Online Reputation As A Determining Factor In Tourist Consumer Behavior

Abstract

Tourists look for information on social networks at all stages of travel planning. Studies have shown that people trust user content more than official resources. A positive online reputation is a prerequisite for the competitiveness of tourist destinations. The vast majority of papers on this topic are theoretical in nature, and their conclusions and suggestions are based on literary analysis and assumptions. The purpose of this study was to fill a gap in the question under study. A conceptual model for measuring the relationship between online reputation and the competitiveness of a tourist destination was tested. Descriptive statistics and reliability analysis were used to process the obtained data. A very high degree of internal consistency is confirmed by the overall Internal consistency reliability coefficient of 0.935. This study provides evidence of the relationship between online reputation and the competitiveness of tourism destinations. The results confirm that tourists use social media at all stages of travel planning and trust user content more than other sources of information.

Keywords: Competitivenessreputationsocial mediatourism destination

Introduction

Reputation is a complex set of evaluative representations, applied in many areas and considered from a psychological, sociological, economic, organizational, marketing and communication points of view (Marchiori & Cantoni, 2011). Reputation can be seen as a long-term positive or negative opinion of people about an object. User-generated content can be thought of as an online version of the traditional word of mouth (Wyrwol, 2014). Researchers have made many attempts to explain how people are affected by information from online sources. The theory of the dual process is the most popular in the research of electronic word of mouth (Cheung & Thadani, 2012).

According to this theory, there are two types of influence on the credibility of received messages: informational and normative. The information impact factor is associated with the assessment of content and source. The regulatory impact category refers to the norms and expectations of members of a group or community. Studies using the theory of the dual process have shown that information and regulatory factors significantly affect the perception of evaluative content (Cheung et al., 2009).

In marketing, trust is recognized as an important factor in consumer behavior on the Internet. For many years, travelers have been using the Internet as an information channel, and recently they themselves began to generate content (Dickinger, 2011). Electronic word of mouth is becoming increasingly popular as it is perceived as informative and trustworthy. The practice has shown that consumers also trust information posted on social networks (Del Chiappa, 2011; Gharib et al., 2019; Yoo & Gretzel, 2011). Travelers believe that online reviews can help them make a purchasing decision (Gabbianelli & Pencarelli, 2020; Jalilvand & Samiei, 2012).

The problem of decision making is central to marketing research on consumer behavior (Kozak, 2010). In the decision-making process, information is a determining factor. Typically, consumers do not have access to all the information and in the decision-making process must determine whether the information is accurate and relevant. Signal theory has proven to be very useful in explaining how consumers use online information as a quality signal in their decision-making process. Consumers are familiar with the concept of reputation and demonstrate the ability to identify the leading opinions of web users regarding tourist destinations (Marchiori et al., 2013).

Although the relationship between online reputation and the competitiveness of tourist destinations is obvious; no serious studies of this relationship have yet been undertaken.

Problem Statement

The concept of competitiveness of tourist destinations has been studied from various points of view, however, its generally accepted definition has not been developed. The competitiveness of a tourist destination is related to its ability to create and integrate a value-added product, constantly attract visitors and provide them with unforgettable positive impressions, and, most importantly, do it all better than other destinations (Amaya-Molinar et al., 2018; Dwyer & Kim, 2003).

There are many models for explaining and measuring the competitiveness of destinations (Gryszel, 2015). In the most commonly used models, indicators are divided into five groups: available resources, auxiliary factors, destination management, situational conditions and demand factors. Demand for tourism services is a special form of demand, which is determined by such factors as socioeconomic, demographic, price and quality factors. Demand conditions comprise three main elements of tourist demand: awareness, perception and preferences. The problem with many studies is that demand factors have not been sufficiently verified. The competitiveness of the destination was usually considered in terms of the destination itself, while the attractiveness of the destination was considered in terms of the tourist. It’s known that an online reputation based on user-generated content can influence tourist decision making, tourist demand and, ultimately, destination competitiveness.

Research Questions

The main objectives of this work were:

  • filling in the currently existing gap in empirical research on the issue under study;

  • checking the adequacy of the proposed research tool;

  • determination of the disadvantages and limitations of the proposed model.

Purpose of the Study

The purpose of this study is to test a pilot conceptual model to measure the relationship between the reputation of a tourist destination on the Internet and its competitiveness.

Research Methods

The purpose of this study is to confirm the relationship between the correlation between online reputation and the competitiveness of tourist destinations.

The questionnaire contains 42 variables grouped in blocks and socio-demographic characteristics.

The proposed model consists of the following elements: components of an online message, attitude to the reputation of the user, trust in social networks, decision-making, demand conditions. The components of online messaging are critical drivers of online discussion. Confidence in social networks affects the user's attitude to the reputation of the destination, as people look for a pronounced online dominant opinion, usually relying on online discussion. Online reviews and the subjective experience of travelers who visited the destination significantly affect the flow of tourists to this destination. The decision-making process is supported by the determining factor of demand, while demand conditions are an essential component of the competitiveness of destinations.

The questionnaire contains 6 sections. Part One informs participants about the purpose of the study and its basic principles. In this part, participants are invited to recall their latest experience in finding information about destinations on the Internet. Sections 2 5 are related to the components of the proposed conceptual model. Part 6 contains socio-demographic information, such as gender, age, purpose of travel, frequency of travel throughout the year, level of Internet use, the most commonly used sources on the Internet, etc. All points on the questionnaire are measured using the traditional five-point Likert scale. The measurements were carried out using the so-called “soft data”. In our case, these are indicators of tourist satisfaction, since tourist satisfaction is a key factor in the competitiveness of the tourist destination. The scale included 42 variables. The target group of the study was tourists who previously searched for information about St. Petersburg on social networks.110 completed questionnaires were received. The online service Google Forms was used to create and collect online profiles through social networks. Descriptive statistics and reliability analysis were most appropriate in this study. Frequencies and percentages were used to describe the proportions of the various points of the questionnaire, a reliability analysis was used to check how a variable or a set of variables corresponded to its measurement object. For this reason, the Internal consistency reliability coefficient was used, showing the internal consistency of the characteristics that describe a single object. Currently, this coefficient is actively used in psychological and sociological studies in order to verify the reliability of the test. It is accepted that for the interpretation of "enough" the coefficient should be greater than 0.7. To analyze the collected data, we used a statistical software package designed for social research (IBM SPSS 20).

Findings

Background information on demographic and other characteristics of the respondents is presented in table 01 .

Table 1 -
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59% of the respondents were women and 41% were men. The largest group was the age group (42%) from 21 to 30 years old, and the smallest in number - from 51 to 60 years (9%) and over 61 years old (6%). A large number of younger respondents turned out to be advanced Internet users (56%) and experts - 25%. These respondents travel 2-3 times a year (22%) and regularly use reviews from other travelers as a source of information (69%). Among these sources of information, the most popular were consumer reviews (47%), followed by social networks (20%), multimedia file sharing (18%), blogs (9%), wikis (5%).

Reliability analysis was performed using SPSS for the calculation of Internal consistency reliability coefficient. The results showed a total Internal consistency reliability coefficient of 0.935, indicating a very high degree of internal data consistency (Table 02 ).

Table 2 -
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The Internal consistency reliability coefficient values of most blocks of the model were more than 0.8. The only exception is the “Decision Making” block with an Internal consistency reliability coefficient value of 0.688, which is only minimally acceptable.

Descriptive statistical analysis was used to describe and generalize the features of the data obtained. Analyzing the first block “Online Review Elements”, it can be noted that average ratings vary from 3.41 to 3.89.

Analyzing the variables, 45% of respondents agreed that the comments are convincing, strong and good. The lowest average score was obtained from the variable “Opinions published online are similar to each other” (3.23), where 45% were unable to express their attitude, and 52% agreed with the wording.

The second block, “Attitude to reputation by the user”, contained six variables that had average ratings from 3.65 to 3.89. The results showed that 72% of respondents tried to understand the online reputation of the destination (3.89). 67% of respondents wanted to know whether travelers had the experience of staying in a destination that they themselves would like to get (3.85).

The second block, “Attitude to reputation by the user”, contained six variables that had average ratings from 3.65 to 3.89. The results showed that 72% of respondents tried to understand the online reputation of the destination (3.89). 67% of respondents wanted to know whether travelers had the experience of staying in a destination that they themselves would like to get (3.85).

Interesting results were obtained for the block “Trust in social networks.” 50% of respondents trust the comments and materials published by other travelers, and trust the reviews, ratings and comments of other travelers more than the ratings presented in official sources. It is noted that only 5% of respondents strongly disagree to recognize the opinion as reliable and 3% doubt the reliability of information sources.

Decision making is the fourth block of the conceptual model and includes 10 variables with an average value in the range from 2.42 to 4.11. The results show that 58% of travelers do not want to attend the destination just because many people go there, and 37% because a friend or family member wants to go there. Moreover, the results indicate that 54% of respondents are influenced by other people.

The last block “Demand conditions”, consisting of ten variables, received an average score of 3.71. A significant part of the respondents (84%) agreed that the destination has a positive image and it is attractive. 75% of respondents believe that the destination provides a sense of security, while 5% disagree. Almost half of the respondents (44%) did not agree with the statements about the low cost of living in a destination and good advertising.

Assessing the results, we can conclude that the online reputation of the destination is positive. The theses are confirmed that travelers use social networks at all stages of travel planning and that people trust user content more than other sources of information. In this regard, all destination stakeholders should work towards improving their overall reputation, and especially their online reputation.

Conclusion

The study was intended to fill an existing gap related to empirical research. Theoretical work previously stated that online reputation is a leading factor in increasing tourism demand and the competitiveness of tourism destinations.

This study, which can be considered a pilot, was undertaken to verify the adequacy of the research tool. In addition, the study revealed weaknesses and limitations of the proposed model. The implementation of such a pilot study cannot guarantee the success of a large-scale study but increases the chances of success.

The article contributes to scientific developments on the issue under study, showing evidence of a correlation between online reputation and the competitiveness of the tourist destination and checking existing measurement tools.

This study has some limitations:

  • the sample size is small;

  • the Internal consistency reliability coefficient value of the “Decision making” block is low and refers to the minimum acceptable (0.67);

  • the results can only be applied to a specific and geographically relatively small destination, and therefore they can only be generalized with caution.

However, these limitations do not detract from the reliability and relevance of the results. They will serve as the basis for further research in this direction.

All limitations must be taken into account to improve the results of future studies. Future studies will use a more comprehensive statistical analysis, large destinations should be their object; the reasons for weak Internal consistency reliability coefficient for the Decision-Making unit should be identified and studied.

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Publisher

European Publisher

First Online

28.12.2020

Doi

10.15405/epsbs.2020.12.04.83

Online ISSN

2357-1330