A Quantified Triple Bottom Line for Tourism

Abstract

The tradition of tourism businesses and regional tourism industries is to measure their value to the host community by jobs, wages, and tax revenues even though every member of that community is affected on a daily basis through a broad variety of impacts. This article demonstrates a conceptual approach for measuring the relative importance of the major dimensions of community quality of life that can be influenced by the tourism industry in order to calculate an indication of overall impact on the well-being of community residents. Furthermore, we have formulated an example conjoint model that values this overall performance in monetary units. This model is successfully implemented using samples of college students and tourism industry professionals in the United States and Cyprus. A monetary version of triple bottom line impacts is calculated for the impacts of changes to a specific hypothetical tourism business. Recommendations are made for the extension and application of this approach to implementing sustainable tourism.

Keywords

tourism impacts triple bottom line corporate social responsibility sustainable communities conjoint analysis

Introduction

A growing shift toward sustainable development has resulted in a renewed significance of the environmental, social, and economic impacts of tourism within communities. The potential positive and negative impacts of tourism have been thoroughly discussed in past studies (Andereck et al. 2005; Brunt and Courtney 1999; Dogan 1989; Haralambopoulos and Pizam 1996; Milman and Pizam 1988; Pizam 1978; Dyer, Aberdeen, and Schuler 2003; Nyaupane, Morais, and Dowler 2006; Tyrrell and Johnston 2001), and a variety of measurement instruments and frameworks have been used in the attempt to measure the community quality of life impacts resulting from tourism development. Many of these instruments have been developed during the quest for sustainable development requiring a shift away from individualism to an emphasis on community and the shared responsibility for socioeconomic well-being and environmental quality (Schumacher 1973; Wackernagel and Rees 1998; Ife 1999; Rogers and Ryan 2001). In 1987, Our Common Future (WCED 1987) articulated the interdependent relationship between community quality of life and the well-known pillars of sustainability: environmental quality, economic prosperity, and social well-being (Rogers and Ryan 2001). In order to achieve the global objectives, Agenda 21 was proposed as a blueprint for local action with an emphasis on community participation in decision making (UNCED 1993).

Community decision makers trying to implement their new sustainability goals have sought measurement tools to simplify their struggle to allocate limited revenues between traditional public services and apparent new initiatives including tourism. For the tourism industry, the most readily available measurements disguise the full range of impacts on quality of life. The actions of tourists and activities of tourism businesses affect nearly every member of a destination community on a daily basis through a broad variety of social, economic, and environmental impacts. There is a significant need to quantify the wider range of social, economic, and environmental impacts and benefits of tourism within the community system. However, quantitative measurements of tourism impacts routinely emphasize jobs, wages, and tax revenues, which are universally understood and can be aggregated for use as simple decision-making indicators. Integrated assessments (i.e., economic, ecological, and societal assessments) have been developed to help bridge the gap between sustainability principles and application (Coffman and Umemoto 2010). Nonetheless, Kidd and Fischer (2007) argue that integrated assessment tools and good governance through participatory planning can compromise the effectiveness of the assessments if individual economic goals are favored over broader community objectives. This suggests that there is a need for the development of an integrated community assessment of the impacts of tourism on community quality of life that incorporates the prevailing values of a community (Olsen, Canan, and Hennessy 1985), measures the performance of the tourism industry, and provides concise and straightforward information in a format that allows decision makers to make informed decisions about tourism within the greater community system.

The purpose of this study is to demonstrate a conceptual approach for (1) measuring the relative importance of the major dimensions of community quality of life that can be influenced by the tourism industry and (2) measuring the performance of tourism businesses in these same dimensions in order to calculate an indication of overall impact on the well-being of community residents. Furthermore, we have empirically demonstrated a model that values this overall performance in monetary units. Although other units of measure could be used, money is the unit used for decisions involving private business and public goods and services (Moons 2003; Dupuis 1985; Pearce and Howarth 2000). It is important to situate social and environmental impacts on a common footing with economic impacts for decisions involving tourism industry development.

A primary contribution of this study is its attempt to address the experimental design issues associated with building a practical but representative model of the contribution of tourism to community quality of life. Given that the primary goal is to build a useful and estimable model, the issues include the following: What quality of life attributes should be included? How can they be linked to tourism development and operations? How can they be explained to community members? How do the attributes relate to one another and quality of life? How can community members be asked about attribute trade-offs? And how can the resulting model be translated into useful results? We have not resolved them all, but have made some progress.

Tourism Impacts and Quality of Life

Beyond Economic Impact

The demand for return on investment (ROI) evidence has been felt by every public entity and every private industry. The reaction has been to measure economic return on investment. The Destination Management Association International (DMAI) response, for example, has been to develop guidelines for measuring performance: “the amount of return is typically what the CVB [convention and visitors bureau] returned to the destination (visitor spending, economic impact, tax dollars)” (DMAI 2005). The voluntary nature of the reporting of social and environmental impacts of tourism on a community has in many instances been used for image or reputation improvement rather than measuring the actual effects on community quality of life. Several reasons for this include the perception that the measuring and reporting of noneconomic returns has little financial benefit or the inability to collect the data needed to measure potential noneconomic returns (Faux 2005). Similarly, the investment in tourism as a mechanism for sustainable development regularly links sustainable tourism projects with poverty alleviation and/or environmental conservation (Epler Wood 2004). While these projects often have lofty goals related to improving the quality of life of local communities, they often fail after donor funds are no longer available, which Epler Wood (2004) argues is because of insufficient evaluation tools that can be used to guide and evaluate the economic, environmental, and social returns on the investments.

A frequent claim for tourism is that it ultimately enhances community quality of life by providing jobs; improving services and infrastructure through tax revenues; and attracting restaurants, shops, festivals, and cultural and sporting events that cater both to visitors and locals (Andereck and Nyaupane 2011). It is also recognized that unmanaged tourism can have negative social and environmental consequences. Very little formal attention has been paid to measuring the broader quality of life impacts of the industry, and certainly nothing compares to the economic ROI calculations.

Nobel laureate in economics Amartya Sen (Sen 1999; Nussbaum and Sen 1993) argued that the definitions of well-being and quality of life need to move beyond the use of economic indicators. Attention to the noneconomic dimensions of quality of life is not new. Since the 1930s, researchers from diverse fields have worked to define, investigate, and measure quality of life (Massam 2002; Eadington 1975). Quality of life can be defined and measured on multiple scales including individual, community, and national levels. For example, each year since 1990 the United Nations has published the Human Development Index, which looks beyond GDP to a broader definition of national well-being. The aggregation of individuals’ quality of life to the community level can be a useful when measuring broader impacts of development on the quality of life in a community. Cutter (1985) suggests, “When an individual’s quality of life is aggregated to the community level, the concept is linked to existing social and environmental conditions. . . . It includes both tangible and intangible measures reflecting local consensus on the community’s values and goals” (p. 1).

Certificate programs and published indicators have given emphasis to social and environmental qualities of life, but policy makers currently have not adopted a standardized method for reconciling the many dimensions of sustainability or valuing those dimensions based on community values. Diener and Seligman (2004, p. 1) proposed that rigorous measurement should be a primary policy imperative and that “well-being needs to be assessed more directly, because there are distressingly large, measurable slippages between economic indicators and well-being.” The values of nonmarket amenities (e.g., air quality, views, culture, heritage, safety, variety) are becoming increasingly important to both tourist markets and residents of destinations.

Considerable progress has been made in the past few decades in the measurement of social amenities and environmental resources (Smith 1996). Market-based efforts to brand sustainable and eco-friendly businesses have provided businesses with a myriad of available certifications (Forest Stewardship Council, http://www.fscus.org; Rainforest Alliance, http://www.rainforest-alliance.org/). Recent initiatives are converging on similar ideas related to the well-being of community residents related to corporate social responsibility (Whitfield and Dioko 2012; Sheldon and Park 2011; Henderson 2007; Kasim 2006; Holcomb, Upchurch, and Okumus 2007; Carroll 1991, 1999), social return on investment (Olsen 2003; Olsen and Nicholls 2005), and sustainable communities (Sustainable Communities Network 2002; Roseland, Connelly, and Hendrickson 2005).

Frameworks for incorporating social and environmental dimensions with economic measures of “tourism yield” have been developed recently (Lundie, Dwyer, and Forsyth 2007; Northcote and Macbeth 2006) as potential assessment and decision-making tools for tourism planning. While both of these studies provide important steps toward measuring the broader quality of life impacts of tourism, they had some limitations. Lundie, Dwyer, and Forsyth (2007) described a hybrid approach to measuring economic and environmental yield of tourism. Even though they did not include a social dimension, they did note the importance of measuring the social value of tourism for a destination. Northcote and Macbeth (2006) presented a conceptual model that included a multidimensional concept of tourism yield that incorporated a “triple bottom line” (TBL) perspective. While making a strong contribution conceptually, they only presented a simple demonstration of the framework’s functionality. Both studies noted that there are necessary trade-offs between quality of life dimensions, but neither presented a rigorous method for calculating these trade-offs.

Several terms have been used to label approaches to assessing impacts and directing planning and decision making for sustainable development. These include Integrated Assessment, Sustainability Assessment (Hacking and Guthrie 2008), 3-E (environment, economic, and equity) Impact Assessment (Sadler 1999), and Extended Impact Assessment (Wilkinson et al. 2004). While many of the sustainability assessments that have been developed focused on project-level assessment (Hacking and Guthrie 2008), there have been efforts to develop assessments at the community level (Olsen, Canan, and Hennessy 1985).

A multitude of concepts and tools, developed to assist in the implementation of sustainable tourism practices, have been developed by international organizations that promote sustainable tourism practices including the World Tourism Organization, World Travel and Tourism Council, UNEP, and UNESCO (Epler Wood 2002; UNEP 2003; WTO 2000). Schianetz, Kavanagh, and Lockington (2007) provided a comprehensive review of sustainability assessments at the tourism destination level that cover a wide range of sociocultural, economic, and environmental issues. These included Sustainability Indicators (WTO 2004), Environmental Impact Assessment (Warnken and Buckley 1998), Life Cycle Assessment (ISO 14040, 1997), Environmental Audits (Ding and Pigram 1995), Ecological Footprints (Gossling et al. 2002), Multi-Criteria Analysis (MCA) (Zografos and Oglethorpe 2004), and Adaptive Environmental Assessment. While the review highlighted the wide range of assessment tools, Schianetz, Kavanagh, and Lockington (2007) are careful to point out that only the “Sustainability Indicators” and MCA were able to be used in a holistic manner encompassing all three sustainability aspects, and even in these cases they fall short to biases as economic indicators are more measurable.

The integration of sustainability dimensions and indicators is one of the major difficulties faced when developing a holistic sustainability assessment. While many of the assessment frameworks currently applied include social, environmental, and economic dimensions, they fail to properly integrate the multiple dimensions. As George (2001, p. 99) argues, “to draw together economic, social, and biophysical objectives into a single list does not integrate them.” To overcome this limitation, Morrison-Saunders and Therivel (2005) argue that separate assessments for each dimension should be brought together at the decision-making level, but this approach is limited as it does not consider the interconnectedness of the dimensions. Arguably, the pursuit of sustainable development requires that the linkages and interdependencies of the environmental, social, and economic systems be considered in a comparable manner so that the trade-offs are revealed in the outcomes (Hacking and Guthrie 2008). At the core of decision making for sustainable development is the clarification of these trade-offs (Sadler, Verocai, and Vanclay 2000).

The clarification of the trade-offs between the multiple dimensions of sustainability is often clouded by the method by which the indicators are integrated. Many studies have adopted a quantitative integration approach where sustainability indicators are assigned a numerical value and then are integrated mathematically (Morse et al. 2001). An often used method to integrate sustainability indicators is to simply add them together, which lends itself to the issues of differing levels of measurement and the weighting of each measure in the aggregation calculations. Even the most rigorous of these methods lend themselves to a certain level of “qualitative integration” as certain subjective value judgments are inevitable (Morse et al. 2001).

This study attempts to address some of these issues by proposing and applying an integrated TBL assessment that incorporates community value weightings. The holistic sustainability assessment has been developed so that the interdependencies and trade-offs of the economic, environmental, and social dimensions are built into the framework. The outcome, which is made empirical in a simple discrete-choice model, is a comparable monetarized value for each dimension, which allows for a straightforward comparison to be made between the dimensions for decision makers, and an assessment tool for tourism businesses to gauge their contributions, economically, socially, and environmentally, based on community importance. The next section outlines the background of the TBL concept and the application of the TBL as an assessment framework.

A Triple Bottom Line for Tourism

The TBL concept is consistent with the sustainable development thinking that emerged in the late 1980s (WCED 1987). Elkington (2004), who originally coined the triple bottom line, suggests that “developing this comprehensive approach to sustainable development and environmental protection will be a central governance challenge—and, even more critically, a market challenge—in the 21st century” (p. 16). The TBL was developed as a framework for measuring and reporting corporate performance beyond the traditional, single “Bottom Line” focused on economic profitability. The TBL adds sociocultural and environmental bottom lines in order to put these dimensions on a more equal footing with the traditional economic benchmark (Elkington 1994). The TBL is meant to be more than just a method of accounting and reporting (Vanclay 2004), as it is hoped that the implementation of the framework would lead to the embracement of the ideals of sustainable development (Elkington 1997; Faux 2005).

The TBL has been advanced as a planning and reporting mechanism and a decision-making framework both for internal management and external reporting (Dwyer 2005). TBL assessments have been adopted by businesses and organizations in many industries as part of a larger group of corporate assessment initiatives. The TBL has also received a strong endorsement from the World Business Council for Sustainable Development, a coalition of 160 international businesses (Vandenberg 2002). Government agencies around the world and at all levels have been required to implement the TBL (Vanclay 2004; Wight 2007). The TBL has also been subject to political misuse such as camouflaging destructive practices, diluting research efforts or environmental activists, or to divert attention away from issues (Buckley 2003).

The tourism industry provides a unique opportunity for the promotion and development of the TBL, as it is made up of many commercial enterprises and forms of tourism that seek to generate gains in conservation, community quality of life, and for multiple stakeholders, simultaneously (Buckley 2003). Through active engagement with the TBL, the tourism sector can provide leadership toward the adoption of the sustainable development philosophy, which reflects the ideals of the societies in which tourism entities operate (Faux and Dwyer 2009).

Conceptually, the TBL has been applied in a variety of tourism settings, likely because of the interrelatedness between the tourism industry and the natural and social environments in which it operates (Faux and Dwyer 2009). Faux and Dwyer (2009) suggest that the TBL approach to hospitality and tourism management offers several benefits, including efficiencies and cost savings, improved market positioning, better stakeholder relationships, improved strategic decision making, and wider destination benefits and competitiveness. While the merits of the TBL approach have been highlighted as a means for tourism development to be more sustainable and for tourism enterprises to be responsible and accountable for their multiple impacts on the communities they operate, most applications of the TBL approach have been more philosophical than practical. In cases where a measurement mechanism is used, this is primarily from the business side only (Faux and Dwyer 2009). There have been some attempts to employ the TBL as a foundation for more robust frameworks for assessment and planning. Notably, Northcote and Macbeth (2006) used the TBL concept as a basis for proposing a multidimensional model to assist assessment and decision making in planning, which they illustrated using Rottnest Island in Australia as a case study. Their model provided a more robust framework for assessing the impact of tourism along multiple dimensions; however, their model falls somewhat short as it is less robust in dealing with the real-world trade-offs that exist between the dimensions of the TBL. In the case study they presented, they were less concerned with the empirical techniques and data collection methods used in their model, instead focusing on the conceptual development and potential application for sustainable destination management. The lack of discussion of the empirical techniques means that they did not account for some of the more challenging aspects of employing the TBL approach in a real-world setting, including the objective development of value weightings for each dimension and the accounting for trade-offs between the dimensions within the model. Similarly, Epler Wood (2004) proposed a “triple bottom line sustainable tourism project development framework” for donors to track their investments in tourism as a sustainable development tool, but stopped short of explaining the empirical techniques for doing so. The TBL has also been used, conceptually, to set a research agenda for pro-poor tourism in the developing world (Font and Harris 2004). In South Africa, the TBL has been used to direct the development of national responsible tourism guidelines meant to encourage more balanced development and to help guide and benchmark the progress of the private sector and rural communities toward economic, including pro-poor, social and environmental tourism benefits (Goodwin, Spenceley, and Maynard 2002; Kotze 2002). On testing the application of the national responsible tourism guidelines among a sample of tourism enterprises, the researchers found that even though they had a detailed methodological framework for assessment, they faced trouble in the collation of data across the enterprises. The aggregation of data is one of the challenges facing the meaningful application of TBL assessments.

TBL sustainability has also been used as a guiding principle in the planning for wildlife tourism, and to further situate the understanding of wildlife tourism within a wider social context (Higginbottom and Scott 2004). The TBL concept has crossed over into guides for independent travelers as evidenced by Lonely Planet’s publication of a guidebook on responsible travel for independent travelers called Code Green (Lorimer 2006) that provided guidelines for travelers to take into consideration TBL issues. Buckley (2008) notes that the Code Green is vague about how these triple bottom issues are assessed, but they do represent a broad-level congruence with the academic literature on TBL. Despite some of the measurement and methodological challenges, the TBL philosophy and assessment framework has proliferated within the tourism industry at multiple levels from consumers, to tourism businesses, to regional, national, and international tourism entities, as reflected in the academic literature. One area that has yet to receive much attention in the academic literature is the orientation of the TBL as a community assessment (Rogers and Ryan 2001).

Community assessments provide public officials and community leaders with information to make decisions about community planning and development. Community assessments can be used to examine the quality of life impacts on a community as well as provide direction for future decisions, but in order to do so effectively, they must incorporate the prevailing values in a community and the various populations within that community (Olsen, Canan, and Hennessy 1985). Community quality of life assessments have traditionally included an objective evaluation of community indicators and a subjective measure of community values. Olsen, Canan, and Hennessy (1985) contend that “it is impossible to assess the quality of life in a community without grounding that assessment in value decisions . . . that all quality of life studies should begin by constructing profiles of the major values in the communities being examined” (p. 328). Andereck and Nyaupane’s (2011) recent study on the impacts of tourism on residents’ quality of life incorporated a subjective measure of community residents’ values. Their measurement compelled residents to compare existing circumstances to a future ideal, thus revealing residents’ perceptions of the quality of life impacts of tourism and personal importance of these attributes in relation to their opinions about the current state of their communities.

The TBL approach can be valuable when recast for community application and becomes a “process for evaluating performance, focusing on the integration of social wellbeing, environmental protection and economic viability goals” (Rogers and Ryan 2001, p. 281). An integrated TBL community assessment of quality of life impacts must provide a means of incorporating the community values into the process. In the next section, the process of incorporating community values is presented. In short, this process uses a conjoint analysis of the quality of life attributes to quantify residents’ importance of these attributes. These importance values are used to weight the three dimensions of TBL, a departure from previous studies. For example, Northcote and Macbeth’s (2006) multidimensional framework of sustainable tourism yield employed weighting estimates based on a post hoc assessment of policy and practices outlined in publically available reports.

While the TBL has been widely adopted, Vanclay (2004) argues that many of the agencies and businesses have lost sight of the wider vision and philosophy of the TBL, instead focusing too much on responding to reporting requirements. A survey of 32 organizations that use the TBL framework in Victoria, Australia, conducted by Vandenberg (2002) found that there was considerable confusion among the organizations about the philosophy and definition of TBL. In addition, several challenges have been noted for integrating the TBL into business and organization operations. Vanclay (2004) suggests that much of the literature on and many of the applications of the TBL have ignored the field of impact assessment, implying that “the naïve adoption of a TBL framework will be a regressive step” (p. 271). Coffman and Umemoto’s (2010) review how the TBL concept was used at the policy and planning level and led to a process that polarized economic and environmental interests during the development of the Hawaii 2050 Sustainability Plan. They argued that “adopting popular notions of sustainability [TBL], without critical examination of how the respective policy frames diverge or interrelate, can lead to ‘tautological traps’” (p. 597).

Another major difficulty for the TBL has been the defining and operationalizing of the noneconomic dimensions. Economic and to some extent environmental impacts have been quantified in TBL studies, but difficulties have been noted in quantifying the social impacts (Koch, Massyn, and Spenceley 2002; Vanclay 2004). Some of the literature strongly suggests that social impacts cannot be precisely defined or quantitatively valued because they are not consistent across a community (Vanclay 2004). A similar difficulty has been noted in the application of the TBL in tourism settings. Dwyer (2005) notes that social and environmental costs and benefits are usually measured using qualitative techniques, and the market values of environmental and social impacts cannot be used to identify individuals’ preferences for these values. Dwyer’s list of challenges for the TBL includes “identifying and selecting appropriate indicators, adopting an appropriate framework for TBL accounting and monitory purposes, and confronting TBL implementation costs” (p. 84). Another challenge in the application of the TBL is to avoid allowing entities to use TBL to legitimize their activities by reporting only positive aspects of their performance (Faux 2005).

As a result of these challenges the TBL has been considered to be intractable (Vanclay 2004), regarded only as a “metaphor” for the task of measuring multidimensional impacts (The Allen Consulting Group 2002). Vanclay (2004) echoes this sentiment, suggesting that TBL is not a decision-making algorithm because it is meant to be a philosophy about corporate social responsibility. It has even been rejected as “an unhelpful addition to current discussions of corporate social responsibility” (Norman and MacDonald 2003, p. 243). Despite these harsh criticisms of the TBL, some have argued that there is an increasing need for quantification of the TBL for accountability purposes (Christiansen 2004), and academic tourism research can make a meaningful contribution in this regard, particularly to the quantification of environmental and social elements (Kelly 2006). The contribution of tourism to environmental and social well-being in a region are well documented in the tourism literature; however, it has been argued that the many of the approaches for assessing these benefits are inadequate because they do not conceptualize these impacts using a common metric (Northcote and Macbeth 2006), such as monetary value (Tooman 1997).

Unmoved by its critics, the authors of this paper are optimistic that a quantified TBL can become an empirical tool for comprehensive analysis of the performance of the tourism industry and its businesses. Only through quantification of impacts, using a common metric, can social and environmental bottom lines be put on equal footing with the economic bottom line. Our research method focuses on a model that describes how community residents perceive the impacts of tourism development and can be applied to measure performance by tourism businesses. To test the methodology we have conducted a discrete choice experiment with students and tourism industry professionals in the United States and Cyprus and interviewed CVB and resort managers in the Phoenix–Scottsdale, Arizona, area.

A Proposed Tourism TBL Estimation Methodology

Our proposed methodology for a quantified tourism TBL includes the formulation of a discrete choice experiment that reflects the breadth of the three bottom lines that can be accurately and feasibly estimated for a single community but still has practical value for local tourism businesses and the tourism industry. Myers (1987) emphasizes that single-community-oriented measures are more relevant than comparisons across communities. The research challenge was to account for this accuracy, feasibility, and practicality in a single procedure. Accuracy was approached by depending heavily on previously developed lists for TBL items. Feasibility was dictated by the average ability and willingness of survey respondents to make a series of choices between sometimes-complex bundles of items. Practicality was determined by the advice and opinions of current tourism business managers. Our procedure consisted of an iterative sequence of model designs, pilot surveys, and business manager interviews.

TBL Dimensions and Attributes

Each community values a unique set of quality of life attributes that might be related to its appreciation (or lack of appreciation) for tourism business activities. The TBL framework suggests that these attributes might be put into social, economic, and environmental categories. The human development, sustainable communities, and sustainable tourism literatures offer a long list of attributes for each of these categories (Pattanaik 1997; UNDP 2007; Miller 2001; Choi and Sirakaya 2006; Sherwood 2007). A very comprehensive list was provided by the Global Reporting Initiative (GRI) (2002), an independent organization established to give support to the TBL and sustainability reporting guidelines (Faux 2005). The GRI list of reporting guidelines includes more than 60 indicators. Our main goal in constructing the indicator lists for our study was to maintain a balance between the economic, environmental, and social dimensions and to reduce the number of the indicators to a more manageable number. This process started with the compilation of all the indicators listed above. Then each member of the research team removed individual indicators that were either less relevant or overlapping. The research team then met to compare lists, and those items that remained were discussed one by one for their usefulness and measurement feasibility in the context of our project. An example of a removed item would be “supplier breakdown by organization and country” or “total materials use other than water, by type.” Ultimately, we selected 10 items from these lists (3 social, 4 economic, and 3 environmental attributes) to represent the three dimensions of the TBL (Table 1). These were chosen to include the widest range within each dimension and rephrased to be easily understood by the wide range of survey respondents and also to correspond to tourism business policies and practices. Based on our analysis of previous literature, we characterized the social dimension of the impacts of a tourism business by its charity, concern for public safety, and openness to the local public; the economic dimension of the impacts by its willingness to trade with local suppliers and business customers, wage rate paid compared to the national average, employment offered to local residents and local taxes paid; and the environmental dimension of the impacts by its water, energy, and material use practices, its green building and infrastructure investments, and its waste treatment policies. Based on the limitations of the experimental design (described later), it was decided this number of attributes (10) was the most that could be included in the study. Each attribute was initially described by a short title and a brief explanation for use in the survey instrument. The descriptions were further refined after interviews with tourism business leaders about their ability to evaluate their practices and policies and our ability to conduct a business audit.

Table 1.

Ten Triple Bottom Line Attributes.

Social

1. Community charity (percentage of gross revenues provided for local charitable contributions).

2. Community health, safety, and security (traffic congestion, security on property, health measures on property)

3. Openness to public and local culture (openness to the nonguest general public)

Economic

4. Local suppliers and customers (goods produced and purchased locally)

5. Average hourly wages and benefits (compared to national average).

6. Employment of local residents (employees who maintain a local residence)

7. Local taxes paid (as a percentage of gross revenues)

Environmental

8. Water, energy, and material practices (energy conservation, use of environment-friendly products)

9. Green building and infrastructure (environment-friendly management, policies, and governance)

10. Waste management and reduction (recycling, wasted reduction)

A set of business assessment items was developed for each of the 10 TBL attributes as the basis of a tourism business performance audit. Ultimately, the audit consisted of 71 questions about whether or not the business conducted certain activities or had adopted certain policies that affected one of the 10 TBL attributes. These were further evaluated in site assessments at three resort properties in the Phoenix–Scottsdale, Arizona, area. The development of the business audit questions lead to the specific wording and definitions used in the final choice experiment. A copy of the business assessment audit score sheet is available from the authors.

TBL Modeling Considerations

Several alternative models were considered for assessing the preferences of the residential community including rankings, scoring, several versions of the stated preference model, and an Analytical Hierarchy Process (Saaty, 1980). After examining the alternative models, we settled on a choice-based conjoint model to estimate a linear scoring equation that assumes that differences in respondent satisfaction can be represented by the sum of values of differences in the 10 social, economic, and environmental attributes. Conjoint experiments developed by Louviere and Hensher (1982) and Louviere and Woodworth (1983) are now common in the valuation literature. Timmermans et al. (1992) and Timmermans and van Noortwijk (1995) have used conjoint analysis to examine housing decisions. Haider and Ewing (1990) have applied it to tourist destination choice. Rigant (2006) used a conjoint analysis to study tourism congestion and carrying capacity. Our conjoint analysis asks respondents to choose between two hypothetical businesses on the basis of which one would contribute more to their community, where each is characterized by a different set of practices and policies. One example survey question is illustrated in Figure 1. Algebraically, the differences between two levels of each attribute are represented by Δx_i (e.g., x_i,high – x_i,low), and the per unit value are represented by β_i in a linear index of the value of the overall difference:

I = β_{1} Δ x_{1} + β_{2} Δ x_{2} + \dots + β_{10} Δ x_{10}

where the subscripts indicate the attribute.

Figure 1.

Example survey question.

It was assumed that the value of this index would determine the respondent’s choice between the hypothetical bundles of attributes and that the probability of choosing a certain bundle in the ith comparison follows a binomial logit distribution. Coefficients can then be estimated by the usual method of maximizing the likelihood of the sample of T observations:

ℓ (β) = \prod_{i = 1}^{T} {[P (I_{i})]}^{y_{i}} {[1 - P (I_{i})]}^{1 - y_{i}}

where $P (I_{i}) = \frac{1}{1 + e^{- I_{i}}}$

P(I_i) is the probability of a value less than or equal to I_i and y_i takes a value of 1 if the first attribute bundle is chosen and 0 if the second attribute bundle is chosen.

The choices made by individuals are personal preferences for their community. Thus, the parameters estimated for the model over all individuals in a sample are weighted averages of the parameters for individuals. This could be viewed as a model of community well-being only if individuals put no value on impacts on other individuals in the community.

Furthermore, do not interpret the estimated coefficients as “part-worth’s” in the traditional conjoint sense since they do not reflect a disaggregation of an easily identifiable total value into its parts. The individual β coefficients measure the relative importance of the difference between a low and high level of one TBL attribute. At best, they measure the relative worth of a single change to the total worth of simultaneous changes in all attributes. Thus, we interpret them simply as average relative importance weights for change.

Experimental Design

The conjoint analysis method consists of asking respondents to choose between alternative bundles of attributes based on their perception about which would maximize their satisfaction. In addition to selecting a practical set of TBL attributes, the method requires that the levels of the attributes be chosen in a way to allow for the efficient estimation of the coefficients. Each of our attributes can take on any of a number of hypothetical levels corresponding to different degrees of business performance. Nonlinearities and interactions can be estimated more efficiently when more attribute levels are evaluated. However, more levels translates into more hypothetical choices for respondents to make on each questionnaire. For example, if we wished to use 7 levels for each of our 10 attributes, the number of possible hypothetical bundles of attribute combinations is an unmanageable 282,475,249. Therefore we limited the number of values of each attribute to two: a “low” value and a “high” value, thereby reducing the number of possible combinations to 1,024. However, this permits only a linear model to be estimated from any practical sample size.

Another difficulty with the hypothetical choice bundle method is that when bundles contain many attributes, then it is difficult for respondents to deliberately consider all of them when choosing between bundles. Mazzotta and Opaluch (1995, p. 509) found that “question complexity appears to become an important issue when alternatives differ by four or more attributes.” Therefore, we limited the total number of chosen pairs of bundles for the survey in which five or fewer attributes were different between the choices.

Finally, we designed a fractional factorial experiment to estimate main effects only (no main effects are confounded with 2-factor interactions), reducing the number of required bundles from 1,024 to 32. In our early experiments, we asked respondents to score each of the 32 bundles in addition to comparing 16 pairs of bundles (see Figure 1). This allowed us to compare the results of the two approaches. We found that paired comparisons were much easier for respondents. In our final discrete choice experiments, we asked respondents to simply choose the best bundle in each of the 16 pairs. The pairings were formed by searching over all possible pairings for the design that minimized the determinant of the matrix of differences. The design matrix for the experiment was thereby developed to minimize the correlations between variables representing the differences between attribute levels. This will minimize variances of the conjoint coefficient estimates (the opposite of the goal for internal consistency, when several variables are used to measure the same concept). A good design depends on the lack of correlations, a characteristic that is sometimes measured by D-efficiency, which is related to the determinant of the correlation matrix in comparison to the correlation matrix of the theoretical “yardstick” of a perfectly orthogonal design. The design described above has a D-efficiency of 54.3%, which could be judged “reasonably efficient.” Unfortunately, this and other traditional efficiency measures do not account for the nonlinear transformation implied by logistic formulation, and it gives only a rough indication of the design’s ability to minimize generalized variances of the estimated coefficients.

Three TBL Experiments

Three experiments have been conducted to explore the feasibility of our proposed methodology: a 2009 class project of senior undergraduate students who participated in both a survey and business manager interviews, a 2009 survey of rural community development professionals in Arizona, and a 2010 survey of Cypriot business professionals. The statistical results are shown in Table 2. Each experiment helped us refine our methodology and confirm the feasibility and practicality of our approach.

Table 2.

Experimental TBL Model Estimates: Three Samples.

Attribute	Cyprus Professionals	Arizona Professionals	ASU Students
Social
1. Community charity	1.08 (0.92)	0.69 (0.59)	1.15 (1.35)
2. Community health, safety, and security	0.97* (0.31)	0.34 (0.26)	1.06* (0.51)
3. Openness to public and local culture	0.53 (0.46)	0.48 (0.36)	0.43 (0.55)
Economic
4. Local suppliers	1.19 (0.71)	0.65 (0.46)	0.91 (1.20)
5. Average hourly wages	0.79 (0.56)	0.92 (0.55)	0.60 (0.80)
6. Employment of local residents	1.28* (0.53)	0.97* (0.45)	0.34 (0.61)
7. Local taxes paid	0.66 (0.66)	0.45 (0.41)	0.19 (0.66)
Environmental
8. Water, energy, and material practices	0.45 (0.44)	0.14 (0.35)	0.57 (0.62)
9. Green building and infrastructure	0.44 (0.39)	0.32 (0.27)	0.85 (0.52)
10. Waste management and reduction	0.59 (0.88)	0.61 (0.54)	1.13 (1.59)
T	21	17	6
T × 16	336	272	96
χ²(9) = −2ln(likelihood ratio)	150.4	79.5	39.2

Note: TBL = triple bottom line. Values within parentheses are standard errors.

In the spring term of 2009, 36 senior Arizona State University tourism students participated in a series of experiments to determine the most effective way to ask survey questions about the preferences for alternative hypothetical tourism businesses. Three choice variables were tested: the rank of each attribute bundle, a numeric rating from 1 to 100 of each attribute bundle, and a choice between 16 pairs of bundles. Ultimately, it was decided that a printed survey using a paired comparison version would be easiest for respondents. The most acceptable format was a side-by-side comparison of two bundles. After being introduced to the nature of the approach, the definitions of the TBL attributes, and the associated business audit questions, the students in six groups of six members each were asked to make group choices between the 16 pairs of attribute bundles representing 32 hypothetical tourism businesses entering the community.

In addition, separate groups of students participated in interviews of resort managers and their staff at three resorts in Phoenix and Scottsdale, Arizona. The business audit was conducted and several changes were suggested in the audit. The results of the class efforts were presented at the Travel and Tourism Research Association Annual Conference in June 2009.

A second experiment was conducted on March 27, 2009, of 17 professionals at a rural Arizona economic development conference as part of a presentation on tourism and the TBL. Each was asked to respond as a resident of their local community rather than as a tourism industry professional. The major change in the method was that respondents were asked to respond individually rather than in groups.

A third experiment was conducted in Nicosia, Cyprus, in 2010 of business consultants and tourism development specialists at a KMPG conference focused on a quantitative TBL as a practical business performance measurement tool. The major change was that the background instructions and the written questionnaire were translated into Greek by local business translators. A special interpretive introduction was given to the survey by a multilingual Cypriot member of the research team.

Exploratory experiments across three groups were not designed for conducting inferential tests about specific parameters of the estimated models but to evaluate the practicality of data collection methods across different life stages, experience, and culture. Each sample was convenient but also uniquely distinct from the others. The ASU student sample consisted of tourism majors involved in the study of sustainable development. The Arizona professional group was chosen specifically to explore differences because of their age and professional employment experience. The Cyprus professionals group was chosen to explore the difference between cultures and the feasibility of using a translated version of that same survey.

The students responded in groups of six rather than individually. Individuals in the other groups responded separately. The differences shown in the table are intended only to illustrate the results that can be achieved by the model. While better than a purely convenience sample or a simulation experiment, the implied test statistics do not carry the credibility of large sample results. Nevertheless, the results shown in the table suggest that professional groups are similar across cultures and after translation, and that the differences for the tourism students are as socially and environmentally oriented as might be expected.

Positive signs for all coefficients indicate that the attributes contribute positively to respondent satisfaction as expected. The coefficients reflected the relative importance of each of the 10 attributes to each sample as they increase from low to high levels. Each of the chi-squared statistics for the model fit is large, but only four coefficients were twice as large as their standard errors (the traditional test of significance at the 5% level).

The results illustrate the differences in importance of attributes in the different groups. Larger numbers indicate greater importance for changes in attributes. Notice the relatively greater importance for employment of local residents by both professional groups and lack of importance to students—in general, ASU placements of tourism students are not in their hometown. Notice also the high importance given to social and environmental attributes by students and the low importance given to these attributes by professionals.

Since the sums of coefficients are not the same across experiments, it is difficult to make direct comparisons. We have experimented with several visual representations of the standardized levels of coefficients. Our favorite representation is the spider chart shown in Figure 2. The chart is divided into three regions corresponding to the three dimensions of the TBL. The light-shaded region at the top right shows the coefficients of the three social indicators, the dark-shaded region at the bottom shows the coefficients of the four economic indicators and the medium-shaded region at the left shows the coefficients of the three environmental attributes. The values of the coefficients for each respondent group are connected by a line so that we can easily compare preferences. (In the original of this drawing, available from the authors, three colored regions highlight the social [yellow], environmental [green], and economic [blue] dimensions.) This diagram makes it more clear that in Arizona, students find social and environmental attributes relatively more important than economic attributes and have a strikingly different pattern from Arizona and Cyprus professionals, who find the economic dimension attributes more important than social and environmental attributes.

Figure 2.

Comparison of the importance of triple bottom line attributes.

Assessing Performance of Tourism Businesses

Our investigation of the potential for the analysis of tourism business performance was conducted in parallel with our first experimental analysis of perceptions of importance. As each of the attributes was identified for the conjoint analysis survey, it was translated into a set of check sheet items that could be asked of tourism businesses. The purpose of the check sheet was to provide measurements of business performance related to each of the 10 attributes—the more checks related to an attribute, the higher the performance. After several rounds of revision by the authors, the check sheet was validated in interviews with managers of three resort properties in the Phoenix–Scottsdale, Arizona area. The items on the check sheets were subsequently revised to reflect the degree to which they could be reliably answered by resort management while avoiding proprietary information. The descriptions of the 10 attributes of the residential survey were also revised to better reflect the tourism business performance measures. While scientific objectivity suggests we should not avoid proprietary business information or adjust theoretical concepts for ease of explanation, our long-term goal of developing tourism industry partnerships requires that we be sensitive to business management practices and policies.

The interviews revealed new areas of resort business performance leading to new check sheet items and different ways to ask questions. In the future, the business check sheet items will need to be refined alongside redefinition of the attributes used in the resident survey.

While the business performance audit was designed to score individual tourism businesses, the contribution of an entire regional tourism industry to community quality of life can be calculated from the scores of individual businesses if they are converted to a common measurement unit. Since different tourism businesses use different types of inputs and production technologies, a single TBL score based on average industry performance will not reflect the diversity in the industry. However, if the scores for individual businesses are converted to monetary units, such as the equivalent in local tax revenues, the dollar values can be added for an overall value contributed by changes implemented by the regional industry.

Monetarizing the TBL

While there has been progress in the monetarization of noneconomic externalities, for example, “willingness to pay” studies (Hacking and Guthrie 2008), the monetarization of the TBL has been a contentious issue in the literature. Several case studies presented in The Mays Report (Mays 2003) attempted to reduce the TBL to a single dollar value. Faux (2005) argued that this narrowed perspective led to confusion among stakeholders and ignored broader social and environmental effects on community well-being. Instead of reducing tourism impacts to single dollar values, Lundie, Dwyer, and Forsyth (2007) used the dollar as a common metric to set the environmental effects of tourism alongside standard economic measures, and they suggested future studies attempt to add the social effects.

The monetarization of the TBL in the model presented in this article uses the dollar as a common metric for the comparison of the economic, social, and environmental impacts of tourism on the community. The coefficients of the index at the heart of the conjoint model provide the means of comparing the relative values of changes in different attributes. These coefficients reflect trade-offs between community amenities (Kahneman and Krueger 2006). For example, the value of a one-unit change in any attribute can be converted to its tax equivalent by multiplying its term in the index by the ratio of gross revenues (GR) to the coefficient of percentage of gross revenues to local taxes (β₇). If the entire index of changes is multiplied by that ratio it is converted into a monetized TBL for the set of changes:

m T B L = \frac{G R}{β_{7}} I = \frac{G R}{β_{7}} β_{1} Δ x_{1} + \dots + (G R) Δ x_{7} + \dots + \frac{G R}{β_{7}} β_{10} Δ x_{10}

Each term in this equation represents the equivalent increase in tax dollars that would be generated by an increase of the respective attribute from its low to high value. The monetary value of the net gain (or loss) in residential quality of life caused by all changes in tourism industry activities can be calculated as the sum of values over businesses.

The conjoint equation can be used to evaluate differences in bundles of attributes for alternative tourism business proposals or changes in bundles for one business over time. The preliminary business audit provides one method to evaluate levels of attributes in each bundle that can be directly translated into low values (few check marks) and high values (many check marks). As estimated here, the equation can only evaluate the change between these two levels, but larger samples and a fuller design might be used to evaluate differences between more levels.

Using the conjoint analysis results from the sample of 17 Arizona tourism professionals, we have calculated the equivalent in tax dollars collected from a business with gross revenues of $1 million that results from increases in all of its performance attributes from low to high levels. The analysis begins by calculating the increase in local taxes paid when the effective tax rate increases from low (2%) to high (5%). For a $1-million-dollar business, this implies $30,000. If all attributes were raised from low to high levels, they would generate benefits to the community equivalent to $372,035 in local tax revenues as shown in the table. Of this, the largest of the three bottom lines would be in economic benefits ($169,067) followed by social benefits ($101,249) and then environmental benefits ($71,719). Table 3 shows the tax equivalent values of all the changes. One can imagine summing the tax-dollar-valued impacts of all tourism businesses in a region to determine the industry’s collective contribution to the quality of life of local residents.

Table 3.

Tax Equivalent Monetary Values of Changes in Community Quality of Life.

Attribute	Beta	Per Tax Dollars	For Tax of $30,000	TBL
Social
1. Community charity	0.69	1.55	$46,424
2. Community health, safety, and security	0.34	0.76	$22,727
3. Openness to public and local culture	0.48	1.07	$32,097	$101,249
Economic
4. Local suppliers and customers	0.65	1.44	$43,234
5. Average hourly wages	0.92	2.04	$61,248
6. Employment of local residents	0.97	2.15	$64,586
7. Local taxes paid	0.45	1.00	$30,000	$169,067
Environmental
8. Water, energy, and material practices	0.14	0.31	$9,236
9. Green building and infrastructure	0.32	0.71	$21,396
10. Waste management and reduction	0.61	1.37	$41,087	$71,719
	5.57		$372,035

Note: TBL = triple bottom line.

Conclusions and Recommendations

This article describes the conceptual development of a quantified triple bottom line for tourism with preliminary empirical results and industry validations. We suggest that this provides a foundation for future steps in the development of a working system that bridges the gaps between sustainable tourism development, community quality of life impacts, and community values. The quantified TBL provides a practical tool for evaluation of tourism impacts within a community system as well as a tool for proactive sustainable tourism development. As with all exploratory projects, we recognize that “additional research is required.” The choice of attributes, the formulation of the conjoint model, and the experimental design each need further investigation. The business activity scoring system and performance measures also need refinement. Finally, research is needed to determine the generally applicable dimensions of the TBL. In particular, the assumption that there are three primary dimensions of community quality of life associated with tourism needs to be tested as in Tyrrell, Paris, and Casson (2010.)

Clearly, larger samples and more refined questions are needed to conduct rigorous tests. In addition, the experimental design trade-offs between choices per respondent, numbers of respondents, and numbers of individuals determining each response need to be further explored along the lines suggested by Lusk and Norwood (2005). It is our opinion that group choices might be more reliable than aggregated individual responses and that sample sizes should be determined after the choices per respondent has been optimized for orthogonality as well as practicality. Respondents can be strategic, lazy, or easily confused.

This research might be extended toward development of a system of evaluating and enhancing tourism’s beneficial role in the community. A vital next step would be the pilot implementation of such a system in a collaborative effort by a progressive community, with support and participation by both tourism businesses and the general public. If successful, the estimated TBL could lead to a valuable exchange of information about business performance goals and community values. In addition, future academic research could focus on applying a quantified TBL approach in a variety of specific contexts, such as the value of heritage tourism sites, the return on investment for pro-poor tourism developments, and the evaluation of specific developments (cruise tourism, airport construction, etc.). We are also confident that the quantified TBL approach presented in this article could be useful in assessing the differing values of different stakeholder groups within a community, or even adapted to other industries. The tourism field is in a unique position to promote the sustainable development movement as it operates within the economic, social, and environmental systems of communities. The sustainability philosophy has been well articulated in relation to tourism, as have the calls for more practical mechanisms for evaluation and planning for sustainability in “real world” situations. This article contributes to the implementation of sustainable tourism through the monetary quantification of tourism business performance using values defined by community residents.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Timothy Tyrrell is professor of Tourism Development and Management and director of the Center for Sustainable Tourism at Arizona State University and emeritus professor of Environmental and Natural Resource Economics at the University of Rhode Island. His primary interests are community development and sustainable revenue management.

Cody Morris Paris is senior lecturer and programme coordinator for Social Science programmes at Middlesex University Dubai and a Senior Research Fellow at the School of Tourism and Hospitality, University of Johannesburg in South Africa. His primary research interests include: Geopolitics and Tourism, Technology and Tourism, Sustainable Development, Mobilities, and Experiential Learning and Tourism.

Vernon Biaett is a retired festival manager who is presently a faculty associate and PhD student in the School of Community Resources & Development at Arizona State University. His primary research has focused on innovative methods to qualitatively understand the behavior of attendees at community festivals and to quantitatively estimate attendance at parades.

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