Diffusion of innovation: The case of ethical tourism behavior

JBR-08727; No of Pages 10 Journal of Business Research xxx (2015) xxx–xxx

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Journal of Business Research

Diffusion of innovation: The case of ethical tourism behavior Alexandra Ganglmair-Wooliscroft ⁎, Ben Wooliscroft 1 Department of Marketing, University of Otago, PO Box 56, Dunedin 9010, New Zealand

a r t i c l e

i n f o

Article history: Received 23 October 2014 Received in revised form 5 November 2015 Accepted 5 November 2015 Available online xxxx Keywords: Ethical behavior Diffusion of innovation Rasch Modeling Consumer innovativeness Tourism

a b s t r a c t Ethical consumption is increasingly important for governments, consumers and researchers. Adopting new ethical tourist behavior requires consumer innovation. Using a sample of ordinary travelers, the research investigates behavioral innovativeness through constructing a hierarchy of Ethical Tourist Behavior (ETB). As ETB fits the Rasch Model, behavior might provide a link between the relatively static individual innovativeness and the dynamic Diffusion of Innovation Model. Universalism, age and gender influence behavioral ethical tourist innovativeness. Using Rasch Modeling and relating results to the levels of adoption and Diffusion of Innovations, companies gain insights about the success potential and uptake of future innovations. © 2015 Elsevier Inc. All rights reserved.

1. Introduction The level of consumption in the developed world is not sustainable (Sheth, Sethia, & Srinivas, 2011). Governments, NPOs, businesses, and consumers accept the need for sustainable development, but investigations reveal a large gap between intention and implementation (Dexhage and Murphy, 2010). The acceleration of sustainable production and consumption patterns is the overarching goal of a major 10-year United Nations initiative that includes six focus areas (UNEP, 2015) including Sustainable Tourism development (Zorba and UNEP Secretariat, 2014). The tourism industry is an essential economic sector for developing and developed countries, with tourists' spending accounting for 9% of worldwide GDP in 2012 (Bonham and Mak, 2014). The industry's environmental impact is responsible for 14% of all greenhouse gases (McKercher and Prideaux, 2011). The high emission levels cause the sector's substantial impact on climate change (McKercher, Prideaux, Cheung, and Law, 2010). Consumers have an increasing interest in ethical and/or sustainable consumption (Carrington, Neville, and Whitwell, 2010). Ethical issues are more salient and consumers start to act accordingly (Newholm and Shaw, 2007). Previous research finds considerable evidence that people act less ethically on holiday than in their daily lives (Dolnicar and Grün, 2009). Tourists undertake different behaviors and ethical holiday behaviors' adoption occurs only slowly (Kroesen, 2013). Activities, practices or ideas that consumers perceive as new ⁎ Corresponding author. Tel.: +64 3 479 8167; fax: +64 3 479 8172. E-mail addresses: [email protected] (A. Ganglmair-Wooliscroft), [email protected] (B. Wooliscroft). 1 Tel.: +64 3 479 8445; fax: +64 3 479 8172.

are innovations (Goldsmith, d'Hauteville, and Flynn, 1998) and the new uptake of ethical holiday behavior is a sign of consumer innovativeness (Roehrich, 2004). This research develops a hierarchy of Ethical Tourist Behavior (ETB) and, using the Rasch Model (Rasch, 1960/80), explores whether reported ETB can provide a link between individual behavioral innovativeness and Diffusion of Innovation (Rogers, 1995). By definition, Rasch's logistic Item Response Curve (Bond and Fox, 2007) and the Diffusion of Innovation's cumulative adoption function are identical. If behavior that people report fits the Rasch Model, the model might act as a link between relatively stable individual innovativeness and the dynamic Diffusion of Innovation Model. The Rasch Model is perfectly suitable for this type of investigation as its results reveal the structure of the behavioral variable; providing information about the relatively static behavioral summary variable, while suggesting information about future diffusion.

2. Background: innovativeness, adoption and diffusion of innovation Companies' success relies on their ability to provide innovative products and services that satisfy customer needs (Hauser, Tellis, and Griffin, 2006). Understanding consumer innovativeness is one of innovation research's essential components (Steenkamp, Hofstede, and Wedel, 1999). Consumer innovativeness is “the consumption of newness” (Roehrich, 2004, p. 671). The conceptualization embraces any idea, practice, or object that appears new to the consumer (Lockett and Littler, 1997). Although most consumer innovativeness research refers to product adoptions (Im, Mason, and Houston, 2007), service adoption receives some attention, often relating to online adoption

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Please cite this article as: Ganglmair-Wooliscroft, A., & Wooliscroft, B., Diffusion of innovation: The case of ethical tourism behavior, Journal of Business Research (2015), http://dx.doi.org/10.1016/j.jbusres.2015.11.006

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behavior, particularly financial service's online adoption (Lassar, Manolis, and Lassar, 2005). Consumer innovativeness takes a view of innovativeness as the general desire to “seek out the new and different” (Hirschman, 1980, p. 285) or focuses on behavior by exploring the “degree to which an individual is relatively earlier in adopting an innovation than other members of his system” (Rogers and Shoemaker, 1971, p. 27). A recent review categorizes studies on consumer innovativeness into three broad research approaches (Bartels and Reinders, 2011). Innate innovativeness refers to a general personality trait while domain-specific innovativeness (DSI) captures innovativeness within a product class, recognizing that involvement with product classes varies (Bartels and Reinders, 2011). Domain specific innovation (DSI) is a stronger predictor of behavioral innovativeness than innate innovativeness and is the most popular approach to measure the construct (Bartels and Reinders, 2011). Consumer innovation research's most direct form refers to the concept's behavioral manifestation; “actualized innovativeness” (Im, Bayus, and Mason, 2003, p. 62), which adheres to Rogers and Shoemaker's (1971) classic definition; adopting relatively earlier than other members of society. The behavior can refer to really-new product acceptance (Jansson, 2011), but in many cases the term refers to a change in consumption patterns by purchasing different products or brands (Roehrich, 2004) that consumers perceive as new. Actualized innovativeness might manifest itself by simply switching from one brand to a different brand (Steenkamp et al., 1999; Wood and Swait, 2002), or by adopting a product that has become newly available in a geographic area (Goldsmith et al., 1998; Steenkamp et al., 1999). Studies exploring innovativeness' behavioral manifestation frequently apply ownership surveys using cross-sectional samples (Im et al., 2003). Researchers ask consumers to indicate on an existing list which items they currently use/own. Product ownership level comparison across the population is the most reliable approach when investigating consumer innovativeness (Im et al., 2007) and studies employ this approach in various contexts (Im et al. (2003). Innovativeness and Adoption of Innovation refer to a relatively static individual characteristic, while the Diffusion of Innovation Model takes a dynamic perspective and looks at an innovation's spread through a population. Diffusion of Innovation research, popular in marketing since the 1970s, explores how “an idea, practice, or object perceived as new” (Rogers, 1976, p. 292) spreads amongst consumers. Diffusion of Innovation studies also apply cross-sectional studies to explore the concept (Rogers, 1995). Rogers (1976, 1995) uses an S-curve to model the cumulative adoption of an innovation over time, reflecting peoples' heterogeneous propensity to innovate — a shape that recent computer models confirm as appropriate (Meade and Islam, 2006). The cumulative diffusion curve's S-shape corresponds with a normal distribution curve representing the

percentage of a population adopting during different time periods (Rogers, 1995) (see Fig. 1). The time element and the relative view inherent in Adoption of Innovation lead to classifying adopters into categories: innovators (2.5%) are the first group in a population to adopt an innovation, early adopters (13.5%) being second, early and late majority (34% each) are the two big groups that follow, with Laggards (16%) being the last group in a population to adopt an innovation (Rogers, 1995). A number of characteristics influence the speed with which a population adopts an innovation; its relative advantage (in economic terms, but also including social prestige), the innovations' convenience and future satisfaction, its compatibility with (past) experiences and existing values (social norms strongly influencing the latter), an innovation's observability (relating to the adoption's social aspect), and its trialability. Increasing complexity relating to usability reduces adoption rates. 2.1 Ethical tourist behavior as actualized innovativeness Governments and public bodies encourage consumers to adopt new additional ethical, sustainable behavior in all aspects of their lives (UNEP, 2015). Research shows that ethical behavior's adoption differs between life domains (Steg and Vlek, 2009) and organizations operating within those life domains encourage people to adopt ethical behaviors. Tourism is a context that encourages the adoption of new ethical behavior (Miller, Rathouse, Scarles, Holmes, and Tribe, 2010). Tourist destinations and individual businesses frequently emphasize sustainable offerings in marketing communication (Peattie and Peattie, 2009; see also TUI Sustainable development (online) and Qualmark Enviro Awards (online) for industry examples). As public bodies and destinations continue to increase marketing efforts to emphasize their sustainable offerings, they introduce ethical tourism ideas and opportunities to consumers — new ideas that require tourists to take up ethical behaviors. The uptake of any form of behavior in a new context is consumer Adoption of Innovation (Roehrich, 2004). Explorations using cross-sectional samples provide information about the uptake of new ethical tourism offerings across a population: “actualized innovativeness” (Im et al., 2003, p. 62), relating to ethical travel behavior. Consumption research uses the terms ‘sustainable’ and ‘ethical’ when referring to the same concept (McDonald, Oates, Alevizou, Young, and Hwang, 2012). In line with the World Tourism Organization who promotes a Global Code of Ethics for Tourism emphasizing the sector's impact on the environment, cultural heritage and societies (UNWTO, online) this research uses the term ‘ethical’ tourist behavior (ETB). Ethical tourist behavior contains a wide range of activities. Some ethical tourist behaviors imply extending everyday behavior to a different context – for example, to not litter or to use available recycling facilities – while others are unique to a tourism context — choosing accommodation with an environmental accreditation. Similar to everyday ethical behavior (Wooliscroft, Ganglmair-Wooliscroft, and Noone, 2014) these ethical activities differ in their intensity and impact on a holiday's overall characteristics. Consumers adopt ethical behavior in a cumulative fashion, a characteristic that fits the Rasch Model. 3. Methodology

Fig. 1. Theoretical diffusion curves (Meade and Islam, 2006).

This study uses a commercial online sample containing 322 respondents, representative of the New Zealand population in terms of age (18 years and above) and gender, who have been on a major holiday (staying away from home for 5 nights or more) in the last three years. The Ethical Tourist Behavior (ETB) hierarchy forms the questionnaire's main part. The following section discusses the item selection process for ETB before exploring Rasch Model's characteristics and suitability for this type of analysis. The third part introduces additional scales and variables in the questionnaire.

Please cite this article as: Ganglmair-Wooliscroft, A., & Wooliscroft, B., Diffusion of innovation: The case of ethical tourism behavior, Journal of Business Research (2015), http://dx.doi.org/10.1016/j.jbusres.2015.11.006

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3.1 Ethical Tourist Behavior (ETB) hierarchy's development: Rasch Modeling Research shows that most people undertake only few ethical behaviors (Wooliscroft et al., 2014), particularly during their holidays (Barr, Shaw, Coles, and Prillwitz, 2010). Specialist websites, magazines and other popular media sources discuss different ethical holiday offerings and provide check lists for ethical holiday behavior, behavior that ordinary tourist generally not yet undertake. Websites and popular media constitute an effective source to elicit a wide range of ethical travel behaviors. This research investigates thirty-four websites and other popular media sources and supplements results with an academic literature search (24 articles relating to Environmental Tourist/Tourism, Ecotourist/-tourism, Ethical Tourist/Tourism, Sustainable Tourist/ Tourism and Responsible Tourist/Tourism. Please contact the authors for a full list of websites and articles). Systematically noting ethical travel behaviors in the sources results in 80 items. The goal is to find behaviors that cover a wide range of ethical behavior, behaviors that are applicable to diverse holiday types and behavior that different proportions in the ordinary travelling population undertake. The items have to be manageable for respondents in an online questionnaire setting. The authors reduce the items in a two-step process: The first step involves removing holiday specific items — for example reducing numbers of stop-overs (requiring a flight), or deposing waste-water at dumpsites (relating solely to campervans/RV holidays). A second pass combines items that relate to very similar behavior; for example combining closing windows and shutting doors into one item. The resulting list includes 33 items that cover a wide range of ethical behaviors, are applicable to different holiday types and vary in their popularity. Table 1 shows the final ETB pool. This study focuses on individuals' subjective evaluation of their behavior. For example, whether respondents believe they save water, or whether they regard their behavior as respectful towards locals. Investigating complex concepts and behaviors like ETB within the general population implies trading off detail and specificity. If items fit the Rasch Model, the model's characteristics imply that people in the sample interpret the items' meaning consistently. The questionnaire presents items with a binary answer format, which eases cognitive demand (Ganglmair-Wooliscroft and Wooliscroft, 2013). Binary questions provide reliable results and decrease response bias (Dolnicar, Grün, and Leisch, 2011). Research shows that binary questions are less likely to result in positive answer behavior (Dolnicar and Grün, 2013) and reduce social desirability bias.

Looking at Table 1, ETB covers a wide behavior range that is likely cumulative, characteristics that fit the Rasch Model (Rasch, 1960/80; Singh, 2004). A probabilistic alternative to Guttman scaling (Wright, 1997), the model states that people who undertake more extreme ethical tourist behavior – for example choosing accommodation with environmental accreditation – will also have a higher probability to engage in comparably easier ethical tourist behavior, for example by not littering. Studies in psychology and education frequently use Rasch Modeling (Bond and Fox, 2007) and the model gains attention in marketing (for example Ewing, Salzberger, and Sinkovics, 2005; Ganglmair-Wooliscroft & Wooliscroft, 2010, Ganglmair-Wooliscroft and Wooliscroft, 2013; Salzberger, 2009; Salzberger, Newton, and Ewing, 2013; Salzberger and Koller, 2013; Soutar and Cornish-Ward, 1997; Soutar and Ryan, 1999; Wooliscroft et al., 2014). Rasch Modeling belongs to the family of logit models (Soutar and Cornish-Ward, 1997). Its elegant equation (Fischer and Molenaar, 1995) is: Pni ðxni ¼ 1Þ ¼

eβn −δi : ð1 þ eβn −δi Þ

RM's formula states that a positive response probability depends on an item's endorsability — for example how easy or difficult respondents find a particular ethical tourist behavior, and a person's characteristic on the concept of interest — how advanced the respondent's ethical tourist behavior is (Bond and Fox, 2007). The item location parameter δi and the person parameter βn, representing the location of items and persons on the continuum, imply the probabilities (Andrich, 1988). Georg Rasch's (1960/80) model measures latent variables like mathematic ability. Soutar and his colleagues (Soutar and Cornish-Ward, 1997; Soutar and Ryan, 1999) and Wooliscroft et al. (2014) successfully apply the Rasch Model to pseudo-latent variables including cumulative ownership patterns, leisure activities and everyday ethical consumption behavior. These pseudo-latent variables – in the current research the summarized Ethical Tourist Behavior (sETB) – reflect a predisposition to act in a certain way that is reasonably consistent over time relative to other people. The probabilistic nature of the Rasch Analysis then primarily accounts for response uncertainty attributed to differences between subjects (Holland, 1990; Borsboom, Mellenbergh, and Van Heerden, 2003). In a Rasch Model, Item Characteristic Curves (ICCs), or Response Expectation Curves (Bond and Fox, 2007), represent the theoretical curve for an item's endorsability/difficulty. Fig. 2 shows a theoretical ICC (Fig. 2a) and ICCs representing multiple items (Fig. 2b), which,

Table 1 ETB items. Please think about your last major holiday. Which of the following ethical choices did you make regarding your last major holiday (always or almost always)? Please consider every choice and tick all that apply. • • • • • • • • • • • • • • • • •

I avoided countries based on their political regime I bought local and seasonal food I bought locally made products & souvenirs I chose a holiday destination close to home I chose environmentally friendly accommodation I chose not to fly I chose to go on a longer holiday to reduce my environmental impact I chose tourist businesses with environmental accreditation I conserved water I did not litter I donated money to local environmental projects at the destination I engaged in outdoor leisure activities I picked up rubbish that was not my own I protected the local environment I protected the local wildlife I purchased carbon off-sets I read information about the natural environment at the destination

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• • • • • • • • • • • • • • • •

I recycled everything possible I reduced my waste I respected the local culture & traditions I re-use towels at the accommodation I re-used water bottles I spend money in locally owned businesses I switched off heating or air-condition when leaving the accommodation I switched off lights when leaving my accommodation I use public transport at the destination I used a sustainable form of transport for my journey to the holiday destination I used businesses that employ locals I visited a holiday destination with an environmentally friendly reputation I was a considerate and respectful photographer I was familiar with and observed local laws I was interested in and gained understanding of the host community While at my holiday destination I walked

Please cite this article as: Ganglmair-Wooliscroft, A., & Wooliscroft, B., Diffusion of innovation: The case of ethical tourism behavior, Journal of Business Research (2015), http://dx.doi.org/10.1016/j.jbusres.2015.11.006

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a) Theoretical ICC

b) Parallel ICCs for different items

c) Well fitting item: ICC & actual

d) Item does not fit: ICC & actual

responses

responses: Fig. 2. ICC curves.

according to Rasch Model's philosophical underpinning, must not cross (Bond and Fox, 2007). In this research context, the horizontal axis of ICC graphs shows ETB's intensity, and the vertical axis reflects the probability of agreeing to an ETB item. Fig. 2c & d shows the visualizations of empirical ICCs with points “representing actual mean scores of groups of homogeneous respondents” (Salzberger and Koller, 2013, p. 1310). Actual values in Fig. 2c closely follow the theoretical Rasch curve, implying that the item fits the model well. Fig. 2d shows a mis-fitting item, responses are not consistent with the model. The Rasch Analysis process provides various investigation tools to explore mis-fitting items, resulting in fitting subitems or in item elimination. Being a logit model, the Rasch Model easily deals with binary items. Despite binary yes/no input, the model presents results on an interval scale, enabling meaningful between-item distance interpretations. Empirical data always deviates from a theoretical model to some degree (Wright, 1992). ICCs are visual representations of the theoretically observed probabilities, with empirical proportions added to visualize the divergence from the model. Rasch Software, like RUMM2020 (Andrich, Sheridan, and Luo, 2003a; Andrich, Sheridan, and Luo, 2003b) also provides researchers with fit indices to determine if the data fits the theoretical model to a satisfactory extent. When conducting a Rasch Analysis, the process resembles a stepwise regression analysis where, using the software's fit indices, every step explores every single item's fit. The Rasch Model emphasizes individual item fit (Salzberger and Sinkovics, 2006) but also includes an overall fit statistic, the Person Separation Index, similar to Cronbach's alpha in classical scale development. This research first explores whether ethical holiday behaviors fit Rasch Modeling assumptions to a satisfactory extent, and the sample adopts ETB in a cumulative and systematic manner. If reported behavior fits the Rasch Model, the model might provide an essential link between individual innovativeness and the Diffusion of Innovation Model. Rasch's logistic ICC (revealing the probability to endorse a particular

ethical tourist behavior), and the cumulative adoption function in Diffusion of Innovation are identical. As the Rasch Model's results reveal the structure of the behavioral variable, the model is perfectly suitable for that type of investigation; providing information about the behavioral summary variable, while suggesting information about future diffusion. 3.2 Other scales and variables included in the questionnaire Goldsmith and Hofacker's (1991) domain specific innovativeness (DSI) scale contains six items that show good validity for a number goods and services (Goldsmith et al., 1998). Much research uses the DSI scale to measure domain specific consumer innovativeness. The scale has favorable psychometric properties and good correlations with actual adoption levels (Roehrich, 2004). Applying DSI to investigate ethical behavior requires the scale's adaptation to the current context. This study modifies Goldsmith and Hofacker's (1991) DSI scale for ethical behavior, ethical consumption innovativeness (ECI). The original DSI contains six items, three negative and three positive (Goldsmith et al., 1998). Mixing negative and positive items can produce problematic results (Salzberger et al., 2013). Changes to items from the original DSI reflect the current context and findings about mixing item directionality. The second author and two colleagues reviewed the items to ensure conceptual compatibility. This process reveals that one original item has no meaningful equivalent (I will buy … even if I haven't heard of it yet) in the current context, resulting in five ECI items. Table 2 shows the original DSI questions and their adaption. The questions use a 7-point Likert type scale anchored by strongly disagree (=1) and strongly agree (=7). The relationship between innovativeness and demographic variables is ambiguous (Hauser et al., 2006) and studies reveal different effects for different product categories (Im et al., 2003). This questionnaire includes demographic information (age, gender, income), variables relating to holiday characteristics (length and place of holiday,

Please cite this article as: Ganglmair-Wooliscroft, A., & Wooliscroft, B., Diffusion of innovation: The case of ethical tourism behavior, Journal of Business Research (2015), http://dx.doi.org/10.1016/j.jbusres.2015.11.006

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4.1 Ethical Tourist Behavior's hierarchy

Table 2 Original DSI and adapted ECI items. Original items: domain specific innovativeness scale (Goldsmith and Hofacker, 1991)

Adapted items: ethical consumption innovativeness (ECI) scale

In general, I am among the first in my circle of friends to undertake an ethical consumption behaviour Compared to my friends, I make a lot of consumption choices on an ethical basis In general, I am the first in my circle of In general, I am the first in my circle friends to know about ethical of friends to know the titles of the consumption issues latest … I know the names of … before other I know about ethical consumption people do issues before other people do If I heard that a new … is available in the If I hear about a new ethical consumption store I would be interested enough to issue, I am interested to find out more buy it Not included: I will buy a new …, even if I haven't heard of it yet.

In general, I am among the first in my circle of friends to buy a new … when it appears Compared to my friends I own a lot of …

time since the last holiday) and values from Schwartz's Universalism dimension (Schultz and Zelezny, 1999): Unity with Nature; Broadminded; Social Justice; Wisdom; Equality; A World at Peace; Protecting the Environment. Respondents indicate on an eight-point scale (ranging from 0 = opposed to my principles, 1 = not important to 7 = supremely important) how important the values are as guiding principles of their lives.

4. Analysis and results The following section provides an overview of the holiday characteristics before discussing the ETB hierarchy's psychometric characteristics and relating results to the characteristics of Diffusion of Innovation (Rogers, 1995). Using ECI, universalism and demographic variables the study investigates possible influences on the subjects' ETB positions. The average age of respondents is 46 years, 53% are females and respondents' average household income (before tax) is $NZ 65,000 (comparable to the New Zealand population in general). Forty-nine percent of respondents' last major holiday was in the previous twelve months (the other half was evenly split by the previous two years), approximately half of the holidays (45%) were domestic holidays, and the holiday's length was relatively evenly split into one, two, and three or more weeks.

Constructing the Ethical Tourist Behavior (ETB) hierarchy follows Rasch Model's prescribed structure in the data. Every step explores each item's fit and, if an item shows misfit, further analysis either removes the item or, if the misfit is due to a systematic bias in a group's answer behavior – for example a certain age group responds systematically different to an item – that item is split (see Salzberger and Sinkovics, 2006 for a detailed discussion of bias in RM). The Rasch Analysis results in five item's removal: I did not fly, I chose a holiday destination close to home, I used a sustainable form of transport for my journey to the holiday destination, I used businesses that employ locals and I was familiar with and observed local laws. Additionally, people 50 years and older respond to I re-used water bottles, and people 65 years and older answer I was interested in & gained understanding of the host community systematically different and the items are split. After these deletions and splits, all items fit the Rasch Model well and the overall ETB Person Separation Index (interpreted like Cronbach's alpha) is 0.88. Appendix A shows the items' full names and their abbreviation, fit statistics and the items' location. Empirical observations in ETB closely follow the theoretically modelled ICCs (see Fig. 2c). Fig. 3 provides an example of three ethical holiday behaviors that are at different stages of diffusion in the population; re-using towels at the accommodation is the most common behavior and considerably further in its diffusion process than to picking up rubbish that is not my own. Choosing to volunteer during ones holiday is in a very early stage of diffusion. Fig. 4 emphasizes the parallel nature of ICCs that the Rasch Model requires (Bond and Fox, 2007) — showing the diffusion of all ethical travel behaviors through the sample. Every ICC represents one ethical behavior. ICCs at the top of Fig. 4 reflect ethical behaviors that are further in their diffusion process while ethical behaviors at the bottom have hardly been adopted. The horizontal axis in Fig. 4 represents respondents' locations on ETB. Cutting through the ICCs vertically shows the pattern of ethical tourist behavior that respondents undertake: Respondents on the left hand side of the graph undertake hardly any ethical behavior, while those on the right have a probability of 90 to 100% of undertaking many ethical holiday behaviors. Fig. 4 represents an item-centric view of ETB. Using the person location (δi) — the Rasch Model also enables calculating the probability of respondents (or groups of respondents) adopting an ethical tourist behavior. Fig. 5 shows the behavioral innovativeness of respondent groups. The figure visualizes Rogers (1995) groups for Adoption of Innovations in Ethical Holiday Behavior. The line thickness represents

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

I re-used towels at the accommodation I volunteered during my holiday

I picked up rubbish that was not my own

Fig. 3. ICCs of three ETBs at different stages of the diffusion process.

Please cite this article as: Ganglmair-Wooliscroft, A., & Wooliscroft, B., Diffusion of innovation: The case of ethical tourism behavior, Journal of Business Research (2015), http://dx.doi.org/10.1016/j.jbusres.2015.11.006

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100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% -4.6

-3.6

-2.6

-1.6

-0.6

0.4

1.4

2.4

3.4

Fig. 4. Diffusion of every ETB item through the population (every curve represents one ETB).

group proportions in the population: The average Innovator (2.5% of the population) is represented by the narrow line at the top right, followed by the average Early Adopter (13.5% of the population). The two thick lines represent average Early and Late Majority (34% each), with the latter being closer to the bottom left. The lowest line in Fig. 5 represents the ETB adoption of an average Laggard (17% of the population). Figs. 4 and 5 show that a satisfactory fitting Rasch Model represents the behavior's level of diffusion (and allows a comparison between different item's adoptions) and the adoption levels of individuals/ respondent groups. The Rasch Model calculates an ETB score for every respondent and projects items and respondents onto one dimension. Fig. 6 provides a different visualization of ETB results, showing the respondent's distribution on the left (represented by circles) and ETB items on the right hand side. Looking at the bottom right hand side of Fig. 6, the ETB hierarchy shows that ethical holiday behavior people most frequently adopt like did not litter, switched off lights and walked at destination is not unique to a holiday situation and is easy to undertake. The next group of items includes a number of holiday specific behaviors like respecting the local culture, spending money in locally owned businesses or re-using towels. The person location mean for this ETB hierarchy lies just below recycling everything possible and being a respectful photographer,

indicating that less than half of New Zealanders adopt ethical behaviors that form the hierarchy's extensive middle ground. These include mainly environmentally friendly behaviors like protecting the local environment or picking up rubbish that wasn't my own, and social ethical behaviors like being interested in the host community. Different age groups interpret the latter systematically different and people over 65 years more often show an interest in their host community than younger tourists. Few respondents adopt ethical tourist behaviors that form the hierarchy's top. The extreme behaviors often relate to activities that influence the holiday's overall characteristics, like avoiding certain destinations and volunteering, or require substantial effort from tourists — for example the search time necessary to select businesses with environmental accreditation. Purchasing carbon off-sets, an ethical behavior whose adoption requires considerable financial commitment, is the most extreme ETB item. 4.2 Variables influencing actual ethical tourist behavior levels Using the Rasch Model's results, the study explores variables potentially influencing ethical tourist behavior levels: domain specific ethical innovativeness, universalism, age and gender. Previous research suggests that domain specific innovativeness (Goldsmith and Hofacker, 1991) – in this study ECI – universalism (Schultz and Zelezny, 1999)

100% 90% 80%

Early Adopters

70%

Innovators

60% 50%

Late Majority

Early Majority

40% 30% 20%

Laggards

10% 0% Fig. 5. Rogers' groups for the adoption of innovations in ethical holiday behavior (line thickness represents group proportion).

Please cite this article as: Ganglmair-Wooliscroft, A., & Wooliscroft, B., Diffusion of innovation: The case of ethical tourism behavior, Journal of Business Research (2015), http://dx.doi.org/10.1016/j.jbusres.2015.11.006

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Fig. 6. Ethical tourist behavior (ETB) hierarchy.

and demographic characteristics significantly explain behavioral innovativeness, even though demographic influences are inconsistent (Hauser et al., 2006). Confirmatory Factor Analysis (CFA) using AMOS 19.0 investigates ECI's internal consistency. The standard protocol (Hair, Anderson, Tatham, and Black, 2006) provides favorable psychometric results: all standardized factor loadings are statistically significant (p b 0.01) and exceed the 0.7 threshold (0.8 to 0.9), supporting construct validity, particularly convergent validity (Hair et al., 2006). Cronbach's alpha (0.9) indicates high internal consistency, the Average Variance Explained (AVE), a convergence summary indicator, is well above the 0.5 cut-off (0.7) and Construct Reliability (CR), a convergent reliability indicator (0.9) is also well above the 0.7 cut-off (Hair et al., 2006). All indicators provide support for ECI's favorable psychometric properties. For the ECI, as well as for Universalism, further analysis will employ the original variables' mean values. The mean value, calculated by averaging the five original variables for ECI is 3.9 on a 1–7 scale. The distribution of ECI is normal (skewness = − 0.1, kurtosis = − 0.2). The study also uses CFA to explore the structure of seven variables that are part of Schwartz's Universalism dimension (Schwartz, 1994). Results are somewhat ambiguous with two variables' standardized factor loadings below the 0.7 cut-off (0.6 for Unity with Nature and 0.7 for Broad Mindedness). Average Variance Extracted (AVE) lies above the cut-off value 0.5 (0.6) and Construct Reliability is 0.9 (Hair, et al., 2006). Cronbach's alpha for the seven variables is 0.9, and no item's removal improves the statistic. Further analysis uses the seven variables' mean value as Universalism's overall indicator. Universalism's mean

value is 5.1 on a zero to seven scale and its distribution is normal (skewness = −0.7, kurtosis = 0.6). The next step explores different ETB levels and its correlation with DSI, Universalism, holiday and demographic characteristics, first on a binary level, then using a multiple regression analysis. ETB correlates significantly but weakly with ECI (representing domain specific innovativeness; Pearson's r = 0.17; p b 0.01). The correlation between ETB and Universalism is stronger (Pearson's r = 0.35; p b 0.01). Age and ETB correlate positively (Pearson's r = 2.7; p b 0.01) and females have a significantly higher mean ETB than males (Independent Sample t-test; t = − 3.34; p b 0.01). Income and ETB do not correlate significantly (p N 0.05). Table 3 Variables explaining ETB. *Significant independent variables in bold; **alternative regression model using stepwise regression provides the same result. B

Std. Error Std. β t

(Constant) −5.38 0.55 Age in years* 0.03 0.01 Universalism* 0.36 0.08 Gender* 0.45 0.16 ECI 0.11 0.07 When was your last holiday? 0.01 0.07 How long was your last major holiday? 0.01 0.00 Income 0.01 0.02 Domestic vs. international holidays 0.11 0.18

0.28 0.27 0.15 0.09 0.01 0.07 0.01 0.04

−9.71 5.17 4.67 2.76 1.61 0.12 1.30 0.26 0.62

Sign. 0.00 0.00 0.00 0.01 0.11 0.91 0.20 0.80 0.54

Adjusted R square = 0.20; F 10.71; sign p b 0.01; method used: enter **. Multiple regression assumptions met according to Hair et al. (2006).

Please cite this article as: Ganglmair-Wooliscroft, A., & Wooliscroft, B., Diffusion of innovation: The case of ethical tourism behavior, Journal of Business Research (2015), http://dx.doi.org/10.1016/j.jbusres.2015.11.006

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Looking at holiday characteristics, ETB shows a weak positive correlation with the length of holidays (Pearson's r = 0.13; p b 0.05) and New Zealanders who spent their holiday overseas report higher levels on ETB than those staying domestically (t = − 2.19; p b 0.01). ETB does not correlate significantly with the time since the last holiday was taken (ANOVA, F = 0.88; p N 0.05). A multiple regression analysis including the above variables finds only three variables remain significant: Age (β = 0.28), Universalism (β = 0.27), and gender (β = 0.15) explaining 20% of behavioral innovativeness, ETB. Table 3 shows the regression analysis in detail. Neither domain specific ethical innovativeness (ECI), nor characteristics of the last holidays explain ETB significantly. 5. Discussion This study contributes to the research stream investigating ordinary people's ethical behavior. The research uses a tourism context as previous studies show that people's ethical travel behavior differs from everyday ethical behavior. Using Rasch Modeling, the research explores New Zealanders' actualized ethical tourist innovativeness by developing ETB and explores variables that influence ETB levels. The final ETB hierarchy contains 27 ethical tourist behaviors that ordinary tourists might undertake during a wide range of holidays. Summarized ETB fits Rasch Modeling assumptions well. All 27 items in the final ETB fit the Rasch Model with values closely fitting theoretical ICCs. The non-crossing nature of ICCs in the Rasch Model supports that ETB's adoption in the population is cumulative and occurs in an orderly sequence. The Rasch Model's results provide information about the structure of ETB. Transformation of summarized ETB represents the Diffusion of Innovation (see Fig. 4). Our results suggest a link between ETB, reflecting an individual, relatively static pseudo-trait, and ethical behavior's diffusion through a population. Further support requires future longitudinal research. The Rasch Analysis results in five item's removal: I did not fly, I chose a holiday destination close to home, I used a sustainable form of transport for my journey to the holiday destination, I used businesses that employ locals and I was familiar with and observed local laws. Additionally, people 50 years and older respond to I re-used water bottles, and people 65 years and older answer I was interested in & gained understanding of the host community systematically different and the items are split. The current ETB results reflect the New Zealand context: As a nation consisting of two main islands, flying is an integral part of many domestic and all non-domestic holidays. New Zealand further has a very limited passenger train network, with either no, or very infrequent services available, making sustainable travel to any destination difficult. Items relating to personal transport do not fit the Rasch Model as their choice depends on factors other than ethical tourist behavior. The choice not to fly or to choose sustainable transport to reach the destination might fit the ETB in other countries with different geographical and infrastructure characteristics. Although people generally perceive New Zealand as ‘green and clean’, the greenhouse gas emission per capita is high: New Zealand's per capita emission is almost twice that of the UK (New Zealand Ministry for the Environment, 2015), in no small part because of dairying, with 30% of the world's cross border dairy trade originating from New Zealand. The country's natural beauty in combination with a very low population density drives the green image. In line with findings for New Zealanders' ethical behavior in everyday life (Wooliscroft et al., 2014), most respondents undertake few, low level ethical tourist behaviors. Very few tourists adopt ethical issues that the academic literature and the popular media frequently discuss. While these ethical behaviors are potential innovations for tourists, they may or may not adopt them in the future. Ethical behavior that most tourists undertake does not incur any costs, nor is the behavior burdensome to undertake. Many respondents undertake behavior that is familiar to them and that they frequently

undertake in their daily lives (Wooliscroft et al., 2014) — for example not littering or turning off lights when leaving the room. Holiday specific aspects like respecting local culture or engaging in outdoor activities are part of the current social norm and many tourists engage in them. The relative popularity of reusing towels reflects accommodation providers' efforts to communicate their role in protecting the environment and providing easy solutions for people to reduce their individual impact. Ethical tourist behaviors that form the hierarchy's middle part still do not incur any extra costs, but require more effort or commitment, for example picking up rubbish that is not my own and choosing holiday destinations with an environmentally friendly reputation. Few respondents adopt them on holidays. The highest, and most difficult ethical tourist behaviors that only a very small group of people undertake generally refer to aspects that impose restrictions or require considerable time and/or monetary commitment — for example, avoiding countries based on their political regime, choosing tourist businesses with environmental accreditation, volunteering or donating to local environmental projects. This result is in line with previous research concluding that tourists are reluctant to take up environmentally friendly behavior that relates to a decrease in convenience (Barr et al., 2010; McKercher and Prideaux, 2011). The popular media and environmental advocates frequently discuss purchasing carbon offsets, but this item is the most extreme ethical behavior in the item set. The ETB question asks for people's behavior, rather than their attitudes, and uses a binary format that reduces positive response tendencies (Dolnicar and Grün, 2013). Looking at the ETB results, and the number of people who do not undertake any, or only very few, ethical tourist behaviors (see the left-hand side of Fig. 6), extensive overreporting due to social desirability appears unlikely, but future studies will examine this influence in more detail. To explore influences on ETB levels, the study modifies DSI (Goldsmith et al., 1998) to capture ethical consumption innovativeness (ECI) and compares domain specific innovativeness to the actualized, behavioral innovativeness that ETB captures. Previous research suggests a positive relationship between domain specific innovativeness (an attitudinal concept) and behavioral innovativeness (Bartels and Reinders, 2011). Exploring this relationship directly, the current study also finds a small but positive correlation between the domain specific ECI and ETB, but when the analysis includes other variables (see Table 3) the relationship is no longer significant. Behavioral innovativeness is context dependent (Steenkamp et al., 1999), and the complex research context (a variety of holiday types, and a broad sample of ordinary tourists), might contribute to the non-significant result. High universalism levels (Schwartz, 1994) correlate with positive environmental attitudes (Schultz and Zelezny, 1999), a finding this study extends to behavior: high importance of Universalism as a guiding value in people's live predicts higher levels on ETB's hierarchy (see Table 3). Increasing age and being female are also significant ETB predictors. Future research will explore whether these findings an age, or a cohort effect explain these findings. 6. Conclusion The current research provides an overview of ethical tourist behavior that the general population adopts. Investigating a complex and diverse topic using a cross-sectional sample requires high abstraction levels, and prevents explanations of ethical tourist behavior's underlying reasons and motivations. RM enables data investigation from a customer and a company point of view — allowing a parallel investigation of customer adoption and company innovation. In order to develop and provide successful innovations, companies need to understand the adoption behavior of customers. Mangers can use the hierarchy to investigate customers' current position and plan marketing communication targeting further reaching ethical tourist behavior as ETB suggests. Companies can compare their currently available ethical service and product offerings

Please cite this article as: Ganglmair-Wooliscroft, A., & Wooliscroft, B., Diffusion of innovation: The case of ethical tourism behavior, Journal of Business Research (2015), http://dx.doi.org/10.1016/j.jbusres.2015.11.006

A. Ganglmair-Wooliscroft, B. Wooliscroft / Journal of Business Research xxx (2015) xxx–xxx

and investigate which products should be available as logical next steps in their portfolio. If consumers wrongly perceive they behave ethically, marketing and communication campaigns can provide the necessary information to correct behavior. For example, when tourists think they save water, while objective measures show otherwise, or if customers are unaware of ethical alternatives, not being aware that one business employs mostly locals. Managers or policy makers, who want to increase uptake of a particular ETB, can see from the hierarchy how much effort a campaign requires to change that behavior; the larger the difference between current behavior and the behavior they propose, the more promotion is necessary. Intensive efforts to change uptake of an ETB item will most likely change the ETB hierarchy, supporting ETB's conceptualization as a pseudo-latent variable. The ETB hierarchy provides evidence of behavior consumers actually adopt. Other behavior might be prominent in the popular media but the hierarchy suggests that consumers are unlikely to adopt that behavior in the (near) future. For example the New Zealand media frequently discusses carbon-offsets, but this behavior is at an extremely early stage of diffusion and results suggest that the majority of New Zealanders will not adopt this ethical behavior in the future. Using the Rasch Model, this research studies the underlying structure of ETB and provides a link between relatively static individual innovativeness and the Diffusion of Innovation's dynamic aspects, a result that future longitudinal studies will have to confirm. Exploring groups within the population that report different adoption levels will provide further information about their comparability to different adoption categories as Rogers (1995) suggests (see Figs. 4 and 5). Longitudinal studies will detect the speed with which an innovation spreads through the population and managers can link this information to Rogers (1995) determinants of adoption speed when designing marketing campaigns; for example by providing signs to increase observability of the ethical behavior and increasing social status. Other industries can use this approach; companies know what they currently offer and what innovations they plan for the future. Looking at consumer behavior for their industry, they gain information about the underlying structure of that behavior and can see which product innovations their consumers might accept in the future and – inferring from products lying higher up in the hierarchy – which product attributes consumers will likely adopt. Future research will apply the same technique to explore behavior's adoption to other industries and life domains. Looking at specific organizations research will also investigate the fit between current ethical product offerings and the customer groups organizations want to target and explore parallels between likely adoption patterns of customers and future innovations the organizations plan. The New Zealand context will influence the results regarding the ethical behaviors' position on the ETB. However, the method — applying Rasch Modeling to investigate cumulative adoptive behavior can be generalized (Wooliscroft et al., 2014). The Rasch Model is particularly suitable for cross-cultural investigations (Salzberger and Sinkovics, 2006) and further research will modify ETB in different locations. Cross-cultural investigations will also highlight social norms relating to ethical behavior in particular countries. The Rasch Model demonstrates that New Zealanders interpret ethical tourist behavior consistently and adopt ETB in a similar and cumulative way. As the time since the last holiday has no effect on the level of ETB, ethical tourist innovations seem to spread slowly through the population. The distribution of respondents along the ETB hierarchy indicates that most ethical tourist behaviors are not yet adopted by ordinary travelers. Transforming behaviors into one summary score acts as a link between relatively static Ethical Tourist Behavior and the Diffusion of Innovation. The Rasch Model provides researchers with a tool to further understand the adoption of behavior in the general population.

9

Appendix A. Item name; abbreviation used in Fig. 6; item location and item fit indices Item

Abbreviation used in Fig. 6

Location Fit p-Value residual

I did not litter I switched off lights when leaving my accommodation I walked at the destination I re-used water bottles: b50

did not litter switched off lights

−2.57 −1.95

1.14 −1.06

0.06 0.05

walked at destination w.bottles b 50y

−1.67 −1.38

0.66 0.42

0.36 0.62

respect loc. culture

−1.33

−2.32

0.05

outdoor act

−1.33

−0.44

0.94

loc. owned busin.

−1.26

−0.31

0.19

reused towel

−1.20

−0.10

0.64

local food turn off heater

−1.14 −1.07

−0.79 −0.86

0.47 0.42

recycled respectful photographer interest host community 65+

−0.63 −0.61

0.88 −1.21

0.08 0.06

−0.41

−0.43

0.08

conserved water locally made products&souven loc.transp

−0.39 −0.33

−0.00 −0.40

0.96 0.98

−0.31

2.35

0.07

red. waste w.bottles 50+

−0.31 −0.21

2.03 −0.73

0.04 0.42

protected local environment protected local wildlife interest in host community b 65y.

−0.12

−2.45

0.08

0.10

−0.76

0.74

0.31

−1.39

0.14

picked up rubbish that wsa not my own read info about local environment

0.53

0.36

0.40

0.78

−0.33

0.35

destination with environmentally friendly reputation environmentally friendly accommodation money to local environmental projects avoid country based on political regime tourist business with environmental accreditation volunteered

0.97

0.18

0.69

1.67

−0.69

0.86

1.89

0.78

0.47

2.05

−0.23

0.50

2.50

−0.81

0.73

2.74

0.48

0.21

carbon off-sets

4.68

0.11

0.56

years I respected the local culture and traditions I engaged in outdoor and leisure activities I spent money in locally owned businesses I re-used towels at the accommodation I bought local and seasonal food I switched off heating or air-conditioning when leaving the accommodation I recycled everything possible I was a considerate and respectful photographer I was interested in & gained understanding of the host community: 65 years plus I conserved water I bought locally made products and souvenirs I used local transport at the destination I reduced my waste I re-used water bottles: 50 years and older I protected the local environment I protected the local wildlife I was interested in & gained understanding of the host community: b65 years I picked up rubbish that was not my own I read information about the natural environment at the destination I visited a destination with an environmentally friendly reputation I chose environmentally friendly accommodation I donated money to local environmental projects at the destination I avoided countries based on their political regime I chose tourist businesses with environmental accreditation I volunteered during my holiday I purchased carbon off-sets

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