Monitoring emotions throughout the onboard customer journey – evidence from the travel and tourism industry

Abstract

The primary objective of this study is to evaluate passengers’ emotions throughout different stages of their onboard customer journey to determine whether assessing passengers’ emotions at a single stage of their journey accurately reflects their overall onboard emotional experience. The study aims to fill this research gap by acquiring data that not only addresses the aforementioned aspects but also validates the applicability of the adapted emotional measurement instrument within the travel and tourism industry. A large-scale survey was conducted, targeting respondents who were ferry passengers traveling for tourism purposes. The study’s results indicate emotional variations throughout the onboard customer journey and affirm the effectiveness of the adapted measurement instrument within the passenger shipping sector. The paper demonstrates the importance of measuring passengers’ emotions at various stages to acquire valid insights and a profound understanding of their emotional experience on board. While extensive research has explored emotions during the customer journey across various sectors, there has been limited quantitative analysis specifically focusing on the emotional experiences of ferry passengers. Future studies could investigate emotional changes in passengers across various and multiple travel and tourism customer journeys, aiming to derive broader and more generalizable conclusions.

Keywords:

• Customer journey
• emotional experience
• onboard experience
• tourism
• travel industry
• passenger shipping
• ferry industry

REVIEWING EDITOR:

• Guangchao Charles Feng
• School of Communication
• Hong Kong Baptist University
• Hong Kong

SUBJECTS:

• Transport Industries
• Service Industries
• Tourism

Introduction

Emotions play an important role both intrapersonally and interpersonally, empowering individuals to respond to significant events by influencing their cognition and attitude (Frijda, 2004). Additionally, emotions contribute to social coordination by eliciting emotional, inferential and behavioral reactions from others (Van Kleef, 2016). As a result, emotions are not merely individual but communal processes, exerting a profound impact on both individual and societal life (Van Kleef & Lange, 2020).

Passengers in the ferry transportation sector are commonly perceived as tourists, and their emotions, as highlighted by Niedenthal and Ric (2017), represent nuanced reactions to events. Throughout the customer journey within the ferry context, passengers encounter numerous touchpoints embedded in the onboard experiencescape, including interactions with the crew and fellow passengers (Pantouvakis & Gerou, 2023). These interactions contribute to a diverse range of emotional experiences, which, being subjective, can significantly influence an individual’s behavior (Amin et al., 2021). Emotions, as demonstrated by Dolan (2002) and Amin et al. (2021), play a central role in shaping the quality and scope of human experiences and behaviors. Recognizing this centrality is crucial for understanding and effectively managing customer journeys within the travel and tourism industry (Yachin, 2018).

The literature widely acknowledges the pivotal role of emotions in the tourist experience (Kim & Fesenmaier, 2015), with scholars emphasizing the potential benefits businesses can derive from evaluating customer emotions (e.g. Magids et al., 2015). Numerous researchers have highlighted that emotions influence various stages of the tourist experience (Prayag et al., 2013) and are integral to understanding tourist behavior (e.g. Choi & Choi, 2019; Hosany, 2012; Hosany et al., 2020; Jiang, 2019). Scholars (e.g. Ayyildiz et al., 2023; Boz & Koc, 2021; Koc & Boz, 2020), underscored the importance of understanding emotions for the internal operations of a tourism and hospitality business. Consequently, effectively managing passenger emotions is fundamental for the success of travel and tourism services (Herjanto et al., 2021).

Despite the significance of evaluating emotions throughout the customer journey, there remains a dearth of empirical evidence regarding the assessment of ferry passenger emotions onboard, as most existing studies predominantly focus on conceptual frameworks. This literature gap underscores the necessity for further research that empirically examines the emotional changes experienced by passengers in real-time customer journeys. This need is particularly pronounced given the growing awareness among scholars that positive and negative emotions can coexist during a single journey (Caruelle et al., 2019; Chepngetich et al., 2019; Ding & Chai, 2015; Koenig-Lewis & Palmer, 2014; Maguire & Geiger, 2015; Manthiou et al., 2020; Pang et al., 2017; Spielmann, 2021). Addressing this gap, this study aims to contribute to a more comprehensive understanding of the emotional dynamics within onboard customer journeys, potentially offering valuable insights for service providers seeking to enhance the emotional experience of ferry passengers.

In light of the aforementioned considerations, the primary objective of this research is to adapt the measurement instrument proposed by Kujur and Singh (2018) with the aim of addressing the following question: Does assessing passengers’ emotions at a single stage of their journey accurately capture their overall onboard emotional experience?

The paper is structured into five sections. Section 2 offers a concise overview of the literature review and hypothesis formation. Section 3 delves into the research methodology, design, measurement instruments and data. Section 4 scrutinises the study results. Finally, Section 5 presents overall conclusions, discusses theoretical and practical implications, and suggests avenues for future research.

Literature review and hypothesis formation

The customer journey is the path that a customer follows from initially recognizing the need to consume to post-usage behaviour, encompassing various stages. This journey includes all interactions between the company and the customer, known as touchpoints, where multiple responses and different experiences are generated. To gain a comprehensive insight into customer behaviour, it is crucial to monitor these various touchpoints (Homburg et al., 2017). As highlighted by Lemon & Verhoef (2016), emotional reactions are created during these customer touchpoints.

Over the years, scholars have made concerted efforts to establish a comprehensive understanding of emotions through diverse definitions and explanations. Despite the multitude of definitions, there is a lack of consistency among them, and many are not sufficiently specific to provide a clear understanding of what an emotion genuinely consists of. Due to the absence of a precise definition, numerous researchers have sought to enhance the comprehension of emotions by elaborating on their constituent elements.

Clore et al. (1987) define emotions as ‘valenced affective reactions to perceptions of situations’. Adopting the affective concept, Cohen & Areni (1991) describe emotions as ‘affective states characterized by episodes of intense feelings associated with a specific referent and instigate specific response behaviors’. Building on Clore et al.’s (1987) valenced tone, Niedenthal and Ric (2017) define emotions as ‘valenced responses to situations that entail synchronized patterns of appraisals, experiences, physiological changes, expressions, and/or behavioral tendencies’. Abdelkader and Bouslama (2014) assert that emotions exhibit a level of intensity, which can be either positive or negative, and are connected to a stimulus causing a disturbance to an individual’s stable condition. According to Servidio and Ruffolo (2016), emotions are symbolic encounters that signify both the qualities of an action and the individual’s response to an external stimulus. These researchers offer distinct perspectives in their definitions, emphasizing both the positive and negative magnitudes of emotional experiences.

In the literature of emotions, two primary theoretical perspectives prevail: the dimensional and the categorical (Volo, 2017). According to Kujur and Singh (2018), the dimensional approach to measuring emotions is based on valence, whereas the categorical approach focuses on the specificity of emotions. In the fields of marketing and management, both dimensional and categorical theories are employed for assessing emotions, with the categorical theory predominantly utilized in the literature on transportation and tourism (e.g. Hosany et al., 2020; Laros & Steenkamp, 2005), since it enhances the understanding of interactive processes.

The inclusion of emotion-related content in tourism studies has emerged primarily from marketing research, particularly in the realm of customer satisfaction studies, rather than directly from psychological investigations. As a result, the application of the emotion concept in tourism has been restricted, with limited consideration given to various theories, measurement methods, and definitions (Volo, 2017). Despite lingering questions about the optimal method for measuring in tourism (Hosany et al., 2020), this study adopts the categorical measurement instrument for emotions.

Customers are influenced by their emotions at different stages of the decision-making process (Roster & Richins, 2009; Maguire & Geiger, 2015). For instance, if customers experience positive emotions during the service, this can motivate them to offer positive recommendations after the conclusion of the service experience (Wu and Gao, 2019). Additionally, emotions can fluctuate over time (Maguire & Geiger, 2015) and customers may not always outwardly express their true feelings, nor do they consistently feel what they express (Greenaway & Kalokerinos, 2019). Given these considerations, it becomes critical to measure emotions during a real experience rather than relying on recall (Caruelle et al., 2019) and to investigate the possibility that positive and negative emotions may coexist during an experience (Manthiou et al., 2020). Consequently, examining the coexistence of positive and negative emotions (emotional changes) during a real customer journey is imperative, as it represents a research gap in the literature (Chepngetich et al., 2019; Manthiou et al., 2020; Spielmann, 2021).

While the customer journey is a relatively new area in academia, with the bibliography spanning the last decade, studies on this topic have been conducted across various sectors including transportation, tourism, healthcare, banking, and retailing. However, research on the customer journey remains limited, with a predominant focus on conceptual frameworks (Canfield & Basso, 2017; Halvorsrud et al., 2016; Kranzbühler et al., 2019; Lemon & Verhoef, 2016; Lipowski & Bondos, 2018; Rudkowski et al., 2019). Academics emphasize the need for research linked to real customer journeys (Manthiou et al., 2020; Verhulst et al., 2020). In the literature, while numerous studies explore the emotional impact on services, empirical studies on emotions during the customer journey are generally scarce. Only a limited number of studies have empirically examined the emotional changes experienced by passengers during their customer journey (e.g. Van Hagen & Bron, 2014), and no such studies have been found in the context of ferry travel. Also it is crucial for both academic researchers and industry managers to gain a deeper understanding of the significant role that emotions play in shaping customer experiences within the field of tourism (Li et al., 2015). In light of recommendations for increased research focus on examining passengers’ emotions (e.g. Song et al., 2020) and acknowledging the likelihood of emotional changes throughout the onboard customer journey, it is hypothesized that emotions on board should be assessed at multiple stages. Accordingly, the following research hypothesis is formulated: Does assessing passengers’ emotions at a single stage of their journey accurately reflect their overall onboard emotional experience?

Methodology

A structured questionnaire was used to collect the data required for the analysis. The study sample consisted of 840 randomly selected participants (N = 840), all of whom were leisure passengers on a passenger RoRo ferry traveling between Greece and Italy. This particular ferry route was selected due to its approximately twenty-two hours duration, allowing for two separate measurements of emotions with a significant time gap between them. To ensure the focus of the study remained on tourism-related factors, passengers traveling for business or non-touristic purposes were intentionally excluded. The random sampling methodology ensured the selection of participants without bias toward specific characteristics or groups. Data were collected over six consecutive itineraries, with the majority of respondents being Greeks, Germans, French, and Italians.

According to scholars (e.g. Ingram et al., 2017; Shaw & Williams, 2009; Yachin, 2018), the tourism customer journey is comprised of three phases: the prospective pre-trip period, the active tourism experience, and the reflective post-trip phase. In this study, emotions are measured during two distinct stages of the active tourism experience phase. The survey is divided into two main phases, Phase A (Part 1) and Phase B (Part 2 and Part 3). During Phase A, respondents complete Part 1, assessing their emotions during the first half of the journey, corresponding to the initial stage of the active tourism experience. In Phase B, the same respondents proceed to complete both Part 2 and Part 3. Part 2 aims to measure passengers’ emotions during the latter half of their journey, reflecting the second stage of the active tourism experience, while Part 3 intends to assess the passengers’ travel purpose and personal characteristics.

Seven items were used to evaluate emotions, categorized into two categories: positive and negative emotions, following the classification by Kujur and Singh (2018). The initial three items measured positive emotions, such as contentment, happiness, and love, while the remaining four items assessed negative emotions, encompassing anger, fear, sadness, and shame. These dimensions (positive & negative) were specifically selected as they have been extensively used for emotional evaluation in the bibliography (e.g. Cachero-Martínez & Vázquez-Casielles, 2021; Falter & Hadwich, 2020; Jang & Namkung, 2009; Kujur & Singh, 2018; Laros & Steenkamp, 2005; Tubillejas-Andrés et al., 2020; Van Kleef & Lange, 2020). The variable of pride was excluded from the positive emotions dimension since the reliability statistics of this variable were not among the acceptable values. All the emotions measurement items were rated on a seven-point Likert–type scale, ranging from ‘Strongly disagree’ to ‘Strongly agree’. The emotions measurement instrument, including items and answer scales, is displayed in Table 1.

Table 1. Items and answer scales.

Emotions measurement instrument^a
Positive emotions (1 = Strongly Disagree, 7 = Strongly Agree)
… I feel contentment (Contended, Peaceful, Fellfield). ^b, c
… I fell happy (Optimistic, Pleased, Thrilled, Enthusiastic).^b^, c
… I feel love (Romantic, Sentimental, Warm-hearted). ^b, c
Negative emotions (1 = Strongly Disagree, 7 = Strongly Agree)
… I feel angry (Frustrated, Irritated, Unfulfilled). ^b, c
… I feel sad (Depressed, Miserable, Helpless). ^b, c
… I feel fear (Scared, Afraid, Worried, Nervous). ^b, c
… I feel ashamed (Embarrassed, Ashamed, Humiliated). ^b, c

^a Adapted from Kujur & Singh (2018).

^b In Part a: ‘During the trip (up to now), there are times that …’.

^c In Part b: ‘From the completion of the part A up to now, there are times that …’.

Confirmatory Factor Analysis (CFA) using the IBM SPSS AMOS (version 26) was performed to refine the emotions scale and confirm the existence of its dimensions, as suggested by the examined literature. CFA was utilized as the measurement instrument for this construct has been widely used in the literature and its applicability has been confirmed by various researchers in the transportation sector (e.g. Majra et al., 2016; Medina-Muñoz et al., 2018; Wozniak et al., 2018). For handling missing values in the data, the single imputation method was employed. Specifically, the mean imputation method was applied because all examined variables followed a normal distribution (Hair et al., 2006). T-tests were then applied to examine possible emotional changes among passengers during the onboard customer journey.

Results

To ensure a high level of construct reliability, it is recommended that the Cronbach’s alpha value be above 0.7. In the current study, the Cronbach’s alpha value for emotions at stage A was found to be 0.797, and for emotions at stage B, it was 0.827. These results indicate that all examined constructs possess a Cronbach’s alpha value above 0.7, thereby demonstrating a highly satisfactory level of construct reliability.

Confirmatory Factor Analysis (CFA) was employed using the Maximum Likelihood Estimation to confirm the structure of the emotions construct at both in stage A and stage B.

The emotion scale consists of two factors, namely positive emotions and negative emotions (Kujur and Singh, 2018). Table 2 displays the standardized regression weights of the emotion scale, illustrating the relationship between each variable and its corresponding latent construct.

Table 2. Standardized regression weights.

Factors	Standardized regression weight*
	Stage A	Stage B
Positive Emotions
…I feel Contentment (Contended, Peaceful, Fulfilled)	0.533	0.783
…I feel Happy (Optimistic, Pleased, Thrilled, Enthusiastic)	0.739	0.870
… I feel Love (Romantic, Sentimental, Warm-hearted)	0.803	0.428
Negative Emotions
… I feel Angry (Frustrated, Irritated, Unfulfilled)	0.635	0.851
… I feel Sad (Depressed, Miserable, Helpless)	0.584	0.798
… I feel Fear (Scared, Afraid, Worried, Nervous)	0.871	0.507
…I feel Ashamed (Embarrassed, Ashamed, Humiliated)	0.598	0.612

* p < 0.001.

The convergent validity of the measurement model is supported by the statistical significance of all standardized regression weights. The absolute and incremental fit indices, presented in Table 3, demonstrate a remarkably high level of satisfaction, indicating a superior model fit. The Chi-square/df ratio falls within the acceptable range of 0 to 5, a widely recognised criterion for assessing model fit (Hooper et al., 2008; Tatoglu et al., 2016). Furthermore, the Root Mean Square Error of Approximation (RMSEA) is below 0.07, as recommended by Steiger (2007) and Hooper et al. (2008). The Goodness of Fit Statistics (GFI), the Adjusted Goodness of Fit Statistics (AGFI), the Comparative Fit Index (CFI), the Normed Fit Index (NFI), and the Tucker-Lewis Index (TLI), also known as the Non-formed Fit Index (NNFI), all demonstrate values exceedingly close to 1. A GFI and AGFI greater than 0.9 (Hooper et al., 2008), and NNFI (TLI), NFI, and CFI values greater than 0.95 (Hooper et al., 2008; Hu & Bentler, 2009) are widely accepted as indicative of a very good model fit. In conclusion, the goodness of fit statistics confirms the robust measurement properties of the instrument under examination.

Table 3. Absolute and incremental fit indices.

Fit Index	Emotions Stage A	Emotions Stage B
GFI	0.995	0.982
AGFI	0.979	0.955
NFI	0.992	0.976
NNFI / TLI	0.986	0.963
CFI	0.995	0.981
RMSEA	0.039	0.068
RMR	0.035	0.046

The results mentioned above confirm that emotions in both stages are comprised of two distinct dimensions. The first dimension, termed positive emotions, includes passengers’ feelings of contentment, happiness, and love. The second dimension, referred to as negative emotions, encompasses their feelings of anger, sadness, fear, and shame.

The paired sample t-test was employed, given than the data from both groups originated from the same participants and followed a normal distribution. This test aimed to determine whether there is a statistical difference between Emotions at stage A (first half of the journey) and Emotions at stage B (second half of the journey). The t-test, establishes the hypothesis by assuming that the means of the two distributions (emotions stage A-emotions stage B) are equal. If the t-test rejects the null hypothesis (H₀: µ₁=µ₂), it indicates that the groups are highly likely to be different.

H2₀: Emotions at stage A and Emotions at stage B are the same.

H21: Emotions at stage A and Emotions at stage B are different.

Before running the test, all variables related to negative emotions were recoded onto the same scale as positive emotions. This recoding was performed to ensure a valid calculation of the difference (Δ. Emotions = Emotions B – Emotions A) between the two observations.

To test the null hypothesis that the true mean difference is zero (Emotions B – Emotions A = 0), a t-test statistical analysis was conducted in SPSS. Table 4 exhibits the t-test analysis comparing emotions at stage A and emotions at stage B.

Table 4. Τ-test analysis between emotions at stage A and emotions at stage B.

Paired samples Test
	M	SDD	SEM	95% CI [Lower, Upper]	T	Df	Sig. (2-tailed)
Emotions B – Emotions A	0.63810	4.35274	0.15018	[0.34332, 0.93288]	4.249	839	0.000

According to the results of t-test analysis, wherein the null hypothesis was rejected (sig = 0), the mean of the emotional difference between stage A and stage B is 0,63810. This difference is statistically significant [t (839) =4,249, p-value < 0,001], indicating that passengers experience emotional changes during the onboard customer journey.

According to Table 5 of percentages, it is also visually noticeable that there are emotional changes from Stage A to Stage B, although those changes are minor. For the items reflecting positive emotions, in stage A, 73,3% and stage B, 73,4% of the passengers, ‘somewhat agree – agree – strongly agree’ that they ‘feel contentment’, 58,6% (A) and 62,4% (B) ‘somewhat agree – agree – strongly agree’ that they ‘feel happy’, 41,7% (A) and 43,5% (B) ‘somewhat agree – agree – strongly agree’ that they ‘feel love’, with 31,7% (A) and 31,3% (B) respondents to ‘Neither Agree or Disagree’. However, for the items reflecting negative emotions in stage A 59,5% and stage B 59,7% of the passengers, ‘Disagree -Strongly Disagree’ that they ‘feel angry’, 67,2% (A) and 67,3,8% (B) ‘Disagree – Strongly Disagree’ that they ‘feel sad’, 71,4% (A) and 77,1% (B) ‘Disagree -Strongly Disagree’ in the question if they ‘feel fear’, and 80,5% (A) and 78,8% (B) ‘Disagree -Strongly Disagree’ if they ‘feel ashamed’. The results of the univariate analysis demonstrate that although most passengers experienced positive emotions in both stages, there were differences in their emotional experiences throughout the onboard customer journey.

Table 5. Percentages of emotions construct items in both stages.

		Strongly Disagree	Disagree	Somewhat Disagree	Neither Agree or Disagree	Somewhat Agree	Agree	Strongly Agree
		%	%	%	%	%	%	%
I feel Contentment	A	0.4%	6.5%	7.5%	12.3%	21.1%	44.0%	8.2%
	B	0.0%	4.9%	7.7%	14.0%	17.9%	45.7%	9.8%
I feel Happy	A	1.3%	6.2%	10.8%	23.2%	17.9%	34.2%	6.5%
	B	0.6%	2.3%	7.2%	27.5%	20.4%	35.8%	6.2%
I feel Love	A	3.2%	13.3%	10.1%	31.7%	18.2%	16.5%	7.0%
	B	3.8%	9.5%	11.9%	31.3%	15.8%	23.3%	4.4%
I feel Angry	A	27.5%	32.0%	9.0%	14.7%	9.5%	6.0%	1.3%
	B	31.7%	28.0%	13.9%	14.8%	6.5%	4.5%	0.7%
I feel Sad	A	33.3%	33.9%	7.9%	12.1%	8.3%	3.0%	1.4%
	B	35.5%	31.8%	10.0%	11.7%	6.9%	3.8%	0.4%
I feel Fear	A	39.7%	34.4%	6.5%	10.9%	6.0%	2.2%	0.4%
	B	39.2%	37.9%	7.3%	8.8%	3.9%	2.9%	0.0%
I feel Ashamed	A	54.2%	26.3%	3.2%	9.6%	2.5%	2.2%	2.0%
	B	49.5%	29.3%	6.2%	8.8%	2.5%	2.9%	0.7%

Both univariate and multivariate analyses support the research hypothesis, confirming that emotions should be assessed at multiple stages throughout the onboard customer journey. Ferry passengers were found to undergo emotional changes. Consequently, this research pinpoints the importance of measuring passengers’ emotions at different stages to obtain valid insights and a meaningful understanding of their emotional experience on board.

Conclusions

It is crucial for businesses to comprehend how emotions evolve throughout the customer journey since it can furnish them with valuable insights into the customer experience, which, in turn, can assist in identifying areas that need improvement (Liao & Wang, 2019). In the last decade, a considerable body of scholarly literature underscores the significant role of comprehending and managing the customer experience, encompassing the monitoring and addressing of emotions at various points in the customer journey (e.g, Koenig-Lewis & Palmer, 2014, Ding & Chai, 2015, Homburg et al., 2017).

This research centers on the highly competitive tourism sector (Schlemmer et al., 2019), provided an opportunity to advance the understanding of customers’ emotional experience and contributes to this growing area of research by exploring customers’ emotions in the ferry context. More specifically, the present study pursued two objectives. The primary aim was to scrutinize the suitability of the adopted emotional measurement instrument proposed by Kujur and Singh (2018) in the context of ferry transportation. The secondary objective was to determine whether assessing passengers’ emotions at a single stage of their journey accurately reflects their overall onboard emotional experience. The study’s results indicate emotional variations throughout the onboard customer journey and affirm the effectiveness of the adapted measurement instrument within the passenger shipping sector.

Lastly, this study advances the understanding of passengers’ onboard emotional experiences in the ferry context, providing actionable insights for both theoretical and practical implications, as well as offering suggestions for future research in the realm of customer experience management in travel and tourism.

Theoretical implications

The utilization of a two-dimensional scale to evaluate passengers’ emotional states, encompassing both positive and negative emotions, has illuminated the dynamic nature of emotional experiences throughout various stages of the onboard journey. This exploration has revealed a nuanced spectrum of emotions, indicating that passengers undergo a diverse range of emotions during their active tourism experience. The confirmation of the initial hypothesis underscores the necessity of investigating passengers’ emotions across multiple stages of their journey on board.

The findings of this study make a significant contribution to the theoretical landscape by emphasizing the importance of assessing emotions at distinct stages of the customer journey. This approach addresses a gap in existing literature, as prior research often overlooked the temporal evolution of emotions during travel experiences. By demonstrating that emotional states fluctuate across different phases, this study enriches the understanding of passengers’ emotional dynamics, offering a more comprehensive framework for future theoretical developments.

Practical implications

The practical implications of this research extend to individuals involved in evaluating passengers’ emotions within the context of travel and tourism customer journeys. Both researchers and practitioners are encouraged to conduct multiple emotion measurements, particularly in scenarios involving diverse touchpoints (such as employees, customers, and services) throughout the journey. This approach ensures a comprehensive and holistic understanding of passengers’ emotional experiences.

To accurately capture the nuances of emotional shifts, it is crucial to measure emotions separately at each stage of the journey. Neglecting this stage-specific approach may result in inaccurate assessments, as emotions experienced during a specific stage might not reflect the overall emotional journey of the passenger. Instances where a passenger exhibits negative emotions during embarkation but positive emotions during the journey and disembarkation, or vice versa, underscore the importance of assessing emotions at each stage to avoid erroneous evaluations. In conclusion, the study emphasizes the potential for misinterpretation when emotions are measured at a single stage.

Limitations and suggestions for future research

The current study solely focused on examining the emotional responses of passengers traveling for leisure purposes across multiple customer journeys within the ferry shipping industry. Another important limitation of this study is that all data were collected on a single ferry during various trips.

This research will serve as a base for future studies to assess the emotions of the passengers in travel and tourism customer journeys and come to broader and more generalizable conclusions. Researchers in future studies may test the examined hypothesis by collecting data embarking on different ships (e.g. multiple coastal line ships or/and cruise ships). Also, further investigation of the customers’ emotional alterations regarding customer journeys in different sea roads and transport areas (e.g. aviation, trains) is strongly recommended.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Anastasia Gerou

Dr. Anastasia Gerou is an Adjunct Lecturer in the Department of Maritime Studies at the School of Maritime and Industrial Studies, University of Piraeus, Greece. Her academic focus lies predominantly in the fields of management and marketing within the maritime sector. Currently, her research interests are oriented towards the domains of marketing management and experience management.