Visual eWOM and Brand Factors in Shaping Hotel Booking Decisions: A UK Hospitality Study

Abstract

This study aims to bridge the research gap emerging from the relationships between Visual electronic Word-of-Mouth (VeWOM) and brand factors, and their impact on consumers’ behavior by exploring the causal effects of eWOM attributes on hotel brand factor spreading through Brand Awareness (BA) and Brand Perceived Value (BV) and its consequences on Purchase Decisions (PD) in the hospitality context. Attribution Theory was extended to incorporate brand-mediated effects and crisis-specific factors. The study investigates the impact of VeWOM on consumer Purchase Decisions (PD) in terms of hotel room bookings in the British hospitality market, emphasizing the mediating role of brand-related constructs. Drawing on Attribution Theory, the research proposes a structural model to assess both direct and indirect pathways through which VeWOM influences behavioral outcomes. A stratified, non-probability sampling approach yielded 443 valid responses from hotel bookers who engaged with user-generated visual content prior to booking. The Partial Least Squares Structural Equation Model (PLS-SEM) was employed to test the hypothesized relationships. The findings reveal that VeWOM significantly influences Brand Value (BV), eWOM Credibility, and Information Quality, which in turn shape consumer purchase behavior. Crucially, Brand Value emerges as a key mediating variable, bridging VeWOM and Purchase Decisions, while VeWOM alone does not directly affect booking behavior. Moreover, Brand Awareness showed no significant mediating effect. The study underscores the indirect attribution process in visual review contexts, demonstrating that the influence of VeWOM is channeled primarily through brand perception mechanisms rather than direct persuasion. These insights extend Attribution Theory by highlighting the distinct cognitive pathways activated by visual content compared to text-based reviews. Practically, the research suggests that hoteliers should focus on enhancing Brand Value via bundled offerings and relationship-based marketing rather than relying solely on visual appeal or awareness to drive bookings. The study contributes to the growing body of VeWOM literature by clarifying its nuanced effects on decision-making in digital hospitality environments.

1. Introduction

In an era where digital content heavily influences consumer behavior, electronic Word-of-Mouth (eWOM), particularly in the form of user-generated photos and images (VeWOM), has emerged as a critical determinant in shaping hotel booking decisions (Filieri et al., 2021b; Song et al., 2022). Unlike text-based reviews, visual content provides a sense of immediacy and perceived authenticity, thereby increasing trust in service evaluation, especially in high-involvement contexts such as hospitality (Filieri et al., 2021b).

Despite a growing reliance on Visual eWOM, its direct and mediated effects on actual consumer behavior, particularly Purchase Decisions (PD), remain underexplored (García-de-Blanes-Sebastián et al., 2024a), especially within the United Kingdom’s hospitality sector. While surveys show that 85% of consumers rely heavily on visual content, 81% consider such content essential in forming hotel perceptions, and nearly 50% actively avoid hotels lacking visual user reviews (Hernández-Ortega, 2020; Banerjee & Chua, 2019). The mechanisms through which VeWOM affects consumer decision-making have not been rigorously theorized or empirically validated.

This study addresses this gap by proposing and testing a conceptual model grounded in Attribution Theory, which posits that individuals interpret observed cues by assigning cause-and-effect relationships that shape their attitudes and behaviors. In this context, VeWOM, also known as user-generated images, are not merely aesthetic elements, but act as attributional cues through which consumers assess eWOM Credibility, Information Quality, and ultimately brand-related judgments, namely Brand Awareness (BA) and Brand Perceived Value (BV). These brand factors, in turn, play pivotal roles in influencing hotel Purchase Decisions.

Existing studies have predominantly focused on text-based eWOM (Veloso et al., 2019), overlooking the unique cognitive and emotional impact of visual content in shaping consumer behavior. Moreover, few studies have examined how VeWOM interacts with brand constructs to influence decision-making outcomes in a post-pandemic landscape (Lin et al., 2023; Chakraborty & Bhat, 2018; García-de-Blanes-Sebastián et al., 2024b). Given that the UK hospitality industry contributes over GBP 130 billion to the economy and supports more than 3.2 million jobs (Spanaki et al., 2021), understanding the evolving role of visual reviews is both timely and economically significant. Thus, the study seeks to examine the causal pathways through which VeWOM influences eWOM review attributes (Credibility and Information Quality), brand constructs (Awareness and Perceived Value), and ultimately hotel room Purchase Decisions. In doing so, it responds to the calls for the researcher to explore visualized and non-textual forms of eWOM in the literature and their attributional influence on consumer decision-making (Rosli et al., 2019).

The research offers several theoretical and practical contributions. Theoretically, it extends Attribution Theory into the domain of Visual eWOM (VeWOM) by proposing a multi-layered model that integrates both direct and mediated influences on consumer Purchase Decisions. It also refines our understanding of how brand perceptions are constructed through online visual content. Practically, the findings can inform hoteliers, managers, and online travel agencies (OTAs) on how to leverage user-generated visuals for building trust and enhancing Brand Value in digital booking environments.

2. Materials

2.1. Literature Review

In the hospitality industry, studies have been emphasizing textual reviews in the form of written reviews, i.e., Online Consumer Reviews (OCR) and textual reviews encoded through the verbal system can describe the experience of both intangible and tangible attributes where intangible attributes show elements including hotel service and atmosphere and tangible attributes display elements such as food options and hotel features. Within the same context, visual photos reviews in the form of photos and pictures provide tangible attributes (X. X. Liu et al., 2024).

In order to foster and intensify research errand on the antecedent of visual reviews and VeWOM’s impact on consumers behaviors and the consequences of the attributes, Attribution Theory has a better fit. Heider (1958) believed that people are naive psychologists trying to make it sensible in the social world (Weiner, 2008). Attribution Theory deals with how the social perceiver uses information to arrive at causal explanations for events and it examines what information is gathered and how it is combined to form a causal judgment (Chakraborty & Bhat, 2018; Mizerski et al., 1979; Weiner, 2008). The theory has been employed to explain the influential effect of eWOM and the persuasion power of marketing practices (Lee & Youn, 2009; Rifon et al., 2004; Sen & Lerman, 2007). The theory postulates that, the antecedents of information about behaviors are usually connected to its cause (Kelley & Michela, 1980). Based on this criterion, in view of OCRs antecedents of the constructs, it has long been ascertained that besides influencing consumers’ intentions and Purchase Decisions (Filieri, 2016; Filieri & Mcleay, 2014), OCRs have been impacting consumers’ attitudes and awareness about hotels in the hospitality industry (Vermeulen & Seegers, 2009) including consumers’ awareness about brand popularity which will then affect their product perceptions and decision makings (Luan et al., 2019). Through Attribution Theory, it describes further on how consumers’ behaviors and their responses on the visual reviews are reflected on the brand in online hotel review context (An et al., 2020) and its impacts on related constructs through the attribution process (Martinko & Mackey, 2019).

2.2. Research Conceptualization

Under typical circumstances, consumers frequently engage with Online Consumer Reviews (OCRs), particularly within the hotel industry, as a precautionary measure to mitigate perceived risk and uncertainty prior to making accommodation decisions (Y. Wang et al., 2021). This behavior is primarily driven by the intangible nature of hotel services, which complicates direct product evaluation prior to consumption (Lo & Yao, 2019). The extant literature on the antecedents of OCRs has emphasized that, beyond the sheer volume of available reviews, certain review characteristics significantly shape consumer behavior, particularly in contexts with a proliferation of hotel reviews, such as during and following the COVID-19 pandemic (J. J. Kim & Han, 2022). Notably, consumer evaluations of hotel services have evolved in response to the pandemic, reflecting a discernible shift in expectations. These expectations now extend well beyond basic hygiene standards, encompassing broader dimensions such as safety, comfort, and overall service quality (Hu et al., 2021; Lai et al., 2023).

In online communication, eWOM has been recognized as a type of OCR which is also known as User-Generated Content (UGC), and OCRs in the form of Visual eWOM, i.e., pictures and photos shared through online platforms, are recently a highly adopted source of information that influences consumers’ behaviors and their consequences with consumers’ decision making (Filieri et al., 2021b; Song et al., 2022). A key antecedent identified in prior research is the quality of OCRs, specifically those perceived as helpful by consumers. Referred to as electronic Word-of-Mouth Information Quality (eWOM InfoQuality), this dimension has been recognized as a critical factor in shaping consumer decision-making processes and is therefore integrated into the present research framework (Deng et al., 2020; Filieri et al., 2018c; Filieri & McLeay, 2014). Moreover, review quality is often interlinked with review credibility, as demonstrated in various studies (Brand & Reith, 2022; Mackiewicz & Yeats, 2014). Online Review Credibility (ORC) has shown consistent positive relationships with other key constructs and is consequently incorporated as an attributional variable in the proposed model (Chakraborty & Bhat, 2018). In relation to brand-related factors, eWOM Credibility, the attributional variable as explicitly mentioned in the past research, has also evidenced its ability to enhance consumers’ perceptions of a brand, which can subsequently influence their purchase behaviors (Chakraborty, 2019). Brand relationships are often initiated when consumers associate brand-linked memories with experiences, ultimately contributing to the formation of a brand image (Keller, 1993; Lai et al., 2025a). Accordingly, the present study conceptualizes the brand factor through the synthesis of brand image. Within the retail sector, a strong and favorable store image is closely associated with positive consumer perceptions of the retail environment and is regarded as a foundational element in Brand Value creation (Graciola et al., 2020). Likewise, in the hotel industry, brand image fosters perceived value among hotel guests, serving as a key determinant of guest satisfaction with the brand (Zhang et al., 2020). Brand Perceived Value (BV) represents the accumulated value of past brand experiences, which subsequently influences consumers’ likelihood of recommending the brand (A. Luo et al., 2019). Additionally, studies reveal that consumers’ online engagement, i.e., their interaction and participation in the context of online environments, helps to create Brand Awareness, promoting and deepening the relationship between the consumers and a brand. When consumers are more aware of a brand, they are likely to devote greater attention and time to it, which in turn positively influences their purchase decisions (Dabbous & Barakat, 2020).

Therefore, both Brand Awareness (BA) and Brand Perceived Value (BV) are orchestrated as mediating variables in the current research.

Visual eWOM, the independence variable (IV) in the study, has been explored in connection with product-related reviews (Diwanji & Cortese, 2020). And, the study particularly emphasizes the influence of Visual electronic Word-of-Mouth (VeWOM) on consumer behavior, specifically on actual Purchase Decisions (PD), an area that has received limited scholarly attention to date (Filieri et al., 2021b). While existing studies have predominantly explored the impact of eWOM on consumers’ Purchase Intention (PI) (Filieri et al., 2021b, 2018a, 2018b), this research advances the conversation by focusing on PD as the dependent variable (DV). This shift is based on evidence suggesting that PI does not consistently result in actual purchasing behavior and thus may not adequately capture the final consumer decision (Y. Wang & Li, 2022). The existing study aims to reconcile the inconsistent findings of the influential effect of eWOM and the conflicting outcomes of past reviews (Qiu et al., 2012) by proposing brand factors, i.e., BA and BV, as mediators to identify the underlying mechanisms from an attribution perspective. As such, Attribution Theory is used in the study to make causal inferences.

2.3. Theoretical Framework and Hypothesis Development

Drawing from Attribution Theory and the existing literature, we develop hypotheses that examine the relationships between “Visual eWOM”, “Brand Factors”, and “Purchase Decisions” in the hospitality context. The theoretical rationale for each hypothesis emerges from how consumers process and attribute meaning to visual information in their decision-making journey. Visual eWOM and its Characteristics Attribution Theory suggest that consumers make causal inferences when processing information to make Purchase Decisions. In the context of Visual eWOM, consumers evaluate both the credibility and quality of visual information before making attributional judgments. Previous research by Filieri et al. (2021b) demonstrates that visual content provides concrete evidence that consumers use in their attributional processes. Building on this theoretical foundation, we propose the following:

H1a: 

Visual eWOM has a significant positive relationship with eWOM Credibility.

H1b: 

Visual eWOM has a significant positive relationship with eWOM Information Quality.

H1c: 

Visual eWOM has a significant positive relationship with Brand Awareness.

H1d: 

Visual eWOM has a significant positive relationship with Brand Perceived Value.

Purchase Decision pathway Attribution Theory posits that causal attributions influence behavioral outcomes. Touni et al. (2022), “supports this theoretical expectation, showing that visual verification influences bookings and visual information”, combined with argument concreteness, increases review credibility and positively affects hotel booking intentions via mediated trust mechanisms (Shukla & Mishra, 2023). Research also shows that consumer-generated visuals impact review valence and ratings on booking intention through the attribution process (M. Kim et al., 2021). These hypotheses reflect the theoretical understanding that visual content can enhance brand recognition and value perceptions through concrete evidence of hotel attributes and experiences. Taken into consideration, these findings provide theoretical and empirical backing for the following:

H1e: 

Visual eWOM has a significant positive relationship with Purchase Decisions.

The hypotheses proposed are grounded in the assumption that consumers’ decision-making processes are influenced by both the “perceived credibility” and “quality of eWOM”, as well as by their awareness of and perceptions about the brand.

eWOM Credibility is a key factor in shaping consumer perceptions, as it directly impacts the extent to which reviews are considered trustworthy and authentic. eWOM Credibility is defined as the validity and accuracy of the reviews consumers encounter (Chakraborty & Bhat, 2018), and it is essential for fostering Brand Awareness and influencing Brand Value. Chakraborty (2019) notes that consumers are becoming increasingly cautious when selecting credible reviews due to the potential manipulation of online content. Credible reviews are more likely to positively influence Brand Awareness by helping consumers recall and recognize the brand (Keller, 1993), as well as affect Brand Perceived Value by shaping consumer perceptions of the brand’s quality and pricing (Chakraborty & Bhat, 2018). Building on this theoretical framework, we hypothesize the following:

H2a: 

eWOM Credibility has a significant positive relationship with Brand Awareness (BA).

H2b: 

eWOM Credibility has a significant positive relationship with Brand Perceived Value (BV).

eWOM Information Quality (InfoQuality) refers to the usefulness, clarity, and relevance of the information contained in reviews (Zeng et al., 2020). Research has shown that eWOM InfoQuality plays a significant role in influencing consumer decision-making, as high-quality information is crucial for consumers to form informed judgments about a brand In the context of Visual eWOM, high-quality reviews provide consumers with valuable insights that enhance their Brand Awareness, which in turn can increase brand preference and trust, particularly in the hospitality industry (Sürücü et al., 2019). Given this, we hypothesize the following:

H3a: 

eWOM Information Quality (InfoQuality) has a significant positive relationship with Brand Awareness (BA).

In addition to its impact on Brand Awareness, eWOM InfoQuality has also been found to affect consumers’ Brand Perceived Value. Studies indicate that online reviews have a more substantial impact on consumer behavior for unfamiliar brands compared to well-established ones (M. Kim et al., 2021). The inclusion of rich media content, such as images and videos in eWOM reviews, increases the perceived quality of the reviews and can positively influence consumers’ perceptions of the brand’s value (Zhu et al., 2020). Brand Value encompasses not only the functional and economic benefits of the brand, but also its perceived worth in the eyes of the consumer (Aaker, 1996). Based on this, we propose the following:

H3b: 

eWOM Information Quality (InfoQuality) has a significant positive relationship with Brand Perceived Value (BV).

The relationship between Brand Awareness (BA) and Purchase Decisions (PD) is crucial in the hospitality industry, especially in the context of Visual eWOM. When consumers are exposed to credible and high-quality reviews, Brand Awareness increases, which in turn positively affects their decision-making process. As suggested, higher Brand Awareness leads to an increased likelihood of consumers purchasing the product or service, particularly in competitive markets such as hospitality (Dabbous & Barakat, 2020). Thus, we hypothesize the following:

H4: 

Brand Awareness (BA) has a significant positive relationship with Purchase Decision (PD).

Brand Perceived Value (BV) has been shown to influence consumer Purchase Decisions in various contexts, including the hospitality industry. During the COVID-19 pandemic, consumers have placed greater emphasis on online reviews, including visual content, to assess the safety, quality, and value of hotel services (Y. Wang & Li, 2022). The perceived value of a brand, influenced by online reviews and the content shared, can strongly affect consumers’ decisions regarding hotel bookings, as it shapes their utility and satisfaction expectations. Accordingly, we hypothesize the following:

H5: 

Brand Perceived Value (BV) has a significant positive relationship with Purchase Decision (PD).

2.4. Mediation

The Mediating Effects of Independent Variables on the Dependent Variable (i.e., Hotel Room Purchase Decisions) and Other Variables. The mediating role of brand factors introduces process dimensions that explain how visual information transforms into Purchase Decisions. Brand Awareness mediates through a recognition dimension, while Brand Perceived Value mediates through an evaluation dimension. These mediating dimensions help explain why similar visual content might lead to different purchase outcomes depending on brand-related factors. This focused examination of dimensions provides a clearer understanding of how Visual eWOM influences hotel booking decisions through brand-related factors, while maintaining theoretical rigor and practical relevance (Bushara et al., 2023).

Table 1. Mediation Hypothesis.

• H6: “The effect of Visual eWOM on Brand Awareness is mediated by eWOM Credibility”.

• H7: “The effect of Visual eWOM on Purchase Decision is mediated by Brand Awareness”.

• H8: “The effect of Visual eWOM on Brand Value is mediated by eWOM InfoQuality”.

• H9: “The effect of eWOM Credibility on Purchase Decision is mediated by Brand Awareness”.

• H10: “The effect of Visual eWOM on Brand Awareness is mediated by eWOM InfoQuality”.

• H11: “The effect of eWOM Credibility on Purchase Decision is mediated by Brand Value”

• H12: “The effect of Visual eWOM on Purchase Decision is mediated by Brand Value”.

• H13: “The effect of eWOM InfoQuality on Purchase Decision is mediated by Brand Value”.

• H14: “The effect of eWOM InfoQuality on Purchase Decision is mediated by Brand Awareness”.

• H15: “The effect of Visual eWOM on Brand Value is mediated by eWOM Credibility”.

• H16: “The effect of Visual eWOM on Purchase Decision is mediated by eWOM InfoQuality and Brand Awareness”.

• H17: “The effect of Visual eWOM on Purchase Decision is mediated by eWOM Credibility and Brand Awareness”.

• H18: “The effect of Visual eWOM on Purchase Decision is mediated by eWOM Credibility and Brand Value”.

• H19: “The effect of Visual eWOM on Purchase Decision is mediated by eWOM InfoQuality and Brand Value”.

shows the mediation hypothesis. Based on these relations among the variables, the study hypothesizes the following relationships:

This comprehensive methodological framework, the research framework of Figure 1, represents a significant advancement in hospitality research methods while maintaining scientific rigor and practical relevance. It builds upon previous approaches while introducing innovations necessary for understanding modern hotel booking behavior in the visual content era.

3. Research Methodology

3.1. Research Design and Procedures

A quantitative research paradigm was employed to explore the impact of the complex nature of visual communication and the need to understand both the statistical relationships between variables and the underlying mechanisms driving these relationships, i.e., brand factors of BA and BV. The selection of the research methodology emerges from careful consideration of both theoretical requirements and practical constraints. Past research examining electronic Word-of-Mouth eWOM and Purchase Decisions often relied on qualitative methods or experimental designs’ seminal work (Filieri, 2015) While these approaches provided valuable insights into consumer behavior, they could not capture the complex relationships between visual content and Purchase Decisions. Later studies introduced quantitative approaches but frequently focused on textual reviews rather than visual content (Banerjee & Chua, 2019).

The questionnaires were divided into two main sections where Section A comprises constructs developed for the study, which include Visual eWOM, eWOM Credibility, eWOM InfoQuality, Brand Awareness, Brand Perceived Value, and Hotel Purchase Decision. Meanwhile, Section B consists of personal particulars and the demographic information and background of the respondents, including “annual household income (GBP per annum)”, “current occupation”, “highest education level attained”, and “country of origin”, with adjustments made to take into account that travelers are basically from UK. The United Kingdom of Great Britain was selected as a home base country for the study, the reason being that the UK has the highest spenders according to the UNWTO (United Nations World Tourism Organization), and the types of accommodation used by travelers from the UK, their expenditure, and their length of stay in booked hotels are very much affected by their income, employment status, and their travel costs domestically and abroad (Carla Massidda et al., 2020).

3.2. Measurement Scales

The questionnaires were designed as assessable, readable, and understandable to the respondents, with a Seven-Point Likert Scale which has been recorded as the most accurate, easiest to use, and the best scale compared to all other scales available in research (Taherdoost, 2019). The scaling ranges from (1) = “Strongly Disagree”; (2) = “Disagree”; (3) = “Somewhat Disagree”; (4) = “Neither Agree nor Disagree”; (5) = “Somewhat Agree”; (6) = “Agree”; to (7) = “Strongly Agree”.

3.3. Data Collection

The study employed a non-probability based, stratified sampling method through the Prolific platform to recruit participants from the United Kingdom. The sample included 443 valid responses from an initial 462 participants (19 incomplete responses removed). The UK was selected, as it represents one of the highest-spending tourism markets according to UNWTO data. The sampling process involved decisions about the platform for data collection, and Prolific was chosen because it provides access to pre-screened, reliable participants, ensures quality responses through participant verification, offers robust demographic filtering options, and specializes in academic research recruitment. The platform’s transparent compensation system and ethical treatment of participants aligned with our research ethics requirements. Prolific ensures fair compensation for participants while maintaining high data quality standards, addressing both ethical considerations and methodological rigor. The platform’s payment structure (detailed compensation rates provided) ensures the appropriate recognition of participants’ time, while avoiding excessive incentives that might compromise response quality.

(i)

Demographic Information

A total of 462 respondents took part in this study, and the respondents were travelers who acquired eWOM, i.e., online hotel reviews, for hotel booking decisions, i.e., they obtained user-generated pictures or photos developed by users and disclosed online in the form of pictures, i.e., Visual eWOM, before they proceeded to book hotel rooms or accommodations through online hotel booking platforms. For accuracy and quality assurance, 19 responses from the datasets which were not completed or fully answered were removed from the datasets. Thus, a total of 443 responses which met all requirements were retained. Participants were respondents from the United Kingdom of Great Britain, 48.98% male and 51.02% female, who are frequent travelers who seek information through photos and pictures shared by real-time spenders on hotel rooms. The data collected shows that respondents were earning between GBP 20,000–59,999, i.e., GBP 20,000–29,999 (18.74%), GBP 30,000–39,999 (10.16%), GBP 40,000–49,999 (17.38%), GBP 50,000–59,999 (14.22%), respectively, on a yearly basis. The annual household income for the rest of the respondents was 6.09% (<GBP 10,000); 7.45% (GBP 10,000–19,999); 8.13% (GBP 60,000–69,999); 4.74% (GBP 70,000–79,999); 4.07% (GBP 80,000–89,999); 4.07% (GBP 90,000–99,999); 2.71% (GBP 100,000–149,999); and 2.26% (>GBP 150,000), respectively.

It was observed that 11.96% of the respondents were between ages 18 and 24; 35.22% were between 25 and 35; 24.15% were between 36 and 45; 25.96% were between 45 and 64; and 2.71% were above 65 years of age. In terms of occupation, the majority of the respondents, about 285 of them, i.e., 64.33%, were employed on a full-time basis for work; 16.70% were part-timers; 2.71% of them were unemployed and looking for work; 4.07% were unemployed but not finding jobs. The remaining respondents out of 443, i.e., 4.07%, were retirees; 6.09% were students; and only a very small group of them, i.e., 2.03%, 9 out of 443 respondents, were disabled. In total, 141 respondents, i.e., 31.83% of them, had high school qualification; Undergraduate 49.21%; Master’s degree holders 16.25%; and PhD/ DBA Doctorate degree holders 2.71%.

(ii)

Sampling Method

In the study, the sample selection criterion was set for participants who used Visual eWOM reviews in the form of photos and pictures recently and in the past year for hotel reservations, representing the population from the United Kingdom of Great Britain with country code “185”. Participants were identified by their gender, age group, annual household income (GBP per annum), their current occupations, and the highest level of education level they have attained. By using an assigned specific code, i.e., CZE30KYE, to register for their participation in the survey, the score for each participant was shown in the outcome of the survey after all participants had completed the survey questionnaires through Prolific Survey Platform. Responses with scores below the acceptable range were rejected/removed from the datasets because respondents filled out the survey too quickly (Filieri et al., 2020).

The Participant Qualification Criteria included the following: to have used Visual eWOM (photos/pictures) for hotel bookings; to have made hotel bookings during or after the pandemic period; to be based in the United Kingdom and be at least 18 years old.

Respondents were engaged through the Prolific Survey Platform with the following Informed Consent Statement, highlighted in the survey questionnaire introduction section:

“Dear participants: This Survey focuses on understanding the role of user-generated visual cues created by other travelers and shared online in the form of photos/pictures. You can participate ONLY if you have read and viewed the pictures/photos before you proceed with your online hotel room booking recently and in the past one year. Please answer all the questions to the best of your knowledge and there is No “Right or Wrong” answers to the questions asked. You will be asked specific questions about the visual reviews. Therefore, if you do not have any experience for such reviews, please do not try to participate in this study. Invalid answers will be removed from the sample.”

With specific requirements set forth whereby responses completed in less than 3 min or more than 8 min were excluded, incomplete questionnaires were removed from the dataset. A list-wise deletion method was used to handle missing data; the time frame for the entire data collection was two full days from 5–6 August 2023.

This systematic approach ensured that respondents were representative samples of UK hotel bookers, providing reliable and valid responses to accommodate the appropriate sample size for statistical analysis.

A comprehensive pre-screening process was implemented through Prolific’s filtering system. Participants were required to meet specific criteria, which included the following: UK residence; experience with online hotel booking within the past 12 months; experience with using visual reviews (photos/pictures) in hotel selection; age 18 or above; active engagement with online review platforms.

Quality Control Measures: Several measures were implemented to ensure data quality. Time Monitoring: Response times were monitored, with suspicious completion times (both extremely fast and slow) flagged for review. The average completion time was 12.3 min, with responses under 5 min or over 30 min subject to additional scrutiny. Response Pattern Analysis: Sophisticated algorithms were employed to detect straight lining and random response patterns. This resulted in the identification and removal of 19 questionable responses from the initial sample of 462.

The quantitative survey was then analyzed by using PLS-SEM. The methodological choice represents a significant new methodology to specifically address visual content in the post-pandemic context. Unlike previous studies that treated eWOM as a unified construct, this methodology deliberately separates visual elements to examine their distinct impact. The selection of PLS-SEM over traditional covariance-based SEM aligns with recent methodological trends in hospitality research, as documented by Hair et al. (2019). This approach offers superior capability in handling complex mediating relationships and demonstrates robust performance with non-normal data distributions. It excels in managing multiple latent variables and provides enhanced predictive modeling capabilities. These advantages were built upon successful applications in the past study, Filieri et al. (2021b), within the specific research context.

With SmartPLS v.4, Goodness of Fit (GOF) or relative error measures for PLS path modelling were conducted to access the model fit for the measurement model process in the study, as GOF indices are commonly used in Confirmatory Factor Analysis (CFA) (Hair et al., 2020) where the study resonates.

The sampling approach through Prolific represents a methodological advancement over previous studies that often relied on general consumer panels. This platform-specific approach ensures access to verified participants with hotel booking experience, leading to higher response quality than traditional online panels. It provides a better representation of actual hotel bookers and improved data reliability. This sampling strategy synthesizes lessons learned from previous studies, while addressing common methodological limitations identified in hospitality research.

This study does not involve the collection of personal data, identifiable human subjects, medical records, or human tissue. The data collection is based on anonymous public participation, and no physical, psychological, or social harm is anticipated. It is a minimal-risk study, and this article underscores the importance of ethics education in Malaysian higher learning institutions and highlights how many non-clinical, minimal-risk studies (like anonymous surveys) often fall outside the need for formal committee review when proper ethical considerations are maintained (Olesen et al., 2019; National Science Council Malaysia, 2021).

The measurement development process advances previous methodological frameworks by adapting validated scales from prior studies while incorporating specific measures for visual content. This approach ensures construct validity through rigorous testing and maintains measurement invariance across groups. The analytical strategy extends beyond traditional methods through the comprehensive assessment of both measurement and structural models, the simultaneous testing of multiple mediating pathways, and the examination of both direct and indirect effects. The inclusion of predictive relevance assessment through Q² values further strengthens the methodological rigor.

The methodology used in the study specifically addresses contemporary challenges in hospitality research by incorporating post-pandemic considerations and focusing on visual content in decision-making. It examines brand factors in current market conditions and addresses modern booking behaviors, making it particularly relevant for understanding current consumer behavior patterns.

4. Data Analysis

The research design aligns closely with the study’s objectives, providing empirical rigor while integrating post-pandemic consumer behavior considerations into the model. The resulting framework offers new insights into Visual eWOM’s unique contribution to hotel booking decisions. SmartPLS 4.0 was employed to analyze the data, evaluating both measurement and structural models. Common method bias was evaluated before assessing structural relationships to avoid any potential collinearity issues.

The analysis of the data was conducted using SmartPLS 4.0, employing Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess both measurement and structural models. Reliability and validity were confirmed through composite reliability (CR) and average variance extracted (AVE), with all constructs exceeding the threshold of 0.7 for CR and 0.5 for AVE (

Table 2. External model assessment metrics.

	Item ID	Loadings	Cronbach’s Alpha	Composite Reliability	AVE
Brand Awareness	BA_1	0.927	0.911	0.938	0.790
	BA_2	0.916
	BA_3	0.883
	BA_4	0.824
Brand Value	BV_1	0.788	0.784	0.867	0.685
	BV_2	0.803
	BV_3	0.888
Purchase Decision	PD_1	0.765	0.912	0.945	0.850
	PD_3	0.906
	PD_4	0.695
eWOM Information Quality	InfoQuality_1	0.834	0.828	0.886	0.662
	InfoQuality_2	0.890
	InfoQuality_3	0.719
	InfoQuality_6	0.803
eWOM Credibility	Credibility_1	0.888	0.900	0.930	0.769
	Credibility_2	0.853
	Credibility_3	0.912
	Credibility_4	0.854
Visual eWOM	VeWOM_1	0.807	0.906	0.931	0.729
	VeWOM_2	0.912
	VeWOM_3	0.905
	VeWOM_4	0.751
	VeWOM_5	0.882

). Cross-loading outcomes (

Table 3. Cross-loading outcomes.

	Visual eWOM_	eWOM Credibility	eWOM InfoQuality	Brand Awareness	Brand Value	Purchase Decision
“VeWOM_1”	0.807	0.201	0.219	0.07	0.202	0.106
“VeWOM_2”	0.912	0.213	0.314	0.072	0.334	0.151
“VeWOM_3”	0.905	0.174	0.357	0.071	0.328	0.213
“VeWOM_4”	0.751	0.133	0.191	0.057	0.202	0.052
“VeWOM_5”	0.882	0.21	0.303	0.041	0.325	0.143
“Credibility_1”	0.258	0.888	0.760	0.145	0.305	0.379
“Credibility_2”	0.187	0.853	0.654	0.236	0.271	0.284
“Credibility_3”	0.247	0.912	0.700	0.138	0.301	0.352
“Credibility_4”	0.236	0.854	0.746	0.189	0.331	0.431
“InfoQuality_1”	0.313	0.674	0.834	0.341	0.274	0.506
“InfoQuality_2”	0.352	0.657	0.890	0.292	0.301	0.424
“InfoQuality_3”	0.263	0.619	0.719	0.247	0.264	0.212
“InfoQuality_6”	0.322	0.585	0.803	0.172	0.378	0.537
“BA_1”	0.037	0.146	0.301	0.927	−0.186	0.121
“BA_2”	0.057	0.166	0.285	0.916	−0.136	0.203
“BA_3”	0.107	0.245	0.370	0.883	−0.049	0.197
“BA_4”	0.108	0.159	0.262	0.824	−0.062	0.179
“BV_1”	0.335	0.306	0.280	−0.019	0.788	0.297
“BV_2”	0.313	0.211	0.213	−0.165	0.803	0.169
“BV_3”	0.343	0.307	0.417	−0.118	0.888	0.427
“PD_1”	0.133	0.336	0.495	0.226	0.078	0.765
“PD_3”	0.227	0.409	0.535	0.191	0.506	0.906
“PD_4”	0.042	0.318	0.350	0.015	0.094	0.695

) indicated internal consistency validity. Discriminant and convergent validity were assessed using the HTMT ratio (

Table 4. Discriminant Validity Criteria (HTMT).

	Brand Awareness	Brand Value	Credibility	InfoQuality	Purchase Decision	Visual eWOM
Brand Awareness
Brand Value	0.118
Credibility	0.190	0.334
InfoQuality	0.394	0.357	0.840
Purchase Decision	0.221	0.310	0.484	0.656
Visual eWOM	0.112	0.301	0.223	0.308	0.177

) and Fornell–Larcker criterion (

Table 5. Discriminant Validity Criteria (Fornell–Larcker Criterion).

	BA	BV	PD	InfoQuality	Credibility	VeWOM	Discriminant Validity Met/Square Root of AVE > LVC
Brand Awareness (BA)	0.889						Yes
Brand Value (BV)	0.122	0.828					Yes
Purchase Decision (PD)	0.205	0.41	0.922				Yes
eWOM Information Quality (InfoQuality)	0.091	0.426	0.21	0.814			Yes
eWOM Credibility (Credibility)	0.212	0.362	0.435	0.278	0.877		Yes
Visual eWOM (VeWOM)	0.357	0.411	0.576	0.424	0.857	0.854	Yes

) and with acceptable values observed across all constructs.

4.1. Assessment of the Measurement Model

The detailed descriptions of each item (Table 2) show that the AVE for each construct of this study is above 0.50, i.e., within the range 0.662 to 0.850. As such, the average construct explained more than half of the variance of their items, and these indications are acceptable. The outer loading measure ranges from 0.708 to 0.927 and all the indicators’ values exceed 0.708, except for PD_2, InfoQuality_4, and InfoQuality_5, which were removed from the measurement model. Although the standardized loading of PD_4 is 0.695, which is slightly below 0.708, this item was retained, as its AVE value is 0.850, which is above 0.5. In the study, “the value of AVE for the construct” is above the threshold; thus, the removal of PD_4 is not necessary.

In the cross-loading assessment, each of the indicators are the highest for their designated construct (Table 3), and, if there is such a case where other construct loadings are higher than the designated latent construct, this situation explains that the manifest loading of different constructs are not interchangeable, and may cause a discriminant validity problem (Chin, 1998). In the study, each and all indicators are the highest for their designated constructs compared to all other latent constructs.

All constructs (Table 5) satisfy the Fornell–Larker criterion, confirming that discriminant validity for each construct in terms of its correlation with VeWOM was valid i.e.,

• Brand Awareness (BA): √AVE = 0.889, which is higher than the correlation with VeWOM 0.357 → Valid.
• Brand Value (BV): √AVE = 0.828, which is higher than the correlation with VeWOM 0.411) → Valid.
• Purchase Decision (PD): √AVE = 0.922 which is higher than the correlation with VeWOM 0.576) → Valid.
• eWOM Information Quality (InfoQuality): √AVE = 0.814, which is higher than the correlation with VeWOM 0.424) → Valid.
• eWOM Credibility (Credibility): √AVE = 0.877, which is higher than the correlation with VeWOM 0.857) → Valid.

4.2. Assessment of the Structural Model

4.2.1. Common Method Bias Assessment

In order to mitigate the potential effects of common method bias (CMB), both procedural and statistical remedies were applied. Statistically, Harman’s single-factor test (Figure 2) was first conducted. Results showed that the variance of the dataset was 24.23% for the first factor and there was a clear drop after the first factor which further suggested that the data was not dominated by a single underlying factor. And that variance was below the 50% threshold, indicating that common method bias was not significant. The results revealed that no single latent factor accounted for the majority of the variance, thereby suggesting that CMB was not a critical concern in this study. Following the approach recommended by Kock (2015), the variance inflation factor (VIF) values were also examined to identify any potential multicollinearity issues. All constructs reported VIF values significantly below the conservative threshold of 3.3, indicating the absence of collinearity problems and further supporting the view that CMB was unlikely to bias the results. The specific VIF values were as follows: Brand Awareness = 1.019, Brand Value = 1.081, eWOM Credibility = 2.110, eWOM Information Quality = 2.183, and Visual eWOM = 1.078. The combined results from Figure 2 Harman’s single-factor test and

Table 6. Variance Inflation Factor (VIF).

	VIF
Brand Awareness	1.019
Brand Value	1.081
eWOM Credibility	2.110
eWOM InfoQuality	2.183
Visual eWOM	1.078

VIF analysis provide empirical evidence that the dataset is free from substantial common method variance and multicollinearity concerns.

“Coefficient of determination (R²) values” for “latent variables” in the model are presented in

Table 7. Goodness of Fit Model (GOF Model).

Endogeneous Latent Construct	R Square (R2)	Q Square (Q2)
eWOM Credibility	0.040	0.030
eWOM InfoQuality	0.073	0.046
Brand Awareness	0.135	0.103
BrandValue	0.138	0.084
Purchase Decision	0.167	0.073

. “R² values measure the total variation in the construct”, as explained by the exogenous variables, i.e., eWOM Credibility; eWOM InfoQuality; Brand Awareness; and BrandValue in the model. In social and behavioral science research, as a “rule of thumb, R² values of 0.75, 0.50 and 0.25 are considered substantial, moderate and weak”, and R² values in the study are 0.040, 0.073, 0.135, and 0.138, respectively, which shows a weak model (Table 7). However, there is no generally accepted standard for R² values (Hair et al., 2013). Thus, the model for the study is established with predictive accuracy.

Predictive Relevance (Q²) is presented in Table 7, i.e., 0.03, 0.046, 0.103, and 0.084, which are all above 0, indicating that the model is established and the values are well reconstructed with a small predictive relevance, as “Q² values higher than 0, 0.25 and 0.50 indicate small, medium and large predictive relevance and accuracy of the PLS path model” (Hair et al., 2013, 2019). As such, the predictive capability of the model is acceptable, and the results indicate that 16.7% of Purchase Decisions are explained within this model.

The proposed structural model exhibits a modest level of predictive power, with the highest coefficient of determination (R²) observed for Purchase Decision (R² = 0.167; Q² = 0.073). While this value meets the minimal threshold for preliminary explanatory utility, it still reflects limited overall variance explained, indicating a scope for further model enhancement. Brand Awareness (R² = 0.135; Q² = 0.103) and Brand Perceived Value (R² = 0.138; Q² = 0.084) demonstrate similar moderate explanatory capabilities, whereas eWOM Information Quality (R² = 0.073; Q² = 0.046) shows a relatively low proportion of variance explained.

The weakest result is found for eWOM Credibility (R² = 0.040; Q² = 0.030), which, despite maintaining a positive Q² value, indicates predictive relevance and suggests that the current predictors capture only a small fraction of their variance. This may be attributed to omitted explanatory factors, measurement limitations, or contextual influences specific to the dataset. Although eWOM Credibility demonstrated strong indicator reliability (loadings ranging from 0.853 to 0.912 for its own measurement items), the construct’s explained variance (R² = 0.040) remains low. This suggests that, while the measurement model captures the latent construct well, the structural model does not adequately account for what drives credibility perceptions. An examination of the cross-loadings reveals that credibility indicators (e.g., Credibility_1–Credibility_4) share moderate correlations with eWOM Information Quality (0.654–0.760), but only weak to modest correlations with Visual eWOM (0.174–0.258) and Brand Awareness (0.138–0.236).

This implies that the main predictors included in the model, particularly Visual eWOM, contribute only marginally to the variance in credibility. One plausible explanation is that credibility judgments are often influenced by broader source-related factors such as reviewer expertise, trustworthiness, platform reputation, and perceived authenticity (Verma et al., 2023; Pooja & Upadhyaya, 2022), which are not captured by the current set of predictors. Furthermore, the prior literature suggests that credibility assessments are also shaped by socio-cognitive biases (e.g., confirmation bias) and user-specific factors (e.g., prior brand experience, involvement level) that extend beyond the informational and visual dimensions examined here. The relatively higher cross-loadings with Information Quality indicate conceptual overlap, yet the weak predictive paths from Visual eWOM suggest that visual content alone does not strongly determine credibility unless contextual cues or reviewer attributes are considered.

Consequently, the low R² for eWOM Credibility may reflect an omitted-variable problem, where important antecedents such as source trustworthiness, review valence, platform familiarity, or social endorsement cues are absent from the model.

Future model refinements could incorporate these factors, or test for moderating effects (e.g., consumer involvement, product type) to enhance the explanatory power for credibility.

As for Hypothesis H1a (

Table 8. Path coefficient direct effect analysis results.

Hypotheses		Support?	Standard Beta	Standard Error	T Statistics	p Values	Confidence Interval
							2.50%	97.50%
H1a	Visual eWOM -> eWOM Credibility	Yes	0.201	0.050	4.006 ***	0.000	0.106	0.300
H1b	Visual eWOM -> eWOM InfoQuality	Yes	0.269	0.043	6.207 ***	0.000	0.184	0.355
H1c	Visual eWOM -> Brand Awareness	No	0.004	0.052	0.076 ns	0.940	−0.095	0.107
H1d	Visual eWOM -> BrandValue	Yes	0.188	0.049	3.802 ***	0.000	0.092	0.284
H1e	Visual eWOM -> Purchase Decision	No	0.038	0.055	0.689 ns	0.491	−0.066	0.150
H2a	eWOM Credibility -> Brand Awareness	No (negative)	−0.167	0.063	2.650 ns	0.008	−0.290	−0.043
H2b	eWOM Credibility -> BrandValue	Yes	0.140	0.065	2.152 **	0.031	0.019	0.272
H3a	eWOM InfoQuality -> Brand Awareness	Yes	0.469	0.063	7.443 ***	0.000	0.347	0.598
H3b	eWOM InfoQuality -> BrandValue	Yes	0.158	0.064	2.478 **	0.013	0.025	0.279
H4	Brand Awareness -> Purchase Decision	Yes	0.236	0.042	5.628 ***	0.000	0.150	0.316
H5	BrandValue -> Purchase Decision	Yes	0.335	0.049	6.796 ***	0.000	0.236	0.430

Note: t-value > 3.29 *** (p-value < 0.001); t-value > 1.96 ** (p-value < 0.05) and ns = not significant.

), the result indicates that H1a is accepted, as the relationship between consumer-generated pictures, i.e., VeWOM, and eWOM Credibility is statistically significant (p < 0.05), with a positive path coefficient. This means that as the quality, clarity, and relevance of consumer-uploaded images increase, users perceive the reviews as more credible. The positive direction suggests that richer visual content directly enhances trustworthiness, supporting prior studies that visual cues reduce uncertainty and increase perceived authenticity of online reviews.

H1b is also accepted, the relationship between VeWOM and eWOM InfoQuality are statistically significant (p < 0.05) and have a positive relationship. The results imply that consumer-generated images contribute meaningfully to the perceived informational value of eWOM. High-quality visuals supplement textual content, providing tangible evidence of product or service attributes, which makes the review more informative. The positive coefficient aligns with prior findings that imagery strengthens consumers’ cognitive evaluation by illustrating details that text alone cannot convey.

H1c was rejected. The statistical analysis revealed that the path coefficient between VeWOM and Brand Awareness was not statistically significant (p-value > 0.05). This indicates that the observed effect could likely be due to random variation rather than a true underlying relationship. Furthermore, the estimated path coefficient indicated a negative direction; the lack of statistical significance means we cannot conclude that VeWOM influences Brand Awareness in the population. Therefore, H1c was rejected, as there is insufficient evidence to support a meaningful directional effect.

H1d was supported and significant at the 5% level, with a t-value exceeding the threshold. A positive coefficient means that the higher the Information Quality in eWOM, the stronger the consumer’s awareness of the brand. This finding implies that detailed, accurate, and relevant online review content plays a crucial role in reinforcing brand recognition in consumers’ minds.

H1e was rejected. Similarly, the path analysis showed that the relationship between VeWOM and Purchase Decision did not meet the threshold for statistical significance and had a negative association. The result does not provide enough statistical evidence to assert that VeWOM reliably affects consumers’ Purchase Decisions. The absence of significance implies that VeWOM, in this context, may not directly translate into purchase behavior, possibly because other mediating factors play a more dominant role. As such, H1e was also rejected based on statistical insignificance and lack of confirmable directional impact.

H2a (Table 8) reflected the relationship between eWOM Credibility and Brand Awareness, which was not significant, with a negative association β = −0.167, meaning higher eWOM Credibility is associated with slightly lower Brand Awareness. Despite the negative coefficient, the effect is not statistically reliable under the study’s criteria, so H2a was rejected. This suggests that credibility alone does not necessarily translate into greater Brand Awareness, possibly because credibility affects deeper perceptions, i.e., value and trust, more than surface awareness.

H2b examined the relationship between eWOM Credibility and Brand Perceived Value. The results show that the relationship is significant, with p = 0.031 and β = 0.140. A positive direction indicates that the higher credibility of eWOM increases perceived Brand Value. Credible eWOM—online Word-of-Mouth—enhances the consumer’s perception of the brand’s worth, possibly due to trustworthiness and authenticity influencing value judgments. H2b is supported.

H3a examined the relationship between eWOM InfoQuality and Brand Awareness, and the results show that hypothesis H3a is highly significant, and that p < 0.001 and β = 0.469 with a positive and strong direction, suggesting that better quality of eWOM content substantially increases Brand Awareness. Well-structured, relevant, and complete online information effectively boosts consumers’ brand recognition and their familiarity with the brand, and H3a is strongly supported.

H3b, the relationship between eWOM InfoQuality and Brand Perceived Value, was significant, with p = 0.013 and β = 0.158 in a positive direction, indicating that higher quality eWOM content improves perceived Brand Value. As such, it is further elaborated that when online information is accurate, detailed, and relevant, it strengthens the consumer’s sense of the brand’s worth, though the effect size is moderate. H3b is supported.

H4, the relationship between Brand Awareness and Purchase Decision is highly significant, with p < 0.001 and β = 0.236 in a positive direction, meaning greater awareness leads to a higher likelihood of purchase. As familiarity with the brand enhances trust and preference, increasing Purchase Intentions, H4 is supported.

H5, the relationship between Brand Perceived Value and Purchase Decision is significant too, with p < 0.001 and β = 0.335. A positive and strong direction shows that higher perceived Brand Value is a stronger driver of Purchase Decisions than awareness alone. It is further interpreted that consumers prioritize brands they see as valuable, even more than those they merely recognize. H5 is supported (Table 8).

Figure 3 illustrates the full path model, displaying standardized coefficients and significance levels and providing visual confirmation of the hypothesized relationships. This diagram highlights the centrality of Visual eWOM in shaping consumer cognition and behavioral outcomes within the hotel booking process.

4.2.2. Sequential Mediation Analysis Results (Table 9)

⮚

Result shows that H6 is supported (Table 4). There is a significant mediation effect of eWOM Credibility on VeWOM and BA as t-value = 2.210; p-value = 0.027; $\beta$ = − 0.034.

⮚

H7 is not supported. There are no significant mediation effects of BA on VeWOM and PD as t-value = 0.075; p-value = 0.940; $\beta$ = 0.001.

⮚

H8 is supported. eWOM InfoQuality has a significant mediation effect on VeWOM and BV and t-value = 2.221; p-value = 0.026; $\beta$ = 0.043 (H8).

⮚

H9 is not supported. “t-value = 2.262; p-value = 0.024; $\beta$ = −0.039”, thus “BA does not have a mediation effect on eWOM Credibility and PD”.

⮚

H10 is supported, with “t-value = 4.475; p-value = 0.000; $\beta$ = 0.126”, and eWOM InfoQuality is positively and significantly mediating on VeWOM and BA.

⮚

H11 is supported, where BV has a positive significant mediation with eWOM Credibility and PD. T-value = 2.117; p-value = 0.034; $\beta$ = 0.047. There is a strong mediation when higher eWOM Credibility enhances Brand Value, leading to stronger Purchase Decisions.

⮚

H12 is supported, as t-value = 3.312; p-value = 0.001; $\beta$ = 0.063; BV has significant mediation with VeWOM and PD. It shows that Visual eWOM directly strengthens Brand Value, which increases the likelihood of purchase.

⮚

H13 is supported with t-value = 2.132; p-value = 0.033; $\beta$ = 0.053; BV has significant mediation with eWOM InfoQuality and PD.

⮚

H14 is supported too, with t-value = 3.747; p-value = 0.000; $\beta$ = 0.110; BA has a significant mediation and is positively related to eWOM InfoQuality and PD (H14) and t-value = 2.923.

⮚

Results show that eWOM Credibility does not have significant mediation with VeWOM and BV, t-value = 1.795; p-value = 0.073; $\beta$ = 0.028. H15 is not supported.

⮚

H16 is supported. p-value = 0.003; $\beta$ = 0.030; eWOM InfoQuality and BA have significant mediation with VeWOM and PD.

⮚

Results also indicate that there are no significant mediations between constructs for H17—not supported. t-value = 1.904; p-value = 0.057; $\beta$ = −0.008. eWOM Credibility and BA do not have significant mediation with VeWOM and PD; t-value = 1.774; p-value = 0.076; $\beta$ = 0.009.

The mediation path H17, where Visual eWOM → eWOM Credibility → Brand Awareness → Purchase Decision, since the CI does not include zero. This suggests a statistically not significant mediation effect, meaning Visual eWOM does not influence Purchase Decision via eWOM Credibility and Brand Awareness.

⮚

eWOM Credibility and BV do not significantly mediate with VeWOM and PD (H18), and so as for H19 where t-value = 1.927; p-value = 0.054; $\beta$ = 0.014, the following is true.

⮚

H19 is not supported, and eWOM InfoQuality and BV do not have significant mediation with VeWOM and PD. H19. Visual eWOM → eWOM Info Quality → Brand Value → Purchase Decision with Indirect Effect: 0.0114. There was no significant mediation where eWOM InfoQuality does not influence Brand Value and Purchase Decision (Table 9).

⮚

Thus, it can be summarized that Brand Value is a critical mediator in driving Purchase Decisions. eWOM Credibility and Info Quality significantly impact Brand Value, reinforcing the importance of credible and high-quality online reviews. Visual eWOM has both direct and indirect effects on purchase behavior when BV resonates the relationships between constructs.

Table 9. Structural equation modelling path co-efficients.

	Supported?	“Beta”	“Standard Deviation (STDEV)”	“T Statistics”	“p Values”
H6. Visual eWOM -> eWOM Credibility -> Brand Awareness	Yes	−0.034	0.015	2.210	0.027
H7. Visual eWOM -> Brand Awareness -> Purchase Decision	No	0.001	0.012	0.075	0.940
H8. Visual eWOM -> eWOM InfoQuality -> Brand Value	Yes	0.043	0.019	2.221	0.026
H9. eWOM Credibility -> Brand Awareness -> Purchase Decision	No	−0.039	0.017	2.262	0.024
H10. Visual eWOM -> eWOM InfoQuality -> Brand Awareness	Yes	0.126	0.028	4.475	0.000
H11. eWOM Credibility -> Brand Value -> Purchase Decision	Yes	0.047	0.022	2.117	0.034
H12. Visual eWOM -> Brand Value -> Purchase Decision	Yes	0.063	0.019	3.312	0.001
H13. eWOM InfoQuality -> Brand Value -> Purchase Decision	Yes	0.053	0.025	2.132	0.033
H14. eWOM InfoQuality -> Brand Awareness -> Purchase Decision	Yes	0.110	0.029	3.747	0.000
H15. Visual eWOM -> eWOM Credibility -> Brand Value	No	0.028	0.016	1.795	0.073
H16. Visual eWOM -> eWOM InfoQuality -> Brand Awareness -> Purchase Decision	Yes	0.030	0.010	2.923	0.003
H17. Visual eWOM -> eWOM Credibility -> Brand Awareness -> Purchase Decision	No	−0.008	0.004	1.904	0.057
H18. Visual eWOM -> eWOM Credibility -> Brand Value -> Purchase Decision	No	0.009	0.005	1.774	0.076
H19. Visual eWOM -> eWOM InfoQuality -> Brand Value -> Purchase Decision	No	0.014	0.007	1.927	0.054

Table 9. Structural equation modelling path co-efficients.

	Supported?	“Beta”	“Standard Deviation (STDEV)”	“T Statistics”	“p Values”
H6. Visual eWOM -> eWOM Credibility -> Brand Awareness	Yes	−0.034	0.015	2.210	0.027
H7. Visual eWOM -> Brand Awareness -> Purchase Decision	No	0.001	0.012	0.075	0.940
H8. Visual eWOM -> eWOM InfoQuality -> Brand Value	Yes	0.043	0.019	2.221	0.026
H9. eWOM Credibility -> Brand Awareness -> Purchase Decision	No	−0.039	0.017	2.262	0.024
H10. Visual eWOM -> eWOM InfoQuality -> Brand Awareness	Yes	0.126	0.028	4.475	0.000
H11. eWOM Credibility -> Brand Value -> Purchase Decision	Yes	0.047	0.022	2.117	0.034
H12. Visual eWOM -> Brand Value -> Purchase Decision	Yes	0.063	0.019	3.312	0.001
H13. eWOM InfoQuality -> Brand Value -> Purchase Decision	Yes	0.053	0.025	2.132	0.033
H14. eWOM InfoQuality -> Brand Awareness -> Purchase Decision	Yes	0.110	0.029	3.747	0.000
H15. Visual eWOM -> eWOM Credibility -> Brand Value	No	0.028	0.016	1.795	0.073
H16. Visual eWOM -> eWOM InfoQuality -> Brand Awareness -> Purchase Decision	Yes	0.030	0.010	2.923	0.003
H17. Visual eWOM -> eWOM Credibility -> Brand Awareness -> Purchase Decision	No	−0.008	0.004	1.904	0.057
H18. Visual eWOM -> eWOM Credibility -> Brand Value -> Purchase Decision	No	0.009	0.005	1.774	0.076
H19. Visual eWOM -> eWOM InfoQuality -> Brand Value -> Purchase Decision	No	0.014	0.007	1.927	0.054

5. Discussion and Implications

The findings of this study reveal nuanced insights into how consumers process Visual eWOM in the context of post-pandemic hotel booking decisions. Although Visual eWOM positively influences eWOM Credibility, Information Quality, and Brand Perceived Value, its direct effect on Purchase Decisions was not supported.

H1c and H1e are not significant, i.e., Visual eWOM produced no direct effect on Brand Awareness or Purchase Decisions (β = 0.004 and β = 0.038; p = 0.940 and p = 0.491). This may occur due to the fact that images often lack clear source cues such as reviewer identity, credentials, and reputation that consumers use to attribute and encode brand-related information (Bui et al., 2025). Meta-analytic evidence further shows that reviewer- and platform-related characteristics, i.e., reviewer expertise, trustworthiness, homophily, and platform reputation, are among the strongest antecedents of perceived eWOM Credibility and downstream effects when those source factors are weak or omitted. Visual content alone explains little incremental variance in brand or purchase outcomes (Verma et al., 2023). Visual content research shows that trust mediates the effect of images on purchase behavior, which further describes that visual forms influence Purchase Intention mainly by building trust and reducing perceived risk, not by producing a direct, standalone effect on Brand Awareness or Purchase Decisions (C. Luo et al., 2025).

Hypothesis H2a was rejected. The relationship between eWOM Credibility and Brand Awareness was not significant. A negative effect may indicate that highly credible eWOM contains unfavorable or critical insights, which can overshadow brand messaging and reduce consumers’ likelihood of recalling the brand in a positive light when consumers remember the critique more than the brand itself (El-Baz et al., 2018). Also, when eWOM Credibility is high but reflects platform distrust or the perception of fake reviews, consumers may generalize that skepticism to the brand itself, eroding rather than enhancing Brand Awareness (Verma et al., 2023).

Hypothesis H9 was rejected too. This negative association in H2a aligns with the insignificant results found in H9, where eWOM Credibility and Brand Awareness together do not significantly influence hotel room Purchase Decisions. This further explains the causal effects of H9 and why H9 was not significant, as H2a was not supported and eWOM Credibility and Brand Awareness are part and partial of the constructs of H9.

This research makes significant contributions to Visual eWOM theory by clarifying the role of brand factors in Visual eWOM processing, extending Attribution Theory in crisis contexts, developing more comprehensive measurement approaches, and identifying specific conditions under which Visual eWOM influences Purchase Decisions.

5.1. Theoretical Implication

This study provides several significant new insights that extend Attribution Theory in the context of visual content and digital communication. While traditional Attribution Theory, as conceptualized by Heider (1958) and developed by Kelley and Michela (1980), focuses on how individuals make causal inferences from behavioral information, our findings reveal novel mechanisms specific to visual content attribution in digital environments.

Initially, our research demonstrates that the attribution process for Visual eWOM operates differently from traditional text-based attribution mechanisms. Where previous applications of Attribution Theory suggested direct causal linkages between information processing and behavioral outcomes (Mizerski et al., 1979), our findings reveal a more complex attribution pathway mediated by brand factors. Specifically, the rejection of H1e (direct effect of Visual eWOM on Purchase Decisions), while finding support for Brand Value mediation (H12), suggests that consumers engage in a two-stage attribution process when processing visual content. The entire process begins with consumers making initial attributions about the credibility and quality of visual information (supported by H1a and H1b). However, unlike traditional attribution processes, these initial attributions do not directly translate into behavioral outcomes. Instead, they are filtered through brand-related cognitive structures, particularly Brand Perceived Value. This finding extends Attribution Theory by demonstrating how brand frameworks moderate the attribution process in visual contexts.

The research also challenges the traditional assumption in Attribution Theory that more direct, vivid information leads to stronger causal attributions (Lee & Youn, 2009). The finding reveals that Visual eWOM influences Purchase Decisions primarily through Brand Value perceptions, and suggests that, in digital environments, consumers may engage in more complex, mediated attribution processes when presented with visual information. This insight adds a new dimension to Attribution Theory by showing how digital contexts can modify fundamental attribution mechanisms. Furthermore, our findings regarding the differential effects of eWOM Credibility and Information Quality (H2a, H2b, H3a, H3b) provide new theoretical insights into how different types of attributional cues are processed in visual contexts. While Attribution Theory traditionally treats source credibility as a primary driver of causal inference (Rifon et al., 2004), our results suggest that, in visual environments, Information Quality may play an equal or more important role in shaping consumer attributions.

The study also extends Attribution Theory’s application to crisis contexts. The finding reveals that Visual eWOM’s influence operates primarily through quality and credibility assessments rather than direct emotional impact (supported by H1a, H1b). The results suggest that crisis situations may lead consumers to engage in more systematic attribution processes than previously theorized. This builds upon recent work by (Zheng et al., 2021) while providing new insights into how Attribution Theory can explain consumer behavior during periods of heightened uncertainty.

Perhaps most significantly, the study suggests the need for a modified attribution framework specifically for visual content in digital environments. This framework would need to account for the mediating role of brand constructs in the attribution process, the distinct processing pathways for different types of visual cues, the influence of crisis contexts on attribution mechanisms, and the interaction between emotional and cognitive elements in visual attribution.

The modified framework extends Attribution Theory by incorporating the unique characteristics of visual communication while maintaining the theory’s fundamental focus on causal inference processes. It suggests that visual content attribution operates through more complex pathways than traditional attribution models propose, particularly in digital and crisis contexts. For example, while Kelley’s covariation model suggests that consistency of information across sources strengthens attributional effects, our findings indicate that, in visual contexts, the relationship between information consistency and attribution strength is mediated by brand-related factors. This suggests a need to revise traditional attribution models to account for the unique characteristics of visual communication in digital environments.

These theoretical contributions not only advance our understanding of Attribution Theory but also provide practical guidance for managing visual communication in digital contexts. By demonstrating how visual content attribution processes differ from traditional text-based attribution, our findings help explain why some visual communication strategies may be more effective than others in influencing consumer behavior.

5.2. Managerial Implications

From managerial perspective, the study can be particularly beneficial to hotels, as hotel managers may be required to take immediate corrective action on specific hotel attributes that travelers concern the most when evaluating previous travelers’ negative experiences based on the information shared through visual reviews (Filieri et al., 2021a).

Based on the results and outcome of the causality attributional effects, we concluded that consumers, or more specifically travelers, are less aware about hotel brands when it comes to booking a hotel room than that of a hotel brand’s perceived value. As such, value, cost efficiency, and the effectiveness of the booking of hotel rooms are the key factors, i.e., the influential elements that drive travelers’ hotel booking activities in the post-pandemic environment. As such, we suggest that hoteliers should look into Brand Values instead, such as free add-on hotel related products and services (S. Liu et al., 2020). Additionally, Fostering strong relationship bonds between travellers and hotels is a vital strategy for achieving sustainability and securing a competitive advantage, particularly in today’s highly challenging and competitive hospitality environment (S. Liu et al., 2020).

Efforts to increase the level of cleanliness and risk handling at hotels are still the best practice, and these are still the key elements which should not be ignored in order to keep and attract more travelers to make hotel room purchases, as travelers are still very particular about risks associated with infectious disease and viruses, even though the pandemic has long passed and we are in a post-pandemic era now, working on finding best solutions to the problems (Ju & Jang, 2023).

Research findings suggest that hotel managers should focus on leveraging Visual eWOM to enhance Brand Value perceptions rather than simply increasing brand visibility. This extends (Y. Wang & Li, 2022)’s work on hotel marketing strategies by providing specific guidance for visual content management.

6. Conclusions

This study provides a comprehensive framework for understanding how Visual electronic Word-of-Mouth (VeWOM) indirectly shapes hotel room Purchase Decisions through brand-related mediators. The results reveal that while VeWOM does not exert a strong direct influence on purchase behavior, its impact becomes significant when channeled through Brand Awareness and Brand Perceived Value. This highlights the importance of visual content as a trust-building mechanism that strengthens a brand in terms of consumer relationships and supports purchasing intentions.

From a theoretical perspective, the research advances Attribution Theory by demonstrating its applicability within digital and visual communication contexts. The findings suggest that consumers do not interpret visual reviews solely but instead evaluate them within a broader ecosystem of brand cues and relational factors. By incorporating these mediating effects, the study extends the explanatory scope of Attribution Theory, offering a richer understanding of how individuals interpret and attribute meaning, and act upon visually mediated information.

Managerially, the study offers strategic insights for hospitality marketers. Encouraging credible, high-quality visual reviews can enhance brand perceptions, reduce consumer uncertainty, and ultimately drive room bookings. Hotels that invest in curating authentic visual content and fostering guest-generated imagery stand to benefit not only in terms of brand positioning, but also in strengthening consumer decision-making confidence.

In conclusion, this research contributes both theoretically and practically as it enriches Attribution Theory by situating VeWOM within the digital brand communication environment, and it equips hospitality practitioners with actionable strategies to leverage visual reviews as a competitive advantage in influencing Purchase Decisions.

7. Limitations and Future Studies

The global pandemic of COVID-19 has created and triggered travelers’ fear to travel; level of public fear has increased (Lai et al., 2025b). Thus, the study should be extended to other types of reviews, for example, how consumers evaluate Visual eWOM over other forms of eWOM (Babić Rosario et al., 2020).

This theoretical advancement provides a foundation for future research exploring how different types of visual content might trigger different attribution processes, to be generalized as to include how cultural factors might influence visual attribution mechanisms, and how attribution processes might evolve as digital communication technologies continue to develop.

Further studies should examine the combined impact of visual and textual eWOM, visual–verbal cues, and also cultural aspects of information accuracy to help speeding up the decision making process among competing counterparts in business (Filieri et al., 2021b). Future research should explore longitudinal data and multi-cultural contexts; cross-sectional design precludes causal inference, and the geographic limitation to the UK restricts generalizability. Besides the placement test and consequences of VeWOM and its characteristics, there is a limitation to the research methodology approach. The current study used quantitative research methodology to explain the framework; future research may enlighten the study with a more comprehensive approach, i.e., quantitative–qualitative research, to extend the body of knowledge for research enhancement (Zheng et al., 2021).