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Article

User-Generated Multimedia Content Impact on the Destination Choice: Five Dimensions of Consumer Experience

by
Iuliana Petronela Gârdan
1,*,
Aurelio Mauri
2,
Ionel Dumitru
3,
Daniel Adrian Gârdan
1,
Silvia Ștefania Maican
4 and
Daniel Moise
3
1
Faculty of Economic Sciences, Spiru Haret University, 060821 Bucharest, Romania
2
Faculty of Arts and Tourism, IULM University, 20143 Milan, Italy
3
Faculty of Marketing, Bucharest University of Economic Studies, 010374 Bucharest, Romania
4
Faculty of Economic Sciences, “1 Decembrie 1918” University, 510009 Alba-Iulia, Romania
*
Author to whom correspondence should be addressed.
Electronics 2022, 11(16), 2570; https://doi.org/10.3390/electronics11162570
Submission received: 19 July 2022 / Revised: 14 August 2022 / Accepted: 15 August 2022 / Published: 17 August 2022
(This article belongs to the Special Issue Impact of COVID-19 on Multimedia Transformation)

Abstract

:
This article proposes a complex approach to the phenomenon known in the literature as the consumption of user-generated multimedia content (UGMC) in the context of the effects generated by the pandemic on the consumption of tourist services. Thus, it analyzes the ways in which the creation and consumption of multimedia content affect the flow experience and the ways in which consumption experiences related to tourist services are transferred to other consumers. The pandemic crisis has fundamentally affected the ways in which consumers interact online with different types of multimedia content. In particular, consumers of travel services have developed behaviors that promote the transfer of trust in safe tourist destinations based on connecting to various forms of multimedia content (pictures, videos, blog posts, etc.) created and distributed online by other consumers who share their own positive consumer experiences. Based on a sample of 673 respondents, we validate a theoretical model that confirms the influence of the different dimensions of the consumption experience of tourist destinations (sensory, emotional, cognitive, conative, and relational). The influence is exerted at the level of the flow experience and exploratory behavior—the experience lived as a combination of elements, of which the emotional dimension is among the strongest, due to the anxiety caused by the COVID-19 pandemic.

1. Introduction

The pandemic crisis has a global dimension and a specific manifestation on multiple layers, being one of the most complex phenomena encountered in modern times by humanity. Within the multitude of effects and consequences, we can notice the major changes that the crisis has produced in the field of information management, both from the broader perspectives of technology and society and from individual perspectives, including interpersonal relationships and the need to inform the individual.
One of the most important and direct effects of the pandemic consists of triggering specific mental and psychological states associated with anxiety and fear of disease exposure and risk, with these particular situations leading to positive perceptions about the economic and environmental benefits of e-commerce platforms. Most people developed react-cope-adapt strategies that implied a constrained behavior within the online environment, with some authors predicting that digitalization and online consumption habits adopted during the lockdown periods will be the future norm for consumption behavior patterns once the pandemic ends [1].
In the context of crises manifestation, people are confronted with information pressure regarding uncertainty and risks. Thereby, they tend to select certain types of media based on the functions that are relevant to them; research in the field has revealed that people manifest the need for additional information, reliable sources, emotional support and close connection with a community [2].
This need for clear information and reliable sources elicited behavior characterized by the effort to actively compare information from different sources, the use of different criteria to assess the quality of information from public broadcasters and the tendency to search for supplementary proof from personal sources (relatives, friends, etc.), which means a constant effort to fight against fake news and the so-called ”infodemic” phenomenon that can affect people’s perception of current social and health-related scenarios [3]. The awareness of health risks and the importance of health-related knowledge acquisition demonstrate that traditional media channels continue to play an important role in the situation of a crisis [4], but at the same time, according to the “socioemotional selectivity theory” [5], in a crisis situation, people also manifest an attention shift from future-oriented goals to emotionally meaningful goals, resulting in a high level of interactivity and social presence on social network and e-commerce platforms [6].
The pandemic has led to a tremendous increase in online multimedia usage and the creation/proliferation of information content on multiple channels (social media networks, blogs, whitepapers, etc.).
Such tendencies will affect consumption behavior in a particular manner in the case of different types of services and products, with the hospitability industry being maybe one of the most affected, among others, because of the hedonic nature of the consumption needs that are behind this type of service and the great importance that the consumption of touristic services can have as a compensatory mechanism after exposure to the frustration of lockdown periods. Research in this field shows that social media has gained tremendous importance with regard to tourist destination choices. About 87% of people under 34 years of age choose their destination according to shared experiences on Facebook [7]. Another extremely popular media channel, YouTube, is a source of inspiration for new travels in the case of 42% of tourists [8]. Other studies also accredited the idea that video blogs are rated as a source of inspiration for future travels by tourists aged 18 to 24, with online videos impacting about 65% of tourists regarding their traveling plans in general, about 48% of tourists regarding the type of travel, and 61% of tourists when they are actually choosing their destination [9,10].
Young tourists (aged 25–34) are the main users of social media in order to collect information, ideas, or inspiration in their process of planning their travels [7]. When it comes to talking about the profile of people that are creating content in the touristic field, we see that young people also have the propensity to share their experiences and pictures about their travels on social media, using them to express themselves and to build their personal image [11]. The typical user-generated multimedia content creator is a person with a high level of education [12], above-average personal income, and Internet usage skills, making frequent trips, and usually planning things ahead [9,13].
The UGMC also offers value to customers through the quality of the content itself, the design quality, and the technical quality, while at the same time being positively related to brand equity. UGMC also has a direct positive influence on brand image, purchase intentions, and sales [14]. The social dimension of customer experience has become prevalent, and interactions through social media require special attention from companies because peers influence other customers’ experiences, transferring information and even emotions [15,16]. Nowadays, companies are facing an increasing fragmentation of media or distribution channels, simultaneously with the proliferation of new types of media, which determines the need for an omnichannel approach [17,18]. Marketers are becoming more and more aware of the importance of communities and networks in building and transferring experiences to modern customers. Therefore, the design and delivery of customer experiences can be considered from multiple perspectives—from the companies’ point of view, from the customers’ point of view, and most of all from both perspectives, entangled in a co-creation process in which customers and organizations are combining resources to create memorable experiences [19,20,21]. Thus, in a broad, holistic view, customer experience encompasses customers’ cognitive, emotional, social, and sensory responses to all interactions (pre-purchase, purchase, post-purchase) with an organization, accrediting the idea that customer experience is process-oriented rather than only an outcome of consumption [22].
Taking into account all of the above, we propose an original study that highlights how the different aspects of the previous consumption experience of other tourists (sensory, cognitive, emotional, conative, and relational elements), communicated with the help of the multimedia content generated by users, becomes extremely important for the intention to travel to a certain tourist destination that offers the lowest degree of anxiety and danger generated by the COVID-19 pandemic. The special situation created by the pandemic requires increased attention from consumers in terms of previous consumption experiences to certify these attributes of tourist destinations. Our paper extends previous research in the field by revealing an empirical model in which different aspects of the consumer experience, mediated by user-generated multimedia content, connect with a particular flow state and exploratory behavior in order to manifest the intention to buy and even the desire to recommend the identified tourist destination. Thus, there are specific aspects correlated with the hedonic dimension of the consumption of tourist services, also increased against the background of the pandemic pressure and with the altruistic social behavior in crisis conditions. This paper is structured as follows: the second part deals with the theoretical framework and hypothesis development, going through the main concepts such as consumption experience; user-generated multimedia content; the sensorial, emotional, cognitive, conative, and relational dimensions of consumers experience mediated through UGMC; the flow experience; exploratory behavior and purchase intention; and establishing the conceptual model proposed. The third part presents the research methodology comprising sampling, data collection and measures, followed by the results, discussion, and implications sections detailing both theoretical and managerial and policy implications. In the final part of the paper, we focus on conclusions, research limitations and possible future research directions.

2. Theoretical Framework and Hypothesis Development

2.1. Consumption Experience

Experience is a dynamic, multidimensional construct. In various situations, consumers are dependent on a rational decision-making process, while in other situations they are largely dominated by emotions. Consumption is an overall holistic experience comprising sensory, behavioral, cognitive, and relational values, rather than just functional features and benefits [23]. If we refer to the consumption experience, we can date the modern view back to the first procedural models of consumer buying behavior that emerged in the 1960s, when experience was viewed through the lenses of the process of goods and services acquisition—a kind of buying experience [24].
Later on, during the 1970s, a new discussion was generated around the idea of satisfaction and loyalty, and along with that, the consumption experience was still seen as being correlated with the buying process. At the same time, a new vision of the concept was highlighted through the work of Alvin Toffler, who, in his book, discussed economic development based on a new sector of the economy which he called “experience industries” [25].
One of the most important moments in the development of the concept of consumption experience from the marketing perspective is represented by the work published by Holbrook and Hirschman in 1982. In their publication, the authors proposed a fundamentally new approach for experience, an approach that emphasizes experience dimensions such feelings and fun, giving to it characteristics close to a more emotional, personal, and individual experience for every consumer, emerging after the interaction with consumed products or services features [19,24,26].
Pine and Gilmore (1998, 1999) [27,28] had a particular approach regarding customer experience within their articles, in which they proposed the “Four Es” of experience, which suggest that people have entertainment, educational, escapist, and esthetic experiences. The four Es are determined by two dimensions: the degree of participation in the experience (weak/passive or active/strong), and the level of connection individuals have with the event, or activity or environmental relationship (from absorption/weak to immersion/strong) [27,28]. These authors posited that the coupling of these two dimensions results in four realms of mutually compatible experience domains that often combine to form uniquely personal encounters. Despite this new approach, more than a decade later, research on customer experience still appeared to be in its infancy [29], even though many specialists in the field have always considered it a fundamental research topic [30].
Later, Schmitt (1999) [23] suggested that five strategic experiential modules can depict the consumption experience and can be used by marketers to manage customer experience: (1) senses, conceptualized as the sensory dimension of experience, referring to sight, sound, touch, taste, and smell; (2) feelings, conceptualized as an emotional or affective dimension of experience referring to customers emotions and moods; (3) thinking, conceptualized from the point of view of the cognition process, corresponding to the cognitive dimension of experience, comprising problem-solving and cognitive engagement; (4) acting, conceptualized as the conative dimension of experience, with a direct relationship with concrete physical actions; and (5) relating, conceptualized as the relational dimension of experience that concern consumers’ desires for self-improvement and needs for social relations to reference groups [31].
For online environments, Gentile, Spiller, and Noci (2007) [32] employed a relatively similar approach to that used by Schmitt (1999), conceptualizing customer experience elements as being sensorial, cognitive, emotional, lifestyle, pragmatic, and relational components. Integrating this kind of broader approach of customer experiences into the S-O-R framework, Pentina, Amialchuk, and Taylor (2011) [33] assumed that customers’ online shopping experiences may comprise elements of interactivity, relational, sensory, cognitive, and pragmatic experiences that can contribute for customer satisfaction. A particular view was developed by Rahmani, Gnoth, and Mather (2019) [34], providing a psycholinguistic approach of emotional components of tourist experience analyzing tourists’ experience blogs. The emotional experience is described as a process with multiple elements depicting positive and negative dimensions alike (anticipation, trust, joy, sadness, disgust, anger)
We considered the framework proposed by Schmitt and confirmed by the other authors cited as being a proper and relevant framework for consumption experience analysis, as we agree with the idea that in a broader view, the tourist experience is not a purely psychological phenomenon, as it encompasses all of the activities and stimulation from physical surroundings, service providers, other customers, companions, etc. [35].
As a final thought regarding the unique situation of touristic services consumption, we consider that holidays create unforgettable memories creation, are important sources for enriched experiences with sensual, emotional, intellectual, physical, and relational stimuli capable of offering joy, provide things to learn and represent unique occasions for individuals’ self-fulfillment.

2.2. The Context of UGMC and S-O-R. Theory within Tourist Destinations Consumption

The general framework of our research starts from the idea related to the importance of UGMC within the touristic services consumption process and the decisional process regarding the future touristic destination. Additionally, besides the accredited role that UGMC can have in terms of touristic destinations consumption, we take into account the S-O-R theory as being capable of describing a reliable mechanism that also can explain this particular field of service consumption.
An important characteristic of UGMC is related to the fact that users are simultaneously creators and consumers of the content [36]. In order to have a more detailed view of user-generated content, we can introduce three specific characteristics: the created content is made public and is published on the Internet, which makes it accessible for everyone; the content has various degrees of creativity depending on the specific personal profile of every individual; the content generated by users usually excludes professional content, being different from professional lines of conduct. User-generated multimedia content can offer enough genuine information for other users and to minimize the risk usually associated with decision-making in the case of intangible services [37].
The user’s motivations to participate within the process of creation and consumption of content are correlated with socio-demographic characteristics, and are also related to the feeling of empowerment, co-creation, the desire of the individuals to express themselves and to have social recognition, and a sense of community [38,39]. Communities help to develop for every user a self-awareness of their sustainable consumption, contributing to the better integration of individuals [40]. Younger tourists have a propensity to post photos more than the older generations [38], and many content creators are doing this because they want to offer public publicity for those destinations that offered them positive experiences as a sign of their appreciation [39].
Furthermore, motivations to create and share content can have an intrinsic background, related to different hedonic consumption elements, such as fun, enjoyment, diversified experiences, and novelty, and an extrinsic one consisting of the possibility to self-express [41]. Motivations to use the content generated by other users can be related to the need to be assured regarding the quality of the touristic destination and to find inspiration according to the latest trends in the industry [42].
An important source of user motivations consists of the ability of content creation to contribute to social status and the effectiveness of social contacts [43]. Users can also elicit a specific capacity to integrate their critical thinking within their consumption of information, even though millennials are believed to have a low capacity for critical thinking, due to their habit of passively receiving information through social media [44].
The creation of content and the involvement of users involves a particular approach regarding touristic services. The majority of popular social-networking applications empower their users with the ability to generate multimedia content and share essential information about their consumption experience, even starting from during the travel itself using pictures, videos, and chats [45,46]. From the point of view of content type analysis, reviews can be quantified from the perspective of two dimensions: quantitative and qualitative. Quantitative aspects can refer, for example, to the number of stars given or the grade associated with an online review, while qualitative features can refer to the ease of use and enjoyment attached to the review [47].
Different social media sites are used as platforms for sharing user-generated online reviews [48]. Furthermore, user-generated online reviews are available on a variety of different websites online, such as on retailer and brand websites, review sites, such as TripAdvisor, and different online communities [47,49].
Four types of user-generated content and applications can be identified, such as [50]:
-
Data gathering—comprises the aggregation and the organization of information which comes from a certain field and can refer to certain processes or phenomena. Usually, data gathering is followed by the editing and annotation of the information for extensive possible future use.
-
Pattern recognition—this type of user-generated content is used to gather essential data regarding the evolution of behaviors or different phenomena over time (pattern of tourists’ movement, for example, in a certain touristic location—a historical town, etc., with the help of geotagged photos and mobile phone communications).
-
Community building—involves using UGMC to aggregate a large number of users, and create content delivered to a specific social localized group, enhancing collaboration between group members.
-
Public art—UGMC can be used “per se” in the creation or development of different artistic projects. Messages and any other form of content can be transformed in various creative ways in order to become effective parts of an artistic opera.
In the framework of our research, the different types of UGMC can offer different degrees of usage value for people. The value that can be associated with UGMC starts from the utility theory (the value is given by the difference between the utility of a generic product and the cost reflected by the price paid to access the specific product). It is possible to recognize two different layers of value—functional value (referring to the practical needs of users satisfied with the functional benefits of the touristic product and the created content (accessibility, ease of use, quality, availability)) and emotional value (enjoyment, popularity and implicit pleasure when interacting with the content) [51]. The value offered by UGMC has to be assessed in the framework of the advantages offered by online channels-the possibility to develop more interactions and an active dialog, and the possibility to perform a quick search for information from multiple sources with a low associated cost [52]. In the context of the restriction of travel due to epidemics and pandemics, attitudes towards travel change as a result of the manifestation of compensatory consumption, which may have implications for stakeholders, marketing managers or tourism specialists. [53].
Our research implies the use of Stimulus-Organism-Response (S-O-R) theory, first introduced by Mchrabian and Russel in 1974 [14]. The context proposed by this theory helps researchers to analyze the possible link and effects that appear when looking at the relationship between individuals and their external environment. The external stimuli (S) can trigger specific internal reactions from the organism (O), reactions that will develop a certain response (R) [54]. This basic theory allows us to explain both the mechanism of consumption experience and the development of UGMC on one side and at the same time that referring to the flow experience and future consumption behavior on the other side.
Different aspects regarding the tourist destination can be seen as environmental triggers that are directly connected with the various dimensions of consumption experience—sensorial, conative, cognitive, affective and relational. Individuals may change their attitudes and elaborate a certain response through the user-generated content, developed after the internal evaluation of the experience. In the same manner, potential consumers that are searching for information can have as stimuli the different types of generated content and corresponding dimensions of it (cognitive, affective, etc.) in order to change their attitudes and eventually their beliefs and to respond through certain future behavior (buying decisions or contributions to the existing user-generated content regarding the tourist destination as an expression of willingness to recommend). Taking into account the above considerations, we proceed to elaborate the hypotheses necessary for the proposed theoretical model.

2.2.1. Sensorial Experience Mediated through UGMC

When talking about the sensorial aspect of customer experience of touristic services, we refer to the interaction between consumers and the physical environment as the ”servicescape”, which is the sum of elements including manmade elements that are controllable by the provider, equivalent to the physical evidence through which the service is provided. These elements can refer to the ambient conditions, the spatial layout and its functionality, buildings, signs, symbols, and artifacts. In a more detailed view, ambient conditions comprise background environmental stimuli such as visual elements (colors, shapes, light, etc.), hygiene and cleanliness, olfactory and temperature elements, and auditory elements. Spatial layout comprises the arrangement of furniture, the room space itself, the architectural and other functional characteristics of the space used by the tourists, and also elements that can depict accessibility and comfort [55].
The sensorial component is manifested in terms such as sense, sensory, sensation, and sensorial [56].
Some researchers also suggested measures to capture the sensory components of consumption, which include hearing, seeing, tasting, smelling, and feeling [57,58]. In this line of research, qualitative type interview techniques have been used to measure comparisons made by subjects between different sensorial attributes of a product [31,59]. Afterward, Gretzel and Fesenmaier [59,60] introduced the Sensory Experience Elicitation Protocol (SEEP) that used open-ended questions in order to measure the associations of sensorial elements in consumers’ minds, taking into consideration tourists’ experiences from the USA. Another example of sensory impressions research is the study by Agapito, Valle, and Mendes (2014) [57], who applied survey questions relating to five human senses (sight, smell, hearing, touch, smell) to analyze sensory tourist experiences in Southwest Portugal and segment visitor experiences into four sensory-informed themes: rural; nature-based; beach-related; and a balanced experience. Several researchers suggested using other methods of capturing people’s sensory experiences, such as the Facial Action Coding System (FACS, which is used for facial expression analysis [59,61].
To properly measure the construct referring to sensorial experience, we consider the items proposed by Edvardsson, Enquist, and Johnston (2010) [55] to be appropriate, comprising: the architecture of the accommodation, the furniture of the rooms, the arrangement of the space, visual elements related to shapes, colors and lighting, hygiene, cleanliness, the olfactory comfort of the accommodation’s location, noise and noise pollution, and pleasant experiences with ambient auditory comfort [55].
User-generated multimedia content (UGMC) is a complex construct that defines on multiple layers the content that is available as a result of the direct consumption experience evoked by consumers in different settings. In the last 20 years, UGMC has mostly been associated by the majority of the scientific literature with the multimedia content developed/shared or spread through the online environment (text, video and audio files, combined media content, etc.). Even if these forms of content are preeminent and have a lot of visibility in terms of accessibility for the majority of potential consumers, there are of course other forms of content generated by users that can be shared and assessed by those interested in consuming certain services or goods—notes, written reviews, contests, etc.
The interaction between individuals that are searching for information before the consumption of touristic services and UGMC referring to diversified experiences in tourist destinations is characterized by a state of ”flow”, defined by specialists as a cognitive immersion of the subject in the field of shared information [62,63]. This profound state of cognitive experience allows people to enjoy the process of co-creation and exploration of tourist destinations’ experiences through the online shared content. Within our research, we refer to the content developed in the online setting and to the flow experience associated with it, as flow is mainly a multidimensional construct studied within the context of mediated human–computer interactions [64,65]. Elements regarding the sensorial experience previously lived by tourists will become strong elements of reference for individuals that immerse themselves into the flow experience determined by the interaction with UGMC connected with these elements. Therefore, we can formulate the first Hypothesis 1 (H1).
Hypothesis 1 (H1).
Sensorial experience related to the tourist destination, mediated through UGMC, will positively influence flow experience within this context.

2.2.2. Emotional Experience Mediated through UGMC

The emotional dimension of consumers’ experience has potentially the most important role in assessing the importance of certain services for individuals, as emotions are interlinked not only with the process of the co-creation of the service but with individuals’ satisfaction with the service itself. Additionally, if we refer to the tourism setting, emotions play a very important role, due to the nature of the consumption needs related to tourist services, which are mainly hedonic in nature. Tourists’ emotions are influenced by their preferences, values, beliefs, and attitudes [66]. Emotions and their orientation—a positive or a negative one—can have different effects on tourists’ satisfaction, meaning that positive emotions such as happiness, pleasure, and joy have a positive influence on tourists’ satisfaction [67,68]. Moreover, the less frequent negative emotions are, the higher the level of satisfaction of the tourists is [69].
When we analyze the issue of emotions in the context of touristic services consumption and further the sharing of this experience, we can take two different lines of discussion: on one hand, the emotional dimension of the touristic services consumption experience itself, and on the other hand, the emotional arousal that individuals may have, which is a prerequisite before they actively search for information regarding other consumers’ previous experiences. The measurement and quantification of emotions are considered very difficult tasks because of their ephemeral nature as intense and conscious affective reactions [70]. Still, because tourists share a lot of information in online reviews, instruments and methods capable of analyzing emotions in the form of sentiment analysis based on their self-reported experience have been developed. These techniques are inexpensive and adequately standardized, consisting of the usage of advanced natural language processing (NLP) techniques. Using different databases such as WordNet or SentiWordNet, the performed analysis detects emotions that can be expressed with unstructured language [71]. Other instruments such as Plutchik’s emotion wheel are capable of linking words with basic emotions, such as anger, fear, anticipation, trust, surprise, sadness, joy, and disgust, being eloquent in the context of UGMC [72].
Different authors have sustained this idea according to different categories of content (movies, video content, pictures, etc.) that can elicit emotions [73,74]. Moreover, video content seems capable of inducing different types of responses—behavioral, physiological, and even moderately intense emotional ones after exposure to subjects [75]; videos are effective in inducing moderately intense emotional, behavioral, and physiological responses coherent with the context of the movie being viewed, and they provide a good context for assessing those dynamic changes in emotional responses [76].
In the same way, photos taken by tourists during their travels allow the transfer of emotional content, thus being possible to exert an influence over other tourists when on websites [77]. Other studies confirmed the fact that the content of images can influence purchase decisions of tourists, both at the destination level and at the final decision level [78], and photographic content, in general, may be more effective in transmitting emotional attributes [79].
The emotional dimension of UGMC is of capital importance for touristic services. The transfer of emotions is considered essential for the decisional process, and this transfer can maintain a high level of interest from any individual immersed in the process of deciding on their future touristic destination. Changes at the level of consumption within the pandemic period have added supplementary pressure from the point of view of the emotional state of consumers. Studies have shown that right after the initial lockdown period in 2020, tourists expressed their need to explore new types of destinations, eliciting powerful positive emotions. In a study performed on more than 21,000 reviews on TripAdvisor, the authors compared results regarding trends for eight basic emotions registered before and after the outbreak of COVID-19 through the analysis of tourists’ self-reported travel experiences. The results are similar to those obtained by Rodway-Dyer and Shaw (2005), which highlight the immediate effect of negative events on visitor behavior [80]. Tourists have redirected their choice of touristic destination towards national parks instead of metropolitan areas, with dominant positive emotions represented by anticipation, trust, and joy [69].
Tourism decision making and consumption are often highly interpersonal and emotional, with tourists needing a large volume of information to ensure their emotional comfort [81]. In order to measure the construct referring to the emotional dimension of the consumption experience, we took into consideration items proposed by Cheung et al. (2021): a positive atmosphere regarding the tourist destination, positive emotions about the tourist destination, and positive feelings about the tourist destination [82].
Information about the emotional dimension of the consumer experience shared by other consumers creates the premise for the manifestation of the state of flow, in which tourists deepen their connection to the specific emotional state of consumption of other consumers, about a set of possible destinations or even a single future choice.
Thus, taking account of all of the above, we can issue the second hypothesis:
Hypothesis 2 (H2).
Emotional experience regarding previous touristic services consumption, mediated through UGMC, positively influences flow experience within the context of tourists’ present new destination decision process.

2.2.3. Cognitive Experience Mediated through UGMC

A very important dimension of consumer experience mediated through UGMC in the field of touristic services is represented by the cognitive dimension of the consumer experience. The cognitive dimension of the consumption experience can be seen as involving mental processes such as perception, memory, language, problem-solving, and abstract thinking. When talking about cognitive processes involved in consumer experience, usually the discussion involves two approaches: the attainment of objectives and the confirmation/disconfirmation of prior expectations [83].
Consumers are involved in consumption as a natural expression of their needs in the form of different self-actualized goals. So, goal-directed behavior is a form of a cognitive process. The measurement of the quality of cognitive experience is performed through assessing benefits that tourists can obtain or the degree of confirmation/disconfirmation of their expectations. UGMC capable of offering such information can be considered a strong motivational factor for other consumers’ capacity to minimize the risk associated with future destination choice.
In terms of tourist services choices, consumers are influenced by their perceived social status and their internalized self-image, and thus they demonstrate socially visible consumption as an expression of their consumer-brand engagement [84]. This type of engagement represents a strong cognitive substrate capable of developing a sense-making process around the consumption of the tourist-specific brand, with individuals actively searching for cognitive cues being capable of justifying their future decisions and tourist destination intentions. Another aspect related to the cognitive dimension of UGMC refers to its functional value. This functional value is built around content information that exhibits rational constructs that can enhance the satisfaction level of practical expectations (lower costs, supplementary benefits like children playground, etc.) [14]. To be able to measure the construct regarding the cognitive dimension of the consumption experience of tourists, we considered the items proposed by Cheung et al. (2021) as being reliable: the functions of tourism and hospitality services, the value of tourism and hospitality services, and the benefits of tourism and hospitality services within a specific touristic destination [82].
The quality of the UGMC itself can also be an important element that is capable of motivating consumers to emerge in a subsequent state of search, namely flow experience, which can stimulate the customer’s internal state of mind, which can include pleasure, satisfaction, arousal, dominance, and comfort.
Considering as a point of reference all of the above, we can conclude with the following hypothesis:
Hypothesis 3 (H3).
Cognitive experience mediated through UGMC positively influences the flow experience within the context of UGMC affecting tourists’ present new destination decision process.

2.2.4. Conative Experience Mediated through UGMC

When it comes to analyzing the destination image concept, scholars have proposed a hierarchical causal model that usually comprises cognitive, affective, and conative components [85,86,87,88,89,90].
Earlier in the process of hypothesis development, we discussed the importance of cognitive and emotional components related to previous touristic destination consumption experiences. The conative component refers to the individual’s actual conduct and actions that are performed within the context of the touristic destination consumption experience. Various studies have highlighted the effect of the emotions developed in connection with a certain destination and of the perceptions (interpreted cognitively) regarding it in the manifestation of a certain behavior or the actions of the consumer [91,92,93].
The conative dimension of prior consumption experience is related to cognitive outcomes and affect, with both cognitive and affective responses influencing attitudes that are capable of predicting a certain behavior [94]. The conative dimension of tourist consumption experience is reflected in different terms such as act, involvement, and behavior [19,95,96]. Consumers interact with the attributes of the touristic destination and explore various benefits—these behavioral outputs of consumption experience can give important clues for other tourists regarding their future possible behavior. In the same line of discussion, we can emphasize the process of learning as being part of consumption experience outcomes related to the conative level—learning by doing and experiencing [31].
These “moments of truth” may elicit supplementary interest from some individuals in the process of searching for accurate and complete information that can predict their future involvement with the concerned touristic destination [57].
The conative dimension of experience relates to how tourists act based on their formation of cognitive and affective images. More studies were performed on conative experience as issues of visitor involvement and co-creation emerged [97]. Tourists have been increasingly perceived as performing a role in tourist settings, and their interactions with other visitors and experience providers can have an important influence on their experience [98]. Knutson, Beck, Kim, and Cha (2007) argued that satisfying or memorable experiences depend on active participation or involvement, or the ‘doing’ implied by conative experience [99]. Conative responses to experiences were also recently highlighted by Zatori et al. (2018) in the concept of ‘experience involvement’ [100]. They underlined the co-creation role of tourists also through the ‘social experience involvement created through social contacts on a tour’. With co-creation and involvement increasingly being emphasized as a means of improving visitor experiences [101,102,103], the conative dimension should also become more important in a range of tourism settings. In our research, in order to measure the construct of conative experience, we took into account elements such as: UGMC relevant to satisfaction related to the involvement of tourists in cultural-religious visits; the satisfaction of tourists that have been involved in sports and recreation activities during their stay on holiday; and satisfaction of tourists that have been involved in shopping and excursions during their stay in a certain destination [104].
Being an ”action”-oriented specific experience, conative experience implies different levels of commitment from tourists. In this context, this type of experience is transmitted through information that clearly expresses the degree of involvement in different actions during the consumption of tourism services. Exposure to such information transforms naturally in a flow experience engagement.
In light of the above, we can issue the following hypothesis:
Hypothesis 4 (H4).
The conative experience, mediated by UGMC, positively influences the experience of the flow in the context of the decision-making process related to the new destinations targeted by tourists.

2.2.5. Relational Experience Mediated through UGMC

In consumer research, the literature on involvement has emphasized the degree to which a person perceives an object, event, or situation as being personally relevant [105]. Celsi and Olson (1988) demonstrated that people’s perceived relevance of objects or activities will affect their involvement and information processing behavior [106].
Relational experiences can be defined as cognitive, experiential, and psychological states [106]. Celsi et al. (1992) [105] stated that personal relevance is represented cognitively in memory as a form of knowledge or belief about whether objects, concepts, or activities are personally relevant. When personal relevance is activated, these beliefs can stimulate emotions or affective responses such as feelings of interest or liking [106]. Personal relevance also affects other cognitive processes [105]. For example, in studies exploring flow, Trevino and Webster (1992) [107] found a positive association between intrinsic interest and focused attention, and Novak, Hoffman, and Young (2000) [108] found that greater importance corresponds to greater focused attention. Another view concerns the interaction between the customer and first-line employees, and the interaction between tourists themselves. Social identities assumed by the consumers can be highlighted by different elements of the tourism destination services. In order to properly measure the construct referring to the relational experience, we took into consideration the items proposed by Sweeney and Soutar (2001): how important the tourist destination was in making a good impression on other people; the degree to which the tourist destination contributed to the social status of tourists; and the extent to which the tourist destination helped individuals to establish contacts and make friends with other tourists [109].
All of the aspects highlighted above can be translated with the help of UGMC to other consumers, and also contribute to their future immersion in comparing different experiences. Thus, we can issue the following hypothesis:
Hypothesis 5 (H5).
The relational experience, mediated through UGMC, positively influences flow experience within the context of the decision-making process related to the new destinations targeted by tourists.

2.2.6. Flow Experience within the Context of UGMC

Flow experience can be defined as the output of the total involvement of people in an action into the form of an overall sensation. During this special state, people are intensely concentrated on the activity and become absorbed with a special focus and awareness, having at the same time a sense of control over the environment [45]. Another characteristic of the flow state is that, despite their unselfconsciousness and complete absorption, the subject has a high level of skill and concentration, visible as a smooth action and control [110].
The flow experience manifests itself when individuals immerse themselves in the activity of searching for and interacting with information specific to that generated by other users/consumers of tourist services. Basically, when considering the antecedents of flow experience within social network usage, and implicitly the use of UGMC, we can take into consideration elements such as skill, challenge, telepresence, and time distortion [111]. Individuals have a particular state of mind concerning their decisional process regarding their future tourist destination, and different studies show that, despite the possible negative factors that can temporarily affect their capacity to immerse themselves in this type of particular experience, such as social overload, information overload, and communication overload, the flow experience helps to reduce users’ perceptions of fatigue and even social media discontinuance intentions [112].
The flow experience, within the context of UGMC immersion, is stimulated by factors that can increase the extraversion of the subject. Previous studies have shown that extraversion has a positive influence on the flow [113,114]. This means that, as we previously explained, different aspects regarding the consumption experience of tourists, among them the emotional experience being a must, can arouse the extraversion state of mind of the individual, thus boosting the flow experience.
The sharing of information over social media is determined by factors such personal and community-related benefits, as well as the social capital gained by those that share their own experiences [115].
Other aspects such as the quality of reviews, the degree of enjoyability, and readability can also contribute to the degree of usefulness of reviews, and finally to the development of the flow experience [49].
In order to properly measure the construct referring to flow experience, we took into consideration the items proposed by Karasakal and Albayrak (2021): continuous action related to browsing social media, travel blogs, or booking reviews; perception about the time compression during browsing; and the state of total engagement in browsing [116].
Interaction with UGMC can help to reduce information asymmetry, which may appear when limited information is available about different tourist destinations, especially within the context of the COVID-19 pandemic. The flow experience is closely related to a future positive exploratory behavior that will have as its main objectives the optimization of decisions regarding tourist destinations
Taking into consideration all of the above, we can develop the following hypothesis:
Hypothesis 6 (H6).
The flow experience within the context of the UGMC immersion of individuals positively influences exploratory behavior in terms of tourist destination finding.

2.2.7. Exploratory Behavior and Purchase Intention

Exploratory behavior has been studied within the psychology field, mainly related to motivational theories, and has also become a familiar topic within the consumption behavior field. Different approaches regarding exploratory behavior have in common the idea that individuals can make multiple attempts to maintain an optimum level of stimulation through different mechanisms, such as the drive to seek variety or the drive to seek novelty [117,118].
The optimal level of stimulation is different from individual to individual, and it is related to individual differences in terms of personality traits, demographic variables, etc. Maintaining this optimal level of stimulation depends on the “arousal potential” of each of the stimuli an individual comes in contact with. Only stimuli that possess enough novelty, ambiguity, uncertainty or incongruity can elicit enough arousal potential [119].
Exploratory behavior has been integrated into the buying decision process in the form of exploratory information searches for information relating to products/services and exploratory purchases [117].
One of the characteristics of the travel-related decision-making process is that decisions made by individuals are dependent on other decisions already made, and thus it takes the form hierarchical process [120].
This aspect is highlighted within the hierarchical travel decision-making model proposed by Fesenmaier and Jeng (2000, 2002) [121,122]. The authors divided the travel planning process into several sub-sections corresponding to different layers of decisions with different levels of importance at the level of the general travel plan. This hierarchical process provides us with a clue as to the possible complexity of trips for many tourists. Because of this level of complexity, many tourists can develop a sense of uncertainty when they are starting to plan. This is why a typical tourist will deal with such uncertainty by considering different scenarios, and often it is possible that a trip is not completely planned at the moment of departure [123]. A detailed information search before the trip can thus diminish the potential risk of making poor decisions regarding the trip. Furthermore, considering how consumers plan their trips, it has been found that previous travel experiences have an impact on the information search and purchase decision making of consumers. At present, the pandemic is having a profound effect on travel planning decisions regarding desired tourist destinations due to the perceived risk associated with the movement of people. For many tourists, the decisional process has become too difficult because of the risks involved. Overall, people feel the need to plan every aspect of the trip very carefully according to precautions and recommendations regarding the pandemic. Thus, their intention to travel has diminished considerably, meaning that the tourism industry will be inevitably affected even in the post-pandemic era [124,125]. Therefore, policy makers in the field of tourism should adopt a creative and flexible strategy for the future, keeping in mind this reality at the level of customers’ perceptions [126].
Behavioral engagement with a tourist destination is the degree to which customers expend energy, effort, and time consuming tourism services associated with the specific tourist destination. If attitudes can be seen, at a more superficial level, as being summary evaluations, intentions are much more clearly expressed in the form of motivations that are relevant to developing a certain behavior. Defining purchase intentions, Spears and Singh considered them to be the equivalent of a conscious plan regarding purchasing a brand [127].
The intention to perform a specific behavior is predicted by people’s attitudes, according to the Theory of Planned Behavior (TPB) and Theory of Reasoned Action (TRA) [128]. In the same line of thought, the hierarchy of effects model also postulates a relationship between attitudes and behavioral intentions [129].
Purchase intentions express the conscious effort of a person to manifest a certain behavior, seen as a motivation for that person, and are different from attitudes [130]. In the context of the hospitality and tourism field, previous research has shown that credibility and interestingness positively impact travel consumption purchase intention [131]. Modern tourists buy experiences and try to minimize the risk associated with dissatisfaction by immersing themselves in social networks and other forms of UGMC relevant to their interests [132]. The measurement of the construct related to exploratory behavior can be performed with the help of items referring to: the subsequent action of browsing in more depth links related to the tourist destination in question; the amount of time spent by the tourists in obtaining details about the intended destination and their propensity to look for additional information about the tourist destination (including by phone or by email) [133].
In this sense, the measurement of the construct regarding the purchase intention can be performed with the help of items such as the intention to try to visit the right tourism destination from the point of view of posts and reviews of other tourists; the effective intention to visit the tourism destination in question; and the degree of intention regarding supplementary information needed about the tourism destination [127,134].
In order to obtain a strong, directed motivation toward the buying decision regarding a certain touristic destination, consumers will exert exploratory behavior after the flow experience, being sensitive to several factors, such as: the perceived value of UGMC, perceived risk, perceived enjoyment, and privacy concerns [135]. Exploratory behavior has an important role from the point of view of the need to reduce anxiety and risk-associated pressure, because it allows consumers to gather and interpret a large volume of information from others.
Considering all of the above, we can issue the following hypothesis:
Hypothesis 7 (H7).
The exploratory behavior in terms of the tourist destination search positively influences the purchase intention regarding a specific tourist service.

2.2.8. Willingness to Recommend

In a content analysis of posts connected to two apparel brands, Smith, Fischer, and Yongjian (2012) found that personal identity, integration, and social interaction motivated a third of user-generated Facebook posts [136]. For many individuals, creating user content is an expression of the connection with the touristic brand’s popularity and the need to be included in a certain social group [137]. Consumers will manifest the willingness to recommend (positive word-of-mouth) as a natural consequence of a certain state of satisfaction and the perceived value and perceived quality of the UGMC [138].
The willingness to recommend is strongly connected with the primary motivation to spend one’s holiday in a certain location [138]. Thus, as we highlighted previously, one of the main motivations for contemporary tourists is to reduce the day-by-day anxiety about COVID-19 and to visit a reliable touristic destination from the point of view of the risk of disease exposure and the possibility to share quality time with the dear ones far from the current life environment. Thus, even though usually the willingness to recommend is also strongly connected with the on-site experience, once the tourist reaches their chosen destination, due to the special conditions created within the context of pandemic crisis, people manifest a willingness to share vital information with other users regarding these specific topics (anxiety due to the hygienic conditions, the low local rate of infections, special protection measures taken by the tourism managers, etc.) as a result of their extensive exploratory behavior regarding UGMC. The importance of such information is so great that individuals put a lot of emotional input into their effort to create subsequent word-of-mouth about these pandemic-specific elements from above.
The measurement of the construct regarding willingness to recommend the tourism destination can be performed using certain items such as: intention to recommend to people that need information about a proper touristic destination; the desire to suggest the destination to relatives and friends; and the individual’s belief that there is not any reason to be concerned about the intended destination [109,139,140].
From the point of view of the above ideas, we can issue the last hypothesis:
Hypothesis 8 (H8).
The exploratory behavior, in terms of the tourist destination search, positively influences the willingness to recommend a specific tourist location.
In the light of all of the above hypotheses, we can propose the following conceptual model of the research, as can be seen in Figure 1.
In order to validate the conceptual model proposed above, we implemented quantitative research, following specific steps. In Figure 2 below, we present the process related to the research undertaken in which, the considered stages can be seen, clarifying the logical connection between the activities and the purpose of the research, similar to the main purpose of the article: analyzing the ways in which tourist service consumers’ interactions with the UGMC through the lens of the five fundamental components of the experience of consumption (sensory, emotional, cognitive, conative, and relational) influence purchase intentions regarding a certain tourist destination and the willingness to recommend the destination. This process is analyzed within the context created by the pandemic, which is characterized by a higher pressure on the buying decisions regarding touristic destinations due to people’s anxiety.

3. Research Methodology

3.1. Sampling and Data Collection

In order to measure the influence of consumption experience related to touristic destinations mediated through sensorial, emotional, cognitive, conative, and relational elements, we employed quantitative research through a questionnaire-based survey; the completion of questionnaires was performed in January 2022. We considered it appropriate to develop this research, taking into consideration a sample extracted from a statistical population composed mainly of young people, based on the conclusions and results of previous research in the literature. These approaches highlighted the fact that, in terms of tourist services, the people who create and consume content regarding consumer experiences in various locations are mostly young, with above-average income and level of education, corresponding to higher education in urban areas. Thus, women have a higher trust in social media than men do, relying more on social media than men and also more often than men when they interact with interesting content on social media platforms [62,141,142]. In the same line, younger users and generation Y increasingly base their travel decisions on UGMC, being more active on social media [143,144,145,146,147,148], with the younger tourists using travel images as a mode of self-expression and an instrument to build their personal image [9,11].
The research was conducted on a sample extracted from students and graduates from three university centers—two situated in Bucharest, the capital of the country, and one in the Central Region Development of Romania. The questionnaire was sent out for completion using an online platform. In total, 234 questionnaires were not valid because they contained data errors (missing answers), which represented about 26% of the total number of completed questionnaires (907). These questionnaires were removed from the database, resulting in a final valid sample of 673 respondents. The Table 1 presents the structure of the final sample, in which we can see the preponderance of women and those with a higher level of education.

3.2. Measures

Answers to the questionnaire were measured with a 7-point Likert (type) balanced scale [149]. The Likert scale was preferred because it is widely used in marketing [150] and attitude-related research [151].
In order to identify the relevance of the items considered, we started with the application of exploratory factor analysis. Exploratory factor analysis (EFA) operates in a similar way to principal component analysis (PCA), both of which are widely used procedures when seeking to reduce the number of variables in the case of collections of continuous variables and obtain a reduced set of synthetic variables called eigenvectors or factors, able to explain most of the total variation [152].
In the first stage, we tested the viability of the model proposed by computing Cronbach’s Alpha. At this stage, the viability was assured at the level of the model, as we can see in Table 2, where the considered indicator has values above the threshold of 0.7 for each of the constructs [153,154,155].
The suitability of the sample was measured with the help of the Kaiser–Meyer–Olkin (KMO) test. The accepted value for this test should be a minimum of 0.5 in order to consider the sample size as being suitable to perform the next in-line factor analysis [156,157].
Thus, in order to explore the relevance of every relationship between the defined variables existing in the model, we performed a confirmatory factor analysis with the help of the IBM-SPSS AMOS program. The results of this analysis are depicted in Table 3, showing good values for the variables of the model. It can be observed within the table that we also show the appropriate theoretical statistical values necessary for goodness of fit, for an easier comparison and for the clarity of conclusions.

4. Results

Within our analysis, in order to assess the eligibility, we used the criteria referring to: P, CMIN/DF, RMR, GFI, AGFI, RMSEA, NFI, RFI, IFI, TLI, CFI, PGFI, PNFI, and PCFI. The values all show a good fit according to the theoretical values from the literature, which allows us to explain all of the proposed hypotheses in this study.
Table 4 shows the structural model results, allowing us to demonstrate the validity of the advanced hypotheses.
According to this table, we may consider the advanced hypotheses to be validated and the conceptual model proposed as being a viable one. This means that variable antecedents for the flow experience (SE—sensorial experience mediated through UGMC; EE—emotional experience mediated through UGMC; CGE—cognitive experience mediated through UGMC; CNE—conative experience mediated through UGMC; and RE—relational experience mediated through UGMC), have a positive effect on FE—flow experience. For three of the variables, namely sensorial experience, cognitive experience and relational experience, the data also show a strong effect on FE. Thereby, from the data revealed by the analysis, it can be observed that the greatest effect on flow experience is exerted by relational experience (β = 0.496; p < 0.01; Critical Ratio test = 6.320 > 1.96) data that validate hypothesis H5. Next, we can see that cognitive experience has a strong and important influence over flow experience (β = 0.485; p < 0.01; Critical Ratio test = 4.104 > 1.96), thus validating hypothesis H3. Sensorial experience registered a strong and significant influence over flow experience (β = 0.431; p < 0.01; Critical Ratio test = 3.083 > 1.96), meaning that hypothesis H1 is also validated. The last two variables from the initial group of antecedent variables, namely the emotional experience and conative experience, have an influence that is not as strong in comparison with the previous ones over flow experience, with values ofβ = 0.250; p < 0.01; Critical Ratio test = 3.366 > 1.96 and β = 0.189; p < 0.01; Critical Ratio test = 3.268 > 1.96, respectively, confirming hypotheses H2 and H4.
The next hypothesis from the model, H6, refers to the influence exerted by the flow experience on the exploratory behavior. In the case of this hypothesis, we can also see values that show a positive and significant relationship between the two variables (β = 0.360; p < 0.01; Critical Ratio test = 8.553 > 1.96). Exploratory behavior, in turn, has a positive and significant influence over the last two variables in the model, namely purchase intention (β = 0.646; p < 0.01; Critical Ratio test = 18.948 > 1.96) and willingness to recommend (β = 0.745; p < 0.01; Critical Ratio test = 19.001 > 1.96), and these data allow us to validate the last two hypotheses from the model—H7 and H8.

5. Discussion

Assessing the influence of other users’ consumption experience on the decisional process of tourists regarding the next destination for their holiday represents a considerable endeavor in the context of the COVID-19 pandemic.
This influence can be considered from several angles, being very nuanced from case to case due to the many variables that can be involved in its measurement. First, the validation of the model, as a whole, confirms the advanced idea in the literature that the decision-making process for choosing a tourist destination is sequential and hierarchical [120,121].
Tourists go through several stages of this process, stages whose analysis reveals the influence of different variables at the level of each one. Thus, taking into account the influence exerted by the first group of variables—antecedents of the “flow” type experience—on the latter, the analysis of the resulting data indicates a positive and significant influence of these variables. However, this influence is also nuanced—the intensity of the relational dimension of the consumer experience mediated by UGMC is observed as being the highest, followed at a short distance by the cognitive dimension of this experience and at a slightly greater distance by the sensory dimension. This situation suggests several implications that can be explained by considering the context that the pandemic creates in terms of the experience of consuming tourist services. This aspect is essential to understand the specifics of our research—to be able to measure with the proposed model the very particular aspects of the direct effects of the pandemic on the consumption behavior of tourists and how they shape their decisions under the pressure of anxiety, depression, and perception of the danger of illness, which are essential factors specific to the pandemic. Thus, we can explain the more intense influence on the variable that describes the flow experience exerted by the relational dimension of the consumer experience by exacerbating the psychological need of tourists to socialize and to relate their consumption experiences and to receive validation from others [165,166].
Additionally, the prevalence of the influence exerted by the dimension related to cognitive experience mediated through UGMC indicates the orientation of tourists in the same pandemic context regarding the need to receive assurances about the functional value of tourist services for the future destination (net benefits that justify rational choice destinations—facilities and logistics that provide security in the face of the pandemic), elements that can corroborate this influence from the point of view of the intensity of the construct measuring the sensory dimension of the consumer experience mediated by UGMC. Certainly, the comfort elements, which ensure a certain level of safety of the tourist in the face of the danger posed by the pandemic (degree of hygiene, comfort given by the arrangement of rooms, hotel circuits, etc.) plus the comfort elements that ensure the appropriate level of satisfaction with the hedonic substrate, are extremely important in determining this perception.
Furthermore, in the model, the validation of the H6 hypothesis regarding the influence exerted by the flow experience on a certain type of exploratory behavior confirms the ideas highlighted in the literature considered for the development of the hypothesis. We discuss the need to reduce the asymmetry of information and to obtain information as accurately as possible regarding the actual potential of the current tourist destination to provide the security sought and the attributes that qualify it as the “ideal choice”.
The validation of hypotheses H7 and H8 confirms the fact that the targeted exploratory behavior regarding high-quality UGMC that can be reliable and capable of ‘’triggering” the final purchase intention can be consistent from the perspective of the pressure exerted by the pandemic conditions. Regarding the specific relationship that can be observed between the exploratory behavior and the willingness to recommend, it becomes clear that the importance of detailed, essential information regarding the attributes of the future destination has a major role in eliciting behavior among modern tourist that normally appears in the post-consumption stage, usually reflecting the effect of a variable that actually measures, from a certain perspective, the degree of loyalty. This indicates the peculiarity of the modified behavior under the effect of pandemic factors—the pressure of a correct decision and the amount of psychic energy consumed in the process of choosing the destination predisposes the consumer to this relatively atypical behavior. The manifestation of exploratory behavior directly proportionally determines the willingness to recommend the destination that convinces the tourist that it might be the best choice in the pandemic setting.
Within our paper, we highlight in a couple of places the importance of social multimedia networks and the involvement of people in communities. From this perspective, one important aspect relies on the value of recommending multimedia content. From this point of view, the fact that for modern consumers of multimedia, the cost effectiveness that emphasizes price with performance is migrating to efficient methods of obtaining cost satisfaction that emphasizes purchaser’s psychological satisfaction is extremely relevant [167]. An interesting method to improve this cost satisfaction is to use the mood inherent in multimedia content. A valid method was considered using values of arousal and valence which express the mood of multimedia content as its internal tag [167].
Tagging represents a method often used to manage a large volume of multimedia content. It implies that users assign short textual annotations to the content (in the form of keywords) capable of describing the specific content and to provide additional information to other users who are interested in it [168]. Mood tagging can be an effective way to connect people to the multimedia content from an emotional point of view. Different categories of content will elicit the interest of different groups of users. In an effort to identify these groups and what is relevant to them, their preferences and their common actions performed within a multimedia social network can be assessed with the help of modelling heterogeneous information in a hypergraph-based data model [169]. Analyzing the human relationships between people from the same category of users, their behavior within social networks and the way in which they interact with the categories of content relevant to them can also be very useful in the context of touristic destination choice, providing decision makers in the field with important clues regarding intended consumption behavior. As a final part of our discussion, we provide a brief comparison between our work results and some other relevant research in the field. In a study conducted on 538 respondents, it was revealed that “emotional and rational content generated by users has a significant impact on tourists’ perceived values, and through that their impulse buying and future purchase intention are also positively influenced” [82]. So, we can assess the fact that as in the case of our model, the future buying decisions are positively influenced by different types of user-generated content. In another study conducted by Tsiakali, the results show that the personality of tourism service consumers influences all of the travel decision-making phases, and at the same time, their behavior is influenced by UGMC more than marketing-generated content [42]. These results add to our findings in an interesting way, with the idea that personality traits are an important element for the choice of touristic destinations, along with the influence exerted by UGMC. The prevalence of different emotional, cognitive, sensorial, conative or relational elements specific for the consumption experience is highlighted in relation to the type of personality of individuals.
In the context of touristic destination image formation and the cognitive or affective dimensions of it, we can take into consideration a study carried out within the context of the Malaysian market on a sample of 342 respondents, showing that perceived usefulness, perceived ease-of-use, aesthetics and homophily were important determinants for platform co-creation experience. The travel satisfaction of respondents was positively influenced by cognitive and affective dimensions of images used within the co-creation process [170]. Additionally, in another work by Sultan et al., research performed on 425 respondents showed that tour-operator content and UGMC have a positive impact on destination image formation. The UGMC has more influence, with the use of social media exerting an influence mediated through affective and cognitive destination images [171]. Such results can be easily compared with our own, from the point of view of the different aspects of the consumption experience and touristic destination image formation.
The above results make clear the idea that the consumption experience translated into UGMC positively influences the touristic destination choice process and touristic destination image formation. This is closely related to our research findings—different aspects of consumption experience determine the flow experience in the context of UGMC and the exploratory behavior, and positively influence purchase intentions and willingness to recommend.

6. Conclusions

6.1. Theoretical Implications

Our proposed model validates the approach suggested by Schmitt (1999) [23], an approach confirmed by many other authors [19,31,35] as being capable of capturing the essential features of the consumption experience framework seen in its integrity (not merely a purely psychological phenomenon).
At the same time, the model highlights several relationships that denote the influence of the pandemic. The order of importance of the constructs with which the consumption experience was measured, from the point of view of the intensity of the relationship established between them and the flow experience, is also relevant concerning the factors that influence the perceptions and behavior of tourists regarding the pandemic.
The relational dimension of the consumption experience appears to have the most intense influence of all five constructs that represent the antecedents of the flow type experience. This information indicates the prevalence of the needs related to socialization and inter-human connection, both with the contact staff and with the other tourists within the chosen destination. The phenomenon of isolation imposed by the authorities at the beginning of the COVID-19 pandemic has determined, for many individuals, the accumulation of frustrations that translate into the future tourist destination choice. Thus, the interaction with a coherent and convincing UGMC from the perspective of the ability of the tourist destination to ensure a quality relational experience is decisive for boosting the state of flow. The unique combination of multimedia inputs that can be created by people as a part of their effort to share their interactive consumption experience has an important role, also emphasized by the pandemic conditions.
As the second most significant variable from the point of view of the relationship intensity, the cognitive dimension of consumption experience measures the perception of the tourists regarding functional benefits and other rational advantages transmitted through UGMC that can also lead to a pronounced flow state.
The flow experience has a special meaning in the context of the COVID-19 pandemic’s pressure upon tourist destination consumers, helping to reduce the anxiety and uncertainty caused by the permanent worries regarding the pandemic. The flow experience, followed by the exploratory behavior of individuals, is transforming in these conditions in a kind of hedonic type-specific consumption state of mind that is capable of balancing the negative feelings induced by the pandemic. Different emotional, rational, conative, and relational cues derived from other consumers’ experiences are helping people to feel more confident and safer with their choices and contribute to their willingness to recommend a certain tourist destination and to share their opinions about their own consumption experience with it.

6.2. Managerial and Policy Implications in the Field

The tourism and the hospitality industry, in general, has been greatly affected by the pandemic, not only from the point of view of net figures (volume of tourists and travel time, etc.) but from the point of view of main consumer behavior profiles, with people making their final traveling decisions depending on their perceptions regarding the risk represented by the pandemic [172].
An awareness of the importance that variables related to consumption experience, flow experience or exploratory behavior hold for the decision-making process regarding visiting a new tourist destination is essential from the perspective of building coherent marketing strategies for players in the field [173]. Additionally, understanding the mechanisms by which anxiety and isolation cause substantial changes in the behavior of tourism service consumers is very useful for optimizing the level of satisfaction delivered to tourists and creating a framework that favors the tourism SME sector.
Our model shows a kind of compensatory mechanism with regard to these factors, through the state flow and the experiential behavior, finalized by the consumption of touristic services itself, associated with a hedonic type of consumption. Finally, this model indicates the importance of information inputs in the form of different categories of media, created by consumers who have, in the current context, the ability to create multimedia content and also to “consume” this content, being substantially driven by conditions caused by the pandemic (isolation, prolonged contact with digital multimedia, online socialization, etc.). For hospitality decision-makers, predicting these future trends offers the possibility for creating new forms of promotion based on testimonials with multimedia content, in which the creative combination of information from videos, images, text and audio content can shape future consumer decisions.

6.3. Research Limitations and Future Directions

The main limitation of this study is related to the size of the sample taken into consideration. While the composition was adequate considering previous research that indicates the prevalence of women and young people at the level of such studies [62,141,142,143,144,145,146,147,148], the volume of the sample could be bigger in order to be suited for the entire statistical population of potential tourists at the national level.
Other limitations came from the fact that we employed cross-type quantitative research, and the type of research more suited to investigating consumption experience is the longitudinal type of research, which can offer a deeper insight when it comes to evaluating the concept of experience itself and its dynamics. Of course, the manner of administrating questionnaires—through online platform sent over email—also has some known issues coming from the fact that some individuals may be reluctant to respond due to the possibility of having their identity disclosed and the uncertainty of the relationship between the person and the answers given online (respondents that did not respond to the questionnaire, etc.). The self-administration of the questionnaire may become a disadvantage if some categories of respondents are more receptive than others; thus, their behavior may affect the representability.
Future research may go into the direction of analyzing in more depth the perceptions of UGMC that are created in different contexts, assuming that every type of UGMC (personal tourists’ blogs, social media pages, discussion groups, YouTube type channels, hotels webpages, and blogs, etc.) can elicit different types of flow experience and so different levels of influence on the decisional process oriented toward a certain travel destination. Moreover, a promising future line of research may consist of qualitative insights regarding the importance of UGMC from the point of view of hedonic consumption experience and a compensatory mechanism that some tourists may develop as a response to the anxiety, depression, and health risks associated with the pandemic.

Author Contributions

Conceptualization, I.P.G., A.M. and D.A.G.; methodology, I.P.G., A.M., I.D., D.A.G., S.Ș.M. and D.M.; software, I.P.G., A.M., I.D., D.A.G. and S.Ș.M.; validation, I.P.G., A.M., I.D., D.A.G., S.Ș.M. and D.M.; formal analysis, I.P.G., A.M., I.D., D.A.G. and S.Ș.M.; data curation, I.P.G., A.M., I.D., D.A.G. and S.Ș.M.; writing—original draft preparation, I.P.G., A.M., I.D., D.A.G., S.Ș.M. and D.M.; writing—review and editing, I.P.G., A.M., I.D., D.A.G. and S.Ș.M.; funding acquisition, I.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Guthrie, C.; Fosso-Wamba, S.; Arnaud, J.B. Online consumer resilience during a pandemic: An exploratory study of e-commerce behavior before, during and after a COVID-19 lockdown. J. Retail. Consum. Serv. 2021, 61, 102570. [Google Scholar] [CrossRef]
  2. Moreno, Á.; Fuentes-Lara, C.; Navarro, C. Covid-19 communication management in Spain: Exploring the effect of information-seeking behavior and message reception in public’s evaluation. El Prof. Inf. 2020, 29, e290402. [Google Scholar] [CrossRef]
  3. Álvarez-Rementería Álvarez, M.; Roman Etxebarrieta, G.; Dosil Santamaria, M. How do we tackle the COVID-19 crisis? Mass media and psychological responses to the health crisis in Spain. J. Sci. Commun. 2021, 20, A05. [Google Scholar] [CrossRef]
  4. Dreisiebner, S.; März, S.; Mandl, T. Information behavior during the Covid-19 crisis in German-speaking countries. J. Doc. 2021, 78, 160–175. [Google Scholar] [CrossRef]
  5. Carstensen, L.L.; Fung, H.H.; Charles, S.T. Socio emotional selectivity theory and emotion regulation in the second half of life. Motiv. Emot. 2003, 27, 103–123. [Google Scholar] [CrossRef]
  6. Anastasiadou, E.; Anestis, M.C.; Karantza, I.; Vlachakis, S. The coronavirus’ effects on consumer behavior and supermarket activities: Insights from Greece and Sweden. Int. J. Sociol. Soc. Policy 2020, 40, 893–907. [Google Scholar] [CrossRef]
  7. Karantzavelou, V. 87 Percent of Travelers under the Age of 34 Use Facebook to Choose Their Destination. 2012. Available online: https://www.traveldailynews.com/post/87-percent-of-travelers-under-the-age-of-34-use-facebook-to-choose-their-destination-50895 (accessed on 12 December 2021).
  8. Ipsos MediaCT. Google Travel Study. In The 2014 Traveler’s Road to Decision; 2014; pp. 1–59. Available online: http://rivetworks.wpengine.com/wp-content/uploads/2015/04/2014-travelers-road-to-decision_research_studies.pdf (accessed on 12 December 2021).
  9. Barišic, P.; Strmota, M.; Ivanda, K. Sociodemographic characteristics of modern tourists as a determining factor in the need recognition for travel. In Proceedings of the FEB Zagreb 10th International Odyssey Conference on Economics and Business, Opatija, Croatia, 12–15 June 2019; University of Zagreb, Faculty of Economics and Business: Zagreb, Croatia, 2019; Volume 1, pp. 754–769. [Google Scholar] [CrossRef]
  10. Crowel, H.; Gribben, H.; Loo, J. Travel content takes off on YouTube. Think Google 2014, 1–11. Available online: http://think.storage.googleapis.com/docs/travel-content-takes-off-on-youtube_articles.pdf (accessed on 8 December 2021).
  11. Lo, I.S.; McKercher, B.; Lo, A.; Cheung, C.; Law, R. Tourism and online photography. Tour. Manag. 2011, 32, 725–731. [Google Scholar] [CrossRef]
  12. Del Chiappa, G. Trustworthiness of Travel 2.0 applications and their influence on tourist behaviour: An empirical investigation in Italy. In Information and Communication Technologies in Tourism; Law, R., Fuchs, M., Ricci, F., Eds.; Springer: New York, NY, USA, 2011; pp. 331–342. [Google Scholar] [CrossRef]
  13. Yoo, K.H.; Gretzel, U. What motivates consumers to write online travel reviews? Inf. Technol. Tour. 2008, 10, 283–295. [Google Scholar] [CrossRef]
  14. Mohammad, J.; Quoquab, F.; Thurasamy, R.; Alolayyan, M.N. The effect of user-generated content quality on brand engagement: The mediating role of functional and emotional values. J. Electron. Commer. Res. 2020, 21, 39–55. Available online: http://www.jecr.org/sites/default/files/2020vol21no1_Paper3.pdf (accessed on 3 January 2022).
  15. Wilson-Nash, C.; Goode, A.; Currie, A. Introducing the socialbot: A novel touchpoint along the young adult customer journey. Eur. J. Mark. 2020, 54, 2621–2643. [Google Scholar] [CrossRef]
  16. Bolton, R.N.; Parasuraman, A.; Hoefnagels, A.; Migchels, N.; Kabadayi, S.; Gruber, T.; Komarova Loureiro, Y.; Solnet, D. Understanding Generation Y and their use of social media: A review and research agenda. J. Serv. Manag. 2013, 24, 245–267. [Google Scholar] [CrossRef]
  17. Lemon, K.N.; Verhoef, P.C. Understanding customer experience throughout the customer journey. J. Mark. 2016, 80, 69–96. [Google Scholar] [CrossRef]
  18. Pauceanu, A.M.; Rabie, N.; Moustafa, A.; Jiroveanu, D.C. Entrepreneurial Leadership and Sustainable Development—A Systematic Literature Review. Sustainability 2021, 13, 11695. [Google Scholar] [CrossRef]
  19. Becker, L.; Jaakkola, E. Customer experience: Fundamental premises and implications for research. J. Acad. Mark. Sci. 2020, 48, 630–648. [Google Scholar] [CrossRef]
  20. Dessart, L.; Veloutsou, C.; Morgan-Thomas, A. Consumer engagement in online brand communities: A social media perspective. J. Prod. Brand. Manag. 2015, 24, 28–42. [Google Scholar] [CrossRef]
  21. Bolton, R.N.; McColl-Kennedy, J.R.; Cheung, L.; Gallan, A.; Orsingher, C.; Witell, L.; Zaki, M. Customer experience challenges: Bringing together digital, physical and social realms. J. Serv. Manag. 2018, 29, 776–808. [Google Scholar] [CrossRef]
  22. De Keyser, A.; Verleye, K.; Lemon, K.N.; Keiningham, T.L.; Klaus, P. Moving the customer experience field forward: Introducing the touchpoints, context, qualities (TCQ) nomenclature. J. Serv. Res. 2020, 23, 433–455. [Google Scholar] [CrossRef]
  23. Schmitt, B. Experiential Marketing. J. Mark. Manag. 1999, 15, 53–67. [Google Scholar] [CrossRef]
  24. Holbrook, M.B.; Hirschman, E.C. The experiential aspects of consumption: Consumer fantasies, feelings, and fun. J. Consum. Res. 1982, 9, 132–140. [Google Scholar] [CrossRef]
  25. Cummings, T.G.; Worley, C.G. Organization Development & Change, 10th ed.; Cengage Learning: Stamford, CT, USA, 2014. [Google Scholar]
  26. Scussel, F.B.C.; Fogaça, N.; Demo, G. Consumption experience: Proposal for a unifying concept. Braz. J. Mark. 2021, 20, 175–198. [Google Scholar] [CrossRef]
  27. Pine, B.J., II; Gilmore, J.H. Welcome to the experience economy. Harv. Bus. Rev. 1998, 76, 97–105. [Google Scholar] [PubMed]
  28. Pine, B.J., II; Gilmore, J.H. The Experience Economy: Work is Theatre & Every Business A Stage; Harvard Business School Press: Boston, MA, USA, 1999. [Google Scholar]
  29. Johnston, R.; Kong, X. The customer experience: A road-map for improvement. Manag. Serv. Qual. Int. J. 2011, 21, 5–24. [Google Scholar] [CrossRef]
  30. Carù, A.; Cova, B. Revisiting consumption experience: A more humble but complete view of the concept. Mark. Theory 2003, 3, 267–286. [Google Scholar] [CrossRef]
  31. Godovykh, M.; Tasci, A.D. Customer experience in tourism: A review of definitions, components, and measurements. Tour. Manag. Perspect. 2020, 35, 100694. [Google Scholar] [CrossRef]
  32. Gentile, C.; Spiller, N.; Noci, G. How to sustain the customer experience: An overview of experience components that co-create value with the customer. Eur. Manag. J. 2007, 25, 395–410. [Google Scholar] [CrossRef]
  33. Pentina, I.; Amialchuk, A.; Taylor, D.G. Exploring effects of online shopping experiences on browser satisfaction and e-tail performance. Int. J. Retail Distrib. Manag. 2011, 39, 742–758. [Google Scholar] [CrossRef]
  34. Rahmani, K.; Gnoth, J.; Mather, D. A psycholinguistic view of tourists’ emotional experiences. J. Travel Res. 2019, 58, 192–206. [Google Scholar] [CrossRef]
  35. Packer, J.; Ballantyne, R. Conceptualizing the visitor experience: A review of literature and development of a multifaceted model. Visit. Stud. 2016, 19, 128–143. [Google Scholar] [CrossRef]
  36. Mendes-Filho, L.; Tan, F.B. User-generated content and consumer empowerment in the travel industry: A uses & gratifications and dual-process conceptualization. PACIS 2009 Proc. 2009, 28. Available online: https://aisel.aisnet.org/pacis2009/28 (accessed on 22 December 2021).
  37. Mendes-Filho, L.; Mills, A.M.; Tan, F.B.; Milne, S. Empowering the traveler: An examination of the impact of user-generated content on travel planning. J. Travel Tour. Mark. 2018, 35, 425–436. [Google Scholar] [CrossRef]
  38. Yoo, K.; Gretzel, U. Use and creation of social media by travellers. In Social Media in Travel, Tourism and Hospitality; Sigala, M., Christou, E., Gretzel, U., Eds.; Ashgate Publishing: Farnham, UK, 2012; pp. 189–205. [Google Scholar]
  39. González-Rodríguez, M.R.; Díaz-Fernández, M.C.; Bilgihan, A.; Shi, F.; Okumus, F. UGC involvement, motivation and personality: Comparison between China and Spain. J. Dest. Mark. Manag. 2021, 19, 100543. [Google Scholar] [CrossRef]
  40. Fonseca, L.M.; Domingues, J.P.; Dima, A.M. Mapping the Sustainable Development Goals Relationships. Sustainability 2020, 12, 3359. [Google Scholar] [CrossRef]
  41. Bigne, E.; Fuentes-Medina, M.L.; Morini-Marrero, S. Memorable tourist experiences versus ordinary tourist experiences analysed through user-generated content. J. Hosp. Tour. Manag. 2020, 45, 309–318. [Google Scholar] [CrossRef]
  42. Tsiakali, K. User-generated-content versus marketing-generated-content: Personality and content influence on traveler’s behavior. J. Hosp. Mark. Manag. 2018, 27, 946–972. [Google Scholar] [CrossRef]
  43. Malthouse, E.C.; Calder, B.J.; Kim, S.J.; Vandenbosch, M. Evidence that user-generated content that produces engagement increases purchase behaviours. J. Mark. Manag. 2016, 32, 427–444. [Google Scholar] [CrossRef]
  44. Menichelli, M.; Braccini, A.M. Millennials, Information Assessment, and Social Media: An Exploratory Study on the Assessment of Critical Thinking Habits. In Exploring Digital Ecosystems; Lazazzara, A., Ricciardi, F., Za, S., Eds.; Springer: Berlin/Heidelberg, Germany; Gewerbestrasse: Cham, Switzerland, 2020; pp. 85–97. [Google Scholar] [CrossRef]
  45. Wu, S.; Wong, I.A.; Lin, Z.C. Understanding the role of atmospheric cues of travel apps: A synthesis between media richness and stimulus–organism–response theory. J. Hosp. Tour. Manag. 2021, 49, 226–234. [Google Scholar] [CrossRef]
  46. Bonfanti, A.; Vigolo, V.; Negri, F. Hotel responses to guests’ online reviews: An exploratory study on communication styles. In Information and Communication Technologies in Tourism 2016. Proceedings of the International Conference in Bilbao, Spain, 2–5 February 2016; Inversini, A., Schegg, R., Eds.; Springer Gewerbestrasse: Cham, Switzerland, 2016; pp. 397–409. [Google Scholar] [CrossRef]
  47. Bernardino, S.; Santos, J.F.; Ribeiro, J.C.; Freitas, A. Determinants of the Effective Use of UGC (User-Generated Content) on Hotel Room Bookings by Portuguese Travellers. Int. J. Online Mark. 2020, 10, 30–43. [Google Scholar] [CrossRef]
  48. Zhang, Z.; Ye, Q.; Law, R.; Li, Y. The impact of e-word-of-mouth on the online popularity of restaurants: A comparison of consumer reviews and editor reviews. Int. J. Hosp. Manag. 2010, 29, 694–700. [Google Scholar] [CrossRef]
  49. Liu, Z.; Park, S. What makes a useful online review? Implication for travel product websites. Tour. Manag. 2015, 47, 140–151. [Google Scholar] [CrossRef]
  50. Krumm, J.; Davies, N.; Narayanaswami, C. User-generated content. IEEE Pervasive Comput. 2008, 7, 10–11. [Google Scholar] [CrossRef]
  51. Dylko, I.; McCluskey, M. Media effects in an era of rapid technological transformation: A case of user-generated content and political participation. Commun. Theory 2012, 22, 250–278. [Google Scholar] [CrossRef]
  52. Teo, T.S.; Yeong, Y.D. Assessing the consumer decision process in the digital marketplace. Omega 2003, 31, 349–363. [Google Scholar] [CrossRef]
  53. Zhang, Y.; Lingyi, M.; Peixue, L.; Lu, Y.; Zhang, J. COVID-19’s impact on tourism: Will compensatory travel intention appear? Asia Pac. J. Tour. Res. 2021, 26, 732–747. [Google Scholar] [CrossRef]
  54. Tian, Z.; Shi, Z.; Cheng, Q. Examining the antecedents and consequences of mobile travel app engagement. PLoS ONE 2021, 16, e0248460. [Google Scholar] [CrossRef]
  55. Edvardsson, B.; Enquist, B.; Johnston, R. Design dimensions of experience rooms for service test drives: Case studies in several service contexts. Manag. Serv. Qual. 2010, 20, 312–327. [Google Scholar] [CrossRef]
  56. Brakus, J.J.; Schmitt, B.H.; Zarantonello, L. Brand experience: What is it? How is it measured? Does it affect loyalty? J. Mark. 2009, 73, 52–68. [Google Scholar] [CrossRef]
  57. Agapito, D.; Valle, P.; Mendes, J. The sensory dimension of tourist experiences: Capturing meaningful sensory-informed themes in Southwest Portugal. Tour. Manag. 2014, 42, 224–237. [Google Scholar] [CrossRef]
  58. Bech-Larsen, T.; Nielsen, N.A. A comparison of five elicitation techniques for elicitation of attributes of low involvement products. J. Econ. Psychol. 1999, 20, 315–341. [Google Scholar] [CrossRef]
  59. Gretzel, U.; Fesenmaier, D.R. Experience-based Internet Marketing: An Exploratory Study of Sensory Experiences Associated with Pleasure Travel to the Midwest United States. In Information and Communication Technologies in Tourism; Frew, A., Hitz, M., O’Connor, P., Eds.; Springer: Vienna, AT, Austria, 2003; pp. 49–57. [Google Scholar]
  60. Gretzel, U.; Fesenmaier, D. Capturing sensory experiences through semi-structured elicitation questions. In The Tourism and Leisure Experience: Consumer and Managerial Perspectives; Morgan, M., Lugosi, P., Ritchie, J.R., Eds.; Channel View Publications: Bristol, UK, 2010; pp. 137–160. [Google Scholar]
  61. Wendin, K.; Allesen-Holm, B.H.; Bredie, W.L. Do facial reactions add new dimensions to measuring sensory responses to basic tastes? Food Qual. Prefer. 2011, 22, 346–354. [Google Scholar] [CrossRef]
  62. Kim, H.; Suh, K.; Lee, U. Effects of collaborative online shopping on shopping experience through social and relational perspectives. Inf. Manag. 2013, 50, 169–180. [Google Scholar] [CrossRef]
  63. Cheung, J.; Vazquez, D. An Exploratory Study to Understand Online Consumers’ Experiential Responses Towards Fashion Visual Content; Academy of Marketing, The Magic in Marketing, University of Limerick: Limerick, Ireland, 2015; pp. 1–12. Available online: https://www.escholar.manchester.ac.uk/api/datastream?publicationPid=uk-ac-man-scw:296657&datastreamId=FULL-TEXT.PDF (accessed on 3 January 2022).
  64. Csikszentmihalyi, M. Finding Flow—The Psychology of Engagement with Everyday Life; Basic Books: New York, NY, USA, 1997. [Google Scholar]
  65. Lee, S.M.; Chen, L. The impact of flow on online consumer behavior. J. Comput. Inf. Syst. 2010, 50, 1–10. [Google Scholar] [CrossRef]
  66. Hosany, S.; Gilbert, D. Measuring tourists’ emotional experiences toward hedonic holiday destinations. J. Travel Res. 2010, 49, 513–526. [Google Scholar] [CrossRef]
  67. Hosany, S.; Martin, D.; Woodside, A.G. Emotions in tourism: Theoretical designs, measurements, analytics, and interpretations. J. Travel Res. 2021, 60, 1391–1407. [Google Scholar] [CrossRef]
  68. Prayag, G.; Hosany, S.; Muskat, B.; Del Chiappa, G. Understanding the relationships between tourists’ emotional experiences, perceived overall image, satisfaction, and intention to recommend. J. Travel Res. 2017, 56, 41–54. [Google Scholar] [CrossRef]
  69. Wang, L.; Kirilenko, A.P. The Impacts of COVID-19 on Tourists’ Emotions Expressed from TripAdvisor Reviews: Emotion Detection of Travel Experiences in Yellowstone National Park; University of Massachusetts Amhers: Amherst, MA, USA, 2021; pp. 1–12. Available online: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=2681&context=ttra (accessed on 8 January 2022).
  70. Vada, S.; Prentice, C.; Scott, N.; Hsiao, A. Positive psychology and tourist well-being: A systematic literature review. Tour. Manag. Perspect. 2020, 33, 100631. [Google Scholar] [CrossRef]
  71. Alswaidan, N.; Menai, M.E.B. A survey of state-of-the-art approaches for emotion recognition in text. Knowl. Inf. Syst. 2020, 62, 2937–2987. [Google Scholar] [CrossRef]
  72. Pröllochs, N.; Bär, D.; Feuerriegel, S. Emotions in online rumor diffusion. EPJ Data Sci. 2021, 10, 51. [Google Scholar] [CrossRef]
  73. Wu, D.; Weng, D.; Xue, S. Virtual reality system as an affective medium to induce specific emotion: A validation study. Electron. Imaging 2016, 4, 1–6. [Google Scholar] [CrossRef]
  74. Riva, G.; Mantovani, F.; Capideville, C.S.; Preziosa, A.; Morganti, F.; Villani, D.; Gaggioli, A.; Botella, C.; Alcañiz, M. Affective interactions using virtual reality: The link between presence and emotions. Cyberpsychol. Behav. 2007, 10, 45–56. [Google Scholar] [CrossRef]
  75. Brown, C.L.; Van Doren, N.; Ford, B.Q.; Mauss, I.B.; Sze, J.W.; Levenson, R.W. Coherence between subjective experience and physiology in emotion: Individual differences and implications for well-being. Emotion 2020, 20, 818–829. [Google Scholar] [CrossRef]
  76. Goldenberg, A.; Gross, J.J. Digital emotion contagion. Trends Cogn. Sci. 2020, 24, 316–328. [Google Scholar] [CrossRef] [PubMed]
  77. Pérez-Vega, R.; Taheri, B.; Farrington, T.; O’Gorman, K. On being attractive, social and visually appealing in social media: The effects of anthropomorphic tourism brands on Facebook fan pages. Tour. Manag. 2018, 66, 339–347. [Google Scholar] [CrossRef]
  78. Pinto, I.; Castro, C. Online travel agencies: Factors influencing tourists’ purchase decisions. Tour. Manag. Stud. 2019, 15, 7–20. [Google Scholar] [CrossRef]
  79. Zhang, K.; Chen, Y.; Li, C. Discovering the tourists’ behaviors and perceptions in a tourism destination by analyzing photos’ visual content with a computer deep learning model: The case of Beijing. Tour. Manag. 2019, 75, 595–608. [Google Scholar] [CrossRef]
  80. Rodway-Dyer, S.; Shaw, G. The effects of the foot-and-mouth outbreak on visitor behaviour: The case of Dartmoor National Park, South-West England. J. Sustain. Tour. 2005, 13, 63–81. [Google Scholar] [CrossRef]
  81. Cohen, S.A.; Prayag, G.; Moital, M. Consumer behaviour in tourism: Concepts, influences and opportunities. Curr. Issues Tour. 2014, 17, 872–909. [Google Scholar] [CrossRef]
  82. Cheung, M.L.; Leung, W.K.; Cheah, J.H.; Ting, H. Exploring the effectiveness of emotional and rational user-generated contents in digital tourism platforms. J. Vacat. Mark. 2021, 20, 152–170. [Google Scholar] [CrossRef]
  83. Keiningham, T.; Ball, J.; Benoit, S.; Bruce, H.L.; Buoye, A.; Dzenkovska, J.; Nasr, L.; Ou, Y.-C.; Zaki, M. The interplay of customer experience and commitment. J. Serv. Mark. 2017, 31, 148–160. [Google Scholar] [CrossRef]
  84. Maeran, R.; Mignemi, G. The role of consumer-brand engagement in tourism consumption: From user-generated content to my generated content. TPM Test. Psychom. Methodol. Appl. Psychol. 2021, 28, 502–520. [Google Scholar] [CrossRef]
  85. Gartner, W.C. Image formation process. J. Travel Tour. Mark. 1994, 2, 191–216. [Google Scholar] [CrossRef]
  86. Beerli, A.; Martin, J.D. Factors influencing destination image. Ann. Tour. Res. 2004, 31, 657–681. [Google Scholar] [CrossRef]
  87. Qu, H.; Kim, L.H.; Im, H.H. A model of destination branding: Integrating the concepts of the branding and destination image. Tour. Manag. 2011, 32, 465–476. [Google Scholar] [CrossRef]
  88. Zhang, H.; Fu, X.; Cai, L.A.; Lu, L. Destination image and tourist loyalty: A meta-analysis. Tour. Manag. 2014, 40, 213–223. [Google Scholar] [CrossRef]
  89. Zhang, H.; Wu, Y.; Buhalis, D. A model of perceived image, memorable tourism experiences and revisit intention. J. Dest. Mark. Manag. 2018, 8, 326–336. [Google Scholar] [CrossRef]
  90. Lv, X.; McCabe, S. Expanding theory of tourists’ destination loyalty: The role of sensory impressions. Tour. Manag. 2020, 77, 104026. [Google Scholar] [CrossRef]
  91. Stojanovic, I.; Andreu, L.; Curras-Perez, R. Effects of the intensity of use of social media on brand equity: An empirical study in a tourist destination. Eur. J. Manag. Bus. Econ. 2018, 27, 83–100. [Google Scholar] [CrossRef]
  92. Cardoso, L.; Dias, F.; de Araújo, A.F.; Marques, M.I.A. A destination imagery processing model: Structural differences between dream and favourite destinations. Ann. Tour. Res. 2019, 74, 81–94. [Google Scholar] [CrossRef]
  93. Marques, C.; da Silva, R.V.; Antova, S. Image, satisfaction, destination and product post-visit behaviours: How do they relate in emerging destinations? Tour. Manag. 2021, 85, 104293. [Google Scholar] [CrossRef]
  94. Manosuthi, N.; Lee, J.S.; Han, H. Predicting the revisit intention of volunteer tourists using the merged model between the theory of planned behavior and norm activation model. J. Travel Tour. Mark. 2020, 37, 510–532. [Google Scholar] [CrossRef]
  95. Rather, R.A. Customer experience and engagement in tourism destinations: The experiential marketing perspective. J. Travel Tour. Mark. 2020, 37, 15–32. [Google Scholar] [CrossRef]
  96. Zollo, L.; Filieri, R.; Rialti, R.; Yoon, S. Unpacking the relationship between social media marketing and brand equity: The mediating role of consumers’ benefits and experience. J. Bus. Res. 2020, 117, 256–267. [Google Scholar] [CrossRef]
  97. Boswijk, A.; Thijssen, T.; Peelen, E. The Experience Economy: A New Perspective; Pearson Education Benelux: Amsterdam, The Netherlands, 2007. [Google Scholar]
  98. Ek, R.; Larsen, J.; Hornskov, S.B.; Mansfeldt, O.K. A dynamic framework of tourist experiences: Space-time and performances in the experience economy. Scand. J. Hosp. Tour. 2008, 8, 122–140. [Google Scholar] [CrossRef]
  99. Knutson, B.J.; Beck, J.A.; Kim, S.H.; Cha, J. Identifying the dimensions of the experience construct. J. Hosp. Leis. Mark. 2007, 15, 31–47. [Google Scholar] [CrossRef]
  100. Zatori, A.; Smith, M.K.; Puczko, L. Experience-involvement, memorability and authenticity: The service provider’s effect on tourist experience. Tour. Manag. 2018, 67, 111–126. [Google Scholar] [CrossRef]
  101. Sørensen, F.; Jensen, J.F. Value creation and knowledge development in tourism experience encounters. Tour. Manag. 2015, 46, 336–346. [Google Scholar] [CrossRef]
  102. Campos, A.C.; Mendes, J.; Valle, P.O.D.; Scott, N. Co-creation of tourist experiences: A literature review. Curr. Issues Tour. 2018, 21, 369–400. [Google Scholar] [CrossRef]
  103. Za, S.; Pallud, J.; Agrifoglio, R.; Metallo, C. Value co-creation in online communities: A preliminary literature analysis. In Exploring Digital Ecosystems; Lazazzara, A., Ricciardi, F., Za, S., Eds.; Springer: Berlin/Heidelberg, Germany; Gewerbestrasse: Cham, Switzerland, 2020; pp. 33–46. [Google Scholar] [CrossRef]
  104. Richards, G.; King, B.; Yeung, E. Experiencing culture in attractions, events and tour settings. Tour. Manag. 2020, 79, 104104. [Google Scholar] [CrossRef]
  105. Celsi, R.L.; Chow, S.; Olson, J.C.; Walker, B.A. The construct validity of intrinsic sources of personal relevance: An intra-individual source of felt involvement. J. Bus. Res. 1992, 25, 165–185. [Google Scholar] [CrossRef]
  106. Celsi, R.L.; Olson, J.C. The role of involvement in attention and comprehension processes. J. Consum. Res. 1988, 15, 210–224. [Google Scholar] [CrossRef]
  107. Trevino, L.K.; Webster, J. Flow in Computer-Mediated Communication: Electronic Mail and Voice Mail Evaluation and Impacts. Commun. Res. 1992, 19, 539–573. [Google Scholar] [CrossRef]
  108. Novak, T.P.; Hoffman, D.L.; Yung, Y.F. Measuring the customer experience in online environments: A structural modeling approach. Mark. Sci. 2000, 19, 22–42. [Google Scholar] [CrossRef]
  109. Sweeney, J.C.; Soutar, G.N. Consumer perceived value: The development of a multiple item scale. J. Retail. 2001, 77, 203–220. [Google Scholar] [CrossRef]
  110. Heckhausen, J.; Heckhausen, H. (Eds.) Motivation and Action, 3rd ed.; Springer Gewerbestrasse: Cham, Switzerland, 2018. [Google Scholar] [CrossRef]
  111. Kang, J.W.; Lee, H.; Namkung, Y. The impact of restaurant patrons’ flow experience on SNS satisfaction and offline purchase intentions. Int. J. Contemp. Hosp. Manag. 2018, 30, 797–816. [Google Scholar] [CrossRef]
  112. Lin, J.; Lin, S.; Turel, O.; Xu, F. The buffering effect of flow experience on the relationship between overload and social media users’ discontinuance intentions. Telemat. Inform. 2020, 49, 101374. [Google Scholar] [CrossRef]
  113. Moon, Y.J.; Kim, W.G.; Armstrong, D.J. Exploring neuroticism and extraversion in flow and user generated content consumption. Inf. Manag. 2014, 51, 347–358. [Google Scholar] [CrossRef]
  114. Cai, X.; Cebollada, J.; Cortiñas, M. Self-report measure of dispositional flow experience in the video game context: Conceptualisation and scale development. Int. J. Hum. Comput. Stud. 2022, 159, 102746. [Google Scholar] [CrossRef]
  115. Munar, A.M.; Jacobsen, J.K.S. Motivations for sharing tourism experiences through social media. Tour. Manag. 2014, 43, 46–54. [Google Scholar] [CrossRef]
  116. Karasakal, S.; Albayrak, T. How to create flow experience during travel: The role of destination attributes. J. Vacat. Mark. 2021, 28, 303–318. [Google Scholar] [CrossRef]
  117. Gañac, C.G. Investigating consumer optimum stimulation level and exploratory online buying behavior. DLSU Bus. Econ. Rev. 2018, 28, 67–85. Available online: https://www.dlsu.edu.ph/wp-content/uploads/2019/03/4ga%C3%B1ac-092618.pdf (accessed on 14 May 2022).
  118. Demangeot, C.; Broderick, A.J. Exploration and its manifestations in the context of online shopping. J. Mark. Manag. 2010, 26, 1256–1278. [Google Scholar] [CrossRef]
  119. Raju, P.S.; Venkatesan, M. Exploratory Behavior in the Consumer Context: A State of the Art Review. In Advances in Consumer Research; Olson, J.C., Abor, A., Eds.; University of Oregon: Eugene, OR, USA, 1980; Volume 07, pp. 258–263. Available online: https://www.acrwebsite.org/volumes/9679/volumes/v07/NA-07/full (accessed on 23 December 2021).
  120. Choi, S.; Lehto, X.Y.; Morrison, A.M.; Jang, S. Structure of travel planning processes and information use patterns. J. Travel. Res. 2012, 51, 26–40. [Google Scholar] [CrossRef]
  121. Fesenmaier, D.R.; Jeng, J.M. Assessing structure in the pleasure trip planning process. Tour. Anal. 2000, 5, 13–27. [Google Scholar]
  122. Jeng, J.; Fesenmaier, D.R. Conceptualizing the travel decision-making hierarchy: A review of recent developments. Tour. Anal. 2002, 7, 15–32. [Google Scholar] [CrossRef]
  123. Jun, S.H.; Vogt, C.A.; MacKay, K.J. Relationships between travel information search and travel product purchase in pretrip contexts. J. Travel Res. 2007, 45, 266–274. [Google Scholar] [CrossRef]
  124. Matiza, T. Post-COVID-19 crisis travel behaviour: Towards mitigating the effects of perceived risk. J. Tour. Futures 2020, 8, 99–108. [Google Scholar] [CrossRef]
  125. Shapoval, V.; Hägglund, P.; Pizam, A.; Abraham, V.; Carlbäck, M.; Nygren, T.; Smith, R.M. The COVID-19 pandemic effects on the hospitality industry using social systems theory: A multi-country comparison. Int. J. Hosp. Manag. 2021, 94, 102813. [Google Scholar] [CrossRef]
  126. Sharma, G.D.; Thomas, A.; Paul, J. Reviving tourism industry post-COVID-19: A resilience-based framework. Tour. Manag. Perspect. 2021, 37, 100786. [Google Scholar] [CrossRef]
  127. Spears, N.; Singh, S.N. Measuring attitude toward the brand and purchase intentions. J. Curr. Issues Res. Advert. 2004, 26, 53–66. [Google Scholar] [CrossRef]
  128. Ajzen, I. The theory of planned behavior: Frequently asked questions. Hum. Behav. Emerg. Technol. 2020, 2, 314–324. [Google Scholar] [CrossRef]
  129. Rehman, F.; Nawaz, T.; Ahmed, I.; Hyder, S. Some insights in the historical prospective of hierarchy of effects model: A short review. Inf. Manag. Bus. Rev. 2014, 6, 301–308. [Google Scholar] [CrossRef]
  130. Filieri, R.; McLeay, F.; Tsui, B.; Lin, Z. Consumer perceptions of information helpfulness and determinants of purchase intention in online consumer reviews of services. Inf. Manag. 2018, 55, 956–970. [Google Scholar] [CrossRef]
  131. Yu, G.; Carlsson, C.; Zou, D. Exploring the influence of user-generated content factors on the behavioral intentions of travel consumers. In Proceedings of the 25th Australasian Conference on Information Systems, Auckland, New Zealand, 8–10 December 2014; pp. 1–12. Available online: https://openrepository.aut.ac.nz/handle/10292/8113 (accessed on 18 December 2021).
  132. Amaral, F.; Tiago, T.; Tiago, F. User-generated content: Tourists’ profiles on Tripadvisor. Int. J. Strateg. Innov. Mark. 2014, 1, 137–145. [Google Scholar] [CrossRef]
  133. Ko, H.; Cho, C.H.; Roberts, M.S. Internet uses and gratifications: A structural equation model of interactive advertising. J. Advert. 2005, 34, 57–70. [Google Scholar] [CrossRef]
  134. Van Reijmersdal, E.A.; Fransen, M.L.; van Noort, G.; Opree, S.J.; Vandeberg, L.; Reusch, S.; van Lieshout, F.; Boerman, S.C. Effects of disclosing sponsored content in blogs: How the use of resistance strategies mediates effects on persuasion. Am. Behav. Sci. 2016, 60, 1458–1474. [Google Scholar] [CrossRef]
  135. Kitsios, F.; Mitsopoulou, E.; Moustaka, E.; Kamariotou, M. User-Generated Content behavior and digital tourism services: A SEM-neural network model for information trust in social networking sites. Int. J. Inf. Manag. Data Insights 2022, 2, 100056. [Google Scholar] [CrossRef]
  136. Smith, A.N.; Fischer, E.; Yongjian, C. How does brand-related user-generated content differ across YouTube, Facebook, and Twitter? J. Interact. Mark. 2012, 26, 102–113. [Google Scholar] [CrossRef]
  137. Muntinga, D.G.; Moorman, M.; Smit, E.G. Introducing COBRAs: Exploring motivations for brand-related social media use. Int. J. Advert. 2011, 30, 13–46. [Google Scholar] [CrossRef]
  138. Lesjak, M.; Navrátil, J.; Pícha, K.; Gilliam, V.L.W.B. The predictors of the willingness to recommend a visit for diversified tourism attractions. Czech J. Tour. 2015, 4, 77–90. [Google Scholar] [CrossRef]
  139. Byon, K.K.; Zhang, J.J. Development of a scale measuring destination image. Mark. Intell. Plan. 2010, 28, 508–532. [Google Scholar] [CrossRef]
  140. Nguyen, Q.; Nguyen, H.; Le, T. Relationships among novelty seeking, satisfaction, return intention, and willingness to recommend of foreign tourists in Vietnam. Manag. Sci. Lett. 2020, 10, 2249–2258. [Google Scholar] [CrossRef]
  141. Persaud, A.; Azhar, I. Innovative mobile marketing via smartphones: Are consumers ready? Mark. Intell. Plan. 2012, 30, 418–443. [Google Scholar] [CrossRef]
  142. Escobar-Rodríguez, T.; Bonsón-Fernández, R. Analysing online purchase intention in Spain: Fashion e-commerce. Inf. Syst. e-Bus. Manag. 2017, 15, 599–622. [Google Scholar] [CrossRef]
  143. Karahasanović, A.; Brandtzæg, P.B.; Heim, J.; Lüders, M.; Vermeir, L.; Pierson, J.; Lievens, B.; Vanattenhoven, J.; Jans, G. Co-creation and user-generated content–elderly people’s user requirements. Comput. Hum. Behav. 2009, 25, 655–678. [Google Scholar] [CrossRef]
  144. Fountain, J.; Charters, S. Generation Y as Wine Tourists: Their Expectations and Experiences at the Winery-cellar Door. In Tourism and Generation Y; Beckendorff, P., Moscardo, G., Pendergast, D., Eds.; CABI: Wallingford, UK, 2010; pp. 47–57. [Google Scholar]
  145. Xiang, Z.; Gretzel, U. Role of social media in online travel information search. Tour. Manag. 2010, 31, 179–188. [Google Scholar] [CrossRef]
  146. Isacsson, A.; Gretzel, U. Facebook as an edutainment medium to engage students in sustainability and tourism. J. Hosp. Tour. Technol. 2011, 2, 81–90. [Google Scholar] [CrossRef]
  147. Leung, D.; Law, R.; Van Hoof, H.; Buhalis, D. Social media in tourism and hospitality: A literature review. J. Travel Tour. Mark. 2013, 30, 3–22. [Google Scholar] [CrossRef]
  148. Zeng, X.; Wei, L. Social ties and user content generation: Evidence from Flickr. Inf. Syst. Res. 2013, 24, 71–87. [Google Scholar] [CrossRef]
  149. Malhotra, N.K. Marketing Research: An Applied Orientation, 7th ed.; Pearson Education Limited: Harlow, UK, 2020. [Google Scholar]
  150. Garland, R. The mid-point on a rating scale: Is it desirable. Mark. Bull. 1991, 2, 66–70. Available online: http://marketing-bulletin.massey.ac.nz/V2/MB_V2_N3_Garland.pdf (accessed on 20 December 2021).
  151. Forman, D.; Nyatanga, L. The process of developing a research questionnaire to measure attitudes to shared learning. Med. Teach. 2001, 23, 595–598. [Google Scholar] [CrossRef]
  152. Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.M.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd ed.; Sage Publications: Thousand Oaks, CA, USA, 2021. [Google Scholar]
  153. Bryant, F.B.; Yarnold, P.R. Principal-components analysis and exploratory and confirmatory factor analysis. In Reading and Understanding Multivariate Statistics; Grimm, L.G., Yarnold, P.R., Eds.; American Psychological Association: Washington, DC, USA, 1995; pp. 99–136. [Google Scholar]
  154. Kline, R. Principles and Practice of Structural Equation Modeling, 4th ed.; The Guilford Press: New York, NY, USA, 2016. [Google Scholar]
  155. Purwanto, A. Partial Least Squares Structural Squation Modeling (PLS-SEM) Analysis for Social and Management Research: A Literature Review. Int. J. Ind. Eng. Manag. Res. 2021, 2, 114–123. [Google Scholar] [CrossRef]
  156. Bulatovic, I. Testing a holistic model of tourist destination loyalty. Tour. Int. Interdiscip. J. 2020, 68, 354–358. [Google Scholar] [CrossRef]
  157. Shrestha, N. Factor analysis as a tool for survey analysis. Am. J. Appl. Math. Stat. 2021, 9, 4–11. [Google Scholar] [CrossRef]
  158. Hooper, D.; Coughlan, J.; Mullen, M. Structural Equation Modelling: Guidelines for Determining Model Fit. Electron. J. Bus. Res. Methods 2008, 6, 53–60. [Google Scholar]
  159. Iacobucci, D. Structural equations modeling: Fit Indices, sample size, and advanced topics. J. Consum. Psychol. 2010, 20, 90–98. [Google Scholar] [CrossRef]
  160. Brown, G.T.L. Measuring attitude with positively packed self-report ratings: Comparison of agreement and frequency scales. Psychol. Rep. 2004, 94, 1015–1024. [Google Scholar] [CrossRef]
  161. Brown, G.T.L. Teachers’ conceptions of assessment: Validation of an abridged version. Psychol. Rep. 2006, 99, 166–170. [Google Scholar] [CrossRef]
  162. Browne, M.W.; Cudek, R. Alternative ways of assessing model fit. In Testing Structural Equation Models; Bollen, K.A., Long, J.S., Eds.; Sage: Newbury Park, CA, USA, 1993; pp. 136–162. [Google Scholar]
  163. Arbuckle, J.L. IBM SPSS Amos 20 User’s Guide; Amos Development Corporation: Mount Pleasant, SC, USA, 2011. [Google Scholar]
  164. Trimurti, C.P.; Utama, I.G.B.R. Bali Tourism Destination Structural Loyalty Model from Consumer Behavior Perspective. Turk. J. Comput. Math. Educ. 2021, 12, 494–505. [Google Scholar]
  165. Cheung, C.; Takashima, M.; Choi, H.; Yang, H.; Tung, V. The impact of COVID-19 pandemic on the psychological needs of tourists: Implications for the travel and tourism industry. J. Travel Tour. Mark. 2021, 38, 155–166. [Google Scholar] [CrossRef]
  166. Di Giunta, L.; Lunetti, C.; Fiasconaro, I.; Gliozzo, G.; Salvo, G.; Ottaviani, C.; Aringolo, K.; Comitale, C.; Riccioni, C.; D’Angeli, G. COVID-19 Impact on Parental Emotion Socialization and Youth Socioemotional Adjustment in Italy. J. Res. Adolesc. 2021, 31, 657–677. [Google Scholar] [CrossRef]
  167. Moon, C.B.; Lee, J.Y.; Kim, D.S.; Kim, B.M. Analysis of Mood Tags for Multimedia Content Recommendation in Social Networks. In Proceedings of the 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN), Zagreb, Croatia, 2–5 July 2019; pp. 452–454. [Google Scholar] [CrossRef]
  168. Ivanov, I.; Vajda, P.; Lee, J.S.; Ebrahimi, T. In tags we trust: Trust modeling in social tagging of multimedia content. IEEE Signal Process. Mag. 2012, 29, 98–107. [Google Scholar] [CrossRef]
  169. Ferraro, A.; Moscato, V.; Sperlì, G. Deep learning-based community detection approach on multimedia social networks. Appl. Sci. 2021, 11, 11447. [Google Scholar] [CrossRef]
  170. Lam, J.M.; Ismail, H.; Lee, S. From desktop to destination: User-generated content platforms, co-created online experiences, destination image and satisfaction. J. Destin. Mark. Manag. 2020, 18, 100490. [Google Scholar] [CrossRef]
  171. Sultan, M.T.; Sharmin, F.; Badulescu, A.; Gavrilut, D.; Xue, K. Social media-based content towards image formation: A new approach to the selection of sustainable destinations. Sustainability 2021, 13, 4241. [Google Scholar] [CrossRef]
  172. Stojanovska-Stefanova, A.; Magdinceva Sopova, M. The impact of Covid-19 on world tourism. In Proceedings of the Sixth International Scientific Conference—TOURISM CHALLENGES AMID COVID-19, Thematic Proceedings, Vrnjaţka Banja, Serbia, 3–5 June 2021; Volume 6, pp. 78–93. [Google Scholar] [CrossRef]
  173. De Keyser, A.; Lemon, K.N.; Klaus, P.; Keiningham, T.L. A Framework for Understanding and Managing the Customer Experience; Marketing Science Institute Working Paper Series 2015, Report No. 15-121; Marketing Science Institute: New York, NY, USA, 2015; pp. 1–47. Available online: https://www.msi.org/wp-content/uploads/2020/06/MSI_Report_15-121.pdf (accessed on 7 December 2021).
Figure 1. The conceptual model proposed.
Figure 1. The conceptual model proposed.
Electronics 11 02570 g001
Figure 2. The research process diagram.
Figure 2. The research process diagram.
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Table 1. Sample profile (n = 673).
Table 1. Sample profile (n = 673).
VariableItemsN%
GenderFemale39959.3%
Male27440.7%
Level of finalized studiesSecondary school 26439.2%
Higher education 22533.4%
Master’s degree 12618.7%
Doctoral degree588.6%
Monthly Income of the respondent *under RON 150013720.4%
RON 1501–2500 14822.0%
RON 2501–350013219.6%
RON 3501–45008011.9%
RON 4501–60008813.1%
Above RON 60008813.1%
Age18–25 years of age32748.6%
26–35 years of age 8312.3%
36–45 years of age17325.7%
46–55 years of age649.5%
56–65 years of age263.9%
Source: * the income groups expressed in EUR are: under approx. EUR 303; approx. EUR 304–505; approx. EUR 506–707; approx. EUR 708–909; approx: EUR 910–1213; EUR 1213 and over.
Table 2. The measurement model results at the level of each construct/item.
Table 2. The measurement model results at the level of each construct/item.
Factor
Loading
Cronbach’s AlphaKaiser–Meyer–Olkin Measure
of Sampling
Adequacy
Sensorial experience mediated through UGMC (SE) 0.7640.749
SE1—Posts of tourists about the architecture of the accommodation, the furniture of the rooms, the arrangement of the space.0.781
SE2—Posts of tourists about visual elements related to shapes, colors and lighting0.650
SE3—Tourists’ posts about the hygiene, cleanliness, olfactory comfort of the accommodation location0.852
SE4—Tourists’ posts regarding noise and noise pollution, pleasant experiences with ambient auditory comfort0.780
Emotional experience mediated through UGMC (EE) 0.9820.784
EE1—Posts that create a positive atmosphere regarding the touristic destination0.980
EE2—Posts that create positive emotions about the touristic destination0.986
EE3—Posts that create positive feelings about the tourist destination 0.980
Cognitive experience mediated through UGMC (CGE) 0.8380.689
CGE1—Posts that describe the functions of tourism and hospitality services within a specific touristic destination.0.834
CGE2—Posts that describe the values of tourism and hospitality services within a specific touristic destination0.917
CGE3—Posts that describe the benefits of tourism and hospitality services within a specific touristic destination0.870
Conative experience mediated through UGMC (CNE) 0.8950.745
CNE1—Posts that describe the satisfaction related to the involvement of tourists in cultural-religious visits0.893
CNE2—Posts that describe the satisfaction of the tourists that have been involved in sports and recreation activities during their stay in a certain destination0.917
CNE3—posts that describe the satisfaction of the tourists that have been involved in shopping and excursions during their stay in a certain destination0.924
Relational experience mediated through UGMC (RE) 0.9130.724
RE1—Posts that describe how important the tourist destination was in making a good impression on other people0.897
RE2—Posts that show whether the tourist destination has contributed to the social status of tourists0.951
RE3—Posts that show to what extent the tourist destination has helped to establish contacts and make friends with other tourists0.924
Flow experience within the context of UGMC (FE) 0.9750.783
FE1—When browsing social media, travel blogs, or booking reviews, nothing else can distract me.0.981
FE2—When browsing social networking sites, travel blogs, or booking site reviews, time seems to pass very quickly.0.974
FE3—When I’m browsing social networking sites, travel blogs, or booking site reviews, I feel totally captivated0.975
Exploratory behavior (EB) 0.8710.712
EB1—Browse in more depth on links related to the tourist destination in question0.908
EB2—I spend more time getting details about the intended destination0.922
EB3—I’m looking for additional information about the tourist destination (including by phone or by email)0.848
Purchase intention toward the touristic destination (PI) 0.8840.706
PI1—I would like to try to get to the tourist destination that seems appropriate based on the posts/reviews0.908
PI2—I intend to go to the tourist destination that I think is appropriate based on the posts/reviews0.938
PI3—I would like to know more about the tourist destination I found in the posts/reviews0.862
Willingness to recommend the touristic destination (WR) 0.8640.689
WR1—I am likely to recommend the tourist destination to those who want advice on travel0.920
WR2—I am willing to suggest these destinations to my relatives and friends for their holidays.0.922
WR3—I would not expect any problems with this tourist destination0.816
Table 3. Goodness-of-fit indexes.
Table 3. Goodness-of-fit indexes.
Model-Fit IndexAbsolute Index
PCMIN/DFRMRGFIAGFIRMSEANFI
Structural model0.0002.2800.0430.9300.9140.0420.956
Threshold value<0.05<5<0.05>0.90>0.90<0.05 *>0.90
Model-Fit IndexComparative IndexParsimony Index
RFIIFITLICFIPGFIPNFIPCFI
Research obtained values0.9500.9750.9710.9750.7600.8400.856
Theoretical statistical values>0.90>0.90>0.95>0.95>0.50>0.50>0.50
Note: Statistical theoretical values are considered according to: [157,158,159]; * [160,161,162].
Table 4. The structural model results.
Table 4. The structural model results.
HypothesesCorrelationsβpStd. ErrorC.R.Decision
H1SE → FE0.4310.0000.1403.083Supported *
H2EE → FE0.2500.0020.0743.366Supported *
H3CGE → FE0.4850.0000.1184.104Supported *
H4CNE → FE0.1890.0010.0583.268Supported *
H5RE → FE0.4960.0000.0796.320Supported *
H6FE → EB0.3600.0000.0428.553Supported *
H7EB → PI0.6460.0000.03418.948Supported *
H8EB → WR0.7450.0000.03919.001Supported *
Note: * Statistical significance of parameter estimates test of the statistic critical ratio (C.R.) needs to be >1.96, p < 0.01 [163,164].
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Gârdan, I.P.; Mauri, A.; Dumitru, I.; Gârdan, D.A.; Maican, S.Ș.; Moise, D. User-Generated Multimedia Content Impact on the Destination Choice: Five Dimensions of Consumer Experience. Electronics 2022, 11, 2570. https://doi.org/10.3390/electronics11162570

AMA Style

Gârdan IP, Mauri A, Dumitru I, Gârdan DA, Maican SȘ, Moise D. User-Generated Multimedia Content Impact on the Destination Choice: Five Dimensions of Consumer Experience. Electronics. 2022; 11(16):2570. https://doi.org/10.3390/electronics11162570

Chicago/Turabian Style

Gârdan, Iuliana Petronela, Aurelio Mauri, Ionel Dumitru, Daniel Adrian Gârdan, Silvia Ștefania Maican, and Daniel Moise. 2022. "User-Generated Multimedia Content Impact on the Destination Choice: Five Dimensions of Consumer Experience" Electronics 11, no. 16: 2570. https://doi.org/10.3390/electronics11162570

APA Style

Gârdan, I. P., Mauri, A., Dumitru, I., Gârdan, D. A., Maican, S. Ș., & Moise, D. (2022). User-Generated Multimedia Content Impact on the Destination Choice: Five Dimensions of Consumer Experience. Electronics, 11(16), 2570. https://doi.org/10.3390/electronics11162570

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