Tourist Innovation in Air Travel

Abstract

This essay explores the recent phenomenon associated with tourists’ adaptability to new services driven by technologies and proposes the concept of tourist innovation as the theoretical underpinnings describing tourists’ adaptability to novel services. To glean the underlying concept of tourist innovation, a series of in-depth personal interviews are deployed. An online survey containing 40 indicators representing the innovation dimensions is distributed that gathers 524 useable responses from air travelers. In the data analysis, a parsimonious model derived from a confirmatory factor analysis validates a four-dimensional solution: (1) novelty seeking, (2) vigilance, (3) hedonic experience seeking, and (4) social distinctiveness. This scale is explained by 10-item tourist innovation measurement, wherein the validity of the resultant scale is achieved.

Keywords

tourist innovation service innovation technology airline scale development

Introduction

In recent years, information technologies (e.g., the Internet and the mobile phone) have become prevailing service conduits that influence tourist perceptions of destinations and travel behaviors. As necessary tools for travelers to acquire information at different stages of their trips, technology-based services (e-commerce) are indeed critical for the tourism industry, as they promote the interactions between providers and tourists while rendering service experiences in a cost-effective and efficient manner. For example, hotel guests and airline passengers may interact with staff online in a timely manner, and restaurant patrons may use a smartphone instead of a credit card for payment.

Although these information technologies have increasingly affected the delivery of services and experiences in tourism, empirical research on tourist propensity for adapting to technology-driven devices is rather limited. In the consumer literature, researchers have identified customers with a high propensity to adopt new products to their needs as “innovative” (Hirschman 1980). This concept has not been widely studied in the tourism field, however. Most research on innovation-related issues have dealt with the innovation of tourism organizations (Orfila-Sintes and Mattsson 2009; Tajeddini 2010; Sundbo, Orfila-Sintes, and Sørensen 2007; Jacob et al. 2003; Bieger and Weinert 2006; Martínez-Román et al. 2015; Hwang and Hyun 2016; Sandvik, Duhan, and Sandvik 2014; Fraj, Matute, and Melero 2015), tourism products/services (Liburd 2005; Frehse 2006; Jin et al. 2015), and tourism destinations (Hjalager 2000; Nordin and Svensson 2007; Mattsson, Sundbo, and Fussing-Jensen 2005; Aarstad, Ness, and Haugland 2015). Few studies have identified or studied vacationing tourists as innovators, nor have they studied their behaviors (Goldsmith and Litvin 1998; Litvin, Kar, and Goldsmith 2001).

Mirroring the concept of innovative consumers that is emerging from the marketing literature, this study examines tourist’s adaptability to technology-driven services, using the term “tourist innovation.” The operational definition of an innovative tourist used to guide the study is one who demonstrates a high propensity to use progressive information technology to learn about a destination and manage services at or in relation to tourism settings. As these human constructs and activities are always varied, universal consensus on consumer behavior in this context is not attainable. Tourists with a high propensity to use, accept, and adopt to new technologies, however, demonstrate this ability for innovation and pleasure-seeking in a manner that can be measured. The conceptualization, manifestation, and more effective measurement of tourist innovation in the context of tourism and technology usage can thus be researched to obtain better insights into tourist trends in information seeking and desires fulfillment in relation to these new media services.

Recognizing the relevance of this issue in light of increased travel trends worldwide, this study aims to explore the recent phenomenon associated with tourists’ adaptability to new services driven by technologies and proposes the concept of tourist innovation as the theoretical underpinnings describing tourists’ adaptability to novel services. Two research questions are developed: (1) How could tourist innovation be measured in the case of technology use? and (2) Does the derived measurement scale achieve construct and nomological validity that could be used in other tourism studies? This study contributes to the current understanding of tourist innovation concepts both in theory and practice. Theoretically, it fills in gaps on the inconsistency of the innovation concept and measurement in consumer research. To our knowledge, this study is the first attempt to conceptualize tourist innovation. It moves away from the more generic consumer research approach to advance current understandings of innovation in the tourist experience industry. Unveiling the underlying dimensionality of tourist innovation may also allow marketers to describe or anticipate the mindsets of innovative travelers and anticipate their needs accordingly.

Literature Review

Conceptualization of Consumer Innovation

Scholarly discussions on customer innovation are represented by three streams: consumer innovation for a single product, innovation for a category of products, and innate innovativeness (Midgley and Dowling 1978). The first two camps suggest that innovation implies the consumers’ tendency to welcome and adopt to any kind of new product/service or a specific type of new product, where the earlier a consumer makes a purchase of a new product or its type, the more innovative that person is. Consumers’ innate innovativeness, on the other hand, is defined as a generalized unobservable predisposition toward innovations that are applicable across product classes (Im, Bayus, and Mason 2003). Compared to the former two approaches on behavior-based innovation, consumer character is concerned with the highest levels of abstraction (Mudd 1990), which provides more generic, context-free indications of human behavior across a wider range of situations (Goldsmith and Hofacker 1991).

An examination of the research reveals various components that help explain the innovation concept from the perspective of psychology. The following section summarizes four ways extant literature explains the drive for innovation: (1) as the need for novelty seeking, (2) as the need for hedonic experience, (3) as the need for product quality, and (4) as the need for uniqueness. Hirschman (1980) compared the similarities and difference of innovation and novelty seeking, categorizing the latter into two types: inherent novelty seeking and actualized novelty seeking. Inherent novelty seeking refers to consumers’ “initiation of behaviors intended to acquire new information,” whereas actualized novelty seeking denotes “the actual acquisition of new information” (Hirschman 1980, 285). Variables to measure inherent novelty seeking include consumers’ willingness to change (Hurt, Joseph, and Cook 1977), newness attraction (Leavitt and Walton 1975), attraction to others’ opinion (Leavitt and Walton 1975), information seeking (Hirschman 1980), and variety seeking (Hirschman 1980) (Table 1). Actualized novelty seeking may lead to three types of behavioral innovation: (1) vicarious innovation, the acquisition of information about a new product; (2) adoptive innovation, the actual adoption of a new product; and (3) use innovation, the use of a previously adopted product to solve a novel problem, or the degree of novelty displayed in each use of a product. In tourism contexts, it is critical to include both tourists’ intentions and their actual behaviors when seeking information about new products and adopting new tourism products.

Table 1.

Consumer Innovation Concepts and Measurements.

	Resource	Concept	Measurements
Novelty seeking	Leavitt and Walton 1975	Open to new experiences and often goes out of his way to experience different and novel stimuli, particularly of a meaningful sort	Forty-item scale
	Kirton 1976	Tend to search for new problems and original solutions within an organization	Thirty-two-item scale with three dimensions: Originality; Efficiency; Rule/Group Conforming
	Hurt, Joseph, and Cook 1977	Willingness to change	Twenty-item unidimensional scale
	Hirschman 1980	Three types: informative innovativeness, adoptive innovativeness, and use innovativeness	N/A
	Price and Ridgway 1983	Use of previously adopted products in novel ways	Forty-four-item scale with five dimensions: creativity, risk preferences, voluntary simplicity, creative use, and multiple use potential
	Manning, Bearden, and Madden 1995	Consumer novelty seeking: the desire to seek out new product information	Eight-item scale to measure consumer novelty seeking
	Im, Bayus, and Mason 2003	An individual’s inherent innovative personality, predisposition, and cognitive style toward innovations that can be applied to consumption domains across product classes	Adopted from Kirton’s (1976) scale
Social differentiation	Goldsmith and Hofacker 1991	Tendency to learn about and adopt innovations within a specific domain of interest	Six-item unidimensional scale; four of six are social-related
	Parasuraman 2000	A tendency to be a technology pioneer and thought leader	Seven-item scale to measure the dimension of Technology Readiness Index
	Tian, Bearden, and Hunter 2001	Consumer’s need for uniqueness: the trait of pursuing differentness relative to others through the acquisition, utilization, and disposition of consumer goods for the purpose of developing and enhancing one’s self-image and social image	Thirty-one items with three dimensions: creative choice counterconformity, unpopular choice counterconformity, and avoidance of similarity
	Vandecasteele and Geuens 2010	Self-reported consumer innovativeness motivated by the self-assertive social need for differentiation	Five-item scale to measure the social dimension of innovativeness
Hedonic experience seeking	Venkatraman and Price 1990	Sensory innovativeness: tendency to engage in and enjoy internally generated experiences	Eight-item scale to measure the sensory innovativeness dimension
	Baumgartner and Steenkamp 1996	Exploratory acquisition of products: tendency to seek sensory stimulation in product purchase through risky and innovative product choices and varied and changing purchase and consumption experiences	Ten-item Exploratory Acquisition of Products scale
	Steenkamp, Hofstede, and Wedel 1999, 56	Predisposition to buy new and different products and brands rather than remain with previous choices and consumer patterns	Adopted from Baumgartner and Steenkamp’s (1996) scale (five of the 10 highest-loading items)
	Wood and Swait 2002	People view novelty and innovation as intrinsically valuable	Six-item scale to measure “need for change”
	Vandecasteele and Geuens 2010	Self-reported consumer innovativeness motivated by affective or sensory stimulation and gratification	Five-item scale to measure the hedonic dimension
Quality experience seeking	Venkatraman and Price 1990, 295	Cognitive innovativeness: tendency to engage with pleasure in new experiences that stimulate thinking	Eight-item scale to measure the cognitive dimension
	Venkatraman 1991	Cognitive innovators, sensory innovators, cognitive-sensory innovators	Adopted Venkatraman and Price’s (1990) scale
	Vandecasteele and Geuens 2010	Self-reported consumer innovativeness motivated by the functional performance of innovations and focuses on task management and accomplishment improvement	Five-item scale to measure the functional dimension

Recent studies have also conceptualized innovation in relation to consumer desire, as their attempt to satisfy certain needs, motivations, or goals. Consumers’ motivated innovation represents their desire to seek benefits in terms of what a new product can do and how this new product can better fulfill their needs. Venkatraman and Price (1990, 295) included a sensory dimension in their innovation concept, which they conceptualized as the “tendency to engage in and enjoy internally generated experiences.” This definition represents consumers’ efforts to gain personal (hedonic) pleasure as an outcome of their new experiences. Roehrich (1995) defined consumer innovation as the need for stimulation (hedonist) and uniqueness (social). Both studies used hedonism as one of the subscales to measure consumers’ need for stimulation. Other studies have incorporated hedonism as a key sensory component to measure consumers’ desire for change (Wood and Swait 2002) or their exploratory acquisition of products (Baumgartner and Steenkamp 1996) (Table 1).

Third, the literature indicated that innovative consumers are also attracted by functional or practical new products (Venkatraman 1991; Hirschman 1984). Following this line of research, Vandecasteele and Geuens (2010, 310) defined the functional dimension in the context of consumers who are “motivated by the functional performance of innovations and focused on task management and accomplishment improvement.” These consumers are task-oriented and want to be productive by using new products, handiness, compatibility efficiency, comfort, ease, quality, and reliability. Vandecasteele and Geuens (2010) measured the functional dimension using five items: time-saving, comfort, functional, convenient, and easier. Compared to other motivated dimensions (e.g., hedonic, cognitive, or social), the functional dimension appears to be the strongest variable for predicting consumers’ buying intentions.

Lastly, existing studies have suggested a social component of innovation, which has been conceptualized as consumers’ expression of the need for uniqueness (Simonson and Nowlis 2000; Roehrich 1995), social (Roehrich 2004), social desirability (Leavitt and Walton 1975; Craig and Ginter 1975), identity building (Tian, Bearden, and Hunter 2001), and perceived visibility (Fisher and Price 1992). Vandecasteele and Geuens (2010) conceptualized this social dimension in the context of consumers’ social need for differentiation. Examples in this category include the status of being unique, opinion leadership, visibility, social rewards, symbolism, sense of belonging, and image. In addition to consumers’ general desirability for being unique, other social elements are included in measuring innovation in a specific domain. Goldsmith and Hofacker (1991) developed a unidimensional scale to measure consumers’ tendency to learn about and adopt innovations within a domain of interest. Within the six-item scale, four items (e.g., “I like to buy a new product before other people do”) are related to social influence on consumers’ decisions to own or purchase a new product. This scale demonstrates high reliability and predictive validity via six mini-studies in product categories, from rock music records and fashion products to household entertainment equipment.

Measurements of Consumer Innovation

After an inventory check on consumer innovation, numerous scales have been identified to measure the innovation concept in consumer research. These scales can be generally categorized into two types: life innovativeness scales and adoptive innovativeness scales (Roehrich 2004). Life innovativeness scales measure consumers’ ability to introduce new things in their lives, whereas adoptive innovativeness scale refers to consumers’ tendency to purchase new products. Life innovativeness scales describe the general attraction to any kind of new thing, not only a new product. Under this premise, consumer innovation is viewed as a trait of “intelligent, creative, selective use of communication for solving problems” (Leavitt and Walton 1975, 549), one who searches for new problems and original solutions within an organization (Kirton 1976), or one who exhibits a “willingness-to-change” (Hurt, Joseph, and Cook 1977, 63). These definitions conceptualize consumer innovation as a personal trait to describe individuals’ tendency to look for new and different options to address their problems or needs. To measure consumer innovation, all researchers of the three studies noted above developed large scales containing items designed to evaluate personality trait dimensions. While these three life innovativeness scales share many features and are intercorrelated in student samples (Goldsmith 2011), they also demonstrate poor predictive validity, assessed by descriptive analyses, in that there are weak correlations with new product purchases (Roehrich 2004). Based on these limitations, there is a need to investigate the nature of these dimensional concepts, and to develop a new scale with more robust analysis to measure and validate the tourist innovation scale.

Adoptive innovativeness scales specifically measure consumers’ tendency to purchase new products. This is the stage where consumers acquire new information and utilize their knowledge about a product to actually make a purchase. Numerous scales have been developed to assess consumers’ adoptive innovativeness, including (1) Goldsmith and Hofacker’s (1991) domain-specific scale, a 6-item unidimensional scale measuring consumer innovation in different product categories; (2) Raju’s (1980) innovation scale, a 10-item scale measuring consumers’ eagerness to buy or know about new products/services; (3) Baumgartner and Steenkamp’s (1996) exploratory acquisition of product (EAP) scale, a 10-item scale measuring consumers’ tendency to seek sensory stimulation in product purchases; and (4) Vandecasteele and Geuens’s (2010) motivated innovativeness scale, a 20-item scale measuring innovation in hedonic, functional, social, and cognitive dimensions. In addition to these four scales, Roehrich’s (1995) scale and Le Louarn’s (1997) scale (not written in English) have also contributed to the understanding of current trends in innovation scale measurement. Nevertheless, most scales in these studies were unidimensional and exhibit different levels of validity and reliability.

Conceptual Model of Tourist Innovation

The preceding literature highlights the concepts, dimensions, and measures of innovation in consumer research over the past decades. Generally adopting innovation measures from consumer literature to identify how tourists respond to new innovations in service sectors, recent studies show that numerous technology innovations have transformed tourism operations (Couture et al. 2015; Beldona, Lin, and Yoo 2012; Tussyadiah 2016; Wang 2015; Hjalager 2010, 2015). A tourist innovation model is thus needed that incorporates three behavioral components: (1) tourist innovation, (2) tourists’ perception on service innovation, and (3) tourists’ purchase intentions.

Literature that has measured innovation in tourism settings is limited. Past research suggested that innovation is a broad term with numerous components and thus recommended that it be relegated to a manageable construct so as to focus on the segment of vacation travelers (Litvin, Kar, and Goldsmith 2001). This focus may be recommended because traveling away from the home environment can potentially increase tourists’ level of hedonic pleasure seeking. Innovation thus provides tourists more opportunities for staying involved, maintaining social interactions, and being open to new experiences (Nimrod and Rotem 2012). In recent years, e-commerce has influenced visitors’ and tourists’ perception of certain destinations and actual travel behaviors. It has become a major tool for travelers to acquire information prior to travel, at the site, as well as reflecting back on the travel experiences after the trip. More recent innovation research in tourism highlights the influence of tourist innovation on their perceptions of museum podcast tours (Kang and Gretzel 2012), for example, and motivation for information acquisition, social communication, and leisure/fashion (Zhang et al. 2015). This emerging research in tourism suggests that further investigation of the concept of innovation in travel markets is warranted.

The link between tourists’ innovation and their intention to purchase innovative services has been found in the literature. In addressing the effects of travelers’ innovative personality on their online shopping behavior, Lee, Qu, and Kim (2007) stated that highly innovative travelers are primarily influenced by their positive attitudes when engaged in online shopping, whereas less-innovative travelers rely on their attitude as well as the opinions of references and others to reduce inherent uncertainly in online transactions. They therefore suggested that less-innovative travelers, being open to other opinions, may be a good target market for online travel marketers, provided that they have a favorable attitude toward online travel shopping. Ngo and O’Cass (2012) developed a theoretical framework to examine the role of technical and nontechnical innovation and its consequences for service quality and firm performance. Their study found that innovation and customer participation has the potential to enhance service quality outcomes. Using a unidimensional measure from consumer research, Couture et al. (2015) verified that a positive relationship exists between tourist innovation and making a tourism purchase online, in both tourism services managed online and the number of online purchases. Beldona, Lin, and Yoo (2012b) also showed that with respect to the use of technology for tourist services, there is a positive relationship between tourist innovation and the perceived values of tourist services.

Compared to traditional distribution channels, more tourism businesses face challenges in identifying and attracting customers in the world of e-commence. This implies that tourists exhibit adoptability of the Internet as an influencing factor when booking travel online (Li and Wang 2008) and using mobile devices (Zhang et al. 2015). Examining tourists’ perceptions of innovation in various sectors, Sidonia and Maria-Cristina (2013) found that tourist innovation is highly important to their decisions to travel in service segments, including accommodations, entertainment, restaurants, and transport companies. To address these issues, this study hypothesizes the following:

Hypothesis 1: Tourist innovation (TI) positively influences perceived service innovativeness (PSI).

Hypothesis 2: Tourist innovation (TI) positively influences tourists’ behavioral intention (BI) to adopt service innovations.

Hypothesis 3: Perceived service innovativeness (PSI) positively influences tourists’ behavioral intention (BI) to adopt service innovation.

Research Methods

This study was operationalized in an air service setting. An airport navigation application (app) was selected as a recent airport innovation because new technologies have been viewed as vehicles for successful customer–industry interactions. They provide convenient channels for airline companies to communicate interactively with air travelers and know their needs. With the extensive advances in technology, a number of innovations, such as GateGuru and Flight+, have been developed to enhance air travelers’ experiences (Radaha 2013). The mobile app used in this study provides airport information on more than 1,000 airports worldwide. It enables air travelers to obtain information about their travel itinerary, how to get to the departure airport, in-between airport navigation, and destination airport. By using this app, passengers may check information such as gate location, flight change, weather, and luggage status.

To conceptualize innovation, this study follows the scale development procedure suggested by Churchill (1979) and DeVellis (2011), yet expands it with multiple steps in each phase that were used in other scale development studies (Arnold and Reynolds 2003; Gaski and Ray 2004). Each step was chosen to achieve a specific study objective for scale development. First, qualitative interviews along with items in the literature review were conducted to explore items that represent the tourist innovation concept. Items with similar meanings taken from the interviews were then assigned, with the academic experts’ opinions placed in the same dimension. Next, a questionnaire survey was formed and distributed among a test sample of 299 undergraduate students to purify the measure and improve item quality. Lastly, the instrument was verified via an online survey to ensure validity and reliability of the scale.

Item Pool Generation and Content Validity

To generate the initial pool of items, qualitative interviews were conducted along with a review of literature on consumer and tourist innovation. To achieve a holistic view, on-site personal interviews and focus groups were held to collect opinions from local hospitality managers, residents, and college students at a large Midwestern university. In total, 23 respondents participated in the qualitative interviews. During data collection, the sample size was determined by the concept of saturation, which occurs when new data do not add much information to the study (Glaser and Strauss 2009; Francis et al. 2010). Participants in the qualitative interviews consisted of 18 men (n = 18, 78.3%) and 5 women (n = 5, 21.7%). Among the three groups of respondents, residents were the oldest group, ranging from 39 to 65 years old (mean = 49 years old), followed by local business managers (mean = 42 years old) and college students (mean = 22 years old).

To conceptualize tourist innovation, participants were asked to use examples to describe their understanding of tourist innovation based on two interview questions: (1) What are the synonyms you use to describe an innovative tourist? and (2) What are the characteristics of innovative tourists? Constant comparative analysis was adopted as a strategy for data analysis. Along with items in the literature (Wang 2014; Vandecasteele and Geuens 2010), an initial pool of 40 items was drafted, extracting eight overarching themes: (1) novelty seeking, (2) eagerness, (3) openness, (4) vigilance, (5) venturesome, (6) hedonic experience seeking, (7) quality experience seeking, and (8) social distinctiveness. Among all eight categories, novelty seeking and social distinctiveness were mentioned the most. Many interviewees described innovative tourists as “creative” and “original” in that they always look for new tourism products in the market. Others mentioned innovators as leaders in purchasing who like to set trends among friends.

The resultant 40 items were then evaluated by faculty and graduate students to improve item quality, face validity, and content validity (DeVellis 2011). First, five faculty members with extensive research experience in tourism and marketing were invited in frequent debriefing sessions to assess the emerging themes and items. Panel experts served two functions in this study: to confirm or invalidate the definition of the tourist innovation concept and to add items that were not included in the initial pool. Next, 30 students and staff were asked to give comments on the wording and clarity of each item. Duplicate statements were deleted in this process. Lastly, review sessions were conducted among five graduate students on the representativeness of measurement items. They were asked to match each item with the predefined dimensions, where each student was given a table with two sections: all themes with their definitions on the left side of the table and a list of all items in a scrambled order on the right side. The participating graduate students were asked to match the item with its corresponding themes. As suggested by panel experts, five items were assigned under each dimension. One of five items was reverse-coded to avoid straight-line patterns or response bias (see Table 2).

Table 2.

Preliminary Pool of Items.

Dimensions	Items
Novelty Seeking	I enjoy trying unusual products.
	I like to try different products on the market.
	I look for new products on the market.
	I like purchasing novel products.
	I am not interested in buying new products. (–)
Eagerness	I am eager to try new products on the market.
	I am longing to buy new products.
	I am passionate about trying new products.
	I am eager to find out new products.
	I am not enthusiastic about buying new products. (–)
Vigilance	I evaluate the trustworthiness of the information about new products.
	I make careful decisions about what I want to buy.
	I do extensive research before acquiring new products.
	When I want a new product on the market, I usually buy it without doing much research before the purchase. (–)
	I do not make unplanned decisions when buying new products.
Openness	I am curious about trying products that I have never used.
	I am open to a variety of product options.
	I prefer to have many alternatives when deciding what to buy.
	I would like to experience new products of different kinds.
	I prefer not having too many choices for what I want to buy. (–)
Venturesome	I cope well with risks associated with trying new products on the market.
	I am fine with the uncertainty of using new products.
	Although there are risks, I see the potential benefits of buying new products.
	I anticipate uncertainty when using new products.
	Buying new products involves too many risks. (–)
Hedonic Experience Seeking	Acquiring new products makes me happier.
	My life is more exciting when I acquire new products.
	Using new products gives me a sense of personal enjoyment.
	I feel very luxurious when using new products.
	I feel nothing special when using new products. (–)
Quality Experience Seeking	If a new product is more functional than existing products, I usually buy it.
	A user-friendly product can often attract my attention.
	I often consider buying products that are more effective than the current options.
	I would buy products that are more efficient than the ones I am using.
	If the product I have works well enough, I avoid buying a newer model. (–)
Social Distinctiveness	It is unnecessary to buy new products to impress others. (–)
	I like to own a new product that distinguishes me from others.
	I prefer to try new products with which I can stand out among my friends.
	I enjoy using new products that make me a visionary leader.
	Using new products makes me a trendsetter.

Item Purification

To purify the measures, an instrument with forty items was first pilot-tested using a convenient student sample at a large Midwest university. An exploratory factor analysis (EFA) with an oblique rotation was performed on all measurement items to identify a priori of each item to its construct, as the resultant factors were expected to be correlated (Tabachnick, Fidell, and Osterlind 2006; Kim, Ritchie, and McCormick 2010; So, King, and Sparks 2014). Notably, at the early stage, it is necessary to “confirm whether the number of dimensions conceptualized can be verified empirically” (Churchill 1979, 69). The EFA results thus provide information on the number of factors based on eliminating and/or combining dimensions and items for representing more valid factor structure (Mitchell and Greatorex 1993; Kim et al. 2015). This practice has been applied in previous tourism scale development studies (Chen and Hsu 2001; Hung and Petrick 2010; Ap and Crompton 1998; Kim, Ritchie, and McCormick 2010; Choi and Sirakaya 2005).

Kaiser’s eigenvalue criterion and scree plot were first used in this study to determine the number of dimensions. Factors with eigenvalues greater than 1 were candidates for further analysis (Tabachnick, Fidell, and Osterlind 2001; Kaiser 1974). Using these criteria, a 10-dimension solution was initially suggested, explaining 64.8% of the total variance. The Kaiser-Meyer-Olkin (KMO) statistic (KMO = .868; p = .000) was greater than .60, indicating its adequacy for factor analysis. According to Churchill (1979), exploratory factor analysis tends to produce more dimensions than can be conceptually identified. Bad dimensions and items should be eliminated to achieve a satisfactory coefficient. Further, item–total correlation, factor loadings, and Cronbach’s alpha were examined to supplement diagnostic information on internal consistency and dimensionality (Churchill 1979). To purify the scale, items with item–total correlations lower than .2 (Streiner and Norman 2008; Nunnally and Bernstein 1994), factor loadings lower than .4 or cross-loaded on more than one factor (Hair et al. 2010; Chen and Hsu 2001), and Cronbach’s alpha lower than .6 (Hair et al. 2010) were candidates for item exclusion.

Based on the above criteria, a hypothetical model with six dimensions was adapted (KMO = .892; p = .000). Two proposed factors (venturesome and novelty seeking) along with two additional factors were removed because of low Cronbach’s alphas (α = .514, α = –.054, and α = –.273) or poor conceptualization (Factor 10). In addition to the items under the above four factors, items 9 and 26 were eliminated because they had loadings under .4, and six factors with 26 items were retained in the model. Overall coefficient score of the instrument was .896, and Cronbach’s alpha for each tourist innovation factor ranged from .708 to .879, which indicates that the proposed scale is a reliable instrument. The remaining six factors are (1) eagerness; (2) vigilance; (3) quality experience seeking; (4) hedonic experience seeking; (5) openness; and (6) social distinctiveness.

Findings

Instrument and Sample Selection

To validate the proposed instrument, an online survey was distributed by a survey sampling company to a pool of air travelers. To be eligible to participate in this online study, respondents must fulfill three criteria: (1) 18 years or older, (2) owner of a smartphone, and (3) used air travel in the past 12 months. Participants who met all three requirements were denoted as the target population.

In addition to the development of the tourist innovation measurement, two endogenous variables were included to assess the robustness of the derived scale in a perception-behavioral model. Respondents’ perceptions were operationalized as perceived service innovation on a new airport navigation application, whereas behavior was operationalized as their intentions to adopt this new service. The perceived service innovation scale was adapted from Agarwal and Prasad’s (1998) study, and intention of innovation adoption scale was measured by a three-item instrument (Agarwal and Karahanna 1998, 2000). A 7-point Likert-type scale was used in both measurements. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were implemented to analyze the data.

During the measurement validation process, a total of 524 complete and usable cases were generated out of 619 attempted surveys (see Table 3). Within the discarded 95 cases, 71 cases were unqualified data, 23 were unengaged data (straight-lining), and 1 case had excessive missing data. Among 524 cases, approximately half (49.6%) of the respondents were female (n = 260). The average age of respondents was 36.48, ranging from 18 to 68 years. More than half (52.7%) were married (n = 276), 70.6% were Caucasian (n = 370), 57.4% had a college degree or higher (n = 301), 60.3% were employed full-time (n = 316), and 61.8% had $60,000 or higher household income in 2012 (n=324). Participants reported that their average times taken for business/vacation trips and on an airplane in the past 12 months were 5.01 and 5.19, respectively, where over three-quarters of the respondents traveled by air on business (n=400, 76.3%) or vacation trips (n=399, 76.1%) between one and five times.

Table 3.

Demographic Profile of Online Respondents.

Variable	Frequency	%	Mean
Gender
Male	264	50.4
Female	260	49.6
Age, years			36.48
18–25	109	20.8
26–35	166	31.7
36–45	130	24.8
46–55	81	15.4
56–68	38	7.3
Marital status
Single	169	32.2
Married	276	52.7
Divorced	35	6.7
Widowed	4	0.8
Separated	6	1.1
Domestic partner	34	6.5
Ethnicity^a
American Indian or Alaska Native	10	1.9
Asian or Pacific Islander	64	12.2
African American	50	9.6
Hispanic	65	12.4
White/Caucasian	370	70.6
Others	7	1.3
Education
Less than a high school diploma	1	0.2
High school graduate	61	11.6
Some college, no degree	101	19.3
Associate’s degree	60	11.5
Bachelor’s degree	182	34.7
Master’s degree	87	16.6
Professional degree	18	3.4
Doctorate	14	2.7
Employment status^a
Full-time employed	316	60.3
Part-time employed	50	9.5
Self-employed	43	8.2
Out of work and looking for work	26	5
Out of work but not currently looking for work	2	0.4
Student	53	10.1
Retired	11	2.1
Military	1	0.2
Unable to work	8	1.5
Homemaker	46	8.8
Others	3	0.6
Household income in 2012
<$20,000	30	5.7
$20,000–$39,999	74	14.1
$40,000–$59,999	96	18.3
$60,000–$79,999	100	19.1
$80,000–$99,999	80	15.3
$100,000–$119,999	69	13.2
≥$120,000	75	14.3

Note: Sample size (n)=524.

Total exceeds 100% because of multiple responses.

Scale Validation: Construct Validity and Reliability

The tourist innovation scale developed in previous stages was validated using online data. Using Analysis of Moment Structures (AMOS22.0) to examine validity and reliability of the measurement scale, CFA was performed in five steps: (1) model specification, (2) identification, (3) estimation, (4) testing fit, and (5) re-specification (Schumacker and Lomax 2004). The six-dimension model with 26 items from the training data was specified and identified with latent and observed variables for CFA in the validation data set. Goodness-of-fit indices showed that the six-factor model produced by the training set did not fit the data well. For example, the ratio of the chi-square (χ² = 1978.280) to the degrees of freedom (df = 284) was 6.966, which was above the cutoff value 5.0 (Schumacker and Lomax 2004). RMSEA (.107) was also higher than the suggested upper confidence interval (.08) (Hu and Bentler 1999). CFI (.834), NFI (.812), GFI (.752), and AGFI (.693) were all below the satisfactory value (>.90) (Hu and Bentler 1999; Byrne 1994; Raykov and Marcoulides 2012).

Based on these observations, the hypothesized model required a model modification. Tourism literature posits that initial measurement models from training data sets do not always fit the data well, which creates the need for model modification or respecification to achieve an acceptable fit and/or factor loading (Kim et al. 2015; Hung and Petrick 2010; Hinkin, Tracey, and Enz 1997). During model modification, the researcher aims to improve the model fit based on the theoretical meanings of the remaining dimensions and items. From the proposed model, a four-dimensional model with 10 items was adopted for this study. In this model, composite reliability and Cronbach’s alpha in all dimensions were greater than .60, and factor loadings for scale items ranged from .65 to .92 (see Table 4). All item factor loadings were significant (p<.001), which indicates rejection of the null hypothesis that the factor loadings were equal to zero. All average variances extracted (AVEs) were greater than 0.50, and the square root of the AVE score for each factor was larger than the correlation score, which indicates that the discriminant validity of the measurement scale was established (Kline 2015; Fornell and Larcker 1981). All these items had high standardized residual covariance with other items. Validity and reliability were retained in the confirmatory model.

Table 4.

Descriptive Statistics and Factor Loading Results of Measurement Items.

	Mean	SD	FL	CR	AVE
Factor 1: Novelty Seeking (α = .867)				.869	.690
I am eager to try new products on the market.	5.49	1.189	.90
I am curious about trying products that I have never used.	5.52	1.254	.81
I enjoy trying unusual products.	5.44	1.181	.79
Factor 2: Vigilance (α = .742)				.839	.839
I do extensive research before acquiring new products.	5.42	1.306	.92
I make careful decisions about what I want to buy.	5.60	1.141	.65
Factor 3: Hedonic Experience Seeking (α = .860)				.863	.759
Acquiring new products makes me happier.	5.23	1.375	.86
Using new products gives me a sense of personal enjoyment.	5.46	1.238	.88
Factor 4: Social Distinctiveness (α = .883)				.886	.722
I enjoy using new products that make me a visionary leader.	4.89	1.545	.83
I prefer to try new products with which I can stand out among my friends.	4.82	1.632	.89
I like to own a new product that distinguishes me from others.	5.06	1.484	.82

Note: All values were significant at p <.001. α = Cronbach’s alpha; FL = factor loading; CR = composite reliability; AVE = average variance extracted. χ²(29) = 83.192; χ²/df = 2.869; comparative fit index = .984; normed fit index = .975; goodness-of-fit index = .968; adjusted goodness-of-fit index = .94; root mean square error of approximation = .06. A 7-point Likert-type scale was used to measure tourist innovation, with 1 = strongly disagree and 7 = strongly agree.

Model Fit for First- and Second-Order Model

Two types of measurement models were examined in this study: first- and second-order model. The first-order model refers to the relationship between latent and observed variables, whereas the second-order model represents the relationship between the latent variables in the first order and a higher level of latent factor (Bollen 1989). For the tourist innovation scale, the first-order CFA model exhibited good model fit (χ²(29) = 83.192, χ²/df = 2.869, CFI = .984, NFI = .975, GFI = .968, AGFI = .94, and RMSEA = .06). A second-order CFA model was tested on the hierarchical relationships between the four latent variables and their higher latent variable (tourist innovation dimension). Four dimensions in the first-order CFA model were used as indicators in the second-order test. The overall fit of the second-order model shows a satisfactory model fit (χ²(31) = 80.57, χ² /df = 2.823, CFI = .983, NFI = .974, GFI = .967, AGFI = .941, and RMSEA = .059).

Hypotheses Testing and Predictive Validity

To examine predictive validity of the resultant scale, a causal relationship between tourist innovation and two endogenous variables (PSI and BI) were built to estimate the predictive power of the instrument (see Figure 1). All measurement items were adopted from previous studies (Agarwal and Prasad 1998; Agarwal and Karahanna 2000). Structural equation modeling (SEM) results revealed that the structural model had an acceptable model fit (χ²(221) = 705.893, χ²/df = 3.194, CFI = .955, NFI = .936, GFI = .893, AGFI = .867, and RMSEA = .065). Tourist innovation was found to be a significant predictor on perceived service innovation (β = .681, p < .001), explaining 46.4% of the variance in attitudes toward PSI. Hypothesis 1 was supported. Tourist innovation was also a significant predictor for behavioral intentions (β = .071, p < .05), explaining 0.5% of the variance in BI. Hypothesis 2 was supported. Lastly, perceived service innovation was found as a significant predictor for behavior intention (β = .907, p < .001), supporting Hypothesis 3.

Figure 1.

Path diagram of tourist innovation.

Further, the relative importance of each dimension was examined (Table 5). Among all four factors, Hedonic Experience Seeking (β = .939, p < .001), Novelty Seeking (β = .903, p < .001), and Social Distinctiveness (β = .845, p < .001) were the three strongest predictors of purchase intentions. Vigilance had the lowest predictive power (β = .504, p < .001) among the four dimensions. The study therefore concludes that the derived innovation scale has acceptable validity and reliability that can be adapted for future studies to measure tourist innovation levels.

Table 5.

Standardized Regression Weight.

	SPC	SE	CR	p^***	R ²
Novelty Seeking→Tourist Innovation	.903	.053	15.914	<.001	.815
Vigilance→Tourist Innovation	.504	.049	7.047	<.001	.254
Hedonic Experience Seeking→Tourist Innovation	.939	.054	17.614	<.001	.882
Social Distinctiveness→Tourist Innovation	.845	–	–	–	.714

***

All values were significant. SPC = standardized path coefficient; SE = standard error; CR = critical ratio.

Conclusion

The current study investigates the vital concept of tourist innovation, which has largely been neglected by tourism researchers to date. Tourist innovation can be conceptualized by four dimensions: (1) novelty seeking, (2) vigilance, (3) hedonic experience seeking, and (4) social distinctiveness. The study finds that these four dimensions could be measured by 10 indicators that demonstrate high validity and reliability. Compared with the current literature on the concept of consumer innovation, this study reveals two issues worth further exploration.

Within the resultant dimensions, the three dimensions of hedonic experience seeking, novelty seeking, and social distinctiveness appear to play the most important role in consumer behavioral intention. In practice, when examining tourist innovation, researchers may thus give these three dimensions more weight than the vigilance dimension. One item to highlight is how novelty seeking could be portrayed as tourists’ openness, eagerness, and quality-experience seeking. This personality trait manifests in the ways that innovative tourists are attracted by new services that are better than the existing options and thus implies that marketing managers ought to monitor levels of tourist’s perceived benefits and value of the hospitality innovation compared to the more ordinary selections (Daniel Kindström et al. 2014; Sandvik, Duhan, and Sandvik 2014; Jin et al. 2015; Nicolau and Santa-María 2013). Within the innovation construct, only two indicators survived in the experience-seeking dimension and vigilance dimension. It is therefore recommended that future studies enhance the stability of the scale by adding more indicators.

This finding departs from the early literature (e.g., Steenkamp and Baumgartner 1992; Im, Bayus, and Mason 2003) in suggesting that innovation directly influences new product adoption behavior. In other studies (e.g., Hurt, Joseph, and Cook 1977), this relationship was not evidenced (Roehrich 2004), suggesting that innovation is not a strong predictor of behavioral intention (e.g., purchase intention). But this study finds that tourists’ perceived service innovation, which touches on individuals’ judgments toward new products, is indeed a strong predictor of behavior intention. To help determine tourists’ behavior intention, service innovation is considered as a viable mediating variable, particularly in predicting the causal relationship between tourist innovation and behavioral intention. Since tourist perception of service innovation contains various parts—including relative advantage, compatibility, and complicity—industry managers and practitioners should center on those three characteristics in their service and delivery strategies in relation to tourists’ purchase intention.

A handful of practical implications associated with the above insights could be applied to the design and promotion of new tourism products and services. The tourism and hospitality industry is dynamic and ever-changing. With the development of new technology and pervasive social media, individuals are bombarded with information that helps them decide vacation destinations, restaurant options, or hotel choices. As their travel behaviors may be influenced by business marketing as well as the traveler’s social contacts, marketers ought to strive for new designs and highlight provisions of the best products to satisfy vacation travelers’ needs. As tourism business owners focus on the hedonic or pleasure-based outcomes (e.g., new rides in theme parks), it is suggested that they incorporate the excitement, fantasy, and happy moods promised by these products in their promotion materials. Marketing managers may also use this study’s measurement to segment consumer markets by using tourist innovation to predict behavior intensions. For example, if a new service procedure is created in the hotel industry, marketers can use this measurement to test if hotel guests would be likely to buy it or not; if a new menu offering is created in a restaurant, restaurant managers may want to see individuals’ responses to the novel product.

This study has a few limitations in research design and data collection processes. Online surveys were used as the primary data collection method. First, the response rate for this method is low compared to other data collection methods, which may cause nonresponse bias. That is, the respondents may have different views or answers from those who were in the pool but who did not participate in the study. To encourage participation, the survey company provided points for respondents who answered online surveys that could be redeemed for cash. This could increase the risk of information reliability, in that without more information other than demographics, it was challenging for this study to judge whether these surveys were truly answered by different respondents.

Despite the limitations, this study was able to construct a multitrait measurement of innovation in the context of tourism. It can contribute theoretically to developing an understanding of the tourist innovation concept and also suggests an acceptable measurement scale. Future studies that investigate the underlying causes for tourist innovation and related behavior may help cross-validate the study findings. Since the objective of this study was to conceptualize tourist innovation, the study did not examine tourists’ usage behavior. Future studies would benefit from examining customers’ new product usage behaviors in a longitudinal term. Also, this study did not examine the influence of other priors and correlates, such as tourists’ profiles, travel experiences, and tolerance of/attraction to innovations. Future studies could also probe the influences of these other variables on innovation, as well as the influence of tourist innovation on other attitudinal and behavioral outcomes, such as perceived value, satisfaction, loyalty, and subsequent word-of-mouth communication. Finally, service innovation is operationalized as a new airport navigation app, which helps airlines obtain information about their flights, airports, and the cities they service. Future research may validate the measurement in other tourism contexts, such as hotels, restaurants, resorts, or cruise line industries. As tourism involves an increasingly dynamic and growing global market, it is advantageous to deploy cross-cultural investigations by testing the derived measurement scales among tourists from different backgrounds.

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

Wei Wang is an assistant professor at The University of Southern Mississippi. Her research focuses on destination marketing and hospitality innovation.

Shu T. Cole is an associate professor at Indiana University. Her research focuses on the travel experience of people with disabilities.

Joseph S. Chen is a professor at Indiana University. His research area is in social impacts and sustainable management in hospitality and tourism.

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