E-WOM and Accommodation

Abstract

Online reviews (ORs) are continuing to foster a renewed spread of word-of-mouth in the travel industry. Travelers are increasingly using ORs to inform them about accommodations and other tourism-related products. As such, it is important to improve our understanding of the behavioral consequences of e-word-of-mouth. In this article, we adopt the elaboration likelihood model to identify what influences travelers to adopt information from ORs in their decision making. We measure the influence of six dimensions of information quality that are part of the central route and two dimensions that are associated with the peripheral route of persuasion. The results of this study reveal that product ranking, information accuracy, information value-added, information relevance, and information timeliness are strong predictors of travelers’ adoption of information from ORs on accommodations. These results imply that high-involvement travelers adopt both central (information quality) and peripheral (product ranking) routes when they process information from ORs.

Keywords

e-word-of-mouth online reviews elaboration likelihood model information quality dimensions information quantity products ranking

Introduction

Blogs, online reviews, and social networking websites are enabling customers to interact virtually and to share information, opinions, and knowledge about all kinds of goods, services, and brands. Web 2.0 applications are empowering online user interaction and collaboration and influencing how travelers create, exchange, and use information (O’Connor 2008; Sigala, Christou, and Gretzel 2012). Some of these applications are fostering the spread of word-of-mouth (WOM) on the web, namely e-WOM (Bronner and de Hoog 2011). E-WOM has been defined as “any positive or negative statement made by potential, actual or former customers about a product or company, that is made available to a multitude of people and institutions via the internet” (Hennig-Thurau et al. 2004, p. 39).

In the travel industry, online reviews (ORs) can be considered as electronic versions of traditional WOM and consist of comments published by travelers on the tourism products, services, and brands they experience. Travelers write ORs to share their own experiences with a product and/or service and describe their levels of satisfaction to help other travelers (Yoo and Gretzel 2008). Internet consumer opinion portals (COPs; Burton and Khammash 2010) are enabling travelers to review any aspect of a vacation, including accommodation, restaurants, destinations, and other tourism-related products such as tour operators. As soon as ORs are published on COPs, they are available for other potential travelers to read and use to inform subsequent decisions (Sigala, Christou, and Gretzel 2012). The importance of ORs is also growing among e-travel agencies that are providing their sponsored products with customer reviews (e.g., venere.com, e-booking.com, expedia.com, hotels.com) or are encouraging travelers to post product reviews on their websites (Mayzlin 2006). In this article, we focus on the influence that online reviews on accommodations have on travelers’ adoption of information from ORs. Information adoption has been defined as the extent to which consumers modify their behavior by utilizing the suggestions made in ORs (Sussman and Siegal 2003; Cheung, Lee, and Rabjhon 2008; Wu and Shaffer 1987). For example, after reading an OR, a traveler may choose to adopt the information received from ORs in their decision-making process and book a particular hotel or select to stay at another more suitable location.

The use of ORs is growing rapidly. For example, Tripadvisor (www.tripadvisor.com), one of the most successful COPs which specializes in travel, enables travelers to write reviews on accommodations all around the world and brings together individuals in discussion forums (Buhalis and Law 2008). Between 2010 and 2013, traffic on Tripadvisor increased from 20 to 60 million monthly visitors, with registered members rising from 15 to 20 million (www.tripadvisor.com 2010, 2013). Tripadvisor has been criticized in the press as consumers could potentially be fooled by fraudulent posts (Morris 2012); however, the growth of this and similar COPs on travel services suggests that e-WOM will continue to play an ever more vital role in travelers’ purchasing decisions.

With such growth, e-WOM and ORs are becoming an increasingly important focus of research in marketing, e-commerce, and e-tourism studies. The popularity of e-WOM among practitioners has spurred a number of research streams. Scholars have measured the influence of ORs on sales of movies, books, and games (Godes and Mayzlin 2004; Liu 2006; Duan, Bin, and Whinston 2005; Chevalier and Mayzlin 2006; Dellarocas, Zhang, and Awad 2007; Zhu and Zhang 2010), as well as investigated the influence that ORs play on consumer behavior (Hennig-Thurau and Walsh 2003; Senecal and Nantel 2004; Smith, Menon, and Sivakumar 2005; Cheung, Lee, and Rabjhon 2008; Sher and Lee 2009). Researchers in the travel and tourism industry have found that ORs affect hotel rooms sales (Ye, Law, and Bin 2009; Vermeulen and Seegers 2009; Ye et al. 2011), and suggested that ORs have higher levels of credibility than other sources of information (Gretzel and Yoo 2008; Akehurst 2009; Dickinger 2011; Fotis, Buhalis, and Rossides 2012). Research has also revealed that positive reviews improve attitudes toward hotels (Vermeulen and Seegers 2009) as well increase travelers’ confidence and reduce their risk when booking accommodation (Gretzel, Yoo, and Purifoy 2007).

The relationship between communication routes and customer involvement and the role that destination websites can play in persuading a customer to visit a particular destination have been explored by Tang, Jang, and Morrison (2012). Other studies have analyzed travelers’ motivations to post reviews (Bronner and de Hoog 2011), and investigated e-ratings and e-complaints behavior regarding hotel services (Del Chiappa and Dall’Aglio 2012). Some researchers have explored the perceived trustworthiness of online reviews channels (Yoo et al. 2009; Dickinger 2011) while others have illustrated how travel blog narratives can be used to identify the factors that drive customer delight at tourism venues (Magnini, Crotts, and Zehrer 2011). However, there is a need for more research that explores the behavioral implications of e-WOM on travelers (Litvin, Goldsmith, and Pan 2008; Vermeulen and Seegers 2009).

Our research aims to investigate the factors that influence the adoption of information from ORs by using Petty and Cacioppo’s (1986) elaboration likelihood model (ELM), which is widely used in the general e-WOM literature (Chan and Ngai 2011). An increased knowledge of the informational influence of ORs on the traveler decision-making process may help tourism marketers to understand the way different information dimensions in ORs will impact travelers’ decisions when booking an accommodation.

In this paper, we build on existing literature and contribute to theory in a number of ways. Many existing studies are based on manipulated experiments, and focus on the influence of either positive or negative reviews on purchase intentions (Park, Lee, and Han 2007; Park and Lee 2008; Gauri, Bhatnagar, and Rao 2008; Vermeulen and Seegers 2009; Lee and Lee 2009; Gupta and Harris 2010). Other researchers who have focused on travel blogs or online diaries that are a form of e-WOM have mainly utilized content analysis or narrative analysis (Banyai and Glover 2012). Instead, we conduct a large-scale survey to explore the determinants of information adoption. Moreover, in this study, we introduce the concept of overall product ranking, which is a type of categorical information relating to the overall evaluation of accommodations provided by travelers, which summarizes the proportion of positive, neutral, and negative reviews for a product (e.g., accommodation) in a category (e.g., destination). In addition, we investigate the influence that different information quality dimensions have on travelers’ information adoption. Although the ELM has been utilized to better understand the communication route of destination websites (Tang, Jang, and Morrison 2012), to our knowledge, this is the only study which has drawn upon ELM theory to explore the factors that influence travelers’ adoption of information from ORs.

Conceptual Model

Information Adoption and the Elaboration Likelihood Model

In our paper, we draw on Sussman and Siegal’s (2003) information adoption model (IAM), which was developed from the Theory of Reasoned Action (TRA; Fishbein and Ajzen 1975; Ajzen and Fishbein 1980) and its derivative, the Technology Acceptance Model (TAM; Davis 1989). The TRA/TAM assumes that people form intentions to adopt a technology or a behavior based on what they believe about the consequences of adoption and their evaluation of the consequences. The IAM was originally constructed in an effort to better understand how people form intentions toward adopting knowledge on particular advocated ideas, behavior, or technology (Sussman and Siegal 2003). However, it can also be used to investigate the adoption of advice (Sussman and Siegal 2003) such as the information contained in ORs (Cheung, Lee, and Rabjhon 2008). In a similar way to Sussman and Siegal (2003), we integrate IAM with ELM but instead of focusing on knowledge transfer within an organization, we explore travelers’ adoption of information from ORs.

A fundamental principle of the ELM model is an “elaboration continuum,” which ranges from low elaboration (low thought) to high elaboration (high thought). Along this continuum, the ELM distinguishes between two routes that influence consumers’ attitude change: a “central route” and a “peripheral route” (Petty, Cacioppo, and Schumann 1983). People who are motivated or willing to process information take the central route, spending more time and providing a rational response using criteria such as information quality (Petty, Cacioppo, and Schumann 1983). On the contrary, people who are less motivated or not willing or able to process information take a peripheral route and use information short cuts, such as source credibility or volume of information to make a decision (Petty, Cacioppo, and Schumann 1983).

The ELM is one of the most frequently used theoretical frameworks in studies on e-WOM (Chan and Ngai 2011) and has been utilized to explain the persuasive power of e-WOM among consumers with different levels of purchasing involvement (Park and Lee 2008; Lee, Park, and Han 2008; Sher and Lee 2009; Gupta and Harris 2010). Drawing on the IAM and ELM, in this study we argue that travelers who adopt information from ORs will incorporate the information obtained into their mental models. They will subsequently take action following the advice or recommendations received from ORs.

The purchase of accommodation is usually considered a complex decision that requires an extensive informational search because of high levels of perceived risk and the fact that it is intangible at the time of purchase (Sirakaya and Woodside 2005; Major and McLeay 2013). Few researchers have investigated the antecedents of consumers’ adoption of information from ORs (Cheung, Lee, and Rabjhon 2008) and none of these have explored the adoption of information from ORs on accommodations. Cheung, Lee, and Rabjhon’s (2008) study on a Chinese food and restaurant virtual community found that information usefulness is one of the strongest antecedents of ORs’ adoption. A similar result was obtained by Sussman and Siegal (2003) in their study on how an individual’s behavior is affected by suggestions and recommendations they received via e-mail in an organizational context.

Both the influence of the central and peripheral routes on travelers’ adoption of information from ORs on accommodations will be investigated in this study. Below we describe each in turn.

Central Route

The ELM theory suggests that consumers who are highly involved with a product or service are more likely to use a central route for information processing, focusing on the quality of arguments (Petty, Cacioppo, and Schumann 1983). The quality of arguments contained within a communication message will determine the degree of informational influence under conditions of high elaboration likelihood (Petty and Cacioppo 1986). Previous research on e-WOM has investigated information quality as a predictor of consumer purchase intentions in high-involvement situations (Park, Lee, and Han 2007; Park and Lee 2008). However, these studies analyzed information quality as a composite construct, failing to discern the importance of different information quality dimensions on consumers’ information adoption in e-WOM. Thus, information completeness, timeliness, accuracy, relevancy, understandability, and value addition (Wang and Strong 1996) are informational quality dimensions that will be further explored in this study.

Peripheral Route

The ELM theory suggests that individuals take a peripheral route when they are less motivated or less capable of thinking about a message, or when they purchase low-involvement products or services (Petty and Cacioppo 1986). Consumers, therefore, make less cognitive efforts and use peripheral cues to evaluate a message. Peripheral cues are simple rules or information short cuts such as brand image and source attractiveness that consumers use to assess a recommendation rather than evaluating the quality of the arguments used by a source (Petty and Cacioppo 1986). Previous studies have adopted information quantity (number of reviews) as a peripheral cue to information processing in e-WOM (Park, Lee, and Han 2007; Park and Kim 2008; Gupta and Harris 2010), and found that consumers associate the number of ORs with a product’s popularity (Chevalier and Mayzlin 2006). In addition, in this study we introduce the concept of overall product ranking.

The overall ranking of products in a specific category (e.g., accommodations in a specific destination) is a common feature in COPs. Overall product ranking refers to an overall score that is often graphically displayed as a number of stars that represents the average rating provided by all customers who have reviewed a product. The overall ranking of accommodation summarizes the relative proportion of positive, negative, and neutral reviews. In a low elaboration situation, consumers may use information quantity (the number of reviews per product) and overall product ranking instead of reading the full content of ORs. Both of these factors are incorporated into our conceptual model. In summary, our conceptual model, which is illustrated in Figure 1, includes elements of both the central (six information quality dimensions) and the peripheral (information quantity and product ranking) routes to persuasion. The inclusion of these variables will improve researchers’ understanding of the informational influence of ORs on travelers’ decisions to adopt information from ORs.

Figure 1.

Research model.

Hypotheses Development

ELM theory suggests that in a high-elaboration context, consumers may consider information quality as a central cue to information processing. Instead, in a low-elaboration situation, consumers may use peripheral cues like overall product ranking and information quantity for adopting information and making a decision without fully reading ORs. In the following paragraphs, we describe these information-processing cues and the hypotheses we test in this study.

Central Route Factors

Information timeliness refers to information that is up to date, current, and represents the state of the art of a product/service (Nelson, Todd, and Wixom 2005). In comparison to traditional WOM, ORs are available 24 hours a day. The most recent ORs are displayed first on COPs, so consumers can easily access the latest reviews published on specific accommodations. This means that travelers can obtain fresh and current reviews on accommodation which distinguishes ORs from printed travel guides, which need more time to be updated and distributed to travelers. Information currency may increase the likelihood that travelers will adopt information from ORs in their decision making process. Thus:

Hypothesis 1: There is a positive relationship between information timeliness and travelers’ adoption of information from ORs.

Information understandability refers to readability, interpretability, and ease of understanding, as well as language, semantic, and lexical expressions used by reviewers (Wang and Strong 1996). ORs are easy to read and understand if the information presented in them is judged as clear, logical, and interpretable. On the contrary, the use of technical words, dialect, or jargon might undermine a review’s clarity. Thus, ORs may be difficult to understand because of a reviewer’s poor writing skills, or the different language and jargon used. Thus, the clarity and readability of reviews may influence travelers’ adoption of information from ORs. Therefore:

Hypothesis 2: There is a positive relationship between perceived information understandability and travelers’ adoption of information from ORs.

Information relevance refers to the extent to which a review is applicable and helpful for a task at hand and depends on different customer needs in specific situations (Wang and Strong 1996). ORs are relevant if they provide the kind of information a customer is looking for. For example, tourists on a honeymoon may be more interested in searching for information about romantic accommodations situated in a scenic and quiet area, possibly close to the main attractions. This target group will generally want to avoid basic, cheap, and less comfortable accommodations, which may be preferred by other market segments such as backpackers. Therefore, the capacity of ORs to satisfy travelers’ needs might affect the decision to adopt information from ORs in their decision making. Thus:

Hypothesis 3: There is a positive relationship between information relevance and travelers’ adoption of information from Ors.

Information accuracy is defined as the correctness in the mapping of stored information to the appropriate state in the real world that the information represents (Nelson, Todd, and Wixom 2005). The accuracy of information depends on travelers’ perceptions that information is accurate, correct, believable, and credible (Wang and Strong 1996). Because comments in ORs are made by independent reviewers, travelers may perceive ORs as correct, candid, and free from bias comments. As ORs are comments provided by travelers for other travelers, they may be perceived as more trustworthy and accurate than other information sources, such as marketing communications (Bickart and Schindler 2001; Senecal and Nantel 2004; Smith, Menon, and Sivakumar 2005; Dickinger 2011). Such evaluations may also be influenced by a traveler’s previous experience with ORs. Therefore, we argue that an increase in the perceived accuracy of the information provided in ORs will lead to an increase in the likelihood of travelers adopting information from ORs. Thus:

Hypothesis 4: There is a positive relationship between the perceived accuracy of information and travelers’ adoption of information from ORs.

Value-added information is the extent to which information is beneficial and provides advantages from their use (Wang and Strong 1996). ORs may empower a traveler’s capacity to make informed decisions by providing information that is generally not easy to access through traditional marketing communications. Marketing communications are generally aimed at emphasizing the positive sides of a product or service while trying to hide or to shadow the negative aspects. However, online reviewers describe both the positive and the negative aspects of accommodations. This enables travelers to get a more comprehensive and critical description of accommodations by facilitating the understanding of their relative strengths and weaknesses. Thus, by reading ORs, travelers may discover aspects that were not considered before (e.g., the presence of loud night clubs in a hotel’s surroundings). Travelers may adopt information from ORs because they are beneficial and advantageous for their decision-making process. Thus:

Hypothesis 5: There is a positive relationship between value-added information and travelers’ adoption of information from ORs.

Information completeness is defined as the extent to which information is of sufficient breadth, depth, and scope for the task at hand (Wang and Strong 1996). Accordingly, a customer may judge a review as complete based on the degree to which information from ORs is comprehensive and exhaustive for booking accommodation. This means that ORs will discuss the main aspects of a room or hotel, such as its location, cleanliness, price of rooms, quality and assortment of breakfast, friendliness of staff, and the like. In offline WOM, the breadth and depth of information available through social networks is limited; thus, peers and friends may have only a limited knowledge on some aspects of accommodations in a destination. On the contrary, in e-WOM travelers can retrieve information about all the services available in the different accommodations (e.g., mini-bar prices, breakfast quality, state of furniture, friendliness of staff), which can facilitate their decision making process. Therefore, the more exhaustive or complete information in ORs is the higher will be the likelihood that travelers will adopt information from ORs. Thus:

Hypothesis 6: There is a positive relationship between information completeness and travelers’ information adoption from ORs.

Peripheral Route Factors

Information quantity is the extent to which the quantity or volume of available data is appropriate for a specific task (Wang and Strong 1996). Information quantity represents the number of ORs per accommodation; it is a peripheral cue to information processing since it is a short cut that consumers may use to make a decision (Park, Lee, and Han 2007). The amount of ORs per product is considered an indicator of product popularity and of the market performance of books (Chevalier and Mayzlin 2006). Travelers may tend to believe that accommodations with a large number of reviews are more popular and more frequently booked than others with fewer reviews.

Research in e-WOM has found contrasting results regarding the influence of the number of reviews on consumers’ purchasing intentions. Some scholars conclude that information quantity influences the adoption of information from ORs by unskeptical consumers (Sher and Lee 2009) and among low-involved consumers (Park, Lee, and Han 2007). Others have determined that the quantity of ORs has an impact on sales of books, games, and movies (Chevalier and Mayzlin 2006; Dellarocas, Zhang, and Awad 2007; Liu 2006; Duan, Bin, and Whinston 2005; Zhu and Zhang 2010). In contrast, Gauri, Bhatnagar, and Rao (2008) collected data from BizRate.com, an online price comparison web site, and found that it is the percentage of positive reviews rather than the total number of reviews that influences the likelihood of repurchase. Davis and Khazanchi (2008) conclude that it is not simply the number of reviews that affects sales but also the efficacy of promotion, the type of product, and the frequency of visualization on other e-commerce websites. However, in this study we hypothesize that a high number of reviews will increase a traveler’s perceptions of the popularity of accommodations and increase the likelihood of the adoption of information from ORs. Thus:

Hypothesis 7: There is a positive relationship between information quantity and travelers’ information adoption from ORs.

Product Ranking refers to a typology of categorical or numerical information based on travelers’ overall (average) evaluation of accommodations in a destination. Rankings and ratings of a product are a typical feature of e-WOM because in WOM it is often difficult to sort out all of the opinions of reviewers and obtain a correct, unbiased summary of product evaluations. Different COPs adopt different scales for building rankings which are graphically illustrated using a number of stars. For example, Tripadvisor.com adopts a five-point scale from 1 (terrible) to 5 (excellent) to rate accommodations. On Booking.com, travelers can adopt a scale from 1 to 10 (terrible to superb). The ranking or numbers of stars represents the average customer’s evaluation of accommodation and summarizes the proportion of positive, neutral, and negative reviews. We argue that product ranking provides a short cut in the information elaboration process, since it restricts the number of alternatives available by displaying accommodations according to traveler average evaluations, from the best to the worst. Thus, travelers may not need to scroll down the entire list or to check all the alternative accommodations available and to read all ORs. By restricting the alternatives available to travelers, the overall ranking of accommodations in a destination might enable the adoption of information from ORs. Current e-WOM literature has not investigated product ranking as an antecedent of travelers’ adoption of information from ORs. The majority of studies have investigated the influence played by either positive or negative reviews (Park, Lee, and Han 2007; Park and Lee 2008; Gauri, Bhatnagar, and Rao 2008; Vermeulen and Seegers 2009; Lee and Lee 2009; Gupta and Harris 2010). However, in a real scenario travelers do not read only positive or negative reviews; rather they may use summary statistics like rankings to understand the proportion of negative and positive reviews for a specific accommodation. Therefore, in this research we argue that overall product ranking may influence travelers’ adoption of information from ORs. Thus:

Hypothesis 8: There is a positive relationship between product ranking and travelers’ information adoption from ORs.

Method

Data Collection

An online questionnaire was created using professional survey-design software and a link to the questionnaire was sent to the study sample by email. The questionnaire was primarily composed of closed-ended questions measured using a 7-point Likert-type scale and was pilot-tested with 30 users of ORs. The sample was selected along purposive lines with a focus on identifying travelers who had recently read ORs when searching for information on accommodations while planning their holidays.

An email was then sent to a convenience sample of academic and administrative staff from three Italian universities. A snowball sampling method was then used to expand the sample and generate additional contacts from nonacademic users. The questionnaire was initially distributed by email to a convenience sample of 55 workers and part-time students with occupations in a variety of industries. This method is appropriate for this study since readers of ORs on accommodations may represent a small percentage of the overall population and therefore might be difficult to identify or to contact. The questionnaire was available in English and in Italian as the research was based in Italy. Questionnaires were translated from English into Italian by an Italian native speaker. A second Italian native speaker translated back the Italian version into English. A total number of 578 respondents completed the survey. Of these, 13 were excluded from subsequent analysis because respondents did not complete all of the questions or because answers were not consistent or accurate.

Construct Measures

The items used to measure the constructs explored in this study are displayed in Table 1. Most of these items have already been used in previous e-commerce, information systems, and e-WOM research (Wixom and Todd 2005; Park, Lee, and Han 2007; Cheung, Lee, and Rabjhon 2008). A scale for measuring product rankings was developed for this study using the procedure recommended by Gerbing and Anderson (1988). The dependent variable was travelers’ adoption of information from ORs, which was measured by two items derived from Wu and Shaffer (1987), and recently adopted by Cheung, Lee, and Rabjhon (2008) in e-WOM research. Involvement with message processing was measured using a scale that was developed and tested by Wheeler, Petty, and Bizer (2005). For the sake of simplicity, only three items were used.

Table 1.

Sociodemographic Characteristics of the Respondents.

Sociodemographic Characteristics of the Respondents
Dimension	Items	Frequency	Percentage
Gender	Female	316	55
	Male	262	45
Age	18-25	80	13.8
	26-35	466	80.6
	36-45	24	4.2
	46-54	8	1.4
	>55	0	0
Education	Primary school	8	1.4
	High School	80	13.8
	Undergraduate	240	41.6
	Postgraduate	250	43.2
Economic Status (€)	70,000 and above	0	0
	50,000-69,000	82	14.2
	30,000-49,000	101	17.5
	10,000-29,000	245	42.4
	Under 9,999	142	24.5
	No answer	8	1.4
Nationality	European	490	84.8
	African	48	8.3
	Middle-Eastern	40	6.9

Sample Profile

The sociodemographic characteristics of the sample are presented in Table 2. The majority of respondents were between 26 and 35 years old (80.6% of respondents), or less than 25 years old (13.8%). The disproportionate predominance of this age cohort may represent a limitation of the study. However, this age cohort represents the typical age of many users of ORs and other social media and is also known as the generation Y cohort (Blackshaw and Nazzaro 2006). The level of education of the sample was high, with 43.2% of the respondents possessing a postgraduate diploma and 41.6% possessing or pursuing an undergraduate degree. The disproportionate predominance of highly educated respondents must be considered a limitation of this study.

Table 2.

Constructs and Items.

Dimension	Items
Information relevance (Wixom and Todd 2005)	The information I get through online reviews is relevant as it matches my needs.
	The information I get through online reviews is appropriate for satisfying my needs.
Information understandability (Wang and Strong 1996)	The information I obtain from online reviews is easy to understand.
	The information I obtain from online reviews is easy to interpret.
	The information I obtain from online reviews is easy to read.
Information accuracy (Wixom and Todd 2005)	The information I obtain from online reviews is correct.
	The information I obtain from online reviews is accurate.
	The information I obtain from online reviews is reliable.
Information completeness (Wang and Strong 1996)	The information I obtain from online reviews is of sufficient depth.
	The information I obtain from online reviews is of sufficient breadth.
Information value-added (Wang and Strong 1996)	The information I obtain from online reviews enables me to understand both the positive and the negative aspects of specific accommodation.
	The information I obtain from online reviews enables me to detect unknown aspects of specific accommodation (related to specific situation or uses).
Information timeliness (Wixom and Todd 2005)	I adopt current comments in online reviews.
	I adopt timely online reviews.
	I adopt up-to-date online reviews.
Information quantity (Park et al. 2007)	I adopt online reviews when the number of reviews per accommodation is large.
	I adopt online reviews when the quantity of reviews per accommodation information is large.
Product ranking (original scale)	The (overall) ranking of different accommodations facilitates the evaluation of the alternatives available.
	(Overall product) rankings help me to rapidly select the best accommodation among several alternatives.
Involvement (Wheeler, Petty, and Bizer 2005)	How much effort did you put into evaluating the given information?
Information adoption (Cheung, Lee, and Rabjhon 2008; Wu and Shaffer 1987)	Did you think deeply about the information contained in the message?
	How personally involved did you feel with the issue you read about?
	I closely followed the suggestions in online reviews and went to the recommended accommodation.
	To what extent does the information in the OR motivate you to purchase the recommended accommodation?

The majority of our sample was composed of individuals from European countries, primarily from Italy, which is acknowledged as a limitation of the study. This sample composition is a response to the need to widen the geographic and cultural scope of e-WOM research, as most existing studies have been conducted in the United States and in Asian countries (Chan and Ngai 2011).

Data Analysis

The most fundamental assumption in multivariate data analysis is the normality of the data (Hair et al. 2010). We used the skewness value to test normality. None of the variables considered in the study ever exceed ±2.58 or ±1.96; thus, the distribution is normal.

Reliability as well as the convergent and discriminant validity of the model was assessed. Reliability was assessed for each construct using Cronbach’s α, which is the most widely used measure of reliability among researchers (Nunnally 1978). Values can range between 0 and 1, with higher values indicating higher reliability among the indicators. Nunnally (1978) suggests that a value of 0.70 indicates good item reliability. Cronbach’s alpha values for the items of the present study were between 0.788 (involvement) and 0.861 (information adoption) (see Table 3). All items had an overall Cronbach’s alpha value of 0.826, which confirms that the model is reliable.

Table 3.

Factor Loadings and Cronbach’s Alpha.

Construct	Items	Factor Loadings
Product ranking	The ranking of different accommodations facilitate the evaluation of the alternatives available.	.77
α = .833	(Overall product) rankings help me to rapidly select the best accommodation among several alternatives.	.74
Information quantity	I adopt online reviews when the number of reviews per accommodation is large.	.67
α = .798	I adopt online reviews when the quantity of reviews per accommodation information is large.	.69
Information relevance	The information I get through online reviews is relevant as it matches my needs.	.78
α = .837	The information I get through online reviews is appropriate for satisfying my needs.	.80
Information value-added	The information I obtain from online reviews enables me to understand both the positive and the negative aspects of specific accommodation.	.86
α = .789	The information I obtain from online reviews enables me to detect unknown aspects of specific accommodation (related to specific situation or uses).	.77
Information understandability	The information I obtain from online reviews is easy to understand.	.86
α = .839	The information I obtain from online reviews is easy to interpret.	.74
	The information I obtain from online reviews is easy to read.	.68
Information timeliness	I adopt current comments in online reviews.	.74
α = .844	I adopt timely online reviews.	.78
	I adopt up-to-date online reviews.	.80
Information accuracy	The information I obtain from online reviews is correct.	.82
α = .836	The information I obtain from online reviews is accurate.	.71
	The information I obtain from online reviews is reliable.	.87
Information completeness	The information I obtain from online reviews is of sufficient depth.	.67
α = .835	The information I obtain from online reviews is of sufficient breadth.	.71
Involvement	How much effort did you put into evaluating the given information?	.77
α = .788	Did you think deeply about the information contained in the message?	.67
	How personally involved did you feel with the issue you read about?	.75
Information Adoption	I closely followed the suggestions in online reviews and went to the recommended accommodation.	.65
α =.861	To what extent does the information in online reviews motivate you to purchase the recommended accommodation?	.77

The convergent validity was assessed using the average variance extracted (AVE) (Fornell and Larcker 1981). As illustrated in Table 4 all AVE scores exceeded the threshold of 0.6 and of 0.5; thus, indicating a strong internal reliability and implying convergent validity (Fornell and Larcker 1981). Evidence of discriminant validity is provided in the lower matrix of squared correlation coefficients and AVE scores as shown in Table 4, where the AVE values for any two constructs were always greater than the squared correlation estimate between any two factors, suggesting discriminant validity (Fornell and Larcker 1981).

Table 4.

Items, Mean, Standard deviation, Skewness and Kurtosis, AVE, and Correlation Matrix.

Items	M	SD	Skew	Kurtosis	1	2	3	4	5	6	7	8	9
1. Product ranking	2.6	1.56	0.502	0.410	0.86
2. Info accuracy	2.9	0.824	0.006	0.176	.233	0.85
3. Info quantity	3.8	1.31	0.472	−0.958	.094	.098	0.63
4. Info relevance	2	0.819	0.218	−1.121	.215	.386	.228	0.83
5. Info value added	2.5	1.20	1.258	1.203	.203	.268	.379	.408	0.71
6. Info understanding	2.7	0.922	1.481	3.209	.257	.193	.068	.298	.378	0.69
7. Info timeliness	3.7	1.81	0.317	−1.050	.122	.160	.174	.107	.289	.154	0.77
8. Info completeness	3.9	1.69	0.037	−0.295	.458	.313	.163	.044	.157	.142	.244	0.83
9. Info adoption	3.2	1.69	0.689	0.401	.629	.386	.036	.365	.321	.211	−.034	.332	0.71

Note: AVE = average variance extracted.

The variance inflation factor (VIF) method was adopted to assess multicollinearity. All of the regression coefficients had VIFs that were below the 5 threshold as recommended by Hair et al. (2010); thus, this result implies that no multicollinearity existed among the constructs that were used.

Results

A stepwise multiple regression analysis was adopted to test hypotheses. The results of the analysis are presented in Table 5. The resulting relationship among some information quality dimensions, product ranking, and travelers’ adoption of information from ORs was strong and highly significant (R² = .518, adjusted R² = .507, F = 65.773, p < .001, df = 5). The model explained approximately 52% of the variance, which is indicative of good overall explanatory power. However, some of the independent variables were not considered to be significant. The strongest antecedents of travelers’ adoption of information from ORs were product ranking (β = .593, p < .001), information accuracy (β = .408, p < .001), and value-added information (β = .231, p < .001). Information relevance (β = .226, p < .001) also showed a strong predicting power, whereas information timeliness (β = .180, p < .001) had a lower but significant influence on travelers’ adoption of information. Information quantity (β = .052, p = nonsignificant), information understanding (β = .045, p = nonsignificant), and information completeness (β = .053, p = nonsignificant) did not have a significant influence on travelers’ adoption of information. Therefore, hypotheses 1, 3, 4, 5, and 8 are supported, while hypotheses 2, 6, and 7 are rejected.

Table 5.

Regression Analysis Results, t Value, VIF.

	Model Β	SE	t Value	VIF
Variable		0.293
Information timeliness	0.180***	0.039	4.577	1.105
Information understandability	0.045 (n.s.)	−1.016	1.259	1.244
Information relevance	0.226***	0.096	2.719	1.346
Information accuracy	0.408***	0.091	4.464	1.235
Information value-added	0.231***	0.064	2.908	1.329
Information completeness	0.061 (n.s.)	0.108	1.108	1.352
Information quantity	−0.052 (n.s.)	0.056	1.086	1.435
Product ranking	0.593***	0.045	13.077	1.095
R ²	0.518
Adjusted R²	0.507
F	65.773***

Note: VIF = variance inflation factor; n.s. = nonsignificant.

***

p < .001, **p < .05, *p < .10.

Discussion and Conclusions

ORs represent a type of user-generated content, and the diffusion and adoption of such reviews is constantly growing among high-involvement travelers. As the importance of ORs on a traveler’s decision is increasing, businesses are interested in gaining a better understanding of the factors that strongly predict travelers’ e-WOM behavior. The findings of the present research provide several theoretical and practical contributions.

This research attempted to fill a gap in the literature by identifying the strongest antecedents of travelers’ information adoption from ORs, including both central and peripheral cues in the research model. In particular, we investigated the role that central cues to information processing, including six information quality dimensions, as well as two peripheral cues (information quantity and product ranking), play in influencing high-involvement travelers’ adoption of information from online accommodation reviews.

From a theoretical point of view, our results are new as no previous study has investigated the influence that both central and peripheral cues have on high-involvement travelers’ information adoption. In particular, the tested model includes a set of factors that have never been tested together previously: information quality dimensions, product ranking, and information quantity. Moreover, we have advanced the literature on e-WOM by identifying new predictors of information adoption from ORs.

The present study has introduced the concept of product ranking, a type of categorical information that is considered a peripheral cue to information processing. Product ranking was found to be the strongest predictor of travelers’ adoption of information from ORs and showed a greater influence than any information quality dimension. This result contrasts with the ELM, which states that high-involvement consumers analyze the quality of the arguments in an advertisement (the influencer) to make a decision (Petty and Cacioppo 1986). Moreover, the results of the present study differ from previous findings in which information quality had a stronger effect than peripheral cues like information quantity in high-elaboration situations (Park, Lee, and Han 2007; Lee, Park and Han 2008; Gupta and Harris 2010). Travelers appear to adopt both a peripheral and a central route to information adoption. The peripheral route is primarily influenced by the ranking of accommodation. This is complemented by adopting a central route and using specific information quality dimensions to process information. Therefore, our results call for a further development of the ELM in the context of ORs on accommodations. Moreover, our results imply that COPs can adopt both a central and a peripheral route to influence high-involvement travelers.

In the present study, we have used product ranking and information quantity to represent peripheral routes to persuasion. Product ranking emerged as the strongest antecedent of high-involvement travelers’ adoption of information from ORs, which is a new finding in e-WOM research. This result may be influenced by the fact that the number and the variety of accommodations such as hotels available in a specific destination are often high. Therefore, travelers may prefer to adopt information short cuts to reduce the number of alternatives that they might consider to book. In doing so, they adopt information on the average score of overall travelers’ evaluations of such accommodation at a destination. According to a rational view, travelers economize on time and effort when they search for information (Solomon 2011) and overall product ranking is a type of categorical information that helps travelers to reduce the number of alternatives as well as facilitates a rapid retrieval of the best ranked accommodations at a destination. Rankings are also helpful for evaluating and comparing different options, thereby reducing the time and the effort needed to identify the most suitable accommodation. For example, travelers looking for accommodation in a destination where they have never been before will have little knowledge of the destination. Thus, they may think that if previous travelers have evaluated a hotel highly, that hotel should be good.

Another significant result comes from the analysis of the influence of different information quality dimensions on travelers’ adoption of information from ORs. Existing studies in e-commerce have used information quality as a composite construct without investigating the influence of each single information quality dimension (Park, Lee, and Han 2007; Park and Lee 2008). The findings of this study highlight that some information quality dimensions are more important than others in predicting travelers’ adoption of information. Information accuracy, value-added information, and information relevance strongly predicted travelers’ adoption of information from ORs; information timeliness was less important in explaining the relationship, while other dimensions like information understandability and information completeness did not have a significant influence.

Among the information quality dimensions, information accuracy was the strongest predictor of traveler’s adoption of information from ORs. Through these findings, we may infer that travelers adopt information because they believe information comes from real travelers who provide an accurate evaluation of accommodations.

Another information quality antecedent relating to the information adoption from ORs is information value-added. The provision of both positive and negative information provides travelers with a critical description of accommodations and helps them to better evaluate their quality. For instance, value-added information may help travelers to detect what has been shadowed by marketing communications. For example, by reading ORs a traveler may discover that a special offer might hide a hotel’s ongoing renovations or that a hotel is located in an unsafe or noisy area. Thus, travelers adopt information contained in ORs in their decision-making process because ORs help them to discover what might not have been reported by the property or by a travel agent’s marketing communications.

Information relevance is another important antecedent of travelers’ adoption of information. ORs provide information on different travel experiences; thus, they potentially satisfy a plethora of information needs because they are written by a variety of target groups, with each one reporting their own needs, opinions, evaluations, and experiences. Thus, travelers adopt information from ORs because they get information that might satisfy their specific needs.

Information timeliness has a moderate influence on the adoption of information. This finding may be due to the fact that travelers not only adopt current information from ORs; but they also incorporate past reviews when making decisions. Contrary to previous findings that revealed that information quantity is an important factor in e-WOM as it predicts product sales (Duan, Bin, and Whinston 2005; Liu 2006; Chevalier and Mayzlin 2006; Dellarocas, Zhang, and Awad 2007), as well as consumer behavior (Park, Lee, and Han 2007; Park and Kim 2008; Sher and Lee 2009; Gupta and Harris 2010), the current research has found that information quantity does not appear to exert a significant influence on travelers’ adoption of information from ORs. The nonsignificant predicting value of this construct may be explained by the fact that travelers do not adopt information volume as an information short cut for detecting product popularity. Today, most COPs contain a larger number of reviews than they did in the past; thus, most websites have an acceptable number of reviews for each individual hotel or accommodation. For example, in 2005, Tripadvisor hosted one million customer reviews, whereas in 2013, this COP hosts more than 75 million customer reviews (Tripadvisor 2005, 2013). This result allows us to infer that travelers will not adopt information based on the volume of reviews; rather they will adopt categorical information such as overall accommodations ranking, which summarizes the evaluations provided by all travelers who have reviewed accommodations in a destination. Moreover, the overall ranking of an accommodation may be an indicator of product quality and appears to be more important than product popularity when adopting information from ORs. For instance, the categorical information about a hotel ranked in first position, which has been reviewed for example by 20 users, will be more influential than the information on a hotel that is ranked 10th and has been reviewed by 80 reviewers. Therefore, a hotel that has been reviewed by 20 customers and another one that has been reviewed by 80 customers will be equal in travelers’ eyes. Consequently, a larger number of reviews is not always favorable.

Information understandability and information completeness did not influence travelers’ adoption of information from ORs. Thus, even if reviews are not always clear or very well written, travelers adopt them for other reasons. Increasingly, language is not a barrier, as most websites have a system of translation from English, which is the most used language on the web. Our results show that travelers are not influenced by the completeness of information. Most travelers appear to only search for information that satisfies their particular needs. Therefore, they are not interested in knowing everything about specific accommodations and may not read all the content in a review; rather they are only interested in the kind of information that satisfies their information needs.

Implications

Practical Implications

The results of this study have important implications for marketing practitioners in the e-tourism and other industries. For instance, COP developers should pay a lot of attention to categorical information, such as product rankings, which help travelers to reduce the accommodation alternatives available for purchase. Moreover, COP designers should attempt to provide more peripheral cues that travelers can use to rapidly obtain the type of information they are looking for. For example, the use of meta-data techniques, such as red and green meta-tags to respectively show the most frequently mentioned strengths and weaknesses of a particular hotel could be used to facilitate travelers’ evaluation of the services of accommodations.

From the managerial side, hotel managers should be aware that through ORs travelers may become comarketers (Sigala, Christou, and Gretzel 2012) by producing reviews that influence other travelers’ decisions more strongly than traditional marketing communications (Bickart and Schindler 2001; Senecal and Nantel 2004). Whereas in the past, a top–down system of product ranking was provided by acknowledged opinion leaders and experts in a particular sector (e.g., Lonely Planet for accommodations), a form of bottom–up evaluation of tourism-related products and services based on customer evaluations is emerging. This might have negative repercussions on the business of official and well-known ratings agencies in the tourism sector. In fact, travelers might be more willing to trust other travelers’ ranking than the ratings provided by business-oriented organizations and groups of tourism experts.

Another important implication of this research is the influence that specific information quality dimensions have on travelers’ adoption of information from ORs. Whereas the role of categorical information (i.e., product ranking) is to orient travelers’ choice, the role of information quality is to provide accurate, critical, relevant, and timely information to travelers.

The accuracy of the information is a powerful influencer in the traveler decision-making process. Accordingly, independent COPs should refine and improve the techniques that they use to guarantee the accuracy of ORs in order to avoid viral marketing activities that spread fraudulent or fake reviews. Thus, COPs might display the Facebook profile of reviewers as a way to increase the perceived reliability of the information provided by reviewers. Websites could also develop a star ranking system to evaluate the expertise of reviewers, which could be based, for example, on the frequency of product/service reviewed and on the experience of use of different tourism products/services in the same category (e.g., for travel websites, this might be the range of accommodations booked and the number of destinations visited).

Travelers are adopting information from ORs to acknowledge the kind of information that accommodation marketers might be hiding or not communicating properly on their website. Since travelers get to know both the negatives and positives of accommodations, marketing campaigns and websites should be more in line with the real quality of the product and services they offer. A recommendation for tourism marketers that comes out from this research is that a more sober and less pompous communication strategy should be adopted when promoting an accommodation. Accordingly, a high discrepancy between what is being said in brand communications and what is being experienced by travelers might motivate more travelers to negatively rate a product or to advise other travelers about its “hidden” weaknesses.

Relevance is another important informational factor that COPs and e-travel agencies should consider. Thus, travelers spend time and effort only to read the information fitting their information needs. Therefore, it is important to segment the information and content according to the information needs of different typologies of travelers. For instance, some COPs provide information about the typical client (e.g., groups, friends, romance, and solo traveler) of a specific accommodation, providing information details about the target group of the accommodation.

In summary, COPs should monitor information quality dimensions because they contribute to influencing travelers’ decisions to adopt information from them. By refining these dimensions, they may attract more reviewers and travelers, improve search engine positioning, and increase brand awareness. Independent COPs are already attempting to increase the amount of users and reviews on their websites in order to improve travelers’ awareness of their brand. In doing so, some of them are not effectively regulating the quality of ORs through strict information quality standards and advanced systems to track false reviewers. This has led to some of them being criticized by the BBC in the United Kingdom about the reliability of posts on websites such as Tripadvisor (Morrison 2012). Nevertheless, some online organizations are already developing some systems to control the quality of the content provided by travelers. Accordingly, Wikipedia is reviewing the quality of its entries through its volunteer administrators (Wikipedians). Since information accuracy is a strong antecedent of travelers’ adoption of information from ORs, similar initiatives should be taken by COPs by using the criteria identified in this study. Moreover, COPs could offer some type of reward mechanism for travelers who post high-quality reviews.

Limitations and Future Research

The present study has a number of limitations. The sample was composed mainly by Italian respondents; however, the decision to conduct the study in this country was made in order to respond to the need to widen the geographic and cultural scope of e-WOM research, as most existing studies have been conducted in the United States and in Asian countries (Chan and Ngai 2011). The present study has tested the model only among high-involvement consumers; therefore, a replication of the study among low-involvement consumers would be useful.

Despite the strength of snowballing for being a convenient and economic method to collect data, the researcher has limited control over the composition of the final sample. However, snowball sampling was appropriate because this research was not aiming to measure the variation of the variables across a population; rather, the purpose was to test the hypothesized relationships among variables (He and Li 2010).

Moreover, the present study did not distinguish two main traveler motives: business and leisure. Business travelers are generally less involved and motivated to spend high amounts of time to search for information on accommodation than holiday makers; therefore, they may be more willing to adopt only peripheral cues to evaluate ORs. On the contrary, leisure travelers might be more involved because travel is a very expensive and risky item and sometimes is the most expensive purchase for a family during the year (Lewis and Chambers 2000). Therefore, leisure travelers might be more willing to adopt only central cues to information processing. In this study, we focus on travelers’ adoption of information from ORs of accommodations. Future research could test our model across different product categories (e.g., high tech products) in order to generalize our results.

Further research could test the model proposed in this research on different typologies of COPs. In fact, results may differ for two typologies of COPs: independent websites (i.e., Tripadvisor.com) and e-merchants (i.e., Booking.com). E-merchants publish ORs written only by travelers who have previously purchased a product, while in an independent website travelers only need a valid email address to publish a review. Lee and Youn (2009) found no difference in the influence that branded review websites and independent COPs exercise on travelers’ WOM recommendations. However, travelers may believe that the accuracy of information provided in independent COPs may be higher than the information provided in brand websites. Therefore, researchers might find a difference in the travelers’ likelihood to adopt information from ORs in the two types of websites.

Despite limitations, the results of this study enhance the understanding of the factors that influence travelers’ adoption of information from online accommodation reviews.

Footnotes

Acknowledgements

The authors would like to thank Dr Bridget Major, the JTR editor, and two anonymous reviewers for their helpful comments on earlier drafts of this paper.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Raffaele Filieri is Senior Lecturer in Marketing at Newcastle Business School, Northumbria University, UK. His research interests include e-tourism, tourism marketing, sustainable tourism and innovation in tourism.

Fraser McLeay is Professor of Strategic Marketing Management and co-chair of the Marketing, Travel and Tourism Management Subject Group at Northumbria University. His research interests include branding, the tourist experience and e-WOM.

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