A netnographic examination of travelers' online discussions of risks

Tourism Management Perspectives 2–3 (2012) 65–71

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Tourism Management Perspectives journal homepage: www.elsevier.com/locate/tmp

A netnographic examination of travelers' online discussions of risks Peter Björk a,⁎, Hannele Kauppinen-Räisänen b a b

Hanken School of Economics, Department of Marketing, P.O. Box 287, FIN-65101 Vaasa, Finland Hanken School of Economics, Department of Marketing, P.O. Box 479, FIN-00101 Helsinki, Finland

a r t i c l e

i n f o

Article history: Received 22 November 2011 Accepted 5 March 2012 Keywords: Netnography Information search Tourist decision making Perceived risk Travel Tourism

a b s t r a c t This research explores online discussions of risk by applying a netnographic approach, which in previous tourism studies has been used to probe travelers' online narratives in general, rather than examining a particular dimension. In the present study, blogs about risk and safety issues were analyzed with the intention of scrutinizing risk categories discussed online and exploring the contingency of risk dimension for cities of different risk levels. Helsinki (Finland), Madrid (Spain), and Cape Town (South Africa) were chosen to represent low-, medium-, and high-risk cities, respectively. Findings from our analysis of the TripAdvisor's forum for risk prove the applicability of the taxonomy of risk categories previously identified in offline contexts and that perceived risk dimensions are destination specific. Insight into risk dimensions that tourists discuss online enable destination marketers to take action, eliminate factors that cause risk perception, refine destination marketing communication, and build strong brands. © 2012 Elsevier Ltd. All rights reserved.

1. Introduction Tourism scholars and practitioners alike have acknowledged the crucial impact of destination image on travelers' destination choices (Balakrishnan, 2009; Pike, 2009). Consequently, destination branding has emerged as an important issue in creating a positive destination image that not only identifies and differentiates the destination, but also reduces perceived destination risk (Berthon, Hulbert, & Pitt, 1999; Lee, O'Leary, & Hong, 2002; Qu, Kim, & Im, 2010). Research has recognized that destination images are formed and perceptions of risks are reduced as travelers' process information from various sources over time, and for those and other communicative reasons, destination marketers and travel companies appear at travel fairs and use advertisements, travel brochures and other destinationspecific literature (Kozak & Kozak, 2008). Scholars and practitioners have observed the widespread use of the Internet as an information-gathering medium and a source for image formation (Castaneda, Frias, & Rodriguez, 2007; Choi, Lehto, & Morrison, 2007). Hence, official tourist boards, tourism bureaus, and convention and visitors bureaus have aimed at marketing destinations and communicating positive images by creating appealing official tourism websites (Lepp, Gibson, & Lane, 2011). In addition, travel companies have created websites not only to communicate an appealing brand identity, but also to develop their own online communities, where they engage in interactive online communication (Casaló, Flavián, & Guinalíu, 2010). Although these firm-hosted ⁎ Corresponding author. Tel.: + 358 40 3521 723. E-mail addresses: peter.bjork@hanken.fi (P. Björk), hannele.kauppinen-raisanen@hanken.fi (H. Kauppinen-Räisänen). 2211-9736/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. doi:10.1016/j.tmp.2012.03.003

business-to-consumer communications performed online serve to build the host's brand, they also influence formation of destination image (Casaló et al., 2010). The Internet has become an attractive way for tourists to not only search for information and form a destination image, but also engage in online communication. Knowledge is transferred through the means of customer-to-customer communication (YouTube.com, Facebook, MySpace.com, and Twitter), and the customer generated content found on travel sites like TripAdvisor, TravelPod, and TravelBlog, are used to get inspiration, travel hints, advice and recommendations, and, of course, to share one's own experiences. These are strong arguments for destination marketers to closely monitor ongoing online communication in social media (Martin & Woodside, 2011). Some argue that customer-to-customer travel blogs have little influence (Volo, 2010) yet worry about their negative impact (Akehurst, 2009). In fact, due to information overload, not only travel blogs, but the entire Internet is considered to negatively affect destination image (Frias, Rodriguez, & Castaneda, 2008; Lepp et al., 2011). This worry stems largely from the huge range of information available online for specific destinations. However, the fact is that travelers share various types of personal destination-specific experiences online, both positive and negative. Focusing on the use of the Internet in forming a destination image, Dwivedi (2009) observed a number of risk-related attributes regarding how travelers described India on online tourism sites. By applying Beerli and Martin's (2004) classification of nine dimensions determining destination image, Dwivedi (2009) was able to detect several attributes suggesting that perceived risk is an essential dimension in forming destination image online. Both studies affirm that online communication is

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fragmented, and that one of the influential issues communicated online is risk, although they do not focus on risk per se. Keeping in mind that the majority of travelers perceive risk to be negative, which may alter their destination choice and travel plans (Kozak, Crotts, & Law, 2007), one could arguably claim that the implicit notion of risk perception communicated online deserves more attention in scholarly studies. This paper aims to answer the following as yet unanswered research questions: what risks are communicated by means of social media? Are taxonomies of risk contingent on different types of destinations? Previous studies of perceived risk linked to tourism and travel have identified six risk categories: functional, physical, financial, social, psychological, and temporal risk (Boksberger & CraigSmith, 2006). This study investigates whether the same risks can be identified in the annotations posted online. Destinations differ in terms of qualities they hold and images they evoke of which perceived risk is one dimension. Iraq, Colombia, Sudan, and Somalia are perceived as the most dangerous countries to visit and the Nordic countries as the least dangerous. The most dangerous cities are Caracas (Venezuela), Ciudad Juarez (Mexico), Mogadishu (Somalia), and Cape Town (South Africa), while Helsinki (Finland) is perceived as among the safest (www.travelersdigest.com). Accordingly, this study explores whether risk annotations are destination specific. Based on the background given above, the objective of this paper is to explore perceived risk communicated online and to contrast the findings to previously presented taxonomies of risk categories (Boksberger & Craig-Smith, 2006; Quintal, Lee, & Soutar, 2010; Sönmez & Graefe, 1998). This paper contributes to the literature by also shedding light on the volatility of the risk categories, here contingent on different types of cities: Helsinki (Finland) representing the category of safe cities (www.mercer.com; www.marketwatch.com), Cape Town (South Africa) is often listed as one of the most dangerous ones in the world (www.mostdangerouscities.org; www.urbantitan. com), whereas Madrid (Spain) is perceived as a city with a medium risk level (Nyiri, 2005; Hideg & Manchin, 2005). This knowledge may be important for destination marketers and travel companies as they aim at understanding images travelers hold for destinations, and as a means of branding a destination in a positive manner (Pan, MacLaurin, & Crotts, 2007). This study follows researchers who have utilized netnography to probe travelers' online narratives. Woodside, Cruickshank, and Dehuang (2007) studied first-person, consumer-generated stories of visitors to Bologna and Florence to map their experiences, thereby deriving important insight to be utilized in the process of building destinations as iconic brands. Moreover, the usefulness of analyzing visitors' stories posted online in the context of destination branding has been confirmed by other researchers (Martin, Woodside, & Dehuang, 2007; Hsu, Dehuang, & Woodside, 2009; Martin & Woodside, 2011). They used the same approach to interpret visitors' experiences of Mumbai, Seoul, Singapore, Tokyo, Beijing, Lijiang, Shanghai, and Xi'an. The usefulness of netnography has also been proved by researchers interested in fields other than tourism (e.g., Kozinets, 2002; Brown, Kozinets, & Sherry, 2003; Langer & Beckman, 2005; Sandlin, 2007; Rokka & Moisander, 2009). In contrast to the abovementioned studies, which all apply a holistic approach to gain consumer insight, the current study is more focused. By taking risk as a unit of analysis, we used the keyword “risk” in the sampling phase to identify a particular type of online discourse. We linked blog entries posted as warnings, based on personal experiences, or as enquiries about potential risk of the cities in focus and analyzed the succeeding blog threads to explore the risk categories and dimensions discussed online. The paper is organized as follows: In the literature review section, we discuss travelers' perceived risk in general, various types of risk in particular, and incidents causing risk perception. In the methodology section, we describe netnography, that is, the steps we took to collect and analyze the data. Then we discuss our findings and conclusions,

as well as the limitations of our research, and give suggestions for future studies. 2. Literature review Within the field of social sciences, perceived risk has been defined as a subjective judgment of the possibility of an adverse outcome (Brun, 1994; Aven & Renn, 2009), an evaluation inherently embedded in consumers' decision-making. Information search, as an integrated part of consumers' decision-making, has been discussed as one strategy consumers use to reduce perceived risk (Sirakaya & Woodside, 2005; Sönmez & Graefe, 1998). Due to the findings of a negative relationship between perceived risk and travel intentions and the fact that travelers search information from both online and offline sources, destination marketers would benefit from closely monitoring the differences between the various risk categories and what potentially pertains to travelers' perception of risk. Indeed, this is a topical issue (e.g., see the special issue of the Scandinavian Journal of Hospitality and Tourism, 2011, 11(3), “Tourism in a Decade of Terrorism, Disaster and Threats — Some Lessons Learned”), which has several consequences on travelers' behavior. “Simply put, destinations perceived as risky are less likely to be visited” (Lepp & Gibson, 2011, p. 289). 2.1. Travelers' perceived risk One of the characteristics shaping consumers' purchase decisions is the perception of a potential loss (Quintal et al., 2010). This judgment of a negative outcome is related to a subjective perception of risk and is based on the fact that consumers have semi-reliable memory and in general limited information and trials to consider (Boksberger & Craig-Smith, 2006). Therefore, perceived risk is to some degree involved in all purchase decisions, particularly in those where the outcome is uncertain (Dholakia, 2001). It is fair to claim that risk cannot be completely avoided when purchasing a tourism service, as it always involves a level of outcome uncertainty. This is due to the fact that travelers are purchasing an intangible service, an experience, and that the bought service cannot be experienced until after purchase (Fakeye & Crompton, 1991, Boksberger & Craig-Smith, 2006). Risk is also involved in travelers' decision to visit a particular place (Reisinger & Mavondo, 2005). Past experience of a destination decreases travelers' sense of destination risk, although a particular place might be perceived as risky in general, such as a destination that has recently suffered a tsunami (Sönmez & Graefe, 1998). From an information sourcing behavior perspective, past experiences relate to travelers' search for internal information, and if this information source proves to be insufficient, such as is the case when a traveler lacks previous experience, they are likely to use external sources to gather information and to reduce the perception of risk (Gursoy & McCleary, 2003; Björk & KauppinenRäisänen, 2011). Tourism researchers have identified several external information-gathering sources: family and friends, destinationspecific literature, media, and travel consultants (Kozak & Kozak, 2008). Currently, the Internet, particularly online communities and travel sites, is a major provider of destination information (Xiang & Gretzel, 2010). Currently, the Internet may still be more frequently used by younger travelers (Kozak & Kozak, 2008); however, it is increasingly used by travelers of all ages, including seniors (Beldona, 2005). Despite the amount of available external online and offline destination information, such information is not always sufficient to reduce the perceived risk. If the perceived potential of a negative outcome falls below an acceptable level, travelers may cancel their trip altogether (Reichel, Fuchs, & Uriely, 2007; Reisinger & Mavondo, 2005; Rittichainuwat & Chakraborty, 2009; Tsaur, Tzeng, & Wang, 1997). Alternatively, they may alter their travel plans, seek

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new options, or exchange already booked trips for those to safer destinations (Kozak et al., 2007; Rittichainuwat & Chakraborty, 2009). Because a potential risk of a tourism service is actualized only after the purchase decision, researchers have categorized different types of travelers according to their novelty-searching behavior and risktaking tendencies. Although one type is attracted to novelty and risky destinations, the need for safety and security determines most travelers' destination choices and travel intentions (Lepp & Gibson, 2008). Hence, as perceived risk is an integral part of travelers' decision-making and a destination's image, when unacceptably high it most often even determines travelers' intentions to avoid a particular destination (Lepp et al., 2011; Rittichainuwat & Chakraborty, 2009). This is supported by consumer studies on brands (Erdem & Swait, 2004), services (e.g., Bansal & Voyer, 2000), and new product development (Forsythe & Shi, 2003). These studies have found that the greater the sense of uncertainty, the greater the perception of risk and the more likely consumers will attempt to reduce that perception. The fact is that the perception of safety and security is increasingly determining current travelers' destination choices (Hall, Timothy, & Duval, 2004; Lepp & Gibson, 2008). 2.2. Various types of perceived risk Research has identified various incidents that evoke travelers' perception of risk, for example, political unrest, wars, and epidemics (Reisinger & Mavondo, 2005; Rittichainuwat & Chakraborty, 2009). To that list, Maser and Weiermair (1998) added diseases, crime, poor hygiene, transportation problems, culture/language barriers, and uncertainty about destination-specific laws and regulations. Travelers are less likely to visit destinations that have suffered, or are likely to suffer, from natural disasters, such as tornados, volcanic eruptions, tsunamis, and floods, or diseases, such as SARS, bird flu, and mad cow disease (Rittichainuwat & Chakraborty, 2009). Today, travelers also fear becoming the target of a terrorist attack (Reisinger & Mavondo, 2005; Rittichainuwat & Chakraborty, 2009). To better understand travelers' perception of risk, researchers have proposed several taxonomies to group and define the various types of risk that travelers perceive as negative. Roehl and Fesenmaier (1992) proposed three types of perceived risk: physical equipment, vacation, and destination risk. Sönmez and Graefe (1998) suggested four: financial, psychological, satisfaction, and time risk. Building on Mitchell and Greatorex's (1993) seminal work, a more recent study by Boksberger and Craig-Smith (2006) identified six: financial, functional, physical, psychological, social, and temporal risk. Functional risk is related to the concern about the consequences of the bought service (Boksberger & Craig-Smith, 2006). Accordingly, it represents a sense of perceived risk that the traveler will not gain the best possible benefit or utility from the vacation. Physical risk refers to a perceived threat directed at the travelers' health or appearance (Quintal et al., 2010), such as physical abuse. Hence, it is related to issues that somehow diminish travelers' sense of personal safety and security. Financial risk is related to the perceived risk of losing money. Basically, it refers to the uncertainty about whether the purchased service is worth the money paid (Boksberger, Bieger, & Laesser, 2007). Travelers might fear that the bought ticket is overpriced, or that a paid vacation will be unused. This perceived risk may also mean the fear of losing money caused by service failure, or the replacement of the paid for service. Social risk is caused by a concern about the opinions of others. This is the fear that one will be affected in a negative way by the perception of other individuals about one's purchase. For example, it may be related to the likelihood of embarrassment due to the selection of a particular service provider or a particular destination (Quintal et al., 2010). Psychological risk means a negative effect on the customer's peace of mind or selfperception caused by the bought service. Accordingly, it is a concern related to emotions the traveler might experience after the purchase

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(e.g., Quintal et al., 2010). Time risk represents the concern about losing time, inconvenience, or delay due to service failure and/or the time spent in getting a failed service replaced (Boksberger et al., 2007). Such risk includes late flights, among other scenarios. 3. Methodology In-depth understanding of risk categories and risk dimensions discussed online was sought by the means of netnography, which employs ethnography's research logic (Kozinets, 1997, 1998, 2006). The usefulness of netnography in gaining an insider's perspective on a given online culture has been tested within the fields of consumer behavior (Rokka, 2010), marketing (Kozinets, 2002), and tourism research (Volo, 2010). In particular, this method allows researchers the unique ability to tap into naturally occurring consumer conservations, and apply an exploratory approach. In this study, it gave us the possibility to map risk categories and risk dimensions, and contrast the findings to risk taxonomies based on offline data (Boksberger & Craig-Smith, 2006). In comparison to its offline counterparts, it has also been described as a faster, simpler, and less expensive method (Rokka, 2010). Access to the customer-to-customer communication conveyed on blogs (not researcher-elicited responses) and travelers' spontaneous annotations opens up a window into tourists' experiences of destination-specific qualities and dimensions of importance in purchase decisions. For example, Woodside et al. (2007) studied visitors' blogs of Bologna and Florence in an attempt to map their experiences and first impressions. Another study using a netnographic approach is that by Dwivedi (2009), who analyzed travelers' queries about India posted on travel message boards. One of his conclusions was that consumers not only construct their own destination image, but also share it with others via the Internet. Further studies have found that visitors share both positive and negative travel experiences by means of computer-mediated communications. Hsu et al. (2009) explored stories travelers shared on blogs about their visits to four Chinese cities. According to the content analysis performed, Beijing was amazing, Lijiang overwhelming, Shanghai modern, and Xi'an great. Negative comments visitors shared concerned the risk of being cheated (Beijing) and the people they encountered being not genuine (Lijiang), cold (Shanghai), or unpleasant (during a particular bus journey in Xi'an). Other cities that researchers have analyzed using the same method are Mumbai, Seoul, Singapore, and Tokyo (Drew, Woodside, & Dehuang, 2007; Martin & Woodside, 2011). 3.1. Data collection and sample Data used in this study includes posted online narratives pertaining to risk discussions linked to three cities and the structures of the blog threads. Netnography consists of a series of designated steps (Belz & Baumbach, 2010), which we followed: 3.1.1. Step 1. Selection of web community for analysis and to decide on entry TripAdvisor, as one of the largest travel community sites providing customer-to-customer communication, was chosen for this study. This free travel guide, with more than 50 million unique monthly visitors and 60 million reviews, assists visitors in gathering travel and destination information, and provides a venue for customer comments, recommendations and warnings (www.TripAdvisor.com). There is a wealth of online communities, which could have been used for this study. However, TripAdvisor was chosen because it offers a large range of communities, and offers the option to specify search criteria. Netnographers must, before entering the field, determine the role they will assume (active or passive) and the level of notification they will use (overt or covert), as illustrated in Fig. 1.

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Notification

68

Overt

Observer

Participant

Covert

Lurker

Spy

Passive

Active

Level of participation Fig. 1. Researcher's position in netnographic studies.

The observer informs bloggers about his or her presence in the community and asks for permission to act as a researcher. He or she takes on a passive role, that of an outsider, and does not interfere in the ongoing discussion. The lurker takes on a passive role, too, but he or she does not reveal his or her identity to the bloggers. The spy is more active than the lurker. The spy immerses him or herself in the discussion, but participates incognito. A participant is active in the ongoing discussions, and he or she announces his or her dual role, as a researcher and participant, explicitly. We used a passive, covert approach in this study, i.e. we did not interfere with the ongoing discussions and “influence the study subject” (Arsal, Woosnam, Baldwin, & Backman, 2010, p. 405). This research position was also practiced by Langer and Beckman (2005), who studied cosmetic surgery. We did not consider our decision not to ask for consent or to communicate our research interest as critical, since the blogs may have been posted months or even years ago, and are public on the Web. No login details are needed to enter the community. Additionally, we identify no single blog or blogger in the analysis. The choice of entry, that is, how a researcher presents him or herself, the research project, its aims, and expected outcome, is not an issue in netnographic studies where the researcher is a lurker. 3.1.2. Step 2. Data collection and analysis TripAdvisor can be tuned to predefined destinations and asked to identify blogs using certain keywords and concepts. The current study is based on narratives linked to three cities, Helsinki (Finland) as a lowrisk city, Madrid (Spain) as a medium-risk city, and Cape Town (South Africa) as a high-risk city. This type of categorization is not a selfevident truth. However, there are lists that position Cape Town among the most dangerous cities in the world (www.mostdangerouscities. org), and Helsinki as a safer city than Madrid (www.mercer.com). The search word used in these three forums was “risk”, and the years for analysis were 2009 and 2010. Content analysis was used to quantify the blog entries. In the first sample, the data generating process resulted in 14 blog threads for Helsinki, 36 for Madrid, and 88 for Cape Town. However, an initial scan of the blogs disqualified some, as they were not relevant for this study. Blogs that did not discuss risk, although they were posted on this forum, and blogs asking for general advice, were not included in this analysis. The final sample consisted of 10 blog threads for Helsinki, 17 for Madrid, and 24 for Cape Town. Each blog thread consisted of one “entry” (question or warning) and one or several “comments/responses”, here defined as blogs. Accordingly, the total number of blogs analyzed for Helsinki was 74, for Madrid 163, and for Cape Town 509. We initially planned to perform the data analysis using the qualitative software Nvivo. However, as the narratives were most often quite short, except for a couple of blogs posted in the Cape Town forum, we instead analyzed the narratives by means of a “paper and pencil” approach. We analyzed the narratives using two steps following a grounded theory approach (Glaser & Strauss, 1967). First, central keywords used by the bloggers were coded and categorized to form first-order

concepts, i.e. basic themes, “the lowest-order themes emerging from textual data” (Arsal et al., 2010, p. 405). These concepts were subject to the constant comparative technique to form categories (Miles & Hubermann, 1984) or organizing themes (Arsal et al., 2010). We applied the logic of qualitative studies and did not categorize the data into predefined structures. However, during the theory generation phase, we noticed that the identified categories resemble the structure of previously identified taxonomies (Boksberger & CraigSmith, 2006). The findings presented in the next section do not add further risk categories to those previously identified, but substantiate existing ones by adding data from online narratives and scrutinizing the thread structure. The covert netnographic approach applied supports a high personal and social distance between researchers and bloggers. Data was collected from 746 blogs to ensure data validity, and analyzed by two researchers to certify validity of results (Merriam, 2002). Reliability is “the extent to which research findings would be the same if the research were to be repeated at a later date” (Veal, 2011, p. 46). We have aimed at explaining all phases of our research in detail, how the data was collected, categorized, and analyzed, for others to follow and to ensure trustworthiness (Maxwell, 1996). 3.1.3. Step 3. Research ethics and member check Kozinets (2002, p. 65) recommended that the researcher fully disclose his or her presence and intentions to online community members during any research, ensure confidentiality and anonymity to informants, and seek and incorporate feedback from members of the online community being researched. Kozinets asserted that all covert studies not applying these recommendations should be condemned. However, others such as Lee (1993) and Langer and Beckman (2005) are more forgiving. There are, according to these authors, occasions when disguised observations are the only option. Studies of sensitive topics, such as cosmetic surgery, are given as one example (Langer & Beckman, 2005). Sandlin (2007) decided on a covert approach using the argument that the online community she was studying (i.e., Budget Living) is public. She pointed out the obvious risk that she would be accused of doing errands for the publisher. Furthermore, she did not want to endanger her status as a “guest” in the community. Our main argument for not using an overt approach in this study was the wish not to interfere with naturally occurring discussions. There is also a time dimension included. Some of the blogs analyzed were posted more than a year ago, and it did not seem to be appropriate to jump into the discussion at this late stage. Some of the threads analyzed were also closed by TripAdvisor, “This topic has been closed to new posts due to inactivity. We hope you'll join the conversation by posting to an open topic or starting a new one”. Member checks to get the community members' comments on the researchers' interpretation of data is not an option in covert studies. These types of studies do not allow for in-depth, interactive discussions with community members, either (Kozinets, 2002). 4. Findings and interpretations This section presents our findings from the travelers' online narratives of risk as follows. First, the online entries are analyzed using the taxonomy of perceived risk discussed in the previous section and the blog threads scrutinized. The entry and comment structure are analyzed to identify the way bloggers respond to particular inquiries. Second, risk dimensions linked to each risk category are presented and analyzed and illustrated by short quotations from the data. 4.1. Risk categories and blog threads Perceived risk linked to tourism can, according to Boksberger and Craig-Smith (2006), be grouped into six different categories, a

P. Björk, H. Kauppinen-Räisänen / Tourism Management Perspectives 2–3 (2012) 65–71 Table 1 Perceived risk structure of Helsinki, Madrid and Cape Town. Risk categories

Functional/performance Physical Financial Social Psychological Time/temporal General Total

Number of entries/(%) Helsinki

Madrid

Cape Town

2/(15.3%) 2/(15.3%) 3/(23.1%) 1/(7.7%) 3/(23.1%) 2/(15.3%) 0/(0.0%) 13 (100.0%)

9/(40.9%) 3/(13.6%) 3/(13.6%) 2/(9.1%) 2/(9.1%) 3/(13.6%) 0/(0.0%) 22 (100.0%)

1/(3.6%) 13/(46.4%) 4/(14.3%) 0/(0.0%) 2/(7.1%) 1/(3.6%) 7/(25.0%) 28 (100%)

structure that emerged in our exploratory study as well. However, further analysis showed that the distributions of the discussions of the different risk categories were biased and destination specific. Specifically, our findings revealed that functional risk dominated for Madrid, whereas the most common “entry” for Cape Town is physical risk (Table 1). Helsinki was characterized by a more even distribution of risk concerns. Furthermore, seven entry inquiries about Cape Town were hard to group into any of the previously identified risk categories. Therefore, an additional risk category was added, a category for general concerns about potential risks bloggers related to a destination. For example one blogger initiated a blog thread by asking “I was wondering about the safety and crime problems one hears about in S. Africa. Any Tips?”. The first reply in response turned the discussion towards Cape Town to be followed up by three other bloggers. We analyzed the structure of the blog threads to determine the number of comments bloggers posted as a response to each entry, how active the responding bloggers were, calculated as a blogger's share of the comments, and which topics were “hot”, based on the number of responses posted, or the length of the thread (Table 2). Finally, we explored whether additional risk categories were brought into the discussion beyond the one imbedded in the entry inquiry. The blog threads analyzed for this study were most often fairly short. Most entries resulted in one to five comments, but there were exceptions, especially when risk in Cape Town was discussed. Blogger activity measured as bloggers' share of posted comments showed that Table 2 Number of comments on entry, blog threads for Helsinki, Madrid and Cape Town. Blog threads Helsinki

Madrid

Cape Town

Number of comments on entry (Total N = 51) 1–5 6–10 11–15 20–25 26 or more

N (%)

N (%)

N (%)

6 (60.0%) 3 (30.0%) 1 (10.0%)

10 (58.8%) 4 (23.5%) 1 (5.9%) 2 (11.8%)

9 (37.5%) 4 (16.6%) 2 (8.3%) 3 (12.5%) 6 (25.0%)

Bloggers share of the posted comments 1–1.5 1.6–2 2.1–2.5 2.6–3 3.1 or higher

N (%)

N (%)

N (%)

8 (80.0%) 2 (20.0%)

12 (70.6%) 4 (23.5%) 1 (5.9%)

Hot topics (based on the longest thread)

Ferry traffic and weather conditions/ the risk of canceled departures/function and time risk 15 (8)

Number of comments in the longest thread (number of different bloggers in that thread)

Food quality in Madrid/the risk to get sick/physical risk 23 (13)

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one or two comments from responding bloggers was most common. However, we observed a shift in the structure of threads consisting of 15 comments or more for Madrid and Cape Town. The analyses of these threads reveal how the responding bloggers gradually started to comment on each other's narratives, resulting in a higher share of posted comments. The risk discussions generating the most input for Helsinki, measured in terms of the thread length, involved the issue of the ferry traffic and the risk of canceled departures due to weather conditions, especially during the winter. The issue that generated the most discussion in the forum for Madrid was an inquiry about food quality and the risk of illness. The number one issue for Cape Town was safety on a general level and the safety of the various areas around Cape Town in particular. The bloggers' answers and comments on entry questions matched very well, and few additional risk categories or dimensions were brought into the discussion. Two of the blog threads on Madrid and Cape Town included added risk categories. The blog threads for Helsinki were in this respect slightly different, because in this case a majority (6 out of 10) included added risk categories. 4.2. Risk categories and dimensions The content of the various risk categories, here defined as risk dimensions, varies between the three cities analyzed, as illustrated in Table 3. Functional risk for Helsinki was most often linked to weather conditions. For example, one of the enquiries posted on the forum for Helsinki was about the risk of canceled departures to Tallinn (Estonia) from Helsinki by ferry due to bad weather conditions. General safety issues for Cape Town unfolded into many dimensions. One was about the option to walk around in particular areas in comparison to taking a cab or renting a car. Another enquiry was about how safe taxis were: “Is it easy/safe to do so on Loader St?” In the case of Madrid, functional risk involved not being able to get to Madrid on time because of problems with online booking and train tickets. Physical risk in Helsinki concerned the cold weather and questions about whether women could safely travel and walk alone in the city. Physical risk dimensions discussed in the forum for Madrid involved the risk of catching swine flu, for example, in comparison to Cape Town, where malaria was a concern. One blogger wrote that she was “being advised to have a vaccination to prevent malaria” and asked, “Is this necessary?” Dimensions of physical risk discussed in these forums added to health concerns linked to the risk of being hurt or injured as a victim of violence. Financial risk in Helsinki mainly involved prices (i.e., whether the city was expensive). Dimensions discussed in the forum for Madrid Table 3 Perceived risk dimensions for Helsinki, Madrid and Cape Town. Risk categories

Risk dimensions illustrated Helsinki

Madrid

Cape Town

14 (58.3%) 4 (16.7%) 3 (12.5%) 2 (8.3%) 1 (4.2%)

Functional

Online booking and train connections

How safe are the taxis?

The risk of getting the swine flue

The risk of getting malaria

Safe areas in CT/the risk to be robbed and hurt/general risk

Financial

Risk for cancelation of the ferry traffic due to bad weather conditions The risk of cold weather and is Helsinki a safe city to travel to How and when to book to get the best price The policy of tipping

Physical

Social Psychological

The risk of not getting a ticket

Time/ temporal

How is Helsinki airport

106 (28)

Price level and the Value for risk of being cheated money How to behave in public places The risk of not getting hold of a ticket Transfer

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were about value for money. One blogger wrote, “Overall we found we were getting ripped off more often than not”. In Cape Town, on the other hand, financial risk mainly concerned bad exchange rates and the excursions not being worth the money. Social risk in Helsinki consisted of questions related to tipping. In Madrid, social risk concerned norms, too, such as whether entering a club in a hen's outfit was acceptable: “Would we…look out of place?” Another example of social risk discussed in Madrid was when a blogger asked whether walking down the street holding an alcoholic drink was okay: “Can you mix a drink in a plastic cup in your hotel room and go for a stroll?” Social risk was not on the agenda in the forum for Cape Town at all. Psychological risk in Helsinki can be linked to psychological unease. One blogger asked, “Approx[imately] how many of the people taking the Tallinn ferry on Sunday tend to be drunks? … Will I get a ‘frustrated’ feeling on these ferries for the 2 hrs?” This risk dimension for Madrid was linked to the risk of being mugged; a worry that apparently had a negative effect on the travelers' peace of mind. One entry included a concern of being falsely accused of stealing and a warning to other travelers. In Cape Town, psychological risk was linked to the general level of safety. Time risk in Helsinki and Madrid involved worries about missing connecting flights or trains and the subsequent problems. This dimension was not as explicit in the narratives linked to Cape Town. The risk dimensions identified in the narratives substantiate the risk categories, thereby enhancing our understanding of how risk perceptions are place contingent. The narratives posted as enquiries in blogs most often included only one risk category at a time, but some blog threads displayed a more complex structure, including two or even three interconnected risk categories. The findings demonstrated how the structure of perceived risk, at least partially, is sequential, as the following blog illustrates: “… I have written to … to see how much their one day excursion for … are. My other debate is whether or not I should try to mirror a similar day trip self-guided on my own or do I risk missing out on something?” The issue about the price of a day excursion is in this case linked to the risk of missing something in self-guided tours. 5. Conclusions, limitations and further studies This study utilized netnography, originally proposed by Kozinets (1997), to study online consumers, to obtain emic, first-person, data on perceived risk linked to travel behavior. An ethnographer has to determine his or her research position, defined by two dimensions participation (active or passive) and level of notification (overt or covert). We assumed a passive, covert lurker position to avoid interfering with the naturally ongoing discourse. This position brings with it some ethical implications. Our main arguments in defense of the chosen research position are that the discussions on the TripAdvisor website are public, no member identity is disclosed, and the findings are presented in a general, abstract way. The findings presented in this paper are based on a content analysis of 51 blog threads including 746 risk-related narratives posted on the online community of the TripAdvisor website during a period of two years (2009–2010). Three cities representing three different risk categories were analyzed. Helsinki (Finland) was chosen to represent low-risk cities (10 blog threads), Madrid (Spain) mediumrisk cities (17 blog threads), and Cape Town (South Africa) high-risk cities (24 blog threads). Previous studies of risk perception have identified different taxonomies of risk. Boksberger and Craig-Smith (2006), for example, categorized risk into functional, physical, financial, social, psychological, and time risk, a structure corroborated in this study. The content analysis did not reveal any further risk categories, despite the fact that a

qualitative exploratory approach was applied. Hence, the study found support for the existence of those six risk categories, although, in the case of Cape Town, we added a new general risk category. It is notable that the responses to the inquiries posted were exact, and digressions from the subject were few. Volo (2010), who studied blogs of tourists visiting South Tyrol (Italy), came to the conclusion that the bloggers kept online narratives on a fairly unemotional level. This appeared to be the case in the current study as well, except for one blog thread on Cape Town. Hence, a common inquiry about risks in different city areas escalated to a discussion about risks in South Africa and resulted in personal attacks and banned blogs. In addition, we noted that risk perception was destination specific and that the discussions of risks within the various risk categories were distributed. Hence, on the one hand, the approach we used revealed that functional risk dominated for Madrid, whereas physical risk dominated for Cape Town (Table 1). Further, Helsinki was characterized by an even distribution of risk concerns within all risk categories (i.e., no risk perception was apparent). On the other hand, the study shows that functional risk in Helsinki primarily concerned weather conditions and the departure of the ferries, in comparison to what was of functional concern in the case of Madrid and Cape Town. Hence, in Cape Town, functional risk concerned modes of exploring city areas, whereas for Madrid it was related to booking issues, among other things. These findings are ample proof of how important it is for destination marketers and travel companies to closely monitor what is discussed online, particularly on travel sites (Carson, 2008; Pan et al., 2007). Accordingly, our findings support those by Dwivedi (2009), who concluded that travel blogs have two implications for destination branding. First, travelers tap into ‘word of mouth’ to form their own image of a destination. Second, travelers also actively share their destination image via the Internet as they report ‘exciting incidents’ (McKee, 2003). As it appears, these ‘visitors'’ take-away images can be benchmarked to a destination's planned brand profile and identity (Martin et al., 2007, p. 41), to identify potential brand collapses (Björk, in press), or to explore brand attributes to enhance destination marketing (Aaker, 1996). Indeed customer-tocustomer communication performed online has an impact on travelers' destination image and cannot be ignored in destinations' brand building strategy. Previous tourism studies have focused on travel stories conveyed on blogs and most often highlighted positive destination experiences (Drew et al., 2007; Hsu et al., 2009; Volo, 2010; Woodside et al., 2007). Unlike previous research, this study contributes to the literature by using a more narrow approach with a focus on risk perception. From the limitations of this study emerge at least two directions for further studies on risk perception. In this study, the concept of risk was used to sort out narratives from a discussion forum for the three cities. Closely related concepts, such as safety, threat, and danger, could be used to get a more extensive picture of how risk perception is discussed online. Another direction would be to examine actions taken by destination marketing organizations in response to what is communicated online in posted narratives. This study could be linked to the theoretical domain of destination branding (Balakrishnan, 2009; Pike, 2009; Kotler & Gertner, 2002). References Aaker, D. (1996). Building strong brands. New York: The Free Press. Akehurst, G. (2009). User generated content: the use of blogs for tourism organisations and tourism consumers. Service Business, 3(1), 51–61. Arsal, I., Woosnam, K., Baldwin, E., & Backman, S. (2010). Residents as travel destination information providers: An online community perspective. Journal of Travel Research, 49(4), 400–413. Aven, T., & Renn, O. (2009). On risk defined as an event where the outcome is uncertain. Journal of Risk Research, 12(1), 1–11. Balakrishnan, M. S. (2009). Strategic branding of destinations: A framework. European Journal of Marketing, 43(5/6), 611–629. Bansal, H. S., & Voyer, P. A. (2000). Word-of-mouth processes within a services purchase decision context. Journal of Service Research, 3, 166–177.

P. Björk, H. Kauppinen-Räisänen / Tourism Management Perspectives 2–3 (2012) 65–71 Beerli, A., & Martin, J. (2004). Factors influencing destination image. Annals of Tourism Research, 31(3), 657–681. Beldona, S. (2005). Cohort analysis of online travel information search behavior: 1995–2000. Journal of Travel Research, 44(2), 135–142. Belz, F. -M., & Baumbach, W. (2010). Netnography as a method of lead user identification. Creativity and Innovation Management, 19(3), 304–313. Berthon, P., Hulbert, J. M., & Pitt, L. F. (1999). Brand management prognostications. Sloan Management Review, 40(2), 53–65. Björk, P. (in press). Brand recovery: A model for quick fix when brand structure collapses. Journal of Travel & Tourism Marketing. Björk, P., & Kauppinen-Räisänen, H. (2011). The impact of perceived risk on information search: A study of Finnish tourists. Scandinavian Journal of Hospitality and Tourism, 11(3), 306–323. Boksberger, P. E., Bieger, T., & Laesser, C. (2007). Multidimensional analysis of perceived risk in commercial air travel. Journal of Air Transport Management, 13(2), 90–96. Boksberger, P. E., & Craig-Smith, S. J. (2006). Customer value amongst tourists: A conceptual framework and a risk-adjusted model. Tourism Review, 61(1), 6–12. Brown, S., Kozinets, R. V., & Sherry, J. F., Jr. (2003). Sell me the old, old story: retromarketing management and the art of brand revival. Journal of Customer Behavior, 2(2), 133–147. Brun, W. (1994). Risk perception: main issues, approaches and findings. In G. Wright, & P. Ayton (Eds.), Subjective probability (pp. 295–320). Chichester: John Wiley & Sons. Carson, D. (2008). The ‘blogosphere’ as a market research tool for tourism destinations: A case study of Australia's Northern Territory. Journal of Vacation Marketing, 14(2), 111–119. Casaló, L. V., Flavián, C., & Guinalíu, M. (2010). Determinants of the intention to participate in firm-hosted online travel communities and effects on consumer behavioral intentions. Tourism Management, 31(6), 898–911. Castaneda, J. A., Frias, D. M., & Rodriguez, M. A. (2007). The influence of the internet on destination satisfaction. Internet Research, 31(3), 657–681. Choi, S., Lehto, X. Y., & Morrison, A. M. (2007). Destination image representation on the web: content analysis of Macau travel related web sites. Tourism Management, 28(1), 118–129. Dholakia, U. M. (2001). A motivational process model of product involvement and consumer risk perception. European Journal of Marketing, 35(11/12), 1340–1360. Drew, M., Woodside, A., & Dehuang, N. (2007). Etic interpretive of Naive subjective personal introspections of tourism behaviour. Analyzing visitors' stories about experiencing Mumbai, Seoul, Singapore, and Tokyo. International Journal of Culture, Tourism and Hospitality Research, 1(1), 14–44. Dwivedi, M. (2009). Online destination image of India: A consumer based perspective. International Journal of Contemporary Hospitality Management, 21(2), 226–232. Erdem, T., & Swait, J. (2004). Brand credibility, brand consideration, and choice. Journal of Consumer Research, 31(1), 191–198. Fakeye, P. C., & Crompton, J. R. (1991). Image differences between prospective, firsttime, and repeat visitors to the lower Rio Grande valley. Journal of Travel Research, 30(2), 10–16. Forsythe, S., & Shi, B. (2003). Consumer patronage and risk perceptions in internet shopping. Journal of Business Research, 56(11), 867–875. Frias, D. M., Rodriguez, M. A., & Castaneda, J. A. (2008). Internet vs. travel agencies on pre-visit destination image formation: An information processing view. Tourism Management, 29(1), 163–179. Glaser, B., & Strauss, A. (1967). The discovery of grounded theory. Chicago, IL: Aldine. Gursoy, D., & McCleary, K. W. (2003). An integrative model of tourists' information search behavior. Annals of Tourism Research, 31(2), 353–573. Hall, M., Timothy, D., & Duval, D. (2004). Security and tourism: Towards a new understanding. Journal of Travel and Tourism Marketing, 15(2–4), 1–18. Hideg, G., & Manchin, R. (2005). Environment and safety in European capitals. EU ICS Working paper Series http://www.europeansafetyobservatory.eu. Hsu, S. -Y., Dehuang, N., & Woodside, A. (2009). Storytelling research of consumers' self-reports of urban tourism experiences in China. Journal of Business Research, 62(12), 1223–1254. Kotler, P., & Gertner, D. (2002). Country as brand, product, and beyond: A place marketing and brand management perspective. Journal of Brand Management, 9(4/5), 249–261. Kozak, M., Crotts, J. C., & Law, R. (2007). The impact of the perception of risk on international travellers school of tourism and hospitality management. International Journal of Tourism Research, 9(4), 233–242. Kozak, N., & Kozak, M. (2008). Information sources available to visitors: A segmentation analysis. Tourism Review, 63(4), 4–12. Kozinets, R. V. (1997). I want to believe: A netnography of X-philes' subculture of consumption. Advances in Consumer Research, 24(1), 470–475. Kozinets, R. V. (1998). On netnography: Initial reflections on consumer research investigations of cybercultures. In J. W. Alba, & W. Hutchinson (Eds.), Advances in consumer research, 25. (pp. 366–371)Provo. UT: Association for Consumer Research. Kozinets, R. V. (2002). The field behind the screen: Using netnography for marketing research in online communities. Journal of Marketing Research, 39(1), 61–72. Kozinets, R. V. (2006). Netnography 2.0. In R. W. Belk (Ed.), Handbook of qualitative research methods in marketing (pp. 129–142). Cheltenham: Edward Elgar Publishing Limited. Langer, R., & Beckman, S. (2005). Sensitive research topics: Netnography revisited. Qualitative Market Research; An International Journal, 8(2), 189–203.

71

Lee, R. M. (1993). Doing research on sensitive topics. London: Sage Publications Ltd. Lee, G. H., O'Leary, J. T., & Hong, G. S. (2002). Visiting propensity predicted by destination image: German long-haul pleasure travelers to the US. International Journal of Hospitality and Tourism Administration, 3(2), 63–92. Lepp, A., & Gibson, H. (2008). Sensation seeking and tourism: Tourist role, perception of risk and destination choice. Tourism Management, 29(4), 740–750. Lepp, A., & Gibson, H. (2011). Tourism and world cup football amidst perceptions of risk: The case of South Africa. Scandinavian Journal of Hospitality and Tourism, 11(3), 286–305. Lepp, A., Gibson, H., & Lane, C. (2011). Image and perceived risk: A study of Uganda and its official tourism website. Tourism Management, 32(3), 675–684. Marketwatch.com bhttp://www.marketwatch.com> Sleeping tight in Luxembourg, Survey ranks the world's most, least safe cities. MarketWatch. Accessed 20 February 2012. Martin, D., & Woodside, A. (2011). Storytelling research on international visitors. Interpreting own experiences in Tokyo. Qualitative Market Research: An International Journal, 14(1), 27–54. Martin, D., Woodside, A. G., & Dehuang, N. (2007). Etic interpreting of naïve subjective personal introspections of tourism behavior: Analyzing visitors' stories about experiencing Mumbai, Seoul, Singapore, and Tokyo. International Journal of Culture, Tourism and Hospitality Research, 1(1), 14–44. Maser, B., & Weiermair, K. (1998). Travel decision-making: From the vantage point of perceived risk and information preferences. Journal of Travel and Tourism Marketing, 7(4), 107–121. Maxwell, J. (1996). Qualitative research design, An interactive approach. Thousand Oaks, CA: Sage Publication Inc. McKee, R. (2003). Storytelling that moves people. Harvard Business Review, 81(6), 51–55. Mercer.com bwww.mercer.com > 2011 Quality of Living worldwide city rankings — Mercer Survey. Mercer. Accessed 20 February 2012. Merriam, S. (2002). Qualitative research in practice: Examples for discussion and analysis. San Francisco, CA: Jossey-Bass. Miles, M. B., & Hubermann, A. M. (1984). Qualitative data analysis: A sourcebook of new methods. CA: Sage publications Inc.. Mitchell, V. W., & Greatorex, M. (1993). Risk perception and reduction in the purchase of consumer services. Service Industries Journal, 13(4), 179–200. Nyiri, Z. (2005). Crime and security in European capitals. EU ICS Working Paper Series http://www.europeansafetyobservatory.eu. Pan, B., MacLaurin, T., & Crotts, J. C. (2007). Travel blogs and the implication for destination marketing. Journal of Tourism Research, 46(1), 35–45. Pike, S. (2009). Destination brand positions of a competitive set of near-home destinations. Tourism Management, 30(6), 857–866. Qu, H., Kim, L. H., & Im, H. H. (2010). A model of destination branding: Integrating the concepts of the branding and destination image. Tourism Management, 32(3), 465–476. Quintal, V. A., Lee, J. A., & Soutar, G. N. (2010). Risk, uncertainty and the theory of planned behavior: A tourism example. Tourism Management, 31(6), 797–805. Reichel, A., Fuchs, G., & Uriely, N. (2007). Perceived risk and the non-institutionalized tourist role: The case of Israeli student ex-backpackers. Journal of Travel Research, 46(2), 217–226. Reisinger, Y., & Mavondo, F. (2005). Travel anxiety and intentions to travel internationally: Implications of travel risk perception. Journal of Travel Research, 43(3), 212–225. Rittichainuwat, B. N., & Chakraborty, G. (2009). Perceived travel risks regarding terrorism and disease: The case of Thailand. Tourism Management, 30(3), 410–418. Roehl, W. S., & Fesenmaier, D. R. (1992). Risk perception and pleasure travel: An exploratory analysis. Journal of Travel Research, 30(4), 17–26. Rokka, J. (2010). Netnographic inquiry and new translocal sites of the social. International Journal of Consumer Studies, 34(4), 381–387. Rokka, J., & Moisander, J. (2009). Environmental dialogue in online communities: Negotiating ecological citizenship among global travelers. International Journal of Consumer Studies, 33(2), 199–205. Sandlin, J. (2007). Netnography as a consumer education research tool. International Journal of Consumer Studies, 31(3), 288–294. Sirakaya, E., & Woodside, A. (2005). Building and testing theories of decision making by travellers. Tourism Management, 26(6), 815–832. Sönmez, S. F., & Graefe, A. (1998). Influence of terrorism risk on foreign tourism decisions. Annals of Tourism Research, 25(1), 112–144. Tripadvisor.com (2009–2010). Fact sheet. http://www.tripadvisor.com/pages/ factsheet.html Accessed 20 February 2012. Tsaur, S. -H., Tzeng, G. -H., & Wang, K. -C. (1997). Evaluating tourist risks from fuzzy perspectives. Annals of Tourism Research, 24(4), 796–816. Urbantitan bwww.urbantitan.com > 10 Most Dangerous cities in the world in 2010. Urban TITAN. Accessed 20 February 2012. Veal, A. (2011). Research methods for leisure & tourism, A practical guide. Edinburgh Gate, UK: Pearson Education Ltd. Volo, S. (2010). Bloggers' reported experiences: Their utility as a tourism data source and their effect on prospective tourists. Journal of Vacation Marketing, 16(4), 297–311. Woodside, A. G., Cruickshank, B. F., & Dehuang, N. (2007). Stories visitors tell about Italian cities as destination icons. Tourism Management, 28(1), 162–174. Xiang, Z., & Gretzel, U. (2010). Role of social media in online travel information search. Tourism Management, 31(2), 179–188.