Perceived Crime and Violence

Abstract

Many travel destinations across the world have endured sporadic or prolonged violence that significantly impacts their respective region’s tourism-reliant economy. This research addresses the issue of violence and travel by examining talk of violence, perceptions of the worsening of crime, fear at the destination, destination satisfaction, and their effects on return intention within the context of travelers who are very familiar with the destination. Study results, obtained from migrants who had spent many winters in the area that borders on a region that had recently erupted in drug-related violence, found the significant effects, direct or indirect, of all variables. Importantly, fear at the destination was found to subsume satisfaction with the destination, which directly impacts intention to return to the destination.

Keywords

violence fear destination satisfaction migrants word-of-mouth travel

Perceptions of crime and violence at a destination will generally deter travel to that destination, at least for visitors inexperienced with the area. But will the same be true for travelers who are very familiar with and have strong ties to a destination faced with nearby violence? Pearce (2012, p. 1024) presents and explains “an understudied category of travel” he terms Visiting Home and Familiar Places (VHFP), which includes family members who return home and repeat visitors who have a longstanding personal and emotional tie to a destination. Given the length and the strength of destination attachment, these return visitors may be less affected by reports of crime and violence at the destination than would transient or leisure travelers, yet research about either VHFP travelers (Pearce 2012) or about effects of traveler-perceived crime, violence, or security is limited (e.g., Boakye 2012; Brunt, Mawby, and Hambly 2000; George 2003).

A common group of VHFP travelers are wintering migrants. These are travelers, mostly retired seniors, who routinely migrate in the winter months from the colder regions of North America to selected U.S. southern states, especially Arizona, Florida, and Texas. Initially, and primarily, these travelers migrate to escape cold weather. Other reasons such as social connections and various activities with others of shared interests and values may enhance the migration experience to emotionally engage the winter migrants with a specific destination and ensure repeated returns (i.e., Gitelson and Crompton 1984). These migrants are considered tourists because they stay in an area temporarily and engage in the same activities as other tourists (Girard and Gartner 1993; Goeldner and Ritchie 2003; Rodríguez 2001).

Academics have noted a lack of research about how visitors to an area perceive crime or other threats to safety in deciding a travel destination (e.g., Boakye 2012; Brunt, Mawby, and Hambly 2000) and about migrants who may face risk and uncertainty (Baláz and Williams 2011) or fear. This study is designed to address these research deficiencies by testing the potential impact of a perceived worsening of violence in a destination on migrants’ perceived fear in their winter destination and on their likelihood of returning. These variables were identified as potentially important factors in this research in keeping with other destination selection models that highlight similar variables such as interaction of positive/negative affective associations, satisfaction from previous visits (Woodside and Lysonski 1989), and personal sources of information such as word of mouth (Um and Crompton 1990; Van Raaij and Francken 1984).

This research is sorely needed for several reasons. First, perceived violence in a region may deter travel, including even part-time residents, to the area, which could significantly impact the region’s economy. Second, this research answers the call by Pearce (2012) for a better understanding of the VHFP topic. Third, although prior tourism research has extensively examined risk and effects of violence on tourism, few studies have examined perceived fear as an emotion or factors that may affect fear in a destination. Although winter migrants differ from tourists in terms of their knowledge about and experience with a given destination, understanding the effect of fear on migrants’ intentions to return should provide us with a low-end, or baseline, estimate of the effect of fear on tourists’ intentions to return. If fear of violence is found to strongly impact migrants’ intentions to return, then the impact may be even stronger for tourists who do not have the same social and economic attachments to the destination that migrants have. Finally, study results may provide insights to help travel destination managers better respond to widespread news reports of heightened violence and crime around the destination.

Context and Background

One popular destination for winter migrants is a four-county region in the southern tip of Texas that borders on Mexico. In 2010, about 144,000 winter migrants were in the four-county area, stayed 4.43 months and spent $802.5 million, and had been coming to the area for an average of nine years (Simpson, Ghaddar, and Sheng 2010). Both the repeated annual visits and prior research (Simpson and Siguaw 2008b) suggests that these winter migrants have a strong bond or identity salience with the far South Texas area. However, in September 2010, the battle for control of the lucrative drug market in neighboring Mexico escalated significantly. The violence received extensive news media coverage through the United States and beyond, with effects felt in the Mexico tourism industry (Bussey 2012), especially in cities that border the United States as a result of the “narco-violence” (Paterson 2011). These news reports of the Mexico violence may well create sufficient levels of fear to impact the decision of many regular winter migrants to return to their South Texas winter destination, even though the migrants are experienced with the relatively low violent crime rate in the region and may not have personally experienced violent crime. In fact, this South Texas area had a violent crime rate per 100,000 inhabitants (Investigation 2010) of 321.1. This compares favorably to the crime rate of other midsized Texas cities such as Lubbock (808), El Paso (452.1), Corpus Christi (605.9), or Waco (543).

The VHFP perspective (Pearce 2012) suggests that the wintering migrants may not be affected by perceptions of crime and violence a few miles away in Mexico because their long-term familiarity with and emotional ties to the area may mitigate fear and negative perceptions, although academic support for this assertion is limited. Additionally, returning to a familiar area normally reduces the risk of a possible unsatisfactory experience at a new, unfamiliar area (Gitelson and Crompton 1984). On the other hand, media reports of significant violence surrounding the destination could evoke fear and deter repeat visits from even the most emotionally attached winter migrant.

Risk and perceived violence have been the subject of considerable research in the tourism literature. For example, Simpson and Siguaw (2008a) identified categories of past travel risk research as (1) perceptions of risk at a destination, which includes tourist events and effects of war or terrorism; (2) effects of perceived risk on travel intentions and satisfaction; and (3) managerial response to travel risk incidents. In general, this risk-related research has determined that perceived risks significantly impact travelers’ plans, tourism demand, and satisfaction with travel (e.g., Dolnicar 2005; Floyd and Pennington-Gray 2004; Lavantis and Azmat 2000; Pizam and Fleischer 2002; Reisinger and Mavondo 2005; Roehl and Fesenmaier 1992).

Some researchers warn that perceived risk, while important, is a cognitive measure and that more research should now focus on the emotional result of perceived risk, such as fear. For example, criminology academics defined fear of crime as an emotional response (e.g., Alper and Chappell 2012; Ferraro and LaGrange 1987; Warr 2000) and noted the need to separate the cognitive from the emotional aspects and assess fear of crime as an emotion. As Warr (2000, p. 454) states, “perceived risk is a proximate cause of fear—not fear itself.”

The psychology literature also provides considerable evidence—and theory—that emotions and cognitions are separate and that emotions affect decision making. Notably, Loewenstein et al. (2001, p. 270) outline a “risk as feelings” hypothesis that “postulates that responses to risky situations (including decision making) result in part from direct (i.e., not cortically mediated) emotional influences, including feelings such as worry, fear, dread, or anxiety.” This hypothesis suggests that anticipated outcomes and subjective probabilities affect cognitive evaluations and feelings that then affect behavior. In the context of travel, Fischhoff et al. (2004, p. 1308) found that potential travelers’ perceived worry had an impact on trip cancellations, which “predicts travel decisions over and above explicit risk judgments.” Other works have found that fear evokes a more pessimistic probability of future terror events (Lerner et al. 2003) and affects optimism as mediated by appraisals of controllability of life events (Lerner and Keltner 2001).

A few studies have examined the role of positive or negative emotions in a travel context (Gnoth et al. 2000; Pearce 2012; Rodríguez del Bosque and San Martín 2008; Yüksel and Yüksel 2007). For example, Rodríguez del Bosque and San Martín showed that positive and negative emotions play a crucial role in satisfaction formation, which in turn affects intentions to return and willingness to recommend the destination. Similarly, Yüksel and Yüksel (2007) found that the emotions of arousal and pleasure mediate effects of perceived risk of tourists on shopping satisfaction and on loyalty intentions at a vacation shopping destination. In another emotions-related travel study, Zins (2002) interviewed tourists to examine the relationship of travel consumption emotions and complaint behavior on satisfaction, finding that positive and negative emotions have a significant influence on satisfaction. Finally, anxiety, as a negative emotion, has been found to impact travel decisions and destination perceptions (Mitchell 1999; Reisinger and Mavondo 2005).

While this prior research strongly suggests the importance of emotions and fear on travel and destination choice, the relationship of fear of crime on return intentions may be different for repeat visitors to a destination. The repeat visitor research has found, in general, that prior experience and the frequency of repeat visits predict future intention to visit the destination (i.e., Campo-Martínez, Garau-Vadell, and Martínez-Ruiz 2010; Sönmez and Graefe 1998). Other similar studies have suggested that prior experience with a destination may mitigate perceptions of risk of violence or terrorism (Lepp and Gibson 2003; Rittichainuwat and Chakraborty 2009), and concern for human-induced risk, such as “crime, terror and political unrest” (Fuchs and Reichel 2011, p. 270). Finally, studies by Baláz and Williams (2011) and Williams and Baláz (2013) suggest that migrants are more risk tolerant than nonmigrants.

In summary, perceptions of risk and fear are important factors in travel decisions and behavior, but significant gaps remain in our understanding of the relationship of risk with travel intentions for migrants and of emotions such as fear for any traveler type, including migrants. Nevertheless, the review of the literature suggests the proposed model of perceived violence effects on winter migrant return intentions as shown in Figure 1. Within the “risk as feelings” hypotheses framework, the model predicts that perceptions of worsening of crime (anticipated outcomes) will affect both satisfaction with the destination (cognitive evaluation) and fear at the destination (feelings or emotions), with both affecting intention to return (behavior). The word-of-mouth communications heard by winter migrants (other factor) will likely affect both the perceptions of worsening of crime in the region as well as fear at the destination. Each of these proposed relationships is discussed next.

Figure 1.

Model of perceived violence effects on winter migrant return intentions.

Violence Word of Mouth

A number of communications methods such as word of mouth (WOM), publicity, and news stories may likely affect fear and perceptions of crime and at a destination. WOM was the sole communication method used in this study however for several reasons. First, WOM is recognized as the most influential form of communication and has been found to impact feelings, cognitions, and behavior towards a wide range of products and services (e.g., Litvin, Goldsmith, and Pan 2008). Second, Jackson’s (2005) review of the crime literature prompts him to write that concerns about crime and risk routinely favor day-to-day communications. This suggests that if crime and violence are salient, as they would be with a proliferation of crime and violence news stories and publicity, then friends and family would talk about the crime and violence. In this way, new stories are subsumed into WOMs. This idea is captured by a model of WOM structure where various communication pieces such as advertisements, media stories, and publicity directly affect word of mouth (Allsop, Bassett, and Hoskins 2007). Third, WOM is interactive and a “significant social force” whereby WOM is generated by networks of consumers (Kozinets et al. 2010). Considering these reasons, WOM was used in this study as a measure of both media and interpersonal communications influences.

Litvin, Goldsmith, and Pan (2008, p. 459) provide an excellent summary of word-of-mouth (WOM) research as applied to tourism to define the term as “communication between consumers about a product, service, or a company in which the sources are considered independent of commercial influence.” The authors cite empirical findings concluding that opinion leaders, close friends, family, or even strangers may generate WOM, which affects purchase intentions. The winter migrants in South Texas likely have strong social ties and strong WOM communications; they are routinely seen clustering together in RV parks, traveling together, and attending social events together. Consequently, WOM is likely to be the most influential form of communication for the winter migrants, as well as other travelers, about the safety of a destination, even more influential than publicity about violence. This WOM about violence, even about a travel destination, will likely heighten perceptions of worsening crime in the area and fear and will likely spread quickly. Given the likelihood of “crime talk” about a region that may be instigated by new stories and the widespread influence of WOM on cognitions and affect, then the following hypotheses are likely:

Hypothesis 1: Higher levels of violence word of mouth will increase the perceived worsening of crime in the destination.

Hypothesis 2: Higher levels of violence word of mouth will increase the fear at the destination.

Perceived Worsening of Crime

Logically, perceptions about the worsening of crime at a migrant’s winter destination will likely fuel the fear fire. Theoretically, the risk-as-feelings hypothesis (Loewenstein et al. 2001) predicts that cognitive evaluations, such as beliefs about crime, will impact feelings and behavior. Surprisingly, however, research has not confirmed a significant relationship between fear as an emotion and perceptions of crime, violence, or victimization (Garofalo 1981). This gap in research applies to retiree travelers in the context of this research as well: “Factors that predict fear of crime in the elderly are largely unknown” (Acierno et al. 2004, p. 386).

Some tourism researchers, however, believe that fear of crime at a destination may arise from multiple sources, including WOM with friends and relatives and from news media (George 2003). As stated previously, more compelling is travel research that has continuously found strong evidence of the impact of perceptions of crime, violence, and terrorism on travel-related outcomes. If perceived crime and violence affect cognitive-emotive processes, then, by way of theories such as the risk as feelings hypothesis, a perceived worsening of crime levels should impact fear at the tourist destination. Therefore, given theory and the limited research:

Hypothesis 3: A greater perception of the worsening of crime in the destination area will increase fear at the destination.

Fear of Crime at Destination

At the heart of this study is the concept of fear of crime at the destination and its effects on intention to return to the destination. Fear, as an emotional response to danger or perceived threats to safety and security, affects the behavior of most people who will work to decrease or remove the fear or adjust their ability to cope with the fear (Tanner, Hunt, and Eppright 1991).

Empirical findings about fear clearly show that it affects decision making and behavior in many different contexts, including travel-related decisions (Chuang 2007; Fischhoff et al. 2004; Gnoth et al. 2000; Yüksel and Yüksel 2007; Zins 2002). For example, Brunt, Mawby, and Hambly (2000) report that 42% of their British sample considered safety and security, which would evoke fear, as concerns in ruling out some travel destinations. Further, research by George (2003) found that the purpose of a visit may impact return intentions, and that visitors of friends and relatives were less likely than business travelers to return to a perceived risky destination. Therefore, consistent with much of the travel literature and using return intention as the behavioral outcome, prior research and theory suggest the following:

Hypothesis 4: Higher levels of fear at a destination will diminish intentions to return to the destination.

Satisfaction with Destination

Satisfaction with a destination has been well documented in the travel literature as one of the most important predictors of loyalty and intentions to return (evidence comes from Baker and Crompton 2000; Campo-Martínez, Garau-Vadell, and Martínez-Ruiz 2010; Huang and Hsu 2009; Rodríguez del Bosque and San Martín 2008). However, surprisingly little research has been conducted on the effects of perceived crime on destination or location satisfaction. Considering that perceptions of crime and insecurity are negative evaluations of a destination, research suggests a link to satisfaction. For example, Alegre and Garau (2010) suggest that negatively perceived destination features could become determining factors in a traveler’s overall satisfaction and return intentions. Also, research by Anderson and Sullivan (1993) and by Mittal, Ross, and Baldasar (1998) indicate that negative evaluations have a much greater effect on dissatisfaction than do positive evaluations on satisfaction.

From the criminology literature, Hipp (2009) found that satisfaction with a neighborhood was increasingly negative with higher rates of perceived crime, but that those households with greater economic and social investment had greater satisfaction levels with their neighborhood. Similarly, Rountree and Land (1996) pointed out that residents of communities with strong social ties to each other are more likely to perceive their neighborhoods as safer. From the literature in travel research, Fuchs and Reichel (2011) found that repeat visits to a tourism area is related to satisfaction, while Lin, Lee, and Wang (2012) found no direct effect of travel risk on satisfaction. Accordingly, we propose the following:

Hypothesis 5: A greater perception of the worsening of crime in the destination area will diminish satisfaction with the destination.

As stated previously, the travel literature is clear that satisfaction affects return intention to a destination. Campo-Martínez, Garau-Vadell, and Martínez-Ruiz (2010) consider satisfaction as the most important influence on return visits, and Baloglu et al. (2003) found that satisfaction is an intervening variable between the perceived attributes of a destination and revisit intention. This is in line with the theoretical models of expectancy-disconfirmation (Churchill and Surprenant 1982; Oliver 1980), where the evaluation of the expected attributes and performance of a product or service determine consumer satisfaction (a cognitive appraisal), which in turn affects behavioral loyalty (Oliver 1999). Thus,

Hypothesis 6: Higher levels of satisfaction with the destination area will increase intention to return to the destination.

Repeat visitors to a destination tend to have higher satisfaction levels with the destination and a lower perception of destination risk (Campo-Martínez, Garau-Vadell, and Martínez-Ruiz 2010; Fuchs and Reichel 2011). This may be true, especially since visitors’ satisfaction is formed partly from the overall experience at a tourism destination (Tribe and Snaith 1998) or from the familiarity that comes from repeat visits. These findings suggest that satisfaction ameliorates perceived risk and fear at a destination.

Also, based on research in psychology by LeDoux (1995), if cognition is defined to broadly include sensory information processing, then emotions such as fear are highly dependent on cognitive processes. According to Westbrook (1987), although postpurchase behavior research usually explains a causal sequence from emotions to satisfaction, the ordering of emotions to satisfaction is not definitive. Instead, negative affect such as fear might be affected or mitigated by satisfaction. Therefore, tourism satisfaction, as a cognitive evaluation (Rodríguez del Bosque and San Martín 2008), should influence a negative emotion such as fear (Izard 1977; Lazarus 1993). Accordingly,

Hypothesis 7: Higher levels of satisfaction with the destination area will diminish fear at the destination.

Methodology

This study was conducted online during June 2011 using a panel of wintering migrants that had been developed over a six-year period. Panel members were recruited by asking respondents who participated in a broader economic impact study of South Texas winter migrants administered biennially to belong to the panel. The 2006 survey of 1,054 respondents yielded about 93 panelists with currently valid e-mails, the 2008 study of 902 respondents generated 94 panelists with valid e-mails and the 2010 study with 1,138 respondents resulted in 153 panelists with valid e-mails. E-mails inviting participation in the online survey were sent to 340 valid e-mail addresses of panel members with a follow-up e-mail sent a week later and a second follow-up e-mail sent about two weeks later. A total of 167 panelists responded for an estimated response rate of 48.8%. This estimate may be understated considering that the panel is composed mostly of seniors, and poor health or death may have prevented responses.

The sample size of 167 is deemed appropriate for testing the proposed model using CFA/SEM because the maximum likelihood estimation (MLE) employed in the analysis has been shown to be robust using data even without the ideal distributional properties (Bagozzi and Yi 2012). Nevertheless, the data were log-transformed to reduce skewness before analysis. In addition, the study’s structural model is not overly complex, consisting of just five constructs that each show sufficient reliability (Iacobucci 2010). As a point of reference, Hair et al. (2010) suggest that a sample size of 150 or more is appropriate for a model with just five constructs and no underidentified constructs. Finally, there were few missing data points and all but one communality statistic is above .50, which shows a low average error variance among indicators (Hair et al. 2010). Table 1 provides the specific characteristics of the sample in this study.

Table 1.

Demographic Profile of Respondents.

Variable	Number Responding	Percentage
Age
51–59	11	7.4
60–64	25	16.8
65–69	43	28.9
70–74	50	33.6
≥75	20	13.4
Average age (years)	68.7

Marital status
Married	133	90.5
Single	1	0.7
Widowed	10	6.8
Divorced/separated	3	2.0
Income ($)
<29,999	15	11.6
30,000–59,999	61	46.8
60,000–79,999	37	28.5
80,000–149,999	15	11.6
>150,000	2	1.5
Education
High school graduate	19	12.7
Some college or a college degree	130	86.6
Gender
Male	67	45.3
Female	81	54.7
Years coming to the area
2–5	39	26.2
6–10	58	38.9
≥11	52	34.9
Average years	9.5

Number of days in the area
90–119	32	19.1
120–159	56	37.9
160–189	39	26.5
≥190	21	14.4
Average days	148

Note: Figures do not add up to the total sample size because some respondents did not provide such information.

The measures used in this study are shown in the appendix and were all assessed using a 5-point Likert-type scale. The satisfaction measure was adapted based on prior works by Westbrook and Oliver (1981) and Arnett, German, and Hunt (2003). The fear of crime and violence scale is original to this study but was developed after reviewing the criminology literature where deficiencies in assessing the measure have been noted. For example, numerous past studies commonly assessed fear of crime using the question “How safe do you feel walking alone in your neighborhood at night?” (Kennedy 1985). This question or a similar one with a slight variation such as using “How worried are you about . . .” has appeared as the fear of crime question in academic research and in a number of national surveys, such as the National Crime Survey, the General Social Survey, the European Social Survey, and the British Crime Survey. The scale in this research is shown in the appendix and included the same “standard” fear of crime question but added other similar items to answer the call by criminology academicians to use multiple items to assess the fear construct (Ferraro and LaGrange 1987; Warr 2000).

The other measures used were customized for this study because no known scales existed to tap the conceptualized constructs. The violence WOM measure was designed to assess the level of “talk” specifically about violence. Because the study population would be considering returning to the destination, the perceived worsening of crime measure was developed to assess expectations of crime in the future in the destination. Finally, return intention was designed to determine intention to return to the destination as indicated by a single item asking about the likelihood of returning to the area the following year.

Results

Most study respondents (142 or 89.3%) were likely or very likely to return to their southern winter destination. Three respondents were neutral about returning, 14 were unlikely or very unlikely to return, and 8 respondents did not answer the question. This return result compares negatively to a previous study of winter migrants in the region that found that 96.9% of survey respondents expected to return to the region (Simpson, Ghaddar, and Sheng 2010). The mean, standard deviation, and variance to this question and for all scale measures are provided in the appendix. After the return intention question, an open-ended question asked respondents to indicate a reason if they did not plan to return to the area the following year. Of the 118 that answered the question with reasons such as health or family matters, 11 (9.3%) cited violence as a reason not to return to the region. As noted previously, the majority of the respondents had been repeat visitors to the same region for several years and reported an average stay of five months during their last winter migration. This suggests a strong affiliation of the migrants with the region, congruent with a VHFP context introduced by Pearce (2012).

Measurement Model

The resulting scales along with their reliabilities, average variances extracted (AVEs), means, standard deviations, and variances appear in the appendix. Results from an AMOS analysis yielded fit indexes that are considered good in general: a chi-square to df ratio (χ²/df) of 1.55, root mean square error of approximation (RMSEA) of .058, Tucker-Lewis index (TLI) of .95, comparative fit index (CFI) of .97, and parsimony normed fit index (PNFI) of .64. These indexes comply with those recommended by Hair et al. (2010), and the scalar estimates revealed that all items loaded significantly (p < .001).

The analysis of convergent and discriminant validity showed that all of the standardized factor loadings and the AVEs of the constructs were greater than .5, which were higher than the squared correlations of the constructs. Cronbach’s alphas for the constructs range from .78 to .93, which are sufficiently high based on the criteria suggested by Nunnally (1978). Results provide evidence of construct validity of the assessment instrument, indicating that the constructs share a proportion of the variance, but at the same time are able to capture distinct traits of the phenomena under study (Hair et al. 2010).

Structural Model

After the CFA demonstrated a good fit of the data, the structural model was estimated in order to examine the relationships in Figure 1. As shown in Table 2, five of seven hypotheses were supported by the analysis. Positive, statistically significant relationships were found between the independent variable violence WOM and perceived worsening of crime (hypothesis 1: γ = .380, p < .001) and between the worsening of crime in the destination and fear at the destination (hypothesis 3: β = .504, p < .001). Negative, significant relationships were found between fear at the destination and intention to return (hypothesis 4: β = –.319, p < .01), between worsening of crime and satisfaction at the destination (hypothesis 5: β = –.289, p < .001), and between satisfaction with the destination and fear at the destination (hypothesis 7: β = –.466, p < .001), as expected. No statistically significant relationship was found to support the hypothesized effects of violence WOM on fear at the destination (hypothesis 2: γ = .098, p < .179) or effects of satisfaction on intention to return (hypothesis 6:β = .045, p < .680).

Table 2.

Path Analysis Results.

	Hypothesized Relations	Standardized Coefficient	Hypotheses Results
Hypothesis 1	Violence WOM → Worsening of crime	.380***	Supported
Hypothesis 2	Violence WOM→ Fear at destination	.098	Not supported
Hypothesis 3	Worsening of crime→ Fear at destination	.504***	Supported
Hypothesis 4	Fear at destination→ Return intention	–.319**	Supported
Hypothesis 5	Worsening of crime→ Satisfaction with destination	–.289***	Supported
Hypothesis 6	Satisfaction with destination→ Return intention	.045	Not Supported
Hypothesis 7	Satisfaction with destination→ Fear at destination	–.466***	Supported

Note: Chi-square to df ratio (χ²/df) = 1.55; root mean square error of approximation = .058; Tucker-Lewis index = .95; comparative fit index = .97; parsimony normed fit index (PNFI) = .64.

***

p < .001; **p < .01; *p < .05.

The path analysis of the complete model revealed the effects of the exogenous and endogenous constructs, either as antecedents or consequences in the relations of the proposed causal chain. The squared multiple correlations identify the variance explained for each construct in the structural model: worsening of crime (R² = .144), satisfaction with destination (R² = .084), fear in destination (R² = .664), and return intention (R² = .121).

Because hypotheses 2 and 6 were not corroborated, tests of mediation effects that could have altered the expected relations were conducted. The tests focused on the roles that perceived worsening of crime and fear at the destination play in the proposed causal chain. Mediating or intervening variables are particularly important in understanding why a relation between constructs exists in a model (Hair et al. 2010), explaining how some phenomena are processed “in-the-head” and in turn account for people’s behavior (Kerlinger and Lee 2000, p. 55).

Mediation Effects

Mediation effects were assessed following the recommendations by Baron and Kenny (1986) and Hair et al. (2010). Those consist of examining the change in model fits (Δχ²/df) as well as the results of three separate regression effects: from the independent variable to the dependent variable (equation 1), from the independent variable to the mediator (equation 2), and from the independent and mediator variables to the dependent variable (equation 3).

First, a model with the mediation of worsening of crime between violence WOM and fear was tested, yielding a model fit (χ²/df) of 1.45. Then, the direct path between violence WOM and fear was added to the model, providing a model fit (χ²/df) of 1.45. Because the direct path did not improve the model fit significantly, there is evidence of a mediation effect (Hair et al. 2010). As seen in the test of the first mediation effect in Table 3, the statistically significant path between WOM and fear at the destination in equation 1 (.293, p < .001) becomes insignificant in equation 3 (.098, p < .179) when perceived worsening of crime is included as a mediator. This provides evidence of the full mediation effect of perceived worsening of crime. Apparently, it is not WOM itself that is important in affecting fear but rather how WOM changes perceptions of the worsening of crime, which then affect fear of crime at a destination.

Table 3.

Test of the Mediating Effects.

Mediator	Dependent Variable	Independent Variable	Standardized Coefficient
Perceived worsening of crime	Equation 1
	Fear at destination	WOM	.293***
	Equation 2
	Perceived worsening of crime	WOM	.384***
	Equation 3
	Fear at destination	Perceived worsening of crime	.504***
		WOM	.098
Fear at destination	Equation 1
	Intention to return	Satisfaction with destination	.241**
	Equation 2
	Fear at destination	Satisfaction with destination	–.469***
	Equation 3
	Intention to return	Fear at destination	–.319**
		Satisfaction with destination	.045

***

p < .001; **p < .01; *p < .05.

Second, a possible mediation of fear of crime on the relationship between satisfaction and intention to return to the destination was tested and resulted in a model fit (χ²/df) of 1.43. The model fit changed only slightly with a fit (χ²/df) of 1.45 when a direct path between satisfaction and return intent was added to the model. As reflected in the second mediation test in Table 3, the statistically significant effect between satisfaction and intention to return to the destination in equation 1 (.241, p < .01) is reduced to a nonsignificant statistical level in equation 3 (.045, p < .680), supporting a full mediation of fear of crime in the model.

Consistent with theory and the literature, the test of mediation has shown that the direct effect between satisfaction and intention to return is statistically significant (.241, p < .01) when fear is not included in the model. However, when we take into account the influence of fear, the direct effect of satisfaction on intention to return disappears. While the direct effects of satisfaction on purchase or return intention are well documented in the literature, the effect may be dissipated, however, by fear, an emotion that appears sufficiently strong to overpower past satisfaction.

Discussion

This research has examined the effects of perceived crime-related variables and destination satisfaction on destination return intention in the context of Visiting Home and Familiar Places. While much research has previously examined risk and violence in travel, few studies have examined the role of emotion, destination satisfaction, and talk of violence. Accordingly, this research adds to the body of research about destination violence and provides insight for managerial response to better manage word-of-mouth communications, especially by finding the significance of violence talk in affecting fear and of destination satisfaction in ameliorating fear in the destination.

First, results show that WOM about violence does not directly impact fear at a destination except through its effect on the perception of worsening crime at a destination, which in turn impacts the levels of fear that visitors feel at the destination. These relationships are consistent with the cognitive-emotive model in marketing proposed by Stephens and Gwinner (1998), since the appraisal of a stressful situation, such as the perception of worsening of crime, leads to elicitation of a negative emotion, such as fear. As visitors hear stories about violent events at the destination (an external stimulus), the information is evaluated and results in perceptions and beliefs about crime (a cognitive process), which is later translated into fear of crime (an emotion).

This finding supports the idea that an emotion such as fear is not aroused just by the environment, but by its juxtaposition to a person’s belief as pointed out by Lazarus (1993). This notion is strengthened by the conceptualization of mediating constructs and their roles from Baron and Kenny (1986, p. 1176), who explained that those effects usually consist of external events that “take on internal psychological significance” and reveal “how and why such effects occur.”

Second, as expected, perceived worsening of crime has an inverse effect on satisfaction with the destination. While most winter migrants to the area expected to return, the return intention rate of 89.3% found in this study was down from a return rate of 96.9% found in a 2010 study of winter migrants in the same area (Simpson, Ghaddar, and Sheng 2010). Using the data from the 2010 study, this decline of 7.8% translates to an overall decline in the number of winter migrant households in the area from 75,000 to 69,160. Because each household was estimated to spend $10,700 while in the area, the overall decline in actual spending would total about $52.6 million. The decline in the number of respondents likely to return is especially troubling since these respondents had been loyal and well satisfied with the region according to prior studies.

Nevertheless, the effects of perceptions of worsening crime found here represent a contribution to the scarce research about the effects of perceptions of crime on satisfaction at a destination but contrasts with the results obtained by Lin, Lee, and Wang (2012), who found no direct effect of travel risk on satisfaction. The difference in results might be explained by the role played by the negative features of a destination in forming overall visitor satisfaction (Alegre and Garau 2010). In this study, variations in the perception of worsening of crime affect satisfaction levels, which then change the extent of the negative emotion, fear. In other words, levels of fear at a destination increase as perceptions of crime and violence increase but the relationship is mitigated by satisfaction derived from current and prior visits, because “cognitions contribute to the formation of emotions” (Rodríguez del Bosque and San Martín 2008, p. 566).

This finding further suggests that without the intervening satisfaction with the destination variable, increasing the number of winter migrants or tourists to an area where crime is perceived to be worsening will be problematic. This reflects that emotions not only serve as antecedents to satisfaction as commonly established in tourist behavior models, but also that satisfaction itself may influence an emotion that, in this case, is fear. Since consumer satisfaction is formed by the accumulation of destination visits (Tribe and Snaith 1998), and because satisfied visitors likely become repeat visitors, then repeat visitors may be less affected by a perception of higher crime levels. This is supported by the work of McDowall (2010), who found that first-time visitors were more concerned about a destination being a safe place than were repeat visitors, suggesting that satisfied, loyal travelers may be less fearful of the destination. Additionally, the findings are consistent with the conceptualization of VHFP travel advanced by Pearce (2012), whereby repeat visitors return to personally relevant locations and familiar settings, even amid talk of violence in the destination.

For transient tourists without as much social and economic investment in a destination as winter migrants, the effect of fear on first-time intentions and return intentions would likely be higher than that observed in this study. Nonmigrant tourists lack the same anchor, or draw, to destinations that migrant tourists have, and are therefore less likely to have fear toward a destination be mitigated by factors such as social networks and commitments, housing investments, or the status quo behavior of relocating. Therefore, the observed effect of fear on return intentions from this study represents a minimum or baseline level that would be expected from nonmigrant tourists. Since fear was a significant driver of return intentions for winter migrants, it is fair to assume that the effect of fear for nonmigrant, transient tourists would be even stronger. Finally, the level of fear as a conceptually distinct construct from risk or concerns about crime (Ferraro 1996) was examined as a predictor of intention to return. Consistent with previous research, the feeling of fear of crime at a destination was found to have an inverse impact on visitors’ intentions to return to the destination. Although a direct relationship between satisfaction and intentions to return to the destination was not supported, satisfaction is fully felt on return intention through its impact on destination fear.

Conclusions

In summary, this investigation adds to the travel research literature by providing a better understanding of the way in which cognitive and emotional constructs work to affect travel destination return intention in the case of VHFP travel. The results of this research highlight the roles that perceived worsening of crime and satisfaction have as predictors and mediators, and how negative emotions like fear impact decision making and behavior in the context of wintering migrants. Theoretically, this study sheds light on the discussion about the cognition–emotion sequence related to satisfaction, by identifying a consumption situation where satisfaction precedes a basic emotion such as fear, which has been seldom studied in tourism (Faullant, Matzler, and Mooradian 2011). As suggested by Hernández-Lobato et al. (2006), satisfaction may be primarily determined by cognitions, whereas attitudinal loyalty, measured by intention to return to the destination, is primarily derived from affect. Thus, these empirically based results contribute to a better understanding of travel behavior and the effects of emotions triggered by destinations; research issues requiring more attention by tourism scholars (Sirakaya and Woodside 2005).

These findings also have implications for destination management organizations (DMOs), visitors and conventions bureaus (VCBs), chambers of commerce, and others involved in attracting tourists and especially longer-stay migrants to areas with perceived violence, given their economic impact on the area. For example, with respect to attracting new visitors, travel destination managers need to work together with local authorities in order to procure a safe environment and hence reduce negative WOM dissemination, especially since personal communication represents a strong influence on travel decision making in the retiree market segment (Viallon 2012).

With respect to past visitors, DMOs and VCBs may launch promotional and communication campaigns specifically targeting prior visitors to recover from negative publicity derived from safety and security issues. This segment of tourists would likely respond more favorably to promotions advocating return because the perceptions of crime and violence on fear would be diminished by higher levels of satisfaction formed from prior visits to the destination. Also, because memories of their travel experience are significant motivators for revisits and an indicator of willingness to spread positive recommendations (Tung and Ritchie 2011), the destination’s branding strategy should aim to “reinforce the recollection of pleasurable memories of the destination” as suggested by Blain, Levy, and Ritchie (2005, p. 331). Such targeted marketing efforts should be more effective than attempting to encourage visits only among new, first-time travelers for whom neither satisfaction level nor memories have yet formed.

Additionally, tourism officials should adopt a preventive approach by implementing programs that foster satisfaction with the destination that may anticipate and reduce the vulnerability of destinations to incidents of crime and violence. For instance, the coordination of tourism business and service providers could help increase the variety and quality of entertainment and leisure activities, in order to meet or exceed expectations of key target markets. In turn, this should positively impact the satisfaction of visitors and likelihood of their return while diminishing the eventual negative effects of fear at the destination.

This study is limited by the sample size and the focus on a single market segment in only one geographic region and the results may not be generalizable to other populations or travel experiences. In addition, other constructs or variables that might influence tourist behavior, such as destination image or features, differences in travel motivation, past experience, effects of other media or destination managers’ promotional efforts were not included in the proposed model.

Future studies should consider incorporating such factors into their research designs. Also, future research may examine the effects of familiarity and place attachment, rather than just measuring past visits, given the centrality of meanings and personal experiences to VHFP travel (Pearce 2012). According to Hammitt, Backlund, and Bixler (2004), familiarity refers to a sense of place knowing, security, and environmental preference based on remembrances and affective components leading to place attachment, which may be an important driver of tourist’s revisit behavior (Tsai 2012). Finally, this study found that not just emotions influence satisfaction with a destination as usually established in the travel-related literature, but that certain negative emotions (in this case fear) may be diminished by a visitor’s level of satisfaction. Further research could be conducted to identify nonrecursive relationships of emotions to satisfaction and vice versa. As suggested by Westbrook (1987), an appraisal of satisfaction may elicit certain emotions that may modify the initial satisfaction appraisal that took place earlier in the causal chain.

Footnotes

Appendix

Acknowledgements

The authors would like to thank the Valley Markets and Tourism Research Center and The University of Texas-Pan American for their strong support of this research.

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

Penny Simpson is Professor, Associate Dean and Director of the Valley Markets and Tourism Research Center at The University of Texas-Pan American. Her works have appeared journals including Journal of Marketing, Journal of the Academy of Marketing Science, Journal of Travel Research, Journal of Business Research, Journal of Strategic Marketing, and Industrial Marketing Management.

Oliver Cruz-Milán is a PhD candidate at the College of Business Administration of The University of Texas-Pan American. He also collaborates with the faculty of the Tourism and Hotel Management program at Universidad del Caribe in Cancun, Mexico. He is pursuing the doctoral degree with the support of the Mexican government through scholarship 308936 from CONACYT-COTACYT.

Justin Gressel is an adjunct professor at The University of Texas-Pan American and is employed at Walt Disney World in Orlando, Florida where his research focuses on pricing in a tourism environment.

References

Allsop

Dee T.

Bassett

Bryce R.

Hoskins

James A.

(2007). “Word-of-Mouth Research: Principles and Applications.” Journal of Advertising Research, 47 (4): 398-411.

Acierno

Ron

Rheingold

Alyssa A.

Resnick

Heidi S.

Kilpatrick

Dean G.

(2004). “Predictors of Fear of Crime in Older Adults.” Anxiety Disorders, 18:385-96.

Alegre

Joaquín

Garau

Jaume

. (2010). “Tourism Satisfaction and Dissatisfaction.” Annals of Tourism Research, 37 (1): 52-73.

Alper

Mariel

Chappell

Allison T.

(2012). “Untangling Fear of Crime: A Multi-theoretical Approach to Examining the Causes of Crime-Specific Fear.” Sociological Spectrum, 32 (4): 346-63.

Anderson

Eugene W.

Sullivan

Mary W.

(1993). “The Antecedents and Consequences of Customer Satisfaction for Firms.” Marketing Science, 12 (2): 125-43.

Arnett

Dennis B.

German

Steve D.

Hunt

Shelby D.

(2003). “The Identity Salience Model of Relationship Marketing Success: The Case of Nonprofit Marketing.” Journal of Marketing, 67 (April): 89-105.

Bagozzi

Richard P.

Youjae

. (2012). “Specification, Evaluation, and Interpretation of Structural Equation Models.” Journal of the Academy of Marketing Science, 40 (1): 8-34.

Baker

Dwayne A.

Crompton

John L.

(2000). “Quality, Satisfaction and Behavioral Intentions.” Annals of Tourism Research, 27 (3): 785-804.

Baláz

Vladimir

Williams

Allan M.

(2011). “Risk Attitudes and Migration Experience.” Journal of Risk Research, 14 (May): 583-96.

10.

Baloglu

Seyhmus

Pekcan

Aykut

Chen

Shiang-Lih

Santos

Joceline

. (2003). “The Relationship between Destination Performance, Overall Satisfaction, and Behavioral Intention for Distinct Segments.” Journal of Quality Assurance in Hospitality & Tourism, 4 (3/4): 149-65.

11.

Baron

Reuben M.

Kenny

David A.

(1986). “The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations.” Journal of Personality and Social Psychology, 51 (December):1173-82.

12.

Blain

Carmen

Levy

Stuart E.

Ritchie

J.R. Brent

. (2005). “Destination Branding: Insights and Practices from Destination Management Organizations,” Journal of Travel Research, 43 (4): 328-38.

13.

Boakye

Kwaku A.

(2012). “Tourists’ Views on Safety and Vulnerability. A Study of Some Selected Towns in Ghana.” Tourism Management, 33 (2): 327-33.

14.

Brunt

Paul

Mawby

Rob

Hambly

Zoe

. (2000). “Tourist Victimisation and the Fear of Crime on Holiday.” Tourism Management, 21 (4): 417-24.

15.

Bussey

(2012). “Amid Murders, Mexico Quietly Thrives.” The Wall Street Journal, February 10: B1.

16.

Campo-Martínez

Sara

Garau-Vadell

Joan B.

Martínez-Ruiz

María Pilar

. (2010). “Factors Influencing Repeat Visits to a Destination: The Influence of Group Composition.” Tourism Management, 31 (6): 862-70.

17.

Chuang

Shih-Chieh

. (2007). “The Effects of Emotions on the Purchase of Tour Commodities.” Journal of Travel & Tourism Marketing, 22 (1): 1-13.

18.

Churchill

Gilbert A.

Surprenant

Carol

. (1982). “An Investigation into the Determinants of Customer Satisfaction.” Journal of Marketing Research, 19 (November): 491-504.

19.

Dolnicar

Sara

. (2005). “Understanding Barriers to Leisure Travel: Tourist Fears as a Marketing Basis.” Journal of Vacation Marketing, 11 (3): 197-208.

20.

Faullant

Rita

Matzler

Kurt

Mooradian

Todd A.

(2011). “Personality, Basic Emotions, and Satisfaction: Primary Emotions in the Mountaineering Experience,” Tourism Management, 32 (6): 1423-30.

21.

Ferraro

Kenneth F.

(1996). “Women’s Fear of Victimization: Shadow of Sexual Assault?” Social Forces, 75 (2): 667-90.

22.

Ferraro

Kenneth F.

LaGrange

Randy

. (1987). “The Measurement of Fear of Crime.” Sociological Inquiry, 57 (Winter): 70-101.

23.

Fischhoff

Baruch

de Bruin

Wändi Bruine

Perrin

Wendy

Downs

Julie

. (2004). “Travel Risks in a Time of Terror: Judments and Choices.” Risk Analysis: An International Journal, 24 (5): 1301-9.

24.

Floyd

Myron F.

Pennington-Gray

Lori

. (2004). “Profiling Risk Perceptions of Tourists.” Annals of Tourism Research, 31 (4): 1051-54.

25.

Fuchs

Galia

Reichel

Arie

. (2011). “An Exploratory Inquiry into Destination Risk Perceptions and Risk Reduction Strategies of First Time vs. Repeat Visitors to a Highly Volatile Destination.” Journal of Travel Research, 32 (2): 266-76.

26.

Garofalo

James

. (1981). “The Fear of Crime: Causes and Consequences.” The Journal of Criminal Law & Criminology, 72 (2): 839-57.

27.

George

Richard

. (2003). “Tourist’s Perceptions of Safety and Security while Visiting Cape Town.” Tourism Management, 24 (5): 575-85.

28.

Girard

T. C.

Gartner

William C.

(1993). “Second Home Second View: Host Community Perceptions,” Annals of Tourism Research, 20 (4): 685-700.

29.

Gitelson

Richard J.

Crompton

John L.

(1984). “Insights into the Repeat Vacation Phenomenon.” Annals of Tourism Research, 11 (2): 199-217.

30.

Gnoth

Juergen

Zins

Andreas H.

Lengmueller

Ruediger

Boshoff

Christo

. (2000). “Emotions, Mood, Flow and Motivations to Travel.” Journal of Travel & Tourism Marketing, 9 (3): 23-34.

31.

Goeldner

Charles R.

Ritchie

J. R. Brent

. (2003). Tourism: Principles, Practices, Philosophies, 9th edition. Hoboken, NJ: Wiley.

32.

Hair

Joseph F.

Jr. Black

William C.

Babin

Barry J.

Anderson

Rolph E.

(2010). Multivariate Data Analysis, 7th edition. Upper Saddle River, NJ: Prentice Hall.

33.

Hammitt

William E.

Backlund

Erik A.

Bixler

Robert B.

(2004). “Experience Use History, Place Bonding and Resource Substitution of Trout Anglers during Recreation Engagements.” Journal of Leisure Research, 36 (3): 356-78.

34.

Hernández-Lobato

Lucio

Solís-Radilla

Maria M.

Moliner-Tena

Miguel A.

Sánchez-García

Javier

. (2006). “Tourism Destination Image, Satisfaction and Loyalty: A Study in Ixtapa-Zihuatanejo, Mexico.” Tourism Geographies, 8 (November): 343-58.

35.

Hipp

John R.

(2009). “Specifying the Determinants of Neighborhood Satisfaction: A Robust Assessment in 24 Metropolitan Areas.” Social Forces, 88 (1): 395-424.

36.

Huang

Songshan

Hsu

Cathy H. C.

(2009). “Effects of Travel Motivation, Past Experience, Perceived Constraint, and Attitude on Revisit Intention.” Journal of Travel Research, 48 (August): 29-44.

37.

Iacobucci

Dawn

. (2010). “Structural Equation Modeling: Fit Indices, Sample Size, and Advanced Topics.” Journal of Consumer Psychology, 20 (1): 90-98.

38.

Investigation, U.S. Department of Justice Federal Bureau of. (2010). “Crime in the United States 2010.” The Federal Bureau of Investigation. http://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2010/crime-in-the-u.s.-2010/tables/table-6 (accessed February 3, 2012).

39.

Izard

Carroll E.

(1977). Human Emotion. New York: Plenum.

40.

Jackson

Jonathan

. (2005). “Validating New Measures of the Fear of Crime.” International Journal of Social Research Methodology, 8 (October): 297-315.

41.

Kennedy

Leslie W.

(1985). “Perception of Social Diversity and Fear of Crime.” Environment and Behavior, 17 (3): 275-95.

42.

Kerlinger

Fred N.

Lee

Howard B.

(2000). Foundations of Behavioral Research, 4th edition. Belmont, CA: Cengage Learning.

43.

Kozinets

Robert V.

de Valck

Kristine

Wojnicki

Andrea C.

Wilner

Sarah J. S.

(2010). “Networked Narratives: Understanding Word-of-Mouth Marketing in Online Communities.” Journal of Marketing, 74 (March): 71-89.

44.

Lavantis

Theodore

Azmat

Gani

. (2000). “Tourism Demand and the Nuisance of Crime.” International Journal of Social Economics, 27 (7-10): 959-67.

45.

Lazarus

Richard S.

(1993). “From Psychological Stress to the Emotions: A History of Changing Outlooks.” Annual Review of Psychology, 44: 1-21.

46.

LeDoux

Joseph E.

(1995). “Emotion: Clues from the Brain.” Annual Review of Psychology, 46 (1): 209-235.

47.

Lepp

Andrew

Gibson

Heather

. (2003). “Tourist Roles, Perceived Risk and International Tourism.” Annals of Tourism Research, 30 (3): 606-24.

48.

Lerner

Jennifer S.

Gonzalez

Roxana M.

Small

Deborah A.

Fischhoff

Baruch

. (2003). “Effects of Fear and Anger on Perceived Risks of Terrorism: A National Field Experiment.” Psychological Science, 14 (March): 144-50.

49.

Lerner

Jennifer S.

Keltner

Dacher

. (2001). “Fear, Anger, and Risk.” Journal of Personality and Social Psychology, 81 (1): 146-59.

50.

Lin

Yi-Hsin

Lee

Yi-Chun

Wang

Shih-Chuan

. (2012). “Analysis of Motivation, Travel Risk, and Travel Satisfaction of Taiwan Undergraduates on Work and Travel Overseas Programs: Developing Measurement Scales.” Tourism Management Perspectives, 2 (3): 35-46.

51.

Litvin

Stephen W.

Goldsmith

Ronald E.

Pan

Bing

. (2008). “Electronic Word-of-mouth in Hospitality and Tourism Management.” Tourism Management, 29 (3): 458-68.

52.

Loewenstein

George F.

Weber

Elke U.

Hsee

Christopher K.

Welch

Ned

. (2001). “Risk as Feelings.” Psychological Bulletin, 127 (2): 267-86.

53.

McDowall

Siriporn

. (2010). “International Tourist Satisfaction and Destination Loyalty: Bangkok, Thailand.” Asia Pacific Journal of Tourism Research, 15 (1): 21-42.

54.

Mitchell

Vincent-Wayne

. (1999). “Consumer Perceived Risk: Conceptualizations and Models.” European Journal of Marketing, 33 (1/2): 163-95.

55.

Mittal

Vikas

Ross

William T.

Jr. Baldasar

Patrick M.

(1998). “The Asymmetric Impact of Negative and Positive Attribute-Level Performance on Overall Satisfaction and Repurchase Intentions.” Journal of Marketing, 62 (January): 33-47.

56.

Nunnally

Jum C.

(1978). Psychometric Theory. New York: McGraw-Hill.

57.

Oliver

Richard L.

(1980). “A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions.” Journal of Marketing Research, 17 (November): 460-69.

58.

Oliver

Richard L.

(1999). “Whence Consumer Loyalty?” Journal of Marketing, 63 (Special Issue): 33-44.

59.

Paterson

Kent.

(2011). “Frontera NorteSur.” New Mexico State University. July 24, 2011. http://fnsnews.nmsu.edu/2011/07/24/fns-special-report-mexico-tourism-woes-worsen-2/ (accessed February 3, 2012).

60.

Pearce

Philip L.

(2012). “The Experience of Visiting Home and Familiar Places.” Annals of Tourism Research, 39 (2): 1024-47.

61.

Pizam

Abraham

Fleischer

Aliza

. (2002). “Severity versus Frequency of Acts of Terrorism: Which Has a Larger Impact on Tourism Demand?” Journal of Travel Research, 40 (3): 337-39.

62.

Reisinger

Yvette

Mavondo

Felix

. (2005). “Travel Anxiety and Intentions to Travel Internationally: Implications of Travel Risk Perception.” Journal of Travel Research, 43 (February): 212-25.

63.

Rittichainuwat

Bongkosh N.

Chakraborty

Goutam

. (2009). “Perceived Travel Risks Regarding Terrorism and Disease: The Case of Thailand.” Tourism Management, 30 (June): 410-18.

64.

Rodríguez

Vicente

. (2001). “Tourism as a Recruiting Post for Retirement Migration.” Tourism Geographies, 3 (1): 52-63.

65.

Rodríguez del Bosque

Ignacio

Martín

Héctor San

. (2008). “Tourist Satisfaction: A Cognitive-Affective Model.” Annals of Tourism Research, 35 (2): 551-73.

66.

Roehl

Wesley S.

Fesenmaier

Daniel R.

(1992). “Risk Perceptions and Pleasure Travel: An Exploratory Analysis.” Journal of Travel Research, 4 (April): 17-26.

67.

Rountree

Pamela W.

Land

Kenneth C.

(1996). “Perceived Risk versus Fear of Crime: Empirical Evidence of Conceptually Distinct Reactions in Survey Data.” Social Forces, 74 (June): 1353-76.

68.

Simpson

Penny M.

Siguaw

Judy A.

(2008a). “Perceived Travel Risk: The Traveller Perspective and Manageability.” International Journal of Tourism Research, 10 (April): 315-27.

69.

Simpson

Penny M.

Siguaw

Judy A.

(2008b). “Destination Word of Mouth: The Role of Traveler Type, Residents, and Identity Salience.” Journal of Travel Research, 47 (2):167-82.

70.

Simpson

Penny M.

Ghaddar

Suad

Sheng

Xiaojing

. (2010). Winter Texan 2010 Survey. College of Business Administration, The University of Texas–Pan American, Edinburg, TX: Valley Markets and Tourism Research Center.

71.

Sirakaya

Ercan

Woodside

Arch G.

(2005). “Building and Testing Theories of Decision Making by Travellers.” Tourism Management, 26 (6): 815-32.

72.

Sönmez

Sevil F.

Graefe

Alan R.

(1998). “Determining Future Travel Behavior from Past Travel Experience and Perceptions of Risk and Safety.” Journal of Travel Research, 37 (November): 171-77.

73.

Stephens

Nancy

Gwinner

Kevin P.

(1998). “Why Don’t Some People Complain? A Cognitive-Emotive Process Model of Consumer Complaint Behavior.” Journal of the Academy of Marketing Science, 26 (3): 72-189.

74.

Tanner

John F.

Jr. Hunt

James B.

Eppright

David R.

(1991). “The Protection Motivation Model: A Normative Model of Fear Appeals.” Journal of Marketing, 55 (July): 36-45.

75.

Tribe

John

Snaith

Tim

. (1998). “From SERVQUAL to HOLSAT: Holiday Satisfaction in Varadero, Cuba.” Tourism Management, 19 (1): 25-34.

76.

Tsai

Shu-pei

. (2012). “Place Attachment and Tourism Marketing: Investigating International Tourists in Singapore,” International Journal of Tourism Research, 14 (2): 139-52.

77.

Tung

Vincent W.S.

Ritchie

J.R. Brent

. (2011). “Investigating the Memorable Experiences of the Senior Travel Market: An Examination of the Reminiscence Bump.” Journal of Travel & Tourism Marketing, 28 (3): 331-43.

78.

Seoho

Crompton

John L.

(1990). “Attitude Determinants in Tourism Destination Choice.” Annals of Tourism Research, 17 (3): 432-48.

79.

Van Raaij

W. Fred

Francken

Dick A.

(1984). “Vacation Destinations, Activities and Satisfactions.” Annals of Tourism Research, 11 (1): 101-12.

80.

Viallon

Philippe

. (2012). “Retired Snowbirds.” Annals of Tourism Research, 39 (4): 2073-91.

81.

Warr

Mark

. (2000). “Fear of Crime in the United States: Avenues for Research and Policy.” In Measurement and Analysis of Crime and Justice, Criminal Justice, edited by Duffee

Washington, DC: United States Department of Justice, pp. 451-89.

82.

Westbrook

Robert A.

Oliver

Richard L.

(1981). “Developing Better Measures of Consumer Satisfaction: Some Preliminary Results.” In Advances in Consumer Research, edited by Monroe

K. B.

Ann Arbor, MI: Association for Consumer Research, pp. 94-101.

83.

Westbrook

Robert A.

(1987). “Product/Consumption-Based Affective Responses and Postpurchase Processes.” Journal of Marketing Research, 24 (August): 258-70.

84.

Williams

Allan M.

Baláz

Vladimír

. (2013). “Tourism, Risk Tolerance and Competences: Travel Organization and Tourism Hazards.” Tourism Management, 35 (April):209-21.

85.

Woodside

Arch G.

Lysonski

Steven

. (1989). “A General Model of Traveler Destination Choice.” Journal of Travel Research, 27 (4): 8-14.

86.

Yüksel

Atila

Yüksel

Fisun

. (2007). “Shopping Risk Perceptions: Effects on Tourists’ Emotions, Satisfaction and Expressed Loyalty Intentions.” Tourism Management, 28 (June): 703-13.

87.

Zins

Andreas H.

(2002). “Consumption Emotions, Experience Quality and Satisfaction.” Journal of Travel & Tourism Marketing, 12 (2/3): 3-18.