects students’ overall appraisal of non-compliant behavior as reasonable, advantageous, or harmless [39]. When students perceive such behavior as a pragmatic solution to common problems such as long walking distances, lack of charging stations, or insufficient parking they are more likely to judge it favorably. This instrumental evaluation is shaped by a perceived imbalance between campus-level restrictions and everyday mobility needs. In this sense, cognitive attitude represents not only a belief about effectiveness, but also an implicit critique of the institutional conditions under which compliance is expected.

Closely related to this is perceived usefulness, which plays a foundational role in shaping students’ instrumental beliefs. It captures students’ perceptions of whether non-compliant behavior contributes meaningfully to goal achievement such as saving time or reducing physical effort. While cognitive attitude reflects an overarching evaluative stance, perceived usefulness is task-specific and utility-oriented [40]. Importantly, perceived usefulness serves as an antecedent to cognitive attitude in this model, indicating that students form global judgments about the acceptability of non-compliance based on its anticipated functional value. This layered structure of reasoning reveals that behavioral intention is grounded in a hierarchy of cognitive assessments, ranging from specific functional beliefs to generalized evaluative dispositions.

Perceived ease of use further contributes to this reasoning chain by lowering the cognitive threshold for justifying non-compliance. Students are more likely to endorse and participate in non-compliant behaviors when these behaviors are perceived as simple, intuitive, or requiring minimal effort [41]. Unlike perceived behavioral control, which concerns students’ belief in their ability to overcome situational barriers, perceived ease of use reflects the inherent simplicity of the behavior itself. In a campus context, where signage is often unclear and restrictions are unevenly enforced, students may find it easier to justify behaviors that are convenient and cause no immediate disruption. The fact that perceived ease of use indirectly enhances both perceived usefulness and cognitive attitude underscores its role in normalizing non-compliant behavior by minimizing perceived behavioral complexity.

Perceived behavioral control represents a different dimension of feasibility. It refers to students’ confidence in their ability to execute non-compliant behaviors, despite external constraints such as monitoring, disciplinary action, or environmental limitations [23]. This construct captures both internal resources (e.g., experience, knowledge of enforcement loopholes) and external factors (e.g., lax regulation, peer behavior). Notably, perceived behavioral control contributes to both behavioral intention and actual behavior, indicating that when students believe that rule-breaking is within their control, they are more likely not only to intend such behavior but also to act on it even in the absence of strong intention. This underscores the importance of campus enforcement visibility and perceived institutional presence in shaping student decision-making.

The model also reveals a complementary relationship between affective and cognitive components of attitude. Rather than acting as a separate pathway, affective attitude is found to be shaped by cognitive judgment [42]. When students believe that non-compliance is useful, easy, and justified, they are less likely to experience guilt or anxiety, and more likely to exhibit neutral or even positive affective responses. This sequential relationship suggests that emotions function to reinforce rather than initiate behavioral intention. In semi-regulated environments, the absence of affective deterrents may further reduce psychological barriers to action.

The influence of subjective norms in this context is largely indirect. While students do not appear to base their behavioral intentions on perceived social approval or disapproval, they do draw inferences from the behaviors of others [43]. Observing peers engaging in non-compliant behavior without consequence may serve as a cue that such actions are acceptable or at least low risk. This normative observation strengthens perceived behavioral control by lowering expectations of enforcement and contributes to cognitive attitude by signaling that the behavior is common, tolerated, or even necessary under existing conditions. Thus, subjective norms operate through the channel of social learning, rather than normative compliance, reinforcing the perception that non-compliance is socially and institutionally feasible.

Taken together, these findings illustrate a cognition-dominant mechanism in which behavioral intention and action are jointly shaped by students’ instrumental evaluations, perceptions of feasibility, and contextual interpretation. Rather than rejecting regulations outright, students engage in rule-violating behaviors when such actions are perceived as efficient, manageable, and low in social or institutional cost. Affective and normative factors, while not absent, operate through their influence on these core cognitive structures. This perspective reframes non-compliance not as a deviant or oppositional act, but as an adaptive behavioral strategy shaped by the interaction of personal reasoning and structural context.

5.2. Practical Implications

The findings of this study offer several practical implications for campus transportation management and policy design. Rather than treating non-compliant e-bike use as a simple matter of rule enforcement or behavioral discipline, the results suggest that interventions should focus on reshaping students’ cognitive evaluations of these behaviors [19]. Since non-compliance is largely driven by perceived usefulness, ease of execution, and behavioral feasibility, effective governance must address the structural and psychological factors that legitimize rule violations in students’ daily decision-making [44].

First, campus administrators should prioritize the removal of functional incentives for non-compliance. For example, expanding the availability of designated parking spaces, improving the distribution and accessibility of authorized charging stations, and ensuring convenient access to academic and residential buildings via compliant routes can significantly reduce students’ perceived utility of non-compliant behaviors. When compliant behavior aligns with students’ goals of saving time and effort, their cognitive and affective evaluations are more likely to shift toward compliance.

Second, management strategies should aim to increase the perceived complexity and potential consequences of non-compliant behavior. This includes improving the visibility of enforcement mechanisms such as random patrols, warning signage, or digital monitoring systems to reduce students’ perceived behavioral control over rule-breaking actions [45]. Notably, the study shows that students act not necessarily because they intend to violate rules, but because they perceive that they can do so easily and without penalty. Small increases in the perceived risk or difficulty of non-compliance may substantially weaken the cognitive justifications that support such behavior.

Third, campus policies should consider using behavioral framing and informational interventions to reshape students’ perceptions of usefulness and ease of compliance. For instance, informational campaigns can highlight the benefits of following regulations such as safety for all road users, environmental sustainability, and the preservation of shared spaces while also addressing common misconceptions (e.g., that unauthorized charging is harmless or that speeding has no consequences) [27]. Presenting compliant behavior as not only rule-abiding but also efficient and socially valued can challenge students’ current utility-based logic and facilitate more favorable cognitive attitudes.

Fourth, efforts to influence social perceptions should focus less on promoting explicit moral norms and more on altering descriptive norms that are students’ perceptions of what is typical or accepted behavior [46]. Highlighting the prevalence of compliant behavior through peer-led programs, student ambassadors, or social media storytelling can recalibrate what students perceive as common and appropriate. When rule-following is made visible and normalized, the social cues that currently support permissiveness may begin to promote conformity with regulations.

Fifth, in addition to regulating e-bike use, campus authorities may consider promoting traditional bicycles as a sustainable and health-enhancing alternative. Compared to electric bikes, traditional bicycles generate no emissions, require no charging infrastructure, and contribute positively to students’ physical well-being. Encouraging their adoption, through measures such as expanding bicycle lanes, providing secure bike parking, offering rental or subsidy programs, and integrating cycling into campus culture, can reduce reliance on e-bikes and mitigate associated non-compliance issues. Future policy design should explore how to shift student preferences toward conventional bicycles, particularly by emphasizing their environmental, health, and community benefits in relation to campus life.

In summary, promoting safe and sustainable e-bike use on campus requires a shift from reactive punishment toward proactive cognitive intervention. By targeting students’ beliefs about usefulness, ease, and feasibility, while altering the structural and social conditions that support those beliefs, university administrators can develop more effective, perception-sensitive strategies for behavioral regulation [47]. These insights are particularly relevant in decentralized environments like university campuses, where formal authority is limited, and individual judgment plays a central role in daily mobility decisions.

5.3. Limitations and Future Research

While this study offers valuable insights into the cognitive mechanisms underlying non-compliant e-bike use among university students, several limitations should be acknowledged, providing directions for future research.

First, the study relies on self-reported data to measure behavioral intention and actual behavior, which may be subject to social desirability bias or recall inaccuracies. Although the survey was conducted anonymously and utilized validated instruments, respondents may underreport or rationalize non-compliant behavior. Future studies could complement self-report methods with behavioral tracking technologies, such as GPS-based mobility logs or on-campus observation, to obtain more objective and granular behavioral data.

Secondly, due to the anonymous nature of data collection, institutional-level characteristics such as campus location (urban vs. rural) and type (public vs. private) were not recorded. This limits the ability to assess how institutional diversity might influence behavioral patterns. While the sample included students from across China, future research should adopt stratified or multi-campus sampling designs that explicitly account for institutional attributes to improve external validity. Cross-national studies could further explore the role of cultural and regional factors in shaping non-compliant e-bike behavior.

Additionally, this study focuses on electric bicycles as a mode of campus transportation, but traditional bicycles, which could also serve as an alternative, were not explored. Compared to electric bicycles, traditional bicycles have a lower environmental impact and contribute to students’ physical health, particularly in campus environments characterized by frequent short-distance travel. Future research could investigate behavioral and policy interventions that encourage students to adopt traditional bicycles instead of electric bikes, thereby enhancing both environmental outcomes and student well-being.

Finally, the study focuses on individual-level decision-making and does not address the institutional, technological, or policy-level dynamics that shape the broader governance of e-bike use. Future research could adopt a multi-level approach that integrates stakeholder perspectives (e.g., campus administrators, security personnel), examines institutional constraints (e.g., budget, space allocation), and explores the design and implementation of behaviorally informed policy interventions.

6. Conclusions

This study investigated the psychological mechanisms underlying non-compliant e-bike use among university students by integrating the TPB and the TAM. The proposed model reveals that behavioral intention is primarily shaped by cognitive attitude, which is significantly influenced by perceived usefulness, perceived ease of use, subjective norms, and perceived behavioral control. Affective attitude plays a reinforcing but secondary role, largely shaped by cognitive evaluations. Actual behavior is jointly predicted by behavioral intention and perceived behavioral control, suggesting that students act not only because they intend to, but also because they feel capable of doing so under weak institutional constraints. Notably, subjective norms influence behavior indirectly through perceived feasibility rather than direct normative pressure, highlighting the role of social cues in shaping risk perception and rule tolerance.

These findings reframe non-compliant behavior as a reasoned and context-adaptive decision, rather than an impulsive or norm-driven response. From a management perspective, this calls for cognition-oriented governance strategies that target students’ instrumental reasoning. Practical measures should focus on reducing the perceived utility and ease of non-compliance, for example, by improving charging infrastructure, clarifying behavioral boundaries, and increasing the perceived difficulty or risk of violations through low-intensity enforcement and normative signaling. Educational campaigns should emphasize not only rule awareness but also the personal and collective costs of seemingly minor violations.

Future research should address the limitations of self-reported behavior by incorporating objective mobility data and extend the model to diverse institutional contexts to assess external validity. Additionally, integrating campus-level structural and regulatory variables could provide a more comprehensive understanding of how individual cognition interacts with institutional design to shape sustainable mobility outcomes.

Ultimately, promoting sustainable mobility on campus through behaviorally informed strategies not only supports low-carbon transitions but also enhances the resilience of transport systems by fostering adaptability, reducing systemic risks, and improving governance in semi-regulated environments.