Understanding Online Consumer Behavior and eWOM Strategies for Sustainable Business Management in the Tourism Industry

Abstract

Electronic word of mouth (eWOM) has been widely used by most consumers on different digital platforms. This review aimed to obtain further insights into online consumer behavior through social networking sites and online reviews sites to help tourism businesses develop sustainable eWOM strategies. To this end, an exploratory study was developed to analyze available literature on eWOM strategies and online consumer behavior. The systematic literature review analysis focused on the following two main topics: (i) tourism and (ii) eWOM. The scientific database, Web of Science, was used to collect relevant literature on the subject. The search terms “Tourism” and “eWOM” were used. Searching the database, Web of Science, yielded a total of 124 articles; upon application of different filters, a total of 14 studies were included in the final dataset. The results of the present study provide new insights into consumer behavior for social sciences and businesses for the adoption of sustainable strategies to increase the influence of eWOM on the tourism industry.

1. Introduction

The continuously developing technologies and the widespread use of the Internet of Things have empowered the evolution of traditional word-of-mouth to electronic word-of-mouth, also known as eWOM [1]. Nowadays, consumers use different social platforms, including social networking sites, consumer review sites, blogs, and social communities, to communicate and share their purchase experiences on products and brands with other consumers [2,3,4,5]. The growing relevance of eWOM strategies, along with the recent Internet trends, has resulted in an increase in the number of consumer online reviews and has had an ever-growing impact on consumers’ purchase decision-making [6]. In this context, electronic word-of-mouth can be considered as a powerful communication in social sciences that has enabled the shift of power from companies to consumers [4], specifically in certain industries, such as the tourism industry [7].

Table 1. Hotel information sources for leisure travelers.

Hotel Information Sources	Men	Women
Metasearch websites (e.g., Expedia, Priceline, Kayak)	40%	44%
Hotel reviews posted on TripAdvisor, Facebook, Twitter, Blogs, etc.	28%	37%
Hotel reviews provided by professionals, Forbes Travel Guide, etc.	31%	31%
Colleagues and business associates recommendation	15%	12%
Friends and family recommendation	49%	58%
Travel—related websites	43%	41%
Google, Yahoo, Bing, or other search engine	48%	47%
Hotel recommended by my organization	12%	9%

Source: [8].

from [8] summarizes the results of a study of 2830 respondents conducted to explore the influence of both positive and negative information on consumers’ choices of hotels and the way consumers search for hotel information.

As can be seen in Table 1, most respondents look first for hotel recommendations from friends and family. Over 40 percent use travel-related websites, metasearch websites, Google, Yahoo, and other search engines. Almost 40 percent of female respondents look for hotel reviews on TripAdvisor and social networking sites [9]. Although there is a small difference between men and women, the results generally show that leisure travelers use many sources for hotel information.

According to the results reported by [8], there are several hotel information sources that consumers consult before the purchase decision. These sources are summarized in

Table 2. Information sources consulted in the early phase of a hotel purchase decision.

Hotel Information Sources	N of Respondents
TripAdvisor	590
Facebook	300
Brand Website	1050
Online Travel Agency	1100
Online Metasearch Engines	650
Online Search Engines	1625
Read a travel book	950

Source: [8].

. Over 1600 respondents began their search for online search engines. Almost one-third of the respondents indicated that they consulted an online travel agency and a brand website. Finally, less than 600 respondents stated that they consulted other hotel information, including TripAdvisor, Facebook, and online metasearch engines.

Table 3. Percentage of visits to most popular sites before the hotel transaction.

Online Booking Websites	5 Weeks +	4 Weeks	3 Weeks	2 Weeks	1 Week	Same Day
TripAdvisor	16%	10%	11%	11%	28%	24%
Booking.com	13%	12%	8%	15%	23%	29%
Expedia	11%	9%	16%	11%	34%	20%
Choice Hotels	10%	9%	11%	17%	12%	40%
Hilton	10%	5%	3%	8%	26%	48%

Source: [10].

shows the percentage of consumer visits on popular websites before booking accommodation [10]. According to the results, the most visited hotel websites are most popular a week before the hotel transaction when consumers are in the purchase process.

Reading online reviews posted by consumers in different eWOM forums has a significant effect on consumers’ buying behavior [11,12]. TripAdvisor is the world’s largest travel website, and over 57% of hotel clients visit TripAdvisor before making a purchase. A recent study has emphasized the growing importance to consumers of online reviews in the hospitality industry that build on the competence and commitment of consumers [1].

The results referred to above in Table 1, Table 2 and Table 3 were used in the present study as the foundation to explore the impact of eWOM strategies on different social platforms and to provide an overview of consumer behavior during the information search phase that was explored using a social science approach. Despite the richness of previous research on eWOM in the tourism industry, the literature is fragmented and needs to be reviewed from specific perspectives [13]. Following [8], the present study aims to thoroughly investigate online consumer behavior to help tourism businesses develop sustainable eWOM strategies.

In this study, the concept of sustainability of hotels was considered from a business perspective. Sustainable businesses were defined as companies with strong foundations that dynamically adapt to changes in the external environment and find new opportunities in the market, taking into account economic changes [14,15,16].

The remainder of this paper is structured as follows. In Section 2, we review previous articles to create a theoretical framework to be used in the present study. In Section 3, we explain the methodology and its implementation. The results of our exploratory analysis on the impact of eWOM on travelers’ decision-making on online review sites and social networking sites are reported in Section 4. Section 5 discusses the implications of our results. Finally, conclusions for further marketing and tourism research that advance our current understanding of eWOM strategies are drawn in Section 6.

2. Literature Review

Over the years, numerous studies have investigated the impact of eWOM strategies on consumer purchase behavior [17,18]. Numerous industrial statistics have also highlighted the significant influence of online reviews on online consumer behavior for sustainable tourism businesses [11].

In the context of recent technological advances, electronic word-of-mouth has become a strong influencing factor in the tourism industry [19]. For instance, [20] reported that eWOM is the most important source of information that affects consumer purchase decisions regarding hospitality and tourism services. The revolution of Information and Communication Technologies (ICTs) in the last decades has transformed both traveler behavior and the tourism industry. The number of travelers who access the Internet to book hotel rooms via third-party intermediaries has grown continuously. Infomediaries, which allow users to exchange information through eWOM, have become particularly relevant in the hospitality industry during the pre-purchase stage [19]. Specifically, a previous study showed that 73% of respondents prefer to read online consumer reviews about a hotel rather than rely on the hotel’s description of itself [5]. Such online reviews are visited by hundreds of millions of potential hotel visitors annually [21].

Overall, available evidence suggests that 78% of online users are influenced by online reviews in their purchase decision-making. Accordingly, online consumer reviews have become relevant sources of information for travelers and play an important role in social sciences and purchasing travel services [22].

At the same time, experiencing continuous growth over the past several decades, tourism has become one of the largest and fastest-growing economic sectors in the world. According to UNWTO, by 2030, it is expected to reach an increase of 57%, representing 1.8 billion international tourist arrivals [23]. Therefore, tourism is considered a continuously developing and highly competitive global industry that involves different sectors worldwide.

In this context, to gain a wider understanding of the continuously increasing impact of eWOM on different social platforms and its effect on the decision-making of hotel consumers, online travel sites and social networking sites should be taken into account. Accordingly, and following several previous studies, such as [24,25], the present study addresses the following research question:

RQ1: Are social networks and other platforms used to develop eWOM strategies for sustainable business management in the tourism sector?

Since the aim of the present study is exploratory, we focused on exploring the trends, rather than formulate a hypothesis to be tested [24].

3. Methodology

Following the guidelines outlined in several previous studies [8,26], a review study was developed to analyze available literature on eWOM and online consumer behavior for sustainable tourism businesses. The systematic literature analysis focused on the following two main topics: (i) tourism and (ii) eWOM.

The scientific databases of Scopus, PubMed, PsyINFO, ScienceDirect, and Web of Science were searched to collect the scientific articles on the subject matter. Following Banerjee et al. [27], we used a randomized controlled process to select the databases using the search terms “tourism” and “eWOM”; the timeframe was set from 2010 to 2018. The database search yielded a total of 135 articles. The Boolean operator AND was applied to optimize the results. All articles were analyzed by first reading the titles and abstracts and then selecting potentially relevant articles that met our inclusion criteria—namely: the papers had to report conclusive results and use adequate terms. This filtering resulted in discarding 84 articles and retaining 51 potentially relevant articles. In the next stage of filtering, the articles had to pass a quality evaluation; that is, only peer-reviewed papers were retained. Other criteria at this stage were the availability of keywords or description terms, relation to the research topic, and appropriate search terms. Those papers that did not meet these criteria (n = 38) were removed, resulting in a total of 14 studies that were included in the final dataset. Further detail on the filtering process is provided PRISMA 2009 Flow diagram (see Figure 1). The filtering criteria were based on AMSTAR [28]. Although the AMSTAR tool was initially designed to assess the quality of the articles from their abstracts, in our review, we followed the indications of [29] as an eligibility gauge.

The aim was to achieve the highest possible amount of evidence from the results reported in high-quality studies. The variables used in AMSTAR to evaluate the quality of the articles included: (i) relationship of the research question to the criteria included in the study, (ii) extraction of data by at least two independent researchers; (iii) quality of the literature review, (iv) identification and definition of concepts; and (v) quality of the references used throughout the study.

4. Exploratory Analysis of the Results

The systematic literature review (SLR) has been extensively used in previous research [18,26,30,31] as a tool for exploratory analysis of the obtained results. The SLR was applied to emphasize the interest of researchers in a specific topic. A literature review is a methodology of exploratory research that consists of collecting and reanalyzing existing literature on a specific subject with the aim of identifying and justifying the conclusions that would bring relevance to the investigation. A literature review should address both primary and secondary sources of information and take these sources of information into account.

Several previous studies, including [32,33], conducted a literature review and performed an exploratory analysis specifically in the tourism sector and social sciences; furthermore, [34] focused on the transformation of word-of-mouth into eWOM and its implications for consumer behavior for tourism businesses sustainability.

The present study was mainly based on the analysis of previous literature (see

Table 4. Literature review of electronic word-of-mouth (eWOM) strategies and online consumer behavior in online review sites for tourism businesses.

Study	Description
[12]	A study of 262 subjects to establish relationships between the probability of making suggestions and eWOM based on the five big personality dimensions: extraversion, agreeableness, conscientiousness, neuroticism, and openness.
[35]	A study of 793 respondents proposes an integrative model of three social network constructs associated with the website (i.e., tie strength, homophily, and source credibility) and their relationship to consumers’ evaluations associated with attitudes and perceived influence of eWOM strategies effectiveness.
[36]	As little is known about the validity and applicability of cultural orientations in countries with perceived inherent similar values, this study identifies the critical factors that influence Chinese and Malaysian users’ attitudes and behavior when processing persuasive eWOM messages.
[37]	This study analyzes the adoption process of consumers when EC-eWOM and SM-eWOM are presented simultaneously. A conceptual model is proposed to reveal the relationship between the adoption of the two types of eWOM.
[38]	The study focuses on the influence of comments written by Facebook friends on the intentions of booking a hotel, the trust in the hotel, the attitude towards the hotel, and the perception of its website. The study also examines the moderator role of Internet users’ trust in those comments on these relations.
[39]	Considering eWOM communication on SNSs as a network based on the users’ social relationships, this study applies social network analysis to examine the communication characteristics of travel-related eWOM on SNSs from the perspective of both ego and whole networks.
[40]	This study focuses on existing frameworks and models to study negative eWOM in leading social networks and its impact on brands, products, and services.
[41]	This study empirically examines how the two social information cues frequently found on online social communities—namely, action-based social information and opinion-based social information—influence consumer purchase decisions. It also explains the moderating role of consumer characteristics, consumer engagement, and consumer expertise.
[42]	The study is based on a destination-specific survey and explores summer holidaymakers’ motivations for social media contributions and their willingness to share content through various social media. The findings in relation to the destination of Mallorca offer an understanding of the adoption of tourist social media in technologically-advanced markets with high levels of ICE use.
[43]	As few studies have directly tested potential antecedents of persuasive eWOM messages among message recipients in a social media context, this study critically examines the determinants of persuasive eWOM messages when message recipients intend to accept and use eWOM messages.
[44]	Influencers can have an important impact on the decision-making of other users. Therefore, the popular eWOM community, Ciao.com, has been modeled as a social network. Using social network analysis techniques, the existence of influencers is justified by the power law distribution of user participation, and then they are identified using their topological features within the social network.
[45]	By developing a conceptual framework for understanding the foundations of digital communication, this study empirically investigates the validity of this framework by examining the factors influencing tourism consumer behavior. The study adopts a conceptual model of eWOM and explores the use of Twitter by tourists.
[46]	This paper examines the acceptance of word-of-mouth information dissemination through social media. The specific focus is on the determinants of acceptance and the effect of acceptance on purchase intention as the result of information on social media pertaining to overseas tourism.
[47]	This conceptual article advances an understanding of electronic word-of-mouth in presenting distinctive credibility profiles towards a proposed influence on destination image and choice.

for a summary). The reviewed articles were selected due to their focus on the same topic of interest.

As can be seen in Table 4, in recent years, the concept of eWOM and social networking sites have become an object of overt-growing research interest. Among these studies, [35] reported that the homophily and tie strength between a website and a consumer are important drivers of source credibility, which influences attitude towards the reviews and the website. The attitude formed through the perceptions of tie-strength, homophily, and source credibility determines the influence of eWOM on consumers’ purchase decisions.

Following the studies conducted in 2014, [36] found that argument quality, source credibility, source attractiveness, source perception, and source style exerted varying influences on Chinese and Malaysian users’ attitude and intention to continue their study abroad. Researchers also showed that e-commerce-eWOM’s usefulness and credibility positively influence the adoption of EC-eWOM but negatively influence that of social media-eWOM. EC-eWOM adoption was found to negatively influence SM-eWOM adoption and mediate the relationship between usefulness, credibility, and SM-eWOM adoption [37].

Furthermore, in a survey of 800 university students, [38] found the influences of comments generated on Facebook on the decision-making process. In addition, [39] established that travel-related eWOM communication via SNSs relies on existing social relationships and ties that can be categorized as strong, middle strength, or weak and that the effect of transmitted information was stronger than that of influential decision-making. Similarly, [40] studied current research of eWOM, social media, and negative eWOM strategies, while [1] highlighted the importance of eWOM in reducing the asymmetry of information.

In another relevant study, [41] discovered that consumer purchase decisions are influenced by two social information cues and that action-based social information is more influential than opinion-based social information. The authors also observed that consumer engagement and consumer expertise play an important moderating role in consumer purchase decisions. In another study, [42] clarified the dominance of visual content, along with the relevance of altruistic and community-related motivations and motivational differences between types of content creators.

Furthermore, [43] examined the determinants of persuasive eWOM messages when message recipients intend to accept and use them. Furthermore, [44] explained that influencers are not determined by the number of performed reviews but by the variety or scope of their reviews and their central position in the consumer network.

In a study on the factors affecting tourists’ decision-making, [45] established that Twitter is an influential marketing channel that should be wisely used in marketing tourism services. As argued by [46], the professionalism of senders and the practicability of eWOM significantly affect the acceptance of information. In addition, the acceptance of eWOM information was found to have a significant effect on the spread of eWOM and customer purchase intention.

Finally, [47] focused on the understanding of electronic word-of-mouth in presenting distinctive credibility profiles towards a proposed influence on destination image and choice.

To provide an in-depth understanding of the articles included in the final sample, a full presentation of these studies is provided in

Table 5. Full presentation of reviewed studies.

	Dependent Variable	Independent Variable	Sample	Key Findings	Topic
[12]	Personality traits	eWOM drivers	262	Some personality traits influence eWOM, but not to a large extent.	Influence of personality traits on eWOM
[35]	Tie strength, Homophily, Credibility	Attitude towards eWOM, perceived influence, purchase behavior	793	Lack of social presence, increasing skepticism about eWOM credibility. Social relationships between consumers and websites.	Attitudes and influence of eWOM for purchase
[36]	Cultural background	Attitudes and Intention	-	Facebook is the most used SNS in Malaysia, as is QQ in China Attitudes and intentions vary	Cultural differences between Malaysian and Chinese eWOM
[37]	EC Volume, EC Rate extremism, EC Integrity, EC Source credibility, Cognitive level, Involvement	Electronic Commerce eWOM usefulness, EC eWOM credibility, EC eWOM adoption, SM eWOM adoption	289	EC-eWOM’s usefulness and credibility positively influence EC-eWOM, but negatively SM-eWOM. EC-eWOM adoption negatively impacts SM-eWOM adoption	Relationships and influence between eWOM adoption in eCommerce and social media
[38]	Facebook eWOM, Positive eWOM, Negative eWOM	Attitude toward hotel, Booking intentions	800	The existence of an influence of Facebook eWOM on user friends and the moderator role of the trust in the decision-making process for hotel booking.	eWOM in Social media and its influence on the user decision-making process
[39]	Contact frequency, Contact duration, Intimacy, Mutual confiding, Social ties, Travel behavior, Ego-network analysis	Whole-network analysis, Density, Graph centralization, Centrality, Subgroups	289	Travel-related eWOM communication via SNS relies on existing social relationships. The effect of transmitted information is stronger than that of influential decision making	Ego and Whole network analysis
[40]	Negative Traditional WOM, Negative eWOM	Strategies, Product categories, Service categories, Company brand	39	Social media influence consumer decision-making process. Impact of negative eWOM on product, strategies, and ROI.	Influence of negative eWOM
[41]	Peer consumer purchase, Peer consumer review, Engagement, Expertise	Consumer Purchase Decision	897	Social information cues influence consumer purchase decisions. Action-based information is more influential than opinion-based information.	Social media credibility
[42]	Media, Types of content	Motivations for sharing	398	Dominance of visual content. Relevance of altruistic and community-related motivations. Motivational differences among types of content creators	Social media and Tourist motivations for sharing content
[43]	Persuasive eWOM messages, Information acceptance, Intention to use	Argument quality, Source credibility, Source attractiveness, Source perception, Source style	78	Argument quality, source credibility, attractiveness perception, and style are key antecedents of persuasive eWOM.	Antecedents of persuasive eWOM
[44]	Number of reviews, Variety of performed reviews, Position in consumer network	Influencer detection	-	Influencers are not determined by the number of performed reviews, but by the variety or scope of their performed reviews and their central position in the consumer network	Social Network Analysis Technique
[45]	Sources of eWOM, Mediating factors, Influencing variables	Outcomes of eWOM and online reviews	500	Twitter is not a panacea, but another marketing channel to be integrated within marketing communications	eWOM and online reviews
[46]	Determinants of acceptance, eWOM acceptance	Purchase intention	1222	Acceptance of eWOM has a significant effect on the spread of eWOM and customer purchase intention. Practicability and professionalism of eWOM should be improved.	eWOM acceptance and dissemination
[47]	Source-receiver relationships, Channel variety, Information solicitation, Message retention, Provider motivations	Destination choice	-	Distinctive credibility profiles towards a proposed influence on destination image and choice	eWOM Dimensions

.

One of the main conclusions that can be derived from the analysis in Table 5 is that most reviewed studies on eWOM in tourism and its impact focused on purchase intention [35,37,38,41,43,46,47]. Exceptions here are studies that sought to expand the knowledge about eWOM in tourism, in general [45] and negative comments, in particular [40]. Several other studies aimed to deepen the understanding of the structure of social networks and influencer marketing [39,44] or explored the impact of eWOM on specific variables, such as cultural differences [36] or personal traits [12].

Then, to deepen our literature review and to unveil the adequate platforms to develop eWOM strategies, we classified the articles included in the final sample according to the social networks and platforms they focused on. The results of this classification are shown in

Table 6. Social networks and platforms used in previous research for eWOM strategies.

	Facebook	TripAdvisor	Twitter	Website	SNS
[12]	-	-	-	-	√
[35]	-	-	-	-	√
[36]	-	-	-	-	√
[37]	-	-	-	√	√
[38]	√	-	-	√	√
[39]	-	-	-	-	√
[40]	-	-	-	√	√
[41]	-	-	-	√	√
[42]	-	-	-	-	√
[43]	-	-	-	-	√
[44]	-	-	-	-	√
[45]	-	-	√	-	-
[46]	-	-	-	-	√
[47]	-	-	-	-	√

Source: Authors.

.

Based on the results reported in Table 6, we can conclude that most of the reviewed studies did not focus on a specific social networking platform and analyzed the data in a generic way (see the last column in Table 6). This suggests that previous studies have predominantly aimed at obtaining a global understanding of social networks. However, each of the social platforms has its specific particularities; therefore, these specific features should be taken into account by tourism businesses to develop specific strategies for each social channel. The reviewed studies also argued that it is necessary to involve experts who dominate these social networks and ensure the success of tourism businesses in a sustainable way. An exception to this pattern is [38], where, despite having done a survey on eWOM, the questions specifically included information about Facebook (see Table 6).

Another study that also focused on a single social network is [45] that reviewed eWOM strategies used on Twitter.

Interestingly, TripAdvisor, a social tourism network par excellence, has not been analyzed specifically in any reviewed studies. This may be due to the difficulty in obtaining the data from TripAdvisor: unlike Twitter that makes it possible to connect to the API directly and thus obtain user reviews or comments, in the case of TripAdvisor, it is necessary to develop an algorithm that would allow researchers to download its data.

To conclude with the analysis of different platforms and information sources of the previous studies on eWOM, it is interesting to note that one of the columns is the company’s own web page. Several reviewed studies [37,38,40,41] used companies’ websites as a source of information on online reviews.

Furthermore, to understand the areas of greatest interest with regard to eWOM strategies, we performed a scientometric analysis of the reviewed studies. A scientometric analysis is an analysis of scientific outcomes using a quantitative and qualitative approach. The first author to use this analysis was [48]; since then, it has been extensively used to gain a deeper understanding of the state of science in systematic literature reviews [7]. In some publications, this analysis was used as the main methodological approach [49]. Following [18], in our analysis, we first included the name of the journal, obtained from the article reference. Then we searched the journal in scientific databases, such as the Web of Science, and noted the Quartile and Category to understand the quality and impact of the reviewed articles better, as well as the categories they belong to. The results of this analysis are summarized in

Table 7. Scientometric analysis.

Journal	Total of Findings	Quartile	Category
Psihologija	1	Q4	Psychology, Multidisciplinary
Computers in Human Behavior	1	Q1	Psychology, Experimental
Journal of Computer Information Systems	1	Q3	Computer Science, Information Systems
Electronic Commerce Research and Applications	1	Q2	Business, Computer Science, Information Systems, Interdisciplinary Applications
International Journal of Hospitality Management	1	Q1	Hospitality, Leisure, Sport, and Tourism
Tourism Management	2	Q1	Environmental Studies, Hospitality, Leisure, Sport, and Tourism Management
2nd European Conference on Social Media (ECSM)	1	-	Communication, Social Sciences
Decision Support Systems	1	Q1	Computer Science; AI Operations Research and Management Science
Journal of Computer Information Systems	1	Q3	Computer Science, Information Systems
Technology Analysis and Strategic Management	1	Q3	Management
International Journal of Information Management	1	Q1	Information Science and Library Science
Electronic Commerce Research	1	Q3	Business and Management
Journal of Travel and Tourism Marketing	1	Q3	Hospitality, Leisure, Sport, and Tourism

Source: Authors.

.

As can be seen in Table 7, previous research on eWOM in tourism has been conducted in the following 14 central categories: (1) Psychology, (2) Multidisciplinary, (3) Experimental, (4) Computer Science, (5) Interdisciplinary Applications, (6) Hospitality, (7) Leisure, Sport and Tourism, (8) Environmental Studies, (9) Communication, (10) Social Sciences, (11) Artificial Intelligence, (12) Operations Research and Management Science, (13) Information Systems, Business, and Management, (14) Information Science and Library Science.

Among all the journals included in the review, only one, Tourism Management, published two articles that were included in the final dataset in the present review.

The eWOM has a computer science dimension since it involves a computer-based analysis of data from social platforms using specific algorithms needed to download user data. Accordingly, some of the reviewed studies were published in journals that belong to the Computer Science category. Another category found in our analysis is Information Systems since the information coming from social networks forms information systems used by, among others, tourism professionals. One more category that can be identified based on the results of the scientometric analysis is that of Hospitality, Leisure, Sport, and Tourism. In fact, eWOM is a particularly relevant topic in the tourism sector, as travelers’ decision-making is usually supported by the comments contributed by previous visitors. Yet another category where the reviewed studies were published was Business and Management; this category is relevant, as reviews published on social networks are extensively used by the management of tourist companies.

With respect to the quality of the publications, 42.85% were Q1, while 7.14% were Q2, 35.71% were Q3, 7.14% were Q4, and 7.14% have no quartile. Overall, most of the reviewed studies belonged to Q1 (i.e., the highest possible ranking) and Q3. Q1 journals included reputed journals, such as Computers in Human Behavior, International Journal of Hospitality Management, Tourism Management, International Journal of Information Management, and Decision Support Systems.

Based on the results reported in this section, we can conclude that our research question (RQ1 “Are social networks and other platforms used to develop eWOM strategies for sustainable business management in the tourism sector?”) can be positively answered by our results.

5. Implications

The results of the present study revealed the importance of eWOM strategies for the tourism industry not only on major websites but also in other types of forums, such as social networks. These venues of eWOM require managerial attention for proper brand management and to reduce the asymmetry of information that consumers get about tourism businesses [1].

The continuous growth of the tourism sector has been supported by the development of ICTs for nearly four decades. As we progress through the 21st century, the digital revolution in social sciences and tourism should be taken into account, as it is one of the important factors that make the industry globally competitive. Nowadays, online review sites and social media websites have become important sources of information for consumers that exert a powerful impact on online consumer behavior [1,12]. Therefore, efficient gathering and analysis of eWOM strategies can help companies to remain competitive in this industry.

However, according to the results reported in Table 6, most previous studies on the topic focused on social networks and platforms in general, rather than thoroughly analyzed tourism platforms, such as TripAdvisor or Booking.com. Accordingly, an important implication for further research on eWOM in the tourism industry is that tourism businesses should become more specific in their marketing activities rather than focus on general social networks and websites. In this way, tourism businesses can develop differentiation strategies and thus create sustainable business management.

The popularity of digital online platforms that contain online reviews and the increasing influence of eWOM on consumer behavior has driven numerous scholars to explore the phenomenon of online reviews. Considering that online reviews have become a powerful marketing tool and a success factor of many business models, in further research, it is necessary to prioritize gaining insights into the behavioral factors that influence consumers’ purchase decision-making through online review sites and social media.

6. Conclusions

This review aimed to investigate online consumer behavior through social networking sites, online reviews sites, and platforms to help tourism businesses develop sustainable eWOM strategies. The results of our review demonstrated the ever-growing scholarly interest in the concept of eWOM strategies in social networking sites and online review sites and platforms. Despite the fact that many previous studies investigated the impact of eWOM on the hotel industry [22,50,51], there is still a need for further research due to the evolution of the industry and consumers. The relevance of the impact of eWOM strategies on the tourism industry has been acknowledged over the years, and further research to fill the gaps in our current knowledge on tourism and marketing is urgently needed.

Since tourists today live in the information era, analyzing the flows of information and identifying information asymmetry are central aspects to be considered [1].

One of the recent trends observed on online platforms and in social networks is the diversification and specialization of content [18]; accordingly, it is necessary to develop the appropriate marketing strategies not only for current placements but also for new platforms specifically developed for tourism businesses.

Moreover, tourism businesses and companies should go the extra mile to adapt their strategies in accordance with recent advances in information technologies. Adapting to the continuously changing behavior of consumers in the online tourism sector should be considered as an opportunity rather than a challenge. Understanding tourists and their online behavior will crucially impact the sustainable development of businesses within the hospitality and tourism sector [13].

In this context, the key directions that should be prioritized are market research, analysis of online consumer behavior, and appropriate use of leverages to influence purchase decision-making. In further research, it would be necessary to review the literature on eWOM in the tourism sector using quantitative techniques that enable measuring the impact of online review sites and social networking sites on consumers’ decision-making related to hotel choices.

Limitations of the present review are related to the sources of the articles, the limited number of studies analyzed, and the number of previous studies consulted. Future studies should seek to get a deeper insight into some of the aspects presented in the paper, such as the information sources for eWOM strategies or the evolution of these websites. Other possible lines of development might be a quantitative analysis of information flows and online consumer behavior.