Question description

 

attached is a document with green highlights where the references need to be updated. the yellow highlight can you find the textbook/journal for it.

peer review reference between 2013-2017

Section 1: Foundation of the Study

Business leaders in developing countries are emphasizing development and promotion of tourism (Bazneshin, Hosseini, & Azeri, 2015).  Altunel and Erkut (2015) argued that providing a superior visitor experience is associated with high levels of tourist satisfaction.  Additionally, more leaders are acknowledging how important tourist satisfaction is in today's competitive world as a means to reap economic benefits (Bazneshin et al., 2015).  The purpose of this comparative study is to examine whether tourists’ motivation to travel and destination image influence tourists’ satisfaction in the British Virgin Islands (BVI). 

Background of the Problem

Researchers have differing views of critical factors for ensuring a positive customer experience in the tourism industry.  Tourism marketers are facing increasing competition, innovation, and branding in a dynamic market worldwide (Hultman, Skarmeas, Oghazi, & Beheshti, 2015), leading destination marketers to adopt innovative strategies to emphasize the destination uniqueness and tourists’ satisfaction (Hultman et al., 2015; Rajaratnam, Munikrishnan, Sharif, & Nair, 2014).  Rajaratnam et al. proclaimed the importance for destination leaders to assess tourist satisfaction so they can better understand how tourist satisfaction is related to destination of choice.  Researchers have noted some destination leaders have not addressed tourist satisfaction, nor attempted to address consumer dissatisfaction (Batista, Couto, Botelho, & Faias, 2014; Fernandes & Correia, 2013).  Pratminingsih, Rudatin, and Rimenta (2014) concluded travel motivation and destination image are fundamental travel behaviors of a visitor in assessing tourist satisfaction.

                                                                                Problem Statement               

Since 2011, the tourism industry has experienced a number of turbulent events resulting in a decrease in the number of travelers (Rahimi, 2016; Estrada & Koutronas, 2016; Hajibaba et al, 2015).  Despite the turbulent events, more than one billion tourists travel internationally, which contributes 9% to the global gross domestic product (Hsieh & Kung, 2013).  The general business problem is when a destination does not meet visitors’ needs, tourists will not be satisfied and a destination will not be competitive (Grigaliūnaitė & Pilelienė, 2014).  The specific business problem is that some tourism officials and managers in the British Virgin Islands (BVI) do not know whether a relationship exists between destination image, push and pull motives to travel, and tourists’ satisfaction.

Purpose Statement

The purpose of the quantitative correlational study is to examine if a relationship exists between destination image, push and pull motives to travel, and tourists’ satisfaction.  The first predictor variable is destination image. Push and pull motives, a predictor construct, consists of thirteen predictor variables: push knowledge, push sightseeing variety, push adventure, push relax, push lifestyles, push family, pull event and activities, pull sightseeing variety, pull easy access and affordability, pull history and culture, pull variety seeking, pull adventure, and pull natural resources.  The criterion variable is tourist satisfaction. I will study the population of departing tourists in the BVI for the period of October 2016 to December 2016, which is the peak of the tourist season at all ports of entry in the British Virgin Islands.  The implication for positive social change is to contribute to the economic enhancement of the British Virgin Islands, which will help to generate employment and entrepreneurship opportunities for residents. 

Nature of the Study

The method for the proposed study is quantitative and the design is comparative.  The quantitative method is most appropriate for the proposed study because researchers use the quantitative method to examine any existing relationships among variables (Westerman, 2012).  Also, how one or more variables affect or influence other variables (Barry et al., 2013).  The proposed study involves examining the potential influence of motivation to travel and destination image on and BVI tourist satisfaction.  Furthermore, within quantitative research, researchers statistically analyze numerical data (Turner, Balmer, & Coverdale, 2013; Venkatesh, Brown, & Bala 2013).  For the proposed study, I will collect and analyze numerical data.  A qualitative method is not appropriate because the method is not suitable when examining the potential influence of variables on one or more other variables, and the method does not produce a single, objective view of reality (Goodbody & Burns, 2011).  A mixed method is not suitable for the proposed study since researchers use mixed method studies to answer qualitative and quantitative research questions within one study (Bromwich & Scapens, 2016); the proposed study has only one quantitative research question.

                The design of the proposed study is correlational.  The correlational design is an appropriate design when the researcher is seeking to examine a non-causal relationship between or among variables (Luft & Shields, 2014).  The current study involves examining if a relationship exists between destination image, push and pull motives to travel, and tourists’ satisfaction.  Destination image, as well as push and pull motives to travel (the two predictor variables), cannot be manipulated and people cannot be randomly assigned to each; therefore, I cannot examine a causal relationship (Luft & Shields, 2014).  The comparative design, a common quantitative design, is not appropriate as the purpose is not to compare variables (Atchley, Wingenbach, & Akers, 2013; Venkatesh et al., 2013).  An experimental design is not appropriate as experimental designs requires researchers to manipulate the independent variables, which is not possible given the nature of the study variables.

Research Question

The study has one research question: What is the relationship between destination image, push knowledge, push sightseeing variety, push adventure, push relax, push lifestyles, push family, pull event and activities, pull sightseeing variety, pull easy access and affordability, pull history and culture, pull variety seeking, pull adventure, pull natural resources, and BVI tourists’ satisfaction?

Hypotheses

Null Hypothesis (H1₀)_: There is no statistically significant relationship between destination image, push knowledge, push sightseeing variety, push adventure, push relax, push lifestyles, push family, pull event and activities, pull sightseeing variety, pull easy access and affordability, pull history and culture, pull variety seeking, pull adventure, pull natural resources, and BVI tourists’ satisfaction.

 

Alternative Hypothesis (H1_a): There is a statistically significant relationship between destination image, push knowledge, push sightseeing variety, push adventure, push relax, push lifestyles, push family, pull event and activities, pull sightseeing variety, pull easy access and affordability, pull history and culture, pull variety seeking, pull adventure, pull natural resources, and BVI tourists’ satisfaction.

Theoretical or Conceptual Framework

The theoretical framework for the proposed study is expectancy-disconfirmation theory.  In 1980, Oliver developed expectancy-disconfirmation theory—a cognitive theory of customer satisfaction—focused on customers making post purchase evaluative judgments concerning a specific buying decision.  According to Oliver (1980), people are either satisfied or dissatisfied as a result of a positive or negative difference between expectation and perception before and after a service is performed. 

The purpose of the quantitative comparative study is to examine if motivation to travel and destination image significantly influence tourist satisfaction.  According to expectancy-disconfirmation theory, pre-travel perceived expectations should affect tourist satisfaction with a destination (Oliver, 1980).  Furthermore, tourists will make judgments about their tourist destination experience based on their original perceived expectations.  If tourist judgments about the destination are positive, they are likely to be more satisfied.  When tourists are satisfied, they will communicate positive experiences to motivate others to make a purchase or a repeat purchase (Mohamad, Ab Ghani, Mamat, and Mamat, 2014).

Operational Definitions

Providing operational definitions of terms that a reader may not understand, and which readers will not find in a basic academic dictionary, is critical.  Below are operational definitions for technical terms, jargon, and special words I refer to in the study.  The definitions come from scholarly sources and are listed in alphabetical order.

                Destination competitiveness. Destination competitiveness refers to a country’s ability to create value and integrate relationships within an economic and social model that takes into account a destination’s natural capital and its preservation for future generations (Dimoska & Trimcev, 2012; Hallmann, Müller, Feiler, Breuer, & Roth, 2012).

Destination image. Destination image is a combination of a tourist’s impression, as well as various tourism products, attractions, and attributes of the destination (Whang, Yong, & Ko, 2015).

Tourist satisfaction. Tourist satisfaction is a psychological state that develops when the travel experience satisfies the traveler’s desires, expectations, and needs (Leung, Woo, & Ly, 2013). 

Tourism sustainability. Tourism sustainability refers to accountability for the current and future social, economic and environmental impact of the destination while addressing the needs of the visitor (Crnogaj, Rebernik, Bradac Hojnik, & Omerzel Gomezelj, 2014; Yüzbaşıoğlu, Topsakal, & Çelik, 2014).

Assumptions, Limitations, and Delimitations

Reflecting on and identifying potential shortcomings and the boundaries of a study is critical (Hesse-Biber, 2016).  By making the shortcomings and boundaries clear to readers, researchers can be transparent, indicate how such shortcomings are addressed in the study, and avoid having others point out the shortcomings.  Researchers often make known shortcomings and boundaries by discussing study assumptions, limitations, and delimitations.

Assumptions

An assumption is an indicator in the study regarding what is true or certain without proof (Foss & Hallerg, 2013; Hesse-Biber, 2016).  This study is based on four assumptions.  The first assumption is that participants who complete the survey will be international visitors to the British Virgin Islands.  The second assumption is that participants will easily and similarly understand the questions on the data collection instrument.  The third assumption is that all participants will answer survey questions honestly and accurately. The fourth assumption is that all visitors will wait until they have completed their stay in the BVI before completing the survey.

Limitations

Limitations refer to potential weaknesses of the study that are out of a researcher’s control ( Hesse-Biber, 2016).  This study includes two limitations.  The first limitation is that with a quantitative correlational study, researchers cannot determine cause and effect.  If results indicate that motivation to travel and destination image influences tourist satisfaction, a third variable may account for any observed relationship.    A third limitation is that the sample of tourists included in the study may not be a true representation of the population.  The sample characteristics may not be the same as the characteristics of most BVI tourists, limiting the generalizability of the findings to all tourists.

Delimitations

Delimitations refer to the boundaries a researcher sets for a study, which the researcher can control (Hesse-Biber, 2016).  The research has six delimitations.  First, the focus is on gathering perceptions of tourists who travel only to the islands within the BVI-- not those who travel to other countries.  The second delimitation is that while there are many other variables that influence tourist satisfaction, the study focuses only on, motivation to travel and destination image.  The third delimitation is that the study population is non-citizens or non-residents entering the BVI for leisure and not business.  The fourth delimitation is that all tourists who visit the BVI outside of the data collection time frame will not be included in the study.  The fifth limitation is related to the population, which is limited to only international visitors departing the BVI.  The focus is on the British Virgin Islands tourism industry and the results may not be generalized to other countries. Finally, the survey is written in English, and therefore only those visitors who can read English will be able to complete the survey.

Significance of the Study

The value of proposed study business practice is that the destinations must remain competitive to maintain and increase the income of residents of the community (Webster & Ivanov, 2014).  Destination competitiveness is associated with the long-term economic prosperity of residents (Zehrer & Hallmann, 2015).  According to Rajaratnam, Munikrishnan,  Sharif, and Nair (2014), tourist satisfaction and the destination attributes influence tourism to the destination.  If the tourist’s experience is satisfying, the tourist leaves permanent footprints on the physical, social, cultural, and economic environments of destinations (Kim, Uysal, & Sirgy, 2013) resulting in repeat visitors.  Therefore, it is tourism leaders’ and stakeholders’ responsibility to ensure sustainable tourism in the BVI, while ensuring the tourist’s experience is satisfying.  However, to gain wider acceptance of the BVI community, tourism leaders need to implement their strategies for developing tourism to the local community, which enhances the territory’s economic growth.

The implications for positive social change include the potential to increase the territory’s economic growth (Ridderstaat, Croes, & Nijkamp, 2014).  Sustainable tourism allows for the future development of tourism to promote businesses’ profitability and sustainability benefiting the local community (Begum, Er, Alam, & Sahazali, 2014).  Growth in the number of tourists usually requires the expansion of infrastructure (e.g., roads, water supply, hospitals, sewage treatment and waste disposal) and tourism facilities (accommodations, restaurants, transportation systems), which are critical factors in the development of tourism in the BVI.  Tourism development leads to employment opportunities through various tourism sectors like hotels, boating, and restaurants, which attract migration to the BVI.  For this reason, the BVI’s environment should maintain industry level to sustain tourism longevity.  Van Vuuren and Slabbert (2012) stated that a destination’s environment is a key factor in motivating tourists to visit a destination.  However, for tourism leaders and stakeholders to implement corrective measures to make the BVI a more marketable tourism product, they need to ensure the social and economic growth of the residents of the territory.  Understanding what factors may influence tourist satisfaction could increase the BVI’s competiveness with other potential destination islands.  Most importantly, an increase in the number of tourists equates to an increase in revenue; increased revenue directly contributes to the economic and social enhancement of the residents of the BVI (Begum et al., 2014).

A Review of the Professional and Academic Literature

The literature review section includes a comprehensive review of literature related to the study topic.  The review section begins with a discussion of the strategy for searching the literature.  A critical analysis and synthesis of literature related to the study theoretical framework, the study independent variables (motivation to travel and destination image), and the study dependent variable (tourist satisfaction) follows a restatement of the study purpose and hypotheses.  Also included is a discussion of the measurement of the study variables.

Literature Search Strategy

Using a structured approach for searching the literature allowed for a comprehensive review of literature.  First, key terms for searching the literature came from the study topic, theoretical framework, and study variables. The initial key terms were tourist satisfaction, destination image, motivation to travel, tourism, customer satisfaction, loyalty, expectancy disconfirmation theory, perceived expectations, tourism destination, and destination competitiveness.   Opting to use the key terms and variations to search databases found in the Walden University Library proved to be most useful in developing this review. Research for this project derived a number of key sources including: Science Direct, Taylor and Francis Online, Sage Journal, Emerald Management, and Hospitality & Tourism Complete.  Valuable information also came from Google Scholar and the EBSCO host database. A search of all referenced databases resulted in identifying valuable literature sourced from peer-reviewed journal articles, books, and relevant government offices (see Table 1). 

 

Table 1

Literature Review Source Content

Literature review content	Total #	# less than 5 years at date of graduation	% total peer-reviewed Less than 5 years at graduation date
Peer-reviewed journals	148	150	96.15%
Books	4	2	0.01%
Non-peer reviewed	2	0	0%
Older articles	2	0	0%
Total	156	152	96.16%

 

Application to the Applied Business Problem

The purpose of the quantitative correlational study is to examine if a relationship exists between destination image, push and pull motives to travel, and tourists’ satisfaction.  The first predictor variable under investigation is destination image.  Push and pull motives, a predictor construct, consists of thirteen predictor variables: push knowledge, push sightseeing variety, push adventure, push relax, push lifestyles, push family, pull event and activities, pull sightseeing variety, pull easy access and affordability, pull history and culture, pull variety seeking, pull adventure, and pull natural resources.  The criterion variable is tourist satisfaction.

Null Hypothesis (H1₀): There is no statistically significant relationship between destination image, push knowledge, push sightseeing variety, push adventure, push relax, push lifestyles, push family, pull event and activities, pull sightseeing variety, pull easy access and affordability, pull history and culture, pull variety seeking, pull adventure, pull natural resources, and BVI tourists’ satisfaction.

Alternative Hypothesis (H1_a): There is a statistically significant relationship between destination image, push knowledge, push sightseeing variety, push adventure, push relax, push lifestyles, push family, pull event and activities, pull sightseeing variety, pull easy access and affordability, pull history and culture, pull variety seeking, pull adventure, pull natural resources, and BVI tourists’ satisfaction.

Theoretical Framework

The purpose of the quantitative correlational study is to examine if a relationship exists between destination image, push and pull motives to travel, and tourists’ satisfaction.  The theoretical framework for the proposed study is based on the Oliver’s (1980) expectancy-disconfirmation theory.  According to this theory, an individual will act in a certain way due to the expectation that the act will be followed by a certain outcome.  Disconfirmation is a visitor’s expectation of the performance of a facet normally attributed with the enhancement of a visitors travel experience such as the aesthetics of a country.  For example, the aesthetics of the BVI includes beaches, the courteousness of locals, the hotel’s accommodations and location to name but a few. According to the theory, due to the actual experience of enjoying these attributes as opposed to relying on perceived expectations, visitors can unreservedly declare whether their perceived expectations were matched or exceeded (Moital, Dias, & Machado, 2013). This example demonstrates the core of the expectancy disconfirmation theory, which gauges whether visitors’ perceptions of their intended stay was disconfirmed. In 1980, Richard Oliver developed expectancy disconfirmation theory—a cognitive theory of customer satisfaction—based on customers making post purchase evaluative judgments concerning a specific buying decision.  This concept defines the importance of visitor’s satisfaction in a destination as an emotional response to their experience.  In other words, if the visitor’s actual experience of the destination complies with previously formed perceptions of the destination, the visitor will make a positive evaluation of the purchase thus signifying that they are a satisfied tourist. (Sukiman, Omar, Muhibudin, Yussof, & Mohamed, 2013).  By this same measure, if a visitor’s actual experience does not comply with their expectations the tourist is likely to be dissatisfied.

An earlier study furthers this notion. According to Oliver (1980), people are either satisfied or dissatisfied as a result of a positive or negative difference between expectation and perception. Their positive or negative difference is based on a comparison between the expectations before a visit or service and the experience after a given service is performed. This essentially indicates a customer’s level of satisfaction. Thus, it is imperative to understand the key role that tourist satisfaction plays in a destination’s ability to remain sustainable and profitable. Furthermore, the degree of customer satisfaction can be related to sustainability in terms of a destination’s competitive advantages and differentiation from alternative destinations (Sukiman et al., 2013). Oliver’s expectancy disconfirmation theory has also been used as an indicator of a destination’s performance as we see in a more recent study. 

A study by Wong and Dioko, (2013) explored the outcomes of customer satisfaction among tourists, and found that the measurement of performance is solely dependent on expectation and or disconfirmation.  Wong and Dioko concluded that in order to outperform the destination competitors, the service provider must deliver a higher level of service that outweighs the value of customer cost.  Similarly, Sukiman et al. (2013) conducted a study measuring tourist satisfaction of international and domestic visitors on a holiday in Pahang, Malaysia.  The study’s aims included three primary objectives;  measuring the gap between tourist expectations and experiences; determining levels of tourist satisfaction using the holiday satisfaction (HOLSAT) model; and recommending improvement strategies.  The results indicated the need to improve strategies for future tourism development in Pahang.

                Although the expectancy disconfirmation of tourist theory is the most commonly used, the notion of tourists having previous expectations prior to receiving the service followed by a comparison of their perceived outcome of the service (Hsu, Wu, & Chen, 2012). Other theories present contrasting views. For example, Oliver and Swain, (1989) used the equity theory to analyze tourist satisfaction based on the relationship between the sacrifices, rewards, expected value, time, and costs visitors’ sustained.  No variable within this study relies on whether the customer receives more value than what they actually spent in terms of price, time and efforts.  Furthermore, normative theory establishes the tourist’s need for norm (Correia, Kozak, & Ferradeira, 2013).  Normative theory allows tourists to compare their present experience of a destination with an alternative or past experience (Correia et al., 2013; Sukiman et al., 2013).  Tse and Wilton (1988) used perceived performance which measures the overall satisfaction based on the actual performance, regardless of the visitor’s prior expectation.  The objective of this study is to understand whether visitors are either satisfied or dissatisfied as a result of a positive or negative difference between expectation and perception before and after their travel experience to the BVI. 

                Although these theories differ, an apparent common theme among destination stakeholders is that of the analysis of tourist satisfaction.  Tourists tend to make judgments regarding their destination experiences based on their original perceived expectations.  If tourist judgments about the destination are positive, they are likely to be more satisfied.  Thus, Gountas and Gountas (2007) recommended that when studying tourist satisfaction, there is a need for greater understanding of the antecedent behind the evaluation, versus the acceptance of the simple assessment. Otherwise, the true knowledge of the clients’ emotional experience would be limited.  Hence, comprehending the antecedent destination image along with push and pull motives to travel behind tourist satisfaction makes Oliver’s (1980) expectancy disconfirmation theory appropriate for this study.

British Virgin Islands

The BVI tourism sector is a key component in the territory’s socio-economic development and prosperity (Cohen, 1995; The British Virgin Islands, n.d.).  Located 60 miles east of Puerto Rico (PR), the BVI has exquisite white sandy beaches, historical sites and numerous cultural attractions. In addition, the BVI has fishing, picturesque blue waters, sailing and dive sites (The British Virgin Islands Tourist Board, n.d.).  The BVI has an excellent environment for tourism development with beautiful waters and unique diving excursions.  Many researchers have identified tourism as the main industry for economic growth in many countries. Tourism is one of the two economic pillars in the BVI that contribute to the country’s economic growth (Njoroge, 2015; The British Virgin Islands Tourist Board, n.d.).  As a result, enhancing the tourism and hospitality industry may be destined to play a pivotal role in the BVI’s future economic prosperity.

During the 1960s when most Eastern Caribbean countries opted towards self-rule to break away from colonialism, the BVI chose to remain dependent, a decision which ultimately affected the determination to decline full membership within the West Indies Federation.  As a result, in 1962 the BVI formally became a dependent territory of the British.  The BVI includes 60 cays and islets with four main islands: Tortola, Virgin Gorda, Anegada, and Jost Van Dyke.  From the early 1960s, the BVI government invested and implemented strategies to contribute to the growth and prosperity in the economy (Cohen, 1995). 

Tourism in the BVI contributes to 40% of the gross domestic product, and the remainder comes from international banking and other industries (The British Virgin Islands, n.d; Development Planning Unit, 2015).  According to the most recent census data in 2013, the BVI’s population is 29,151, with an average household monthly income of $2,452.73, and an average expenditure of $1000.00 (Development Planning Unit, 2015).  More than half of the BVI population came from migration. Many nationalities came to the BVI to seek employment, particularly in the hospitality industry, which includes yacht charters (Cohen, 1995). 

One noted reason for developing the yacht chartering industry within the BVI tourism product was to highlight the uniqueness of its intact pristine natural environment.  The natural environment is alluring to persons visiting the BVI, and is responsible for accrediting the BVI the name “Nature’s Little Secret” (Cohen, 1995; The British Virgin Islands Tourist Board, n.d.).  Cohen (1995) stated that the opening of Little Dix Bay Resort and the first yacht chartering company in 1969 aided in the prosperous economy.  The BVI has a visitor expenditure of $458.50 million--a 1.09 % increase from the year 2013 (Development Planning Unit, 2014).  Of the total expenditures, 53% attributes to yacht charter, 27% hotels, 10% other, and 7% cruise ship (Development Planning Unit, 2014).  The Development Planning Unit has indicated that in 2014, 513,118 tourists visited the BVI--an increase of 1% arrivals from 2013.  Of these arrivals, 70% came from America, 7% from Canada, 4% from the United Kingdom (UK), 4% from France, 3% from Germany, and 13% from other nationals.

British Virgin Islands government officials see the tourism industry as a priority for maintaining and improving the well-being of the Territory.  Political stability in tourism allows the people of the BVI to further develop and enhance infrastructure, such as widening the Territory’s airspace and roads and accommodating the expansion of a cruise ship pier, which are all necessary for tourism to flourish.  However, Dwyer, Pham, Forsyth, and Spurr (2014) noted that government support and the current tourism budget allocated for marketing and promotion activities of tourism in the BVI is insufficient compared to other Caribbean islands.  Due to the accessibility to these islands, the alternative Caribbean islands have a competitive advantage to the BVI.  Thus far, the BVI tourism product includes guaranteed sustainability (The British Virgin Islands Tourist Board, n.d.). 

A tourism destination is a destination that has various products and services to meet visitor needs.  A visitor selects a tourist destination based on whether they believe the destination has all the desired amenities (Buhalis & Amaranggana, 2013).  Therefore, Lamsfus and Alzua-Sorzabal (2013) stated most tourists’ perceptions of a destination are a result of information gathered from various travel information boards.   To identify one destination over another destination necessitates a look at a combination of various components in the destination that can satisfy the traveler’s perception prior, during, and after their trip (Soteriades, 2012).  Additionally, Buhalis (2000) stated that to be categorized as a tourism destination, the destination must have been structured with the six As: (a) attractions (natural, man-made, artificial, purpose built, heritage, and special events); (b) accessibility (transportation comprised of routes, terminals, and vehicles); (c) amenities (accommodation, catering facilities, retailing, and other tourist services); (d) available packages (pre-arranged packages by intermediaries and principals); (e) activities (what consumers do during their visit at the destination); and (f) ancillary services (services used by tourists such as banks, telecommunication, post, newsagents, hospitals, etc.). 

                The potential to mainstream tourism was established as agriculture became limited, and to some non-existent (O’Neal, 2012).  As time progressed through slavery in the BVI, agriculture became dominant and soon after mass production of sugarcane became the norm of most British Caribbean colonies (O'Neal, 2012).  Likewise, sugarcane became the main crop of the BVI, which allowed the BVI to conduct foreign trade with Danish West Indies islands particularly St. Thomas and other nearby islands (O'Neal, 2012).  In the mid-1960s the BVI began to seek interest in financial services and tourism known as the Twin pillars (The British Virgin Islands Tourist Board, n.d).  O’Loughlin (1962) recommended in his report, that the BVI pursue tourism as their main source of economic development which may likely bring higher standard of living to the population.  The acceptance of the O’Loughlin report was validated by the construction of Laurance Rockefeller’s Little Dix Bay Hotel in Virgin Gorda in 1964 (Cohen, 2010) as the promotion and development strategy of the tourism era in the BVI.  Thereafter, Prospect Reef hotel was constructed with 131 rooms in Road Town, Tortola (O'Neal, 2012).  The Development Planning Unit (n.d.) has indicated that in 1981, 154,500 tourists visited the BVI--an increase of 782% arrivals from 1967.  Of the twin pillars, tourism is the most important industry as it employs a large percentage of both local and non-nationals skilled and professional positions in the territory, equating to numerous local entrepreneurs within the industry (O’Neal, 2012).  The main reason for tourism is to temporarily escape from everyday life routines, stress, and constraints (Rasouli, & Timmermans, 2014).  During this economic growth of the BVI, Graham (1969) labeled the BVI as the Sunny Success Story because of the fast growth that was noted in any other British Caribbean islands. Therefore, it is vital that the BVI maintain and continue the transformation of tourism-based economy in the BVI.

Tourism Motivation

Many researchers who have studied tourist behavior, try to understand what tourists do and why they make the decisions to do what they do (D’Avanzo & Pilato, 2014).  Tourists’ motivation is one of the major factors behind choosing a particular destination over another as it relates to the ultimate goal of remaining profitable. (Pratminingsih, Rudatin, & Rimenta, 2014).  The focus of earlier researchers was to understand the reasons why tourists travel (Crompton, 1979; Dann 1981), and these reasons are a crucial factor for comprehending tourist behavior (Tangeland, Vennesland, & Nybakk, 2013).  However, before examining the various sources, it’s necessary to establish a definition of motivations in order to establish a baseline for presenting the research. According to (Zhang & Peng, 2014) motivation is a set of needs that persuade persons to act and to find a way to obtain satisfaction. With this definition as a base-line, the research indicates that motivation is one of the major factors that drives tourists’ decisions to choose a destination of choice (Pratminingsih et al., 2014).  As a major factor in understating tourist motivations, researchers who have studied tourist behavior have focused primarily on understanding two main factors; what tourists want to do on their vacation and 2) why they make the decisions to do what they do (D’Avanzo & Pilato, 2014).   While the research landscape of most recent years focuses on what tourists want to do on their vacation and why they make the decisions to do what they do (D’Avanzo & Pilato, 2014)

Some of the earliest research into tourist motivations revealed a more fundamental approach and includes the basic question why tourists travel (Crompton, 1979; Dann 1981) as a crucial factor in understanding tourist behavior (Tangeland et al., 2013).   The literature also strongly reflects that tourism officials, who are aware of tourist behavior, have used insights into these questions to further develop strategies to: capitalize on benefits from tourist behavior, tourist expectations, and travel experiences to encourage future travel (Assaker & Hallak, 2012; Battour, Ismail, & Battor, &  Awais, 2014; Kinley, Forney, & Kim, 2012).  Building on our earlier definition of motivation being a set of needs that persuade persons to act and to find a way to obtain satisfaction (Zhang & Peng (2014), Crompton (1979) offers the perspective that inspiration or enthusiasm can influence an individual to accomplish an event as a quest for personal satisfaction. With this understanding, tourist motivations fall into four travel market segments: a) business travel, b) government or corporate business travel, c) visitation of friends and relatives, and d) pleasure vacation travel. 

Maslow’s (1954) hierarchy of needs model is a useful tool for understanding tourism motivation. His five-stage model depicts a hierarchal pyramid of needs based on physiological needs. These needs are further divided into two categories of higher level needs and lower need for self-actualization (Adiele & Abraham, 2013). Maslow’s five-stage model pyramid is arranged in descending order from top to bottom and depicts the following:  biological and physiological needs, safety needs, love and belongingness needs, esteem needs, and self-actualization needs. The model advances the idea that before the feat of self-actualization is accomplished, it is imperative that the hierarchy of needs according to the Maslow’s pyramid be attained. Maslow proposed that his model helps to explain the process by which psychological needs are fulfilled. His model proposes that lesser needs are achieved before higher needs. For example, if an individual is hungry or homeless, (a base or lower need) considering the virtues of career opportunities (a need to fulfill self-actualization) will be irrelevant.   Following this notion, this example holds that after all other needs are realized accordingly, self-actualization then occurs.  Further expansion of this model shows the placement of lower or basic psychological needs such as hunger, thirst, shelter, and sexuality at a higher priority level than needs promoting self-actualization (Adiele & Abraham, 2013; Maslow, 1954). Next, in the Maslow’s pyramid model hierarchy is the safety needs aspect. This describes needs such as security, protection from pain, fear, and anxiety. Safety needs also include the need for sheltering dependency, order, and lawfulness (Adiele & Abraham, 2013; Maslow, 1954).  Next, it is believed that after the previous needs are achieved, there is now a need for belongingness, which  involves love, affection, emotional security, social acceptance, and a sense of identity (Adiele & Abraham, 2013; Liu & Mattila, 2015).  As we move into the higher needs or esteem needs (Maslow, 1954) we see the focus of the needs elevate to less basic needs such as achieving goals and gaining approval as well as recognition from ones’ peers (Adiele & Abraham, 2013).  At the top of the needs pyramid is self-actualization, which is self-fulfillment through the realization of potential and ability based on  the need for comprehension and insight into society and the world (Maslow, 1954; Moscardo, Dann, McKercher, 2014).   In contrast to Maslow’s pyramid model, other studies present a slightly different perspective on tourism motivational factors.

Although Crompton (1979) was the first to expound on the classification of tourist motivations into push and pull tourism factors, Dann (1981) was the first to use these terms push and pull factors. In Crompton’s research, two distinct types of socio-psychological motivations were identified as drivers of the fundamental aspects of tourist’s decision making process. The first driving force is the initial decision to travel, whereas the second plays a role in deciding to choose a particular destination, location, or event (Crompton, 1979).  There are various visitor-inspired tourist motivation theories, however researchers have widely accepted the theory of push and pull motivational factors (Battour et al., 2014; Dann, 1981; Chung, Koo, & Kim, 2014; Seebaluck et al., 2015; Bhargava, 2013; Wang, Luo, & Tang 2015). The concept behind this theory is that people travel based on a push by internal forces and a pull from external forces while considering the composition  of a destination’s attributes (Chung, Koo, & Kim, 2014; Gursoy et al. 2015; Kraftchick et al., 2014).There are two types of motives towards travel. The first;

Push Motives to Travel. Push motives derive from Maslow’s hierarchy of needs model and are considered to be intrinsic motivations which provide fundamental goals and needs that are the basis of behavior motivation (Maslow, 1954; Chung et al., 2014; Hunag, 2015).  Accordingly, Prebensen, Woo, Chen, and Uysal, (2012) and Jensen Lindberg and Østergaard, (2015) argued that push factors correlate to a tourist’s need to make a trip, the experience, and or the destination they seek. Therefore, these needs have influenced the individual to act on them from an emotional conundrum requiring them to  mentally escape from their daily routine (Lew & Williams, 2015; Radicchi, 2013).  Nassar, Mostafa, and Reisinger (2015) identified the following four push factors of motivation to travel to a destination: (a) leisure and recreation; (b) visiting friends and relatives; (c) health and wellness, and (d) religion.  Mody et al., (2014) and Lehto (2014) identified additional common push factors such as (a) novelty, (b) seekers, and (c) socializers.  Šimková and Holzner (2014) claimed that escaping from daily the routine, and workplace, and fulfilling social needs such as meeting other people, and experiencing something unique or unusual are the needs of the tourist.  Crompton (1979) singled out eight motivational push factors:

1.           Escape is the change in environment, which allows travelers to explore, discover, evaluate and reevaluate the destination

2.           Relaxation is an individual’s method of attaining mental rest often via engaging in activities outside their normal routine

3.           Prestige is the traveler’s desire to travel to the destination that does not have heavy tourist traffic

4.           Regression is the traveler’s vacation that allows them to distance themselves from their normal surroundings to engage in behavior that is outside the scope of their usual practice

5.           Enhancement of kinship relationships is the traveler’s desire to be brought closer together with family and friends to strengthen bonds

6.           Facilitation of social interaction is the traveler’s desire for socialization, meeting new people and experiencing different aspects of life

7.           Novelty is the tendency of tourists  to desire experiencing new activities and  unvisited destinations

8.        Education is the tourist’s desire to learn the history of their destination location to enhance their vacation experience

 

Pull Motives to Travel.  Pull factors are considered as extrinsic motivations, which are a result of the attractiveness of the image of the destination (Seebaluck et al., 2015).  The destination image refers to characteristics that attract visitors to visit it.  Pull motives are categorized into four categories; historical and heritage attractions; cultural and cuisine experiences; rest and relaxation facilities; and family and friend bonding opportunities (Leong et al., 2015).  Many tourists evaluate the destination image based on the destination’s characteristics (Gössling et al., 2012; Zhang et al., 2014). Hence, the ideal situation requires the needs of the visitors due to the above factors that individuals use to decide on their destinations. Chen and Chen (2015) stated that a combination of push and pull factors attract different type of travelers seeking various values.

                Some would argue that pull factors are more straightforward identifiable because they are external and the visited destinations can be visibly compared (Lai, & Vinh, 2013; Tangeland et al., 2013; Prebensen et al., 2012; Ziegler, Dearden, & Rollins, 2012). However, the particular pull factors that attract one visitor to a destination could significantly vary from the pull factors that attract another visitor to the same destination (Prebensen et al., 2012; Prayag & Hosany, 2014). The destination choice originates from tourists’ assessments of a location’s qualities and includes factors such as natural and cultural attractions, social opportunities, physical amenities and facilities, nightlife, and ambiance (Kim & Brown, 2012; Lacher, Geoffrey et al. 2013; Prayag & Hosany 2014). Kassean and Gassita, (2013) and Mussalam & Tajeddini, (2016) listed culture link, accessibility, products, quality, advantage, events, ecological attributes, shopping, and natural amenities as examples of pull motivations.

History of Push-Pull Motivation to Travel. The push-pull concept has been the guideline for many motivation studies for tourism (Chen & Chen, 2015; Li et al., 2013; Tangeland et al., 2013) and many push pull factors has been distinguished by the empirical studies.  Crompton (1979) conducted one of the earliest investigations into motivation to travel. Crompton identified nine common push pull motivation factors behind an individual decision to travel: (a) escape, (b) exploration, (c) relaxation, (d) prestige, (e) regression, (f) enhancement of family relationship, (g) social interaction, (h) novelty, and (i) education.  Furthermore,  Li, Zhang, & Cai, (2013)  used eighty-two push-pull items and identified ten push-pull motivational factors; (a) escape and relax, (b) fulfillment of unprecedented experiences, (c) business, (d) child education, (e) development, (f) relationship and family togetherness, (g) natural scenery, (h) self-development, (i) shopping, and (j) nostalgia. An additional push-pull motivation factor analysis was conducted by Scholtz, Kruger, & Saayman (2013) and found six motivational factors; (a) escape, (b) finances (c) socializing and exploration (d) family, (e) wildlife experience.  Moreover, Chen, Bao, and Huang (2014), surveyed persons to understand what their motivations were and four motivation to travel factors were identified: (a) interaction, (b) self-actualization, (c) destination experience and, (d) escape and relaxation.  For aforementioned four studies, Dan’s (1977) concepts were the underlying basis for identifying the push-pull motivation to travel factors to examine why an individual would be motivated to travel.

Similarly, Correia, Kozak, and Ferradeira (2013) examined the relation of motivation to travel and tourist satisfaction and found three push pull motivation to travel factors which are: (a) novelty, (b) knowledge, and (c) facilities. In Lee and Hsu’s (2013) study, they also found three push pull motivational factors: (a) cultural experiences, (b) leisure, and (c) psychology and self-expression. Additionally, Li and Ryan (2014) explored what motivates Chinese tourists to visit North Korea, and discovered that tourists were curious and mysterious, noting that curiosity was the most significant factor behind the decision to visit a country. The more the destination is mysterious, the more visitors want to travel to the location. However, some travelers would rather not visit a destination that is too crowded (Li & Ryan, 2014).  Mody et al., (2014) identified the motivational responsibility for international and domestic travelers visiting India.  Three push pull motivational factors singled out were (a) novelty, (b) seekers, and (c) socializers (Mody et al., 2014).

There is no single instrument established as the benchmark for motivational factors. In fact, methods of measurement vary, and have been devised to fit specific parameters of specific research. However, for the purposes of this review a series of valid questions related to each variable have been identified as a reasonable basis for measurement. The following table indicates the variable and related questions:

Table 3

Variable and related questions

 

Variable	Definition	Question
Tourism Motivations	(Zhang & Peng (2014): motivation is a set of needs that persuade persons to act and to find a way to obtain satisfaction.	What is your primary reason for traveling? Why did you take this particular vacation? Why did you decide to travel at this time? What do you hope to get out of this vacation?
Push Factors	(Prebensen, Woo, Chen & Uysal, (2012): push factors correlate to a tourist’s need to make a trip, the experience, and or the destination they seek	Rate the following travel reasons from most important to least important: Escape, Relaxation, Prestige, Regression, Relationships, social interaction Novelty, Education. Of these topics, which is the most important and why? How long since your last vacation?
Pull Factors	(Seebaluck et al., 2015):  Pull factors are considered as extrinsic motivations, which are a result of the attractiveness of the image of the destination	What about this location made you want to visit? How did you find out about this location? What attributes of this location attraction did you enjoy most? How did you learn of this location?
Destination Image	(Gunn, 1972): the accumulated mental images that a person has of a destination as a result of their interaction with the tourism products and services	What did you like about the images you saw of the destination? What was your impression of the location based on the images you saw? What image attracted you the most? What image attracted you the least? How did the image make you feel about this destination?
Tourist Satisfaction	(Belanche, Casaló & Guinalíu, (2012) and Dayour & Adongo, (2015): the essence of consumer’s experiences with products and services	Would you come back to this location? What was your favorite part of the visit? Which was your favorite part of this visit? Which was your least favorite part of this visit? Is this location better or worse than other locations you have visited and why? What was your first impression of the location upon arrival? Did the location live up to your expectations?

 

While this table does not include every conceivable question that might be asked of a customer, the questions presented would garner measurable responses from guests. Aside from questions that could be posed in a survey setting, previous literature also suggests that researchers adapt scales to explore specific motivational factor. Some authors aim is to determine whether any other items should be included in the measurement tool to identify push pull motivation to travel factors (Caber & Albayrak, 2016). The measurement of push pull factors is assessed based on the attributes of the destination which represent the perceptions of the destination (Prayag & Hosany, 2014). Hence, understanding the visitor’s push pull motivation to travel to a destination may explain a visitor’s choices and may also prompt repeat visitation (Wang, Luo & Tang, 2015). Travelers’ are either pushed by psychological factors or pulled by external forces based on the destination’s attributes or both (Leong, Yeh, Hsiao, & Huan, 2015; Seebaluck et al., 2015).  Travelers search for simultaneous satisfaction of their needs and wants, which makes their motivational factors multifaceted (Bhargava, 2013).    Along with push pull motives, the characteristics of the destination helps the individual determine which destination to visit.  Therefore, Kim, Oh, and Jogaratnam, (2006) and Mohammad and Som’s, (2010) classification of push pull factors was captured in a survey modified to fit the needs of the BVI.  The information used came from the BVI Tourist Board’s website, pamphlets and other government documents about the BVI. 

In summary, targeting tourists’ by the activities they peruse enables tourism authorities in to identify and understand tourist travel-related behavior by observing their patterns and needs (Kinley et al., 2012).  Many scholars have studied the relationship between motivation and visitor satisfaction while defining the motivation factors that are influenced by tourist satisfaction respectively in their study (Battour, Battor & Ismail, 2012; Lee & Hsu, 2013; Lee, Kang, & Lee, 2013).  Almeida, Correia & Pimpão, (2014) proclaimed that other scholars have suggested that offering fresh air as a motivational factor is insufficient for a satisfactory experience.  For this reason, there are many other factors that affect the tourism destination selection to ensure tourist satisfaction (Guillet, Law & Leung, 2012; Prayag et al., 2015).

 Destination Image

The destination image is an independent variable in the proposed study that references the impressions a tourist may acquire based on different pre-conceived notions about a destination (Battour et al., 2014; Crompton, 1979; Ramseook-Munhurrun et al., 2015).   In tourism literature, there is no agreed upon universal definition of destination image; however, Gunn (1972) was among the first scholars to propose a theory of destination image formation.  His theory purports that images represented the accumulation of mental images that a person has of a destination as a result of their interaction with the tourism products and services.  This baseline definition led to other researchers examining various aspects of destination image formation (Rajesh, 2013; Llodrà-Riera et al., 2015; Özdemir & Şimşek, 2015).  One such study focused on understanding the influences of destination image on traveler’s intentions to travel to certain destinations (Jalilvand et al., 2012; Özdemir & Şimşek, 2015; Ryu, Lee & Gon Kim, 2012; Zhang, Fu, Cai & Lu, 2014).  Another related study focused on the relationships between destination image and relevant variables such as tourist service quality, tourists’ satisfaction, and their impact on intentions to return to a particular destination (Stylos et al., 2016; Tan, & Wu, 2016; Tosun, Dedeoğlu, & Fyall, 2015; Zhang, Fu, Tai & Lu, 2014).  As the research reflects, image and imagery proved to be a very informative variable in understanding the impact of destination image which lended to a deeper look into travelers’ image-related decisions.

Cognitive and affective are two concepts of destination image that have been derived from more intensive studies. Imagery, being the powerful tool that it is, allows us to formulate pre and post judgments regarding destination image based on any external stimuli received (Agapito, Oom do Valle, & da Costa Mendes, 2013; Prayag & Ryan, 2012).  According to Zhang, Fu, Cai & Lu (2014) cognitive concept refers to the interpretation of knowledge and beliefs regarding the physical attributes of a destination. Affective concept on the other hand refers to the individual’s feelings as it pertains to the attributes and the natural environments (Költringer & Dickinger, (2015).   This would imply that cognitive stimuli fall under the pre judgements of knowledge and beliefs, whereas affective stimuli are formed after or post visit as it deals with feelings about attributes and natural environments (Prayag et al., 2015).  Agapito, Oom do Valle & da Costa Mendes, (2013) agreed with the cognitive and affective concepts examined by Zhang, Fu, Cai and Lu (2014) but also introduced a third concept.  Agapito, Oom do Valle & da Costa Mendes theory stated that destination image comprised of three components: cognitive, affective, and conative.  The cognitive component is the evaluation of destination attributes; affective refers to one’s emotions and feelings towards the intended destination; and lastly conative component speaks to a person’s intention to visit a destination (Choi, Lu & Cai, 2015; Ryu, Decosta & Andéhn, 2016; Xie and Lee, 2013). With the third conative component being introduced further research touched on other aspects of tourist behavior.   Elliot and Papadopoulos (2015) claimed that while cognitive and affective components added an emotional consideration to the destination images, visitors based their decision to recommend the destination to others as a result of the conative component (Llodrà-Riera et al., 2015). The belief is that global evaluations refer to the overall image perceptions of visitors (Hosany & Prayag, 2013; Kaplanidou et al., 2012). Some researchers have stated that all three components should be measured together in order to satisfy the tourist interests and personal needs (Papadimitriou, Apostolopoulou & Kaplanidou, 2013; Servidio, 2015).

Destination image influences tourists’ buying behavioral patterns towards a specific destination, and as a result affected tourist satisfaction. Destination image impacts tourist satisfaction, which in turn affects intentions of revisit (Phillips et al., 2013; Suhartanto & Triyuni, 2016).  Ramseook-Munhurrun, Seebaluck and Naidoo (2015) stated that in the mind of a visitor, destination image could be very persuasive as it determines both purchasing decisions and a visitor’s intentions to visit or revisit.  Tawil & Al Tamimi, (2013)  also listed and described three components of destination image which are as follows: the product -  meaning the quality of the destination’s attributes; the behavior and attitude of the destination hosts – meaning how the destination accommodated you; and the environment – meaning weather, scenery and facilities.  Island destinations image is equated to an exotic destination which includes pristine beaches, white sand, blue sea, landscape, biodiversity, and vibrant culture to attract visitors (Lucrezi & van der Walt, 2016; Seebaluck et al., 2013).  Ramseook-Munhurrun et al., (2015) argued that beaches are major attractions for the tourism industry and the beaches were considered as a motivational factor for tourists to visit island destinations. We’ve examined many aspects of destination image and its impacts on tourist behavior; however, additional studies have drawn distinctions based on the quality of a destination image as it relates to customer satisfaction.

Assaker and Hallak, (2013) agreed with Zhang et al., (2014), in that destination image does impact future returns based on consumer satisfaction. They argued that the more favorable the destination image was the higher the result in overall customer satisfaction would be.  Many researchers have stated that customer satisfaction influences future customer behavior (Heung & Gu, 2012; Ryu et al., 2012; Seebaluck et al., 2013). Additionally, researchers have argued that a positive image of a destination reinforces the traveler's decision to visit; however, a negative image will deter a traveler from visiting (Chen, Chen & Okumus, 2013; Chen & Phou, 2013; Jalilvand, et al., 2012; Zhang, et al., 2014).  In contrast, Prayag and Ryan (2012) recognized that destination image directly and indirectly impacts customer satisfaction and behavioral intentions. Quintal, Phau & Polczynski, (2014) proclaimed that when the positive image overshadows a negative image, tourists are more eager to visit. A logical correlation can be drawn, based on Quintal et al. that positive destination images produce greater customer satisfaction. That being said, a destination must align its destination image with its customer satisfaction goals as they interrelated factors that impact the tourist buying process (Stylos et al., 2016).  Furthermore, Ryu et al., (2016) and Cucculelli and Goffi, (2016) advances the notion that tourists’ perceptions of a destination do affect the destination image and its sustainability. This further exemplifies the importance of destination image as it relates to a tourist destination maintaining a competitive stance in the industry. 

For a destination to remain competitive, the destination must find strategies to maximize earnings and always maintain the positive destination image comparable with alternative destinations (Mwaura, Acquaye & Jargal, 2013).  The destination should implement strategies to promote and attract more visitors in this competitive environment.  Mwaura, Acquaye & Jargal (2013) stated that although promotional campaigns can be expensive, the awareness that the campaign brings to the destination enhance the images of the destination.  In fact, destinations are compelled to maintain and enhance their images to increase tourism receipts, income, employment and government revenues among other contributions of international tourism (Ramseook-Munhurrun et al., 2015).

In each study, destination image is conceptualized based on a study by (Dolnicar, & Grün, 2012).  The cognitive, affective, and conative components of destination image should be included in the destination image evaluation process because the exclusion of any component may result in an incomplete measurement (Echtner & Richie, 1991).  Echtner and Richie (2003) recommended that future researchers should use guidelines for measuring destination image based on four criteria: attributes and holistic components; functional and psychological characteristics of the attributes and holistic components; integrated, unique and common features of a particular destination; and use of qualitative and quantitative methodology to measure destination image quality. Having examined various push-pull motivations to travel and the impact of destination image, we can logically advance the review to an analysis of the next variable—tourist satisfaction and its essential role in understanding and maintaining profitability. 

Tourist Satisfaction

  Tourist satisfaction or Customer satisfaction is an integral component of marketing that affects customer retention, profitability and competiveness (Kärnä, 2014).  Customer satisfaction is the key to securing customer loyalty and long-term financial performance (Bayraktar et al., 2012; Kärnä, 2014).  Understanding customer satisfaction brings positive reaction to an organization such as long-term benefits, customer loyalty, and organizational profitability (Bayraktar et al., 2012; Chen, 2012; Kärnä, 2014; Malik, 2012).   In every market, an organization must define customer satisfaction.  Belanche, Casaló & Guinalíu, (2012) and Dayour & Adongo, (2015), identified satisfaction as the essence of consumer’s experiences with products and services.  The consumer’s intent to repurchase a product or services is based solely on the key indicator called quality (Ali, dey & filieri, 2015).  Quality is a clear and concise indication of how customers emotionally evaluate their experiences (Altunel & Erkut, 2015).

Some researchers have done extensive investigation into tourist satisfaction with their chosen tourism destinations (Rajesh, 2013; Battour et al., 2012; Rajaratnam et al., 2014).  Some aimed to understand the influences of tourist satisfaction based on their intentions to travel to a destination (Assaker & Hallak, 2012).  Additionally, some scholars examined other influences of tourist satisfaction such as tourist service quality and their impact on intentions to return.  Additionally, this study looked into previously mentioned variables of motivation, destination image and tourist satisfaction (Kim, Holland & Han, 2013; Marković & Raspor Janković, 2013; Prayag, Hosany & Odeh, 2013; Rajesh, 2013).

Tourist satisfaction is the tourists’ overall evaluation of the destination experience, which fulfills their desires, expectations and needs (Ramseook-Munhurrun et al., 2015).  Whereas, it’s stated that tourist satisfaction is the visitor’s emotional response that precedes their cognitive responses to the service experience (Cong, 2016).   As mentioned in various studies, tourist satisfaction may be seen as the tourist assessment of the destination’s characteristics.  Therefore, if the tourists are satisfied with their experience, they are likely to return to the destination and recommend the destination to others (Araslı & Baradarani, 2014). A tourist’s perception of a destination is also influenced by various factors that affect the level of satisfaction (Araslı & Baradarani, 2014).  Some factors are identified as accommodations, restaurants, attractions, environment, accessibility, and safety (Chew & Jahari, 2014).  Moreover, Belanche, Casaló and Guinalíu (2012) and Dayour and Adongo, (2015) agreed that the destination's products and services impact tourist satisfaction.  Hence, the outcome of high levels of satisfaction leads to repeated purchase and a positive word-of-mouth referrals (Confente, 2014; Ramseook-Munhurrun et al., 2015).  Wasfi and Kostenko, (2014) stated that there are three reasons to guarantee that consumers are satisfied; positive word-of-mouth; recurrence in customer visits; and ensuring that complaints are dealt with in a timely manner.  Word-of-mouth leads to recommendation of a product or service to family and friends and repeat customers bring a steady source of income (Yeoh, Othman, Ahmad, 2013).   It is important to remember that dealing with complaints may be very expensive and time-consuming; however, positive handling of complaints leads to a good reputation for an organization (Ogbeide, Böser & Harrinton, 2015).

The majority of research on visitors’ satisfaction have been based on the model of expectancy disconfirmation (Correia et al., 2013; Radder & Han, 2013).  Customer satisfaction is deemed the consumer’s expectation prior to purchase and their evaluation after the perceived performance (Ali et al., 2015; Altunel & Erkut, 2015; Chodzaza & Gombachika, 2013; Kursunluoglu, 2014). In other words, expectancy disconfirmation speaks to the before (expectation) and after (perceived performance) comparison in the mind of the customer that dictates their level of satisfaction with a product or service. Chodzaza and Gombachika, (2013) defined the expectancy disconfirmation concept of customer satisfaction as the consumer's perceived notion about a product or service that affects customer satisfaction (Chodzaza & Gombachika, 2013).  The disconfirmation occurs when the perceived performance does not match customer expectation. Confirmation is achieved when the product or service exceeds the customer expectation (Ottenbacher, Harrington & Fauser, 2016). Hence, when expectation is below the consumer perceived performance, dissatisfaction occurs (Oliver, 1980; Zhao et al., 2014).  In addition, while it is not the goal of any destination, it is possible for satisfaction to still occur when a tourist has low expectations and receives a poor experience (Chan, 2014). While it has been adequately established that the tourism industry pays close attention to guest satisfaction in terms of expectations matching perceived notions, other research suggests that satisfaction is based on emotions.

To further expound on the understanding of customer satisfaction, it is important to distinguish between the overall satisfaction and the tourist satisfaction with an individual attribute of the tourism experience (Rajesh, 2013).  Rajesh (2013) stated that satisfaction might be a psychological state of mind that the tourist brings to the destination, based on the destination preconceptions. Rajesh (2013) also noted that there are three types of satisfaction related to tourist experiences: (a) Emotional Response: response to an emotional or cognitive judge (b) Object of customer satisfaction: a response to specific focus of the trip, and (c) a response to a particular moment of the trip. In terms of products, these three stages would occur prior to purchase, after the purchase, and after consumption respectively. Past researchers have indicated that tourist satisfaction is also an excellent indicator of repurchase intention (Phillips, et al., 2013; Su, Swanson, & Chen, 2016). Organizations should always satisfy their customers in order to retain them. It is also essential for tourism officials to be able to identify areas which affects the tourist’ experience and the destination product while modifying services that can be rectified to maximize satisfaction (Battour, et al., 2012; Hosany & Prayag, 2013). It is an imperative that tourism officials continuously try to improve the tourism experience by understanding the components that affect their ability to increase consumer satisfaction and visitation (Simpson & Siguaw, 2013).  These ultimately results in the improvement of the financial feasibility and success of the organization.

Summary

This review examined a litany of research that draws distinct parallels and relationships between the factors that influence tourist behavior based on the Oliver’s (1980) expectancy-disconfirmation theory.  This theory states that an individual will act in a certain way due to the expectation that the act will be followed by a certain outcome.  Disconfirmation is a visitor’s expectation of the performance of a facet normally attributed with the enhancement of a visitor’s travel experience such as the aesthetics of a country.  In support of this theory, several sources were reviewed that focused on the three primary variables in discussing the expectancy disconfirmation theory: tourism motivations, destination image and tourist satisfaction. Key points were made about the interrelated nature of each of these variables and how they relate to the industry’s desire to understand sustaining profitability.

First, parallels were drawn between why tourist are motivated to travel (Pratminingsih et al., 2014) and what components drive people to travel. Building on the baseline definition of motivations as presented by Zhang and Peng, (2014) it was revealed that motivations are a direct result of eight internal push factors and several external pull factors that are the root of travel motivations (Crompton, 1979).  Through an examination of these factors the literature revealed that destination image, another significant variable that influences tourist behavior, played a large role in traveler’s decision making processes and even helped shape push-pull factors  (Jalilvand et al., 2012; Özdemir & Şimşek, 2015; Ryu et al., 2012; Zhang et al., 2014).  Furthermore, the research revealed that not only was destination image influential in shaping traveler’s motivations (Gunn,1972) before taking a trip, it was heavily influential in shaping travelers’ level of satisfaction when the actual experience is compared with the expectation (Kärnä, 2014). Satisfaction being the factor that affects customer retention, profitability and competiveness (Kärnä, 2014), it was made clear that there is a direct relationship between tourism motivations, destination image and the customer’s ultimate satisfaction (Battour, et al., 2012; Hosany & Prayag, 2013).   

Transition

Section 1 began with a discussion on the importance of understanding tourist satisfaction for leaders can improve the BVI tourism industry.  However, some tourism officials and managers in the British Virgin Islands (BVI) do not know whether a relationship exists between destination image, push and pull motives to travel, and tourists’ satisfaction.  Therefore, I will use a quantitative correlational study to examine if a relationship exists between destination image, push and pull motives to travel, and tourists’ satisfaction.  According to Oliver (1980) theory expectancy-disconfirmation theory, an individual will act in a certain way due to the expectation that the act will be followed by a certain outcome.

Section 2 outlined the role of the researcher, lists eligibility criteria for participants, and describes research method chosen for the proposed study.  The section provides information on the sampling technique used; a discussion of the data collection, the instrument used for data collection, and its reliability and validity; and, finally, the data analysis.  Section 3 includes a presentation of the findings, a discussion regarding the applicability to professional practice, information on the implications for social change, recommendations for action and further research, reflections, and the conclusion of the study. 

Section 2: The Project

Section 2, begins with a restatement of the purpose of the study, followed by the role of researcher, the study participants, the research method, and the population and sample strategy.  I also discuss issues associated with conducting ethical research, the instrumentation, and the data collection and analysis techniques.  The section ends with a discussion of study validity.

 

Purpose Statement

The purpose of the quantitative correlation study is to examine if a relationship exists between destination image, push and pull motives to travel, and tourists’ satisfaction.  The first predictor variable is destination image.  Push and pull motives is a construct consisting of thirteen predictor variables: Push knowledge, push sightseeing variety, push adventure, push relax, push lifestyles, push family, pull event and activities, pull sightseeing variety, pull easy access and affordability, pull history and culture, pull variety seeking, pull adventure, and pull natural resources.  The criterion variable is tourist satisfaction. I will study the population of departing tourists in the BVI for the period of October 2016 to December 2016, which is the peak of the tourist season at all ports of entry in the British Virgin Islands.  The implication for positive social change is to contribute to the economic enhancement of the British Virgin Islands, which will help to generate employment and entrepreneurship opportunities for residents.  

Role of the Researcher

In a quantitative study, the role of the researcher may include (a) gather data, (b) analyze and interpret the data, and (c) present the study results (Eide & Showalter, 2012; Freire, Santos, & Sauer, 2016).  For the current study, data collection will occur using a survey; participants will receive a paper survey during the process of clearance upon entering the BVI.  Because I will not have direct access to the participants as they enter the BVI, a BVI government official (immigration officer) will distribute the survey at the time of each visitor’s arrival, reducing any potential bias towards the study (Breiby, 2015).  Participants will complete a paper-and-pencil paper survey, anywhere convenient to them, because the likelihood that Internet service may not be available (McPeake, Bateson, & O’Neill, 2014). 

Working in various sectors of the tourism industry before 2015, I read documents where authors suggested some tourists may not be satisfied with the BVI tourism product.  Numerous external factors in the BVI contribute to tourists’ experience, which includes sea and land-based activities.  As a resident of the BVI, either a professional or personal relationship or interaction with any of the participants of the study will not be permitted. 

Participants

To participate in the study, participants must meet specific eligibility criteria.  The participants must be non-citizens or non-resident visitors of the BVI entering at any port of entry into the BVI.  Only individuals 18 or older can participate in the study as a measure of protection to the participants.  The participants must be tourists departing the BVI during the period of October 2016 to December 2016.  Having visual aid will help in encouraging visitors to participate (Alameda-Pineda et al, 2016; Farrell et al, 2014; Kumer, Recker & Mendling, 2016).  To gain access to the participants, all monitors located at all ports of entry will display several advertisements informing the visitors about the survey.  The advertisements will include a description of the survey purpose, the benefits of participating, the eligibility criteria, the directions for survey completion and return, and directions for obtaining a survey.   The advertisement will also include a statement that all participants must read the implied consent form prior to completing the survey (See Appendix B).  Because direct access to the participants may be limited, a BVI government official (immigration officer) will provide eligible individuals with a paper survey, which will include written instructions guiding participants how to complete the survey and where to return upon completion at the end of their visit.  Participants may misplace their survey, therefore, alternate avenues where visitors can collect a survey (Edelman et al, 2013; Kaur Mann & Kaur, 2016; Khan et al, 2013).    In the event of any misplaced surveys, participants will also have the opportunity to obtain a survey from either ferry terminals or airport departure lounges.  Participants will have the option to withdraw from the study at any time, either by not completing or by not turning the survey into a lock box at any port of departure.   

Research Method

The method for the proposed study is quantitative.  The quantitative research method is the appropriate method for studies when researchers gather numeric data to examine the relationship between or among variables when answering the research question(s) (Reinholds et al., 2015; Rozin, Hormes, Faith, & Wansink, 2012; Zhang et al, 2016) or examine how one or more variables affect or influence other variables (Barry et al., 2013; Pekar & Brabec, 2016; Seisonen, Vene & Koppel, 2016; Zhang et al, 2016).  Further, researchers use the quantitative method to test null hypotheses using parametric and non-parametric statistical tests (Aoyagi et al., 2015; Sanfilippo et al., 2016; Schneider, 2015).  Quantitative researchers use statistical procedures to evaluate relationships among the various distinct variables in the study (Olesen & Petersen, 2016; Schneider, 2015; Aoyagi et al., 2015).  Quantitative researchers also collect data from a sample, hoping to be able to generalize the results to a larger population (Cokley & Awad, 2013; Hitchcock & Newman, 2013; Schneider, 2015).  The proposed study involves examining the relationships that exist between destination image, push and pull motives to travel, and tourist satisfaction in the BVI.  Furthermore, within quantitative research, researchers statistically analyze numerical data (Turner, Balmer, & Coverdale, 2013; Venkatesh, Brown, & Bala 2013; Schneider, 2015).  Therefore, by using a quantitative method, the researcher will test whether a statistical relationship exists between destination image, push and pull motives to travel, and BVI tourist satisfaction.

This study will involve collecting numeric data using Likert-type items to examine the relationship (if any) between study variables.  A quantitative methodology is selected for this study, as the focus is on identifying any potential correlational relationship among variables and testing the null hypotheses (Barry et al., 2013; Sanfilippo et al., 2016; Schneider, 2015).  For this quantitative research, a deductive method is essential; therefore, a qualitative or mixed method would not be appropriate (Feisinger, 2013; Venkatesh et al., 2013; Zandvanian & Daryapoor, 2013).

Research Design

The design of the proposed study is correlational.  The correlational design is an appropriate design when the researcher is seeking to examine a non-causal relationship between or among variables (Bleske-Rechek, Morrison, & Heidtke, 2014; Croker, 2012; Mosing et al, 2016).  The study objective is to examine whether a non-causal relationship exists among two independent variables (push and pull motive to travel and destination image) and one dependent variable (tourist satisfaction).  The researcher cannot manipulate the independent variables, nor randomly assign participants to levels of the independent variable, supporting the requirements when conducting an experimental design (Al-Jarrah et al, 2015; Thorarensen, Kubiriza, & Imsland, 2015).  In this study, destination image, as well as the push and pull motive to travel cannot be manipulated, and people cannot be randomly assigned to each: therefore, the researcher cannot examine results using an experimental design (Chirico et al, 2013; Benredouane, 2016; Howard, Best & Nickels, 2014).  The comparative design is not an appropriate design, because the objective is not to make comparisons between variables (Atchley, Wingenbach, & Akers, 2013; Sharma, 2013; Yu-Jia, 2012).

Population and Sampling

The target population for this study includes BVI tourists for the period of October 2016 to December 2016.  The estimated population of tourists for this time period is 152,190 (development Planning Unit, 2015), which includes visitors from places such as the United States, Canada, the United Kingdom, France, Germany, Holland, Italy, Sweden, Spain, Argentina, Brazil, Venezuela, Organization of Eastern Caribbean States countries, the French West Indies, and the Netherland Antilles (Development Planning Unit, 2015). The population includes non-citizens or non-resident visitors entering at any of the 10 ports of entry in the BVI.  Individuals access the 10 ports of entry from Tortola (which has four air and seaports), Virgin Gorda (which has three air and seaports), Anegada (which has two air and seaports), and Jost Van Dyke (which has one seaport).

I will use a nonprobability convenience sampling technique to identify participants for the study, as opposed to a probability sampling method, using random selection (Azzalini, 2016; Baker et al., 2013; Gluth, Rieskamp, & Büchel, 2012).  Using a nonprobability sampling method, researchers unsystematically select participants; therefore, because not all members of the population are guaranteed to have an equal chance of inclusion in the sample (Azzalini, 2016; Baker et al., 2013; Skowronek & Duerr, 2009). 

The most common nonprobability sampling techniques are purposive and convenience sampling (Baker et al., 2013; Guest, Bunce, & Johnson, 2006; Ho, 2016).  Convenience sampling refers to the availability of potential participants or on the convenience of the researcher, which may not represent the target population (Baker et al., 2013; Guest et al., 2006; Wallace, Clark, & White, 2012).  Convenience sampling allows a researcher to make generalizations based on the sample studied; hence, one drawback is the internal bias by the researcher (Agyemang, Nyanyofio, & Gyamfi, 2014; Dutang, Goegebeur and Guillou, 2016; Nagara & Okoli, 2016).  Convenience sampling is the most used sampling techniques because it is fast and inexpensive, and the participants are more readily available (Bornstein, Jager, & Putnick, 2013; Dutang, Goegebeur & Guillou, 2016; Nagara & Okoli, 2016).  In contrast, random sampling is relatively simple, but very costly, with results that are more generalizable (Asendorpf et al., 2013; Barr, Levy, Scheepers, & Tily, 2013; Janssen, 2013).

The sample size should be large enough to satisfy the analysis used (Button et al., 2013; Laud & Dane, 2014; Thorarensen, Kubiriza, & Imsland, 2015).  A researcher must choose a population capable of providing a sample size adequate for generating sufficient data (Holland & Kopp-Schneider, 2015; Muskat, Blackman, & Muskat, 2012). Having a robust sample size is imperative for a researcher to interpret the study results accurately (Arseneau & Balion, 2016; Button et al., 2013; Holland & Kopp-Schneider, 2015).  

To determine the needed sample size, I used a sample size calculator and conducted a power analysis.   The sample size calculator is G* Power, G*Power is a statistical software package researchers use to conduct an apriori sample size analysis (Faul, Erdfelder, Buchner, & Lang, 2009).  A power analysis, using G*Power version 3.1.9 software, was conducted to determine the appropriate sample size for the study. An apriori power analysis, assuming a medium effect size (f = .15), a = .05, indicated a minimum sample size of 135 participants is required to achieve a power of .80. Increasing the sample size to 236 will increase power to .99.  Therefore, the researcher will seek between 135 and 236 participants for the study.  Using a medium effect size (f = .15) and a = .05 is the appropriate for this proposed study as displayed in Figure 1.

 

 

Figure 1. Power as a function of sample size.

 

Ethical Research

The researcher’s sole responsibility is to protect participants and to ensure the research results (Eide & Showalter, 2012).  In this study, to comply with the Belmont Report ethical guidelines, I will take specific steps to protect the rights and confidentiality of research participants (U.S. Department of Health and Human Services, 1979).  The first step is to ensure participants receive and read the information on the implied consent form prior to completing a survey.

I will not have direct access to the participants therefore, to gain access to the participants a BVI government official (immigration officer) will notify and confirm that all prospective participants are 18 years or older and that participant read the implied consent form before completing the survey.  The survey will include written instructions reminding the participants of when to complete the survey and where to return the completed survey at the end of their visit.  As participants stand in line waiting to be processed by an official at all ports of entry for admittance, visitors will be able to view several advertisements about the survey displayed on monitors.  The advertisements will establish ethical assurances by explaining rights of study participants and protecting the participants’ rights to privacy, ensuring confidentiality, and maintaining honesty (U.S. Department of Health and Human Services, 1979; Arango et al., 2016).

A BVI government staff (immigration officer) will notify all prospective participants that to participate, they must be over the age of 18 and must be identified as a non-citizen or non-resident visitor.  The implied consent letter indicates the measures I will follow when conducting my research (see Appendix B).  In the case of a misplaced survey, participants will also have the opportunity to receive an additional survey in the departure lounge either at ferry terminals or at airports. 

Participants will have the option to withdraw from the study at any time, either by not completing the survey, and or by not turning the survey in to the appropriate entity.  To avoid coercion, there will be no incentives associated with participating in this study.  I choose not to include incentives to ensure participants’ decision to participate in the study is not altered by financial gain (Fein & Kulik, 2011).

To help protect the rights of participants, data will be stored for 5 years on a secure computer.  The data collected will be password protected and only accessible to the researcher.  After 5 years, the data will be electronically erased from the computer.  In addition, I will keep the completed surveys and any printed information will be locked away and destroyed by secure shredding after 5 years (Cronin-Gilmore, 2012).

Data Collection Instruments

No existing instrument exists to gather data on all the variables for the study.  The variables in the study are not unobservable psychological constructs, and using an existing instrument is typically most appropriate when measuring such constructs (Barry et al., 2013; Davies et al., 2013; Slaney & Racine, 2013). Instead, I will create a self-developed survey, with individual survey items to measure the study variables.  Although thought to be more challenging and labor intensive to develop, there are certain advantages that come with developing a unique, purpose-specific survey.  For example, a self-developed survey ensures the inclusion of the variables and concepts a researcher must measure based on a detailed review of the literature (Buchanan, Siegfried & Jelsma, 2015; Bettoni et al, 2014; Granko et al, 2014).  Opting for a self-developed survey allows a researcher to prepare each question specific to the research questions of the study (Buchanan, Siegfried & Jelsma, 2015; Bettoni et al, 2014; Granko et al, 2014).  The instrument for the proposed study is a paper survey (see Appendix C).  In addition, a self-developed instrument can facilitate the ability to systematically address issues of validity and reliability explained under the Data Collection section. Table 2 includes a summary of the variables in the survey, listed in the order they will appear in the survey instrument.

 

 

 

 

 

 

 

 

Table 3   

Variable Measurement

Variable	Survey item #	Level of measurement
Demographic (gender)	1	Ordinal
Demographic (purpose of visit)	2	Ordinal
Demographic (islands to be visited)	3	Ordinal
Demographic variable (BVI arrival method)	4	Ordinal
Demographic variable (nationality)	5	Ordinal
Demographic (has been to the BVI before)	6	Ordinal
Demographic (household income)	7	Ordinal
Destination image (predictor variable)	8	Nominal
Push and pull motives to travel (predictor variable)	9	Nominal
Tourist satisfaction (criterion variable)	10	Ordinal

 

Demographic Survey Items

Structuring the format of the survey is crucial to the success of the study. The first section of the survey instrument includes demographic questions. The demographic information collected is gender, purpose of visit, islands to be visited, BVI arrival method, nationality, has been to the BVI before, household income.  I will measure each demographic variable using a single question at an ordinal level. 

Destination Image

The second section of the survey instrument includes questions to gather the data on the study’s independent and dependent variables (destination image, push and pull motives to travel, and tourist satisfaction).  I will measure the first independent variable, destination image, using a single question at the nominal level of measurement.  Assaker and Hallak (2013) and Stylidis, Belhassen, and Shani (2014) studied destination image and measured this variable using a single item because of the various dimensions of destination image.  The intent is to use a modified version of the single item Assaker and Hallak used to measure destination image.  Assaker and Hallak’s single item was, “How would you describe the image that you have of that destination before the experience”?  Participants provided answers using a 5-point Likert-type scale.  The scale ranged from 1 (not all satisfied) to 5 (extremely satisfied), with high scores indicating exceptional levels of destination image and lower scores indicating unsatisfactory levels of destination image.  Similar to previous studies, the emphasis will be on an overall evaluation of destination image, using the scale above, rather than analyzing the individual components of the destination image construct (Assaker & Hallak, 2013; Prayag et al., 2015; Zhang et al. 2014).

Push and Pull Motives to Travel

The second section of the survey instrument also includes questions, at the nominal level of measurement, to gather the data on study’s independent construct of push and pull motives to travel.  Kim, Oh, and Jogaratnam (2006) and Mohammad and Som’s (2010), studied push and pull motives to travel and measure the variable using multiple items. The intent is to use a modified version of Kim, Oh, and Jogaratnam and Mohammad and Som used to measure push and pull motive to travel to fit the needs of the BVI.  Push and pull motives consists of thirteen predictor variables: push knowledge, push sightseeing variety, push adventure, push relax, push lifestyles, push family, pull event and activities, pull sightseeing variety, pull easy access and affordability, pull history and culture, pull variety seeking, pull adventure, and pull natural resources.   Participants will provide answers using a 5-point Likert-type scale.  The scale ranges from 1 (not at all satisfied) to 5 (extremely satisfied), with high scores indicating exceptional levels of push and pull motives to travel and lower scores indicating unsatisfactory levels of push and pull motives to travel.

Tourist Satisfaction

I will measure the dependent variable, tourist satisfaction, as a continuous variable at the ordinal level of measurement.  Assaker and Hallak (2013) studied tourist satisfaction and measured this variable using a single item to understand the visitor overall satisfaction with the visitor visit to a destination.  Assaker and Hallak’s satisfaction scale contained one item intended to measure the overall tourist satisfaction with visitors experience to the BVI.  This single item from Assaker and Hallak is, “How would you describe your overall satisfaction with your stay in that destination”?  Participants provide answers using an ordinal, 5-point Likert-type scale.  The scale will range from 1 (not all satisfied) to 7 (extremely satisfied), with high scores indicating exceptional levels of tourist satisfaction and lower scores indicating low levels of tourist satisfaction.

Instrument Reliability and Validity

                Because there is no existing survey instrument available for my study, no published reliability and validity information available.  Therefore, I will conduct a pilot test to assess validity and reliability of the instrument using specific methods described in the Data Collection Technique section below.

Data Collection Technique

I will use a paper survey to collect data. Some researcher has stated that data collection is often the most costly and time intensive portion of research (Baker et al., 2013; Dunn et al., 2015; Pires et al., 2016).  Paper surveys have the advantages of allowing participants to complete a survey anywhere, to reduce bias towards the researcher and paper responses are at a higher rate than web-based surveys (Cahill et al., 2015; Hohwü et al., 2013; McPeake, Bateson, & O’Neill, 2014).  Disadvantage of using paper surveys is the high cost of printing not associated with using a web-based survey (Cahill et al., 2015; Sue & Ritter, 2012; Binu & Misbah, 2013). 

Following Walden’s Institutional Review Board (IRB) approval, but before distributing the survey to participants, I will conduct a field test and a pilot test to assess validity and reliability of the instrument.   Researchers use field test to assess the survey instrument for content validity (Chakraborty et al, 2016; Li, Scott & Walters, 2014; Harshman & Yezierski, 2016). The field test for this study will include four experts in the areas of academics and business practice to assess the survey instrument for content validity.  Leggett et al (2016), suggested the following guidelines for assessing questionnaire validity, the field test will involve gathering information to answer three questions:

1. Does the instrument look like a survey?

2. Is the survey appropriate for the study population?

3. Does the survey include all of the questions needed to answer the study research question and achieve the study objectives?

In addition, the field test involves a test for readability of the survey instrument.  Flesch Reading Ease and Flesch-Kincaid Grade Level tests are methods used for checking readability (Hartley, 2016; Eltorai et al, 2015; Lenzner, 2013). Because of the field test and readability tests, I will modify the survey instrument before conducting the pilot test to assess instrument reliability. 

                Conducting test-retest procedures of the survey instrument will enhance the internal validity of the instrument based on any difficulties observed to gather evidence of reliability (Mello, Merchant, & Clark, 2013; Rickards, Magee, & Artino Jr, 2012; Van Teijlingen & Hundley, 2002).  I will then administer the survey to a small convenience sample from visiting tourist using the test-retest procedure using 5 days for test-retest interval.  Allowing the period longer than 5 days may make the factors I am measuring to change and may alter the scores in independent variable (tourist satisfaction) (Becken & Wilson, 2013; Breiby, 2015; Dubois et al., 2016).

Conducting test-retest procedures of the survey instrument will enhance the internal validity of the instrument based on any difficulties observed to gather evidence of reliability (Chang & Chang, 2016; Mello, Merchant, & Clark, 2013; Rickards, Magee, & Artino Jr, 2012).  Researchers use the Pearson’s correlation coefficient and Spearman’s Rho to measure instrument reliability (Baumester et al, 2016; Harshman & Yezierski, 2016; Karyadi, VanderVeen & Ciders, 2014).  I will calculate the reliability of Questions 9—the questions measuring each of the study variables—using the Pearson’s correlation coefficient for ratio variables and Spearman’s Rho for ordinal variables. 

Completion of the pilot study, I will publish and print the survey for distribution.  Individuals identified as non-citizens or non-resident visitors, who are 18 years and older and entering the BVI at any ports of entry will receive a paper survey from a BVI government official (immigration officer).  The paper survey will include written instructions guiding participants how to complete the survey and where to return upon completion at the end of their visit.  In addition, all monitors located at all ports of entry and departure lounges will display several advertisements informing the visitors about the survey.  Because many ports in the BVI are normally open for extended hours, reoccurring advertisements should motivate more visitors to participate in the surveys.  In the event of any misplaced surveys, participants will also have the opportunity to obtain a survey from either ferry terminals or airport departure lounges. 

Data Analysis

The research question for the proposed study is, What is the relationship between destination image, push knowledge, push sightseeing variety, push adventure, push relax, push lifestyles, push family, pull event and activities, pull sightseeing variety, pull easy access and affordability, pull history and culture, pull variety seeking, pull adventure, pull natural resources, and BVI tourists’ satisfaction? The hypotheses are below.

Null Hypothesis (H1₀)_: There is no statistically significant relationship between destination image, push knowledge, push sightseeing variety, push adventure, push relax, push lifestyles, push family, pull event and activities, pull sightseeing variety, pull easy access and affordability, pull history and culture, pull variety seeking, pull adventure, pull natural resources, and BVI tourists’ satisfaction.

 

Alternative Hypothesis (H1_a): There is a statistically significant relationship between destination image, push knowledge, push sightseeing variety, push adventure, push relax, push lifestyles, push family, pull event and activities, pull sightseeing variety, pull easy access and affordability, pull history and culture, pull variety seeking, pull adventure, pull natural resources, and BVI tourists’ satisfaction.

Statistical Analyses

For statistical data analysis, I will multiple regression.  Multiple linear regression is the appropriate method of quantitative data analysis when there are one interval dependent variable and more than one interval or categorical independent variable (Donneau et al, 2015; Mehmood & Ahmed, 2016; Wang, Chiou, & Muller, 2016).  The criterion variable in this study is tourist satisfaction, which will have an ordinal level of measure.  The predictor variables in this study are destination image and push and pull motives to travel, which have ordinal measurement levels.  Therefore, because this study involves more than two continuous variables, simple regression analysis cannot be used (Bakrania et al, 2015; Luchman, 2014; Rybak, Sternberg & Pfeiffer, 2013). Multiple regression analysis will help determine how much independent variables explain the variation in the dependent variable and the independent variable will improve the accuracy in predicting the values of the dependent variable (Gho & Zhang, 2014; Luchman, 2014; Nimon & Owsald, 2013).

Simple linear regression and analysis of variance (ANOVA) are two types of quantitative statistics; however, they do not meet the needs for this study.  ANOVA is appropriate when we have categorical IVs and a continuous DV—where we compare means (Dios et al, 2013; Hesamian, 2015; Pekar & Braver, 2016).  Also, in ANOVA, the researchers seek to find the means among groups (Dios et al, 2013; Hesamian, 2015; Thorarensen, Kubiriza, & Imsland, 2015), which is not an objective of this study.  With simple linear regression, the goal is to predict the value of a dependent variable based on the value of an independent variable (Ardhakupar, Sridhar & Atrey, 2014; Brown, 2014; Wang, Chiou, & Muller, 2016).  This study includes examination of the relationship (if any) between two independent variables and a dependent variable; therefore, simple linear regression cannot be considered for this study. 

Assumptions

Researchers proposed five assumptions to be tested when using multiple regression analysis: (a) measurement error, (b) normality, (c) linearity, (d) multicollinearity, and (e) homoscedasticity (Dormann et al., 2013; Kim, Sugar, & Belin, 2015; Kock & Lynn, 2012).  Next is a discussion of each assumption of multiple regression.

Measurement error.  Conducting multiple regression analysis may include the assumption of no error in the measure of variables.  Cronbach’ alpha is a common test for measurement when measuring multiple items (Osborne & Water, 2002; Tonetto & Desmet, 2016; Valim et al, 2014). Therefore, for the variable push pull motives to travel, I will perform Cronbach’ alpha test for measurement of error.

Normal distribution.  I will have to perform a visual inspection and create a histogram of each variable to test the assumption of normal distribution.  Conducting a Shapiro-Wilk test will determine whether normal distribution of each variable exists.  In the research, the assessment of normality will determine the specific statistical tests researchers utilize: parametric or non-parametric (Punzo, Browne & McNicholas, 2016).  Parametric test produces a bell-shaped curve versus a non-parametric (Fernandes, Madeiros & Veiga, 2014; Punzo, Browne & McNicholas, 2016; Urbano, 2015).  Researchers can use bootstrapping procedures when the data failed to meet the statistical assumption of normality.

Linear relationship.  Another assumption for multiple regression that determines whether a linear relationship exists between variables.  To test for linearity assumption, I will create and inspect a scatter plotter of predicted and residual values for each variable (Singh et al, 2016; Yan & Zhang, 2015; Li, 2015).  If linear relationships do not exist, researchers can use bootstrapping procedures to examine any possible influence of assumption violations.

Homoscedasticity.  Conducting a scatterplot analysis will help test for assumptions of homoscedasticity (Francq & Govaerts, 2014; Punzo, Browne & McNicholas, 2016; Urbano, 2015).  To test whether a violation of homogeneity exists, I will conduct a Goldfeld-Quandt test for homogeneity of variance (Francq & Govaerts, 2014; Punzo, Browne & McNicholas, 2016; Urbano, 2015).

Multicollinearity.  Multicollinearity exists when a possible predictor-predictor redundancy phenomenon occurred (Amini & Roozbeh, 2016; Kock & Lynn, 2012; Chandra & Sarkar, 2015).  Using a normal probability plot (P-P) of the regression standardized residual tested for multicollinearity (Amini & Roozbeh, 2016; Aslam, 2014; Chandra & Sarkar, 2015).

Violation of assumptions.  Violating assumptions can result in errors.  There are two types of errors, which can occur when using inferring statistical significance of the analysis.  Type I error when the researchers reject the true null hypothesis and Type II error results when the researchers do not reject a false null hypothesis (Delorme et al, 2016; Li & Mei, 2016; Liu et al, 2015).  For example decreasing the p value, from .05 to .01, reduces the possibility of a Type I error, but also increases the likelihood of a Type II error (Delorme et al, 2016; Li & Mei, 2016; Liu et al, 2015). If the violation of an assumption exists, Punzo, Browne and McNicholas (2016) suggested that researcher should use of bootstrapping procedures.  Therefore, I will use the bootstrapping procedure to mitigate any violations of assumptions.

Interpreting Results

Descriptive designs examine the current condition of a situation or circumstance (Correia & Kozak, 2016; Montilla & Kromrey, 2016; Olya & Altinay, 2016; Scott & Walters, 2014).  I will use descriptive statistics to examine the distribution of data. Some of the measures included the standard deviation, mean, and variance.  I will use a pre-established probability standard of .05 for the alpha, or p value, which is common in tourist satisfaction (Correia & Kozak, 2016; Liu, Horng et al, 2016; Olya & Altinay, 2016). The related confidence interval for an alpha of .05 is 95%.  A medium effect size (f ² = .15) is appropriate based on a review of 29 articles where tourist satisfaction, as measured by destination image or motivation to travel, was the outcome measurement (Correia & Kozak, 2016; Li, Scott & Walters, 2014; Olya & Altinay, 2016).

Software and Data

Common software researchers use to analyze statistical data include Statistical Package for the Social Sciences (SPSS), Statista, and Microsoft Excel (Ayatollahi et al., 2013; Ahman et al., 2013; Cori et al., 2013). Because SPSS is commonly use by tourism industry researchers, I will use SPSS (e.g., Cheng & Lin, 2014; Manaf et al., 2015; Xu & Shieh, 2014). 

Before conducting data analysis, researchers visually inspect the survey data for missing, incomplete, or unusual information (Cai & Zhu, 2015; Kim, Sugar & Belin, 2015; Zvoch, 2014).  The purpose of data clean is to detect errors and remove these errors for quality improvement (Cai & Zhu, 2015; Kim, Sugar & Belin, 2015; Zvoch, 2014).  Data cleaning is important in statistical analyses (Cai & Zhu, 2015; Kim, Sugar & Belin, 2015; Zvoch, 2014). To address missing data, the most popular method used is deletion of any cases that have missing data (Kim, Sugar & Belin, 2015; Punzo, Browne & McNicholas, 2016; Zvoch, 2014). Because the use of paper survey the likelihood of missing data is minimal, therefore, I will adopt this procedure for any missing data.

Study Validity

Study validity is the final consideration of the project.  Validity is an important aspect of the study, which involves the integrity of conclusions drawn from the research (Barry et al, 2013; Baumeister et al, 2016; Chakraborti et al, 2016).  There are two types of validity: internal validity and external validity (Baumeister et al, 2016; Chakraborti et al, 2016; Pericci & Pereira, 2016).   

 Internal Validity

                Le Borgne et al (2016) stated some internal validity could occur in instrumentation, statistical regression, selection, and testing. Williams (2015) stated that internal validity supports the notion that observed covariation correlates to a causal relationship.  This study is a correlational study, and therefore, there are no threats to the internal validity.

Statistical conclusion validity.  In statistical conclusion of validity, there are two types of errors Type I and Type II.  Rejection of a true null hypotheses is Type I error and non-rejection of a false null hypotheses is when Type II error occurs (Kratochwill & Levin, 2014; Le Borgne et al., 2016; Pericci & Pereira, 2016).  Three statistical conclusion of validity are instrument validity, data assumption, and sample size.

Reliability of the instrument.  Research study reliability mirrors the consistency of the study and instrument; therefore the researchers should verify the survey instrument for reliability (Barry et al., 2013; Rickards, Magee, & Artino Jr., 2012; Trani, Babulal & Bakhsh, 2015).  Reliability increases the trustworthiness of the measurement tool and enabled subsequent researchers to reach similar conclusions in replications (Almeida, Ferreira & Cavalcante, 2015; Barry et al., 2013; Trani, Babulal & Bakhsh, 2015).  To ensure reliability of the proposed study, I will compute Cronbach’s alpha using the variable push and pull motives to travel.  Because Cronbach’s Alpha is relevant when there are multiple items within the scale (Baral, 2015; Osborne & Water, 2002; Tonetto & Desmet, 2016).

Data assumption.  Five assumption was identified in the Data Analysis section: normal distribution of variables, a linear relationship between the dependent variables, homoscedasticity and lack of collinearity among the independent variables, and measurement error (Behr, 2015; Kim, Sugar, & Belin, 2015; Osborne & Water, 2002).  Therefore, a violation of assumptions can results in errors, resulting in the use of a nonparametric procedure such as discriminant analysis to analyze the data (Benner, Gugercin & Willcox, 2015; Behr, 2015; Saart, Gao & Kim, 2013).  Bootstrapping procedures will address violations of assumptions (Benito, Solana & Lopez, 2014).  Again, I will use bootstrapping to address violations of assumptions.

Sample size.  Kouvelioti and Vagenas (2015) stated that statistical validity depends on the sample size.  Having an insufficient sample size for this study may result in an incorrect inference about the study.  For this study, I will conduct a G*Power 3.1.9.2 analysis to calculate a sufficient sample size (Faul et al., 2009).  A priori power analysis indicates a minimum sample size of 135 assuming a medium effect size (f = .15), with a = .05 to achieve a power of .80 while power of .99 requires a sample size of 236.  Therefore, a sample size of between 135 and 236 participants is appropriate for the study

External Validity

                External validity is the ability of generalization to the larger population (Raina, 2015).  External validity refers to an instrument’s ability to measure attributes of the study’s constructs (Walls et al., 2011).  Threats to external validity represent factors that reduce the ability to generalize the study results within a larger population of study (Khorsan & Crawford, 2014; Oo, 2016; Raina, 2015).  Therefore, using nonprobability sampling may limit the ability to generalize the results of the study to other population.  Whereas, probability sampling assumes that each participant of the population has the same equal chances for selection and is the preference for making statistical inferences to the population ( Barratt, Ferris & Lenton, 2015; Raina, 2015; Sarais et al, 2016). 

Transition and Summary

Section 2 began with role of the researcher and the participants, who are visitors to the BVI.  The research method and design is a quantitative correlational study using a paper survey to collect data through convenience sampling.  Section 2 concluded with a discussion on data analysis process using multiple linear regression and methods I will use to test the study’s validity.

Section 3 includes a presentation of the findings, a discussion regarding the applicability to professional practice, information on the implications for social change, recommendations for action and further research, reflections, and the conclusion of the study. 

References

Agyemang, C. B., Nyanyofio, J. G., & Gyamfi, G. D. (2014). Job stress, sector of work, and shift-work pattern as correlates of worker health and safety: A study of a manufacturing company in Ghana. International Journal of Business and Management, 9(7), 59–69. doi:10.5539/ijbm.v9n7p59

Alameda-Pineda, X., Staiano, J., Subramanian, R., Batrinca, L., Ricci, E., Lepri, B., . . . Sebe, N. (2016). SALSA: A novel dataset for multimodal group behavior analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence IEEE, 38(8), 1707-1720. doi:10.1109/tpami.2015.2496269

Al-Jarrah, O. Y., Yoo, P. D., Muhaidat, S., Karagiannidis, G. K., & Taha, K. (2015). Efficient machine learning for big data: A Review. Big Data Research, 2(3), 87–93. doi:10.1016/j.bdr.2015.04.001

Almeida, A. T., Ferreira, R. J., & Cavalcante, C. A. (2015). A review of the use of multicriteria and multi-objective models in maintenance and reliability. IMA Journal of Management Mathematics, 26(3), 249-271. doi:10.1093/imaman/dpv010

Altunel, M. C., & Erkut, B. (2015). Cultural tourism in Istanbul: The mediation effect of tourist experience and satisfaction on the relationship between involvement and recommendation intention. Journal of Destination Marketing & Management, 4(4), 213-221. doi:10.1016/j.jdmm.2015.06.003

Amini, M., & Roozbeh, M. (2016). Least trimmed squares ridge estimation in partially linear regression models. Journal of Statistical Computation and Simulation, 86(14), 2766–2780. doi:10.1080/00949655.2015.1128433

Ardhapurkar, P. M., Sridharan, A., & Atrey, M. D. (2014). Prediction of temperature distribution in heat exchanger for mixed refrigerant Joule–Thomson cryocooler. Indian Journal of Cryogenics, 39(1), 169. doi:10.5958/2349-2120.2014.00821.8

Asendorpf, J. B., Conner, M., De Fruyt, F., De Houwer, J., Denissen, J. J., Fiedler, K., & Perugini, M. (2013). Recommendations for increasing replicability in psychology. European Journal of Personality, 27(2), 108-119. doi:10.1002/per.1919

Assaker, G., & Hallak, R. (2013). Moderating effects of tourists’ novelty-seeking tendencies on destination image, visitor satisfaction, and short-and long-term revisit intentions. Journal of Travel Research, 52(5), 600-613. doi:10.1177/0047287513478497

Aslam, M. (2014). Using heteroscedasticity-consistent standard errors for the linear regression model with correlated regressors. Communications in Statistics - Simulation and Computation, 43(10), 2353-2373. doi:10.1080/03610918.2012.750354

Atchley, T. W., Wingenbach, G., & Akers, C. (2013). Comparison of course completion and student performance through online and traditional courses. The International Review of Research in Open and Distributed Learning, 14(4), 105-116. Retrieved from http://www.irrodl.org

Azzalini, A., & Menardi, G. (2016). Density-based clustering with non-continuous data. Computational Statistics, 31(2), 771–798. doi:10.1007/s00180-016-0644-8

Baker, R., Brick, J. M., Bates, N. A., Battaglia, M., Couper, M. P., Dever, J. A., ... & Tourangeau, R. (2013). Summary report of the AAPOR task force on non-probability sampling. Journal of Survey Statistics and Methodology, 1(2), 90-143. doi:10.1093/jssam/smt008

Bakrania, K., Edwardson, C. L., Bodicoat, D. H., Esliger, D. W., Gill, J. M., Kazi, A., . . . Yates, T. (2015). Associations of mutually exclusive categories of physical activity and sedentary time with markers of cardiometabolic health in English adults: A cross-sectional analysis of the Health Survey for England. BMC Public Health, 16(1). doi:10.1186/s12889-016-2694-9

Barr, D. J., Levy, R., Scheepers, C., & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68(3), 255-278. doi:10.1016/j.jml.2012.11.001

Barry, A. E., Chaney, B., Piazza-Gardner, A. K., & Chavarria, E. A. (2013). Validity and reliability reporting practices in the field of health education and behavior: A review of seven journals. Health Education & Behavior, 41(1), 12-18. doi:10.1177/1090198113483139

Batista, M. D. G. C., Couto, J. P. A., Botelho, D. R., & Faias, C. (2014). Tourist satisfaction and loyalty in the hotel business: An application to the island of São Miguel, Azores. Tourism & Management Studies, 10(1), 16-23. Retrieved from http://tmstudies.net/index.php/ectms

Baumeister, S. E., Ricci, C., Kohler, S., Fischer, B., Töpfer, C., Finger, J. D., & Leitzmann, M. F. (2016). Physical activity surveillance in the European Union: Reliability and validity of the European Health Interview Survey-Physical Activity Questionnaire (EHIS-PAQ). International Journal of Behavioral Nutrition and Physical Activity, 13(1). doi:10.1186/s12966-016-0386-6

Bazneshin, S. D., Hosseini, S. B., & Azeri, A. R. K. (2015). The physical variables of tourist areas to increase the tourists’ satisfaction regarding the sustainable tourism criteria: Case Study of Rudsar Villages, Sefidab in Rahim Abad. Procedia-Social and Behavioral Sciences, 201, 128-135. doi:10.1016/j.sbspro.2015.08.141

Becken, S., & Wilson, J. (2013). The impacts of weather on tourist travel. Tourism Geographies, 15(4), 620-639. doi:10.1080/14616688.2012.762541

Begum, H., Er, A. C., Alam, A. F., & Sahazali, N. (2014). Tourist's perceptions towards the role of stakeholders in sustainable tourism. Procedia-Social and Behavioral Sciences, 144, 313-321. doi:10.1016/j.sbspro.2014.07.301

Behr, A. (2015). Stochastic data envelopment analysis. Production and Efficiency Analysis with R, 161-182. doi:10.1007/978-3-319-20502-1_7

Benner, P., Gugercin, S., & Willcox, K. (2015). A survey of projection-based model reduction methods for parametric dynamical systems. SIAM Review, 57(4), 483-531. doi:10.1137/130932715

Benredouane, S., Berrama, T., & Doufene, N. (2016). Strategy of screening and optimization of process parameters using experimental design: application to amoxicillin elimination by adsorption on activated carbon.Chemometrics and Intelligent Laboratory Systems, 155, 128-137. doi:10.1016/j.chemolab.2016.04.010

Bettoni, E., Ferriero, G., Bakhsh, H., Bravini, E., Massazza, G., & Franchignoni, F. (2014). A systematic review of questionnaires to assess patient satisfaction with limb orthoses. Prosthetics and Orthotics International, 40(2), 158-169. doi:10.1177/0309364614556836

Bleske-Rechek, A., & Kelley, J. A. (2014). Birth order and personality: A within-family test using independent self-reports from both firstborn and laterborn siblings. Personality and Individual Differences, 56, 15-18. doi:10.1016/j.paid.2013.08.011

Bornstein, M. H., Jager, J., & Putnick, D. L. (2013). Sampling in developmental science: Situations, shortcomings, solutions, and standards. Developmental Review, 33(4), 357-370. doi:10.1016/j.dr.2013.08.003

Breiby, M. A. (2015). Exploring aesthetic dimensions in nature-based tourist experiences. Tourism Analysis, 20(4), 369-380. doi:10.3727/108354215x14400815080361

Bromwich, M., & Scapens, R. W. (2016). Management accounting research: 25 years on. Management Accounting Research, 31, 1-9. doi:10.1016/j.mar.2016.03.002

Brown, J. D. (2014). Simple linear regression. Linear Models in Matrix Form, 39-67. doi:10.1007/978-3-319-11734-8_2

Buchanan, H., Siegfried, N., & Jelsma, J. (2015). Survey instruments for knowledge, skills, attitudes and behaviour related to evidence-based practice in occupational therapy: A systematic review. Occupational Therapy International, 23(2), 59-90. doi:10.1002/oti.1398

Button, K. S., Ioannidis, J. P., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S., & Munafò, M. R. (2013). Power failure: Why small sample size undermines the reliability of neuroscience. Nature Reviews Neuroscience, 14(5), 365-376. Retrieved from http://www.nature.com/nrn/index.html

Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 14, 1-10. doi:10.5334/dsj-2015-002

Chakraborty, N. M., Fry, K., Behl, R., & Longfield, K. (2016). Simplified asset indices to measure wealth and equity in health programs: A reliability and validity analysis using survey data from 16 countries. Global Health: Science and Practice, 4(1), 141-154. doi:10.9745/ghsp-d-15-00384

Chandra, S., & Sarkar, N. (2015). A restricted class estimator in the mixed regression model with autocorrelated disturbances. Statistical Papers, 57(2), 429-449. doi:10.1007/s00362-015-0664-4

Chirico, R. D., Frenkel, M., Magee, J. W., Diky, V., Muzny, C. D., Kazakov, A. F., ... & Brenneke, J. F. (2013). Improvement of quality in publication of experimental thermophysical property data: Challenges, assessment tools, global implementation, and online support. Journal of Chemical & Engineering Data, 58(10), 2699-2716. doi:10.1021/je400569s

Cokley, K., & Awad, G. H. (2013). In defense of quantitative methods: Using the "Master's Tools" to promote social justice. Journal for Social Action in Counseling & Psychology, 5(2), 26-41. Retrieved from http://www.jsacp.tumblr.com

Correia, A., & Kozak, M. (2016). Tourists' shopping experiences at street markets: Cross-country research. Tourism Management, 56, 85-95. doi:10.1016/j.tourman.2016.03.026

Crnogaj, K., Rebernik, M., Bradac Hojnik, B., & Omerzel Gomezelj, D. (2014). Building a model of researching the sustainable entrepreneurship in the tourism sector. Kybernetes, 43(3/4), 377-393. doi:10.1108/k-07-2013-0155

Cronin-Gilmore, J. (2012). Exploring marketing strategies in small businesses. Journal of Marketing Development and Competitiveness, 6(1), 96. Retrieved from http://www.na-businesspress.com/jmdcopen.html

 Davies, N. M., Smith, G. D., Windmeijer, F., & Martin, R. M. (2013). Issues in the reporting and conduct of instrumental variable studies. Epidemiology, 24(3), 363-369. doi:10.1097/ede.0b013e31828abafb

Delorme, P., Micheaux, P. L., Liquet, B., & Riou, J. (2016). Type-II generalized family-wise error rate formulas with application to sample size determination. Statistics in Medicine, 35(16), 2687-2714. doi:10.1002/sim.6909

Development Planning Unit (2015).  BVI Statistics Economy. Published by the Central Statistics Office, Development Planning Unit

Dimoska, T., & Trimcev, B. (2012). Competitiveness strategies for supporting economic development of the touristic destination. Procedia-Social and Behavioral Sciences, 44, 279-288. doi:10.1016/j.sbspro.2012.05.031

Dios, K. D., Manibusan, A., Marsden, R., & Pinkstaff, J. (2013). Comparison of bioanalytical methods for the quantitation of PEGylated human insulin. Journal of Immunological Methods, 396(1-2), 1-7. doi:10.1016/j.jim.2013.07.007

Donneau, A., Mauer, M., Lambert, P., Lesaffre, E., & Albert, A. (2015). Testing the proportional odds assumption in multiply imputed ordinal longitudinal data. Journal of Applied Statistics, 42(10), 2257-2279. doi:10.1080/02664763.2015.1023704

Dormann, C. F., Elith, J., Bacher, S., Buchmann, C., Carl, G., Carre, G., & Lautenbach, S. (2013). Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography, 36, 27-46. doi:10.1111/j.1600-0587.2012.07348.xFe

Dunn, T. J., Baguley, T., & Brunsden, V. (2014). From alpha to omega: A practical solution to the pervasive problem of internal consistency estimation. British Journal of Psychology, 105(3), 399-412. doi:10.1111/bjop.12046

Dutang, C., Goegebeur, Y., & Guillou, A. (2016). Robust and Bias-Corrected Estimation of the Probability of Extreme Failure Sets. Sankhya A, 78(1), 52-86. doi:10.1007/s13171-015-0078-3

Edelman, L. S., Yang, R., Guymon, M., & Olson, L. M. (2013). Survey methods and response rates among rural community dwelling older adults. Nursing Research, 62(4), 286-291. doi:10.1097/nnr.0b013e3182987b32

Eide, E. R., & Showalter, M. H. (2012). Methods matter: Improving causal inference in educational and social science research: A review article. Economics of Education Review, 31, 744-748. doi:10.1016/j.econedurev.2012.05.010

Eltorai, A. E., Naqvi, S. S., Ghanian, S., Eberson, C. P., Weiss, A. C., Born, C. T., & Daniels, A. H. (2015). Readability of Invasive Procedure Consent Forms. Clinical and Translational Science, 8(6), 830-833. doi:10.1111/cts.12364

Estrada, M. A., & Koutronas, E. (2016). Terrorist attack assessment: Paris November 2015 and Brussels March 2016. Journal of Policy Modeling, 38(3), 553-571. doi:10.1016/j.jpolmod.2016.04.001

Farrell, E. H., Whistance, R. N., Phillips, K., Morgan, B., Savage, K., Lewis, V., . . . Edwards, A. (2014). Systematic review and meta-analysis of audio-visual information aids for informed consent for invasive healthcare procedures in clinical practice. Patient Education and Counseling, 94(1), 20-32. doi:10.1016/j.pec.2013.08.019

Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G* Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149-1160. doi:10.3758/brm.41.4.1149

Fein, E. C., & Kulik, C. T. (2011). Safeguarding access and safeguarding meaning as strategies for achieving confidentiality. Industrial and Organizational Psychology, 4(04), 479-481. doi:10.1111/j.1754-9434.2011.01378.x

Feinsinger, P. (2013). Research methodologies in applied and basic ecology: which am I following, and why?. Revista Chilena De Historia Natural, 86(4), 385-402. Retrieved from https://revchilhistnat.biomedcentral.com/

Fernandes, M., Medeiros, M. C., & Veiga, A. (2014). A (semi)parametric functional coefficient logarithmic autoregressive conditional duration model. Econometric Reviews, 35(7), 1221-1250. doi:10.1080/07474938.2014.977071

Foss, N., & Hallberg, N. (2013). How symmetrical assumption advance strategic management research. Strategic Management Journal, 35, 903-913. doi:10.1002/smj.2130

Francq, B. G., & Govaerts, B. B. (2014). Measurement methods comparison with errors-in-variables regressions. From horizontal to vertical OLS regression, review and new perspectives. Chemometrics and Intelligent Laboratory Systems, 134, 123-139. doi:10.1016/j.chemolab.2014.03.006

Freire, J. R. D. S., Santos, I. C. D., & Sauer, L. (2016). Knowledge generation in agricultura research. Ciência Rural, 46(7), 1301-1307. doi:10.1590/0103-8478cr20150745

Gay, L. R., Millers, G. E., & Airasian, P. W. (2014). Educational research: Competencies for analysis and application (11^th ed.). New York, NY: Pearson.

Gluth, S., Rieskamp, J., & Büchel, C. (2012). Deciding when to decide: Time-variant sequential sampling models explain the emergence of value-based decisions in the human brain. The Journal of Neuroscience, 32(31), 10686-10698. doi:10.1523/jneurosci.0727-12.2012

Goodbody, L., & Burns, J. (2011). A disquisition on pluralism in qualitative methods: The troublesome case of a critical narrative analysis. Qualitative Research in Psychology, 8, 170-196. doi:10.1080/14780887.2011.575288

Granko, R. P., Wolfe, A. S., Kelley, L. R., Morton, C. S., & Delgado, O. (2014). Assessing the self-development potential of a pharmacy management practitioner through self-assessment survey. American Journal of Health-System Pharmacy, 72(2), 149-157. doi:10.2146/ajhp140004

Green, S. B., & Salkind, N. J. (2014). Using SPSS for windows and macintosh: Analyzing and understanding data (6th ed.). Upper Saddle River, NJ: Prentice Hall.

Grigaliūnaitė, V., & Pilelienė, L. (2014). Satisfaction and loyalty of Lithuanian rural tourists: Segmentation and managerial implications. Regional Formation and Development Studies, 14(3), 64-75. doi:10.15181/rfds.v14i3.864

Guest, G., Bunce, A., & Johnson, L. (2006). How many interviews are enough? An experiment with data saturation and variability. Field methods, 18(1), 59-82. doi:10.1177/1525822x05279903

Hajibaba, H., Gretzel, U., Leisch, F., & Dolnicar, S. (2015). Crisis-resistant tourists. Annals of Tourism Research, 53, 46-60. doi:10.1016/j.annals.2015.04.001

Harshman, J., & Yezierski, E. (2016). Test–retest reliability of the adaptive chemistry assessment survey for teachers: measurement error and alternatives to correlation. Journal of Chemical Education, 93(2), 239-247. doi:10.1021/acs.jchemed.5b00620

Hartley, J. (2016). Is time up for the Flesch measure of reading ease? Scientometrics, 107(3), 1523-1526. doi:10.1007/s11192-016-1920-7

Hallmann, K., Müller, S., Feiler, S., Breuer, C., & Roth, R. (2012). Suppliers' perception of destination competitiveness in a winter sport resort. Tourism Review, 67(2), 13-21. doi:10.1108/16605371211236105

Hesamian, G. (2015). One-way ANOVA based on interval information. International Journal of Systems Science, 47(11), 2682-2690. doi:10.1080/00207721.2015.1014449

Hesse-Biber, S. (2016). Doing interdisciplinary mixed methods health care research: working the boundaries, tensions, and synergistic potential of team-based research. Qualitative Health Research, 26(5), 649-658. doi:10.1177/1049732316634304

Hitchcock, J., & Newman, I. (2013). Applying an interactive quantitative-qualitative framework: How identifying common intent can enhance inquiry. Human Resource Development Review, 12, 36-52. doi:10.1177/153448431242127

Hohwü, L., Lyshol, H., Gissler, M., Jonsson, S. H., Petzold, M., & Obel, C. (2013). Web-based versus traditional paper questionnaires: a mixed-mode survey with a Nordic perspective. Journal of Medical Internet Research, 15(8), e173. doi:10.2196/jmir.2595

Holland-Letz, T., & Kopp-Schneider, A. (2015). Optimal experimental designs for dose–response studies with continuous endpoints. Archives of Toxicology, 89(11), 2059-2068. doi:10.1007/s00204-014-1335-2

Howard, D., Best, W., & Nickels, L. (2014). Optimising the design of intervention studies: Critiques and ways forward. Aphasiology, 29(5), 526-562. doi:10.1080/02687038.2014.985884

Hsieh, H. J., & Kung, S. F. (2013). The linkage analysis of environmental impact of tourism industry. Procedia Environmental Sciences, 17, 658-665. doi:10.1016/j.proenv.2013.02.082

Hultman, M., Skarmeas, D., Oghazi, P., & Beheshti, H. M. (2015). Achieving tourist loyalty through destination personality, satisfaction, and identification. Journal of Business Research, 68(11), 2227-2231. doi:10.1016/j.jbusres.2015.06.002

Karyadi, K. A., Vanderveen, J. D., & Cyders, M. A. (2014). A meta-analysis of the relationship between trait mindfulness and substance use behaviors. Drug and Alcohol Dependence, 143, 1-10. doi:10.1016/j.drugalcdep.2014.07.014

Kaur Mann, A., & Kaur, N. (2016). Survey of Web Database Clustering Techniques. IJSR International Journal of Science and Research, 5(2), 685-688. doi:10.21275/v5i2.nov161214

Kim, K., Sun, J., Jogaratnam, G., & Oh, I. K. (2006). Market segmentation by activity preferences: Validation of cultural festival participants. Event Management, 10(4), 221-229. doi:10.3727/152599507783948666

Kim, K., Uysal, M., & Sirgy, M. J. (2013). How does tourism in a community impact the quality of life of community residents? Tourism Management, 36, 527-540. doi:10.1016/j.tourman.2012.09.005

Kim, S., Sugar, C. A., & Belin, T. R. (2015). Evaluating model‐based imputation methods for missing covariates in regression models with interactions. Statistics in Medicine, 34(11), 1876-1888. doi:10.1002/sim.6435

Khan, W. Z., Xiang, Y., Aalsalem, M. Y., & Arshad, Q. (2013). Mobile phone sensing systems: A survey. IEEE Communications Surveys & Tutorials, 15(1), 402-427. doi:10.1109/surv.2012.031412.00077

Khorsan, R., & Crawford, C. (2014). How to assess the external validity and model validity of therapeutic trials: A conceptual approach to systematic review methodology. Journal of Evidence-Based Complementary and Alternative Medicine, 2014, 1-12. doi:10.1155/2014/694804

Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM: An illustration and recommendations. Journal of the Association for Information Systems, 13, 546–580. Retrieved from http://aisel.aisnet.org/jais/

Kratochwill, T. R., & Levin, J. R. (2014). Meta- and statistical analysis of single-case intervention research data: Quantitative gifts and a wish list. Journal of School

Psychology, 52, 231–235. doi:10.1016/j.jsp.2014.01.003

Kummer, T., Recker, J., & Mendling, J. (2016). Enhancing understandability of process models through cultural-dependent color adjustments. Decision Support Systems, 87, 1-12. doi:10.1016/j.dss.2016.04.004

Laud, P. J., & Dane, A. (2014). Confidence intervals for the difference between independent binomial proportions: comparison using a graphical approach and moving averages. Pharmaceutical Statistics, 13(5), 294-308. doi:10.1002/pst.1631

Le Borgne, F., Giraudeau, B., Querard, A. H., Giral, M., & Foucher, Y. (2015). Comparisons of the performance of different statistical tests for time‐to‐event analysis with confounding factors: practical illustrations in kidney transplantation. Statistics in Medicine. 35(7), 1103–1116. doi:10.1002/sim.6777

Leedy, P. D., & Ormrod, J. E. (2013). Practical research: Planning and design (10th ed.). Upper Saddle River, NJ: Pearson Education.

Leggett, L. E., Khadaroo, R. G., Holroyd-Leduc, J., Lorenzetti, D. L., Hanson, H., Wagg, A., . . . Clement, F. (2016). Measuring resource utilization. Medicine, 95(10), E2759. doi:10.1097/md.0000000000002759

Lenzner, T. (2013). Are readability formulas valid tools for assessing survey question difficulty? Sociological Methods & Research, 43(4), 677-698. doi:10.1177/0049124113513436

Leung, D., Woo, G. J., & Ly, T. P. (2013). The effects of physical and cultural distance on tourist satisfaction: A case study of local-based airlines, public transportation, and government services in Hong Kong. Journal of China Tourism Research, 9(2), 218-242. doi:10.1080/19388160.2013.784572

Li, C. (2015). A test for the linearity of the nonparametric part of a semiparametric logistic regression model. Journal of Applied Statistics, 43(3), 461-475. doi:10.1080/02664763.2015.1070803

Li, S., Scott, N., & Walters, G. (2014). Current and potential methods for measuring emotion in tourism experiences: A review. Current Issues in Tourism, 18(9), 805-827. doi:10.1080/13683500.2014.975679

Li, Y., & Mei, Y. (2016). Effect of bivariate data’s correlation on sequential tests of circular error probability. Journal of Statistical Planning and Inference, 171, 99-114. doi:10.1016/j.jspi.2015.11.001

Liu, C., Horng, J., Chou, S., Chen, Y., Lin, Y., & Zhu, Y. (2015). An empirical examination of the form of relationship between sustainable tourism experiences and satisfaction. Asia Pacific Journal of Tourism Research, 21(7), 717-740. doi:10.1080/10941665.2015.1068196

Luchman, J. N. (2014). Relative importance analysis with multicategory dependent variables: An extension and review of best practices. Organizational Research Methods, 17(4), 452-471. doi:10.1177/1094428114544509

Luft, J., & Shields, M. D. (2014). Subjectivity in developing and validating causal explanations in positivist accounting research. Accounting, Organizations and Society, 39, 550-558. doi:10.1016/j.aos.2013.09.001

McPeake, J., Bateson, M., & O’Neill, A. (2014). Electronic surveys: how to maximise success. Nurse researcher, 21(3), 24-26. doi:10.7748/nr2014.01.21.3.24.e1205

Mehmood, T., & Ahmed, B. (2016). The diversity in the applications of partial least squares: an overview. Journal of Chemometrics, 30(1), 4-17. doi:10.1002/cem.2762

Mello, M. J., Merchant, R. C., & Clark, M. A. (2013). Surveying emergency medicine. Academic Emergency Medicine, 20(4), 409-412. doi:10.1111/acem.12103

Mohammad, B. A. M. A. H., & Som, A. P. M. (2010). An analysis of push and pull travel motivations of foreign tourists to Jordan. International Journal of Business and Management, 5(12), 41. doi:10.5539/ijbm.v5n12p41

Mohamad, M., Ab Ghani, N. I., Mamat, M., & Mamat, I. (2014). Satisfaction as a mediator to the relationships between destination image and loyalty. World Applied Sciences Journal, 30, 1113-1123. Retrieved from http://www.wasj.org/

Montilla, J. M., & Kromrey, J. (2016). Construct Validity of the Scale: Students' Choice Process. Case at the University of Los Andes, Venezuela. Ciencia e Ingenieria, 37(1), 19-27. Retrieved from http://erevistas.saber.ula.ve/cienciaeingenieria

Mosing, M. A., Madison, G., Pedersen, N. L., & Ullén, F. (2015). Investigating cognitive transfer within the framework of music practice: Genetic pleiotropy rather than causality. Developmental Science. 19(3), 504–512. doi:10.1111/desc.12306

Moustakas, C. (1994). Phenomenological research methods. Sage Publications.

Muskat, M., Blackman, D. A., & Muskat, B. (2012). Mixed methods: Combining expert interviews, Cross-impact analysis and scenario development. The Electronic Journal of Business Research Methods, 10(1), 09-21. doi:10.2139/ssrn.2202179

Nimon, K. F., & Oswald, F. L. (2013). Understanding the results of multiple linear regression beyond standardized regression coefficients. Organizational Research Methods, 16(4), 650-674. doi:10.1177/1094428113493929

 Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 460-469. doi:10.2307/3150499

Olya, H. G., & Altinay, L. (2016). Asymmetric modeling of intention to purchase tourism weather insurance and loyalty. Journal of Business Research, 69(8), 2791-2800. doi:10.1016/j.jbusres.2015.11.015

Osborne, J. W., & Waters, E. (2002). Four assumptions of multiple regression that researchers should always test. Practical Assessment, Research & Evaluation, 8(2). Retrieved from http://pareonline.net/

Ott, L., Longnecker, M., & Ott, R. L. (2001). An introduction to statistical methods and data analysis (Vol. 511). Pacific Grove, CA: Duxbury.

Pekár, S., & Brabec, M. (2016). Marginal Models Via GLS: A Convenient Yet Neglected Tool for the Analysis of Correlated Data in the Behavioural Sciences. Ethology, 122(8), 621-631. doi:10.1111/desc.12306

Pericchi, L., & Pereira, C. (2016). Adaptative significance levels using optimal decision rules: Balancing by weighting the error probabilities. Brazilian Journal of Probability and Statistics, 30(1), 70-90. doi:10.1214/14-bjps257

Pires, I. M., Garcia, N. M., Pombo, N., & Flórez-Revuelta, F. (2016). From data acquisition to data fusion: a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices. Sensors, 16(2), 184. doi:10.3390/s16020184

Pratminingsih, S. A., Rudatin, C. L., & Rimenta, T. (2014). Roles of motivation and destination image in predicting tourist revisit intention: A case of Bandung–Indonesia. International Journal of Innovation, Management and Technology, 5(1), 19-24. doi:10.7763/ijimt.2014.v5.479

Prayag, G., Hosany, S., Muskat, B., & Del Chiappa, G. (2015). Understanding the relationships between tourists’ emotional experiences, perceived overall image, satisfaction, and intention to recommend. Journal of Travel Research, doi:10.1177/0047287515620567

Punzo, A., Browne, R. P., & Mcnicholas, P. D. (2016). Hypothesis testing for mixture model selection. Journal of Statistical Computation and Simulation, 86(14), 2797-2818. doi:10.1080/00949655.2015.1131282

Rahimi, R. (2016). Tourism and violence. Tourism Management, 55, 238-239. doi:10.1016/j.tourman.2016.02.018

Rajaratnam, S. D., Munikrishnan, U. T., Sharif, S. P., & Nair, V. (2014). Service quality and previous experience as a moderator in determining tourists’ satisfaction with rural tourism destinations in Malaysia: A partial least squares approach. Procedia-Social and Behavioral Sciences, 144, 203-211. doi:10.1016/j.sbspro.2014.07.288

Rickards, G., Magee, C., & Artino Jr, A. R. (2012). You can't fix by analysis what you've spoiled by design: Developing survey instruments and collecting validity evidence. Journal of Graduate Medical Education, 4, 407–410. doi:10.4300/JGME-D-12-00239.1

Ridderstaat, J., Croes, R., & Nijkamp, P. (2014). Tourism and long‐run economic growth in Aruba. International Journal of Tourism Research, 16, 472-487. doi:10.1002/jtr.1941

Rozin, P., Hormes, J. M., Faith, M. S., & Wansink, B. (2012). Is meat male? A quantitative multimethod framework to establish metaphoric relationships. Journal of Consumer Research, 39, 629-643. doi:10.1086/664970

Rybak, M. E., Sternberg, M. R., & Pfeiffer, C. M. (2013). Sociodemographic and lifestyle variables are compound- and class-specific correlates of urine phytoestrogen concentrations in the U.S. Population. Journal of Nutrition, 143(6), 986S-994S. doi:10.3945/jn.112.172981

Saart, P., Gao, J., & Kim, N. H. (2013). Semiparametric methods in nonlinear time series analysis: A selective review. Journal of Nonparametric Statistics, 26(1), 141-169. doi:10.1080/10485252.2013.840724

Sarais, V., Reschini, M., Busnelli, A., Biancardi, R., Paffoni, A., & Somigliana, E. (2016). Predicting the success of IVF: External validation of the van Loendersloot's model. Human Reproduction, 31(6), 1245-1252. doi:10.1093/humrep/dew069

Schneider, J. W. (2015). Null hypothesis significance tests. A mix-up of two different theories: the basis for widespread confusion and numerous misinterpretations. Scientometrics, 102(1), 411-432. doi:10.1007/s11192-014-1251-5.

Seisonen, S., Vene, K., & Koppel, K. (2016). The current practice in the application of chemometrics for correlation of sensory and gas chromatographic data. Food Chemistry, 210, 530-540. doi:10.1016/j.foodchem.2016.04.134

Sharma, D., & Kibria, B. G. (2013). On some test statistics for testing homogeneity of variances: a comparative study. Journal of Statistical Computation and Simulation, 83(10), 1944-1963. doi:10.1080/00949655.2012.675336

Singh, P., Engel, J., Jansen, J., de Haan, J., & Buydens, L. M. C. (2016). Dissimilarity based Partial Least Squares (DPLS) for genomic prediction from SNPs. BMC Genomics, 17(1), 1. doi:10.1186/s12864-016-2651-0

Skowronek, D., & Duerr, L. (2009). The convenience of nonprobability Survey strategies for small academic libraries. College & Research Libraries News, 70(7), 412-415.

Stokes, M. E., Davis, C. S., & Koch, G. G. (2012). Categorical data analysis using SAS. SAS institute.

 Stylidis, D., Belhassen, Y., & Shani, A. (2014). Three Tales of a City Stakeholders’ Images of Eilat as a Tourist Destination. Journal of Travel Research, 4(6) 702-716. doi: 10.1177/0047287514532373.

Thorarensen, H., Kubiriza, G. K., & Imsland, A. K. (2015). Experimental design and statistical analyses of fish growth studies. Aquaculture, 448, 483-490. doi:10.1016/j.aquaculture.2015.05.018

Tonetto, L. M., & Desmet, P. M. (2016). Why we love or hate our cars: A qualitative approach to the development of a quantitative user experience survey. Applied Ergonomics, 56, 68-74. doi:10.1016/j.apergo.2016.03.008

Turner, T. L., Balmer, D. F., & Coverdale, J. H. (2013). Methodologies and study designs relevant to medical education research. International Review of Psychiatry, 25, 301-310. doi:10.3109/09540261.2013.790310

Urbano, J. (2015). Test collection reliability: A study of bias and robustness to statistical assumptions via stochastic simulation. Information Retrieval Journal, 19(3), 313-350. doi:10.1007/s10791-015-9274-y

U.S. Department of Health and Human Services. (2014). Human subjects research (45 CFR 46). Retrieved from http://www.hhs.gov/ohrp/humansubjects/guidance/belmont.html

Valim, M. D., Marziale, M. H. P., Richart‐Martínez, M., & Sanjuan‐Quiles, Á. (2014). Instruments for evaluating compliance with infection control practices and factors that affect it: an integrative review. Journal of Clinical Nursing, 23(11-12), 1502-1519. doi:10.1111/jocn.12316

Van Vuuren, C., & Slabbert, E. (2012). Travel motivations and behaviour of tourists to a South African resort. Tourism & Management Studies, 13, 457–467. doi:10.1002/jtr.820

Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: Guidelines for conducting mixed methods research in information systems. MIS Quarterly, 37, 21-54. Retrieved from http://www.misq.org

Van Teijlingen, E., & Hundley, V. (2002). The importance of pilot studies. Nursing Standard, 16(40), 33-36. doi:10.7748/ns2002.06.16.40.33.c3214

Walls, J. L., Phan, P. H., & Berrone, P. (2011). Measuring environmental strategy: Construct development, reliability, and validity. Business & Society, 50(1), 71-115. doi:10.1177/0007650310394427

Wallace, S., Clark, M., & White, J. (2012). ‘It's on my iPhone’: attitudes to the use of mobile computing devices in medical education, a mixed-methods study. BMJ Open, 2(4), e001099. doi:10.1136/bmjopen-2012-001099

Wang, J. L., Chiou, J. M., & Müller, H. G. (2016). Functional Data Analysis. Annual Review of Statistics and Its Application, 3, 257-295. doi:10.1146/annurev-statistics-041715-033624

Webster, C., & Ivanov, S. (2014). Transforming competitiveness into economic benefits: Does tourism stimulate economic growth in more competitive destinations? Tourism Management, 40, 137-140. doi:10.1016/j.tourman.2013.06.003

Westerman, M. A. (2012). Conversation analysis and interpretive quantitative research on
psychotherapy process and problematic interpersonal behavior. Theory and
Psychology, 21(2), 155-178. doi:10.1177/0959354310394719

Whang, H., Yong, S., & Ko, E. (2015, in press). Pop culture, destination images, and visit intentions: Theory and research on travel motivations of Chinese and Russian tourists. Journal of Business Research. doi:10.1016/j.jbusres.2015.06.020

Yan, R., & Zhang, L. (2015). Linearity tests under the null hypothesis of a random walk with drift. Statistical Papers, 57(2), 407-418. doi:10.1007/s00362-015-0659-1

Yu-Jia, H. (2012). The moderating effect of brand equity and the mediating effect of marketing strategy on the relationship between service quality and customer loyalty: The case of retail chain stores in Taiwan. International Journal of Organizational Innovation, 5(1), 155-162. Retrieved from http://ijoi-online.org

Yüzbaşıoğlu, N., Topsakal, Y., & Çelik, P. (2014). Roles of Tourism Enterprises on Destination Sustainability: Case of Antalya, Turkey. Procedia-Social and Behavioral Sciences, 150, 968-976. doi:10.1016/j.sbspro.2014.09.109

Zandvanian, A., & Daryapoor, E. (2013). Mixed methods research: A new paradigm in educational research. Journal of Education and Management Studies, 3(4), 525-31. Retrieved from http://jems.science-line.com/

 Zehrer, A., & Hallmann, K. (2015). A stakeholder perspective on policy indicators of destination competitiveness. Journal of Destination Marketing & Management, 4(2), 120–126. doi:10.1016/j.jdmm.2015.03.003

Zhang, D., Yan, M., Niu, Y., Liu, X., van Zwieten, L., Chen, D., ... & Zheng, J. (2016). Is current biochar research addressing global soil constraints for sustainable agriculture?. Agriculture, Ecosystems & Environment, 226, 25-32. doi:10.1016/j.agee.2016.04.010

Zhang, H., Xiaoxiao, F., Liping, A. C., & Lin, L. (2014). Destination image and tourist loyalty: a meta-analysis. Tourism Management, 40, 213-223. doi:10.1016/j.tourman.2013.06.006

Zvoch, K. (2014). Modern Quantitative Methods for Evaluation Science Recommendations for Essential Methodological Texts. American Journal of Evaluation, 35(3), 430-440. doi:10.1177/1098214013514128

 

 

 

 

 

 

 

 

Chapter 7 of the Publication Manual of the American Psychological Association, sixth edition, includes numerous examples of reference list entries. For more information on references or APA style, consult the APA website or the Walden Writing Center website.

Appendix A: National Institutes of Health Training Certificate

 

 

 

 

 

 

 

 

 

 

 

 

 

Appendix B: Implied Consent Form

Motives to Travel, Destination Image, and British Virgin Islands Tourists’ Satisfaction

 

You are invited to take part in a research study about whether a relationship exists between destination image, push and pull motives to travel, and BVI tourists’ satisfaction. The researcher is inviting non-citizen or non-resident visitors to the British Virgin Islands, who are at least 18 years of age. This part of a process for “implied consent” to provide you information about the study and what your participation will entail.

 

This study is being conducted by a researcher named Sherrine Augustine, who is a doctoral student at Walden University, is conducting a correlation study.

 

Background Information:

The purpose of this study is to examine if a relationship exists between destination image, push and pull motives to travel, and tourists’ satisfaction.

  

Procedures:

If you agree to be in this study, you will be asked to:

·         The study is expected to take no more than 5-7 minutes to complete the survey;

·         Be sure to complete the survey after your travel experience to the BVI is complete.

 

Here are some sample questions:

 

On a scale from 1 to 5 (one being “Not at all satisfied” and seven “Extremely satisfied” please rate how you feel about the following statements by an indication of a tick (ü) to each statement.

 

1.        How would you describe the image that you have of that destination before the experience?

2.        How would you describe your overall satisfaction with your stay in that destination?

 

Voluntary Nature of the Study:

This study is voluntary. Everyone will respect your decision of whether or not you choose to be in the study. No one will treat you differently if you decide not to be in the study. If you decide to participate in the study, you can withdraw from the study at any time, either by not completing or by not turning the survey into a lock box at any departing port of departure.

 

Risks and Benefits of Being in the Study:

Being in this type of study involves some risk of the minor discomforts that can be encountered in daily life, such as stress or becoming upset. Being in this study would not pose risk to your safety or wellbeing. The study could potentially benefit the British Virgin Islands tourism industry by providing information that may improve tourist satisfaction among its visitors.

 

Payment:

While there is no compensation for your participation, I, as well as the tourism industry, will be grateful for your selflessness and decision to participate in this short survey.

 

Privacy:

Any information you provide will be kept anonymous. The researcher will not use your personal information for any purposes outside of this research project. Also, the researcher will not include your name or anything else that could identify you in the study reports. Data will be kept secure in a locked cabinet that will only be accessible to the researcher. Data will be kept for a period of at least 5 years, as required by the university. After 5 years all data collected will be shredded.

 

Contacts and Questions:

You may ask any questions you have now. Or if you have questions later, you may contact the researcher via sherrine.augustine@waldenu.edu or 1-284-441-0407. If you want to talk privately about your rights as a participant, you can call Dr. Leilani Endicott at 612-312-1210 or 1-800-925-3368 extension 3121210. She is the Walden University representative who can discuss this with you. Walden University’s approval number for this study is IRB will enter approval number here and it expires on IRB will enter expiration date.

 

Please keep this consent form for your records.

 

Statement of Implied Consent

 

I have read the above and I feel I understand the study well enough to make a decision about my involvement. By returning the survey into a lock box at any departing port of departure, I understand

That I am agreeing to the terms describe above.

 

 

Regards,

 

 

 

Sherrine Augustine

Appendix C: Survey Questions

Make a selection to the following statements by an indication of a tick (ü) to each statement

1.        Gender

Male	o
Female	o

2.        Purpose of Visit

Vacation	o
Business	o
Seeking Work	o
Other	o

3.        Which island will you be visiting?

Tortola	o
Virgin Gorda	o
Anegada	o
Jost Van Dkye	o
Other	o

4.        You arrived to the BVI by

Air	o
Private Air	o
Cruise Ship	o
Ferry	o
Private Charter	o

5.        Nationality ___________________________

6.        Has you been to the BVI before?

Yes	o
No	o

7.        Which category best describes your household income?

Less than $20,000	o
$20,000-$39.999	o
$40,000-$59,000	o
$60,000-$79,999	o
$80,000-$99.999	o
$100,000-$149,999	o
$150,000-$199,999	o
Above $200,000	o

 

Please indicate your level of satisfaction with the following statement regarding your travel experience in the BVI (choose the response that most closely applies to your level of satisfaction):

 

8.        Destination image.  How would you describe the image that you have of that destination before the experience?

Not at all satisfied	Slightly Satisfied	Unsure	Very Satisfied	Extremely Satisfied
o	o	o	o	o

9.        Push Motives of motivation to travel.                                

               

		Not at all satisfied		Slightly Satisfied		Unsure		Very Satisfied		Extremely Satisfied
Knowledge
Learning new things or increasing knowledge
Experiencing new and different lifestyle
Seeing as much as possible
Seeing and experiencing a foreign destination
Travelling to historical places
Sight seeing
Sightseeing Variety
To fulfill my dream of visiting a foreign land/country
To sightsee touristic spots
To explore cultural resources
Adventure
Finding thrill or excitement
Having fun or being entertained
Being darling and adventuresome being free to act the way I feel
Reliving past good times
Relax
Doing nothing at all
Getaway from demand of home
Change from busy jobs
Escaping from the ordinary
Lifestyles
Experiencing simple lifestyle
Rediscovering myself Travel bragging
Talking about a trip after returning home Indulging in luxury
Going places friends have not been
Family
Visiting friends or relatives
Family togetherness
Visit places family came from home
Feeling a home away from home

Pull Motives of motivation to travel.

 

	Not at all satisfied	Slightly Satisfied	Unsure	Very Satisfied	Extremely Satisfied
Event and activities
Activities for entire family
Festivals and event
Sightseeing Variety
To fulfill my dream of visiting a foreign land/country
To sightsee touristic spots
To explore cultural resources
Easy Access and affordable
Affordable tourist destination Safe destination
Value of money
History and culture
National Park Culture and traditions
Outstanding scenery
Variety seeking
Traditional food Outdoor activities Exotic atmosphere
Adventure
Weather/climate
Natural resources
Natural reserves Beautiful beaches

 

10.     Tourist satisfaction.  How would you describe your overall satisfaction with your stay in that destination?

Not at all satisfied	Slightly Satisfied	Unsure	Very Satisfied	Extremely Satisfied
o	o	o	o	o