A Longitudinal Survey Exploring the Psychological Determinants of Concealed Smartphone Use While Driving: Insights from an Expanding Theory of Planned Behavior

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In the context of legislation banning the use of smartphones while driving, concealed smartphone use while driving (CSUWD) has become a prevalent form of distracted driving, which has negative consequences for driving behavior and road safety. This study aims to investigate the psychological determinants of CSUWD using the Extended Theory of Planned Behavior. The results of this current study provide important insights for formulating non-legal intervention measures to prevent CSUWD-related traffic injuries and strengthen road safety.

Abstract

Concealed smartphone use while driving (CSUWD), a prevalent and covert form of distracted driving, poses significant threats to road safety. However, the psychological determinants underlying this illegal behavior remain underexplored. A two-wave longitudinal study based on the expanding theory of planned behavior (TPB) investigates the intention and prospective behavior of CSUWD in China. In the first wave, 256 respondents assessed the standard TPB constructs, alongside extended constructs of descriptive norms, moral norms, and perceived risks. Subsequently, 156 participants reported their actual behavior in the second wave. Hierarchical multiple regression results revealed that the traditional TPB variables accounted for 57.1% of intention variance and 45.2% of behavior variance, while extended variables contributed an additional 11.7% to intention variance. All variables, except perceived crash risk, emerged as significant determinants of intention. Notably, the perceived risk of being caught and fined inversely correlated with intention, suggesting a potential disinhibition effect. Both perceived behavioral control and intention were significant determinants of subsequent behavior. The findings underscore the validity of TPB in predicting CSUWD, informing the design of non-legal interventions (e.g., public education advertisement, road awareness campaigns, and technological interventions) to mitigate CSUWD-related distracted driving and promote sustainable transportation systems.

1. Introduction

1.1. Concealed Smartphone Use While Driving (CSUWD)

Distracted driving poses a significant global hazard to road safety. Among the numerous sources of distraction, smartphone use while driving (SUWD) is the most prevalent form of driver distraction, including two different types of sub-behaviors: obvious and concealed. Compared to the obvious SUWD, CSUWD involves drivers deliberately positioning their smartphones in obscured locations to conceal the act from external observers, such as beneath tinted windows or behind the steering wheel (see Figure 1) [1]. This covert behavior reflects premeditated intention, as drivers are aware of its illegality, yet choose to engage in it [2]. It also challenges law enforcement agencies to detect and regulate it, exacerbating its public safety risks.

1.2. The Hazards of CSUWD

CSUWD represents a perilous behavior that exacerbates driver distraction by compelling individuals to divert their visual, cognitive, and manual attention from the roadway to their concealed smartphones [3]. This interaction modality engenders a higher degree of visual-manual distraction than general SUWD, as the spatial separation between the driver’s gaze and the hidden device necessitates continuous head adjustments and manual manipulations. Consequently, this intensified visual, cognitive, and physical diversion significantly elevates the risk of collision or near-miss incidents [4]. Multiple simulator-based empirical studies [5,6] have consistently demonstrated that performing visual-manual interactions with smartphones while driving impairs key performance metrics, with the magnitude of impairment escalating as the angular displacement between the driver’s forward vision and the smartphone’s hidden location increases.

1.3. The Prevalence of CSUWD

The prohibition of SUWD has been legislated in most countries and regions for years, including the United States, the United Kingdom, Australia, and China (covered by this survey). However, recent global epidemiological studies on SUWD indicate that penalty-based enforcement has had limited or no discernible impact on drivers’ road safety compliance, traffic collision rates, or injury-related claims [7,8,9]. A qualitative research by Rudisill et al. [10] suggests a key reason is that drivers deliberately conceal their smartphone usage to impede law enforcement by obscuring evidence and complicating arrests. Consistent with this example, multiple studies highlight that drivers frequently use covert smartphones to evade the traffic police’s detection and penalties [1,2,3,4]. Notably, an online survey by Oviedo-Trespalacios [11,12] revealed a paradoxical trend—drivers’ attempts to evade arrest, punishment, and legal sanctions may prompt drivers to increase CSUWD, contradicting the intended deterrent effects deliberately.

1.4. The Current Research

By comparison, law enforcement agencies may encounter greater complexity in addressing CSUWD than other road safety issues, such as speeding, drunk driving, and traffic violations. Consequently, in addition to existing regulations, exploring alternative measures is imperative to mitigate this behavior collectively [13]. One viable approach is to delve into the underlying motivations that prompt CSUWD, which can facilitate the development of more targeted strategies against CSUWD, such as initiating safety awareness campaigns, implementing public education programs, and advancing in-vehicle technologies [14]. For instance, a recent study examined various factors influencing the likelihood of CSUWD behavior, aiming to construct a secure and sustainable transportation system [15]. Previous research has demonstrated that understanding key psychological determinants significantly enhances the design and testing of road safety message content targeting other unsafe and illegal driving behaviors [16,17,18]. Therefore, given the pervasive nature and substantial risks to road safety posed by CSUWD, unraveling the underlying psychological mechanisms driving drivers’ engagement in this behavior is paramount.

2. Theoretical Framework, Expanded Constructs, and Conceptual Model

2.1. Theory of Planned Behavior (TPB)

Transportation psychology and behavioral science scholars often utilize theoretical frameworks such as the TPB to analyze the core decision-making mechanisms underlying unsafe and illegal driving behaviors. According to TPB [19], the most proximate predictor of behavior is behavioral intention, which is shaped by the interplay of three key components: attitude, subjective norms, and perceived behavioral control. Attitude refers to how an individual evaluates a behavior, viewing it as positive or negative based on their behavioral beliefs about the perceived benefits or drawbacks of that action. Subjective norms represent the perceived social pressures individuals experience from significant others regarding whether to engage in a behavior, influenced by normative beliefs about others’ approval or disapproval. Perceived behavioral control describes an individual’s assessment of the ease or difficulty in performing a behavior, shaped by control beliefs that identify barriers or facilitators influencing the action. Occasionally, perceived behavioral control can directly influence behavior [20].

To date, the TPB has been extensively employed to forecast a diverse array of risky driving behaviors, encompassing fatigued driving [21], speeding [22], drunk driving [23], and distracted driving [24]. However, the number of studies explicitly applying the TPB framework to identify the psychological factors associated with CSUWD is relatively limited. Notably, Gauld [1] was among the first to propose that the TPB is a crucial theoretical framework for comprehending the various factors that influence CSUWD behavior. Building on this, Gauld et al. [2] conducted TPB-based focus group interviews, uncovering various behavioral, normative, and control beliefs related to CSUWD and general SUWD. In summary, overt and covert smartphone usage while driving emerges as fundamentally distinct, characterized by varying underlying motivators and their relative significance. Gauld et al. [3] further advanced this line of inquiry by integrating the concepts of moral norms, mobile phone involvement, and anticipated regret into an expanded TPB model, thereby examining the psychological factors influencing intention and behavior about CSUWD. Similarly, Eren et al. [4] incorporated expected action/inaction regret and problematic mobile phone use into an expanded TPB model, delving into the psychological factors impacting young drivers’ intentions and actions related to CSUWD.

Regarding research methods, the limited TPB-based studies on CSUWD have all employed a two-wave longitudinal survey to collect data for the self-management questionnaire [1,3,4]. Specifically, the first survey (Time 1) assessed drivers’ attitudes, subjective norms, perceived behavioral control, and intentions towards CSUWD. The second survey (Time 2), conducted a week later, evaluated drivers’ subsequent CSUWD behaviors. This temporal interval setting avoids the issue of insignificant changes in variables due to a short time frame while mitigating the impact of memory biases or external environmental changes that could occur over a more extended period. The longitudinal survey design is prevalent in behavioral science, offering a robust platform to capture the dynamic relationships and temporal shifts in causal associations among variables. On the one hand, a longitudinal survey design helps to reveal the dynamic changes in the mechanism of action of each variable during the transformation from behavioral intention to actual behavior. On the other hand, through two data collections, it is possible to more accurately assess the correlation between behavioral intention and subsequent actual behavior, thereby verifying the applicability of the TPB in explaining complex driving behaviors.

2.2. Expanded Constructs

Even though existing research has highlighted the explanatory value of the TPB regarding CSUWD [1,2,3,4], its predictive capacity remains limited. A significant amount of variance is still unaccounted for, necessitating further investigation. Therefore, expanding the TPB by integrating additional variables may assist in explaining a greater proportion of the variations in intention and behavior associated with CSUWD [25]. To date, the existing literature on the psychological determinants of risky driving behavior not only encompasses the standard TPB variables but also incorporates other factors such as descriptive norms [26,27,28,29,30], moral norms [3,31,32,33,34], and perceived risks [35,36,37,38,39]. However, the precise manner in which these constructs contribute to the intention and behavior related to CSUWD is yet to be fully understood. Therefore, this study aims to integrate these three variables and establish an extended TPB model to explain the driver’s intentions and behaviors related to CSUWD.

Subsequent literature pertains to the three central constructs of descriptive norms, moral norms, and perceived risks, potentially augmenting the explanatory power and interpretability of CSUWD.

2.2.1. Descriptive Norms

Including additional normative variables can prove advantageous since subjective norms often exhibit the weakest predictive power owing to their restricted components, as highlighted in the prior meta-analysis concerning the TPB [25]. Descriptive norms mirror an individual’s beliefs regarding specific behavior, formed by observing others’ actions. Studies conducted by Donmez’s team [26,27] revealed that drivers reported a higher incidence of distracted driving when they believed other drivers frequently engaged in technology-related distractions. Moreover, research by Berenbaum et al. [28] and Brown et al. [29] indicated that descriptive norms significantly influence drivers’ intentions to engage in SUWD. Nicolls et al. [30] also identified a substantial correlation between descriptive norms and the intention to practice smartphone activities during driving. Consequently, adults who perceive that most drivers around them frequently engage in CSUWD are more inclined to partake in such behavior.

2.2.2. Moral Norms

Moral norms play a pivotal role in shaping an individual’s moral compass when distinguishing between right and wrong, particularly in the context of specific actions [3,31,32,33,34]. Integrating moral norms into unsafe and illegal behavior investigations can offer valuable insights. For instance, Nemme and White [31] discovered that moral norms significantly forecasted drivers’ intentions and subsequent behaviors regarding speeding under the influence of drugs or alcohol (SUWD). Similarly, Gauld et al. [3,32] advocated including moral norms to deepen our understanding of the motivations and actions related to CSUWD. Studies conducted by Kim [33] and Khanjani et al. [34] consistently revealed that moral norms deter the intention to engage in SUWD. Hence, it stands to reason that drivers who perceive CSUWD as morally reprehensible are less inclined to engage in such behavior.

2.2.3. Perceived Risks

Generally, risk perception plays a significant role in dangerous driving activities [35,36,37,38,39]. Coincidentally, drivers also face two significant risks when they consciously engage in CSUWD: the perceived risks of crashing and getting caught and fined. For instance, Prat et al. [35] observed a positive correlation between the perceived risks of crashing and the SUWD. In contrast, Przepiorka et al. [36] found that the perceived risk of crashing negatively impacted SUWD. They also discovered that the perceived risk of being caught and fined did not significantly influence this behavior. Sullman et al. [37] suggested that these two perceived risks did not notably affect intentions regarding SUWD among Ukrainian drivers. Phuksuksakul et al. [38] further concluded that the perceived risks of crashing did not significantly influence either intentions or actual behavior related to SUWD. However, Sullman et al. [39] later argued that the perceived risks of crashing and getting caught and fined are crucial factors in determining SUWD behaviors in the UK. In light of these inconsistent findings, there is a strong need for further research to examine the causal relationship between these two risk perceptions and CSUWD.

2.3. The Research Gaps and Conceptual Model

In summary, there are still some gaps in the existing scientific literature regarding the psychological causes of CSUWD that need to be addressed. Firstly, while an increasing number of TPB-based studies [26,27,28,29,30,31,32,33,34,35,36,37,38,39] have examined the psychological determinants of general SUWD behaviors, a paucity of research specifically focuses on the intentions and behaviors related to CSUWD. Secondly, unlike previous longitudinal investigations [1,2,3,4] that primarily concentrated on specific smartphone actions while driving, such as concealed texting or social media, this study delves into a wide array of smartphone usage behaviors, offering a more intricate understanding of the phenomenon. Thirdly, the existing TPB model fails to provide adequate explanatory power for CSUWD. Beyond the traditional constructs of TPB, it remains unclear how descriptive norms, moral norms, and perceived risks influence CSUWD [26,27,28,29,30,31,32,33,34,35,36,37,38,39]. Hence, the present study seeks to bridge these knowledge gaps by conducting a two-wave longitudinal survey to uncover the psychological determinants of CSUWD through an augmented version of the TPB. On the one hand, this endeavor contributes to the extant body of literature on CSUWD by offering novel insights. On the other hand, it furnishes valuable guidance for devising non-legal countermeasures (e.g., public awareness campaigns, road safety initiatives, and in-vehicle technological interventions) aimed at curtailing the pervasive and hazardous practice of CSUWD and fostering the advancement of sustainable transportation.

Drawing upon the theoretical underpinnings of the TPB and the preceding literature, an extended TPB model was devised (refer to Figure 2), giving rise to the following hypotheses (H) to steer the inquiry.

H1: 

The three classical TPB components—attitude (H1a), subjective norms (H1b), and perceived behavioral control (H1c)—will synergistically forecast the intention to engage in CSUWD.

H2: 

The augmented variables of descriptive norms (H2a), moral norms (H2b), perceived risks of crashing (H2c), and perceived risks of being caught and fined (H2d) will contribute additional explanatory power to the intention behind CSUWD.

H3: 

Perceived behavioral control (H3a) and the behavioral intention (H3b) will predict the future actual engagement in CSUWD.

3. Materials and Methods

3.1. Respondents

To participate, individuals were required to possess a valid Chinese driver’s license, own a personal vehicle and smartphone, and be residents of Chengdu and Zhenjiang, China. The recruitment process yielded a diverse pool of participants: 86 were engaged through educational lectures, 103 were directly approached in parking lots of local shopping centers, and 67 were acquired using a snowball sampling strategy via popular social media platforms like TikTok, Facebook, and REDnote. The recruitment snapshot of respondents can be found in Figure 3.

As detailed in

Table 1. Respondents’ information from the Time 1 survey (N = 256) and Time 2 survey (N = 156).

Demographics	Category	Time 1 Survey (N)	Time 2 Survey (N)
Gender	Male	120	71
	Female	136	85
Age	18–35	139	90
	35–50	80	41
	50–59	37	25
Education level	Junior or below	11	7
	Senior	75	39
	College or above	170	110
Driving location	Largely urban area	157	105
	Both urban and rural areas	76	38
	Largely rural area	23	13

, 256 respondents between 18 and 59 (Mean = 41.75, Standard Deviation = 7.33) participated in the Time 1 survey. The majority held a college degree and frequently drove within urban environments. Gender distribution was relatively even. Owing to the dedicated efforts of the research team, 156 participants completed the Time 2 survey, leading to an attrition rate of 39.1% between the T1 and T2 surveys. The sample size was determined by the widely accepted general rule that each observed variable should have 5 to 10 observations when latent variables have multiple indicators [1,2,3,4]. Moreover, the sample size was similar to previous two-wave longitudinal surveys related to distracted driving behavior [3,4,14,31]. Although the Time 2 sample showed an average age of 0.74 years lower than Time 1, with a marginally lower proportion of male participants, these discrepancies remained within a 5% margin. Furthermore, the distributions of respondents’ education level and driving location in the Time 1 and Time 2 surveys were also very similar.

3.2. Measures

Similar to the existing TPB literature on SUWD and CSUWD [3,4,14,31], this research adopted a longitudinal, two-wave survey design with a one-week interval between data collection points. Participants completed a self-administered questionnaire encompassing diverse aspects. Firstly, the questionnaire elicited social demographic details, such as age, gender, and educational attainment. Secondly, at Time 1, the survey employed the well-established TPB constructs for self-management, incorporating variables like attitude, subjective norms, perceived behavioral control, and intention. Additionally, it included supplementary variables addressing descriptive norms, moral norms, and perceived risks. The measurement items were informed by extant English literature and interpreted into Chinese for practical purposes. In order to enhance the cultural adaptability to the Chinese context, we consulted with three traffic safety experts and two professional translators to ensure the questionnaire’s reliability and validity. Furthermore, to prevent potential problems that might arise during large-scale investigations, a pilot test involving 20 Chinese drivers was conducted, and the wording of the items was fine-tuned before finalization.

Following the proposal of Ajzen [19,20], the questions were also designed according to the principle of behavior, action, context, and timeframe. Consistent with CSUWD-related literature [1,3,4], the target behavior for this study was “CSUWD.” The scenario presented to the participants was as follows: “Imagine that you are driving a regular car, commuting on the city roads with a speed limit of 60 km per hour. The current weather conditions are excellent, and the temperature is enjoyable.”

Table 2. The measurement items, adapted source, and Cronbach’s Alpha of the variables.

Variables	Measurement Items	Supporting Literature	Cronbach’s Alpha
INT	I will engage in CSUWD.	[1,2,3,4]	0.841
	I will use my smartphone in a concealed manner while driving.
	I will covertly interact with the smartphone while driving next week.
ATT	For me, CSUWD is unwise (1) to wise (7).	[1,2,3,4]	0.830
	For me, CSUWD is unnecessary (1) to necessary (7).
	For me, CSUWD is unpleasant (1) to pleasant (7).
SN	People important to me think it is okay for me to engage in CSUWD.	[1,2,3,4]	0.845
	People important to me approve of me engaging in CSUWD.
	People important to me want me to engage in CSUWD.
PBC	I believe that I can drive well even when engaging in CSUWD.	[1,2,3,4]	0.881
	I am confident that I can engage in CSUWD and still drive safely.
	I have complete control over whether or not to engage in CSUWD.
DN	The drivers beside me will engage in CSUWD next week.	[26,27,28,29,30]	0.774
	The drivers beside use smartphones in a concealed manner while driving.
	The drivers beside me covertly interact with the smartphone while driving.
MN	I think that CSUWD is wrong.	[3,31,32,33,34]	0.869
	CSUWD is against my tenets.
	I will feel compunctious to engage in CSUWD.
PRC	The chances of crashing for CSUWD are high.	[35,36,37,38,39]	0.816
	I would be concerned about CSUWD due to crashing.
	If I engage in CSUWD, there is a high probability of crashing.
PRCF	The chances of getting caught and fined for CSUWD are high.	[35,36,37,38,39]	0.822
	I would be concerned about CSUWD due to getting caught and fined.
	If I engage in CSUWD, there is a high probability of getting caught and fined.

INT: intention; ATT: attitude; SN: subjective norms; PBC: perceived behavior control; DN: descriptive norms; MN: moral norms; PRC: perceived risks of crashing; PRCF: perceived risks of getting caught and fined.

presents each construct’s measurement items, supporting literature, and Cronbach’s alpha coefficients. Attitude was evaluated using three seven-point semantic differential scales, anchored as follows: (1) unwise to (7) wise, (1) unnecessary to (7) necessary, and (1) unpleasant to (7) pleasant. The remaining scales were rated on a seven-point Likert scale, ranging from (1) strongly disagree to (7) strongly agree. All Cronbach’s alpha values were above 0.75, ensuring the reliability of the questionnaire.

The Time 2 survey questionnaire elicits information on drivers’ self-reported SUWD behaviors during the preceding week. The survey items were presented exclusively to participants who had the opportunity to engage in SUWD the previous week. These individuals were asked to answer three questions concerning the frequency of CSUWD. The items, which were adapted from established literature [3,4,14,31], included queries such as, “How often did you use your smartphone in a concealed manner while driving in the past week?” and “How often did you covertly interact with your smartphone while at the wheel during the past seven days?” Participants’ responses were captured using a seven-point Likert scale, with options ranging from (1) never to (7) very often. To safeguard the anonymity of the participants and uphold data privacy, each respondent was assigned a unique identification code. This coding system ensures the confidentiality of their responses and facilitates the correlation and comparison of data between the two surveys.

3.3. Procedures

Each participant in the study completed two online surveys, with a one-week interval between them. At the commencement of the Time 1 survey, respondents were presented with an information sheet that detailed the informed consent, the objectives of the project, the anticipated benefits and risks, and the confidentiality protocol. Submission of the completed survey was deemed as granting consent to participate. Following this, respondents were requested to answer a specific question that generated a unique identification code, which was documented to connect their responses to the Time 2 survey. The Time 1 survey was estimated to take around ten minutes to finish. Upon completion of the Time 1 survey, participants were invited to provide an email address to receive the Time 2 survey after a week. The Time 2 survey required approximately three minutes to complete. After finishing both surveys, participants could receive 50 CNY as compensation. The Ethics Committee of the School of Arts at Jiangsu University in China authorized this two-wave longitudinal survey initiative on 11 December 2024 (Approval Number: 2024-12-005).

4. Results

The data collected from the questionnaires were analyzed using the IBM SPSS 30.0 statistical toolkit. A confidence level of 95% was selected for the study. The scores for each variable were determined by calculating the mean of all the measurement items. Further, a one-way MANOVA conducted on all the main questionnaire variables (e.g., intention, attitude, subjective norm, perceived behavioral control) confirmed that there were no significant differences between the responses depending on whether participants completed the follow-up questionnaire or not (Wilks’s = 0.89, F(8, 147) = 1.35, p = 0.164).

4.1. The Results of the Time 1 Survey

Firstly, we evaluated the dataset’s suitability for the intended processing by assessing the validity of the underlying assumptions, including linearity, normality of the residuals, and homoscedasticity. Subsequently, we explored the potential issue of multicollinearity, analyzing bivariate correlations and further verifying through the computation of variance inflation factors. Finally, we performed descriptive statistical analysis and employed hierarchical multiple regression (HMR) as our primary analytical method.

In this study, the independent variables comprised demographic factors—specifically, gender and age—alongside the standard variables of the TPB: attitude, subjective norms, and perceived behavioral control. Moreover, we incorporated additional expanding variables such as descriptive norms, moral norms, and perceived risks, which were evaluated at each stage of the MHR. The dependent variable for this analysis was the intention to engage in CSUWD.

4.1.1. Descriptive Statistical Analysis of the Time 1 Survey

Table 3. Means, standard deviations, and bivariate associations of Time 1 survey (N = 256).

Variables	Means	Standard Deviations	Sex	Age	ATT	SN	PBC	DN	MN	PRC	PRCF	INT
Sex #	-	-	-	−0.02	0.10	−0.07	0.18 *	−0.03	0.10	−0.16 *	−0.05	0.04
Age	38.6	9.51	-	-	−0.32 **	−0.35 **	0.07	0.11	0.17 *	0.41 **	−0.16 *	0.12
ATT	3.95	1.77	-	-	-	0.42 **	0.38 **	0.29 **	−0.37 **	−0.39 **	−0.20 *	0.27 **
SN	2.93	1.42	-	-	-	-	0.28 **	0.32 **	−0.36 *	−0.17 *	−0.10	0.18 *
PBC	4.03	1.13	-	-	-	-	-	0.30 **	−0.34 **	−0.44 **	−0.42 **	0.35 **
DN	4.46	1.69	-	-	-	-	-	-	−0.15 *	−0.43 **	−0.46 **	0.33 **
MN	3.32	1.70	-	-	-	-	-	-	-	0.52 ***	−0.53 ***	−0.55 ***
PRC	2.15	1.02	-	-	-	-	-	-	-	-	0.60 ***	−0.21 *
PRCF	2.07	0.98	-	-	-	-	-	-	-	-	-	0.51 ***
INT	3.74	1.05	-	-	-	-	-	-	-	-	-	-

# Woman = 0, Man = 1; * p < 0.05, ** p < 0.01, *** p < 0.001; ATT: attitude; SN: subjective norms; PBC: perceived behavior control; DN: descriptive norms; MN: moral norms; PRC: perceived risks of crashing; PRCF: perceived risks of getting caught and fined; INT: intention.

presents these variables’ means, standard deviations, and bivariate correlations. The descriptive statistics reveal that participants’ reported intention to engage in CSUWD is above the average. Additionally, they hold a relatively upbeat assessment of their CSUWD behavior and believe they have significant control over this conduct. However, significant others do not observe this behavior in them to the same extent.

Regarding the expanding variables, the higher scores on descriptive norms suggest that respondents generally believe that CSUWD is a prevalent behavior among drivers in their vicinity. Furthermore, participants’ self-moral evaluations of CSUWD behavior were of average intensity. The lower scores on perceived risks indicate that respondents believe there is a low probability of experiencing a crash or being apprehended and fined by law enforcement if they engage in CSUWD behavior in the forthcoming week. Attitude, subjective norms, perceived behavioral control, descriptive norms, and perceived risks of getting caught and fined all demonstrated significant positive correlations with CSUWD intention. Conversely, moral norms and the perceived risks of crashing exhibited significant negative correlations with CSUWD intention. Notably, sex and age did not significantly correlate with CSUWD intention.

4.1.2. HMR Predicting CSUWD Intention

This study utilized the HMR model to explore the psychological determinants of intention to engage in CSUWD [3,4,14,31]. To address potential demographic effects, gender and age were incorporated as covariates in Step 1. In Step 2, the traditional TPB variables—attitude, subjective norm, and perceived behavioral control—were introduced. Step 3 included additional variables—descriptive norms, moral norms, perceived risks associated with crashing, and the likelihood of getting caught or fined. The key aim was to ascertain whether the augmented TPB model, incorporating these additional variables, offers a more robust explanation of the variance in CSUWD intentions compared to the conventional TPB variables alone.

Table 4. HMR: Predicting CSUWD intention (N = 256).

Step	Variables	B	95% CI	β	F	R2	ΔR2
1	Sex #	−0.11	(−0.04, 0.23)	−0.06	3.66	0.002	0.002
	Age	−0.13	(−0.21, 0.27)	−0.07
2	Sex	0.10	(−0.04, 0.23)	0.05	90.44 ***	0.573 ***	0.571 ***
	Age	−0.11	(−0.21, 0.27)	−0.06
	ATT	0.50	(0.37, 0.69)	0.42 ***
	SN	0.26	(0.01, 0.31)	0.20 **
	PBC	0.45	(0.31, 0.59)	0.37 ***
3	Sex	0.08	(−0.04, 0.23)	0.04	136.09 ***	0.690 ***	0.117 ***
	Age	−0.10	(−0.21, 0.27)	−0.05
	ATT	0.48	(0.37, 0.69)	0.39 ***
	SN	0.21	(0.01, 0.31)	0.14 *
	PBC	0.43	(0.31, 0.59)	0.35 ***
	DN	0.35	(0.22, 0.54)	0.26 **
	MN	−0.24	(−0.53, −0.07)	−0.15 *
	PRC	−0.11	(−0.22, 0.15)	−0.08
	PRCF	0.25	(0.10, 0.38)	0.16 *

# Woman = 0, Man = 1; * p < 0.05, ** p < 0.01, *** p < 0.001; ATT: attitude; SN: subjective norms; PBC: perceived behavior control; DN: descriptive norms; MN: moral norms; PRC: perceived risks of crashing; PRCF: perceived risks of getting caught and fined; INT: intention.

displays that the HMR results from Step 1 had no statistically significant findings (p = 0.011). However, the model in Step 2 showed statistical significance (p < 0.001), and a significant portion, precisely 57.1%, of the variance in CSUWD intention was explained. All three standard TPB variables—attitude, subjective norms, and perceived behavioral control—were identified as significant positive determinants of the intention to engage in CSUWD. In Step 3, the expanding TPB variables substantially enhanced the explanatory power regarding CSUWD intention, accounting for an additional 11.7% of the variance (p < 0.001). The variables of descriptive norms, moral norms, and perceived risks of getting caught and fined emerged as significant determinants in this model. In the final analysis, the overall model showed statistical significance (p < 0.001). This model demonstrated strong explanatory power, accounting for 69.0% of the variance in the dependent variable. All standard TPB variables and additional factors were found to be significant determinants of intention to engage in CSUWD, except the perceived risk of crashing. The following factors were identified as significant positive determinants of intention: attitude, subjective norms, perceived behavioral control, descriptive norms, and the perceived risks of getting caught and fined. Conversely, moral norms were identified as a significant negative determinant.

4.2. The Results of the Time 2 Survey

Notwithstanding the myriad challenges, a relatively high attrition rate of 39.1% was evident in the Time 2 survey. As previously discussed, participants who reported engaging in SUWD practices during the interval week were presented with questions designed to assess their actual CSUWD behavior, thereby reducing the sample size in the Time 2 survey. However, in line with current TPB-based longitudinal investigations into distracted driving behaviors [3,4,14,31], the culminating sample of 156 respondents was deemed suitably conservative for a streamlined analysis. In this study, instead of endeavoring to identify all potential extensions of the TPB variables in forecasting future CSUWD behavior, the final HMR model exclusively focused on predicting the original TPB variables as key determinants of CSUWD behavior.

4.2.1. Descriptive Statistical Analysis of the Time 2 Survey

Table 5. Means, standard deviations, and bivariate associations of Time 2 survey (N = 156).

Variables	Means	Standard Deviations	ATT	SN	PBC	INT	BEH
ATT	3.82	0.84		0.42 **	0.34 **	0.35 **	0.44 **
SN	2.99	0.75			0.22 **	0.26 **	0.19 *
PBC	4.26	0.81				0.66 ***	0.38 **
INT	3.68	0.90					0.68 ***
BEH	3.67	1.01					1

* p < 0.05, ** p < 0.01, *** p < 0.001; ATT: attitude; SN: subjective norms; PBC: perceived behavior control; INT: intention; BEH: behavior.

displays each construct’s means, standard deviations, and bivariate associations. The average score for behavior was marginally high, indicating that respondents actively engaged in CSUWD during the intervening week. Both intention and perceived behavioral control showed a statistically significant correlation with behavior. There was a strong positive correlation between intention and perceived behavioral control (r = 0.68), which raises potential concerns about multicollinearity. However, following the TPB, it is appropriate to include both variables in the predictive analysis of behavior. Therefore, both variables were retained for the hierarchical multiple regression analyses to predict CSUWD behavior.

4.2.2. HMR Predicting CSUWD Behavior

Table 6. HMR: Predicting CSUWD behavior (N = 156).

Step	Variables	B	95% CI	β	F	R2	ΔR2
1	INT	0.61	(0.37, 0.77)	0.52 ***	78.44 ***	0.452 ***	0.452 ***
	PBC	0.27	(0.04, 0.33)	0.21 **
2	INT	0.60	(0.40, 0.77)	0.48 ***	80.05 ***	0.452 ***	0.000
	PBC	0.25	(0.05, 0.32)	0.20 **
	ATT	0.06	(−0.01, 0.22)	0.04
	SN	−0.04	(−0.10, 0.13)	−0.01

* p < 0.05, ** p < 0.01, *** p < 0.001; ATT: attitude; SN: subjective norms; PBC: perceived behavior control; INT: intention; BEH: behavior.

delves into the predictive power of the TPB in forecasting actual CSUWD behavior over the previous week. Drawing on the TPB framework, perceived behavioral control and intention emerge as pivotal predictors of behavioral outcomes, forming the basis of the initial analytical step. The combined influence of intention and perceived behavioral control accounts for a substantial 45.2% of the behavioral variance (p < 0.001). In the second stage, attitude and subjective norms are incorporated into the model. Consistent with expectations, these variables do not contribute significantly to the explained variance beyond the effects of perceived behavioral control and intention (p = 0.574). Overall, the model demonstrates robust statistical significance (p < 0.001). The study reaffirms that perceived behavioral control and intention are key positive drivers of subsequent CSUWD behavior.

5. Discussion

5.1. H1: Utility of the Standard TPB Variables to Explain CSUWD Intention

H1 has been completely recognized. When sex and age were controlled in Step 1, the traditional TPB variables accounted for 57.1% of the variance in drivers’ intentions to engage in CSUWD in the imminent week. This study’s findings further support that the TPB is a meaningful theoretical framework for elucidating the psychological underpinnings of CSUWD intentions and behaviors [1,2,3,4]. However, it is crucial to highlight that the explanatory power of TPB in the current context is lower than the findings reported by Gauld et al. [1,3,4], where TPB accounted for approximately 69.0% of the variance in intention. One potential explanation for this disparity could be that the present survey did not specifically focus on distinct types of smartphone usage, such as calling, texting, or engaging with social media, as was the case in Gauld et al.’s study. Instead, the current study encompassed general smartphone usage, including making calls, sending texts, and employing social media platforms. Hence, we can infer that TPB’s explanatory strength might be more pronounced for specific smartphone functions than general usage, which has also been observed in TPB-based investigations about SUWD [40].

Specifically, the three core constructs of TPB—attitude, subjective norms, and perceived behavior control—were valuable determinants of drivers’ CSUWD intention in the coming week. First, the more positive the driver’s evaluation of CSUWD is, the higher the tendency to secretly engage in SUWD. Furthermore, drivers who enjoyed greater approval from significant people, such as parents, friends, and relatives, also tended to use their smartphones while driving without being noticed. Additionally, those who perceived CSUWD as less complicated and experienced less resistance towards it were more likely to express a firm intention to engage in CSUWD. Among these factors, attitude had the most significant influence, closely followed by perceived behavioral control, and subjective norms had a minimal impact. The above findings are consistent with TPB-based research on CSUWD [1,2,3,4], suggesting a potential universal principle governing the psychological predictors of smartphone-related driver distractions. Moreover, preliminary TPB-based observations indicate that the predictive power of subjective norms on the intention to use smartphones while driving may not always be substantial [33,39]. Consequently, it can be postulated that CSUWD and SUWD may be influenced by distinct normative beliefs, a claim that requires further empirical evidence for support.

5.2. H2: Utility of the Additional Variables to Explain CSUWD Intention

H2 has been partly validated. When demographics and standard TPB variables were controlled, the expanding variables demonstrated a significant effect, explaining an additional 11.7% of the variance in drivers’ intentions to engage in CSUWD the following week. Factors such as descriptive norms, moral norms, and the perceived risks of getting caught and fined have been identified as important predictors. However, the perceived risks associated with crashing do not appear to have a substantial impact. Descriptive norms have enhanced the ability to predict drivers’ intentions regarding CSUWD, aligning with previous studies on the intention to engage in SUWD [26,27,28,29,30]. Hence, adding descriptive norms as an expanding TPB variable is beneficial. In addition, individuals who believe that CSUWD is morally objectionable tend to be less likely to use CSUWD, reinforcing the existing conclusions [31,32,33,34]. Namely, whether hidden or apparent, moral norms are significant determinants of SUWD intentions. Moral norms can be valuable indicators in cases where behavior is considered unsafe. Additionally, descriptive norms and moral norms have a more substantial normative impact than subjective norms in this study, which supports the ongoing argument that adding additional normative variables to extend the TPB model is essential to overcome the limitations of subjective norms alone [25].

Unlike previous literature [35,36,37,38,39], the conclusion that the perceived risks of crashing inhibit drivers’ intentions to engage in CSUWD was not recurrent. Several points might explain this unexpected result. One possible reason is the driver’s blind confidence in their driving skills, causing them to disappreciate the likelihood of CSUWD-related crashing [5,6]. Another reason could be that drivers wrongly believe the gains of CSUWD outweigh the possible threats of crashing, contributing to their misjudgment [36,37,38]. Furthermore, the perceived risks of getting caught and fined seem to play a positive role in the intention to engage in CSUWD, contradicting existing literature on classical deterrence theory [39]. In other words, drivers who feel a higher risk of being caught and fined for CSUWD are not necessarily deterred from such behavior; this perception might encourage it. One explanation for this behavior is that drivers make a deliberate, premeditated choice. It would be beneficial to explain this using the resistance theory or the dual-process model [41]. In other words, although they realize the adverse outcomes, such as being caught and fined, they still plan to engage in CSUWD. On the one hand, traffic police face challenges in observing SUWD activities outside the tinted window and enforcing the law strictly [10,11,12]. On the other hand, this stems from a lack of in-depth understanding of the legal implications, knowledge, and norms surrounding CSUWD. As a result, there is a need for stricter law enforcement and increased education regarding CSUWD behaviors.

5.3. H3: Utility of the TPB to Explain Subsequent CSUWD Behavior

The CSUWD behavior over the previous week was self-reported in the Time 2 survey (N = 156). Given the relatively high attrition rate, it is imperative to approach this finding with circumspection, as the statistical power is less than optimal. Bearing this limitation in mind, H3 receives robust support. The results suggest that the original TPB variables account for 45.2% of drivers’ CSUWD behavior variance. Moreover, significant variations beyond the standard TPB variables warrant further exploration. Extending beyond the rational principles of the TPB, certain non-rational factors—such as habits and automaticity—may provide additional insight into why individuals engage in CSUWD across diverse contexts [42,43,44].

Both intention and perceived behavioral control were crucial in predicting the behavior of CSUWD. This finding differs slightly from previous research [1,2,3,4], which indicated that intention was the sole determinant and that perceived behavioral control did not significantly predict self-reported CSUWD behavior. A possible reason for this discrepancy is that Gauld et al.’s research [1,2,3,4] focused on specific smartphone functions, such as texting or social media, which are fundamentally different from general smartphone usage in this study. Therefore, different motivating factors may influence specific and general smartphone use functions associated with CSUWD, highlighting the importance of exploring the psychological predictors associated with the subdivision of CSUWD behavior using the expanding TPB. Additionally, this longitudinal study took place in China and recruited a group of drivers aged 18 to 59, who may have unique characteristics compared to respondents in Gauld et al.’s research [1,2,3,4], including differences in demographic information, roadway conditions, and cultural influences.

5.4. Implications of Road Safety Interventions

The research result provides meaningful suggestions for road safety departments to develop targeted interventions to reduce CSUWD-related driver distraction. Due to the existing enforcement challenges associated with CSUWD, it is essential to introduce attached non-legislative programs, such as public health advertisements, road safety campaigns, and in-vehicle technologies, to tackle this unsafe and common distracted driving behavior.

5.4.1. Interventions from the Traditional TPB Variables

Based on the classic TPB framework, tailored interventions should prioritize attitude and perceived behavior control, followed by subjective norms. Specifically, the behavioral, normative, and control beliefs associated with CSUWD are the focus of the intervention efforts [2]. For example, while individuals may hold positive views regarding the convenience of using smartphones while driving, fears about potential accidents and legal consequences may act as deterrents. Moreover, efforts should be made to mitigate the influence of disapproving attitudes from significant others like parents, relatives, and coworkers. It is also crucial to dispel the pervasive myth that drivers can safely multitask behind the wheel without repercussions. Ultimately, raising awareness about the potential adverse outcomes of CSUWD on road safety—such as rear-end collisions and lane encroachments—is imperative.

Furthermore, in addition to the public awareness campaigns, it would be beneficial to implement more targeted intervention strategies, such as counter-habit strategies, smartphone-blocking technologies, and policy implications for law enforcement design.

5.4.2. Interventions from the Expanding Variables

Regarding expanding variables, interventions should prioritize descriptive and moral norms, followed by the perception of the risk of being caught and fined. For example, campaigns could widely stress that many drivers opt not to engage in CSUWD behavior. Moreover, interventions should concentrate on moral norms by emphasizing the inherent risks of crashes, legal repercussions, and the advantages of adhering to road traffic regulations. It is also crucial to address drivers’ cognitive dissonances when they believe that the benefits of CSUWD outweigh the associated risks. One effective strategy is to illustrate the adverse effects of CSUWD on driving performance through on-road or simulated driving experiments. Similar to practices in the United Kingdom and Australia, introducing high-definition roadside cameras may help enhance the perceived likelihood of apprehension and punishment. However, further assessment of their efficacy, ethical, and technical aspects is required, because relevant studies have shown that exposing the location of the law enforcement cameras may have a suppressive effect on law enforcement related to smartphone-related distracted driving [15,43].

Currently, the predominant viewpoint advocates for implementing socio-technical system approaches to mitigate technology-based distractions. Within this framework, all stakeholders should be held accountable for any driving-related damages that arise from these distractions [45,46,47].

5.4.3. Interventions from the ‘Intention-Behavior’ Gap

Apart from reducing CSUWD intentions, developing other intervention methods that bridge the ‘intention-behavior’ gap is important. At this point, technological intervention might be the most effective approach. For example, smartphone blocking applications (e.g., Do Not Disturb While Driving, Android Auto, and AT&T DriveMode) that automatically turn off smartphone functions while driving could be a practical recommendation, which has been supported by some of the literature [48,49,50].

Furthermore, it is essential to consider other in-vehicle technology-based distractions when designing intervention strategies. As previously noted, in-vehicle information systems and smartwatches are increasingly becoming sources of driver distraction [51]. Hence, performing a combined program that intervenes with other in-vehicle technologies used while driving is crucial, especially when working on interventions to prevent risky SUWD behaviors.

5.5. Limitations and Future Study

This study represents the first longitudinal survey to explore the psychological factors underlying CSUWD among Chinese drivers based on the extended TPB model. However, several limitations warrant further investigation. Firstly, self-reported measures of risky and illegal driving behaviors by social media and snowballing may be susceptible to recall biases and social desirability effects. Although the anonymous nature of the survey may alleviate these issues, future research should utilize objective methods such as simulated driving, naturalistic driving, or roadside observations to validate the findings. Secondly, the demographic homogeneity of the respondents limits the generalizability of the results. Future studies should include more diverse groups and regions to ascertain if these findings are specific to Chinese drivers or applicable to other populations. In addition, the Chinese cultural adaptability of measurement items still requires further exploration using exploratory factor analysis and confirmatory factor analysis. Thirdly, the expanding variables’ predictive power on CSUWD behavior could not be fully explored due to the high attrition rate. Future research should consider expanding the participant pool and employing advanced statistical techniques such as structural equation modeling to address this. Fourthly, while the extended TPB model developed in this study offers explanatory value, there is still room for improvement. Incorporating additional social-demographic (e.g., income, education level, and geographical location), contextual factors (e.g., traffic conditions, time of day, presence of passengers, and in-vehicle technology), or alternative theoretical frameworks (e.g., situational action theory and the theory of normative social behavior) could further enhance the model’s predictive capabilities. Fifth, the study treats CSUWD as a unified behavior but does not differentiate between types of smartphone use (texting, calling, browsing), which vary in risk and frequency. Therefore, these constructs will likely influence the CSUWD intention differently, and further research is needed.

Finally, it is essential to note that the practical implications proposed in this study are currently theoretical and still require further development. A more concrete proposal for policy interventions or behavioral campaigns would be beneficial. In the future, randomized controlled trials will be necessary to empirically assess their potential effectiveness in reducing CSUWD.

6. Conclusions

The two-wave longitudinal survey effectively utilizes an expanding TPB model to delve into the psychological determinants of intentions and prospective behaviors related to CSUWD. In addition to the conventional TPB components—attitude, subjective norms, and perceived behavioral control—the study adds the expanding constructs of descriptive norms, moral norms, and perceived risks. Intriguingly, the perceived risks of crashing do not emerge as pivotal determinants of CSUWD intentions. Conversely, the perceived risks of being caught and fined counterintuitively positively influence CSUWD intentions. Both intention and perceived behavioral control are crucial predictors of actual CSUWD behavior. These findings offer a robust foundation for developing TPB-based interventions, encompassing public education programs, road safety campaigns, and in-vehicle technologies, to curb the prevalence and hazards of CSUWD-related distractions, strengthen road safety, and advance sustainable transportation systems.