Introducción
Indeed, the tourism industry worldwide has experienced a major hiatus as a result of the COVID-19 pandemic (Buckley & Cooper, 2021). However, the vision of tourists play an important role in tourism development (Jangra et al., 2021; Serrano-Arcos et al., 2021) and tourists can have unique tourist experiences with a destination’s multimedia through content such as images, text, videos and audios (Lupu et al., 2021) With the recent explosion of virtual tours (Njerekai, 2019), virtual platforms are becoming increasingly important as entertainment tools. Together to this, consumption of multimedia content is increasing globally and systematically generates new research questions in the tourism marketing and destination management fields (Palazzo et al., 2021).
The tourist destination image - hereinafter TDI - means a strategic effort for all tourism actors and has been researched intensively during the last decades. However, regarding its measurement, it is problematic when travellers subjectively evaluate the place (Wullur & Sutapa, 2019). For this reason, this research that uses electroencephalography to obtain metric data directly from the subject’s brain has to be understood as a methodological proposal, to reduce this subjective bias.
The TDI is a multidimensional construct consisting of cognitive and affective dimensions (Beerli-Palacio & Martín-Santana, 2017; Elliot & Papadopoulos, 2016; Hernández et al., 2016; Huete Alcocer & López Ruiz, 2019; Kani et al., 2017; M. Y. Lai et al., 2020; Marine-Roig & Anton Clavé, 2016) that has been developed under the prism of different definitions and formation models, and therefore allowing more than one interpretation (Garzón-Paredes & Royo-Vela, 2021). The findings suggest that greater conceptual work is required (Duignan, 2021) and the same can be said regarding TDI measurement methodologies and scales which shows to the researcher, perhaps because of the destination diversity, a wide set of methods and tools (Garzón-Paredes & Royo-Vela, 2021). In this sense, research on the TDI framework that use simultaneously virtual reality, multivariate analysis and neuroscience to investigate urban or rural cultural destinations, in which heritage is present, is scarce. According to (Kim et al., 2017), to understand relationship and effects between culture and tourism is an interesting and necessary research topic, however it has been little studied so far.
Since an important part of the tourist product can be culture, cultural tourism plays a fundamental role both in the attractiveness of destinations and its image (Huete-Alcocer et al., 2019). Thus, the contribution of cognitive and affective images to the image of a destination can be related to its local heritage. Intangible culture as well as the tangible culture such as historical and architectural heritage can increase the cognitive and emotional response to a destination and consequently, its image, its attractiveness and its competitiveness (Folgado-Fernández et al., 2017; George, 2017), it can be said that the cognitive and emotional components of the TDI can be studied through the historical, architectural, and cultural heritage of cities (Royo-Vela, 2009).
Therefore, the research is driven to identify and measure, through electroencephalography (Yulita et al., 2020), the impact of cultural and architectural heritage emulated by virtual reality on the mind of a tourist in the form of cognitive and emotional responses (Barrile et al., 2020) measured as brain waves (Rawnaque et al., 2020). Data obtained from a neuromarketing experiment and directly from the human brain is used to carry out an analysis using the partial least squares regression algorithm PLS, And then compared with the results of a measurement scale obtained through a survey applying structural equation models; thus evaluating, the effects of the cultural and architectural heritage on the TDI. It is the general hypothesis of this research that the emotional and cognitive impact that architectural and cultural heritage has on the minds of tourists is intense and generates a positive image of the destination.
Objetivos
Compare the results obtained in an experiment through the electroencephalogram, with the results obtained through a measurement scale thanks to a survey, applying the PLS least squares algorithm, to analyze the cognitive and emotional responses of subjects immersed in the virtual reality of the cultural tourist destination.
Método
Neuromarketing and PLS-SEM: The current investigation uses electroencephalography experimentation to collect brain wave data as responses to the stimulation of virtual reality in a sample of 25 people residing in Ecuador randomly selected between the ages of 21 and 60 years. Before the experiment, each participant has given written consent to sign, and they are informed that participation is optional and that they reserve the right to withdraw at any time (Gholami Doborjeh et al., 2018)
The data collection procedure is performed in a physiology laboratory equipped with an electroencephalogram EEG (Aldayel et al., 2020); virtual reality (VR) goggles; a projector; headphones, and an iPhone 6 smartphone. The phone is inserted into virtual reality device to project videos of tourist sites containing architectural heritage; additionally, a computer records all the data. The information generated by the electroencephalogram (EEG) is complex (González-Morales et al., 2020); therefore, it should be organized in a way that facilitates visual analysis.
To approve the hypotheses, wave amplitude must be high; that is, a stronger stimulus will produce a steeper wave. This experiment focuses on the investigation of alpha and beta waves, an important characteristic inherent to the behaviour of alpha activity, is the variation that is usually visible in its amplitude.
The alpha rhythm is the most critical finding to declare a subject as alert or awake. Alpha α waves are electromagnetic oscillations in the frequency range of 8-12 Hz.
Beta waves are in the frequency range of 12 to 30 Hz as a result of strong neuronal activity. The electric brain waves: delta, theta, alpha, beta and gamma, coexist in the brain and vary in frequency according to the amount of electricity generated by a stimulus. This makes it possible to break down the waves into high, medium or low frequencies; the more significant the impact achieved by the stimulus in a human, the more electricity will be generated in the brain and the higher the frequency recorded by the electroencephalography equipment. Subsequently, two wave points from each range (alpha and beta) are randomly taken from the combined waveforms. Information is gathered in this manner to analyse the variables in the system of structural equations. These data are measured in Hertz (Hz symbol). The structural model is composed of cognition (cognitive image), emotion (affective image) and image (global image), the beta points are associated with cognition, the alpha points, with emotion and, the general image is the result of a compound of cognition plus one of emotion; the construct is created with the sum of the average of the alpha and beta waves In other words, the metric data of the observable variables in the structural model are taken directly from the participants’ brains. They are associated with the constructs depending on what they represent for the human mind, and the general image is measured with the summation of the metric variables cognition plus emotion. α + β. In accordance with (Chin, 1998) The PLS procedure is designed to explain the variance - R2 - of the dependent construct, this procedure is robust in small and medium samples. In terms of the data, an initial concern is related to the sample size, depending on the number of relationships to be evaluated, the widely used empirical rule states that the total sample size is 10 times the greater of two possibilities 1) The block that has the largest number of indicators or 2) the dependent variable that is affected by the largest number of independent variables. In the present model, the first possibility is equal to 2 since the latent variables have two observable variables each, both the α variables and the β variables, and in the second possibility it is also equal to two, since the number of hypotheses that arrive at the dependent variable image is equal to 2. (Chin, 1998)
That means that the minimum size of the sample in this investigation is 2 x 10 = 20, however the sample under analysis contains 25 cases.
To implement the PLS technique, it is necessary to verify the adequacy of the data as well as the test power for the dependent variable - R2 -; For this, it is necessary (to evaluate the reflective model, the reliability of the indicator, the reliability of internal consistency, the convergent validity as well as the discriminant validity), It is also necessary to consider the formative evaluation of the internal model (endogenous constructs, the variance, the size of the effect, the relative predictive relevance of the indirect and total path as well as the coefficient of effect and significance) -. (Hair et al., 2012)
The processing data software is SMART-PLS. (Ávila & Moreno, 2007) the modelling of structural equations with partial least squares PLS-SEM is a method of multivariate analysis of the second generation that currently has a significant acceptance in the scientific community, mainly in the areas of social sciences and economics. Being a robust and flexible alternative, it allows working with estimates of simultaneous equations through multiple regressions. It aims to increase the explanatory capacity of the empirical verification of the theory, the development of computer programs has also contributed to its use.
In the configuration, a full Bootstrapping used with 5000 subsamples applied, in the bootstrap confidence interval method with correction of bias and acceleration (BCA). The type of two-tailed test, the significance level of 0.05, the schism of route weights (route), 5000 maximum interactions and the stop criterion (10 ^ -X) = 7.
The latent variables evaluated are cognition, emotion and image. The investigated model proposes that cognition is hierarchical to emotion and, in turn, these two constructions hierarchically influence the image of the destination.
According to the classification shown in table 3, the α waves, associated with emotion, are between 10Hz and 12Hz and the β waves, associated with cognition are between 22Hz and 30Hz. Outside this range, the impact is passive or very stressful. In other words, cognitive and emotional components structure the image of destination measured from brain waves, by the use of the electroencephalogram, four random metric data are taken as observable variables. Two points are for alpha emotion (α) and Two points for beta cognition (β). For the structural analysis in the smartPLS software, observable variables (points taken from brain waves) are associated with each latent variable." Additionally, a survey with a measurement scale for tourist destinations with cultural heritage is applied to the study subjects.
Principales Resultados
The implicit objective of this research is to demonstrate that an analysis PLS-SEM can be carried out with data obtained from brain electrical waves by applying neuroscience techniques to measure a stimulus, this fact has been proven. On the Pearson correlation coefficient (R2), a measure of linear relationship between two quantitative random variables, it can be said that the adjusted (R2), for the endogenous variable virtual image of the destination, the variable is 0.687, which means that 68% of the variance of this variable is explained by the model. The study shows that the emotional and cognitive impact of destinations endowed with historical architectural, cultural heritage, emulated with virtual reality, positively influence the image of the virtual destination.
Since the observable variables respond to completely interchangeable and correlated indicators, since they are taken from the same frequency waves of the brain, the model is reflective.
The structural model is composed of cognition (cognitive image), emotion (affective image) and image (global image), the beta points are associated with cognition, the alpha points, with emotion and, the general image is the result of a compound of cognition plus one of emotion; the construct is created with the sum of the average of the alpha and beta waves. The results of the discriminant or divergent validity analysis verified that the constructions determined in the model measure their concept and not the rest of the constructions. Below the complete results of Bootstrapping. The variance between the same constructs shows that they correlate more significantly with their same indicators than the others. The convergent reliability and validity analysis shows that the average extracted variance is> 0.5, which indicates that the construction explains more than half of its variation than the other composing indicators. In composite reliability (CR), the indicator of all constructions is > 0.8, so they are acceptable. The values of t also are significant since having a value> 1.96; the average extracted variance is substantial >0.67 in all factors. The loads are significantly higher than 0.82, and the standardized factor weights are significantly greater than 0.77 All hypotheses are favourable the t value is > 1.96. and P-values less than 0.05 The study shows that emotional and cognitive impact of a destination characterized by the heritage and emulated with virtual reality positively influences the image of the virtual destination. The positive emotional and cognitive impact of heritage on the virtual tourist is confirmed.
Regarding hypothesis number 1. H1: The virtual cognitive image (cognitive response) influences the virtual affective image (emotional response) the t value is 2,799 the P-Value is 0,005 and the beta standardized is 0,447; the values are favourable, for this reason, hypothesis No.1 is approved
About hypothesis number 2. H2: The virtual cognitive image (cognitive response) positively influences the image of the destination. the t value is 2,215 the P-Value is 0,027 and the beta standardized is 0,450; the values are also favourable; hypothesis No.2 is approved.
Regarding hypothesis number 3. H3: The virtual emotional image (emotional response) positively influences the image of the destination. the t value is 2,862 the P-Value is 0,004 and the beta standardized is 0,541; likewise, the values are also favourable and hypothesis No.3 is approved.
The model ratifies all quality indicators and approve the three hypotheses.
Conclusiones
The findings suggest greater conceptual work, in this sense, research on the TDI framework that use simultaneously virtual reality, multivariate analysis and neuroscience to investigate urban or rural cultural destinations, in which heritage is present, is innovative.
Intangible culture, as well as, the tangible culture such as historical and architectural heritage can increase the cognitive and emotional response to a destination and consequently, its image, its attractiveness and its competitiveness, it can be stated through this study that that the cognitive and emotional components can be studied through the historical, architectural, and cultural heritage.
This research shows that it is possible to measure, the impact of the cultural and architectural heritage emulated with virtual reality, on the mind of a tourist, in the form of cognitive and emotional responses through brain bio-electric waves with the help of electroencephalography. Therefore, this article has sought to open a debate as a means to significantly evaluate several important developments in modern tourism communication.
The positive emotional and cognitive impact of heritage on the virtual tourist has been confirmed, as well as, that the models are possible, it was also discovered that brain waves can vary according to the stimulus design, which significantly affects the results in the multivariate analysis In other words, the brain's bioelectric waves can vary according to the importance that the subject gives to the stimulus, the stimuli can have specific objectives and their impact can be measured with the EEG.
It has been demonstrated that it is possible to measure TDI with neuroscience tools and techniques, since audio-visual stimuli can be analysed psych-physiologically by the impact they cause on brain bioelectric waves, thanks to this technique it was identified that virtual trips generate a favourable TDI in the virtual tourist.
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