Fear of missing out (FOMO) refers to the customer's perception of being anxious for not engaging in an experience. FOMO is an anxiety feeling positively associated with social media usage that one cannot catch up on something important in life. Fear of missing out (FOMO) marketing appeals initiated from social media usage were found to significantly affect consumer purchase decisions including choice of destination. Consumers usually browse social media and social networking sites such as forums and reviews in online tourism agents (OTAs) when they make travel decisions. Although FOMO is expected to affect tourists' perception and urgency in making a tourism decision, the use of FOMO-laden message to promote travel destination through different types of influencers has not yet been widely studied. This study fills this research gap by examining the effect of using FOMO laden content to promote travel destination through different types of influencers. An online experiment was conducted with four experimental conditions in which different influencers share about a destination using the same FOMO-laden message: (1) travel KOL, (2) tourists who post user-generated-content (UGC), (3) personal friends, and (4) a control condition with the absence of influencer and FOMO message. The 984 respondents were randomly assigned into one of the four experimental conditions. Data collected was analysed using PLS-SEM and PLS-MGA. Results indicated that anticipated elation, anticipated envy, and social influence predicted 30.2% variance of FOMO and FOMO explained 31.6% of variance of intention to visit the destination promoted. Multi-group analysis (PLS-MGA) found that exposure to message shared by travel KOL and personal friends significantly strengthen the FOMO feeling of participants resulting in strong intention to visit the destination promoted. UGC posted by tourists showed similar effect as the non-FOMO laden control group and are less significant in driving the FOMO feeling that leads to visit intention. Findings of this study provide insights into how effectiveness of destination promotion can be enhanced by using FOMO-laden message on social media through influential influencers.

Introduction In an age of rapid development at the information technology front, the viability of ‘smart travel destinations’ is increasingly becoming a reality (Buhalis & Amaranggana, 2014, 2015). Advances in mobile technology have allowed travel destinations to leverage the location-based wireless tracking capabilities afforded by 3/4G telecommunication networks, Bluetooth connectivity, GPS and Wi-Fi networks (Choe & Fesenmaier 2017; Eriksson, 2002). The benefits of these wireless tracking technologies include precise information on spatial behaviour (Edwards, Dickson, Griffin, & Hayllar, 2010), relevant location-based services (LBS)(Pedrana, 2014), navigational services (Eriksson, 2002), as well as recommender services (Tussyadiah & Wang, 2016). With this kind of data available to them, destination management organisations (DMOs) are able to develop more customise tourist engagement strategies which will help them communicate specifically tailored results to tourists (Edwards & Griffin, 2013). While the focus of current tourism research has been the benefits of these wireless tracking technologies (WTTs) to the destination, little research has been done to examine tourists’ perceptions of these technologies. The current exploratory study will investigate tourist perceptions of three prominent kinds of WTTs with differing levels of control at a travel destination: (1) wireless tracking only (WT only; low control); (2) Wi-Fi wireless tracking (Wi-Fi WT; moderate control); and (3) application-based tracking (App; high control). Theoretical development The current study applies the Expectancy-Value Theory in examining tourist perceptions of WTTs at a travel destination. The Expectancy-Value Theory suggests that motivation for a behaviour is determined by the desirability of the outcome i.e. benefits to the tourist (Sparks, 2007). In the context of this study, perceived personalisation and perceived innovativeness serve as benefits to tourists. Perceived personalisation is defined as the ability of a DMO to recognize and treat its tourists as individuals through personal messaging, targeted banner ads, special offers on bills, or other personal transactions” (Imhoff, Loftis, & Geiger, 2001). Perceived innovativeness reflects the degree to which a new product is seen to possess new and unique attributes and features (Fu, Jones, & Bolander, 2008). Studies have shown that perceived personalisation and perceived innovativeness positively impact on attitudes toward the product (Baek & Morimoto, 2012; Fu & Eliott, 2013), which in the context of this study relates to both the WTT itself as well as the destination. However, mere presence of WTTs can often provoke concerns about manipulative intent (Lee-Wingate & Xie, 2010) and privacy (Shilton, 2009). Inferences of manipulative intent is defined as tourist perceptions that a company is attempting to persuade via inappropriate, unfair or manipulative means (Campbell, 1995). Privacy concerns refer to the degree to which a tourist is worried about the potential invasion of the right to prevent the disclosure of personal information to others (Baek & Morimoto, 2012, p. 63). Inferences of manipulative intent and privacy concerns have been found to negatively impact on attitudes toward the product (Lee-Wingate & Xie, 2010; Shilton, 2009). Thus, the ability of a travel destination to emphasise the pros and minimise concerns for the cons of WTTs will result in more positive attitudes towards the WTT as well as the destination, which in turn, will positively impact on intention to visit the destination (based on arguments in tourism research suggesting that both attitudes toward products and the destination itself may have an impact on intention to visit e.g. Elliot, Papadopoulos & Kim 2011; Lee & Lockshin 2012). The hypothesised model for this study can be seen in Figure 1. Methodology The conceptual model was tested using data from the United States via the Amazon Mechanical Turk (MTurk) platform. A total of 750 responses were acquired but only 615 were included for analysis (responses were excluded due to incomplete data or straight-lining). A between-subjects experimental design was implemented respondents viewing a stimulus for either (1) wireless tracking only (WT only; low control); (2) Wi-Fi wireless tracking (Wi-Fi WT; moderate control); or (3) application-based tracking (App; high control). A pretest of the stimulus confirmed the levels of control proposed by the researchers. Respondents were first told to imagine their next travel destination and were then shown a stimulus. In the WT only condition, respondents were told that the destination was tracking the movement of tourists when their smartphones wireless, Bluetooth or mobile reception was turned on. In the Wi-Fi WT condition, respondents were informed that the destination would track tourists logged on to the destination’s Wi-Fi network. In the App condition, respondents were notified that the destination has an app system which allows the destination to track tourists and send them personalised push notifications. The difference between these three conditions was the level of perceived control that tourists had over the tracking of their location within the destination. Respondents then rated the WTT and destination with regards to inferences to manipulative intent, privacy concerns, perceived personalisation, perceived innovativeness, attitude toward the WTT, attitude toward the destination, and intention to visit the destination. The measures for each of these scales were chosen for their reliability and relevance to the current study. Structural equation modelling then examined the hypothesised relationships for significance. Results and discussion Exploratory and factor analysis was conducted to ensure the unidimensional of the scales. Composite reliabilities ranged from 0.70 to 0.95 and the average variance extracted scores ranged from 0.70 to 0.87, suggesting strong internal validity for all scales. All measures were also tested for convergent and discriminant validity which were both supported. Then, the hypotheses were examined using a multigroup analysis with structural equation modelling in AMOS 22. The goodness-of-fit indices for the structural model was deemed acceptable (χ²/df=1.67; RMSEA=0.03; CFI=0.97; NFI=0.94; IFI=0.90) (model comparisons revealed no significant differences at a model level suggesting that the model applied across the different groups). The results of the path analysis revealed five hypotheses which were fully supported (H1a, H2a, H3a, H3b and H6b). The remaining six hypotheses (H1b, H2b, H4a, H4b, H5 and H6a) were only partially supported with significant parameter estimates noted for either one or two of the conditions. The full result of the path analysis can be seen in Table 1. The results suggest that inferences of manipulative intent significantly decreased attitude toward the WTT, highlighting the need for destinations to be transparent about the reasons for tracking tourists. Specifically, the concealed tracking of tourists’ movements was particularly damaging to attitude toward the destination. Privacy concerns also negatively impacted on attitude toward the WTT for all conditions, but surprisingly privacy concerns appeared to significantly increase attitude toward the destination under the App condition. A potential explanation for this is the fact that despite potential for privacy infringements, tourists possess control over usage of the application, thereby moderating the ability of the destination to track them. However, this result warrants greater investigation in future studies. Perceived personalisation was noted to positively impact on attitudes toward the WTT and destination suggesting that tourists positively regarded the benefits of personalisation that the WTT afforded them. Further, perceived innovativeness appeared to positively impact on attitude towards the WTT for the App condition, but more interestingly, positively impacted on attitude toward the destination for the WT only condition. This may potentially suggest that while tourists did perceive manipulative intent in the wireless tracking of their whereabouts they also perceived this to be an innovation. Theoretically, this study extends the tourism literature with regards to the installation or application of wireless tracking technologies. It highlights the aspects that appeal to tourists as well as the concerns that they may have. From a managerial perspective, the results suggest a need for transparency as well as the empowerment of tourists to choose the degree to which their whereabouts are tracked within the destination. It offers further insights into which technologies are best suited to be leveraged in order to develop stronger tourist engagement at the destination. The implications of these results apply to destination managers, marketers as well as policy makers. A successful balance between obtaining valuable information about tourists and providing them with a choice whether or not to be tracked is crucial in ensuring favourable perception of the travel destination.

This research examined whether the (in)congruence between the geographical distance between the viewer and the destination, and the dynamic distance experienced via zoomin and zoom-out affects the recommendation likelihood of the travel destination. Specifically, when the viewer’s motivation is utilitarian (e.g., travelling for work), we expect the congruence effect (H1): a higher recommendation likelihood when the geographic distance is congruent with the dynamic distance; that is, the viewer is more likely to recommend the travel destination when the destination is geographically far away from (close to) with a zoom-out (zoom-in) view. By contrast, when the viewer’s motivation is hedonic (e.g., travelling for fun), we expect the incongruence effect (H2): a higher recommendation likelihood when the geographic distance is incongruent with the dynamic distance; that is, the viewer is more likely to recommend the travel destination when the destination is geographically far away from (close to) with a zoom-in (zoomout) view. We test these ideas in an experimental study.

오늘날 인터넷의 출현과 확산으로 인하여 정보의 홍수를 이루게 되었고, 고객들은 자신이 원하는 제품이나 서비스를 선택하기 위해서 정보를 탐색하는 작업이 더욱 어려워지게 되었다. 이러한 고객들에게 좀 더 편리하게 자신이 원하는 제품이나 서비스를 선택하도록 도와주는 것이 추천 시스템으로써, 고객 관계 관리의 중요한 부분으로 자리잡게 되었다. 본 연구에서는, 인터넷상의 여행사 사이트 등에서 고객이 여행지를 선택할 때 고객이 관심을 가질만한 여행지를 추천하여 줌