Tourism marketing: risk perception
Table of Contents:
Perceived risks of tourism in Bangladesh
Nazmoon Akhter and Azizul Hassan
Tourism is one of the major contributing sectors in the economic growth of a country, creating revenues and employment and supporting cultural value and entertainment. The World Tourism Organization’s “Tourism 2020 Vision” (UNWTO, 2003) anticipates that international tourist arrivals will be more than 1.56 billion during year 2020. However, planning and anticipation time before the trip is not a simple task as a tourist must make decisions regarding timing, transportation mode, budget, secondary destinations and activities along with the primary destination. Besides, tourism is connected with risk and fear for both physical integrity and belongings of the travellers. For this reason, tourists’ perceived risk influences tourists’ choice of destinations (Fuchs and Rcichel, 2006). Tourists try to avoid high-risk destinations and select ones that they consider safe (Sonmez and Graefe, 1998a). Thus, successful tourism development relies on the risk reduction that is related with a destination and an event along with infrastructural improvements and heightened awareness on the world stage.
Perceived risk is reviewed across various disciplines, namely geology (Burton et al., 1978), sociology (Douglas and Wildavsky, 1982), psychology (Kahn and Sarin, 1988), marketing (Bauer, 1960; Dholakia, 2001) and tourism (Carter, 1998; Lcpp and Gibson, 2008). In the field of tourism, Roehl and Fesenmaier (1992) define three groups of tourists on the basis of their risk perception: risk neutral, considering no risk involved in tourism or their destination; functional risk, considering the probability of mechanical, equipment or organizational problems associated with tourism; and place risk, perceiving fairly risky vacations and very risky destinations that have been recently visited by tourists during their vacations. Some scholars have reported that perceived risk generates from various potential losses (Dholakia, 2001; Jacoby and Kaplan, 1972; Roselius, 1971). Performance risk arises from purchases that will not perform according to buyers’ desire or expectation (Horton, 1976). Financial risk indicates potential net financial loss that is having the possibility of repairing, replacing or price refunding of purchased product (Laroche et al., 2004). Psychological risk means the possibility of anxiety or psychological discomfort like worry and regret, generated from post-purchase affective reactions (Roehl and Fesenmaier, 1992). Social risk is the probability that purchaser motive to purchase a product may be influenced by other people’s opinions (Murray and Schlacter, 1990). Physical risk is concerned about the potential threat of a person’s health or appearance due to the consumed product (Mitchell, 1998). Time risk has the potentiality of consuming time or wasting time due to the products purchased (Roselius, 1971). Physical risks (Sonmez and Graefe, 1998a; 1998b), financial risks (Um et al., 2006), health risks (Larsen et al., 2007) and social risks (Carter, 1998) are imperatively related to travel. Roehl and Fesenmaier (1992) found seven different types of risk involved in travel decisions, namely equipment, financial, physical, psychological, satisfaction, social and time risks. They found different risk perceptions among tourists, where some tourists arc more risk averse than others. A strongly influential “generalization effect” of perceived risk is exited which can result in serious economic losses. Besides, when the consumer has faced certain types of risks, his/her behaviour has changed from delaying the purchase to following strategies designed to reduce risks to a “tolerable” level. For this reason, it is much more challenging to investigate tourists’ risk perception about destination and risk reduction strategies that encourage them to return to the risky destination.
Perceived risk is the uncertainty that a consumer has when buying items. Mitchell et al. (1999) reports that researchers, i.e. practitioners and academicians, have focused on perceived risk that is related to various fields, including food technology, banking services, dental services, apparel catalogue shopping, intercultural comparisons and so on. Mowen and Minor (1998) defined perceived risk as a consumer’s perception regarding risk based upon assessing the possibility of negative outcomes as well as the likelihood of occurring such outcomes. According to several literatures regarding consumer behaviour (Engel et al., 1995; Assael, 1995; Mowen and Minor, 1998; Schitfman and Kanuk, 2007), consumer perceived risk is classified in different ways such as physical risk, indicating probability of physical harm to the consumer arisen from product malfunction; performance risk, representing the probability of not operating the purchased product as expected; financial risk, defining probability of losing investment in the product; social risk, meaning the fear that the purchased product will not conform to the standards of the reference group; psychological risk, indicating the fear that the purchased product will not suit the consumer’s self-image; time risk, explaining the risk of taking time excessively for product consumption; and opportunity loss risk, defining the possibility that by taking an action, the consumer will miss out on alternative preferred activities. According to Quintal, Lee and Soutar (2010) and Dowling and Staelin (1994), perceived risk is the perception of probable loss because of uncertainty involved in purchasing product or service. Additionally, Rcichel et al. (2007) classified consumers’ risk perception into seven categories: physical risk (i.e. potential harm result from consuming products and services),
Perceived risks of tourism in Bangladesh 177 monetary risk (i.e. potential loss of the money invested in a product/service purchase), performance risk (i.e. the possibility of the product/service not reaching consumers’ expectations), social risk (i.e. anxiety that the product will not be approved by the reference group), psychological risk (i.e. the possibility' that the product will not be consistent with self-image), time risk (i.e. concern that consuming product/service will be time consuming), and opportunity’ loss risk (i.e. fear that the consumer will forgo other better consumption options due to making the decision of purchasing a particular product/service).
Tourists’ perceived risk
Travel intention is influenced mainly by’ destination image regarding its risk level. Roehl and Fesenmaier ( 1992), who primarily’ research on risk perception in tourism, determined three basic factors of perceived risk, namely phy'sical-cquipment risk, vacation risk and destination risk. Promsivapallop and Kannaovakun (2017) report that risk perception regarding travel has negative effect on tourists’ intention to travel only’ in risky destinations but no effect on their intention to visit low-risk or no-risk destinations. Further, Promsivapallop and Kannaovakun (2018) examine travel risk determinants and their relation with young educated adults who lived in Germany and visited Thailand through considering their role, past experience, types of gender and their intention to travel. They’ found six determinants of tourists’ risk perception regarding travel, namely crime and false practice risk, health risk, hazard risk, communication risk, over-commercialization risk and political risk, which have relationships with tourists’ roles and their past travel experience. They stated that over-commercialization risk is a more crucial risk which has relationships with tourist roles and their travel experience than other types of risks, where political risk is a less concerned risk having a negative impact on tourists’ travel intention.
Garg (2013) stated that tourists perceive terrorist activities, SARS, swine flu, earthquakes and tsunamis as key travel risk concerns at the time of choosing destinations and they try' to avoid destinations having high risk from these factors. Low-risk perception about a destination has positive effect on that destination. In this regard, Tavitiyaman and Qu (2013) argued that travellers with low-risk perceptions about a destination will normally’ establish a more positive image about that destination, and improve travellers’ visit intention to that destination which results in gaining greater overall satisfaction than travellers with high-risk perceptions. Rittichainuwat et al. (2018) examine tourists’ risk perception and travel decisions using variables like demographics, knowledge about safety’ and country’ of residence where samples were gathered in Thailand, Japan, Australia and Indonesia. They’ explained that tourist risk perception was influenced by tsunami occurrence probability’ and was destination specific. Additionally’, Kozak et al. (2007) found that the majority of travellers have changed their decisions to travel to a destination due to elevated risk. They’ also observed that different continents perceive travellers’ risk differently and concluded that travellers from varied nations may face different degrees of risk.
Parrey et al. (2018) investigated both government initiatives and media influence as mediating roles between perceived risks and destination image regarding conflict zones. They found that unrest (terrorist) and political risk is mostly followed by both psychological risk and socio-cultural risk which is against the assumption that unrest (terrorist) risk is the crucial source of risk that domestic tourists perceived during visiting any conflict zone. Further, they found that the media’s role improves the risk perception and government initiatives decrease the destination’s risk image as well as its competitiveness. Moreover, they affirmed that performing best under the dimension of control as government initiatives plays very weak role as compared to performing under the dimension of concern as media for the destination image in the conflict zone. Mitchell and Vassos (1997) and Mitchell et al. (1999) found a list of 43 risk factors about a holiday package, which ranged from serious occurrences like natural disasters to trivial matters like when a tour representative did not participate in activities. Moreira (2007) investigated stealth and catastrophic risks to the development of tourism destinations. He explains that stealth risks are more significant than catastrophic risks to residents and tourists alike. Stealth risks are the gradual degradation of neutral or positive present conditions diffused over time such as air quality, while catastrophic risks include the frequency of sudden negative impacts on present reality by serious accidents or natural disasters such as earthquakes and typhoons. According to both domestic and international markets, Dolnicar (2005) analysed various risks or “fears” such as political risk, composing of terrorism and political instability; environmental risk, involving natural disasters and landslides; health risk, indicating lack of access to healthcare and life-threatening diseases; planning risk, representing unreliable airlines and inexperienced operators; and property risk, including theft and loss of luggage. Fuchs and Reichel (2006) investigated destination risk perception among foreign tourists visiting Israel and identified six destination risk perception factors: human-induced; financial; service quality; social-psychological; natural disasters and car accidents; and food safety problems and weather. In this regard, Reisinger and Mavondo (2005,2006) established an integrative theory of risk perception and anxiety as determinants of international travel intentions. They found significant differences in perceptions of international tourists about travel risk and safety, anxiety and intention to travel. They explained that tourists from the United States, Hong Kong and Australia perceived more travel risk, felt less safe, were more anxious and reluctant to travel as compared to tourists from the United Kingdom, Canada and Greece. In another study (2005), they utilized path analysis to show travel risk perception as a function of cultural orientation and psychographic factors and found that terrorism and socio-cultural risk emerged as the most significant predictors of travel anxiety where intentions to travel internationally were determined by both travel anxiety levels and level of perceived safety.
Reichcl et al. (2007) reported that perceived risk of backpackers’ experience is composed of multi-dimensional phenomenon which varies according to an individual’s characteristics such as gender, past backpacking experience and preference for fellow travellers. In addition, Dayour et al. (2019) explored backpackers’
Perceived risks of tourism in Bangladesh 179 risk perceptions towards smartphone usage and consequent risk reduction strategies. They explained that backpackers’ innovativeness, trust and familiarity with a smartphone are established as inhibitors of their perceived risk where levels of trust had a significant positive impact on their intentions to reuse the device, as did their satisfaction levels with the device and travel. They found that backpackers used a mix of both cognitive and non-cognitive measures to manage their risk perceptions. Adam (2015) investigated backpackers’ risk perceptions and risk reduction strategies in Ghana and explored backpackers’ perceived risks, determinants of perceived risk and risk reduction strategies. He showed that there are six dimensions of backpackers’ perceived risks in Ghana, namely expectation, physical, health, financial, political and socio-psychological risks. He used a binary logistic regression model to determine backpackers’ perceived risk in Ghana where he found that religion, continent of origin, sex, repeat visits and travel arrangements were significant determinants. He also focused on risk reduction strategies by backpackers where risk reduction strategies were found to vary by type of perceived risk.
A more comprehensive explanation to the concept of travel risk is given by Cui ct al. (2016) who summarized three views of tourism risk perception including tourist’s subjective feelings of adverse effects which may occur during the trip, tourist’s objective evaluation of the adverse effects, and tourist’s cognitive of exceeding the threshold part of the expected adverse effects during travel. Further, Fennell (2017) developed a comprehensive model about travel perceived risk and fear where he found six components: characteristics of tourists, fearinducing factors of a trip, strategies to reduce fear, travel stage, fear intensity and fear responses.
Risk reduction strategies
The significant role of information as a means for risk reduction is highlighted by several other scholars. According to Mitchell et al. (1999), if the tolerance level of the consumer about risk is crossed, it will either result in abandonment of the purchasing process or the consumer’s engaging in risk reduction. They state that consumers seek to reduce the uncertainty or consequences of an unsatisfactory decision by following risk reduction, or “risk handling” strategies. They explain that uncertainty can be reduced by collecting additional information and by “the importance of a name that can be trusted”. They also argue that “risk tolerance” directly affects the risk threshold at which consumers begin to engage in risk reduction strategies, as “risk tolerance” not only represents a level of risk that the consumer cannot tolerate, but also represents the ability of the consumer to absorb the risks involved in the decision. Byzalov and Shachar (2004) argued that advertising increases consumers’ tendency to purchase the promoted product as the informative content of advertising helps to resolve some of the uncertainty that risk-averse consumers face and thus reduces the risk associated with the product. According to Boshoff (2002), tourism can follow several strategies to reduce risk perceptions and hence to directly or indirectly enhance the purchaseintentions of prospective buyers. He highlighted that such risk strategies include providing potential buyers with general information about the service, providing potential buyers with price information and providing a service guarantee prior to actual purchase. In this regard, Tideswell and Faulkner (1999) report that familiarity with a destination and information search behaviour can also be usefol tools for risk reduction. Law (2006) suggested several risk reduction strategies such as free insurance coverage, local government guarantees of tourists’ personal safety, an increase in transparency of information related to risk incidents and the introduction of surveillance or protection measures to deal with the aforementioned risks of pandemics, terrorist attacks and natural disasters.
Mitchell (1993) identified factors such as age, socio-economic group and education that can influence the use of risk reduction strategies. He explained that increased age of people lowers the propensity of the search and processing of information. Higher educational levels tend to increase levels of searching, but not in all product categories. He also explained that consumers with high self-confidence and high risk perception tend to use more risk reduction strategics.
Tan (1999) investigated Internet shopping risk reduction strategies and found that reference groups appeal as the most preferred risk reliever for Internet shopping, particularly product endorsements by expert users. In addition, he also finds that the marketer’s reputation, brand image and specific warranty strategies effectively reduce risk for potential Internet shoppers.
Due to the significance of tourism on the one hand and the considerable effects of perceived risks on tourism on the other hand, it is important to investigate the factors related to perceived risk and how this risk can be minimized which is beneficial to both travellers and marketers.
Travel and tourism in Bangladesh
Travel and tourism provides a great contribution to a nation’s economy through creating jobs, driving exports and generating prosperity. In Bangladesh, both domestic and international tourists play crucial roles in its economic sector. The direct contribution of travel and tourism is 2.2% of total GDP in 2017 and is forecast to rise by 6.1% in 2018. The total contribution of travel and tourism to GDP is 4.3% of GDP in 2017, and is forecast to rise by 6.4% in 2018. Additionally, travel and tourism directly supported 1,178,500 jobs (1.8% of total employment) during 2017 which is expected to rise by 3.0% in 2018. The total contribution of travel and tourism to employment, including jobs indirectly supported by the industry, was 3.8% of total employment (2,432,000 jobs) in 2017 that is expected to rise by 2.5% in 2018. In Bangladesh, domestic travel spending generated 97.4% of direct travel and tourism GDP in 2017 compared with 2.6% for visitor exports (i.e. foreign visitor spending or international tourism receipts). Domestic travel spending is expected to grow by 6.3% in 2018 to BDT725.9 billion and visitor exports are expected to grow by 6.3% in 2018 to BDT19.5 billion (World Travel & Tourism Council [WTTC], 2018).
In addition, Bangladesh’s visitor arrivals have dropped 73.9% in December 2018 as compared to an increase of 23.6% in 2017 (CEIC Data, 2018). On the other hand, foreign tourist arrivals in Bangladesh have increased over the last five years. In 2014, the number of foreign tourists is about 0.16 million which slightly reduced in 2015 reaching 0.14 million, before increasing again to 0.20 million in 2016, about 0.26 million in 2017, around 0.27 million in 2018 and about 0.20 million up to July in 2019 (Imam, 2019).
The sample for this study is the international tourists and domestic tourists, who stayed in Chattogram in Bangladesh and visited Cox’s Bazar, Saint Martin, Dhaka and Sylhet from July to December 2019.
In the study, apart from using primary data to conduct the research, a number of articles and textbooks have been reviewed to find out related variables and existing models on the perceived risk of tourism and various information have been supplemented through the browsing of related web pages on the internet. Then a printed survey questionnaire was prepared on the basis of reviewed literature to collect primary data where such data were collected by direct personal visit to the respondents.
Questionnaire survey method is administered among the respondents by providing an 83-item questionnaire developed by the researchers on the basis of reviewed literatures to gather primary data where 12 questions are about demographic characteristics of the respondents, 54 questions are about the perceived risks of tourism in Bangladesh and the remaining 17 questions were about the strategies to reduce risks associated with tourism. The respondents were asked to rank each of 71 items on a 5-point Likert scale (5 = Strongly Agree . . . 1 = Strongly Disagree).
Mode of data analysis
The study used Statistical Product and Service Solutions (SPSS) version 22.0 and AMOS 23 software to analyse the data. Five main statistical tools were employed in the analysis, namely exploratory factor analysis (principal component analysis), confirmatory factor analysis, binary logistic regression, one-way analysis of variance (ANOVA) and ordinal logistic regression.
A sophisticated method of statistics, factor analysis (principal component analysis) by varimax rotation method, is used in this present study to obtain interpretable dimensions of perceived risk of tourism in Bangladesh. Here, researchers have followed the initial factor matrices to varimax rotation procedures to provide orthogonal common factors (Kaiser, 1974). Finally, dimensions regarding the perceived risk of tourists are made on the basis of factors scores. After that, the first-order confirmatory factor analysis (CFA) is performed in the overall dataset through the maximum likelihood (ML) estimate to measure the validity’ of a theoretical construct (Byrne and Gavin, 1996). Then, CFA results are evaluated to confirm the undimensionality' and reliability of each contract. The model fit indicators evaluated are CMIN/DF, RMSEA, SRMR, GFI, AGFI, CFI, PCLOSE and HOELTER.
To discuss the model fit of SEM, the study' considers the criteria of the various model fit indices as follows:
Further, to assess the determinants of tourists’ perceived risk, a binary’ logistic regression function is used as such tool has the ability’ to accept independent variables of varying measurement levels (Pallant, 2005). Additionally, binary' logistic regression is an appropriate tool for categorical dependent variables in a binary
Table 11.1 Criteria of model fit indices
Source: Data compiled by the authors, 2020
Perceived risks of tourism in Bangladesh 183 format (Pallant, 2005). In using binary regression, the tourists’ perceived risk dimensions are turned into one (by summing the scores and dividing them by the number of items making up the dimension). Then, the response to the dimension is recorded into a binary function where “strongly agree” and “somewhat agree”, indicating the presence of perceived risk, arc coded as one (1) and “strongly disagree” and “somewhat disagree”, indicating the absence of perceived risk, are coded as zero (0) (Adam, 2015). The risk dimension is regressed against a set of independent variables to determine which factors influence tourists’ perceived risks. Further, ANOVA is used to assess the differences in risk reduction strategies across tourists’ perceived risks dimensions. Lastly, ordinal logistic regression is used in the study as the dependent variables i.e. risk reduction strategies are categorical (Likert scale) and the value of each category has a significant chronological order and each value is certainly higher than the previous one.
Identifications of variables that influence tourists’ perceived risks in Bangladesh
The variables influencing tourist’s risk perception in Bangladesh are presented in Table 11.2.
Table 11.2 List of variables that represent tourists’ perceived risks in Bangladesh
Table 11.2 (Continued)
Source: Data compiled by the authors, 2020
Table 11.3 Variables that represent tourists’ risk reduction strategies in Bangladesh
Source: Data compiled by the authors, 2020
Identifications of variables that represent tourists’ risk reduction strategies in Bangladesh
The variables that represent tourists’ risk reduction strategies in Bangladesh are presented in Table 11.3.
Data analysis and findings
This section is discussed under the following heads.
Analysis and findings of sample respondents
The total number of respondents involved in the interviews is 320, of whom 60.9% are males and the remaining 39.1% are females. In terms of age, 64.4% respondents lie within the age group of 18-30 years. This is followed by 31-40 years (30.0%), 41-50 years (3.4%), less than 18 (1.3%) and above 50 years (0.9 %) respectively. Most of the respondents are Muslims (82.5%) which is followed by Hindus (13.1%), Christians (2.2%), Buddhists (1.6%) and atheists (0.6%), respectively. Among respondents, 45.9% have bachelor degrees, 45.0% have postgraduate degrees, 6.6% have completed high school and the remaining 2% have master’s degrees. Regarding relationships, 60.9% respondents are single and the remaining 39.1% are married. In terms of occupation, 45.9% respondents arc students, 40.9% are private jobholders, 6.9% are businessmen, 4.7% are housewives and the remaining 1.6% are private jobholders. Among the sample, 6.3% are international tourists and 93.7% are domestic tourists. Most of the respondents
(48.4%) are visiting the tourist place at the time of visiting their friends and relatives while about 45.9% are visiting tourist spots during holiday and the remaining 5.6% are visiting tourist spots for other purposes such as honeymoon, study tour and so on. Most of the respondents (44.7%) stay the tourist places for two days. Most of the respondents (60%) have visited tourist places in Bangladesh before while about 40% are visiting for the first time. Additionally, 47.5% are visiting with friends and have mostly organized their trip by themselves (69.1%) without using travel intermediaries. Most of the respondents (43.3%) have travelled with a budget of less than or equal to Tk. 10,000 (Table 11.4).
Table 11.4 Demographic analysis
Source: Data compiled by the authors, 2020
Appropriateness of data for factor analysis
Kaiser-Meyer-Olkin (KMO) is a method of representing the appropriateness of data for factor analysis to show sampling adequacy. The value of KMO statistics varies between 0 and 1. Kaiser (1974) argued that values greater than 0.5 are acceptable. Again, Bartlett’s test of sphericity (Bartlett, 1950) is another statistical tool used in the study to verify its appropriateness. This test will be significant with a value less than 0.5. The values of KMO and Bartlett’s test for adequacy of the sample of the study have been presented in Table 11.5.
Table 11.5 shows that in this study, the value of KMO is 0.770 which is greater than 0.5, indicating that the sample taken to process the factor analysis is acceptable. Besides, the significance value of Bartlett’s test of sphericity is also less than 0.5, indicating that the data is appropriate for the factor analysis.
After testing appropriateness of data, the next step has been carried out to show factor analysis to simplify the diverse relationship that exists among a set of observed variables.
188 Nazmoon Akhter and Azizul Hassan
Table 11.5 KMO and Bartlett’s test
Source: Data compiled by the authors, 2020
For factor analysis, principal component analysis (PCA) followed by varimax rotation is performed. It is required to mention here that factors loading greater than 0.3 are considered significant, 0.4 are considered more important and 0.5 or greater are considered very significant (Hair et al., 2003). For parsimony, only those factors with loading above 0.5 are considered significant (Hair et al., 2003; Pal and Bagai, 1987; and Hair et al., 1998). Communality values indicate the proportion of the variance in the response to the variables that are explained by the identified factors. Moreover, a factor’s eigenvalue, which is the sum of the squares of its factor loading, has also been computed hereafter to indicate how well each factor fits the data from all of the respondents on all of the variables.
Determinants ofperceived risks of tourism in Bangladesh
In order to find out the determinants of tourists’ perceived risk in Bangladesh, 54 reasons are taken into consideration. In this case, the Bartlett’s test of sphericity and KMO of sampling adequacy are applied to evaluate the adequacy of the survey data for factor analysis. Then, a minimum 0.30 value of communality of each item has been considered for further analysis in principle components analysis. As a result, two items, XI0 and X43, have been eliminated from the analysis. Therefore, a principal component factor analysis with varimax rotation is performed for the remaining 52 items/reasons of which four items, X24, X31, X50 and X54, have been deleted due to have factor loading less than 0.40 and 48 items with factor loading greater than 0.40 have produced a clear structure of factor. Here, six-factor results are emerged from the output with eigenvalues greater than 1 (Table 11.6). The total variance of 51.520% found afterwards indicates that the six-factor solutions explain 51.520% of the total variance where the first factor explains the most and is about 12.454%; the second, third, fourth, fifth and sixth factors explain 11.725%, 10.659%, 6.044%, 5.322% and 5.315%, respectively. These six factors of tourists’ perceived risks along with their ordered variables have been named and their results arc shown in Table 11.6.
Table 11.6 shows that the name of six factors of tourists’ perceived risks are financial and communication risk, political instability and natural risk, health and time risk, physical risk, social risk and psychological risk where the results of Cronbach’s alpha coefficients of the six dimensions arc 0.883, 0.870, 0.853, 0.663, 0.607 and 0.604, respectively.
Tabic 11.6 Principle component analysis with rotated component matrix and communalities
Source: Data compiled by the authors, 2020
Result of various dimensions ofperceived risk
The relationship between each latent variable of perceived risk is displayed in Figure 11.1.
From Table 11.6, the main dimension of financial and communication risk is government committed in promoting the destination’s positive image (0.715), while the main dimension of political instability and natural risk is risk of kidnapping at the tourist spots of Bangladesh (0.862). Furthermore, the main dimension of health and time risk is risk of ill hygiene and cleanliness at hotels (0.753), the main dimension of physical risk is experience or witnessing of violence (0.697) and the main dimension of social risk is the thought of vacationing at tourist spots of Bangladesh giving a feeling of unwanted anxiety (0.628). Finally, the main dimension of psychological risk is not reflecting personality (0.668). All
Figure 11.1 Path diagram of tourists’ perceived risk
Source: Data compiled by the authors, 2020
Perceived risks of tourism in Bangladesh 191 dimensions affect each variable significantly (Table 11.3). The results of model fit indices in the study reports that CMIN/DF = 2.267, CFI= 0.868, GFI=0.853, AGFI-0.81, RMSEA = 0.075, SRMR = 0.0631, Hocker’s N returns value at 5% significant level =119 and Hoelter’s N returns value at 1% significant level =127 are found in the model. Based on overall indices, this sample has an acceptable fit to the model as chi-square, CMIN/DF, CFI, RMSEA, RMR and SRMR lie in the acceptable ranges. Although there are some indicators that do not meet the criteria goodness of fit, overall the model has met the criteria of goodness of fit (Meesala and Paul, 2016) as CMIN/DF, CFI, RMSEA, SRMR and Hoelter’s N return values lie in the acceptable ranges (Table 11.7).
Table 11.7 Result of regression weight
Table 11.7 (Continued)
Source: Data compiled by the authors, 2020
Notes: *, **, *** Significant at alpha 10 %, 5 %, and 1 % respectively, S.E: Standard Error, CR: Critical Ratio, CP: Constant Parameter
Results of binary logistic regression
The binary logistic regression model is a good predictor of tourists’ perceived risk as indicated by the omnibus tests of model coefficients and the Hosmer and Lemeshow test. To be a good predictor, the alpha values of the omnibus tests of model coefficients need to be less than 0.05 and the Hosmer and Lemeshow test has to be greater than 0.05 (Pallant, 2005). The study is reliable as the alpha values of both tests fulfilled the requirement as shown in Table 11.8.
The model predicted 47.7% (Nagelkerkc Rsquare) and 35.7% (Cox and Snell R square) of tourists’ perceived risk in Bangladesh as shown by the pseudo R2 values. However, despite the significance of the model in predicting tourists’ perceived risk in Bangladesh, not all the predictor variables arc significant in predicting tourists’ perceived risk. Six out of the 13 predictor variables are found to be significant to the model. Age, type of tourist, purpose of visit and travel budget emerge as the most significant predictors of tourists’ perceived risk in Bangladesh (Table 11.9).
Under age, tourists within the 31-40 age group are more likely to associate Bangladesh with risk compared to the age group greater than 50. This is
Table 11.8 Results of model fit
Source: Data compiled by the authors, 2020
Table 11.9 Results of binary logistic regression
Table 11.9 (Continued)
Source: Data compiled by the authors, 2020
Notes: *, **, *** Significant at alpha 10%, 5%, and 1% respectively, RC: Reference Category
Perceived risks of tourism in Bangladesh 195 followed by tourists’ age groups within 18-30, less than 18 and 41-50. Under religion, Muslim tourists are more likely to associate risk with Bangladesh than atheist counterparts. This is followed by Hindu tourists. Under occupation, students are 0.294 times less likely to risk as compare to housewives, which is followed by private jobholders. Under tourist type, international tourists are 0.087 times less likely to associate risk as compared to domestic travellers in Bangladesh. Tourists who are travelling during holidays are 1.649 times more likely to associate risk than those who are travelling for other purposes such as honeymoon, physical treatment and so on. This is followed by tourists who are travelling to visit to meet their friends and relatives. Tourists who have tourist budget Tk. 40,001-50,000 are 107.454 times more likely to associate Bangladesh with risk as compared those have tourist budget greater than Tk. 50,000. This is followed by tourists having budget Tk. 20,001-30,000, Tk. 10,001-20,000, less than or equal to Tk. 10,000 and Tk. 30,001-40,000, respectively (Table 11.9).
Descriptive statistics of risk reduction strategies
On the whole, 17 risk reduction strategics are considered (Table 11.10). The most used risk reduction strategy is to consult with people who had previously visited the destination as indicated by a mean score of 4.2000. This was followed by
Table 11.10 Descriptive statistics of risk reduction strategies
Source: Data compiled by the authors, 2020
seek advice from police (4.0969), seek advice from local tourist boards (4.0281), rely on information referred by WTO (3.9031), choose a popular destination (3.8500), travel in the company of friends (3.8188), gather information from travel agencies (3.6563), search for information on the Internet (3.6281), avoid places crowded by locals (3.6219), make use of local guides (3.6063), avoid public transport when alone (3.4938), search for information from friends and relatives and make decisions in cooperation with relatives and friends (3.4406), make use of travel intermediaries (3.2688)and read articles about tourist places where I want to visit (3.1406). The strategics that are not preferred by the sample tourists are travel in the company of other nationals (2.9063), watch television programmes about tourist places where I want to visit (2.7750) and dress like locals (2.5906).
Perceived risk by risk reduction strategies
Now the central tendency as to mean of the data and their dispersion as to standard deviation have been calculated along with one-way ANOVA and are shown in Table 11.11. The tabic reveals that the average results of financial and communication risk, political instability and natural risk, health and time risk, physical risk, social risk and psychological risk are 3.1049, 3.4688, 3.2875, 3.0406, 2.718 and 2.9438, respectively, with standard deviation of 0.79253, 0.786, 0.82529, 0.55028, 0.66155 and 0.72056, respectively. This shows that most of the respondents of the commercial banks in Bangladesh are satisfied with the first four dimensions and unsatisfied with the last two dimensions. However, the results of one-way ANOVA explain the variation in risk reduction strategies across the various perceived risk dimensions at different significant levels (Table 11.11). The results report that statistically significant differences exist between the various dimensions of perceived risk and type of risk reduction strategy except the variation between physical risk dimension and RRS12, i.e. search for information on the Internet. Tourists with the perceived risks dimensions want to use almost all types of risk reduction strategy discussed in Table 11.11 to keep safe and enjoy their tour.
Results of ordinal regression analysis
The results of regression analysis help us to understand how dependent variables are affected by various independent variables included in the 17 types of risk reduction strategy. For this independent variable ordinal regression is performed separately. Regression results presented six tourists’ perceived risks dimensions and 17 types of risk reduction strategy variables where tourists’ perceived risk dimensions have both a positive and negative relationship with the types of risk reduction strategies but only positive relationships are statistically significant (Table 11.12). The model also shows global significance according to the likelihood ratio and pseudo R2 measures that provide indication of model explanatory power.
Table 11.11 Risk reduction strategies by perceived risk
Source: Data compiled by the authors, 2020
Notes: *, **, *** Significant at alpha 10%, 5% and 1%, respectively
Table 11.12 Results of ordinal regression model
Source: Data compiled by the authors, 2020
Notes: *, **, *** Significant at alpha 10%, 5%, and 1%, respectively, OR: ordinal regression result
The table shows that the tourists who perceived financial and communication risk want to follow almost all types of risk reduction strategies except RRS8, RRS9 and RRS16. Tourists with political instability and natural risk dimension resort to the use ofRRSl, RRS4, RRS5, RRS7, RRS8, RRS9, RRS10, RRS13, RRS15 and RRS16. Tourists who associated Bangladesh with health and time risk use 14 risk reduction strategies: RRS1, RRS3, RRS5, RRS6, RRS7, RRS8, RRS9, RRS10, RRS11, RRS12, RRS14, RRS15, RRS16 and RRS17. Those who perceived Bangladesh to be associated with physical risk use RRS3, RRS8, RRS10, RRS11, RRS12 and RRS13 risk reduction strategies. Tourists who perceived social risk in Bangladesh use RRS1, RRS2, RRS6, RRS7, RRS9, RRS12, RRS14, RRS15, RRS16 and RRS17 as risk reduction strategies. Tourists who associated Bangladesh with psychological risk use RRS2, RRS3, RRS5 and RRS17 risk reduction strategies. In the model, the exponentiated coefficient offers an idea of magnitude marginal effects on the probability of observing higher categories for risk reduction strategies against lower categories. For instance, a one-unit increase in the financial and communication risk would increase the odds 509.384% that tourists follow RRS1, while all other variables in the model are held constant under ordinal regression result one (OR1) (Table 11.12).
Conclusion and implications
This chapter examined the perceived risk of tourists in Bangladesh as well as the determinants of the perceived risk. The chapter also determined and assessed the risk reduction strategies used by tourists in Bangladesh. To conduct the study, primary data was collected from 320 tourists of whom 6.3% arc international tourists and the remaining 93.7% are domestic tourists who stayed in Chattogram in Bangladesh and visited Cox’s Bazar, Saint Martin, Dhaka and Sylhet from July to December 2019.
The study found six dimensions of tourists’ perceived risk on Bangladesh, namely financial and communication risk, political instability and natural risk, health and time risk, physical risk, social risk and psychological risk where these six factor explain 12.454%, 11.725%, 10.659%, 6.044%, 5.322% and 5.315%, respectively and 51.520% in total of the total variance. The main dimension of financial and communication risk is government committed in promoting destination’s positive image, while the main dimension of political instability and natural risk is risk of kidnapping at the tourist spots of Bangladesh. Furthermore, the main dimension of health and time risk is risk of ill hygiene and cleanliness at hotels, the main dimension of physical risk is experience or witnessing of violence and the main dimension of social risk is the thought of vacationing at tourist spots of Bangladesh giving a feeling of unwanted anxiety. Finally, the main dimension of psychological risk is not reflecting personality. In addition, the study results report that a good number of individual factors affect perception of travel risk. Age, type of tourist, purpose of visit and travel budget emerge as the most significant predictors of tourists’ perceived risk on Bangladesh, suggesting that demographic and travel characteristics of tourists are relevant in understanding tourists’ risk perceptions. Additionally, the study also shows that tourists follow
Perceived risks of tourism in Bangladesh 201 both consumption behaviour modification and information search as risk reduction strategies. The study also revealed that the usage of risk reduction strategies of tourists relied on the type of risk perceived. Thus, tourism planners should pay attention to which risks might cause stress among tourists, an awareness that should also inform marketing strategies.
The study has indicated that financial and communication risk is a highly perceived risk among tourists, implying that improvement in service provision as well as standardization of services at reasonable costs and good communication facilities will help to allay such risk. Subsequently, the Ministry of Tourism, Bangladesh Parjatan Corporation and hotel industry in Bangladesh should educate and train employees in tourists’ facilities on quality service delivery. Additionally, the study revealed that tourists are a heterogeneous group since certain demographic and travel characteristics such as age, type of tourist, purpose of visit and travel budget influenced their perceptions of risk on Bangladesh. This means that further research is required on the dynamic features of tourists especially in relation to risk perceptions. Although this study has developed the risk reduction strategies used by tourists, issues such as socio-demographic characteristics, past travel experience and so on that influence their risk reduction strategies largely remain unexplored. In this regard, future studies on tourists should delve into the risk reduction strategies. As another limitation, qualitative data such the top travel concerns is not considered in the study. In this regard, open-ended questions might be helpful to report which are the top travel concerns.
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