The Multitasking Distortion in Railway Passenger Well-Being: Intrinsic Motivation, Digital Media Usage, and the Moderating Role of the On-Train Environment

Abstract

The widespread adoption of information and communication technology enables passengers to transform travel into productive time. However, constant internet connectivity creates multitasking environments that do not necessarily improve passengers’ subjective well-being. To support sustainable urban mobility and social sustainability, public transport must provide high-quality travel experiences. Based on a survey of Tokyo railway passengers (N = 461), the association between digital media usage and subjective well-being was evaluated concerning motivation, media format, and the on-train environment. Cluster analysis and multi-group structural equation modeling revealed that intrinsic motivation strongly predicted onboard well-being. While analog media users exhibited high onboard well-being, digital media usage demonstrated a multitasking distortion: onboard well-being decreased despite high intrinsic motivation. This negative impact was prominent in High-Duration/High-Adaptation environments characterized by abundant travel time and habituation. Furthermore, enhanced onboard well-being significantly spilled over to overall life satisfaction. These findings suggest that constant connectivity in High-Duration/High-Adaptation environments consumes cognitive resources, obstructing travel’s restorative potential. Improving passenger well-being and maintaining modal shifts requires designing environments as cognitive shelters that support intentional disconnection.

1. Introduction

Traditional transport planning and urban economics conceptualize travel as a derived demand for destination activities [1]. Consequently, travel time is viewed as an unproductive deadweight loss, making travel time savings the primary justification for infrastructure developments like increasing railway speeds.

Advancements in information and communication technology (ICT) have altered this premise. Constant internet connectivity enables contemporary passengers to multitask—working, acquiring information, and socializing—while traveling [2]. Following Ettema and Verschuren [2], travel-based multitasking is conceptualized as the simultaneous engagement in secondary onboard activities during physical displacement. Under this framework, utilizing any media format during a commute constitutes an active form of travel-based multitasking. This highlights the positive utility of travel, framing travel time as an activity resource rather than a mere cost [3,4]. Furthermore, Mobility as a Service (MaaS) and smart city initiatives prioritize maximizing passenger productivity through seamless communication environments [5].

However, whether ICT-driven connectivity unconditionally improves passengers’ subjective well-being (SWB) remains debatable. While alleviating boredom, constant internet connectivity causes attention fragmentation and information overload, potentially depleting cognitive resources and inducing fatigue [6]. Indeed, recent research has increasingly scrutinized the psychological consequences of digital engagement, demonstrating associations between screen time and negative affect [7], as well as the occurrence of mobile gaming burnout [8]. Simultaneously, the direct link between transit infrastructure developments—such as new metro lines—and passengers’ subjective well-being has garnered significant attention in the transport literature [9]. Consequently, the psychological transition and temporary time-out inherently provided by travel [10] might be encroached upon by fragmented information consumption. If passengers use digital media seeking worthwhile travel time but inadvertently reduce their SWB, a multitasking distortion exists. Targeting railway passengers in the Tokyo metropolitan area, this mechanism is empirically investigated concerning: (1) motivation for onboard activities, (2) media format, and (3) the physical on-train environment.

1.1. Redefining Travel Value in the Context of High-Density Rail Travel

The concept of worthwhile travel time (WTT) reconceptualizes travel as a meaningful duration rather than an unproductive loss, emphasizing the value judgments passengers attribute to it. Comprising multidimensional elements like productivity, enjoyment, and fitness [11], contemporary travel time is transforming into discretionary time that enhances individual well-being.

Beyond individual benefits, enhancing this psychological value of travel has emerged as a critical challenge for global urban transport policy. With passenger demand projected to grow significantly [12], realizing sustainable urban environments necessitates a radical modal shift from low-utilization private vehicles to integrated public transport networks [13]. To successfully promote and maintain this modal shift, aligning with the eleventh sustainable development goal (SDG 11) regarding sustainable cities and communities conceived by the United Nations [14], travel time must be transformed from a mere economic cost into high-quality time that enhances passenger well-being. This transformation indicates that the experiential quality of public transport is decisive alongside traditional criteria like time and fare.

To empirically investigate how to enhance this experiential quality, Japanese railway travel provides a highly significant field. Unlike automobiles requiring constant operation, railways offer a hands-free transition zone releasing passengers from daily obligations [10]. This environment allows autonomous activity selection, strongly motivating WTT maximization. Consequently, the train interior represents an optimal site for examining how ICT usage affects well-being.

Within Japan, the Tokyo metropolitan area faces a unique challenge; despite possessing a highly reliable network, significant crowding remains unresolved. Because such crowded environments are associated with psychological stress and elicit a desire for withdrawal [15], digital media may function not merely to kill time, but as coping mechanisms to psychologically escape uncomfortable physical surroundings.

Because these stressful conditions are experienced routinely, obligatory, repetitive daily travel is deliberately focused upon in this study. Converting these stressful routines into WTT yields substantial social impact; improving this repetitive experience forms the foundation for accumulating onboard well-being and cumulatively enhancing overall life satisfaction via bottom-up spillover [16]. Indeed, empirical evaluations of new transit infrastructure have demonstrated that metro system developments can significantly improve both travel satisfaction and overall life satisfaction among residents, supporting the bottom-up spillover mechanism [9].

1.2. From “Activity Types” to “Quality of Motivation”

Despite the recognized importance of enhancing WTT, previous empirical studies have predominantly adopted an activity-based approach, categorizing onboard behaviors to analyze their impacts on subjective evaluations. However, findings regarding the relationship between specific activity types and SWB remain inconsistent [17]. This discrepancy indicates that superficial activity classifications—merely observing what passengers are doing—fail to fully capture the quality of their psychological experiences.

To address this limitation, the analytical focus in this study is shifted from activity types to the underlying quality of motivation. Grounded in self-determination theory (SDT), it is posited that motivation exists on a continuum from intrinsic to extrinsic motivation and amotivation [18]. While intrinsic motivation yields the highest SWB, amotivation characterized by an absence of agency and intention causes the most significant decline in well-being.

Applying this SDT framework clarifies the nuanced concept of killing time during Japanese commutes. While strategic time-killing functions as an extrinsic coping strategy to reduce environmental discomfort [19], passive digital media usage driven by algorithms or mindless scrolling without clear intentions closely resembles amotivation, ultimately impairing psychological enrichment.

Therefore, while accounting for differences in media format, whether onboard activities are driven by intrinsic enjoyment, extrinsic adaptation, or amotivation is examined. Because highly autonomous activities induce Flow [20]—a state of deep concentration directly enhancing travel quality—focusing on these motivational mechanisms elucidates the distortion between motivated behavior and well-being within digitalized travel spaces.

1.3. Media Formats and Cognitive Processes

Alongside motivation quality, the media format mediating activities acts as a critical determinant governing users’ cognitive processes and emotional experiences [21]. Digital media, characterized by multifunctionality and constant connectivity, offer unparalleled convenience by enabling passengers to access vast information and seamlessly connect with society. Indeed, these devices function as highly effective tools for alleviating boredom during travels. However, from a cognitive psychological perspective, this continuous connectivity entails structural trade-offs. The brain drain hypothesis indicates that a smartphone’s mere presence automatically attracts attention, thereby reducing available cognitive capacity [6].

Furthermore, expending self-regulation resources to manage the continuous influx of notifications risks ego depletion [22,23]. Preventing cognitive depletion caused by constant digital connection is essential for protecting the mental well-being of citizens in modern society, which directly aligns with the third sustainable development goal (SDG 3) concerning good health and well-being proposed by the United Nations [24]. Consequently, the very features that make digital media exceptionally convenient can inadvertently obstruct higher-order well-being experiences, such as Flow.

Conversely, traditional analog media (e.g., paper books) provide unique psychological utility precisely due to their physical constraints. Tactile feedback and fixed layouts assist deeper comprehension [25], while interruption-free environments activate brain circuits enabling deep reading [26]. Aligning with Borgmann’s [27] concept of focal things, analog media function as cognitive shelters that block external interference.

In high-stress trains, digital media usage serves as an immediate coping strategy, shifting consciousness toward cognitively demanding virtual environments to escape physical crowding. Analog media, however, restore a sense of control by naturally restricting information influx. This difference in media format, interacting with motivation, constitutes the objective psychological mechanism generating the multitasking distortion—demonstrating that extreme convenience does not necessarily equate to high well-being.

1.4. Research Objectives and Significance

Building on this theoretical background, the mechanisms underlying the multitasking distortion are empirically elucidated in this study. Specifically, the relationship between digital media usage and SWB among Tokyo railway passengers is examined from three perspectives: (1) motivation quality, (2) media format, and (3) on-train environment, testing the following four hypotheses:

H1: 

The Primacy of Motivation Quality.

When onboard activities are selected based on intrinsic motivation, passengers’ SWB increases. Conversely, extrinsic motivation or amotivation negatively associates with SWB, verifying the universal applicability of SDT even within highly constrained travel behaviors.

H2: 

The Distorting Role of Media Formats.

Although media selection correlates with motivation quality, the media format independently affects SWB. Even after controlling for motivation, utilizing digital media—prone to inducing attention residue—yields a significantly lower positive (or a negative) effect on SWB compared to analog media.

H3: 

The Moderating Role of On-train Environment.

The physical on-train environment moderates the relationship between digital media usage and onboard well-being. Specifically, the negative impact (distortion) of digital media manifests more prominently in High-Duration/High-Adaptation environments with fewer physical stressors. Because subjective perceptions of constant internet connectivity excessively consume cognitive resources, they obstruct travel time’s inherent restorative potential, despite conditions conducive to rest.

H4: 

Bottom-up Spillover to Life Satisfaction.

As discussed in Section 1.1, the domain-specific SWB (onboard well-being) experienced during daily commutes transcends fleeting emotions, significantly influencing overall life satisfaction. This qualitative commute improvement serves as a foundation for urban residents’ long-term well-being [27].

1.4.1. Significance of the Study

The academic contribution of this study lies in theoretically explaining the multitasking distortion experienced by contemporary passengers through SDT and cognitive psychology, thereby challenging conventional activity-based approaches. Practically, these findings suggest that future transport services must embrace environmental designs that protect passengers’ cognitive shelters, supporting intentional disconnection alongside analog immersion.

1.4.2. Ethics Approval Statement

This work was approved by the ethics committee of the University of Tokyo (UTSP-24021). This study was performed following the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Informed consent was obtained from all participants.

2. Materials and Methods

2.1. Participants

Participants were recruited between 29 October and 11 November 2024, using a crowdsourcing service (Lancers Inc., Tokyo, Japan). The target population comprised adults aged 18 and older with Japanese nationality who had used a railway on the day of or the day prior to their participation in the survey. The questionnaire, created on the online survey platform Qualtrics, was distributed to this population.

Prior to the survey, the required sample size to achieve sufficient statistical power to detect a small effect using ANCOVA via G*Power 3.1 [28]. Assuming an effect size f = 0.20, α = 0.05, power = 0.95, four groups, and three covariates, the required sample size was 434. Anticipating dropouts during the screening for railway use, a target of 600 responses was established. A total of 570 responses were collected. Responses were deemed valid if participants met the criteria for recent railway use and passed an attention check (an item verifying compliance with instructions), resulting in a final sample of 461 participants for analysis. The final sample comprised 310 men and 151 women, with a mean age of 43.02 years (SD = 10.32).

2.2. Measurements

2.2.1. Activities and Media Format

First, participants were asked to report the media they used during their commute and then select their specific activity from 22 items (e.g., browsing social networking sites (SNS), reading, sleeping, thinking). Participants were instructed to select a single “dominant activity” on which they spent the most time [29]. The correspondence between media and activities was categorized as follows: digital media included activities using smartphones or tablets (e.g., SNS, watching videos, playing games, emailing, listening to music); analog media included activities using paper books, newspapers, or physical documents; and no media included activities not utilizing any media format (e.g., thinking, looking out the window, conversing, sleeping). Additionally, participants were prompted to select or specify any activities not covered by these categories under others.

2.2.2. Motivational Regulation

To measure motivational regulation for onboard activities, the Scale for Meaning of Travel Behavior (SMTM; 12 items) [29] was used. This scale assesses the subjective meaning attached to onboard activities and comprises 12 items encompassing concepts such as enjoyment, immersion, usefulness, and killing time. Participants rated the extent to which these items applied to their activity during their most recent trip on a 7-point Likert scale ranging from 1 (completely disagree) to 7 (completely agree). The factor structure of this scale was confirmed through preliminary analysis (factor analysis) described later, and subscales were constructed based on those results.

2.2.3. Onboard Well-Being (OWB)

To measure subjective onboard well-being, the Japanese version (9 items) [30] of the Satisfaction with Travel Scale (STS) [31] was used. The STS consists of nine items measuring three dimensions of travel evaluation with three items each: cognitive evaluation, affective evaluation—activated, and affective evaluation—deactivated. Participants responded using a 7-point semantic differential scale (e.g., 1 = very bad/bored to 7 = very good/enthusiastic). A factor analysis was also conducted on this scale to confirm its validity in the Japanese passenger environment, defining the variable based on its structure.

2.2.4. Subjective Happiness and Life Satisfaction

As dependent variables, the Japanese version (4 items) [32] of the Subjective Happiness Scale (SHS) [33] and the Japanese version (5 items) [34] of the Satisfaction With Life Scale (SWLS) [16] were used. The SHS consists of four items, such as “Some people are generally very unhappy. Although they are not depressed, they never seem as happy as they might be. To what extent does this characterization describe you?”, rated on a 7-point scale. The SWLS comprises five items, such as “In most ways my life is close to my ideal,” also rated on a 7-point scale.

2.2.5. On-Train Environment Indicators

Objective and subjective environmental indicators were measured to conduct cluster-based environmental classification. Specifically, the following were assessed: trip direction (0 = outbound/travel, 1 = inbound/returning), years of continuous route use, sectional travel time (the riding time on the longest segment if transfers were involved, or total riding time if no transfers), travel time window (1 = 7:00–8:59, 2 = 9:00–11:59, 3 = 12:00–16:59, 4 = 17:00–18:59, 5 = 19:00–21:59, 6 = after 22:00), seating status (1 = seated with vacant seats, 2 = standing with vacant seats, 3 = standing with no vacant seats), accompanying persons (1 = none, 2 = family/friends, 3 = colleagues/business associates), and crowding level. The crowding level was evaluated on a 5-point scale based on Ministry of Land, Infrastructure, Transport and Tourism guidelines [35]: 1 (spacious enough to have vacant seats), 2 (able to hold onto a strap or stanchion), 3 (able to read a newspaper if folded), 4 (bodies touching with a feeling of pressure, but able to manage reading a paperback book), and 5 (bodies leaning with every train lurch, completely immobilized). Higher scores indicated greater perceived crowding by the participants (M = 2.37, SD = 1.23).

2.3. Procedure

A retrospective method requiring participants to recall a specific travel episode in detail was employed. Although potentially susceptible to memory bias, this approach is suitable for assessing detailed psychological processes in a large sample [31].

First, participants were randomly assigned to recall either their most recent outbound commute (n = 232) or inbound return trip (n = 229) using a railway. A railway trip was broadly defined as a single, continuous boarding segment on any rail-based transit; for commutes involving multiple transfers or modes, participants were instructed to recall the single railway segment with the longest boarding time.

Next, participants reported their recalled trip’s environmental and logistical details, including duration, start time window, seating status, accompanying persons, and crowding level [35]. They then indicated their dominant onboard activity, the media format used, and their motivational regulation (SMTM) [28]. Furthermore, onboard well-being [27,29], life satisfaction [15,32], and subjective happiness [30,31] were assessed. Finally, basic demographic information and the total one-way travel time from home to the workplace were collected.

2.4. Analytical Strategy

Analyses were conducted using R (version 4.3.3) [36] and the lavaan package [37]. The procedure was as follows: (1) exploratory factor analyses on the SMTM and STS to define analytical variables; (2) hierarchical cluster analysis (Ward’s method, squared Euclidean distance) to classify the on-train environment; (3) ANCOVA to examine differences in onboard well-being by media format, controlling for environmental and demographic covariates, with Holm-adjusted multiple comparisons; and (4) multi-group structural equation modeling (MG-SEM) to evaluate the moderating effect of the environment on the mechanisms linking motivation, media format, and onboard well-being.

3. Results

3.1. Preliminary Analysis: Factor Structure and Reliability of Scales

An exploratory factor analysis (maximum likelihood method, Promax rotation) was conducted to examine the structures of the SMTM and STS (see Supplementary Materials for factor loadings). For the 12-item SMTM, the scree plot and Kaiser criterion supported a two-factor solution. Factor 1, termed intrinsic motivation (α = 0.78), comprised items reflecting enjoyment and immersion. Factor 2, termed extrinsic motivation (α = 0.68), included items reflecting usefulness and killing time. Despite the latter’s slightly lower reliability, both demonstrated acceptable internal consistency for subsequent analyses.

Regarding the nine-item STS, Ettema et al. [31] proposed a three-factor structure. However, the data’s eigenvalues (5.21, 1.37, 0.74) and scree plot indicated a dominant first factor. Furthermore, extracting a three-factor solution yielded high inter-factor correlations (rs = 0.44–0.68), aligning with recent findings [17] that the subscales essentially represent a single higher-order concept. To avoid multicollinearity, mitigate respondent bias inherent in retrospective methods, and ensure model parsimony for the multi-group structural equation modeling (MG-SEM), a single-factor solution was adopted. This integrated factor was defined as onboard well-being (OWB; α = 0.90).

Finally, life satisfaction (SWLS) and subjective happiness (SHS) also demonstrated high internal consistency (αs = 0.91 and 0.89, respectively). Arithmetic means were calculated for all constructs to use as scores for subsequent analyses (

Table 1. Descriptive Statistics and Pearson’s Correlation Coefficients.

		Mean	SD	α	Pearson’s Correlation Coefficients
					SWLS		SHS		OWB		Intrinsic
1	SWLS	3.08	1.10	0.912	---		---		---		---
2	SHS	4.12	1.24	0.894	0.785	***	---		---		---
3	OWB	4.06	0.98	0.904	0.313	***	0.323	***	---		---
4	Intrinsic Motivation	4.45	0.91	0.779	0.168	***	0.130	***	0.327	***	---
5	Extrinsic Motivation	3.56	0.84	0.676	0.139	***	0.183	***	0.192	***	0.100	*

Note. * p < 0.05, *** p < 0.001.

). OWB significantly and positively correlated with intrinsic motivation (r = 0.33, p < 0.001), extrinsic motivation (r = 0.19, p < 0.001), and age (r = 0.19, p < 0.001).

3.2. Classification of Travel Environments

To examine the heterogeneity of the effects of the physical and temporal characteristics of the on-train environment on passengers’ activity selection and onboard well-being, a hierarchical cluster analysis (Ward’s method, squared Euclidean distance) using the ten environmental, travel-related, and socio-demographic variables listed in

Table 2. Comparison of Cluster Characteristics.

Variables	Low-Duration/ Low-Adaptation (n = 312)	High-Duration/ High-Adaptation (n = 149)	Statistic	p	Effect Size
Duration of use (years)	7.19 (5.83)	14.48 (10.15)	t(196) = −8.14	<0.001	d = −0.97
Time of day (score)	2.55 (1.13)	2.01 (0.58)	t(456.4) = 6.71	<0.001	d = 0.54
Total travel time (min)	39.22 (13.93)	65.94 (23.76)	t(198.1) = −12.72	<0.001	d = −1.51
Section travel time (min)	17.46 (10.13)	37.81 (20.02)	t(185.1) = −11.71	<0.001	d = −1.44
Crowding level (score)	2.25 (1.18)	2.62 (1.29)	t(270.7) = −2.97	0.003	d = −0.30
Age (years)	41.37 (10.10)	46.48 (9.94)	t(295.7) = −5.14	<0.001	d = −0.51
Trip direction (To work = 1)	49%	52%	χ2 = 0.36	0.616	V = 0.00
Seat Availability (Available = 1)	46%	40%	χ2 = 1.76	0.220	V = 0.04
Stance (Seated = 1, Standing = 0)	55%	47%	χ2 = 2.90	0.109	V = 0.06
Companion
(Family/friends = 1)	1.30%	0.70%	χ2 = 0.35	0.911	V = 0.00
(Colleagues/Business partners = 1)	0.00%	0.70%	χ2 = 0.35	0.911	V = 0.00

Note. Values for continuous variables are presented as Mean (Standard Deviation), and categorical variables are presented as percentages. Statistical significance was tested using Welch’s t-test for continuous variables and Pearson’s χ² test for categorical variables. Effect sizes are reported using Cohen’s d and Cramer’s V, respectively. Crowding level was measured on a 5-point scale (1 = Not crowded/Plenty of seats, 5 = Very crowded/Immobile).

was conducted. Based on the shape of the dendrogram and interpretability, the respondents were classified into two groups with distinct characteristics: the first group was termed the Low-Duration/Low-Adaptation Group and the second group the High-Duration/High-Adaptation Group (Table 2). Importantly, these labels denote relative differences within the sample rather than absolute extremes of commuting conditions.

The first group, the Low-Duration/Low-Adaptation Group (n = 312), had a comparatively short total travel time (M = 39.2 min) and a shorter continuous usage period for the relevant route (M = 7.2 years). Notably, their subjective crowding level was significantly lower than that of the High-Duration/High-Adaptation Group (M = 2.25). Although physical crowding was relatively mild, this was interpreted as an environment less suitable for immersive activities (i.e., where motivation is less likely to increase) due to comparatively less temporal leeway and environmental habituation.

The second group, the High-Duration/High-Adaptation Group (n = 149), consisted of veteran railway passengers with a long total travel time (M = 65.9 min) and a very long usage history for the relevant route (M = 14.5 years). The crowding level was significantly higher compared to the first group (M = 2.62), indicating that the physical environment was not necessarily comfortable. However, this value remained within the level of being able to read a newspaper (scale = 3).

Examining the effect sizes (Cohen’s d), the differences in travel time (d = 1.44) and usage history (d = 0.97) were larger than the difference in crowding level (d = 0.30). In other words, the factors making this group High-Duration/High-Adaptation were not the physical availability of vacant seats, but rather the abundant time resources available for activities and the habituation to adapt to mild crowding stress.

Before proceeding to the model testing, potential recall bias arising from the response timing was evaluated. We compared respondents who completed the survey on the day of train travel (n = 259) with those who responded on the following day (n = 202) across all constructed scales, environmental cluster classifications (High/Low Duration/Adaptation), media use, and demographic attributes. As detailed in Table S6 in the Supplementary Materials, no significant differences were detected across any variables after Holm’s multiplicity adjustment, with the sole exception of household income (d = 0.29, p_adj = 0.031). The effect sizes (Cohen’s d and Cramér’s V) remained below 0.20 for almost all variables, indicating negligible to small differences (e.g., age: d = 0.22, p_adj = 0.200, n.s.). These results confirm that systematic discrepancies due to the recall interval were virtually absent across the primary constructs, warranting the analysis of the combined sample.

3.3. Relationship Between Media Usage and OWB

First, Hypothesis 1 regarding the relationship between motivation and well-being was partially supported. As shown in Table 1, intrinsic motivation showed a moderate positive correlation with onboard well-being (OWB; r = 0.33, p < 0.001). However, contrary to the negative association predicted in H1, extrinsic motivation also showed a weak but significant positive correlation with OWB (r = 0.19, p < 0.001). This is consistent with the prediction that the higher the autonomous meaning attached to travel, the more positive the experience becomes. Furthermore, relating to Hypothesis 3 (the spillover effect), OWB showed significant positive correlations with subjective happiness (SHS; r = 0.32, p < 0.001) and life satisfaction (SWLS; r = 0.31, p < 0.001). Although the absolute values of the correlation coefficients were moderate, it was confirmed that the quality of daily travel experiences is statistically linked to overall life evaluation.

Next, to verify whether well-being differed by media format, an analysis of covariance (ANCOVA) with media format (digital media, analog media, no media, others) as the independent variable and OWB as the dependent variable was conducted (Figure 1). Four variables were included as covariates to control for confounding factors: crowding level, total travel time, seated status, and age. The sample sizes for the media categories were 343 for digital media, 25 for analog media, 88 for no media, and 5 for others. Although responses for analog media accounted for less than 10% of the total, no impact on the model estimation was observed.

The ANCOVA results indicated that the covariates of age (F(1, 453) = 9.85, p = 0.002, η_p² = 0.02), seated status (F(1, 453) = 6.92, p = 0.009, η_p² = 0.02), and crowding level (F(1, 453) = 7.55, p = 0.006, η_p² = 0.02) had significant effects on OWB. However, even after statistically controlling for these factors, the main effect of media format remained significant with a moderate effect size (F(3, 453) = 10.20, p < 0.001, η_p² = 0.06). To ensure that these findings were not biased by the highly unbalanced group sizes and potential violations of normality, extensive robustness checks were executed using a non-parametric Kruskal–Wallis test (H(3) = 41.44, p < 0.0001) and a bootstrap ANCOVA with 2000 resamples. Both alternative statistical approaches yielded identical, highly robust patterns of significance, with the bootstrap 95% confidence intervals consistently supporting the significant advantages of analog activities (the comprehensive statistics of these robustness checks are provided in Table S7 in the Supplementary Materials).

The results of multiple comparisons using the Holm method showed that the adjusted mean for analog media users (M_adj. = 5.02) was the highest, significantly higher than that of digital media users (M_adj. = 4.05) (t(453) = 5.09, p < 0.001, d = 1.07). Furthermore, the OWB for analog media users was significantly higher than that of the no media group (M_adj. = 3.87; t(453) = 5.46, p < 0.001, d = 1.28) and the others group (M_adj. = 3.88; t(453) = 2.57, p = 0.043, d = 1.27). Conversely, no statistically significant difference in OWB between digital media users and the no media group (t(453) = 1.63, p = 0.312, d = 0.20) or the others group (t(453) = 0.42, p > 0.999, d = 0.19).

While these results demonstrate a significant positive association between analog media usage and onboard well-being, the self-selected nature of the media formats precludes any definitive causal interpretation. Nevertheless, the findings suggest that the choice of analog media is linked to higher well-being, whereas digital media usage, the method chosen by the majority (approximately 75%), does not improve travel comfort beyond the state of doing nothing.

3.4. Examination of Environmental Differences via MG-SEM

To examine the moderating effect of the on-train environment on the onboard well-being generation process through travel behaviors, multi-group structural equation modeling (MG-SEM) was conducted for Group 1 (Low-Duration/Low-Adaptation Group) and Group 2 (High-Duration/High-Adaptation Group). The R package lavaan was used for the analysis. As a prerequisite for cross-group comparisons, a model assuming strong measurement invariance (scalar invariance) was adopted, constraining all factor loadings and intercepts to be equal across groups. The analysis supported both metric invariance (Δχ² (25) = 32.41, p = 0.147; ΔCFI = −0.002) and scalar invariance (ΔCFI = −0.001; ΔRMSEA = −0.001) based on standard heuristic criteria (ΔCFI < 0.010), demonstrating robust scale comparability across the groups (the comprehensive invariance testing results are detailed in Table S8 in the Supplementary Materials). Consequently, the structural equation model assuming strong measurement invariance (scalar invariance) was adopted, constraining all factor loadings and intercepts to be equal across groups.

To ensure model parsimony, demographic variables (e.g., age) and seated status examined in Section 3.3 were excluded; however, preliminary analyses confirmed that controlling for these variables did not change the signs or significance of the primary path coefficients. Furthermore, to address the potential confounding effect of trip duration on media engagement, an additional sensitivity analysis controlling for total travel time was conducted. This analysis confirmed that the primary results—most notably the significant negative path from digital media usage to onboard well-being—remained robust (see Table S8 in the Supplementary Materials).

The model fit indices were χ²(960) = 2720.57, p < 0.001. Although CFI (0.81) and RMSEA (0.09) remained at moderate levels, SRMR (0.10) was close to the acceptable range, and the goodness-of-fit index (GFI = 0.97) and adjusted goodness-of-fit index (AGFI = 0.96) showed extremely high values. These results support that the model has a structure capable of adequately explaining the observed data (Figure 2). Examining the coefficients of determination (R²), a notable difference in the model’s explanatory power depending on the environment was found. The explained variance of onboard well-being (OWB) remained at 20.2% in Group 1, which had a lower quality environment, whereas it reached 43.0% in Group 2, which had a higher quality environment. This suggests that under High-Duration/High-Adaptation environments, individual psychological and behavioral factors, such as motivation and media format, reflect more clearly on well-being (Figure 2).

Based on the estimated parameters, three key characteristics regarding the relationships among motivation, media format, and well-being were identified.

First, a positive association between motivation and behavior was found. In both groups, a significant positive covariance between intrinsic motivation and digital media usage was observed (Group 1: φ = 0.41, p < 0.001; Group 2: φ = 0.45, p < 0.001). This indicates that passengers who strongly desire to enjoy or spend their travel time meaningfully are more likely to select digital media, such as smartphones, as a means to do so.

Second, a multitasking distortion between motivation and well-being was observed. Despite being selected with high intrinsic or extrinsic motivation, the path coefficients from digital media usage to OWB were negative in both groups. This negative effect was statistically significant only in the highly stable Group 2 (β = −0.17, p = 0.045)—an effect that, importantly, strengthened when controlling for total travel time (see Table S8); conversely, in the less stable Group 1, the coefficient showed a negative trend but did not reach significance (β = −0.13, p = 0.115). To formally evaluate if this negative association differed across environments, a series of multi-group chi-square difference tests were conducted. The results revealed no statistically significant group differences in the path from digital media usage to OWB (Δχ² (1) = 0.186, p = 0.667), nor in any other structural paths, including the path from intrinsic motivation to OWB (Δχ² (1) = 0.034, p = 0.853; see Table S9 in the Supplementary Materials). This lack of statistical moderation suggests that the multitasking distortion—where digital media usage negatively associates with onboard well-being—is not a localized phenomenon unique to a specific environment. Rather, the cognitive interference and attention fragmentation induced by digital media represent a robust, generalizable psychological process that consistently operates across varying temporal resources and environmental conditions.

Third is the contrast with analog media. Unlike digital media, the path coefficients from analog media to OWB were not significant in either Group 1 (β = 0.05, p = 0.538) or Group 2 (β = 0.02, p = 0.801). Although descriptive statistics showed that OWB was higher among analog media users, the direct effect of analog media usage itself on boosting OWB was limited within the model controlling for motivation and environmental factors. Importantly, however, the significant negative effect (suppression effect) seen with digital media was not observed.

Finally, the spillover effect was examined. The paths from onboard well-being (OWB) to subjective happiness (SHS; Group 1: β = 0.28, p < 0.001; Group 2: β = 0.51, p < 0.001) and to life satisfaction (SWLS; Group 1: β = 0.31, p < 0.001; Group 2: β = 0.44, p < 0.001) were significant and positive in both groups. This indicates positive associations that are consistent with a bottom-up spillover mechanism, although the cross-sectional nature of the data precludes the definitive establishment of causal directionality.

4. Discussion

The primary objective of this study was to elucidate the realities of the multitasking distortion occurring between ICT use and onboard well-being among railway passengers. By synthesizing the findings across three perspectives—motivation, media format, and the on-train environment—a clear structural mechanism was revealed. First, supporting the bottom-up spillover hypothesis [16], onboard well-being was strongly predicted by intrinsic motivation rather than the activity itself (H1), subsequently enhancing overall life satisfaction (H3). Second, despite intrinsic motivation driving the selection of digital devices, users of analog media formats experienced significantly higher onboard well-being (H2). Crucially, this study identified that the multitasking distortion—where highly motivated digital use paradoxically impairs well-being—manifested exclusively within High-Duration/High-Adaptation environments (β = −0.168, R² = 43.0). In such autonomous settings, the structural flaw of digital media consuming users’ cognitive resources becomes starkly evident, functioning as a vital premise for the detailed mechanisms discussed in the subsequent sections.

4.1. Digital Media as an Impediment to Immersion

This section elucidates the mechanisms underlying the observed multitasking distortion—how digital media usage driven by high intrinsic motivation paradoxically diminishes onboard well-being in immersion-conducive environments. This phenomenon can be explained by two primary factors: media-specific attention fragmentation and the opportunity cost generated by the on-train environment.

First, digital media structurally facilitate attention fragmentation. Although digital media usage strongly correlated with the intrinsic intention to enjoy the commute, devices like smartphones potentially consume limited cognitive capacity merely through their physical presence or notification potential [6]. Even if the subjective perception is one of enjoyment, the constant application switching inherent in digital operations may generate attention residue [38], potentially obstructing sustained flow. This psychological strain is conceptually aligned with digital-media burnout, such as the “being tired but still gaming” phenomenon, where individuals compulsively continue digital engagement despite experiencing cognitive fatigue [8]. Consequently, this hypothetical attention depletion is proposed as a plausible explanation that offsets the cognitive utility typically gained from immersion, resulting in a failure to achieve the onboard well-being commensurate with the user’s initial motivation.

Second, although the statistical strength of this negative association did not significantly differ between the environments (Δχ² (1) = 0.186, p = 0.667), its practical salience is closely linked to opportunity costs. In “Low-Duration/Low-Adaptation” environments—which represent relatively demanding conditions for sustained attention rather than extreme discomfort—achieving restorative experiences is challenging, and environmental factors primarily constrain well-being, resulting in a lower explained variance (R² = 0.202). In contrast, a High-Duration/High-Adaptation on-train environment possesses the latent potential to function as a restorative “transition time” [10], as reflected in the substantially higher explained variance (R² = 0.430). The statistical equivalence of the negative digital path across both groups suggests that constant digital connectivity consistently impairs well-being. However, because the environment is comparatively favorable, digital multitasking actively obstructs potential recovery opportunities, and this opportunity cost manifests as a significant (β = −0.168) decline in onboard well-being. When environmental constraints are relaxed, well-being is directly governed by behavioral choices regarding media format and task engagement.

4.2. Promoting Immersion and Restoration Through Physical Constraints

Descriptive statistics and analysis of covariance (ANCOVA) demonstrated that railway passengers utilizing analog media formats (e.g., paper books) experienced significantly higher onboard well-being than digital users or non-users. Crucially, this superiority persisted even after controlling for confounding factors like age and seating availability. Regarding these effects, it is important to clarify the apparent discrepancy between the preliminary ANCOVA results and the MG-SEM findings. While the ANCOVA indicated a significant main effect of analog media on onboard well-being, this direct effect dissipated in the MG-SEM. This is not a logical contradiction but rather a methodological refinement. Because ANCOVA does not explicitly model the covariance between motivation and media format, it detects an apparent direct effect of the media format. However, when the MG-SEM explicitly incorporated this covariance structure, the effect of the media format was subsumed by the underlying motivation. This demonstrates that it is not the physical characteristics of analog media itself that enhance well-being, but rather the high level of autonomous motivation characteristic of passengers who actively choose such formats. Given the convenience of digital technology, this outcome is not self-evident. It suggests that the “physical constraints” digital tools seek to eliminate paradoxically serve as vital resources promoting psychological immersion and restoration. This mechanism aligns with Borgmann’s [27] concept of focal things and psychological flow.

First, the inconvenience of analog media enforces focal engagement. In contrast to the digital device paradigm, objects demanding active mastery and effort are termed focal things [27]. Psychologically, the restrictive nature of paper books—lacking hyperlinks and requiring physical page-turning—anchors attention to a single modality. Unlike digital attention fragmentation, this single-task environment acts as structural scaffolding to regulate distractible attention, facilitating deep immersion or flow states [20]. Consequently, high analog well-being corroborates that deep focal engagement in a single object, rather than convenience, drives onboard well-being during constrained commutes. However, the affective fulfillment derived from such engagement is dynamically bounded by individual psychological characteristics and engagement styles. While Self-Determination Theory posits that intrinsic motivation fosters positive affects [18], its expression during travel depends on whether media use represents voluntary, goal-directed immersion [20] or compulsive, algorithm-driven scroll behavior [7,39]. Passengers possessing higher trait self-control or conscientiousness can successfully channel their intrinsic motivation into constructive, restorative activity during transit, shielding themselves from cognitive overload [6,23]. Conversely, for individuals prone to compulsive device engagement, even intrinsically driven digital usage can trigger cognitive depletion and attention residue [8,22]. Recognizing these individual-level boundary conditions clarifies that multitasking distortion is not merely a uniform environmental effect, but an interactive process between digital device architecture and individual self-regulatory capacities.

Second, analog media construct a robust cognitive shelter. Managing social and physical intrusions in the on-train environment is critical for well-being [2]. While travelers use media to isolate themselves, the media format dictates success [40]. Digital media’s constant internet connection leaves users perpetually vulnerable to digital notifications, even if physically shielded from the clamor. In contrast, analog immersion enables a complete “double disconnection” from both physical environments and digital interference. Only within this secure cognitive shelter can users fully attain the restoration of depleted directed attention [41].

In summary, the superiority of analog media lies structurally in protecting users from both external distractors and digital intrusion, thereby firmly guiding them toward single-object immersion.

4.3. Practical Implications: Designing for Autonomous Disconnection

4.3.1. Evidence-Based Design Implications

First, regarding the immediate implications of the empirical findings, transportation policy and mobility space design are proposed in this section. Traditionally, transportation planning has prioritized travel time savings, crowding alleviation, and connectivity via in-vehicle Wi-Fi. However, the multitasking distortion confirmed in this study suggests that providing physical comfort and internet connectivity cannot maximize passengers’ subjective well-being. To promote and maintain a modal shift from private vehicles to railways, aligning with the eleventh sustainable development goal (SDG 11) regarding sustainable cities and communities conceived by the United Nations [24], travel time must be recontextualized from a mere economic cost into high-quality time that enhances passenger well-being. Consequently, mobility design must shift from pursuing transport efficiency toward providing high-quality travel experiences where passengers can autonomously engage.

Second, the empirical results indicate that environmental development requires a psychological approach to support social sustainability. Preventing cognitive depletion caused by constant digital connection is essential for protecting the mental well-being of citizens in modern society, which directly aligns with the third sustainable development goal (SDG 3) concerning good health and well-being proposed by the United Nations [14]. Because intrinsic motivation (and its underlying autonomy) primarily determined onboard well-being levels, outright bans or coercive interventions against digital use must be avoided. Such external pressures risk transforming users’ motivations into controlled motivation, inducing amotivation in the long term [18]. Instead, an autonomy-supportive environment may be cultivated, presenting non-digital alternatives that empower railway passengers to autonomously choose disconnection.

4.3.2. Future Policy Recommendations and Conceptual Proposals

The observed multitasking distortion suggests several conceptual policy recommendations for future mobility space design. First, operators could investigate providing spatial “scaffolding” to help passengers detach from smartphones and reclaim immersion in physical reality or inner thoughts. One direction is adapting the long-distance “Quiet Car” concept —such as Amtrak’s Quiet Car [42], European railway “quiet zones” (e.g., Jain & Lyons [10]), or “quiet carriages” on Chinese high-speed rail (e.g., Tang et al. [43])—for daily commutes. While these existing services primarily target acoustic silence, the empirical findings conceptually suggest a need for spaces supporting attention liberation from digital media. Establishing non-digital recommended areas, where lighting and seating afford paper media formats or resting, aids autonomous disconnection. Furthermore, viewing natural landscapes through train windows can be reevaluated. According to Attention Restoration Theory [41], while smartphones (Hard Fascination) consume cognitive resources, passive attention to nature (Soft Fascination) restores them. Ensuring visual access to natural elements is proposed as a restorative approach functioning like a digital detox.

Second, a highly plausible direction for future policy and research involves promoting coexistence with technology (Calm Technology) is advocated. The negative impacts of digital media usage stem from cognitive depletion during active smartphone operation, not from digital technology inherently. Auditory experiences like audiobooks allow diverse experiences while minimizing device manipulation. Conversely, passive media like in-car digital signage provide information with minimal cognitive load. Implementing an information environment that remains in the periphery rather than monopolizing the user’s center of attention is suggested [44]. Instead of encouraging individual immersion, designers may provide ambient information, creating balanced digital environments where passengers can rest.

4.4. Limitations and Future Research

Regarding potential mood biases, while our statistical comparison of recall intervals (same-day vs. previous-day) confirmed no systematic differences across OWB or any primary constructs (Supplementary Table S6), the potential influence of temporary mood states requires nuanced consideration. First, the Onboard Well-Being (OWB) measure employed in this study (adapted from the Satisfaction with Travel Scale [31]) specifically assesses evaluative well-being—reflecting stable, domain-specific judgments of the travel experience—rather than momentary state moods. Consequently, evaluative travel judgments tend to be relatively robust against minor mood fluctuations at the time of survey completion.

At the same time, a continuous “Emotional Spillover Effect” across the travel episode also merits attention [30]. Specifically, pre-commute emotional states (e.g., baseline morning stress) may influence subsequent travel perceptions, while current mood state during survey completion could affect retrospective recall. Furthermore, post-survey mood state—representing the immediate affective state following travel—is also relevant, as it serves as a psychological link through which onboard experiences spill over into broader life satisfaction (H4). While cross-sectional retrospective designs cannot fully isolate momentary state mood fluctuations from enduring evaluative judgments, future research utilizing Experience Sampling Methods (ESM) [23] can track baseline affect prior to boarding, real-time in-transit emotional trajectories, and immediate post-commute mood states to empirically disentangle momentary mood bias from structural travel evaluations.

Despite these general limitations, the specific context of the Japanese on-train environment provides a crucial and forward-looking baseline for elucidating the formation process of well-being in mobility spaces. Because the railway cabin minimizes physical constraints and maximizes multitasking freedom, the findings proactively anticipate the psychological challenges humans will face in future mobility contexts, such as the widespread adoption of Autonomous Vehicles and MaaS. As vehicle cabins increasingly resemble “living rooms,” it is highly probable that a similar multitasking distortion will manifest there. Furthermore, the efficacy of High-Duration/High-Adaptation environments and proposed cognitive shelters heavily relies on the strict normative compliance characteristic of “Tight” cultures like Japan [45]. Consequently, cross-cultural research is imperative to evaluate the moderating role of looser social norms on individual-environment interactions. Finally, to overcome the limitations of subjective reporting, future studies must adopt a multimodal assessment approach. By integrating objective physiological metrics via wearable devices (e.g., heart rate variability) to capture affective components with high-resolution Experience Sampling Method (ESM) to accurately assess the subjective perception of intrinsic motivation, the mechanisms of the travel experience can be unraveled with unprecedented clarity.

5. Conclusions

Through an empirical analysis of Tokyo railway passengers, the structural multitasking distortion between ICT use and travel well-being was elucidated. While High-Duration/High-Adaptation environments promote immersion and enhance onboard well-being via analog media formats, digital media usage depletes cognitive resources and impairs well-being. This challenges the premise that constant technological connectivity necessarily improves life quality; instead, it indicates that balancing autonomous motivation with physical constraints is essential for fulfilling travel.

Travel time is increasingly recognized as more than an unproductive duration. Amidst continuous digital connectivity, the moderately isolated on-train environment serves as a cognitive shelter to reclaim rest and immersion. By transcending transport efficiency to design environments that restore attentional resources, urban planners can actively utilize passengers’ subjective perceptions and value judgments to support long-term well-being and social sustainability (SDG 3). Such autonomy-supportive designs can transform daily travel from a stressful routine into a restorative process, thereby contributing to sustainable urban mobility (SDG 11).