Consumer Purchasing in Sustainable Tourism: Attraction Sustainability and Its Impact on Decision-Making by Heather L. Rubright August, 2014 Director of Thesis: Dr. Carol Kline, PhD Major Department: Sustainable Tourism The green movement has generated an increase in research on consumer behavior towards green products and services. The purpose of this study was to explore the factors that influence consumers to choose sustainable attractions and to develop a better understanding of whether the sustainable features of an attraction impact sustainable attraction selection by visitors. The results show that the environmental practices of an attraction were not as important to visitors as other factors such as reputation, price, and the activities at the site. The results also demonstrate that when selecting among green factors, certification of a site and eco-furnishings play the largest role in determining the likelihood of visitation to a sustainable attraction. Consumer Purchasing in Sustainable Tourism: Attraction Sustainability and Its Impact on Decision-Making A Thesis Presented to the Faculty of the Department of the Graduate School East Carolina University In Partial Fulfillment of the Requirements for the Degree M.S Sustainable Tourism By Heather L. Rubright August, 2014 ©Copyright by Heather L. Rubright 2014 All Rights Reserved ACKNOWLEDGEMENTS First I would like to thank my committee for working with me on this project. You have all provided much appreciated guidance, advice, and support. Mr. Naar, one of our first conversations facilitated progression of these research ideas in my head and your help securing participating attractions was invaluable. Dr. Viren, thank you for your continuous encouraging words and the assistance you provided in terms of suggestions, ideas, and additional literature. Dr. Oliver, your independent study course reaffirmed my interest in consumer behavior and sustainability marketing during a crucial time in my thesis process. Thank you for the thoughts, conversations, and articles, which so conveniently related to both the course as well as my thesis. Dr. Kline, I have so much to thank you for. This would not have come to fruition without your guidance, insight, attention to detail, dedication, drive, flexibility, and pure desire to produce great work. Thank you for being my chair, professor, mentor, and friend through this process. Dr. Long, thank you for this program. It has been amazing and it has saved me from an unhappy dead end career path. I have loved the content of the program and the places it can take all who are involved. Last, but certainly not least, I would like to thank my family. Thank you to my parents, who provided vital support and encouragement to follow my dreams. And to my husband, Derek, you once again have been my rock. Thank you for your support, love, and encouragement through this process. It has been stressful, difficult, and chaotic to say the least but without you, it would not have been possible. Thank you for believing in me, my abilities, and my dreams. TABLE OF CONTENTS LIST OF TABLES…………………………………………………………………………………………………………..viii LIST OF FIGURES…………………………………………………………………………………………………………….x CHAPTER 1: INTRODUCTION………………………………………………………………………………………….1 Green consumerism……………………………………………………………………………………………..2 Sustainable tourism……………………………………………………………………………………………..2 Sustainable attractions…………………………………………………………………………………………3 Perceived consumer effectiveness………………………………………………………………………...5 Research questions………………………………………………………………………………………………6 CHAPTER 2: LITERATURE REVIEW…………………………………………………………………………………7 Green consumerism……………………………………………………………………………………………..7 Sustainable tourism……………………………………………………………………………………………..8 Everyday green products versus travel decisions………………………………………………...12 Certification programs……………………………………………………………………………………….16 Hotel, restaurant, and attraction research…………………………………………………………...17 Consumer behavior motivators…………………………………………………………………………..20 Rational…………………………………………………………………………………………………..21 Sociological……………………………………………………………………………………………..21 Psychological…………………………………………………………………………………………..22 Perceived consumer effectiveness……………………………………………………………25 CHAPTER 3: METHODS…………………………………………………………………………………………………29 Sample………………………………………………………………………………………………………………29 Survey design and distribution…………………………………………………………………………...31 Analysis……………………………………………………………………………………………………………..41 CHAPTER 4: RESULTS…………………………………………………………………………………………………...43 Descriptive results……………………………………………………………………………………………..43 Test results………………………………………………………………………………………………………..52 Results summary………………………………………………………………………………………………..81 CHAPTER 5: DISCUSSION……........................................................................................................................ ....84 Implications of test results………………………………………………………………………………….84 Practical implications…………………………………………………………………………………………89 Limitations, academic implications, and future research……………………………………...91 Conclusion…………………………………………………………………………………………………………94 REFERENCES………………………………………………………………………………………………………………..96 APPENDICIES Appendix A: Initial contact email to sites…………………………………………………………...102 Appendix B: Participant solicitations….……………………………………………………………..104 Appendix C: Sustainable Attraction Survey..………………………………………………………105 Appendix D: ECU UMC IRB approval letter.………………………………………………………..115 LIST OF TABLES Table 3.1 Attraction facts………………………………………………………………………………………………30 Table 3.2 General attraction selection survey questions…………………………………………………34 Table 3.3 Sustainable attraction features survey questions…………………………………………….36 Table 3.4 PCE survey questions…………………………………………………………………………………….38 Table 3.5 Green purchasing survey questions………………………………………………………………..39 Table 3.6 Green travel purchasing survey questions………………………………………………………40 Table 3.7 Survey solicitation schedule……………………………………………………………………………41 Table 3.8 Analysis table………………………………………………………………………………………………...42 Table 4.1 Socio-demographic summary of respondents………………………………………………….43 Table 4.2 Site distribution……………………………………………………………………………………………..44 Table 4.3. Influential factors for attraction selection……………………………………………………….45 Table 4.4 Resources utilized in planning visit to attraction……………………………………………..45 Table 4.5 NC GreenTravel……………………………………………………………………………………………..46 Table 4.6 Non-green and green factors influencing attraction selection…………………………..46 Table 4.7 Perceived consumer effectiveness…………………………………………………………………..49 Table 4.8 Green behaviors…………………………………………………………………………………………….50 Table 4.9 Collinearity statistics for the nine non-green independent variables………………..53 Table 4.10 Skew and kurtosis of nine non-green variables……………………………………………..56 Table 4.11 Bivariate correlations of non-green independent variables with CSA……………..57 Table 4.12 Summary of multiple regression for variables predicting CSA………………………..58 Table 4.13 Collinearity statistics for the fifteen green independent variables…………………..59 Table 4.14 Skew and kurtosis of fifteen green variables………………………………………………….61 Table 4.15 Bivariate correlations of green factors with CSA……………………………………………62 Table 4.16 Summary of multiple regression for variables CSA………………………………………...63 Table 4.17 Summary of revised multiple regression for variables predicting CSA…………….64 Table 4.18 Variance CSA explained by each independent variable within a multiple regression analysis……………………………………………………………………………………………………….65 Table 4.19 Bivariate correlations of green factors with ICV…………………………………………….68 Table 4.20 Summary of multiple regression for variables predicting ICV…………………………69 Table 4.21 Summary of revised multiple regression for variables predicting ICV……………..70 Table 4.22 Variance ICV explained by each independent variable within a multiple regression analysis……………………………………………………………………………………………………….71 Table 4.23 Collinearity statistics for the eight PCE independent variables……………………….72 Table 4.24 Skew and kurtosis of eight PCE variables………………………………………………………74 Table 4.25 Bivariate correlations of independent variables with dependent variables……..75 Table 4.26 Summary of multiple regression for variables predicting CSA………………………..77 Table 4.27 Summary of revised multiple regression for variables predicting CSA……………78 Table 4.28 Variance of CSA explained by each independent variable within a multiple regression analysis……………………………………………………………………………………………………….79 Table 4.29 Pearson correlations for green purchase independent variables…………………….80 Table 4.30 Pearson correlations for green travel purchase independent variables…………..81 LIST OF FIGURES Figure 3.1 Map of attraction locations……………………………………………………………………………31 Figure 4.1 Likelihood of increased visitation due to sustainable practices……………………….48 Figure 4.2 Likelihood of choosing sustainable attraction………………………………………………...48 Figure 4.3 Histogram of dependent variable CSA……………………………………………………………54 Figure 4.4 Normal P-P Plot of regression standardized residual for CSA………………………….55 Figure 4.5 Scatterplot of standardized residuals for CSA…………………………………………………56 Figure 4.6 Histogram of dependent variable CSA……………………………………………………………60 Figure 4.7 Normal P-P plot of regression standardized residual for CSA………………………….60 Figure 4.8 Scatterplot of standardized residuals for CSA…………………………………………………61 Figure 4.9 Histogram of dependent variable ICV…………………………………………………………….66 Figure 4.10 Normal P-P plot of regression standardized residual for ICV………………………...67 Figure 4.11 Scatterplot of standardized residuals for ICV………………………………………………..68 Figure 4.12 Histogram of dependent variable CSA………………………………………………………….73 Figure 4.13 Normal P-P plot of regression standardized residual……………………………………73 Figure 4.14 Scatterplot of standardized residuals…………………………………………………………..74 Chapter 1: Introduction The study of consumer behavior is an integral component in the development and marketing of many products and services. Their success is dependent on the consumer’s decision to purchase, including in the case of sustainable or green products and services. Sustainable tourism consists of various sectors including but not limited to accommodations, dining, transportation, retail, visitor information, tour operators, and attractions. There has been a fair amount of research conducted on green consumer behavior and motivations in regards to hotels and the sustainable aspects of restaurants. In contrast, consumer selection of attractions, including the potential influence of sustainability features, has not been researched to the same extent. Understanding consumer choice in regards to attractions is critical because their choice of destination is frequently based upon the attractions. For example, Chan and Baum (2007) found that ecotourists were primarily attracted to a certain destination because of its attributes, including natural attractions, wildlife, local lifestyle, and eco-activities. According to Weaver (2006, p. 92), attractions “influence the type, location and volume of tourist activity in a destination.” While many different criteria affect a consumer’s decision, it is important to understand whether, and to what degree, visitors consider sustainable features of attractions during their purchasing decision. Perceived consumer effectiveness, or the perceived level of impact that one’s actions has upon a particular situation, is one possible concept to consider when exploring this topic, and will be the theoretical foundation in the current study. 2 Green consumerism Both consumers’ attitudes and behaviors have shifted due to increased awareness of growing global environmental concerns (Lee, Han, & Willson, 2011). The development and progression of the green consumer movement has prompted a surging interest in the topic of consumer behavior. One reason for this is the long-term implications for businesses as consumers increasingly encourage companies to adopt sustainable approaches (Dembkowski & Hanmer-Lloyd, 1994). The “green movement” has additionally created a variety of new and innovative products and services that consider the environmental implications involved. Green practices, green behaviors, and green consumption can be thought of as actions that individuals choose to incorporate into their lives, regularly or sporadically, that benefit the environment or that have a less negative impact than an alternative option. As consumers have the ability to shape and influence product options with their purchasing power, understanding their motivations and decision-making processes of green purchasing is a critical endeavor. As the range and variety of available green products has expanded over time, services with a green focus have begun to emerge in the marketplace. This expansion eventually evolved to include the products and services associated with the tourism industry. Sustainable tourism The concept of sustainable tourism emerged in the early 1990s as a result of the broader idea of ‘sustainable development’, which can be defined as ‘development that meets the needs of the present without compromising the ability of future generations to meet their 3 own needs’ (World Commission on Environment and Development, 1987, p. 43). Sustainable tourism follows the same premise, and considers the triple bottom line, which includes economic, social, and environmental performance measures. As the product and service offerings in the tourism industry evolved to incorporate sustainable features, green consumers began to notice and patronize those establishments. For example, Foster, Sampson, and Dunn (2000) found that out of the six service firms they studied, four of which were tourism related, one of the main drivers of environmental action taken by the companies was found to be environmentally conscious consumers. In each case, consumer demand had some amount of influence on the companies’ environmental decisions (Foster et al., 2000). In terms of actions that tourism sectors can and have taken, hotels can now be LEED certified, restaurants use more local and organic food products, and attractions are also integrating green practices such as energy and water efficiency and waste reduction, with the goals of attracting sustainability-minded tourists. Sustainable attractions Similar to the concept of sustainable tourism, a sustainable attraction would be one that incorporates certain practices and programs with the goal of reducing the negative impacts on the environment, promoting the local economy, and preserving the cultural aspects of a destination or region. Attractions that incorporate sustainable or green features may or may not be taking action with the specific intention of being sustainable. The primary goal in some cases may be for other reasons such as financial or regulation driven. Furthermore, the reasons an organization implements certain green practices can generate confusion for consumers. A consumer or tourist may believe an attraction is adopting environmentally 4 friendly practices with the primary objective of benefiting the environment or community, when in fact that is only a secondary advantage. As a result, consumer perceptions about the green practices of a site may be distorted unless organizational intentions are transparent and accurately conveyed to the public. While consumer behavior related to hotels and restaurants has been widely studied (Choi, Parsa, Sigala, &Putrevu, 2009; Han, Hsu, & Lee 2009; Han & Kim, 2010; Kim, Kim, & Goh, 2011; Kim, Njite, & Hancer, 2013; Lee, Han, & Willson, 2011; Tsai & Tsai, 2008), consumer “purchasing” behavior at attractions have been researched less so. However, attractions are a vital component for destinations wishing to appeal to travelers and draw them to their location, often being the main draw for many tourists to a specific destination. It has been stated that attractions are the part of the tourism system that are “most intimately connected to the destination and its identity as a location for tourist activity” (Weaver, 206, p. 92). When tourists travel, shelter and food are obvious necessities, however attractions are not. Nevertheless, it is often the attractions that are the reason for the tourism in the first place. Out of the $1.4 trillion generated by the travel and tourism industry in 2011, 10% of that, or roughly $140 billion was from recreation and attractions (SelectUSA, n.d., para. 1). Attractions are also an important research topic in sustainable tourism because of their potential to impact the triple bottom line. Lastly, in terms of consumer consumption, in order to become a more sustainable society, overall understanding of how and why consumers choose attractions as a product, and whether sustainability features play a role, is an important piece of the puzzle. Attractions can be defined as the main motivation for leisure travel and consist of both natural and developed sites (Goeldner & Ritchie, 2011). This tourism sector includes 5 cultural attractions, natural attractions, events, recreation, and entertainment attractions (Goeldner & Ritchie, 2011). Attractions vary in terms of ownership, and exist in the public, private, and non-profit sectors of the economy. All of the stakeholders involved however, will be better suited to develop and improve desirable qualities of attractions, including green features, once tourist motivations can be identified. This could in turn increase profits and drive competitive advantage by appealing to a broader and better-understood market. For example, PGAV Destination Consulting (2008) reported that 70% of attraction visitors are more likely to visit attractions that pursue green practices rather than those that continue business as usual. Furthermore, 30% of attraction visitors have already made the choice to visit a green attraction (PGAV Destination Consulting, 2008). With information such as this, all forms of attractions, as well as tourism marketers, can better appeal and cater to the consumer’s preferences while also addressing sustainability issues. Perceived consumer effectiveness When exploring consumer behavior and decision-making, there are a variety of possible theoretical foundations to consider. As research has shown there to be a positive correlation between environmental concern and environmentally-friendly behavior (Straughan & Roberts, 1999; Kim & Han, 2010), one applicable theoretical option is perceived consumer effectiveness (PCE). This theory suggests that an individual is more likely to engage in certain behaviors if he/she believes that those particular actions will have a beneficial social or environmental impact (Belz & Peattie, 2009). PCE has been applied to varying types of products and behaviors including but not limited to pollution abatement (Kinnear, Taylor, and Ahmed, 1974), a sustainable food product (Vermeir & 6 Verbeke, 2006), and various sustainability related activities (McDonald & Oates, 2006; Ellen, Wiener, & Cobb-Walgren, 1991). Research questions The purpose of this research was to explore the rationale behind consumer selection of attractions, whether sustainability factors played a role, and whether there are correlations between an individual’s green purchase behaviors and their selection of sustainable attractions. The following specific questions were explored: 1) What factors influence an individual to select an attraction to visit? 2) How much of an impact do the sustainable features of an attraction have on the selection of that attraction? 3) How much does perceived consumer effectiveness explain the selection of sustainable attractions? 4) Is there a correlation between everyday green purchases and the selection of sustainable attractions? 5) Is there a correlation between green travel purchases and the selection of sustainable attractions? 7 Chapter 2: Literature Review Green consumerism A clear concept of green consumption first appeared in the 1970s as “societal marketing” and later expanded to include environmental issues (Peattie, 2010). During the 1980s, ecological concerns among the public increased due to large-scale disasters (e.g. Exxon Valdez oil spill, the nuclear meltdown of Chernobyl) and increasing evidence of environmental degradation (Peattie, 2010), which further led to changes in consumer behavior towards eco-friendly businesses (Han & Kim, 2010). The exact origins of green consumerism are difficult to precisely determine, however it became popular in both the academic and practitioner literature in the 1990s. Since the early 1990s, “more than 75% of the population used environmental criteria regularly in their purchasing decisions” (Choi, Parsa, Sigala, & Putrevu, 2009, p. 98). Additionally, public opinion polls have found that consumers will select a green product over a more traditional, less environmentally friendly product if all else is equal (Ginsberg & Bloom, 2004). American consumers have also stated that they have purchased a product solely due to the fact that the product advertising or label suggested it was environmentally safe or biodegradable (Ginsberg & Bloom, 2004). This evidence indicates an increasing need to understand the green consumer movement for the benefit of marketers, businesses, consumers, and society in general. There has also been a push for consumers to become the motivating force for transformation. Since the late 1980s public policy’s position towards social and environmental issues has started to shift towards the individual citizen and consumer to be the socio-environmental change agent (Barr & Prillwitz, 2012). In fact, in the United 8 Kingdom’s most recent sustainable development strategy the first chapter is titled “Helping People Make Better Choices,” solidifying the idea that consumer choice is vital in regards to environmental sustainability (Barr & Prillwitz, 2012). Leaders in the marketing field have studied consumer decision-making since the 1950s in regards to tangible, manufactured products, which provided the earliest models for decision-making in tourism for service products (Sirakaya & Woodside, 2005). In the late 1980s and early 1990s, service markets such as tourism started to be included in green marketing and the green movement (Belz & Peattie, 2009). As a result, there has been a rise in research regarding consumer decision-making in tourism, as well as the emerging field of sustainable tourism. Sustainable tourism Before aspects of sustainable tourism consumer decision-making can be discussed, it is important to recognize the fact that consumers may not be able to make sustainable travel choices if they are not familiar with the concepts of environmental sustainability and sustainable tourism. PGAV Destination Consulting (2008) found that the public believes environmental sustainability encompasses air and water quality, alternative energy sources, environmentally friendly cleaning products and natural insecticides. In this study, the public did not perceive environmentally sustainable to include climate change or global warming (PGAV Destination Consulting, 2008). Similarly, recent research by the European Commission showed that two thirds of consumers find it difficult to understand which products are better for the environment (Roth, 2011). In regards to travel, Miller, Rathouse, Scarles, Holmes, and Tribe (2010) found that although individuals would describe 9 themselves as concerned about environmental issues, there was still confusion as to how tourism related to the environment. Additionally, even if consumers are aware of the relationship between the environment and tourism there may still be a lack of engagement and action. Hjalager (2000) found that when respondents were asked about the importance of environmental issues for travel choices, there was a tendency for responses to be politically correct. It is still unknown whether the respondent will investigate environmental standards of a travel product before actually engaging in the decision- making process (Hjalager, 2000), and therefore, it is unclear whether awareness results in action. However, in order to promote sustainability and encourage more pro- environmental behavior, an increase in awareness and education among tourism consumers is important (Miller et al., 2010). For example, Han and Kim (2010) noted that the public’s increasing environmental concern is motivating environmentally responsible management, at least in the hotel industry. Furthermore, in regards to the phrase ‘sustainable tourism’, the Global Sustainable Tourism Council (GSTC) suggests that sustainable tourism is a term and concept unclear to consumers, partly because the industry has not defined it well (“Travel Forever”, n.d., slide 6), and partly because the industry uses multiple terms such as sustainable tourism, responsible tourism, ecotourism, and green tourism (“Travel Forever”, n.d., slide 6). For all of these reasons, it is understandable that there is confusion for consumers when selecting sustainable travel choices. To compound matters, there is disagreement amongst and within academia and the marketing industry as to the appropriate target audiences for sustainable products, and consequently segmentation strategies. Pomering, Noble, and Johnson (2011) suggest that 10 tourism and sustainable tourism should not be considered separately, as all forms of tourism need to move towards more sustainable outcomes. Furthermore, Peattie (1999) proposes focusing on the purchase rather than the consumer as an alternative to conventional market segmentation, and McDonald and Oates (2006) similarly state that it may be beneficial to “focus on consumers’ perceptions of green issues rather than their identifiable characteristics.” Results from a study done by McDonald, Oates, Thyne, Alevizou, and McMorland (2009, p. 143) help to give weight to Peattie’s previous work that “there is no such thing as a green consumer.” The authors suggest one reason for this is because of inconsistencies in individual purchasing behaviors (McDonald et al., 2009). They also state that there is just as much debate about the concept of green tourists as there is about green consumers (McDonald et al., 2009). This discussion may not necessarily indicate an opposition to segmentation, but rather supports the opinion that a psychographic segmentation approach may be more effective than a traditional demographic approach for understanding green consumer behavior (Straughan & Roberts, 1999). Conversely, there are others who believe that the green traveler must be defined. This is a reasonable assertion for certain research and marketing purposes. For example, the CMIGreen Annual Green Traveler Survey Report only utilizes results obtained from green travelers. This segment is comprised of respondents who consider themselves to be very or extremely eco-conscious and who took at least one overnight vacation in the past year (Roth, 2011). CMIGreen, a green tourism marketing research organization, does acknowledge, however, that a green traveler can range anywhere from an upscale tourist who desires a comfortable but green hotel to a self-sufficient eco-adventurer (Roth, 2011). 11 An additional example of consumer segmentation involves the United Kingdom where there has been a focus on broad segmentation of the UK population in order to classify individuals in terms of their “sustainable lifestyles” (Barr & Prillwitz, 2012). The authors assert that this segmentation helps in the understanding and promotion of behavior change in regards to environmental action (Barr & Prillwitz, 2012). Another instance of the segmentation of green consumers is the typology created by McDonald, Oates, Alevizou, Young, and Hwang (2012), in which they found three categories of green consumers; Translators, Exceptors, and Selectors. These groups possess varying degrees of green, which translate to differing approaches of green consumption (McDonald et al., 2012). The Translators are partially green. They are motivated by a sense of doing what they believe is the right thing in each situation, not holistically, and they are open to change. The Exceptors are the greenest group in the typology. Sustainability is a priority in every facet of their lives, they are change seekers, and they have the “most sophisticated understanding of sustainability” (McDonald et. al., 2012, p. 454). The largest group in the typology is the Selectors. They are green in one aspect of their lives but in all other areas they are grey. Only one issue motivates them, and they do not focus on sustainability holistically (McDonald et al., 2012). The act of segmenting travelers according to their green consumption behaviors is beneficial so that their travel activities and patterns can be tracked. Unfortunately, one drawback is that the increasing complexity of segmentation and typology of green consumers could potentially make those same consumers more difficult to influence using traditional promotional tools and methods (McDonald et al., 2012). This is a valid and 12 beneficial dialogue to have as understanding consumer decision making would be difficult without defining who the consumer actually is. Everyday green products versus travel decisions The issue of whether consumers make travel decisions in the same way that they make other consumer choices is a relevant matter. In regards to green consumerism in general, Moisander (2007) suggests that environmentally friendly consumption is highly complex intellectually, morally, and in practice. However, there is disagreement on exactly what qualifies as behavior that protects the environment (Moisander, 2007). Further, Moisander (2007, p. 408) suggests that the focus of attention on the individual consumer should be shifted towards “whole communities of consumers instead”. This means that people would need to be studied, addressed, and targeted as members of the communities, groups, and organizations they belong to (Moisander, 2007) rather than as sole individuals. This concept would have significant implications for marketing if it were to be applied to sustainable tourism. There is some evidence to show that the correlation between everyday green consumer choices and green traveler choices may be positive (Roth, 2011). Sharpley (2006) noted that tourists are becoming ‘greener’ as they are demanding more environmentally appropriate tourism experiences. The 2010-2011 report from CMIGreen, showed that respondents were more committed to sustainability both at home and on the road from the previous year (Roth, 2011). Additionally, since 2009, 5% more of the respondents “acted on their environmental concerns while traveling” and specific green travel practices were also up from the previous year (Roth, 2011, p. 6). Furthermore, there 13 was a 7.5% increase in the number of respondents who said they “researched and booked greener accommodations”, and a 4-5% increase each in those who ate local cuisine and traveled by local transportation (Roth, 2011, p. 6). Another example of evidence that shows a positive correlation between everyday green consumer choices and green traveler choice was a study on done by Bergin-Seers and Mair in 2009. Miller’s Green Consumer Scorecard was used, and it was found that tourists who were considered to be more green at home were also more likely to change their travel plans or patterns in order to use fuel efficient flights, chose environmentally friendly accommodations, or make a donation to conserve the environment (Bergin-Seers & Mair, 2009). Conversely there is research that indicates that green purchase decisions and the relationship to green travel purchases is a much more complicated issue. For example, while it has been found that consumers often are willing to buy green products, they consider the price, appearance, and functionality before assessing the environmental status of that product (Tsai & Tsai, 2008; Firth & Hing, 1999). In their 2008 study, Tsai and Tsai found that the Taiwanese say they are very environmentally conscious, however very few turn their words into action. In regards to hotels, they found that the promotion of environmental conservation was not a primary concern and consumers seemed to exhibit opposite green consumption behaviors (Tsai & Tsai, 2008). It is suggested that one reason for this may be that many individuals have high expectations for the service quality of hotels and that “luxuries not directly associated with daily life are demanded by consumers” (Tsai & Tsai, 2008, p. 303). Similarly, McDonald et al. (2009) found that although sustainability criteria was discussed during the decision-making process regarding airline flights, it was often compromised in favor of other aspects, such as 14 journey time, price, and convenience. One consideration is that consumers often perceive green products to be of lower quality than their traditional counterparts or may not even effectively deliver their environmental guarantees (Ginsberg & Bloom, 2004). This is despite previously mentioned public opinion polls, which show that consumers prefer green products over less environmentally friendly ones when all else is equal (Ginsberg & Bloom, 2004). These studies imply that sustainable tourism products and services may need to be evaluated based on specific factors and in categories separate from everyday consumer products when considering consumer decision-making. This is also supported by the fact that McDonald et al. (2009) concluded that green values are not translated into purchases similarly across different product and service sectors. It is important to note however that there is some consistency in purchase decisions within sectors (McDonald et al., 2009) Peattie (2010) suggested that there are several possible reasons for these gaps between attitude and behavior in the research; the studies could be over-reporting the strength of an individual’s environmental attitude or intentions or there may be a gap due to the fact that much of the research relies on self-reporting, which may also be overstated. It may also be necessary to draw further distinctions between everyday green consumers and sustainable travelers. As opposed to other types of consumption, visitors are isolated from any negative impacts at the destination level and common sense and codes of conduct are voluntary (Peattie, 2010). Also, the consumer movement for everyday green products has not had many similarities to the green or sustainable tourism movement. Hjalager (2000) suggests several reasons there has been an absence of a green consumer movement in tourism. First, there is no concise definition of a sustainable tourism product. Second, the 15 purchase of travel related products are not frequent or repetitive. Third, there are not well- defined certifications to identify tourist products with environmental characteristics. Lastly, Hjalager states the consumption styles are vague and therefore they do not set clear standards. Furthermore, some individuals have been found to believe that small everyday actions in their homes could have a greater impact than any changes to tourism behavior, which may correspond to the frequency of those actions (Miller et al., 2010). Also interesting is the fact that the individuals who recognized the impacts of tourism felt as though they earned the right to travel because they took other pro-environmental actions throughout the rest of the year (Miller et al., 2010). This may be another area for future research that involves the link between perceived travel rights and the resulting personal responsibilities attached to those rights. Another aspect of decision-making to consider is that the type of involvement and level of decision-making can be different for varied consumer purchases (Sirakaya & Woodside, 2005). For example, most tourism purchases can be considered high involvement and are extensive in nature due to the monetary and non-monetary costs involved in the decision-making process (Sirakaya & Woodside, 2005), whereas purchasing a salad dressing would be considered with lower involvement and risk on the part of the consumer. In another example, Thorgerson, Jorgensen, and Sandager (2012, p. 194) found that consumers in their study used very little time and effort on decision making when buying milk, an “everyday repeat-purchase product,” even when the product had a green attribute. There has however been research to show that interest and involvement in environmentally friendly product information does translate to purchase intentions for 16 higher involvement products, such as a hybrid vehicle (Oliver & Lee, 2010), which bodes well for high involvement tourism decisions. Moreover, the consideration of the environment in consumer decision-making is only one third of the whole that should be taken into account in regards to sustainable choices. In terms of sustainable tourism, even though the majority of the discussion has focused on the environment, the effects of tourism on sociocultural values have been previously recognized and impacts on the environment can be linked to impacts on communities (Pomering et al., 2011). Responsible tourists not only place an emphasis on environmental concerns but also “a desire to show respect for local communities, and to share the economic benefits of tourism directly with local people” (Weeden, 2011, p. 215). Certification programs As previously stated, research has shown that it has been difficult and confusing for tourists to make sustainable travel choices. Several reasons for this may be that there are not well-defined certifications available for the identification of environmentally friendly tourist products (Hjalager, 2000), or that the proliferation of ecolabels had created confusion for customers to the point where they prefer to ignore the messages altogether (Font, 2002). Font (2002) noted that at the time there were over 100 ecolabels for tourism, hospitality, and ecotourism. Esparon, Gyuris, & Stoeckl (2013) similarly expressed that the abundance of competing programs and the lack of uniform standards, creates a challenge for consumers who wish to choose a reliable program. These issues indicate that trusted certification programs for sustainable tourism products would have significant value for consumers. Research has shown that consumers view certification programs and ecolabels 17 as positive and find them to be important (Esparon, et al., 2013; Puhakka & Siikamaki, 2012). Additionally, according to GSTC, 59% of travelers would be influenced by a green rating index (“Travel Forever”, n.d., slide 15). Lastly, certification has been said to benefit consumers by providing a guarantee of quality and reliability (Esparon, et al., 2013). In order to ease the complexity of certification programs in tourism, and help clarify sustainable tourism choices, more simple, efficient, effective, and universal certification organizations and standards would be advantageous. Font (2002), noted that only in the late 90s were there efforts to create international umbrellas for environmental certification, beginning with Green Globe in 1998. He states that international labels are likely the only labels that will influence tourist purchases (Font, 2002). Currently, there are emerging environmental standards such as Global Sustainable Tourism Council’s criteria. According to GSTC their guidelines will define “sustainable tourism in a way that is actionable, measurable, and credible” and will set a global minimum standard (Travel Forever”, n.d., slide 9). This agency will undoubtedly certify thorough, systemic sustainability, but whether this will be sufficient to encourage travelers to choose sustainable businesses and organizations that have such a certification designation is still undetermined. Hotel, restaurant, and attraction research There has been a great deal of consumer behavior research done on hotels and restaurants, some of which involves environmentally friendly characteristics of the facilities and products. For example, Han and Kim (2010) have studied decision making of hotel customers utilizing the theory of planned behavior. By incorporating service quality, 18 satisfaction, overall image, and frequency of past behavior into the theory of planned behavior they were able to better understand a consumer’s intention to revisit a green hotel (Han and Kim, 2010). Tsai and Tsai (2008) have also researched consumer behavior related to environmental ethics in green hotels. They utilized the consumer behavior theory in order to discover a positive relationship between the environmental ethics of consumers and hotel related consumption behaviors (Tsai & Tsai, 2008). Similarly, Choi, Parsa, Sigala, and Putrevu (2009) studied consumer behavior in the lodging industry. The results of their study found that consumers demonstrated high willingness to pay for hotels that employed environmentally responsible practices (Choi et al., 2009). Han, Hsu, and Lee (2009) researched the roles of customer attitudes in decision making for eco-friendly hotels. Specifically they found that consumers’ attitudes toward green behaviors and overall image of a green hotel resulted in positive relationships towards visit intentions, word of mouth intentions, and willingness to pay (Han et al., 2009). Lee, Han, and Willson (2011) explored critical factors involved in consumers’ decision-making processes concerning eco-friendly hotels and found that the expected outcomes held by consumers were positively related to both visit intention as well as word of mouth intention (Lee et al., 2011). In terms of restaurants, Kim, Kim, and Goh (2011) have looked at food tourist’s behaviors and their intention to revisit. By using the modified theory of reasoned action, they found a positive correlation between perceived value, intention to revisit, and satisfaction (Kim et al., 2011). Kim, Njite, and Hancer (2013) studied consumer emotions in regards to their intention to choose eco-friendly restaurants. The theory of planned behavior was utilized in the study and it was discovered that subjective norm was the best 19 predictor of behavioral intentions for consumer selection of an eco-friendly restaurant (Kim et al., 2013). Hu, Parsa, and Self (2010) studied consumer behavior in the context of green restaurant selection. They found that consumers’ knowledge about the sustainable practices of a restaurant and the consumers’ environmental concerns were both important determinants of patronization intentions (Hu et al., 2010). While there is an abundance of studies on sustainable hotel selection, flights and restaurants, the same amount of attention has not been given to consumer behavior in regards to attractions. Because attractions have the potential to influence the volume of tourist activity to a region, knowing more about consumer purchasing is important. There are a variety of attractions types, each of which has the potential to attract various and wide ranging segments of tourists. Attractions can be built or natural, they can be owned and managed by various entities, and they can have varying specific attributes (Weaver, 2006). All of these factors can and do affect the decisions a tourist makes when selecting a site to frequent. Some consumer behavior research has been done on tourists visiting specific types of attractions. For example, to develop a consumer profile and better understand wine tourists, their attitudes and consumer behavior characteristics have been explored (Asero & Patti, 2011). Similarly, another aspect of consumer behavior, motivation, has been examined in sports tourists with the purpose of identifying travel motives for this specific group of tourists (Kurtzman & Zauhar, 2005). There are also studies that have researched tourist satisfaction (one potential factor in consumer behavior) in protected areas (Okello & Yerian, 2009), the attractiveness of sustainable forest destinations to tourists (Lee, Huang, & Yeh, 2010), which can affect tourist motivations and preferences, and also 20 destination attributes (including attractions) that draw ecotourists to ecolodges (Chan & Baum, 2007). Finally, while tourist consumer behavior in the context of certain specific attractions, and consumer behavior in the context of green purchases (including hotels and restaurants) exists, there seems to be very little research on the intersection of these two areas. Very few studies have explored consumer behavior, and more specifically sustainable or green attractions as a whole. This study is an attempt to address these gaps in the literature. Consumer behavior motivators Greater understanding of consumer behavior would provide destination marketers, non- profit organizations and private sector businesses beneficial information about attracting visitors. There are three general theoretical approaches that can assist in understanding, explaining, and predicting consumer behavior from a sustainability perspective: rational, sociological, and psychological explanations (Belz & Peattie, 2009). While they are all useful in the attempt to understand consumer behavior, none are capable of explaining it in totality (Belz & Peattie, 2009). Additionally, these perspectives are not exhaustive and do not contain all of the potential influencers of consumer behavior and decision-making. They also “cannot easily explain the behavior of all consumers, across all types of consumption at all times” (Belz & Peattie, 2009, p. 87). Despite these potential drawbacks, the three broad theoretical perspectives do however capture the majority of key ideas that help to explain consumption behavior in regards to sustainability (Belz & Peattie, 2009). 21 Rational Rational explanations often involve the economics of sustainable consumption, and how consumers weigh the functional benefits with the relative affordability (Belz & Peattie, 2009). These models are not necessarily the most effective at promoting sustainable choices because not all social and environmental costs are reflected in the prices that consumers pay (Belz & Peattie, 2009). The concept of perceived costs and benefits, which do include non-economic costs, is a broader approach to rational consumer choice (Belz & Peattie, 2009). This is best explained that as perceived benefits minus perceived costs equals perceived net benefits, consumers will choose the choice with the highest perceived net benefits (Belz & Peattie, 2009). For example, Chen (2013) introduced a new concept of ‘green perceived value’ based on Patterson and Spreng’s (1997) definition of perceived value. Green perceived value is a “consumer’s overall appraisal of the net benefit of a product or service between what is received and what is given based on the consumer’s environmental desires, sustainable expectations, and green needs” (Chen, 2013). Additionally, Chen (2013) found that green perceived value is positively related to green satisfaction, green trust, and green loyalty in the context of electronics consumers. Sociological Sociological explanations include theories that involve how individuals think others will perceive their consumption behaviors and how that influences their place in society (Belz & Peattie, 2009). One example of a sociological explanation would be social norms, which can sway individuals to imitate individuals with pro-environmental behaviors (Miller et al., 2010). There are two types of norms to consider. They include what we perceive to be 22 common practice or normal (descriptive norms) and behaviors we perceive to be morally right (injunctive social norms) (Peattie, 2010). For example, it was found that the use of normative appeals encouraged more hotel guests to reuse their towels (Peattie, 2010). Additionally, Lee (2008) found that out of seven green purchasing behavior predictors studied, social influence was the most important. The implications for this could suggest that if individuals believe that they are part of a collective group, working towards a goal, then they are more likely to participate in any given behavior. This can be applied to sustainable tourism planning as well as personal behaviors during travel. For example, if individuals are motivated by social influence or feel as though they are part of a collective group, they may be more inclined to select eco-friendly travel products or services if they learn one of their peers or friends did similarly. As a result, more sustainable choices may require more collective, community-based solutions (Peattie, 2010). Psychological Finally, there are psychological theories to assist in the understanding of consumer behavior. Most of this research focuses on consumers’ attitudes and beliefs. According to Sirakaya and Woodside (2005) the decision-making process can be influenced by a number of psychological and internal factors such as attitudes, motivation, beliefs, intentions, values, lifestyles, and images (Sirakaya & Woodside, 2005). One psychological theory that utilizes internal variables is the consumer behavior theory, which involves three components of attitude, cognitive (belief), affective (feeling), and behavior (reaction) (Tsai &Tsai 2008). This theory asserts that consumer behavior can be affected when the three components of attitude are perfectly compatible (Tsai & Tsai, 23 2008). There has been disagreement however as to whether there is indeed a correlation between the components (Tsai & Tsai, 2008). Additionally, the effects of the cognitive and affective components can vary from the resulting behavior (Tsai & Tsai, 2008). The issue of whether there is in fact a correlation between cognition, affect, and behavior adds further uncertainty as to whether environmentally conscious consumers may or may not actually make environmentally conscious consumer decisions. Another theory that has been used to research consumer behavior in various contexts, including green hotels, is the Theory of Planned Behavior (TPB), which is an extension of the Theory of Reasoned Action (Han & Kim, 2010). The TPB considers both volitional control as well as non-volitional control in order to explain behavior (Han & Kim, 2010). The volitional part of the theory refers to the assumption that individuals make a reasoned choice because they are rational and motivation-based in the decision-making process (Han & Kim, 2010). The TPB incorporated non-volitional control as well, which relates to perceived behavioral control (Han & Kim, 2010). An essential part of both of these theories is an individual’s intention, which “provides the most accurate prediction of particular behaviors” and “indicates an individual’s readiness/willingness to engage in a particular behavior” (Han & Kim, 2010, p. 661). Intention is based on attitude toward the behavior, subjective norm, and perceived behavioral control (Han & Kim, 2010). “Attitude toward the behavior refers to the degree of an individual’s positive or negative evaluation/appraisal of behavior performance” (Han & Kim, 2010, p. 661). Subjective norm refers to social pressure needed for engagement in a particular behavior, and perceived behavioral control “reflects an individual’s perception of the ease or difficulty in performing a specific behavior” (Han & Kim, 2010, p. 661). Again, perceived behavioral 24 control is the non-volitional factor in this theory (Han & Kim, 2010). This model has been effective in predicting the power of a customer’s intention to revisit green hotels (Han & Kim, 2010). Han and Kim (2010) used an extended TPB model in order to show that not only do attitude, subjective norm, and perceived behavioral control aid in the ability to determine a customer’s intention to revisit a green hotel, but overall image, customer satisfaction, and frequency of past behavior contribute as well. Additionally, in terms of attitudes, Belz and Peattie (2009) note that perceived personal relevance, social responsibility, and trust are three important sets of attitudes to consider in regards to consumer willingness. Perceived personal relevance relates to “the extent to which consumers see a connection between their lives and consumption behavior and a particular issue” (Belz & Peattie, 2009, p. 83). An area of concern associated with this is the potential disconnect between the problem frame and the personal frame (Belz & Peattie, 2009). The problem frame refers to global environmental challenges while the personal frame refers to an individuals’ home, life, work, and family (Belz & Peattie, 2009). The common thread among these theories is the connection between attitude and behavior. Attitude and behavior are also essential components to the idea of self-efficacy and the theory of perceived consumer effectiveness, which involves an important set of attitudes and beliefs related to personal relevance (Belz & Peattie, 2009). Bandura (1997) created the term self-efficacy in order to describe the degree that an individual believes himself or herself to be capable of exercising control over behaviors necessary for generating certain desired outcomes. It can be said that perceived consumer effectiveness could be considered “self-efficacy with regard to the behavioral domain consumption and the outcome domain environmental preservation (Hanss & Bohm, 2013). 25 Therefore, PCE is essentially self-efficacy in the specific context of consumer behavior (Hanss & Bahm, 2013). Perceived consumer effectiveness Perceived consumer effectiveness is also related to behavioral control (Vermeir & Verbeke, 2006), and as previously stated, refers to an individual’s belief that his or her actions can have a beneficial impact on social or environmental issues (Belz & Peattie, 2009). This theory suggests that consumers are more likely to engage in behaviors that they believe will make a difference (Belz & Peattie, 2009) and allows them to exert their influence as a purchaser through such beliefs (Peattie, 2001). The original purpose of PCE was to explore purchases, however it can also be adapted and applied to study other facets of consumption behavior (Belz & Peattie, 2009). In 1974, Kinnear, Taylor, and Ahmed defined PCE as a measure of the extent a consumer believes he or she can be effective in regards to pollution abatement. Their results indicated that consumers who “could be useful in pollution abatement demonstrated higher than average concern” (Kinnear et al., 1974, p. 22). Since then, this theory has been used extensively to explain environmental attitude and behavior. Ellen, Wiener, and Cobb- Walgren (1991) demonstrated that PCE is distinct from environmental concern and can contribute to the prediction of certain pro-ecological behaviors. Their results showed that motivating consumers to express their concerns through actual behavior is partly a “function of increasing their perception that individual actions do make a difference” (Ellen et al., 1991, p. 102). Berger and Corbin (1992, p. 80) note that PCE has been found to be modeled more effectively as a separate construct from attitude and thus consider it as an 26 “estimate of the extent to which personal consumption activities contribute to a solution to the problem.” Their research examined whether PCE would moderate the relationship between environmental attitudes and personal consumer behaviors (Berger & Corbin, 1992). Indeed, they found that individuals who perceive themselves to have more personal efficacy also have higher correlations between environmental attitudes and consumer behavior (Bergin & Corbin, 1992). Roberts (1996) also confirmed that PCE is an effective predictor of environmentally conscious consumer behavior. His survey study determined that the higher an individual’s PCE, the greater the likelihood that the individual would participate in general ecologically conscious consumer behaviors (Roberts, 1996). Furthermore, in a later study, Straughan and Roberts (1999) point out that individual environmental concern does not automatically lead to proactive behavior unless the individual feels as though they can be effective tackling environmental issues. However, Kim (2011), in her study on the effects of collectivism, values, and attitudes on environmentally friendly purchases did not find that PCE improved the prediction of green buying behavior, despite finding that environmental attitudes did have a weakly positive effect on green buying behavior. Kim (2011) acknowledged that a possible limitation to the study was the fact that undergraduate students were used as the sample, and therefore may not represent the general consumer. Tan and Lau (2011) conducted a survey of university students in Malaysia in which they were asked questions about environmental attitudes, green purchase attitudes, the frequency of green purchase behaviors, and PCE. They concluded that both PCE and green purchase attitude were significantly related to green purchase behavior (Tan & Lau, 2011). There was not however a significant relationship between environmental attitude and green purchase behavior (Tan & Lau, 27 2011). This particular finding was not consistent with other research studies. The concept of PCE, or environmental self-efficacy, was also studied in conjunction with environmental values in order to create an environmental propensity framework (EPF) to segment automobile customers with the goal of encouraging the adoption of hybrid vehicles (Oliver & Rosen, 2010). Generally, it has been found that people who have shown higher PCE are likely to be more environmentally concerned (Tan, 2011), and PCE also has significant correlation to different types of environmental behaviors such as recycling, choosing more environmentally friendly products, and consciously reducing household electricity usage (Roberts, 1996). In the context of sustainable tourism, Kim and Han (2010) found that PCE plays an important part in explaining hotel customers’ environmentally friendly decision- making process along with environmentally conscious behaviors. They found the connections between environmental concerns, PCE, and environmentally conscious behaviors to be positive and significant (Kim & Han, 2010). Additionally, these variables also aid in the prediction of intention to pay conventional hotel prices for a green hotel (Kim & Han, 2010). This theory has great implications for sustainable tourism in several ways. When people feel as though they have the power to act and those actions can have positive results, they are more inclined to take that action (Wesley, Lee, & Kim 2012). For this to happen, those individuals must believe that their efforts can contribute to the solution of a problem and the behavioral change will occur “when the consumer is convinced that behavior will have an impact on bringing about change” (Wesley et al., 2012, p. 34). Therefore, if this theory can be effectively applied to sustainable tourism, 28 there is a greater chance that consumers have the potential to be the driving force for the continued promotion and implementation of sustainable tourism practices industry wide. Research on attitudes, values, intentions, and norms and their impact on behaviors have dominated this area of research despite the fact that there has been growing evidence that “their influence varies across different types of behavior and contexts” (Peattie, 2010). Additionally, it can be argued that there is not one single unifying theory for changing behavior, as individual motivations are too complex and multifaceted (Miller et al., 2010). Nevertheless, psychological and sociological theories for consumer behavior are still relevant explanations to consider in sustainable tourism decision-making and behavior. Furthermore, as theories such as the Theory of Reasoned Action, consumer behavior theory, and the Theory of Planned Behavior are several of the widely popular and frequently employed theories that are used to explain consumer behavior, PCE offers a refreshing and often underutilized perspective. With this in mind, this study attempted to address the following issues: 1) What factors influence an individual to select an attraction to visit? 2) How much of an impact do the sustainable features of an attraction have on the selection of that attraction? 3) How much does perceived consumer effectiveness explain the selection of sustainable attractions? 4) Is there a correlation between ‘everyday’ green purchases and the selection of sustainable attractions? 5) Is there a correlation between green travel purchases and the selection of sustainable attractions? 29 Chapter 3: Methods Sample The sample was drawn from visitors to North Carolina attraction sites that are part of NC GreenTravel, an initiative that was developed through a partnership with N.C. Division of Environmental Assistance and Customer Service, the Center for Sustainable Tourism at East Carolina University, the N.C. Division of Tourism, Film & Sports Development, and Waste Reduction Partners. It is a program that encourages economic growth and environmental stewardship in the tourism industry. The NC GreenTravel attractions have met or exceeded the initiative’s standards for a green attraction site (e.g. the use of post- consumer recycled paper, energy efficient lighting, aerators in sinks). For more information about NC GreenTravel standards, visit http://portal.ncdenr.org/web/deao/ncgreentravel. The sample was considered a convenience sample as the survey solicitation was distributed on social media sites (Facebook, Twitter) and through member or marketing email lists. As this study’s intent was to explore the behaviors of general attraction attendees, all participants were included, whether they were residents or tourists. Although this study intends to explore the impact of sustainability features on attractions in general, the three selected participating sites include two state parks and one zoological park. State parks are a popular and often frequented attraction site for many of the country’s individuals and families. More than 725 million people visited state parks in the United States in 2009 (Esprit & Smith, 2011). Furthermore, as state parks struggle to cope with declining budgets, sustainability features have become appealing options both in order to increase visitation as well as to decrease expenditures (Esprit & Smith, 2011). The North Carolina state park system consists of 34 parks, 4 recreation areas, and 17 natural 30 areas (Greenwood & Vick, 2008). The park system makes a significant economic contribution to North Carolina’s economy, provides jobs, and has a considerable impact on the income of local residents (Greenwood & Vick, 2008). The sites selected for this study were Grandfather Mountain, Chimney Rock at Chimney Rock State Park, and the North Carolina Zoo. They represent a geographic dispersion throughout the state (Figure 3.1). Table 3.1 provides general information about the parks included in the study. Table 3.1. Attraction facts. Site Established Annual visitation Acreage Key features Grandfather Mountain 1952 250,000 720 5,946 foot peak and mile-high swinging bridge Chimney Rock 1902 >250,000 1,000 535-million- year-old monolith North Carolina Zoo 1974 >700,000 2,200 Over 1600 animals 31 Figure 3.1. Map of attraction locations. Request for participation in the study and assistance in obtaining respondents was done through individual contact with the manager or director of the park. Once the program manager reviewed the survey materials, the survey was posted on social media sites and sent to email addresses of attraction members. Survey design and distribution The instrument was comprised of both previously constructed and tested survey questions as well as adapted questions (Berger & Corbin, 1992; Ellen, Wiener, & Cobb-Walgren, 1991; Kim, 2011; Lee, 2008; Roberts, 1996; Roth, 2011; Tsai & Tsai, 2008). These survey questions were chosen in order to effectively answer the study’s research questions. Furthermore, the questions asked respondents about their actual past behavior in an 32 attempt to reduce social desirability bias. This effect refers to situations in which individuals provide socially desirable responses in order to create a more favorable impression of themselves or appear to conform to societal norms (Roxas & Lindsay, 2012). This phenomenon is likely to be a significant issue when dealing with environmental sustainability practices (Roxas & Linsay, 2012). Requesting information about actual behavior encourages respondents to answer about facts that occurred as opposed to intentions. Nonetheless, this is an issue to be cognizant of when analyzing the results as it has been found that self-reported behaviors are “more closely associated with a conscious over-reporting of desirable behaviors” (Randall & Fernandes, 1991). One potential way to attempt to minimize the social desirability bias is to ensure anonymity to respondents (Randall & Fernandes, 1991), which this study conveys in the informed consent page. The survey consisted of four main sections. In order to orientate the respondent, the first section began with the following general definitions of sustainable tourism and tourist attraction. Sustainable tourism aims to preserve the environment, economy and cultural aspects of a destination. It also promotes the sustainability of tourism products and services such as airlines and other transportation, lodging, restaurants, attractions, cruises, tour operators, etc. and A tourist attraction refers to something interesting or enjoyable that people want to visit, see, or do; it can include but is not limited to parks, museums, aquariums, sporting events, concerts, festivals, and the beach. After the definitions were presented, there were two categorical survey questions geared towards exploring factors that influence an individual’s attraction choice (Table 33 3.2). The first question asked in terms of general attractions, while the second question asked about the specific NC GreenTravel attraction from which the respondent was obtained. The other two questions in this section were a categorical question regarding what resources are used to gather attraction information and a question that asked the respondents whether they have heard of the NC GreenTravel initiative. 34 Table 3.2. General attraction selection survey questions. Research Question 1: What factors influence an individual to select an attraction to visit? Research Dimension: Attraction Selection Source Original Question Adapted Question 1 Asero & Patti, 2011 and 2 Roth, 2011 (2nd Annual Green Traveler Study 2010-2011) and 1Indicate the importance of different tools in your decision to visit a winery. -Word of mouth -Reputation of winery/wine -Internet -Events -Brochures -Wine region guides -Prices -Mass media 2Which of these were the main factors influencing your most recent choice of vacation destination? Mark all that apply. -Desire to explore the destination -Geographical location -Visit friends/family -Price/good deal - Activities available there -Environmental/sustainable/socially responsible considerations -Recommendation from friend/family -Other Please think about the last vacation you took and specifically the attractions you visited. Which of the following characteristics of the attractions most influenced your decision to visit them? Please select the top three.. -Convenient location 2 -Friend/family wanted to visit 2 -Price/good value 1 -Because of the activities available there 2 -Special events at the attraction 1 -Because of environmental/sustainable/socially responsible practices of the attraction site 2 -Reputation 1 -Don’t remember -Other 1 Asero & Patti, 2011 and 2 Roth, 2011 1Indicate the importance of different tools in your decision to visit a winery. -Word of mouth -Reputation of winery/wine -Internet -Events -Brochures -Wine region guides Now please think about the last time you visited xxxx(specific NC GreenTravel site). Which of the following factors most influenced your visit? Please select the top three. -Convenient location 2 -Friend/family wanted to visit 2 -Price/good value 1 35 -Prices -Mass media 2Which of these were the main factors influencing your most recent choice of vacation destination? Mark all that apply. -Desire to explore the destination -Geographical location -Visit friends/family -Price/good deal -Activities available there -Environmental/sustainable/socially responsible considerations -Recommendation from friend/family -Other -Because of the activities available there 2 -Special events at the attraction 1 -Because of environmental/sustainable/socially responsible practices of the attraction site 2 -Word of mouth 1 -Reputation 1 -Internet 1 -Brochures 1 -Don’t remember -Other 1Asero & Patti, 2011; 2 Dodds, Graci, & Holmes, 2010; 3 Roth, 2011 Tools/resources/methods used to find out about… -Travel publications, guidebooks and/or websites -Green/environmental publications and/or websites -Word of mouth -Email newsletters -Tourism office or visitor bureau publications and/or websites -Friends/family -Local newspaper/travel section -Facebook or other social networking -Travel agent -TV advertisements -Billboards -Brochures -Online review -Other Still thinking about your last vacation, what resources did you use to learn about the attractions you visited? Please select all that apply. -Attraction website -Guidebook 2 -Green/environmental publications and/or websites 3 -Word of mouth 1 -Magazine or newspaper articles 3 -Email newsletters 3 -Travel agent 2 -Friends/family 2 -Facebook or other social networking 3 -TV advertisements 3 -Billboards 3 -Brochures 1 -Online review 1 -Other (please specify) 36 The second section inquired as to the importance of “green” initiatives as well as traditional reasons for selecting an attraction (Table 3.3). Additionally, this section examined the likelihood of a respondent seeking out and choosing a more sustainable attraction site while on vacation, and the likelihood that the sustainable practices of an attraction increase the chance of visitation to that site; these questions will be used as the dependent variables in the analyses. Table 3.3. Sustainable attraction features survey questions. Research Question 2: How much of an impact do the sustainable features of an attraction have on the selection of that attraction? Research Dimension: Impact of Sustainability Features on Selection Source Original Question Adapted Question 1 Asero & Patti, 2011 and 2Roth, 2011 1 Indicate the importance of different tools in your decision to visit a winery. -Word of mouth -Reputation of winery/wine -Internet -Events -Brochures -Wine region guides -Prices -Mass media 2 When making a hotel reservation, what are the top five motivators that make you choose one hotel over another? Please rank up to five. -Advertising in green/alternative media -Online review of property 2Which of these were the main factors influencing your most recent choice of vacation destination? Mark all that apply. -Geographical location Generally speaking, in the past two years, how important were each of the following when selecting one attraction over another to visit? 4-point Likert scale (1-Not important, 2-Somewhat important, 3-Important, 4-Very important) -Advertising or promotional material 2 -Reputation of attraction 1 -Online review 2 -Convenient location 2 -Friend/family wanted to visit 2 -Price/good value 1 -Activities available there 2 -Special events at the attraction 1 -Environmental/sustainable/socially responsible practices of the attraction site 2 37 - Activities available there -Environmental/sustainable/socially responsible considerations -Recommendation from friend/family 1 Cheng, Su & Tan; 2 Firth & Hing, 1999; 3 Hu, Parsa, & Self; 4Kim, Njite & Hancer, 2013; 5 Kline, Tucker & Hoggard, 2014; 6 PGAV Destination Consulting, 2008 Environmental initiatives - LEED certification of the facility - Green sustainable dining options on site or nearby -Green building and construction -Eco-friendly furnishings -Carbon offset in scenic areas -Energy efficiency and conservation -Water efficiency and conservation -Recycling -Composting -Air quality in scenic areas -Non-toxic cleaning and chemical products -Local residents’ awareness of low-carbon environmental protection -Using biodegradable, recyclable utensils, cups and packaging -Natural landscape -Use of hybrid company vehicles Each of the following is a sustainability initiative that attractions could potentially adopt. As you think about attractions you have visited within the last two years, how important were each of the following in influencing your desire to visit? 4-point Likert scale (1-Not important, 2-Somewhat important, 3-Important, 4-Very important) -Certification as a sustainable or green site 6 -Green sustainable dining options on site or nearby 5 -Built with eco-friendly materials 3 -Eco-friendly furnishings 2 -Carbon reduction or offset programs 1 -Energy efficiency 4 -Water efficiency 4 -Recycling 3 -Composting 3 -Indoor air quality 1 -Non-toxic cleaning chemicals 3 -Involvement in local environmental efforts 1 -The use of biodegradable products 4 -Natural landscape 6 -The use of hybrid company vehicles 6 Roth, 2011 Are you likely to seek out and choose greener vacation options for these travel products in the coming year? -Cruise -Airline -Hotel -Restaurant -Tour -Rental Car Using a scale from1 to 10 with 1 being the least likely and 10being the most likely, how likely are you to seek out and choose sustainable attraction sites while on vacation in the coming year? Using a scale from 1 to 10 with 1 being the least likely and 10 being the most likely, how likely is it that the sustainable practices of an attraction increase the chance of you visiting that site? Roth, 2011 Are you aware of the Global Sustainable Tourism Criteria set by The Global Partnership for Sustainable Have you heard of the NCGreen Travel initiative? -Yes 38 Tourism Criteria (GSTC Partnership)? -No -Unsure The third section of the survey asked respondents to indicate their level of agreement to eight PCE questions (Table 3.4). The scales ranged on a 4-point Likert scale from strongly disagree to strongly agree and included an ‘unsure’ option. A 4-point scale was chosen in order to avoid confusion among various degrees of agree and disagree. Furthermore, additional scale points do not necessarily enhance reliability (Chang, 1994). An even number of points was chosen as it forces respondents to have an opinion, which encourages deeper processing of the item and minimizes social desirability bias (Smyth, Dillman, Christian & Stern, 2006; Garland, 1991). Table 3.4. PCE survey questions. Research Question 3: How much does perceived consumer effectiveness explain the selection of sustainable attractions? Research Dimension: Perceived Consumer Effectiveness Source Original Question Adapted Question Ellen, Wiener, & Cobb-Walgren, 1991 There is not much that any one individual can do about the environment 5-point Likert scale 4-point Likert scale (1-Strongly disagree, 2-Disagree, 3- Agree, 4-Strongly Agree) Ellen, Wiener, & Cobb-Walgren, 1991 The conservation efforts of one person are useless as long as other people refuse to conserve 5-point Likert scale 4-point Likert scale (1-Strongly disagree, 2-Disagree, 3- Agree, 4-Strongly Agree) Roberts, 1996 When I buy products, I try to consider how my use of them will affect the environment and other consumers Likert scale (number of options unknown) When I buy products (such as groceries or cleaning products), I try to consider how my use of them will affect the environment and other consumers 4-point Likert scale (1-Strongly disagree, 2-Disagree, 3- Agree, 4-Strongly Agree) Roberts, 1996 When I buy products, I try to consider how my use of them will affect the environment and other consumers Likert scale (number of options unknown) When I buy travel products (such as a hotel or a restaurant), I try to consider how my use of them will affect the environment and other consumers 4-point Likert scale (1-Strongly disagree, 2-Disagree, 3- 39 Agree, 4-Strongly Agree) Roberts, 1996 Each consumer’s behavior can have a positive effect on society by purchasing products sold by socially responsible companies Likert scale (number of options unknown) 4-point Likert scale (1-Strongly disagree, 2-Disagree, 3- Agree, 4-Strongly Agree) Kim, 2011 I feel capable of helping solve the environmental problems 7-point Likert scale (ranging from ‘strongly disagree’ to ‘strongly agree’) 4-point Likert scale (1-Strongly disagree, 2-Disagree, 3- Agree, 4-Strongly Agree) Kim, 2011 I can protect the environment by buying products that are friendly to the environment 7-point Likert scale (ranging from ‘strongly disagree’ to ‘strongly agree’) 4-point Likert scale (1-Strongly disagree, 2-Disagree, 3- Agree, 4-Strongly Agree) Kim, 2011 I feel I can help solve natural resource problems by conserving water and energy 7-point Likert scale (ranging from ‘strongly disagree’ to ‘strongly agree’) 4-point Likert scale (1-Strongly disagree, 2-Disagree, 3- Agree, 4-Strongly Agree) The third section also asked respondents ten green purchasing questions on a 4-point Likert scale from strongly disagree to strongly agree and included an ‘unsure’ option (Table 3.5). Table 3.5. Green purchasing survey questions. Research Question 4: Is there a correlation between ‘everyday’ green purchases and the selection of sustainable attractions? Research Dimension: Green Purchase Behaviors Source Original Question Adapted Question Roberts, 1996 For three GPB items, the original 5-point Likert scale (ranging from ‘Always true’ to ‘Never true’) was replaced with … …a 4-point Likert scale (1-Strongly disagree, 2- Disagree, 3-Agree, 4-Strongly Agree) Berger & Corbin, 1992 Have you in the last year, or will you next year? -Gone/go out of your way to seek out biodegradable products -I have gone out of my way to seek out biodegradable products 4-point Likert scale (1-Strongly disagree, 2- Disagree, 3-Agree, 4-Strongly Agree) Kim, 2011 For three GPB items, the original 5-point Likert scale (Never, rarely, sometimes, often, always) was replaced with… …a 4-point Likert scale (1-Strongly disagree, 2- Disagree, 3-Agree, 4-Strongly Agree) 40 Tsai & Tsai, 2008 I purchase products made from recyclable materials (5-point Likert scale) 4-point Likert scale (1-Strongly disagree, 2-Disagree, 3- Agree, 4-Strongly Agree) Lee, 2008 When I want to buy a product, I look at the ingredients label to see if it contains things that are environmentally damaging Likert scale (number of options unknown) 4-point Likert scale (1-Strongly disagree, 2-Disagree, 3- Agree, 4-Strongly Agree) Lee, 2008 I prefer green products over non-green products when their product qualities are similar Likert scale (number of options unknown) 4-point Likert scale (1-Strongly disagree, 2-Disagree, 3- Agree, 4-Strongly Agree) Lastly, the third section asked seven green travel purchasing questions on a 4-point Likert scale from strongly disagree to strongly agree and included an ‘unsure’ option (Table 3.6). Table 3.6. Green travel purchasing survey questions. Research Question 5: Is there a correlation between green travel purchases and the selection of sustainable attractions? Research Dimension: Green Travel Purchase Source Original Question Adapted Question Roth, 2011 and U.S. Travel Care Code Which measures have you taken to be a “greener” traveler in the past 12 months? -I turned off lights and/or air conditioning when I left the room -I reused hotel sheets and towels to conserve resources -I recycled -I brought and used a reusable water bottle -I purchased locally-made crafts -I traveled by train or other public transportation -I walked and/or bicycled to most activities -I ate organic and/or vegetarian meal(s) -I helped spread the word about green travel by sharing my experience/advice with others -I researched and booked “greener” accommodations -I rented a high-mileage, more fuel-efficient car Which measures have you taken to be a “greener” traveler in the past 12 months? For each of the following items, please select the option that best represents your level of agreement. 4-point Likert scale (1-Strongly disagree, 2-Disagree, 3-Agree, 4-Strongly Agree) -I brought and used a reusable water bottle -I purchased locally-made crafts -I traveled by train, subway, bus or other public transportation -I ate organic and/or vegetarian meal(s) -I researched and booked “greener” accommodations -I rented a high-mileage, more fuel-efficient car -I have used a carbon offset program to counter my carbon footprint 41 The last section of the survey included demographic questions such as age, gender, household income, educational level, ethnicity, and residential zip code. The survey instrument was piloted and reviewed with two East Carolina faculty members, two faculty members outside of the University, three PhD candidates, and one practitioner. It was then created on a web-based platform and a link to the survey was posted on social media websites and was also sent to the recipients of site newsletters. Reminders were posted on social media. Table 3.7 summarizes the solicitation schedule for each attraction. Table 3.7. Survey solicitation schedule. Chimney Rock Grandfather Mountain North Carolina Zoo First solicitation 5/8 5/1 5/1 Second solicitation 5/13 5/20 Final solicitation Facebook posts 5/12 and 5/19 5/6 5/19 Twitter posts (tweets) 5/2 Analysis Data was analyzed in SPSS 20.0. A combination of descriptive summaries, multiple regression, and Pearson’s correlation was used to answer the research questions. Please see Table 3.8 below for the corresponding analysis for each research question. 42 Research Question Independent Variable Dependent Variable Analysis Type 1: What factors influence an individual to select an attraction to visit? N/A N/A Descriptive of SQ 1, & 3 2: How much of an impact do the sustainable features of an attraction have on the selection of that attraction? 5: Factors’ level of importance (interval) (non-green) 8: Likelihood of seeking out sustainable attraction sites Regression 6: Qualities’ level of importance (interval) (green) 8: Likelihood of seeking out sustainable attraction sites Regression 6: Qualities’ level of importance (interval) (green) 7: Likelihood that sustainable practices of attraction increase chance of visitation Regression 3: How much does perceived consumer effectiveness explain the selection of sustainable attractions? 9: PCE (interval) 8: Likelihood of seeking out sustainable attraction sites Regression 4: Is there a correlation between everyday green purchases and the selection of sustainable attractions? 10: Green Purchase Behaviors (interval) 8: Likelihood of seeking out sustainable attraction sites Pearson’s correlation 5: Is there a correlation between green travel purchases and the selection of sustainable attractions? 11: Green Travel Purchase Behaviors (interval) 8: Likelihood of seeking out sustainable attraction sites Pearson’s correlation Table 3.8. Analysis table. 43 Chapter 4: Results After three weeks of data collection, 681 useable surveys were collected from the three participating sites. Surveys with more than 5% missing data were deleted. Descriptive results Demographics A profile of the sample was created to determine the distribution of the socio-demographic variables (Table 4.1). The majority of respondents were female (71.8%), between the ages of 35-44 (23.6%), and White (92.5%). The majority had some college education or were college graduates (60.2%), while another 23.5% had obtained a post-graduate degree. Most respondents (43.6%) reported income between $50,000-$100,000. Most respondents (77.8%) were North Carolina residents. Table 4.1. Socio-demographic summary of respondents. Variable Percentage of respondents Gender (n=670) Male 28.2% Female 71.8% Age range (n=661) 18-24 3.6% 25-34 13.8% 35-44 23.6% 45-54 22.2% 55-64 22.4% 65-74 12.9% 75+ 1.5% Race (n=671) White 92.5% Non-white 3.4% Prefer not to answer 2.5% Education (n=671) High school or some high school 10.3% 44 Technical or trade school 6.0% College graduate or some college 60.2% Graduate school, JD, or MD 23.5% Household income (n=631) Less than $50,000 33.0% $50,000-$100,000 43.6% $100,000-$150,000 15.7% $150,000-$200,00 4.4% Greater than $200,000 3.3% Residency (n=658) NC Resident 77.8% Non-NC resident 22.2% Site distribution The three participating sites were Grandfather Mountain, Chimney Rock, and the North Carolina Zoo (Table 4.2). Table 4.2. Site distribution. Site Percentage of respondents Grandfather Mountain 45.4% North Carolina Zoo 39.8% Chimney Rock 14.8% Influencing factors for attraction selection Two questions asked respondents about the characteristics of attractions that most influenced them in the attraction selection process. This was asked both generally, in terms of the last vacation taken, as well as specifically, in terms of the particular site that had promoted the survey. When thinking about the last vacation taken, the top three influencing factors respondents selected were because of the activities available there (64.6%), reputation of attraction (52.3%), and price/good value (48.0%). The three factors that respondents said most influenced their visitation to the specific attractions (Grandfather Mountain, NC Zoo, or Chimney Rock) were reputation of the attraction 45 (55.2%), because of the activities available at the attraction (47.9%), and friend/family member wanted to visit (45.7%). Table 4.3. Influential factors for attraction selection. Answer options General n Specific n Because of the activities available at the attraction 64.6% 440 47.9% 326 Reputation of the attraction 52.3% 356 55.2% 376 Price/good value 48.0% 327 34.9% 238 Friend/family member wanted to visit 37.2% 253 45.7% 311 Convenient location 23.9% 163 30.7% 209 Because of environmental/sustainable/socially responsible practices of the attraction site 23.6% 161 30.4% 207 A special event occurring at the attraction 18.2% 124 15.0% 102 Don't remember .6% 4 1.3% 9 Resources An attraction’s website (80.0%) was the resource respondents said they most used to learn about an attraction to visit. Other highly utilized resources were friends/family (45.8%), word of mouth (41.3%), and online reviews (37.0%). It is apparent that Internet resources are valuable marketing tools however it is also interesting to note that Facebook and other social networking sites (27.3%) and email newsletters (17.0%) received fairly low response percentages. Table 4.4. Resources utilized in planning visit to attraction. Answer options Response percent n Attraction website 80.0% 545 Friends/family 45.8% 312 Word of mouth 41.3% 281 Online review 37.0% 252 Brochures 28.5% 194 Guidebook 27.9% 190 Facebook or other social networking 27.3% 186 Magazine or newspaper articles 18.4% 125 Email newsletters 17.0% 116 Green/environmental publications and/or websites 6.0% 41 Travel agent 4.3% 29 46 Billboards 4.1% 28 TV advertisements 3.8% 26 NC GreenTravel The majority of respondents (85.4%) had not heard of the NC GreenTravel initiative. Table 4.5. NC GreenTravel. Answer options Response percent n Yes 8.2% 56 No 85.4% 581 Unsure 6.3% 43 Non-Green and Green Attraction Selection Factors The three most important non-green factors when selecting an attraction to visit were found to be the reputation of the attraction (92.4% noted this was important and the very important responses), price/good value (85.2%), and activities available there (82.9%). The least important for selecting an attraction was advertising and promotional material (22.0%). The three most important (again combining important and very important) sustainability initiatives (or green factors) were found to be natural landscape (79.4%), indoor air quality (71.4%), and recycling (62.8%). The items found to be least important were use of hybrid company vehicles (34.1%), green sustainable dining options on site or nearby (27.8%), and carbon reduction offset programs (27.0%). Table 4.6. Non-green and green factors influencing attraction selection. Answer options Not important Somewhat important Important Very important n Non-green Reputation of attraction 1.6% 5.9% 46.0% 46.4% 674 Price/good value 1.5% 13.4% 42.9% 42.3% 674 Activities available there 2.2% 14.9% 49.2% 33.7% 671 Friend/family wanted to visit 6.8% 17.8% 45.1% 30.3% 663 Convenient location 6.6% 24.6% 45.3% 23.5% 667 Online review 9.1% 31.9% 36.5% 22.5% 668 Environmental/sustainable/soc 12.4% 33.7% 15.6% 661 47 ially responsible practices of the attraction site 38.3% Special events at the attraction 13.9% 41.5% 33.6% 11.0% 655 Advertising or promotional material 22.0% 48.0% 22.0% 8.0% 663 Green Natural landscape 6.4% 14.2% 36.0% 43.4% 677 Indoor air quality 9.5% 19.1% 41.9% 29.5% 675 Recycling 13.5% 23.7% 36.5% 26.3% 674 Non-toxic cleaning chemicals 16.6% 30.2% 32.0% 21.1% 668 Water efficiency 15.3% 28.8% 37.4% 18.4% 673 The use of biodegradable products 16.0% 31.1% 35.1% 17.8% 669 Energy efficiency 16.5% 30.9% 35.8% 16.9% 674 Involvement in local environmental efforts 17.2% 30.8% 35.8% 16.2% 673 Composting 22.2% 36.0% 29.8% 12.1% 672 Green sustainable dining options on site or nearby 27.8% 35.3% 26.3% 10.6% 669 Certification as a sustainable or green site 23.7% 39.0% 28.1% 9.3% 670 Carbon reduction or offset programs 27.0% 38.2% 26.0% 8.8% 673 Built with eco-friendly materials 23.7% 39.2% 29.2% 7.9% 674 Use of hybrid company vehicles 34.1% 37.6% 21.7% 6.6% 668 Eco-friendly furnishings 26.3% 40.4% 26.7% 6.5% 673 Dependent Variables When asking respondents about how likely it is that the sustainable practices of an attraction would increase their selection of that site, answers varied widely. Nearly one- third (31.5%) of the respondents felt that it was somewhat likely, selecting 6 or 7 out of 10, while another 27.6% felt it was very likely or extremely likely (8, 9, or 10 out of 10). 48 Figure 4.1. Likelihood of increased visitation due to sustainable practices. Similarly, the majority of respondents (41.9%) indicated that it was likely they would seek out and choose sustainable attractions CSA while on vacation in the coming year (7, 8, 9, or 10 out of 10). Figure 4.2. Likelihood of choosing sustainable attraction. Perceived Consumer Effectiveness The intention of this question was to gauge the respondents’ personal environmental beliefs in regarding whether their actions can make a difference concerning environmental 10.4 4.4 6.9 4.7 14.6 12.2 19.3 12.4 6.2 9 0 5 10 15 20 25 1 2 3 4 5 6 7 8 9 10 Percent L ik e li h o o d 11.8 5.2 7.4 4.9 15.1 13.7 16.8 11.2 6.5 7.4 0 5 10 15 20 1 2 3 4 5 6 7 8 9 10 Percent L ik e li h o o d 49 issues. The results show that respondents felt as though they could contribute positively to environmental matters. For example, the majority of individuals feel that a consumer’s behavior can have positive effects on society (90.1% agreed and strongly agreed), that individuals are capable of helping to solve environmental issues (80.4%), and they try to consider how travel products will affect the environment (51.7%). Similarly, the majority of individuals disagree with the statement that there is not much one person can do about the environment (91%) and the statement that the conservation efforts of one person are useless (84.6%). The agreement (agree and strongly agree) and disagreement (disagree and strongly disagree) statements are combined above. Table 4.7. Perceived consumer effectiveness. Answer options Strongly disagree Disagree Agree Strongly agree Unsure n There is not much that any one individual can do about the environment 44.5% 46.5% 6.3% 1.8% .9% 681 The conservation efforts of one person are useless as long as other people refuse to conserve 35.3% 49.3% 10.8% 3.5% 1.0% 679 Each consumer’s behavior can have a positive effect on society by purchasing products sold by socially responsible companies 2.2% 4.1% 45.9% 44.2% 3.5% 679 I feel capable of helping solve the environmental problems 1.5% 12.8% 56.5% 23.9% 5.3% 678 I can protect the environment by buying products that are friendly to the environment 1.5% 3.5% 51.0% 40.4% 3.5% 678 I feel I can help solve natural resource problems by conserving water and energy 1.2% 4.6% 50.3% 39.9% 4.0% 676 When I buy everyday household products (such as groceries or cleaning products), I try to consider 3.9% 17.9% 45.9% 26.4% 5.9% 675 50 how my use of them will affect the environment and other consumers When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the environment and other consumers 7.4% 34.0% 40.0% 11.7% 6.9% 677 General Green Purchase Behaviors and Green Travel Purchase Behaviors The next set of questions inquired about actual green purchasing behaviors rather than intention to purchase, in an attempt to minimize bias of providing socially acceptable responses. Nearly all (94.3% agree and strongly agree combined) respondents reported trying to buy energy efficient appliances, 74.5% of individuals reported that they have switched products for ecological reasons, and 91.0% reported that they purchase products made from recyclable materials. Similarly, 86.4% of respondents agree with preferring green products to non-green products when other qualities are similar. However, 48.3% of individuals disagreed with the statement that they have gone out of their way to seek biodegradable products. When respondents were asked about their green travel purchase behaviors, the three most common response choices were purchasing locally-made crafts (87.5% agreed or strongly agreed), bringing and using a reusable water bottle (85.7%), and eating organic and/or vegetarian meal(s) (56.4%). Table 4.8. Green behaviors. Answer options Strongly disagree Disagree Agree Strongly agree Unsure n Green Purchase Behaviors I try to buy energy efficient household appliances 1.3% 2.6% 44.4% 49.9% 1.8% 680 I prefer green products over non-green products when their product qualities are similar 2.7% 7.0% 48.5% 37.9% 4.0% 676 51 When I have a choice between two equal products, I purchase the one less harmful to other people and the environment 1.8% 8.6% 47.0% 37.8% 4.9% 677 I purchase products made from recyclable materials 1.3% 5.0% 58.2% 32.8% 2.6% 680 I have switched products for ecological reasons 2.9% 18.4% 46.3% 28.2% 4.1% 678 I make special effort to buy household chemicals such as detergents and cleaning solutions that are environmentally friendly 2.9% 21.1% 46.1% 25.4% 4.4% 681 When I want to buy a product, I look at the ingredients label to see if it contains things that are environmentally damaging 4.0% 33.8% 39.7% 18.6% 4.0% 678 I will not buy a product if the company that sells it is ecologically irresponsible 4.0% 28.2% 42.7% 16.4% 8.7% 677 I prefer green products over non- I have gone out of my way to seek out biodegradable products 7.0% 41.3% 31.5% 14.9% 5.3% 676 I have convinced members of my family or friends not to buy some products that are harmful to the environment 7.2% 29.6% 42.6% 13.0% 7.7% 679 Green Travel Purchase Behaviors I brought and used a reusable water bottle 1.9% 11.3% 37.0% 48.7% 1.0% 679 I purchased locally-made crafts 1.6% 8.4% 47.6% 39.9% 2.4% 676 I ate organic and/or vegetarian meal(s) 12.1% 29.5% 32.5% 23.9% 1.9% 677 I rented or bought a high- mileage, more fuel-efficient car 11.4% 34.6% 29.7% 19.2% 5.2% 677 I traveled by train, subway, bus or other public transportation 17.8% 43.7% 21.2% 13.3% 4.0% 679 I researched and booked “greener” accommodations 15.4% 51.3% 21.3% 5.0% 7.1% 677 I have used a carbon offset program to counter my carbon footprint 22.0% 50.9% 11.7% 3.9% 11.6% 674 52 Test Results Research Question 1, What factors influence an individual to select an attraction to visit?, was addressed using the descriptive information in Table 4.3. The top three influencing factors respondents selected when thinking about their last vacation, were because of the activities available there (64.6%), reputation of attraction (52.3%), and price/good value (48.0%). The three factors that most influenced respondents’ visitation to the specific attractions were reputation of the attraction (55.2%), because of the activities available at the attraction (47.9%), and friend/family member wanted to visit (45.7%). Research Question 2, How much of an impact do the sustainable features of an attraction have on the selection of that attraction?, was answered using three multiple regression models; the importance of the non-green factors relating to the likelihood of seeking out and choosing sustainable attractions (CSA); the importance of the green factors relating to CSA; and the importance of the green factors relating to the likelihood that the sustainable practices of attractions increase the chance of visitation (ICV). Prior to the regression analysis, the data was checked to ensure it met the assumptions of the analysis. The following will discuss sample size, multicollinearity and singularity, normality, outliers, linearity and homoscedasticity, and independence of residuals. To conduct a multiple regression, Tabachnick (2001) recommends n = 50 + 8m, with m equaling the number of predictor variables. Stevens, as cited in Pallant (2005), uses 15m as a guideline for the number of cases desired for a regression. By both measures, the study sample is large enough at n=681. Multicollinearity refers to the relationship among the independent variables (Pallant, 2005). Multicollinearity exists when the independent variables are highly correlated. When assessing mulitcollinearlity, Pallant (2005) 53 recommends a bivariate correlation below .9. All of the independent variables were below the suggested r=.9 correlation, however all but one variable were lower than Pallant’s (2005) suggested r=.3. The only variable between r=.3 and r=.9 was Environmental/sustainable/socially responsible practices of the attraction site (r=.640). Two other measures of multicollinearity, Tolerance and Variance Inflation Factor (VIF) were examined. Tolerance indicates how much of a specified variable is not explained by the other independent variables in the model "and is calculated using the formula 1-R2 for each variable” (Pallant, 2005, p. 150). The tolerance measure should be .10 or above, a number smaller than this would indicate the correlation with other variables as very high. The VIF is the inverse of Tolerance and is calculated as 1 divided by Tolerance; a VIF score higher than 10 (Pallant, 2005) would be a concern. All of the independent variables performed well on these collinearity diagnostic tests (Table 4.9), therefore the decision was made to include them in the initial regression model. Table 4.9. Collinearity statistics for the nine non-green independent variables. Collinearity Statistics Variable Tolerance VIF Non-green Advertising or promotional material .846 1.182 Online review .803 1.245 Reputation of attraction .798 1.252 Convenient location .674 1.483 Price/good value .663 1.508 Friend/family wanted to visit .855 1.169 Environmental/sustainable/socially responsible practices of the attraction site .907 1.102 Activities available there .790 1.266 Special events at the attraction .819 1.221 54 A histogram showing normality, the normal P-P plot of regression, and a scatterplot of standardized residuals were utilized in order to evaluate the assumptions for the dependent variable. Normality of the dependent variable was assessed by examining a histogram of the data (Figure 4.3), where the x-axis portrays the mean and standard deviations of the data. Figure 4.3. Histogram of dependent variable CSA (n=617). Additionally, the Normal P-P Plot of Regression for the model indicates no major deviations from normality, with the points “lying in a reasonably straight diagonal line from bottom left to top right” (Pallant, 2005). 55 Figure 4.4. Normal P-P Plot of regression standardized residual for CSA. The Scatterplot of Standardized Residuals is roughly rectangularly distributed and most points are concentrated in the center along the 0 point (Pallant, 2005). Additionally, the scatterplot confirms the absence of a great number of significant outliers based on Tabachnick's suggestion that standard residual values should fall between -3.3 and 3.3 (2001). 56 Figure 4.5. Scatterplot of standardized residuals for CSA. In determining the skewedness (tilt) of data, skew values should fall between +2 and -2. In determining kurtosis (peakedness) of data distribution, a common rule of thumb is also +2 to -2. Non-green factors data for this study are presented in Table 4.10 below. All values related to skew and kurtosis fall between the +2 to -2 guidelines. Table 4.10. Skew and kurtosis of nine non-green variables. Non-green variables Skew Kurtosis Advertising or promotional material .451 -.353 Online review -.148 -.843 Reputation of attraction -.930 1.003 Convenient location -.366 -.483 Price/good value -.676 -.158 Friend/family wanted to visit -.605 -.256 Environmental/sustainable/socially responsible practices of the attraction site -.080 -.752 Activities available there -.563 -.042 Special events at the attraction .122 -.623 An initial regression with the nine predictor variables was run for the purpose of data reduction. The motivation for running the initial regression was not to test the full 57 model, but to explore the unique contribution to variance in the dependent variable for each of the independent variables so that further, more educated analyses might be run. Tabachnick (2001) suggests that each of the independent variables show some relationship with the dependent variable, recommending a .3 correlation or above, however overall correlations of the independent variables with non-green Factors were all low except for Environmental/sustainable/socially responsible practices of the attraction site (ESSRP) with a correlation of .640 (Table 4.11). Table 4.11. Bivariate correlations of non-green independent variables with CSA. Variable Correlation with CSA Non-green Advertising or promotional material .126 Online review .070 Reputation of attraction .172 Convenient location .021 Price/good value .082 Friend/family wanted to visit .069 Environmental/sustainable/socially responsible practices of the attraction site .640 Activities available there .169 Special events at the attraction .146 Model Summary 1- Non-Green Factors and CSA - Initial Multiple Regression Results of the regression model were statistically significant, F (9, 642) = 50.734, p < .0005 and the predictor variables accounted for 41.9% of the variance in CSA (R2=.419). Beta coefficients from the regression analysis are presented in Table 4.12. ESSRP makes the largest unique contribution to the model. It is the only statistically significant variable at the .05 level with a ? = .623. For every one unit increase in ESSRP, CSA increases by 1.842 units (?= 19.598). 58 Table 4.12. Summary of multiple regression for variables predicting CSA (N = 642). Variable B Std. Error ? t Sig. Non-green Advertising or promotional material .093 .572 .018 .162 .871 Online review -.061 .098 -.021 -.618 .537 Reputation of attraction .062 .134 .016 .467 .641 Convenient location -.199 .115 -.064 -1.730 .084 Price/good value .038 .133 .011 .289 .773 Friend/family wanted to visit .023 .100 .007 .228 .820 Environmental/sustainable/ socially responsible practices of the attraction site 1.842 .094 .623 19.598 .000* Activities available there .224 .121 .063 1.849 .065 Special events at the attraction .121 .103 .039 1.173 .241 *p<.05 Based on the output from the initial run, additional regression models using these independent variables and CSA were not tested as only one variable was found to be significant. Discussion of Assumptions for Model Summary 2 Sample size was previously discussed and continues to apply to this regression model, therefore it will not be discussed here. The other assumptions will however be reviewed as this regression utilizes a different combination of independent variables and dependent variable. Multicollinearity and Singularity All of the correlations between the independent variables, except for Eco-Friendly Furnishings and Built with Eco-Friendly Materials, fell between r=.3 and r=.9 indicating a healthy correlation between them. The two other measures of multicollinearity, Tolerance 59 and VIF were also examined for this data set. In terms of Tolerance, the values for Energy Efficiency (.099) and Water Efficiency (.099) were both below .10. Similarly, the VIF values for these two variables were 10.105 and 10.073, respectively. Because of this, these two variables were not included in the initial regression model. Additionally, the correlation between Eco-Friendly Furnishings and Built with Eco-Friendly Materials was high (.916), therefore Built with Eco-Friendly Materials was not included in the initial regression. Table 4.13. Collinearity statistics for the fifteen green independent variables. Collinearity Statistics Variable Tolerance VIF Green Built with eco-friendly materials .138 7.236 Eco-friendly furnishings .136 7.357 Carbon reduction or offset programs .233 4.295 Energy efficiency .099 10.105 Water efficiency .099 10.073 Recycling .263 3.796 Composting .319 3.134 Indoor air quality .505 1.980 Non-toxic cleaning chemicals .309 3.234 The use of biodegradable products .235 4.261 Natural landscape .615 1.627 Use of hybrid company vehicles .376 2.658 Involvement in local environmental efforts .327 3.056 Certification as a sustainable or green site .231 4.332 Green sustainable dining options on site or nearby .288 3.476 Normality of the dependent variable was assessed by examining a histogram of the data (Figure 4.6), where the x-axis portrays the mean and standard deviations of the data. 60 Figure 4.6. Histogram of dependent variable CSA. Additionally, the Normal P-P Plot of Regression for the model indicates no major deviations from normality, with the points “lying in a reasonably straight diagonal line from bottom left to top right” (Pallant, 2005). Figure 4.7. Normal P-P plot of regression standardized residual for CSA. The Scatterplot of Standardized Residuals is roughly rectangularly distributed and most points are concentrated in the center along the 0 point (Pallant, 2005). Additionally, the 61 scatterplot confirms the absence of outliers based on Tabachnick's suggestion that standard residual values should fall between -3.3 and 3.3 (2001). Figure 4.8. Scatterplot of standardized residuals for CSA. The skewedness (tilt) and kurtosis (peakedness) of data distribution for Green factors data are presented in Table 4.14 below. All values related to skew and kurtosis fall between the +2 to -2 guidelines. Table 4.14. Skew and kurtosis of fifteen green variables. Green variables Skew Kurtosis Built with eco-friendly materials .232 -.759 Eco-friendly furnishings .311 -.696 Carbon reduction or offset programs .335 -.777 Energy efficiency -.074 -.934 Water efficiency -.151 -.912 Recycling -.331 -.926 Composting .178 -.901 Indoor air quality -.546 -.527 Non-toxic cleaning chemicals -.076 -1.057 The use of biodegradable products -.073 -.944 Natural landscape -.862 -.091 Use of hybrid company vehicles .517 -.626 62 Involvement in local environmental efforts -.063 -.941 Certification as a sustainable or green site -.775 -.899 Green sustainable dining options on site or nearby -.347 -.743 Tabachnick (2001) suggests that each of the independent variables show some relationship with the dependent variable, recommending a .3 correlation or above. All correlations of the independent variables with CSA were above .3 (Table 4.15). Table 4.15. Bivariate correlations of green factors with CSA. Variable Correlation with CSA Green Built with eco-friendly materials .615 Eco-friendly furnishings .643 Carbon reduction or offset programs .628 Energy efficiency .596 Water efficiency .601 Recycling .528 Composting .537 Indoor air quality .391 Non-toxic cleaning chemicals .531 The use of biodegradable products .571 Natural landscape .372 Use of hybrid company vehicles .560 Involvement in local environmental efforts .581 Certification as a sustainable or green site .656 Green sustainable dining options on site or nearby .637 Model Summary 2- Green Factors and CSA - Initial Multiple Regression An initial regression with 12 predictor variables was run for the purpose of data reduction. Results of the regression model were statistically significant, F (12, 662) = 58.694 p < .0005 and the predictor variables accounted for 52.0% of the variance in CSA (R2= .520). Beta coefficients from the regression analysis are presented in Table 4.16. Eco-Friendly Furnishings (.218) makes the largest unique contribution to the model, followed by 63 Certification as a Sustainable or Green Site (.217). For every one unit increase in Eco- Friendly Furnishings, CSA increases by .659 units (? = 4.203). For every one unit increase in Certification as a Sustainable or Green Site, CSA increases by .629 units (? = 3.844). Table 4.16. Summary of multiple regression for variables CSA (N = 662). Variable B Std. Error ? t Sig. Green Eco-friendly furnishings .659 .157 .218 4.203 .000* Carbon reduction or offset programs .323 .155 .112 2.085 .037* Recycling .222 .124 .083 1.792 .074 Composting -.109 .133 -.039 -.819 .413 Indoor air quality -.065 .108 -.023 -.600 .549 Non-toxic cleaning chemicals .272 .129 .102 2.105 .036* The use of biodegradable products -.177 .153 -.064 -1.155 .249 Natural landscape .001 .101 .000 .012 .991 Use of hybrid company vehicles .009 .129 .003 .068 .945 Involvement in local environmental efforts .144 .131 .052 1.098 .273 Certification as a sustainable or green site .629 .164 .217 3.844 .000* Green sustainable dining options on site or nearby .445 .139 .162 3.205 .001* Based on the output from the initial run, an additional regression model was tested using eight predictor variables. This model used independent variables Eco-Friendly Furnishings, Carbon Reduction or Offset Programs, Recycling, Non-Toxic Cleaning Chemicals, the Use of Biodegradable Products, Involvement in Local Environmental Efforts, 64 Certification as a Sustainable or Green Site, and Green Sustainable Dining Options on Site or Nearby. Model Summary - Green Factors and CSA - Revised Multiple Regression Results of the regression model were statistically significant, F (8, 662) = 88.325, p < .0005 and the predictor variables accounted for 51.9% of the variance in CSA. Certification as a Sustainable or Green Site (.215) had the largest unique contribution to the model, followed by Eco-Friendly Furnishings (.213), and Green Sustainable Dining Options Onsite or Nearby (.162). Table 4.17. Summary of revised multiple regression for variables predicting CSA (N = 662). Variable B Std. Error ? t Sig. Eco-friendly furnishings .641 .154 .213 4.153 .000* Carbon reduction or offset programs .302 .151 .105 1.994 .047* Recycling .173 .112 .065 1.540 .124 Non-toxic cleaning chemicals .236 .116 .089 2.036 .042* The use of biodegradable products -.186 .151 -.067 -1.231 .219 Involvement in local environmental efforts .143 .129 .052 1.113 .266 Certification as a sustainable or green site .622 .160 .215 3.886 .000* Green sustainable dining options on site or nearby .446 .136 .162 3.284 .001* *p<.05 65 Independent Variable Evaluation The square of the value in Part Correlations explains the percent of variance in R2. In other words, this predicts how much R2 value would drop if this variable were omitted (Pallant, p. 154). In this case, 1.28% of variance in CSA is explained by Eco-friendly furnishings, while Certification as a Green or Sustainable Site explains 1.10 of this variance. Table 4.18 below presents each unique variance of the eight predictor variables. Table 4.18. Variance CSA explained by each independent variable within a multiple regression analysis. Independent variables Part correlation Percentage of CSA variance explained Eco-friendly furnishings .113 1.28% Carbon reduction or offset programs .054 .29% Recycling .042 0.18% Non-toxic cleaning chemicals .055 .30% The use of biodegradable products -.033 0.11% Involvement in local environmental efforts .030 0.09% Certification as a sustainable or green site .105 1.10% Green sustainable dining options on site or nearby .089 .79% To answer research question 3, How much does perceived consumer effectiveness explain the selection of sustainable attractions?, a multiple regression analysis was done in order to explore perceived consumer effectiveness and its relation to CSA in the coming year. The independent variables of perceived consumer effectiveness were analyzed using multiple regression to determine if they predict CSA. 66 Discussion of Assumptions for Model Summary 3 Sample size was previously discussed and continues to apply to this regression model therefore it will not be discussed here. The other assumptions will however be reviewed as this regression utilizes a different combination of independent and dependent variables. Multicollinearity and Singularity The assumption results for these Green Factor independent variables were discussed previously in the Model Summary 2 section, however they will be reviewed again for this particular Model Summary. Again, because of the VIF and the Tolerance values for Energy Efficiency and Water Efficiency, these two variables were not included in the initial regression. Collinearity statistics for the fifteen green independent variables were previously presented in Table 4.13. Normality of the dependent variable was assessed by examining a histogram of the data (Figure 4.9), where the x-axis portrays the mean and standard deviations of the data. Figure 4.9. Histogram of dependent variable ICV. 67 Additionally, the Normal P-P Plot of Regression for the model indicates no major deviations from normality, with the points “lying in a reasonably straight diagonal line from bottom left to top right” (Pallant, 2005). Figure 4.10. Normal P-P plot of regression standardized residual for ICV. The Scatterplot of Standardized Residuals is very roughly rectangularly distributed and most points are concentrated in the center along the 0 point (Pallant, 2005). There do appear to be a few outliers based on Tabachnick's (2001) suggestion that standard residual values should fall between -3.3 and 3.3, however this is normal given the large sample size and further action is not necessary. 68 Figure 4.11. Scatterplot of standardized residuals for ICV. The skewedness (tilt) and kurtosis (peakedness) of data distribution for Green factors data were presented in Table 4.14. All correlations of the independent variables with ICV were above .3 (Table 4.19). Table 4.19. Bivariate correlations of green factors with ICV. Variable Correlation with ICV Green Built with eco-friendly materials .627 Eco-friendly furnishings .648 Carbon reduction or offset programs .644 Energy efficiency .608 Water efficiency .616 Recycling .563 Composting .566 Indoor air quality .412 Non-toxic cleaning chemicals .551 The use of biodegradable products .587 Natural landscape .401 Use of hybrid company vehicles .568 Involvement in local environmental efforts .599 Certification as a sustainable or green site .663 69 Green sustainable dining options on site or nearby .635 Model Summary 3- Green Factors and ICV - Initial Multiple Regression An initial regression with 12 predictor variables was run for the purpose of data reduction. The motivation for running the initial regression was not to test the full model, but to explore the unique contribution to variance in the dependent variable for each of the independent variables so that further, more educated analyses might be run. Results of the regression model were statistically significant, F (12, 662) = 63.233 p < .0005 and the predictor variables accounted for 53.9% of the variance in ICV (R2= .539). Beta coefficients from the analysis are presented in Table 4.20. Certification as a Sustainable or Green Site (.212) makes the largest unique contribution to the model, followed by Eco-Friendly Furnishings (.181). Both of these variables are statistically significant at the .05 level and are displayed in Table 4.20. For every one unit increase in Certification as a Sustainable or Green Site, ICV increases by .610 units (? = 3.833). For every one unit increase in Eco- Friendly Furnishings, ICV increases by .540 units (? = 3.541). Table 4.20. Summary of multiple regression for variables predicting ICV (N = 662). Variable B Std. Error ? t Sig. Green Eco-friendly furnishings .540 .290 .181 3.541 .000* Carbon reduction or offset programs .388 .151 .136 2.576 .010* Recycling .32 .121 .121 2.658 .008* Composting -.030 .129 -.011 -.235 .814 Indoor air quality -.060 .105 -.021 -.566 .571 Non-toxic cleaning chemicals .322 .126 .122 2.560 .011* The use of biodegradable products -.243 .149 -.089 -1.630 .104 Natural landscape .072 .099 .024 .728 .467 70 Use of hybrid company vehicles .003 .126 .001 .023 .982 Involvement in local environmental efforts .166 .128 .060 1.298 .195 Certification as a sustainable or green site .610 .159 .212 3.833 .000* Green sustainable dining options on site or nearby .338 .135 .124 2.500 .013* *p<.05 Based on the output from the initial run, an additional regression model was tested using eight predictor variables. This model used independent variables Eco-Friendly Furnishings, Carbon Reduction or Offset Programs, Recycling, Non-Toxic Cleaning Chemicals, the Use of Biodegradable Products, Involvement in Local Environmental Efforts, Certification as a Sustainable or Green Site, and Green Sustainable Dining Options on Site or Nearby. These eight variables were chosen based on their significance levels and Beta values. Model Summary - Green Factors and ICV - Revised Multiple Regression Results of the regression model were statistically significant, F (8, 662) = 95.221, p < .0005 and the predictor variables accounted for 53.8% of the variance in ICV. Certification as a Sustainable or Green Site (.211) had the largest unique contribution to the model, followed by Eco-Friendly Furnishings (.178). Table 4.21. Summary of revised multiple regression for variables predicting ICV (N = 662). Variable B Std. Error ? t Sig. Eco-friendly furnishings .532 .150 .178 3.546 .000* Carbon reduction or offset programs .375 .147 .132 2.548 .011* 71 Recycling .308 .109 .116 2.815 .005* Non-toxic cleaning chemicals .302 .113 .115 2.682 .007* The use of biodegradable products -.232 .147 -.085 -1.577 .115 Involvement in local environmental efforts .179 .125 .065 1.425 .155 Certification as a sustainable or green site .607 .156 .211 3.898 .000* Green sustainable dining options on site or nearby .345 .132 .126 2.612 .009* *p<.05 Independent Variable Evaluation The square of the value in Part Correlations explains the percent of variance in R2. In other words, this predicts how much R2 value would drop if this variable were omitted (Pallant, 2005, p. 154). In this case, 1.08% of variance in ICV is explained by Certification as a Green or Sustainable Site, while Eco-friendly furnishings explains .88% of this variance. Table 4.22 below presents each unique variance of the eight predictor variables. Table 4.22. Variance ICV explained by each independent variable within a multiple regression analysis. Independent variables Part correlation Percentage of ICV variance explained Eco-friendly furnishings .094 .88% Carbon reduction or offset programs .068 .46% Recycling .075 .56% Non-toxic cleaning chemicals .071 .50% The use of biodegradable products -.042 .18% Involvement in local environmental efforts .038 .14% Certification as a sustainable or green site .104 1.08% Green sustainable dining options on site or nearby .069 .48% 72 Discussion of Assumptions for Model Summary 4 Sample size was previously discussed and continues to apply to this regression model, therefore it will not be discussed here. All of the independent variables were below Pallant’s (2005) recommended r= .9 correlation value, however only three were above the recommended r=.3 value. The variables that fell below r= .3 therefore may have minimal relationship to the dependent variable. The two other measures of multicollinearity, Tolerance and VIF were also examined for this data set. In terms of Tolerance, all values were above .10. Similarly, all VIF were below 10. Table 4.23. Collinearity statistics for the eight PCE independent variables Collinearity statistics Variable Tolerance VIF PCE There is not much that any one individual can do about the environment .593 1.686 The conservation efforts of one person are useless as long as other people refuse to conserve .577 1.732 Each consumer’s behavior can have a positive effect on society by purchasing products sold by socially responsible companies .559 1.788 I feel capable of helping solve the environmental problems .695 1.439 I can protect the environment by buying products that are friendly to the environment .430 2.323 I feel I can help solve natural resource problems by conserving water and energy .543 1.843 When I buy everyday household products (such as groceries or cleaning products), I try to consider how my use of them will affect the environment and other consumers .585 1.708 When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the environment and other consumers .705 1.417 Normality of the dependent variable was assessed by examining a histogram of the data (Figure 4.1), where the x-axis portrays the mean and standard deviations of the data. 73 Figure 4.12. Histogram of dependent variable CSA. Additionally, the Normal P-P Plot of Regression for the model indicates no major deviations from normality. Figure 4.13. Normal P-P plot of regression standardized residual. The Scatterplot of Standardized Residuals confirms that the assumptions were not violated and that there are not a great number of significant outliers. 74 Figure 4.14. Scatterplot of standardized residuals. The skewedness (tilt) and (peakedness) of PCE variable data are presented in Table 4.24 below. All values related to skew fall between the +2 to -2 guidelines. In terms of kurtosis, there were three variables that fell outside the more lenient criteria of +3 to -3. They were Each consumer’s behavior can have a positive effect on society by purchasing products sold by socially responsible companies, I can protect the environment by buying products that are friendly to the environment, and I feel I can help solve natural resource problems by conserving water and energy. The fact that they fell on the positive end, signifies that the distribution is fairly peaked and clustered in the center with long, thin tails (Pallant, 2005). This can result in an underestimate of the variance, however this risk is reduced with large sample sizes of more than 200 cases, therefore these cases were left in the model (Pallant, 2005). Table 4.24. Skew and kurtosis of eight PCE variables. PCE variables Skew Kurtosis 75 There is not much that any one individual can do about the environment .789 .960 The conservation efforts of one person are useless as long as other people refuse to conserve .676 .560 Each consumer’s behavior can have a positive effect on society by purchasing products sold by socially responsible companies -1.822 3.991 I feel capable of helping solve the environmental problems -1.448 2.591 I can protect the environment by buying products that are friendly to the environment -1.882 4.704 I feel I can help solve natural resource problems by conserving water and energy -1.846 4.361 When I buy everyday household products (such as groceries or cleaning products), I try to consider how my use of them will affect the environment and other consumers -1.089 .982 When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the environment and other consumers -.646 .207 Tabachnick (2001) suggests that each of the independent variables show some relationship with the dependent variable, recommending a .3 correlation or above. Only three variables had correlations above .3. All other correlations of the independent variables with CSA were just slightly below .3 (Table 4.25). Table 4.25. Bivariate correlations of independent variables with dependent variables. Variable Correlation with CSA PCE There is not much that any one individual can do about the environment -.257 The conservation efforts of one person are useless as long as other people refuse to conserve -.210 Each consumer’s behavior can have a positive effect on society by purchasing products sold by socially responsible companies .257 I feel capable of helping solve the environmental problems .337 I can protect the environment by buying products that are friendly to the environment .283 76 I feel I can help solve natural resource problems by conserving water and energy .217 When I buy everyday household products (such as groceries or cleaning products), I try to consider how my use of them will affect the environment and other consumers .395 When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the environment and other consumers .425 Model Summary 4- PCE and CSA - Initial Multiple Regression An initial regression with 8 predictor variables was run. Results of the regression were statistically significant, F (8, 670) = 32.617 p < .0005 and the predictor variables accounted for 28.3% of the variance CSA (R2= .283). Independent Variable Evaluation Beta coefficients from the regression analysis are presented in Table 4.26. When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the environment and other consumers (.262) makes the largest unique contribution to the model, followed by I feel capable of helping solve the environmental problems (.161). Both of these variables are statistically significant at the .05 level and are displayed in Table 4.26. For every one unit increase in When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the environment and other consumers, CSA increases by .681 units (? = 6.689). For every one unit increase in I feel capable of helping solve the environmental problems, ICV increases by .451 units (? = 4.081). 77 Table 4.26. Summary of multiple regression for variables predicting CSA (N = 670). Variable B Std. Error ? t Sig. There is not much that any one individual can do about the environment -.620 .163 -.162 -3.793 .000 The conservation efforts of one person are useless as long as other people refuse to conserve .006 .148 .002 .039 .969 Each consumer’s behavior can have a positive effect on society by purchasing products sold by socially responsible companies .141 .128 .048 1.100 .272 I feel capable of helping solve the environmental problems .451 .110 .161 4.081 .000 I can protect the environment by buying products that are friendly to the environment .189 .152 .062 1.238 .216 I feel I can help solve natural resource problems by conserving water and energy -.193 .132 -.065 -1.464 .144 When I buy everyday household products (such as groceries or cleaning products), I try to consider how my use of them will affect the environment and others .382 .109 .151 3.514 .000 When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the environment and other consumers .681 .102 .262 6.689 .000 *p<.05 Based on the Beta and significance statistics from the initial run, an additional regression model was tested using seven predictor variables. Model Summary - PCE and CSA - Revised Multiple Regression Results of the regression were statistically significant, F (7, 670) = 37.333, p < .0005 and the predictor variables accounted for 28.3% of the variance in CSA. The variable that had the largest unique contribution to the model (.681) was When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the 78 environment and other consumers, followed by There is not much that any one individual can do about the environment (-.616), and then I feel capable of helping solve the environmental problems (.450). Table 4.27. Summary of revised multiple regression for variables predicting CSA (N = 670). Variable B Std. Error ? t Sig. There is not much that any one individual can do about the environment -.616 .129 -.161 -4.770 .000* Each consumer’s behavior can have a positive effect on society by purchasing products sold by socially responsible companies .142 .128 .049 1.109 .268 I feel capable of helping solve the environmental problems .450 .110 .161 4.109 .000* I can protect the environment by buying products that are friendly to the environment .188 .151 .062 1.242 .215 I feel I can help solve natural resource problems by conserving water and energy -.193 .132 -.065 -1.465 .143 When I buy everyday household products (such as groceries or cleaning products), I try to consider how my use of them will affect the environment and others .382 .108 .151 3.524 .000* When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the environment and other consumers .681 .101 .262 6.713 .000* *p<.05 Independent Variable Evaluation Over four percent (4.88%) of variance in CSA is explained by When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the environment and other consumers, while There is not much that any one individual can 79 do about the environment explains 2.46% of this variance. Table 4.28 below presents each unique variance of the three predictor variables. Table 4.28. Variance of CSA explained by each independent variable within a multiple regression analysis. Independent variables Part correlation Percentage of CSA variance explained There is not much that any one individual can do about the environment -.157 2.46% Each consumer’s behavior can have a positive effect on society by purchasing products sold by socially responsible companies .036 .13% I feel capable of helping solve the environmental problems .135 1.82% I can protect the environment by buying products that are friendly to the environment .041 .17% I feel I can help solve natural resource problems by conserving water and energy -.048 .23%% When I buy everyday household products (such as groceries or cleaning products), I try to consider how my use of them will affect the environment and others .116 1.35% When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the environment and other consumers .221 4.88% To answer Research Question 4, a Pearson product-moment correlation was done to explore the relationship between the ten green purchase behaviors and CSA in the coming year. Pearson Correlation Coefficients range from +1 to -1, with a zero denoting no relationship. Preliminary analyses were performed to ensure no violation of the assumptions of normality, linearity and homoscedasticity. Eight out of the ten Green Purchase Behavior variables had positive and medium strength correlations with CSA. Medium correlation strength refers to r= .30 to .49 or r= -.30 to -.49 (Pallant, 2005). The remaining two Green Purchase Behavior variables had weak positive correlations with CSA. 80 Coefficient of determination was also calculated for each variable. This indicated how much variance the variables share. Therefore, I try to buy energy efficient household appliances and CSA share 12.9% of their variance, which denotes that this particular independent variable helps to explain 12.9% of the variance in respondents’ scores for CSA. All Pearson correlations and coefficients of determination are presented in Table 4.29. Table 4.29. Pearson correlations for green purchase independent variables. Variable r n Sig. Coefficient of determination I have convinced members of my family or friends not to buy some products that are harmful to the environment .359 675 .000* 12.9% I will not buy a product if the company that sells it is ecologically irresponsible .434 673 .000* 18.8% I try to buy energy efficient household appliances .230 676 .000* 5.3% I have switched products for ecological reasons .395 674 .000* 15.6% I make special effort to buy household chemicals such as detergents and cleaning solutions that are environmentally friendly .399 677 .000* 15.9% When I have a choice between two equal products, I purchase the one less harmful to other people and the environment .329 673 .000* 10.8% I purchase products made from recyclable materials .277 676 .000* 7.7% When I want to buy a product, I look at the ingredients label to see if it contains things that are environmentally damaging .424 674 .000* 18.0% I prefer green products over non-green products when their product qualities are similar .319 672 .000* 10.2% I have gone out of my way to seek out biodegradable products .398 672 .000* 15.8% *p<.05 81 The relationship between the seven green travel purchase behaviors and CSA in the coming year (Research Question 5) was investigated using Pearson product-moment correlation. Again, preliminary analyses were performed to ensure no violation of the assumptions of normality, linearity and homoscedasticity. Two out of the seven Green Travel Purchase Behavior variables had positive and medium strength correlations with CSA. Medium correlation strength refers to r= .30 to .49 or r= -.30 to -.49 (Pallant, 2005). The remaining five Green Travel Purchase Behavior variables had weak positive correlations with CSA. Coefficient of determination was also calculated for each variable. This indicated how much variance the variables share. Therefore, I researched and booked “greener” accommodations and CSA share 11.6% of their variance, which denotes that this particular independent variable helps to explain 11.6% of the variance in respondents’ scores for CSA. All Pearson correlations and coefficients of determination are presented in Table 4.30. Table 4.30. Pearson correlations for green travel purchase independent variables. Variable r n Sig. Coefficient of determination I brought and used a reusable water bottle .251 675 .000* 6.3% I purchased locally made crafts .195 672 .000* 3.8% I traveled by train, subway, bus or other public transportation .126 675 .001* 1.6% I rented or bought a high-mileage, more fuel-efficient car .179 673 .000* 3.2% I ate organic and/or vegetarian meal(s) .316 673 .000* 10.0% I researched and booked “greener” accommodations .340 673 .000* 11.6% I have used a carbon offset program to counter my carbon footprint .226 670 .000* 5.1% *p<.05 Results summary A total of 884 surveys were collected, with 681 of those being usable. The data, analyzed using several multiple regression models and Pearson’s correlation, resulted in several 82 significant findings. Descriptive data was used to answer the first research question. The top three influencing factors for selecting an attraction that respondents chose were because of the activities available there (64.6%), reputation of attraction (52.3%), and price/good value (48.0%). In order to answer the second research question, three multiple regression models were used. The first model was statistically significant and revealed that only the ESSRP independent variable contributed to the model in which CSA was the dependent variable. The second regression model indicated that Eco-Friendly Furnishings and Certification as a Sustainable or Green Site had the largest unique contribution (.211 and .178 respectively) to the model in which CSA was the dependent variable. The third multiple regression model was also statistically significant and similarly showed that Certification as a Sustainable or Green Site and Eco-Friendly Furnishings had the largest unique contribution (.215 and .213 respectively) to the model in which ICV was the dependent variable. The third research question was answered using a multiple regression model, which was statistically significant and the predictor variables accounted for 28.3% of the variance in CSA. When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the environment and other consumers made the largest unique contribution in both the initial model as well as the revised model. The fourth research question was answered using Pearson’s correlation, which indicated that eight out of the ten Green Purchase Behavior variables had positive and medium strength correlations with CSA. The fifth research question revealed that two out of the seven Green Travel Purchase Behavior variables had positive and medium strength correlations with CSA and the remaining five Green Travel Purchase Behavior variables 83 had weak positive correlations with CSA. In the final chapter these results will be discussed as well as limitations and future research. 84 Chapter 5: Discussion The purpose of this study was to explore whether and to what extent the sustainable features of an attraction have on a consumer’s decision to frequent that site. This research also investigated the relationships between green purchase behaviors and a consumer’s likelihood of seeking out a sustainable attraction. PCE was used as the theoretical foundation and insight into its relationship with CSA was also examined. The following section reviews the test results and draws conclusions, provides practical and academic implications, explores limitations of the study, and offers recommendations for future research. Implications of test results To answer the first research question, What factors influence an individual to select an attraction to visit?, the descriptive results from two of the survey questions were used. The questions asked which factors of attractions most influence the respondents during the selection process. One question referenced attractions in general while the other listed the same characteristics as answer choices but referenced a specific attraction. For both general and specific attractions, the top two choices were because of the activities available there and reputation of attraction. Despite the fact that because of environmental/sustainable/socially responsible practices of the attraction site was an answer choice, it ranked 6th out of 7 choices for both general attractions as well as specific attractions. These results supported the findings of Tsai and Tsai (2008) in that consumers often consider the price, appearance, and functionality before assessing the environmental 85 status of the product. Similarly, McDonald et al. (2009) found that sustainability criteria was compromised in favor of other factors such as price and convenience. Firth & Hing (1999) also discovered that backpackers chose price, location, services, and facilities over implementation of ecofriendly practices when selecting hostels. There may be several reasons that the environmental initiatives of an attraction did not play a greater role in the respondent’s selection criteria. For example, McDonald et al. (2009, p.141) noted “sustainability criteria is not used consistently across product sectors” and consumers focus on different green criteria in different product segments. Therefore, environmental factors may be considered for certain products but not taken into account at all for other types of products (McDonald et al., 2009). Another reason that the environmental aspect may not have been a larger deciding factor in consumer attraction selection could be explained by the fact that consumers treat vacation related decisions and purchases as luxuries, or that consumption behaviors are different or even opposite than that of daily life (Tsai & Tsai, 2008). Similarly, consumers feel as though they ‘earn’ the right to choose environmentally unfriendly options on vacation because of the more environmentally friendly actions they take at home (McDonald et al., 2009). In order to satisfy the second research question, How much of an impact do the sustainable features of an attraction have on the selection of that attraction? , multiple regression tests were used on three different combinations of independent and dependent variables. First, a model for Non-Green Factors and CSA was generated. This model was statistically significant, however the only independent variable that made a significant contribution to the model was Environmental/Sustainable/Socially Responsible Practices 86 of the Attraction Site. This result reflects the sentiment expressed by Choi, et al. (2009) who stated that greater than 75% of the population uses environmental criteria when deciding on a consumer purchase. The second multiple regression test examined the relationship between the Green Factors independent variables and CSA. These independent variables consisted of sustainability initiatives that could potentially be adopted by tourist attractions. The revised regression model was found to be statistically significant and Certification as a Sustainable or Green Site and Eco-Friendly Furnishings had the greatest unique contributions to the model. The third multiple regression test looked at the Green Factor independent variables with ICV. This model was also significant and similar to the above model, Certification as a Sustainable or Green Site and Eco-Friendly Furnishings again had the largest unique contribution to the model. It is interesting to note that these two factors were only considered to be of moderate importance for respondents when selecting attractions in the descriptive results. It is also noteworthy that the multiple regression models for both CSA and ICV paired with Green Factors yielded the same two independent factors as having the greatest unique contribution. Lee, Han, and Willson (2011) noted that furnishings were one of the factors that could be considered a strong feature of positively viewed guestrooms in green hotels. Additionally, mindclick (Abrams, 2012) reported that business travelers rated the sustainable furnishings of a hotel room as important, if not more so, than the operational efforts such as LEED certification and Energy Star ratings. Research has suggested that tourists make choices based on whether they can directly see or feel the environmental 87 aspects, rather than less visible initiatives such as energy or water efficiency (Esparon et al., 2013; Puhakka & Siikamaki, 2012). For example, PGAV Destination Consulting (2008) reported that LEED Certification, which is the standard system for sustainable facilities, ranked very last as an outward sign of environmental commitment valued by the attraction visitors that they surveyed. Similarly, a study by Dodds et al. (2010) of tourists in Thailand and Indonesia revealed that the respondents reported environmental issues that could be seen and felt such as waste, water cleanliness, and the marine environment. This may be one possible explanation for the fact that Eco-Furnishings had a higher Beta value than other Green Factor choices such as Recycling or Involvement in Local Environmental Efforts. The latter items cannot be directly seen or felt and furthermore, unless those sustainability initiatives are conveyed to the consumer, they may go completely unnoticed. The other top contributing factor for these two models was Certification as a Sustainable or Green Site. This finding supports other research done on consumer perception of certification programs. Esparon et al. (2013) found that at “accommodations, visitors perceived most attributes of certification to be important” and that certification operators performed “better” than non-certified operators on multiple features. Other research has also supported the consumer benefits of certification or ecolabel development for tourism products and services (Puhakka & Siikamaki, 2012). To answer Research Question 3, How much does perceived consumer effectiveness explain the selection of sustainable attractions?, multiple regression was used and the model was statistically significant. The variable that had the largest unique contribution to the model was When I buy travel products (such as a hotel room or a restaurant meal), I try to consider how my use of them will affect the environment and other consumers. This signifies 88 that individuals, who believe they can make a difference in terms of travel products, also intend to seek out and choose a sustainable attraction. The consideration of the environment when buying everyday household products did not result in the same findings. The relationship between PCE and CSA supports past studies that have shown there is a positive correlation between environmental concern and environmentally friendly behavior (Straughan & Roberts, 1999). Similarly, Tan & Lau (2011) found that PCE and green purchase attitude was significantly related to green purchase behavior. In the context of tourism, Kim & Han (2010) found that PCE played a meaningful role in explaining hotel customers’ environmentally friendly decisions. However, these results were contrary to the findings by Kim (2011) in which PCE did not improve the prediction of green buying behavior. It should be noted, that PCE models have been found to be highly effective in predicting environmentally friendly behaviors and higher correlational values would have been expected in the results. Further research between PCE and consumer purchase intention in regards to tourism attractions would be constructive. To explore correlations between ‘everyday’ green purchases (GPB) and CSA, and between GTPB and CSA (Research Questions 4 and 5), Pearson’s Correlation was used. Almost all of the GPB showed positive and medium strength correlations with CSA. Only two of the GTPB resulted in positive and medium strength correlations with CSA. These results are quite different for two sets of seemingly similar independent variables. It is possible that GPB generally had higher correlations than GTPB due to the fact that individuals are more familiar with everyday green products rather than green travel products. Cleaning products and grocery items are regularly visible to consumers therefore 89 it is more likely that consumers have greater familiarity with those items. Green or sustainable travel products and options may be less known. The fact that there were positive and medium strength correlations between GPB and CSA support the previous findings by Bergin-Seers and Mair (2009) in which tourists who had green behaviors at home were also more likely to exhibit green behaviors while traveling. It is puzzling however that the relationships between GTPB and CSA were not stronger. A modification to possibly explore these results further will be discussed in the future research section. Practical implications It is important to consider the implications of this study for the tourism industry and attractions in particular. For example, an improved understanding of the impact of sustainable features that attraction management adopts would be beneficial for established sites, as well as future developments. Given that consumers appear to both appreciate and value sustainable initiatives that they can see and feel more than those they cannot, it is essential for management and marketing to implement and effectively convey those elements. For example, Weaver (2006) suggests that green conventional tourism products are not as visible to conscientious travelers as organic products are to the conscientious grocery shopper. Therefore, informed choice is limited by visual clues, knowledge of the product, as well as due to a lack of resources that communicate product information to consumers (Weaver, 2006). This realization provides marketers rationale and motivation for the successful conveyance of tourist attraction products and services. This will establish and convey the visibility and experience of environmental initiatives needed to appeal to consumers. 90 This research as well as previous research has indicated that consumers hold positive views of tourism certification programs, and has also shown them to be beneficial for consumers. Visitors utilize certification to identify sustainable tourism businesses and products, however these programs have been found to be more important for accommodations rather than attractions (Esparon et al., 2013). This suggests that increased effort in conveying the importance of certification attributes for attractions is needed (Esparon et al., 2013). Furthermore, as the majority of respondents were not familiar with NC GreenTravel, this is a prime opportunity to increase the marketing efforts of this program in order to boost program recognition. The totality of this information signals fundamental action on the part of both attractions and certification associations. Attractions can and should confidently seek out and implement the necessary initiatives in order to obtain sustainable or green certification that will appeal to tourists, while also increasing the visibility of this endorsement. Additionally, it will be essential for certifying associations to make every possible effort to reach out to and educate not only the green travelers, but also all travelers, to obtain widespread support and recognition of certification programs. In terms of PCE and CSA, a better comprehension of the relationship between an individual’s beliefs about their environmental actions and their intention to select sustainable attractions would be useful when planning and implementing a variety of sustainable initiatives. If attraction management has the ability to convey to tourists that their actions will benefit the environmental efforts of the site, it may be more likely that tourists will choose to frequent those businesses. Furthermore, travel can also be thought of as an opportunity for individuals to choose the lifestyle they would like to have. For 91 example, if consumers are unable to participate in green behaviors in their everyday lives, they may be more inclined to do so when on vacation. This is an important consideration for destinations and sites that chose to incorporate green initiatives and the opportunity for environmentally friendly behaviors. Limitations, academic implications and future research Several limitations must be considered when reviewing this research study. First, the survey instrument was distributed online, therefore visitors to the parks with limited Internet access were not able to take the survey. Additionally, as the attractions distributed the solicitation emails and requests on their own, the list was not available to the researcher, nor was date of distribution of the survey solicitation guaranteed to be consistent across sites. Additionally, as the participating sites were located in North Carolina, the results cannot be generalized to other locations within the state or nationally. The sites are unique and offer different recreational opportunities which prevents generalization to other attractions. Additionally, all three of the participating sites were state owned which may have affected the results. Another potential limitation of the study could be attributed to the adaptation of the survey questions, which could have affected the validity of the research. For example, Green Factors and CSA had very similar results to Green Factors and ICV, which may suggest the two dependent variable questions did not measure different constructs as intended. Also, it may be possible that GTPB were not sufficiently related to sustainable attractions and each product, or attraction must be evaluated and considered 92 independently. This thought is supported by previous research that found that green values do not translate into purchases similarly across different product and service sectors (McDonald et al., 2009). An additional limitation is that the survey instrument has not been tested previously in a variety of other settings and research topics. This may warrant modification and additional testing of the survey instrument. It is possible that ‘composite scores’ could have been generated for three of the independent variable sets. . For example, instead of reviewing each Beta score individually for each of the PCE variables, an overall PCE composite score could be created which may result in more significant statistical values as it generates an overall score instead of a value for each variable within the construct. Lastly, social desirability is an area of potential concern in any study that measures an individual’s environmental attitudes and behaviors (Roxas & Lindsay, 2012). The answers represented the respondents perceived attitudes and preferences, and not necessarily what they actually do. There were several measures taken in an attempt to minimize this effect. First, the survey was not administered face-to-face thereby allowing the respondent to answer more comfortably (and presumably truthfully). This assurance of anonymity is one way to reduce the social desirability bias (Randall & Fernandes, 1991). Additionally, the majority of the survey questions attempted to ask respondents about actual past behavior as opposed to intention. There are several implications for the academic community in regards to this study. First, as there appears to be limited research on consumer purchasing behaviors involving sustainable attractions, this exploratory study presents a first attempt at clarifying decision-making processes of attraction visitors. Developing and testing a GTPB index, 93 based on some of the results of this study, would allow for greater research options in regards to green purchasing behaviors and sustainable tourism. It is important to clarify the fact that consumer behavior is a complicated and multifaceted topic and there are many ways of addressing and explaining decisions consumers make. As such, there may be a multitude of factors contributing to consumer decisions and examining the topic from one perspective provides insight, however may not fully examine all of the intricacies simultaneously. The results from this study may reflect the fact that there are a variety of factors involved in such a complex topic. This was an exploratory study intended to investigate how much of an impact sustainability features of an attraction and the green purchase behaviors of individuals have on the selection of sustainable attractions. There are a variety of opportunities for future research based on the results and conclusions of the study. A few of these are: ? Determine how much of an impact the existent sustainable features of a specific attraction have on the selection of that particular attraction by researching attractions with a large number of visible sustainability initiatives; the Green Factor independent variables would then consist of only the features the attraction had incorporated, while the dependent variable would measure a visitor’s intention or likelihood of visiting that particular attraction due to one or more of the previously mentioned Green Factor variables. ? Use the individual PCE independent variables to create a composite score and test correlation with the dependent variable. ? Use the individual GPB and GTPB independent variables to create a composite score and test correlation with the dependent variable. 94 ? Further explore the Green Factors by dividing them into two categories of items that can be directly seen and felt by tourists vs. ones that cannot. ? Explore visitor selection of sustainable attractions in other domestic and international contexts. ? Compare results of local visitors to those who traveled from out of state ? Investigate differences between families versus individuals, males versus females, or between different age groups ? Adapt the survey instrument to accommodations and administer to hotel guests to explore differences and similarities ? Investigate PCE and consumer purchase intention in regards to tourism attractions through qualitative methods. ? Compare consumer’s intentions to their actual behavior in regards to sustainable travel products and services Conclusion The results of this exploratory study show that there is a connection between particular green features of attractions and the selection of sustainable sites. There is limited academic literature concerning the importance and influence of specific factors in the selection process for sustainable attractions and additional research is needed in order to fully understand the totality of variables that affect consumer decision-making for these tourism sites. Although a complex topic, consumer behavior in this context is an essential piece in the progression and promotion of sustainable tourism. 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Specifically, I will be looking at how visitors select attractions, in addition to whether and how much sustainable features of those attractions play a role in their decision making process. I am also hoping to discover whether there is a relationship between a person’s selection of sustainable attractions and their purchase behaviors of other green products. As you are an attraction that is recognized by the NC GreenTravel program, your guests can provide a unique and valuable perspective on guest preferences for sustainability features. The information that we gather will be valuable to you, as it will provide increased insight into how your guests feel about sustainable features of attractions, and will showcase the fact that you are a NC GreenTravel member. With this in mind, I would like to send a weblink to an electronic survey to your mailing lists and social media outlets. I welcome your input on how we can best distribute it in order to obtain a promising number of responses. Once we are ready to distribute the survey, I will provide you three solicitation emails to forward to your contact lists. I will also provide ideal dates for those to be sent out. Please consider offering an incentive to the respondents for completing the survey, e.g. “you will be entered into a drawing for admission for four to….” While this isn’t necessary, it typically increases the response rate and will provide you and us with more reliable and complete results. This project will meet requirements for the completion of my master’s thesis, and I will be supervised by my thesis committee chair, Dr. Carol Kline, Assistant Professor with the Center for Sustainable Tourism. If we are able to work with you, I would provide your organization with a technical report that summarizes the results of my study. The technical report will target your destination organization individually, and will hopefully be useful in your future marketing efforts. I would like to schedule a phone appointment to discuss the project further and make sure my project and the technical report meets your needs. Please respond with any questions or comments you may have regarding this project, and I look forward to working together. Thank you in advance, Heather Rubright Graduate Student, Center for Sustainability: Tourism, Natural Resources, and the Built Environment 103 East Carolina University 104 Appendix B: Participant solicitations Email solicitation The Center for Sustainability at East Carolina University, in partnership with (particular site) wants to learn about how visitors select attractions to visit and if being a green attraction plays a role in that choice. To help us answer these questions, we have developed a short survey, which can be accessed at the link provided below. We hope you will give a few minutes of your time to share your opinions with us. Your opinions will provide (particular site) with valuable information. As an incentive to participate, one respondent will be selected to receive four complimentary admission tickets to (particular site). Thank you for your time and interest in this study! Second email solicitation Thank you for taking the time to participate in Round 1 of the Attraction Sustainability Survey! Even if you did not participate in Round 1, we would welcome your input in Round 2. This survey is being conducted in partnership with The Center for Sustainability at East Carolina University to learn about how visitors select attractions to visit and if being a green attraction plays a role in that choice. The survey can be accessed at the link provided below. We hope you will give a few minutes of your time to share your opinions with us. Your opinions will provide (particular site) with valuable information. As an incentive to participate, one respondent will be selected to receive four complimentary admission tickets to (particular site). Thank you for your time and interest in this study! Newsletter solicitation (Chimney Rock) The Center for Sustainable Tourism at East Carolina University in partnership with Chimney Rock, is interested in learning about how visitors choose which attractions to visit and if being a green attraction plays a role in their choice of where to visit. To help us answer these questions, we are developing a short survey, a link to which will be available in an upcoming newsletter. We hope many of you will be willing to give a few minutes of your time to share with us your opinions on the survey, which we hope will provide us and Chimney Rock with valuable information to help Chimney Rock continue to be your "Favorite State Park". . As an incentive, we will be selecting a respondent for xxxx. 105 Appendix C: Sustainable Attraction Survey 106 107 108 109 110 111 112 113 114 115 Appendix D: ECU UMC IRB approval letter