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Mohd Haniff Osman
1,2*
, Muhamad Nazri Borhan
1
, Nur Arzilah Ismail
2
1
Smart and Sustainable Township Research Centre, Faculty of Engineering and Built Environment,
National University of Malaysia, 43600 UKM Bangi, Selangor, Malaysia
2
Applied and Computational Engineering Mathematics, Department of Engineering Education, Faculty
of Engineering and Built Environment, National University of Malaysia, 43600 UKM Bangi, Selangor,
Malaysia
*Corresponding Author
22 December 2025; Accepted: 28 December 2025; Published: 05 January 2026
ABSTRACT
This study investigates how Airport Rail Link (ARL) users without prior experience of service disruption
evaluate disruption management strategies during unexpected disruptions. While previous studies have focused
on passengers who have encountered disruptions, little is known about the expectations and perceptions of users
who have never faced such events. Integrating the Expectancy Disconfirmation Model (EDM) with Justice
Theory, this study examines the roles of normative expectations, perceived fairness and performance in shaping
disconfirmation judgments. To address methodological challenges in capturing expectations for hypothetical
scenarios, a comic-strip-based questionnaire was developed. This visual approach depicted realistic disruption
scenarios and response actions, enabling respondents to assess fairness across distributive, procedural and
interactional dimensions before comparing perceived performance with initial expectations. The instrument was
pretested and piloted to ensure its clarity and feasibility in terms of timing. Data were collected from 290 ARL
users who confirmed they had never experienced a service disruption. Responses were analysed using Partial
Least Squares Structural Equation Modelling to test hypothesized relationships and mediation effects. Results
reveal that perceived fairness of response actions strongly influences disconfirmation and fully mediates the
effect of expectations, highlighting the critical role of performance perception in shaping judgments. Importance-
Performance Map Analysis further identified perceived fairness as the most influential construct. The findings
underscore the importance of transparent communication and fairness-driven response actions in building trust
among regular users who may hold idealized expectations. From a methodological perspective, the study
demonstrates the value of visual stated preference instruments for eliciting meaningful responses in hypothetical
contexts. Practical implications include designing proactive communication strategies and fairness-oriented
response measures to enhance resilience and user confidence in the face of unexpected disruptions.
Keywords: Airport Rail Link Service; Disruption Management; Expectancy Disconfirmation Model; Justice
Theory; Visual Stated Preference Method
INTRODUCTION
Disruption management refers to the process of minimizing the cascading effects of service interruptions while
operating within existing resource constraints (Osman et al., 2016). A significant challenge arises when
disruptions occur unexpectedly, as opposed to planned disruptions (Kaewunruen & Osman, 2023). Unexpected
disruptions pose a significant challenge for operators, particularly in time-sensitive transport systems, such as
Airport Rail Link (ARL) services that connect metropolitan areas to airports. When ARL services fail without
warning, passengers risk missing flights due to delays in reaching the airport and completing boarding
procedures (Malandri et al., 2017). Unlike general rail disruptions, ARL failures carry heightened operational
and temporal constraints, making practical response actions critical.
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Previous studies have primarily examined service users who have experienced disruptions, focusing on how
prior encounters shape expectations and satisfaction (Auld et al., 2020; Currie & Muir, 2017; Hien et al., 2024;
Lu et al., 2025). These users may have a baseline understanding of how operators typically manage such
situations, which leads to specific expectations regarding their responses. However, a substantial gap exists in
understanding ARL passengers who regularly use the service but have never faced a disruption. These passengers
often form normative expectations based on idealized assumptions or experiences with other transport modes
rather than firsthand knowledge of ARL recovery processes (Hjortskov, 2020). Such expectations may overlook
operational realities, such as the time required to arrange shuttle buses or coordinate alternative transport (Rahimi
et al., 2019; Wang et al., 2022). When disruptions occur, these passengers’ evaluations of fairness and
performance can significantly influence their trust and loyalty, which are key factors in sustaining ridership and
reputation (Hien et al., 2024).
The evaluation of response actions during disruptions necessitates a multidimensional approach that considers
for both cognitive and fairness-based judgments (Yim et al., 2003). To assess these evaluations, this study
integrates the Expectancy Disconfirmation Model (EDM) and Justice Theory. EDM provides a cognitive lens,
positing that satisfaction results from the comparison between perceived performance and prior expectations
(Oliver, 1980). The results of this comparison can be classified into three possible outcomes: positive
disconfirmation occurs when perceived performance exceeds initial expectations. In this case, the response
action "over-delivered," resulting in a pleasant surprise; negative disconfirmation occurs when perceived
performance falls short of initial expectations. Here, the response action is "under-delivered”, leading to
dissatisfaction, and a simple confirmation occurs when the perceived performance aligns perfectly with the initial
expectations. Justice Theory complements EDM by introducing three dimensions of fairness: distributive,
procedural and interactional. As stated by Liao et al., (2022), distributive justice refers to the fairness of
outcomes, such as compensation or alternate transport; procedural justice focuses on the fairness and
transparency of the processes used to handle disruptions; and interactional justice assesses the quality of
interpersonal treatment, emphasizing respect and empathy during recovery interactions. Integrating EDM and
Justice Theory offers a multidimensional lens for understanding how passengers without prior experience judge
response actions (Chih et al., 2012; Hien et al., 2024).
Traditional EDM studies rely on survey-based evaluations, often in textual form, allowing respondents to answer
at their convenience (Van Ryzin, 2005). Nevertheless, this approach may fail to capture abstract scenarios for
respondents lacking prior exposure (Cherchi & Hensher, 2015). To address this, the present study employs a
visual approach using comic-strip narratives embedded in questionnaires. This design immerses respondents in
realistic disruption scenarios, enabling them to evaluate response actions across fairness dimensions before
comparing them to their expectations (Schiebler et al., 2025).
Accordingly, the objectives of this study are to: i) evaluate the EDM for ARL disruption management through
the lens of Justice Theory, ii) investigate whether standard planned response actions effectively disconfirm
normative expectations among ARL passengers without prior disruption experience, and iii) offer practical
implications for ARL operators to build trust and confidence among this passenger segment. By focusing on this
overlooked group, the study contributes to the literature by extending research beyond disruption-experienced
users to include regular passengers who have yet to encounter service failures.
LITERATURE REVIEW
Capturing public expectations for unpredictable events presents methodological challenges. Revealed Preference
(RP) instruments cannot capture choices of non-users or new alternatives, prompting the use of Stated Preference
(SP) designs to elicit choices under hypothetical scenarios. Traditionally, these scenarios are presented in written
form. Nonetheless, text-based approaches often fail to convey abstract concepts effectively to non-users,
potentially compromising response quality. To help respondents grasp abstract disruption scenarios, transport
researchers increasingly embed visual elements into surveys. For instance, Auld et al., (2020) visually presented
disruption response options such as waiting for service restoration or using a shuttle bus, within survey
instruments. Similarly, Kalyanpad et al., (2020) integrated the Google Maps API into RP and SP experiments
to provide interactive, map-based questionnaires. These innovations aim to improve accuracy, facilitate
understanding and create a more engaging user experience.
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Beyond methodological considerations, theoretical frameworks guide the evaluation of service satisfaction. The
EDM provides a cognitive basis for assessing discrepancies between perceived performance and initial
expectations. A 2025 meta-analysis confirms strong positive links among expectations, perceived performance
and satisfaction, particularly for predictive expectations and services, underscoring the relevance of EDM in
service contexts, such as transportation (Schiebler et al., 2025). In the context of service recovery, EDM explains
how users, particularly existing ones, form expectations about reliability and the response actions taken. These
expectations may include assumptions about response speed, clarity of communication and assistance provided
during delays (McCollough et al., 2000). When disruptions occur, users evaluate whether the operator’s actions
align with or exceed these expectations. For instance, quickly deploying shuttle buses and proactively rebooking
flights can build trust and loyalty (Matikiti et al., 2018; Migacz et al., 2018).
While EDM helps capture the expectation-performance gap, it does not fully address perceptions of fairness,
which are crucial during service failures (Migacz et al., 2018). Justice Theory offers the evaluative basis for
fairness across three dimensions: distributive (outcomes/compensation), procedural (processes/policies) and
interactional (interpersonal treatment). Effective communication and compensation can mitigate negative
disconfirmation (Wang et al., 2022). In contrast, poor transparency and delayed responses exacerbate
dissatisfaction, eroding trust and loyalty (Naohiko et al., 2017). Emerging evidence also shows that service users
compare their treatment to others, shaping perceived justice across dimensions in multi-stage recovery processes
which is relevant for crowded, time-pressured ARL disruptions (Aguilar-Rojas et al., 2024).
METHODOLOGY
This study employed a mixed-method approach, combining visual survey design and structural equation
modelling, to examine how normative expectations influence perceived fairness and disconfirmation of response
actions among ARL users without prior disruption experience. The methodology consisted of four key
components: research model development, instrument design, data collection and data analysis, as illustrated in
Figure 1.
Figure 1: Sequential Stages of Research Methodology
Research Model
The conceptual model integrates the EDM with Justice Theory to capture both cognitive and fairness-based
evaluations of response actions. Three primary constructs were operationalized:
i. Justice-based Expectations (EXP): Pre-formed beliefs about what constitutes a fair response during an
unexpected disruption.
ii. Perceived Justice of Response Action (PCV): Evaluation of the fairness and appropriateness of the
operator’s actions as depicted in the scenario.
iii. Disconfirmation of Response Action (DSC): The extent to which PCV differs from initial EXP,
indicating whether the response was better, worse or about the same as expected.
Based on these constructs, the following hypotheses were tested:
i. H1: EXP significantly influence DSC,
ii. H2: PCV positively affects DSC,
iii. H3: EXP positively influence PCV, and
iv. H4: PCV mediates the relationship between EXP and DSC.
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Figure 2 illustrates how the EDM and Justice Theory jointly explain user disconfirmation during ARL service
disruptions. EDM captures the expectation-performance gap, while Justice Theory addresses fairness across
distributive, procedural and interactional dimensions, leading to disconfirmation.
Figure 2: Conceptual Model Integrating Justice Theory Dimensions with the EDM To Explain How Response
Actions Shape Expectations, Perceptions and Subsequent Disconfirmation.
Instrument Development
The creation of the comic strip narrative followed a structured framework adapted from Friesen et al. (2018),
emphasizing the need for a detailed storyboard. The narrative was structured around three elements:
transportation issue, people and information flow, following guidelines from Salomon and Singer (2011).
The story centres on Peter, a first-time user of the ARL service. He is initially scheduled to arrive at the airport
with a comfortable 90-minute buffer before his flight's boarding time. Tension arises when a service suspension
disrupts his plans. Peter's inexperience with the ARL operator's response procedures, combined with heavy rain
outside the station, influences his critical decision. Rather than seeking alternative transportation, he decides to
stay at the station. He is entirely reliant on the train operator’s responses. This highlights user vulnerability
during a service failure. The information flow element illustrates the crisis communication and response actions
of the ARL operator, crafted using real-world procedures (Itani et al., 2019; Piner & Condry, 2017).
A hybrid workflow combining artificial intelligence with human illustration was employed to visually represent
the narrative. Tools like StoryNest and StoryboardThat quickly generated visuals, which an illustrator then
refined to enhance emotional depth and ensure contextual accuracy.
Finally, questionnaire items were anchored to specific points in the comic narrative to capture EXP, PCV and
DSC sequentially. By anchoring the items to specific sequential points in the visual narrative, the instrument
sought to capture immediate cognitive and emotional reactions rather than generalized post-hoc reflections.
Questionnaire items were adapted from established service recovery literature (Chih et al., 2012; Matikiti et al.,
2018; McCollough et al., 2000) and measured on a five-point Likert scale ranging from “Strongly Disagree” to
“Strongly Agree”. The instrument was pretested by experts and piloted with 30 passengers to ensure clarity and
the feasibility of timing. The comic-based questionnaire can be accessed at:
https://forms.gle/e9soTHhmBBn9CPQKA
Data Collection
To mitigate potential response biases common in stated preference designs, several safeguards were
implemented. First, the survey was distributed at major transit hubs in Kuala Lumpur to ensure respondents were
in a 'commuter mindset'. Next, random distribution of QR codes allowed participants to complete the comic-
based questionnaire at their convenience, reducing the likelihood of rushed or 'straightlined' responses. Finally,
participants were required to indicate at the end of the questionnaire whether they had previously used a train to
access the airport and confirm whether they had ever experienced a train service disruption.
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Data Analysis
Data analysis utilized Partial Least Squares Structural Equation Modelling (PLS-SEM) to test hypothesized
relationships among constructs and assess mediation effects. PLS-SEM was chosen for its ability to handle
complex models that include latent variables, as well as its suitability for relatively small sample sizes. The
implementation of PLS-SEM, facilitated by SmartPLS 4 software, follows a two-stage approach as
recommended by Hair et al., (2017).
i. Measurement Model Assessment: The first stage evaluates the reliability and validity of the measurement
model, encompassing assessments of indicator reliability, internal consistency reliability, convergent
validity and discriminant validity.
ii. Structural Model Assessment: The second stage evaluates the structural model by examining the
significance of the path coefficients (using bootstrapping), the coefficient of determination, and the
model's predictive relevance.
After completing the model assessments, we conducted an Importance-Performance Map Analysis (IPMA). The
primary objective of this analysis was to validate the relative importance and performance of EXP and PCV in
influencing DSC among ARL users.
RESULTS
Out of the 650 invitation cards distributed, 557 individuals participated in the study, resulting in a participation
rate of 86%. Among these participants, 290 individuals (approximately 53%) had no prior experience with ARL
service disruption, which forms the relevant sample for this study. Importantly, the 290 participants provide
sufficient statistical power for PLS-SEM analysis. The power analysis indicates that 90 participants are needed
to achieve 80% statistical power at a 5% significance level for detecting R² values of 0.10. Thus, the structural
model (as illustrated in Figure 3) can demonstrate adequate power to detect minimal effects and ensures reliable
statistical outcomes.
Measurement Model Assessment
Before assessing the structural model, we evaluated the reliability and validity of the reflective measurement
models for the three latent constructs: EXP, PCV and DSC. The results in Figure 4 show that all measurement
constructs achieved high levels of internal consistency. The Cronbach’s alpha values ranged from 0.788 to 0.858,
while the Composite Reliability (CR) values fell between 0.875 and 0.894, both of which exceed the 0.70
threshold. The constructs demonstrated convergent validity, with Average Variance Extracted (AVE) values
exceeding 0.50: 0.541 for EXP, 0.586 for PCV and 0.701 for DSC. This indicates that each construct accounts
for over half of the variance in its indicators. Furthermore, the indicators displayed in Figure 3 show statistical
significance with a p-value of less than 0.05, and most values exceed 0.7. The structural model still included
indicators Exp1 and Exp2, despite their outer loadings being below 0.7, because their respective construct shows
strong internal consistency and convergent validity.
Figure 3: PLS-SEM Results Showing Indicator Outer Loadings, f² Effect Sizes on Paths and R² Values for
Endogenous Constructs.
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Figure 4: Reflective Measurement Model Results
Discriminant validity was confirmed using two approaches. First, the Fornell-Larcker criterion was satisfied, as
the square root of each construct’s AVE exceeded its correlations with other constructs (Table 1). Second,
Heterotrait-Monotrait (HTMT) ratios (Table 2) were all below 0.90, confirming that the constructs are distinct.
Table 1: Discriminant Validity by Fornell-Larcker Criterion
Construct
DSC
EXP
PCV
DSC
0.837
-
-
EXP
0.409
0.736
-
PCV
0.744
0.477
0.766
Table 2: HTMT Ratio Results
Construct
DSC
EXP
EXP
0.443
-
PCV
0.884
0.510
Structural Model Assessment and Hypothesis Testing
The structural model was evaluated for multicollinearity, explanatory power and predictive relevance. VIF
(Variance Inflation Factor) values (Figure 5) were well below the conservative threshold of 3.0, indicating no
concerns about multicollinearity. The model explained 55.8% of the variance in DSC (R² = 0.558) and 22.8% of
the variance in PCV (R² = 0.228). Stone-Geisser’s Q² values for PCV (0.180) and DSC (0.358) exceeded 0.15,
confirming predictive relevance.
Effect size analysis revealed that EXP had a negligible direct effect on DSC (f² = 0.008). At the same time, PCV
exerted a powerful influence (f² = 0.880), surpassing Cohen’s threshold for a significant effect (≥ 0.35).
Bootstrapping results (Table 3) showed that the path from EXP to DSC was non-significant (t-value = 0.631, p-
value = 0.528), so H1 was not supported. Conversely, PCV → DSC was positive and highly significant (t-value
= 7.432, p-value < 0.001), providing support for H2. The path from EXP → PCV was also significant (t-value
= 4.760, p-value < 0.001), supporting H3. Mediation analysis confirmed complete mediation. The indirect effect
of EXP on DSC through PCV was significant (Indirect Effect = 0.339, t-value = 3.488, p-value < 0.01), with a
95% confidence interval [0.195, 0.560] that excluded zero. Therefore, H4 was supported.
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Figure 5: Endogenous Variable VIF, R
2
and Q
2
Table 3: Statistical Hypothesis Tests
Path
β
t-value
p-value
Decision
EXP → DSC
0.069
0.631
0.528
Not support H1
PCV → DSC
0.711
7.432
0.000
Support H2
EXP → PCV
0.447
4.760
0.000
Support H3
EXP → PCV→ DSC
0.339
3.488
0.001
Support H4
IPMA results (Table 4) validated the structural findings. PCV emerged as the most influential driver of DSC
(importance score = 0.759), followed by EXP (importance score = 0.582). Performance scores were moderate
and similar across constructs (PCV = 65.560; EXP = 68.698), indicating generally positive user perceptions.
Table 4: Importance and Performance Scores for Endogenous Variables
Construct
Importance
Performance
EXP
0.409
69.871
PCV
0.711
66.438
DISCUSSION AND MANAGERIAL IMPLICATIONS
The findings reveal that EXP did not have a direct influence on DSC (β = 0.069, p-value > 0.05). This finding
diverges from prior research examining passengers who have experienced disruptions (Hien et al., 2024; Matikiti
et al., 2018). A plausible explanation lies in the sample composition: respondents were train users who had never
encountered a disruption. Their expectations were likely shaped by generalized assumptions or idealized service
standards rather than firsthand experience with recovery processes. For example, the public may be unaware of
the logistical constraints that ARL operators face, such as the limited time window for arranging shuttle buses
during disruptions (Itani et al., 2019).
Interestingly, the total effect of EXP on DSC was fully mediated by PCV, as indicated by the significant indirect
path (β = 0.339, p-value < 0.05). This suggests that passengers without disruption experience cannot accurately
judge whether their expectations were met until they understand the operator’s recovery measures. In this study,
the comic-strip questionnaire played a pivotal role by visually depicting the disruption scenario and response
actions through the lens of justice theory. This approach enabled respondents to evaluate performance across
distributive, procedural and interactional dimensions before comparing it to their initial expectations.
The significant path from PCV to DSC (β = 0.711) underscores the critical role of performance perception in
shaping disconfirmation judgments. IPMA further reinforces this finding, identifying PCV as the most influential
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construct (importance score = 0.711), surpassing EXP (importance score = 0.409). These results align with
established literature emphasizing the primacy of perceived performance in satisfaction and disconfirmation
processes (McCollough et al., 2000).
From a managerial perspective, ARL operators should recognize that passengers who have never experienced
disruptions may hold unrealistic expectations. Transparent communication strategies such as social media
updates, website FAQs and visually engaging content, can help set realistic expectations and reduce uncertainty
during disruptions. Specifically, tangible solutions such as shuttle buses (distributive justice), timely and
accurate updates (procedural justice), and empathetic staff interactions (interactional justice) are essential for
fostering positive perceptions. Failure to uphold these principles risks deterring regular users. In contrast,
successful implementation can enhance the service’s reputation and foster customer loyalty.
CONCLUSION
This study advances the understanding of disruption management in ARL services by focusing on passengers
who have used the service but have never experienced a disruption; a group often overlooked in prior research.
By integrating the EDM with Justice Theory, the findings reveal that justice-based expectations alone do not
directly influence disconfirmation for this segment. These expectations, instead, can be disconfirmed when the
service users are presented with clear and structured information about professional response actions, even under
chaotic conditions.
From a practical perspective, ARL operators should prioritize proactive communication strategies and fairness-
driven response actions to maintain trust among regular users who have never faced disruptions. Clear, timely
updates and empathetic interactions can help manage normative expectations and foster confidence in the dace
of unexpected service failures.
While this study provides foundational insights into Kuala Lumpur’s transport infrastructure, the findings may
reflect cultural and institutional norms specific to the Malaysian rail sector. Future studies should extend this
framework to airport rail systems in diverse global contexts to account for variations in operational settings and
passenger demographics, thereby enhancing external validity. Another promising avenue is to examine
demographic differences, such as those between young university students and mature travellers, as these groups
may exhibit distinct preferences for survey formats. Younger respondents might find visual narratives more
engaging, whereas older travellers may favour traditional text-based questionnaires. Understanding these
differences could inform the development of inclusive survey instruments and tailored communication strategies
for disruption management.
Although this study did not directly compare comic-strip questionnaires with traditional text-based formats, the
visual approach appears promising for immersing respondents in realistic disruption scenarios. By depicting both
disruption events and response actions visually, the instrument enabled respondents to assess fairness across
distributive, procedural and interactional dimensions before forming judgments. This suggests that visual survey
designs may enhance stated preference research, particularly among respondents with limited prior exposure to
service failures. A direct quantitative comparison between visual and text-based formats remains an important
avenue for future research to isolate the effect size of visual elements. The current approach was adopted to
mitigate cognitive barriers faced by non-users in complex disruption contexts.
ACKNOWLEDGEMENTS
This research was supported by Universiti Kebangsaan Malaysia under Grant No. GGPM-2023-002. The authors
gratefully acknowledge the assistance of Khaleeda Aleesa Kamaruzaman for her valuable contributions to
questionnaire development and data collection.
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