DESTINATION BRAND VALUE, CONSUMER INVOLVEMENT AND THE PERCEPTION OF FAIR PRICING OF MEDIA HOSTING FEES ON DIGITAL AUCTION PLATFORMS

Abstratc:

With the growing competitiveness in the hosting sector, several strategies have been used to conquer the consumer and maximize daily sales. Digital platforms are the most used means for companies to achieve this goal. Therefore, this work aimed to analyze how the perception of fair price of hosting media diaries on digital auction platforms can be influenced by the brand value of the destination and the respective involvement of the consumer by it. This descriptive study, of a quantitative nature, adopted the unique cross-section. In total, 416 respondents composed the sample, whose data were analyzed by the multiple linear regression technique and correlations between the variables. The results indicated that only relevance demonstrated a positive influence in relation to the perception of price justice. Thus, it was possible to verify that price is an element of great importance in the negotiation and not dependent on the personal relationship that the consumer has with the destination and the equipment that composes it, nor on its representativeness. Therefore, what prevails for the consumer is, on the negotiation, to get the biggest possible price reduction and, after that, to acquire the hotel's daily.

Keywords:
Involvement; Brand value; Price fairness; Digital auction

Resumo:

Com a crescente competitividade no setor de hospedagem, diversas estratégias têm sido utilizadas para conquistar o consumidor e maximizar as vendas de diárias. As plataformas digitais são os meios mais utilizados para que as empresas alcancem essa finalidade. Diante disso, este trabalho teve como objetivo analisar como a percepção de justiça de preço de diárias de meios de hospedagem, em plataformas de leilões digitais, pode ser influenciada pelo valor da marca do destino e o respectivo envolvimento do consumidor por ele. Este estudo descritivo, de natureza quantitativa, adotou o corte transversal único. No total, 416 respondentes compuseram a amostra, cujos dados foram analisados pela técnica de regressão linear múltipla e correlações entre as variáveis. Os resultados indicaram que apenas a relevância demonstrou influência positiva em relação à percepção de justiça de preço. Assim, foi possível constatar que o preço é um elemento de grande importância na negociação e não dependente da relação pessoal que o consumidor tenha com o destino e com os equipamentos que o compõe, nem tão pouco com a representatividade do mesmo. Portanto, o que prevalece para o consumidor é, na negociação, conseguir a maior redução possível do preço e, após isso, adquirir a diária do hotel.

Palavras-chave:
envolvimento; valor da marca; justiça de preço; leilão digital

Resumen:

Con la creciente competitividad en el sector hotelero, se han utilizado varias estrategias para ganarse al consumidor y maximizar las ventas de tarifas diarias. Las plataformas digitales son el medio más utilizado por las empresas para lograr este propósito. Por lo tanto, este estudio tuvo como objetivo analizar cómo la percepción de la justicia de precios de las tarifas diarias de los medios de alojamiento, en las plataformas de subastas digitales, puede verse influenciada por el valor de marca del destino y el respectivo envolvimiento del consumidor por el mismo. Este estudio descriptivo, de naturaleza cuantitativa, adoptó la sección transversal única. En total, 416 encuestados componían la muestra, cuyos datos se analizaron mediante la técnica de regresión lineal múltiple y correlaciones entre las variables. Los resultados indicaron que sólo la relevancia demostró una influencia positiva en relación con la percepción de la justicia de precios. Así, se pudo comprobar que el precio es un elemento de gran importancia en la negociación y no depende de la relación personal que el consumidor tenga con el destino y con los equipos que lo componen, ni tan poco con su representatividad. Por lo tanto, lo que prevalece para el consumidor es, en la negociación, conseguir la mayor reducción de precio posible y, después, comprar la tarifa diaria del hotel.

Palabras clave:
Envolvimiento; Valor de marca; Justicia de precios; Subastas digitales.

INTRODUCTION

The evolution of technology has changed consumer behavior when it comes to purchasing services, especially, in the hospitality and tourism sector. The tourism industry has become one of the most successful and one of the largest users of digital platforms in e-commerce (Huang et al., 2017). This online environment is often used by consumers to base their decisions on which vacation destination they wish to visit or in which accommodation facility they should book their accommodation (Browning, So & Sparks, 2013).

For Zhu et al. (2019), consumers turn to various online platforms during their travel planning to obtain detailed, comprehensive, and personalized information about the services they wish to purchase. This certainly includes checking the prices proposed by suppliers (Pan, Zhang & Law, 2013). For consumers, price is important as they develop a value criterion associated with the product (Malik et al., 2018). In the case of the hospitality industry, accommodations have different prices and service standards, which allows for a charge from the personalized services offered, leading to different experiences to each customer (Chung & Petrick, 2013).

In this vein, the comparison of the amounts to be paid for each daily rate, which is a process performed by the consumer, results in a personal judgment, which can range from "reasonable" to "acceptable." Bolton, Warlop & Alba (2003) call this process the perception of price fairness. For the authors, it is possible that the consumer has a perception of price justice more accurate, when he searches for information about different establishments, since, being in possession of this information, he can observe discrepancies and / or equivalences in the prices charged by different companies.

According to Chiang & Jang (2007), the consumer's perception in relation to price justice is one of the main determinants in the decision-making process. As a result, in order to improve this perception, suppliers use some marketing approaches, highlighting the price negotiation strategy (Legohere, Poutier & Fyalll, 2013).

For Sun, Ni & Wang (2016), price negotiation has been recurrent in purchases in the virtual environment. Inviting the interested consumer to participate in price negotiation works as a tool to boost sales (Levy & Gvili, 2019). This negotiation can happen with the participation of the consumer in existing auctions on virtual platforms. According to Koo (2010), this new service model has had an accelerated growth, allowing the democratization of the discount for a segment of the population that, before, did not have bargaining power and that, now, can participate in the negotiation.

For Baker & Song (2007), the growth of auctions is due to the ease of participation of virtual transactions in the electronic environment. These have been used as sales strategies, which aim to lower the purchase price, through virtual interaction with the seller (Gvili & Levy, 2019). In hotel daily rate auctions, the initial amount is proposed by the establishment and the participant negotiates with the service provider to reduce the price.

Thus, to gain a better understanding about this negotiation process, it is necessary to analyze what factors may influence consumer decision making (Haddad, Hallak & Assaker, 2015). In the present research, a consumer's evaluation will be analyzed in relation to their perception associated with the brand value of a destination and that of their engagement with it.

The evaluation and combination of the characteristics of a destination create a unique identity, a brand (Williams, Gill & Chura, 2004), whose construct for a perception of value is referred to as the brand value of a destination, which, in the scope of this research, will be analyzed against the perspective of the consumer's image of the destination, the awareness of the locality as a tourist destination, the quality of the destination relative to its infrastructure, and the consumer's loyalty to that destination (Konečnik & Gartner, 2007). It is realized that a destination's brand is one of the main areas of interest in tourism marketing due to its significance as an intangible asset of service companies (Deshmukh & Dhaudhari, 2019).

Similarly, in consumer behavior studies, it is important to understand their preferences in the decision-making process (Engel et al., 1993). One should understand how products should be launched, how best to determine pricing, design channels, craft messages, and also develop other marketing activities (Silva, 2013).

With this, it is verified that several factors reflect the consumer's behavior at the time of purchase decision, being their level of involvement with the product they want to buy one of them (Fonseca & Rossi, 2000). Therefore, this research addressed the consumer's situational involvement with the tourist destination chosen for a trip, considering factors such as relevance; pleasure it can provide; personal symbolism in visiting it, in addition to the importance and likelihood of risk in making a purchase that does not meet the needs of the trip.

Tying the situational engagement to the tourist destination becomes essential to list the factors that are determinants for consumer choice, so that these destinations are benefited with the financial spending made by tourists, especially in the accommodation sector (Pereira & Ferreira, 2014). For Law, Chan & Goh (2007), consumers who search for means of lodging in digital platforms may have a perception of price justice more sharpened. Thus, for a better understanding of how the tourism and hospitality sector is affected, it is necessary to investigate the relationship of this decision-making process with price and other factors that may influence decision-making (Santos, Machado & Mayer, 2018).

Thus, there is a theoretical gap in relation to both the constructs presented and the auction context, so this research can bring several implications for service marketing. Besides contributing with the possibility of directing the service to a specific public, new management strategies may arise for hotel companies. It may also influence the planning and promotion of tourist destinations.

Given the above, the following question arose: How does the brand value of a tourist destination and the consumer's involvement with this destination influence the sense of perceived price fairness in the purchase of accommodation services in digital auction platforms?

THEORETICAL BACKGROUND

Perception of Price Fairness

Price is the fundamental element in the company-customer relationship, since it determines the degree of utilization of a product (Santos, Duarte & Jesus, 2020). Zeithaml (1988) defines price as the amount that the consumer invests to acquire a good or get access to a service. According to Ye, Wang & Law (2014), price is treated in a particular way in the hospitality industry.

In the hospitality field, the studies about the aspects related to price necessarily go through the identification of the value perceived by the consumer (Toni & Mazzon, 2014). Perceived value is the interpretation of the subject with respect to the price, comparing it with the expected advantages from the acquisition of a product (Zeithaml, Bitner & Gremler, 2014). In this case, the perceived value may vary, according to several factors, such as price, hotel brand, as well as hotel characteristics, for example. If the perceived value is close to the consumer's expectations, there is a tendency to increase the intention to purchase the service (Cross & Dixit, 2005).

In this sense, it is identified that if the price presented has the value perceived by the consumer as a little lower than expected, there is the perception of it being favorable and fair. However, if the consumer perceives that there was an increase in prices, the interpretation is that the price is unfavorable and unfair (Chiang & Jang, 2007). Xia, Monroe & Cox (2004) argue that the perception of price fairness is a judgment made, the answer to which is equivalent to analyzing whether the price is acceptable or fair, comparing the amounts charged and the references used. If there is too much difference between the consumer's reference and the amount charged, there may be a risk of perceiving price unfairness.

For Santos, Mayer & Marques (2019), perceived price fairness is a consumer's affective and cognitive evaluation of whether the amount charged is fair, acceptable, or reasonable. In the hospitality sector, this evaluation may involve affective aspects, such as emotions and feelings related to the tourist destination chosen for the trip, for example. Moreover, it is also related to cognitive aspects, such as memory and attention to the prices advertised on digital platforms, and decision making when buying a hotel room rate.

According to Gumussoy & Koseoglu (2016), guests become more loyal and satisfied when they have the perception that they are paying a fair price. Loyal customers have a more positive perception of price fairness, which impacts their behavioral intentions in terms of rebooking, reviews, and positive recommendation to others (Haddad, Hallak & Assaker (2015).

Destination Brand Value

Branding is considered a powerful means of securing competitive advantage, being able to offer future and stable revenues (Kotler & Keller, 2019). The general principles, which are used to designate and understand product brands, are equivalent to those for establishing the branding of tourist destinations (Yang et al., 2015). This occurs, according to Boo, Busser & Baloglu (2009), since the tourist, in this case, consumer, relates to a destination as if it were a product.

The brand value of a destination is an important tool for marketing because it aims to form a positive image of the place for the target audience (Barnes, Mattsson & Sorensen, 2014). From the analysis of the dimensions awareness, image, quality and loyalty, the brand becomes a factor of attraction or rejection for the choice of a destination for a leisure trip.

Brand awareness, according to Keller (2003), consists in thinking about the brand and it comes to mind easily. This awareness of the existence of a brand is the initial step to build it and value it, even before that place is recognized as a likely destination for a trip (Gartner & Ruzzier, 2010). Therefore, destination managers must create a brand that is identified, immediately, by the consumer, when it is thought of or visualized. With this, hypothesis H1a arises:

H1a: Destination awareness influences the perceived price fairness of accommodation room rates on digital auction platforms.

When a brand is recognized, consumers attach meaning by associating it with its image (Keller, 2003). The image encompasses a set of varieties related to the destination. For Kim & Richardson (2003), image portrays itself as a set of ideas, beliefs, feelings, and expectations of that destination, which accumulate over time. With that, we have H1b:

H1b: The image of the destination influences the perception of fairness in the price of daily accommodation on digital auction platforms.

In the tourism industry, the image that tourists have of a destination can influence their perception of its quality (Gomez, Lopez & Molina, 2015). In this industry, it is possible to state that the abundance of online reviews provides information about price and quality of a destination or hotel (Santos, Machado & Mayer, 2018). In destination brand value, service quality is relevant, in the tourism industry, as consumers seek that the value received is consistent with the price paid (Malik et al., 2018). Thus, H1c is established:

H1c: Destination quality influences the perceived price fairness of accommodation room rates on digital auction platforms.

Given the above, it can be stated that there is a balance between the perceived quality of a brand and the intention of a future purchase or willingness to buy the product, creating what is called brand loyalty (Sweeney et al.,1995). This loyalty is represented by the level of attachment that a traveler has to the brand of a respective destination (Kladou & Kehagias, 2014), thus meaning that the traveler trusts that destination brand more than others and that he/she is willing to pay a higher value for the products offered in that location (Bianchi, Pike & Ling, 2014). Consumers, who are loyal to a particular brand, attach less importance to price (Diaz & Cataluña, 2011), which allows H1d to be established:

H1d: Destination loyalty influences the perceived price fairness of accommodation room rates on digital auction platforms.

Involvement and the consumer decision making process in hospitality

According to Fonseca & Rossi (2000), the purchase decision making is a process composed of steps to be performed by the consumer to supply a need or desire. The pattern of each consumer is defined by his buying behavior and is totally related to the benefits and experiences he has or longs to have about a specific product (Assael, 1998). One of the factors that can affect the purchase decision process is the consumer's level of involvement with the product.

For Krummenauer et al. (2016), involvement is the level of importance caused by a stimulus that occurs in a specific situation related to the purchase or consumption of a particular product. According to Frias, Silva & Seabra (2017), there is a great influence of the level of involvement of a consumer with a product in the purchase decision process, in the purchase and post-purchase, thus allowing to differentiate the types of consumers.

According to Solomon (2016), involvement is the relevance of the object perceived by an individual based on their inherent needs, values, and interests. As well as it is possible to state that different factors can affect involvement, these being related to the person, the object or the situation.

For Houston & Rothschild (1977), there are the lasting involvement and the situational involvement, the latter being the focus of this study, because it addresses how situations differ and impact on the tendency to cause interests or behaviors. Thus, characteristics such as price, complexity of attributes, and frequency of purchase directly contribute to situational involvement (Houston & Rothschild, 1977).

The approach used to measure the consumer's situational involvement is distributed into five factors: relevance, pleasure, symbolic value, risk importance and risk probability (Rossi & Fonseca, 2000). In relevance, the individual relates his self-image and core values to the product (Arora, 1982), because he believes that his involvement with a specific product can be related to a positive highlight of his self-image (Rossi & Fonseca, 2000). The following is H2a:

H2a: Destination relevance influences the perceived fairness of accommodation room rates on digital auction platforms.

The "pleasure" dimension refers to the emotional appeal that the product can provoke (Laurent & Kapferer, 1985). Consumers derive satisfaction from using goods and services that provide fantasy and excitement (Mowen & Minor, 2003). In this context, the pleasure provided to the tourist when visiting the destination is related, representing the feelings of fun and excitement in being in the chosen place. Next, H2b is verified: H2b: The pleasure in visiting the destination influences the perceived price fairness of lodging accommodations rates on digital auction platforms.

The symbolic value is formed by a public meaning, through identity and social approval, which occurs when the individual appropriates an object, which there is already a consensus about its meaning (Richins, 1994). In this dimension, the experience of consuming a product is used to determine the consumer's social identity and represent meanings to others in the same social group (Solomon, 2016). Thus, depending on how they feel, consumers purchase specific products so that they can be seen by others or feel good in front of others (Sweeney & Soutar, 2001). In this way, H2c arises:

H2c: The symbolic value of the destination influences the perceived fairness of the price of accommodation rates in digital auction platforms.

The fourth and fifth dimensions refer to risk, importance, and the probability of it happening. The perception of risk is an important factor that favors the creation of the bond between the product and the consumer (Sheth & Venkatesan, 1968). In tourism activity, the greater the diversity of the services involved, such as transportation, lodging and food, the greater the probability that the service will not function as desired (Otto & Ritchie, 1996). The following are hypotheses H2d and H2e:

H2d: The importance of risk, when choosing a hotel for a trip, influences the perceived price fairness of lodging facility rates on digital auction platforms.

H2e: Risk probability, when choosing a hotel for a trip, influences the perceived price fairness of lodging facility rates on digital auction platforms.

Consumer engagement is a key factor driving interaction in online shopping (Han & Kim, 2017), which may be related to behavior on auction sites (Levy & Gvili, 2019) and, in turn, may influence the price sense of hotel room nights researched for a trip, affecting the perception of price fairness in the face of digital auction platforms.

According to Mallikraj & Alagarsamy (2020), the level of product engagement has a significant effect on brand equity dimensions such as brand loyalty, brand quality, and brand awareness. As a result, consumers' involvement with the product enhances brand awareness and creates strong, favorable and unique associations, influencing their purchase decisions (Hutter et al., 2013). Thus, hypothesis H3 arises:

H3: Consumer engagement with the destination influences the brand value of the destination chosen for making a trip.

METHODOLOGY

The research adopted a conclusive study approach, guided by descriptive precepts. It has a quantitative nature and used a cross-sectional analysis, as data collection from the population was conducted only once (Malhotra, 2019).

Research Population and Sample

The population of this research was composed of Brazilian individuals of both sexes. The individuals were 18 years of age or older and had access to the Internet. The sample size reached a total of 416 respondents. The sampling chosen was non-probability, and the sampling technique applied was snowball sampling.

Data Collection Instrument

The instrument used for data collection was a structured questionnaire, composed of 3 groups of questions, totaling 37 items. A 5-point Likert scale was used, where respondents specified their degree of agreement with each statement.

Table 1
Collection Instrument Composition

In order to contextualize the research, before the statements, it was presented a scenario of a hotel room at the destination chosen by the respondent, its characteristics and the price proposed by a platform of daily rates bidding in hotels. The added questions, which referred to the individual's participation in hotel daily rates auctions, were multiple choice, as shown in Table 2. These questions were designed according to what is observed in digital auction platforms, in which the buyer has the option to accept the suggested price or bid to reduce and analyze the new price proposal from the hotel.

Table 2
Complementary items

Finally, the last group of questions encompassed the sociodemographic questions, which refer to the personal information of the study respondents. The constructs destination brand value and destination engagement contained questions related to a specific destination to be analyzed. Therefore, an initial contextualization with options of tourist destinations was required. In this case, the respondent chose, among four alternatives, the destination that most interested him/her to take a future trip. For this, four distinct destination scenarios were designed based on the segmentation adopted by the Brazilian Ministry of Tourism, namely: a) Sun and beach (Fernando de Noronha - PE), b) Cultural (Gramado - RS), c) Ecotourism (Bonito - MS) and d) Adventure (Chapada Diamantina - BA).

Data Collection

Before collecting data through the online distribution of the instrument, a pre-test was conducted with ten participants. After validation, the questionnaire was sent to the target audience through the WhatsApp application, in addition to sending direct messages on Instagram. The page containing the collection instrument was available on the Internet from August 4 to 11, 2020.

Data Analysis

Data analysis was performed on SPSS Statistics 24.0 software. Socio-demographic and contextual data were measured using descriptive statistics. Cronbach's alpha, factor analysis, and multiple linear regression analysis were used to analyze the latent variables.

ANALYSIS AND DISCUSSION OF THE RESULTS

Characterization of the sample

In the sociodemographic profile, it was verified that most of the respondents are female, with 69.5% of the respondents. Regarding marital status, 45.7% are married. As far as the level of education is concerned, 32.8% have a post-graduate degree. The family inc ome with the highest concentration of answers was equivalent to R$5,225.00 to R$10,450.00, corresponding to 24.5%. About the number of individuals who live in the same house, 32.5% reported having two people.

It was also shown that 38% of the survey participants travel with their partner. 86.5% of the respondents have not visited the destination chosen to answer the survey instrument, and 84.5% have never purchased on a digital auction platform. The average age of the participants was 38.48 years.

Regarding the respondent's willingness to pay the daily rate proposed in the ad, 43.8% would pay the ad rate. We verified the fact that the participant could negotiate the price charged in the ad. Of these, 38.7% said they would propose a 15% reduction in the value. Finally, 32.7% said they would make the reservation immediately.

Factor Analysis and Cronbach's Alpha

In the exploratory factor analysis, the varimax rotation method was used. In this phase, the dimensions image, awareness, symbolic value and risk probability did not reach the minimum value of the desired index of factor loading (ID4, ID5, ID7, CD1, CD4, CD5, SIM1 and PROR1). Similarly, in the communality (H²), it was found that in four dimensions, the values did not reach the minimum expected index of 0.5 (ID1, ID5, ID6, ID7, QD1, SIM1, and PROR1, as shown in Table 1.

Table 1:
Summary of data reliability and dimensionality

Variables	C.F.	H 2	KMO	df	Variables	C.F.	H 2	KMO	df
ID1	0,562	0,337	0,547	21	CD1	0,239	0,766	0,469	10
ID2	0,850	0,766			CD2	0,597	0,606
ID3	0,835	0,739			CD3	0,511	0.671
ID4	0,432	0,784			CD4	-0,150	0,641
ID5	0,299	0,365			CD5	-0,579	0,586
ID6	0,643	0,452
LD1	0,817	0,648	0,781	3	QD1	0,527	0,277	0,556	6
LD2	0,815	0,664			QD2	0,789	0,623
LD3	0,805	0,668			QD3	0,821	0,674
LD4	0,751	0,563
REL1	0,863	0,745	0,648	3	PR1	0,950	0,874	0,763	3
REL2	0,824	0,540			PR2	0,935	0,874
REL3	0,735	0,680			PR3	0,935	0,902
SIM1	0,278	0,077	0,507	3	IMR1	0,904	0,636	0,682	3
SIM2	0,908	0,824			IMR2	0,883	0,817
SIM3	0,919	0,845			IMR3	0,798	0,780
PROR1	-0,284	0,081	0,505	3	PJP1	0,864	0,746	0,713	3
PROR2	0,886	0,785			PJP2	0,860	0,740
PROR3	0,903	0,815			PJP3	0,835	0,698

• Source: Data collection (2020)

The items with inadequate indices were removed from the model and a new factor analysis was performed. However, it is agreed that the researcher can decide to keep the item if he/she considers it to be essential to the research (De Guimarães et al., 2018). Thus, it was decided not to remove the item (QD1) from the quality dimension, since this item is necessary for this research, since it refers specifically to hotel services, the context of this research.

Next, the factor analysis was performed to verify if the indicators suggested by the scales were aligned with the constructs. For this, the H2, KMO, cumulative variance, and Cronbach's alpha tests were used. According to Table 2, all dimensions reached the adequate KMO index, that is, they were above 0.5. In addition, the cumulative variance criterion can also be used, thus demonstrating the percentage of explanation of each dimension for the model. The awareness (51%) and destination quality (52%) dimensions were below the indicated value, which is 60%. Therefore, the items do not explain the dimensions they represent. Finally, the minimum Cronbach's index in all dimensions presented an alpha above 0.7, therefore, considered of high reliability.

Table 2:
Summary of data reliability and dimensionality

Variables	C.F.	H 2	KMO	Barlett			V.Exp.*	Cronbach
				df	Qui2	Sig
ID2	0,895	0,800	0,500	1	184,314	0,000	80,014	0,743
ID3	0,895	0,800
CD2	0,719	0,517	0,500	1	0,468	0,494	51,681	0,063
CD3	0,719	0,517
QD1	0,527	0,277	0,556	3	116,372	0,000	52,478	0,744
QD2	0,789	0,623
QD3	0,821	0,674
LD1	0,817	0,648	0,781	6	529,496	0,000	63,599	0,779
LD2	0,815	0,664
LD3	0,805	0,668
LD4	0,751	0,563
REL1	0,863	0,745	0,648	3	290,862	0,000	65,488	0,736
REL2	0,824	0,540
REL3	0,735	0,680
PR1	0,950	0,874	0,763	3	1048,992	0,000	88,357	0,933
PR2	0,935	0,874
PR3	0,935	0,902
SIM2	0,925	0,856	0,500	1	291,597	0,000	85,566	0,830
SIM3	0,925	0,856
IMR1	0,904	0,636	0,682	3	510,505	0,000	74,445	0,819
IMR2	0,883	0,817
IMR3	0,798	0,780
PROR2	0,908	0,824	0,500	1	224,691	0,000	82,373	0,786
PRO3	0,908	0,824
PJP1	0,864	0,746	0,713	3	421,288	0,000	72,797	0,812
PJP2	0,860	0,740
PJP3	0,835	0,698

• Source: Data collection (2020). * V.Exp. = explained variance.

Multiple linear regression

For the next step of the analysis, multiple linear regression, it was necessary that some assumptions were met, such as normality, homoscedasticity, linearity and independence. To analyze the normality of the data, the asymmetry and kurtosis was performed, identifying that all items presented indices between -1 and 1, therefore meeting the assumption. To verify homoscedasticity for variance explanation, a scatter plot between the standardized residual and the standardized predicted value was performed, thus demonstrating the homoscedasticity of the data by presenting a rectangular format, showing it to be homogeneous.

To meet the linearity assumption, Pearson's correlation was performed. The classification used in the research followed the following indexes: r = 0.10 to 0.30 (weak); r = 0.40 to 0.6 (moderate); r = 0.70 to 1 (strong). It was found that there is only a strong correlation between loyalty and relevance (0.718), indicating a moderate to strong relationship. Therefore, there is no strong relationship between the other dimensions in this study. Such data confirmed the linearity assumption. To verify the independence of the errors (residuals), the Durbin-Watson test was performed. The test value for this model was 1.813, thus being at the appropriate index, since the index should be around 2. Thus, all assumptions were met to perform the multiple linear regression analysis.

The average of answers in the constructs was, in general, intermediate, according to Table 3. Only the pleasure in visiting the destination portrayed higher values, expressing that the research participants have a higher level of agreement with the statements of these constructs. In addition, the symbolic value, the probability of risk and the perception of price fairness, variable, presented lower values than expected.

Table 3:
Descriptive statistics of the composite variables

Variables	Average	D. P.*	N
IMAGE	3,18	1,03172	416
QUALITY	3,83	,760	416
LOYALT	3,91	,641	416
RELEVANCY	3,33	,812	416
PLEASURE	4,24	,871	416
SYMBOLIC	2,15	1,07875	416
IMPORTANCERISK	3,66	,979	416
RISKPROBABILITY	2,65	,99379	416
PRICE FAIRNESS	2,74	,67541	416

• Source: Data collection (2020). * P.D. = standard deviation

By performing the backward multiple regression method, the results indicated that there is no statistical significance of the importance of risk, since it was automatically removed from model 2. In view of this, it is already admissible to consider that H2d has been rejected. Other models were suggested with the exclusion of some variables, namely: loyalty (model 3), pleasure (model 4) and symbolic value (model 5). As summarized in Table 4, presented below.

Table 4:
Multiple Regression (backward method)

Model	Variables entered	Removed variables	Method
1	RISK IMPORTANCE, QUALITY, LOYALTY, SYMBOLISM, RISK LIKELIHOOD, PLEASURE, IMAGE, RELEVANCE		Enter
2		IMPRISCCO	Backward (criterion: Probability of F to be removed >= ,100). Backward (criterion: Probability of F to be removed >= ,100). Backward (criterion: Probability of F to be removed >= ,100). Backward (criterion: Probability of F to be removed >= ,100).
3		LOYALTY
4		PLEASURE
5		SYMBOLIC

• Source: Data collection (2020)

After verifying the rejection of H2d, it is evident that the respondents consider it irrelevant whether the inadequate choice of a hotel room at the desired destination is a nuisance or something annoying (Rossi & Fonseca, 2000). Therefore, it does not demonstrate the respondents' involvement related to the importance of risk.

Thus, the results of this research contradict previous studies, such as that of Santos et al (2017), which suggested that participants attach importance to risk when purchasing a service. And that of Rossi & Fonseca (2000) who state that at the time of acquiring a service consumers attribute a high degree to the importance of risk. It is argued that such divergences arise from the fact that respondents do not care about the quality, location or services offered by the hotel, but rather about the price they are willing to pay for the accommodation.

Table 5 below presents the models accepted in the analysis. As described below, the adjusted R² value shows that all observed models present a low intensity of influence of the dimensions of the independent variables in relation to the perception of price justice, representing less than 4% of explanation in all models analyzed.

Table 5:
Model Summary

Model	R	R squared	Adjusted R-squared	Standard error of the estimate	Durbin-Watson
1	,226a	,051	,032	,66438	1,813
2	,226b	,051	,035	,66357
3	,226c	,051	,037	,66279
4	,225d	,051	,039	,66207
5	,220e	,049	,039	,66202

• Source: Data collection (2020)

Next, the variance (ANOVA) was checked, presented in Table 6. The focus was to ascertain the Sig value, which indicated statistical significance in all models, since the maximum acceptable value is 0.005 (Hair et al., 2009).

Table 6:
ANOVA

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	9,664	8	1,208	2,737	,006b
	Residual	179,653	407	,441
	Total	189,316	415
2	Regression	9,663	7	1,380	3,135	,003c
	Residual	179,653	408	,440
	Total	189,316	415
3	Regression	9,647	6	1,608	3,660	,001d
	Residual	179,670	409	,439
	Total	189,316	415
4	Regression	9,597	5	1,919	4,379	,001e
	Residual	179,719	410	,438
	Total	189,316	415
5	Regression	9,190	4	2,297	5,242	,000f
	Residual	179,653	407	,441
	Total	189,316	415

• Source: Data collection (2020)

Finally, in the last tests of the multiple linear regression the coefficient indices were presented, shown in Table 7. The first factor that was observed was the Sig of each dimension. Thus, in model 2, no variable reached the desired significance index. In models 3 and 4, one notices that only the relevance dimension (0.004 and 0.002 respectively) statistically affects the dependent variable. In model 5, relevance (0.003) and risk probability (0.004) are statistically significant in relation to the perception of price fairness.

Another index analyzed in Table 6 is the value of Beta (B). It can be seen that, in all models, the only variables that exerted positive strength on the perception of price fairness were image and relevance, according to: model 2 (0.68 and 0.148), model 3 (0.69 and 0.140), model 4(0.69 and 0.132) and model 5 (0.69 and 0.122).

Therefore, because they had Sig above 0.005, the index required for the hypothesis to be accepted (Hair et al, 2009), hypotheses H1b (image), H1c (quality), H1d (loyalty), H2b (pleasure) and H2c (symbolic value) were rejected. In summary, only H2a (relevance) was accepted and exerts a positive influence and H2e was accepted exerting a negative influence on the dependent variable of this research.

Table 7:
Coefficient

Model		Non-standardized coefficients		Standardized Coefficients	t	Sig.	95.0% confidence interval for B
		B	Standard Model	Beta			Lower limit	Upper limit
1	(Constant)	2,796	,312		8,957	,000	2,182	3,409
	RISK PROBABILITY	-,089	,035	-,131	-2,514	,012	-,158	-,019
	SYMBOLIC	-,031	,032	-,050	-,973	,331	-,094	,032
	IMAGE	,068	,040	,104	1,708	,088	-,010	,146
	QUALITY	-,089	,054	-,100	-1,625	,105	-,196	,019
	LOYALTY	-,014	,074	-,013	-,192	,848	-,159	,131
	RELEVANCE	,148	,064	,178	2,305	,022	,022	,274
	RISK	-,014	,046	-,018	-,308	,758	-,104	,076
	IMPORTANCE OF RISK	-,001	,037	-,002	-,036	,972	-,075	,072
2	(Constant)	2,795	,311		8,997	,000	2,184	3,406
	RISK PROBABILITY	-,089	,034	-,131	-2,625	,009	-,156	-,022
	SYMBOLIC	-,031	,032	-,050	-,983	,326	-,094	,031
	IMAGE	,068	,040	,103	1,710	,088	-,010	,146
	QUALITY	-,089	,054	-,100	-1,630	,104	-,195	,018
	LOYALTY	-,014	,074	-,014	-,194	,847	-,159	,131
	RELEVANCY	,148	,064	,178	2,309	,021	,022	,274
	PLEASURE	-,015	,043	-,019	-,336	,737	-,100	,071
3	(Constant)	2,767	,274		10,082	,000	2,227	3,306
	RISK PROBABILITY	-,089	,034	-,131	-2,631	,009	-,156	-,023
	SYMBOLIC	-,031	,032	-,050	-,980	,328	-,094	,031
	IMAGE	,069	,039	,105	1,752	,080	-,008	,146
	QUALITY	-,090	,054	-,101	-1,658	,098	-,196	,017
	RELEVANCY	,140	,048	,168	2,908	,004	,045	,235
	PLEASURE	-,014	,043	-,019	-,335	,738	-,099	,070
4	(Constant)	2,733	,255		10,734	,000	2,232	3,233
	RISK PROBABILITY	-,090	,034	-,132	-2,656	,008	-,156	-,023
	SYMBOLIC	-,031	,032	-,049	-,964	,335	-,093	,032
	IMAGE	,069	,039	,106	1,765	,078	-,008	,146
	QUALITY	-,090	,054	-,101	-1,668	,096	-,196	,016
	RELEVANCY	,132	,042	,159	3,159	,002	,050	,214
5	(Constant)	2,726	,254		10,711	,000	2,226	3,226
	RISK PROBABILITY	-,096	,033	-,142	-2,900	,004	-,161	-,031
	IMAGE	,069	,039	,105	1,762	,079	-,008	,146
	QUALITY	-,092	,054	-,103	-1,702	,089	-,198	,014
	RELEVANCY	,122	,040	,146	3,012	,003	,042	,201

• Source: Data collection (2020)

As verified, in the table above, H1b was rejected, contradicting the statement of Chagas (2008), who considers that the image influences the purchase behavior of the tourist. It is understood, with this result, that it is likely that the desire to take a trip was not related to the cultural aspects of the destination. And it may be related to other tourist attractions or equipment belonging to the destination.

The data also showed that H1c, corresponding to brand quality, was also rejected. Result that refutes the findings of Malik et al (2018), which confirm that quality and price fairness are positively related. These authors also came to the conclusion that consumers are willing to pay higher prices to purchase quality products. This negative influence may be precisely because of the connection of the quality of services offered at the destination to the high prices and high cost required to attend these facilities that have superior quality.

As seen, H1d is another hypothesis that was rejected. This finding contradicts the research of Diaz & Cataluña (2011), as these authors state that non-loyal consumers have a higher perception of price fairness, significantly influencing price justice. However, it corroborates with the same result of Garretson, Ficher & Burton's (2002) study, which argues that loyalty exerted negative influence on price justice. The possibility that a good part of the respondents in this research are not loyal to the destination can be attributed to the economic issue, since in times of financial crisis, subjects tend to choose cheaper destinations (Rodrigues & Mallou, 2014).

The positive influence of H2a (relevance) means to say that research participants tend to show higher perceived price fairness when purchasing hotel daily rates on digital auction platforms, when traveling to the destination becomes essential beneficial and necessary. This result is in agreement with the arguments of previous studies about this variable. According to Zaichkowsky (1985), personal relevance is the main element of the involvement construct, because, for Celso & Olson (1988), the individual identifies the attributes and benefits of the service by relating them to his or her needs, values, and goals. Thus, the sense of price justice becomes higher when the consumer attaches greater relevance to the desired destination to take a trip.

As for H2b, referring to the pleasure variable, this hypothesis was rejected. This result contradicts several studies, such as Diaz & Cataluña (2011), which state that consumers who associate the purchase of a service with pleasure or consider its use pleasant, tend to have a greater sense of price justice. A trip, many times, demands a lot of expenditure, mainly, with lodging, besides transportation, food and tours (Souza & Mendes Filho, 2019). It is possible that pleasure is associated with the high financial cost required to accomplish a trip. Thus, for the consumer, one of the options is to reduce costs and seek less expensive lodging or other accommodation options.

As already mentioned, the H2c was also rejected, corroborating the findings of the research of Toni & Mazzon (2014), in which the trip to the destination did not provide an increase in status in the consumer's social group nor reflected in the improvement of their self-image, having a negative impact on price. With the Internet, one notices a great personal exposure of individuals on social networks portrayed by posting photos and videos. This result may be related to an increase in the search for lower daily rates, due to the economic uncertainties of much of the Brazilian population during the period of the survey.

Finally, H2e concerning the probability of risk was accepted, but exerting a negative influence on the perception of price fairness. This result confronts Nicolau's (2013), which shows a positive influence of the probability of risk on the perception of price justice. Tourists are willing to pay a higher price to have more safety and risk reduction. One of the plausible explanations for this result being contrary to the one cited above is the consumer's easy access to the internet and digital platforms, since through them it is possible to conduct online surveys and analyze comments and ratings from users of hotel services. Thus, the probability of making an unwanted choice is reduced.

FINAL CONSIDERATIONS

It is evident that the consumer who attributes more relevance to a destination becomes more loyal, preferring that destination to a similar one, besides indicating it to other individuals of his acquaintance. Relevance translates the degree of importance with which the consumer perceives an object. Thus, the more relevant (important) the destination is, the greater is the perception of price fairness in the purchase of daily rates in digital auctions.

Another important finding was the correlation between relevance and destination loyalty. The results also found that most respondents, of this research, have never participated in the modality of negotiation of auctions of daily rates. Given these results, it is necessary to consider some issues that are implicit in this research for a better understanding of the results. The most important issue to be presented is the impact of the Covid-19 pandemic.

According to Mecca & Gedoz (2020), tourism is among the most affected economic activities. Tourism is sensitive to all situations that may offer risks at the time of travel, such as weather hazards, geological hazards, terrorism and pandemics (Beni, 2020). And with the pandemic of COVID-19, travel was instrumental in the rapid global spread of the pandemic (Baum & Hai, 2020).

In general, in the current economic situation of the country due to the pandemic, the analyses about consumer behavior, in relation to the price of services, could portray an atypical reality. This can be considered due to the family financial insecurity caused by layoffs, salary reduction, and the very uncertainty of remaining employed. These are factors that may affect the sense of price justice, since taking a trip may not be the essential thing, considering the reality of many Brazilians in this moment of crisis.

With this, it was noticed that, regarding the willingness of the research participant to pay the price of the daily rate, a large portion of the respondents, on digital auction platforms, would try to negotiate with the establishment a price reduction in the percentage value of 15%, because more than half of them would not pay the amount proposed by the ad. This may be the reason why the dimensions quality, loyalty and pleasure had a negative relationship with the dependent variable.

Study limitations

The application of the collection instrument was carried out during the Covid-19 pandemic. During this time of global crisis, much of the population faced economic and psychosocial difficulties. Another limitation occurred regarding the collection instrument because possibly some respondents showed no interest or desire to visit any of the available options. In addition, the consumer's lack of familiarity with daily shopping can be listed as a limitation of this research.

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Publication Dates

• Publication in this collection
  05 June 2023
• Date of issue
  May-Aug 2023

History

• Received
  12 Sept 2021
• Accepted
  29 Nov 2022