International Journal of Tourism Research, Int. J. Tourism Res., 16: 113–125 (2014) Published online 2 August 2012 in Wiley Online Library (wileyonlinelibrary.com) DOI: 10.1002/jtr.1905

A Tool For Measurement of Innovation Newness and Adoption in Tourism Firms DEJAN KRIZAJ1*, ANDREJ BRODNIK2,3 and BORIS BUKOVEC4 1 University of Primorska, Faculty of Tourism Studies Portoroz – Turistica, Portoroz, Slovenia 2 University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia 3 University of Primorska, Department of Information Sciences and Technologies, Koper, Slovenia 4 Faculty of Organisation Studies Novo mesto, Novo mesto, Slovenia ABSTRACT The paper focuses on the newness characteristic of realized innovations and their adoption in tourism firms. For that purpose it investigates three research problems: (i) measurement of newness level and adoption of tourism innovations; (ii) definition of tourism innovations taxonomy (needed for the measurement); and (iii) statistical analysis of innovations’ adoption in tourism destinations (result of the measurement). The main aim of the research was to develop and validate the tool used for such measurements. The tool should help researchers and managers in tracking and benchmarking how innovative tourism firms are. Copyright © 2012 John Wiley & Sons, Ltd. Received 25 January 2012; Revised 17 June 2012; Accepted 20 June 2012

key words

tourism innovation; adoption of innovations; innovation taxonomy

INTRODUCTION Innovation is a complex phenomenon and, as such, is described in many theories (Schumpeter, 1961; Drucker, 1985; Sundbo, 1995; Rogers, 2003; Fagerberg et al., 2006). In a wider social context, innovation is defined as an idea, practice or object perceived as new by an individual or other unit of adoption (Rogers, 2003). From the economic perspective, innovation is described as an activity in which an invented entity is further developed into a commercially useful entity being accepted in a social system (tribe, company, society, etc.) (Schumpeter, 1961; Drucker, 1985). ‘Economic’ innovations are not just technological innovations and can represent novelties in the form of a product, a process, organizational behavior, market behavior and so on. Incompatibility between general and service innovation has been asserted by many authors (Sundbo, 1997; Sundbo, 1998a; De Jong and Vermeulen, 2003; Stevens and Dimitriadis, 2005), and in a similar way, the specifics of tourism innovations have been further discussed (Jacob et al., 2003; Walder et al., 2006; Hall and Williams, 2008). The main issues raised relate to different characteristics, measurement specifics and different descriptive models of innovation in the service industry and tourism. In introductory sections of the literature on tourism, one can usually find references to the growing numbers of tourists, a positive assessment of the economic impact of tourism and facts about tourism as one of the world's fastest growing sectors (Hall and Williams, 2008). Following the initial emphasis, that tourism is a significant industry, published content usually diverts to some other, primary, purpose of the research or discussion. The vast majority of *Correspondence to: D. Krizaj, University of Primorska, Faculty of Tourism Studies Portoroz–Turistica, Obala 11a, SI‐6320 Portoroz, Slovenia. E‐mail: [email protected]

Copyright © 2012 John Wiley & Sons, Ltd.

authors therefore do not reach beyond this point in dealing with tourism innovation processes that cause changes and growth. The global tourism innovation process that Hall and Williams mention and which needs more attention consists of a remarkable series of small, gradual changes and a handful of revolutionary leaps, which repeatedly re‐ define tourism and broaden its reach. The X,Y,Z co‐ordinate system in Figure1 shows a tourism firm in stage p0 which represents the firm's state before adopting an innovation; i.e. in a less developed phase. If a firm realizes some innovation steps in terms of creating/ adopting innovations in the directions of X (product), Y (process) or Z (market), it moves in the proposed direction. If the firm moves in all three directions at the same time, then, according to Bieger and Weinert (2006), it is innovating its whole business model. Regardless of which term is used by different theories to denote this movement and whatever direction the firm is taking in the proposed co‐ ordinate system (single or several axes), one thing is certain: the firm is changing its stage from position p0 =(0,0,0) to position p1 =(x,y,z), from a less to a more developed phase. Similar logic can be used in a basic block diagram, where the central block is the innovation process that transforms the firm's stage from p0 to p1. There are (a) some input variables to the firm in stage p0, (b) innovation process mechanisms and (c) output variables of the firm in stage p1 that are dealt with in tourism innovation research. The same – as for a tourism firm – can as well be assumed for each ‘actor’ of the tourism process: the tourist, employee and environment in which the tourism process takes place. They all sense change or are changed in the tourism innovation process that transforms them from stage p0 to stage p1. They can all be passive or active during the process, and they all receive and/or create some inputs and outputs. The paper concentrates on tourism firms’ perspective of this process.

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Figure 1. Innovation co‐ordinate system and block diagram. Source for the coordinate system: Bieger and Weinert (2006).

Further explanation of innovation block diagram parts, shown in Figure1, can be extracted from the extensive Hjalager's (2010) tourism innovation literature review. In the review, several tourism innovation issues are divided into different sections and topics, which can be distributed to the innovation basic block diagram inputs and outputs. Originating from Hjalager's terminology of sections and topics, input innovation process variables can be classified as follows: determinants, driving forces, knowledge sources and policies. Output innovation process variables can be divided into the following: extents, effects, implications and impacts. Various tourism innovation research projects and papers usually deal with some of the outlined variables and mechanisms themselves, filling the research gaps and trying to contribute to the understanding of parts and the whole picture of the innovation process (ibid.). This paper focuses on the newness characteristic of realized innovations and their adoption in tourism firms. For that purpose, the paper opens three research problems: • measurement of newness level and adoption of tourism innovations • definition of tourism innovation taxonomy (needed for the measurement) • statistical analysis of innovations’ adoption in tourism destinations (result of the measurement) During the presented research project, a dedicated tool was developed that incorporates these three problems. The final goal of the paper is to validate the developed tool used for the measurement of tourism innovations’ newness level and their adoption. For that purpose, theoretical and measurement innovation issues are discussed in the following section. Furthermore, the research project's methodology is described, followed by pilot results and tool validation conclusions. The paper ends with a discussion and suggestions for improving and testing the developed tool.

THEORY Innovation literature is extensive and can be categorized in different ways (Dosi, 1988; Johne and Snelson, 1988; Garcia and Calantone, 2002; Becheikh et al., 2006; Hjalager, 2010). Copyright © 2012 John Wiley & Sons, Ltd.

Four main innovation literature orientations (as suggested by Johannessen et al., 2001) focus on individual, structural, interactive and systemic perspectives. The individual perspective stresses factors connected with personal characteristics (Caird, 1994, Patterson, 2004) and the structural perspective concentrates on organizational aspects (Slappendel, 1996; Ormrod et al., 2007). In the interactive perspective, the connection between action and structure is analyzed (Hung, 2004), whereas the perspective of the innovation systems concentrates on national and regional influences on innovation activity (Lundvall, 2010). Tourism innovation can be approached from several angles, as well. Such multi‐ perspectives approach is, for instance, used by Sundbo et al. (2007). Here, multi‐level analyses based on a general discussion of models of innovation systems are applied through three standpoints: firm (Orfila‐Sintes et al., 2005; Pikkemaat and Peters, 2005; Orfila‐Sintes and Mattsson, 2009), network (Novelli et al., 2006; Erkuş‐Öztürk, 2009) and system (Baidal, 2004; Hall, 2009). At all these levels, the generated results of the research improve theoretical understanding of input and output tourism innovation process variables such as those previously mentioned: determinants, driving forces, extents, implications, impacts and so on. (Hjalager, 2010). They can all be conducted by primary and secondary research instruments. Primary empirical surveys commonly try to reveal tourism innovation tendencies by focusing on the internal organizational structure and provide multidimensional scales for measuring innovative performance and capabilities at the firm or other micro level (Jacob et al., 2003; Orfila‐Sintes et al., 2005; Pikkemaat and Peters, 2005; Perez et al., 2006; Volo, 2006). Most of the secondary innovation surveys and scoreboards are based on the Schumpeterian concept of innovation performance and measure innovation at a national or regional stage and provide a composite index of their innovation levels and undertakings (Sundbo et al., 2007; Hall, 2009; Camisón and Monfort‐Mir, 2012). Camisón and Monfort‐Mir outline several limitations of a too strict Schumpeterian approach to tourism innovation. As suggested within multi‐perspective approaches to innovation literature categorization and review (Johannessen et al., 2001; Sundbo et al., 2007), there are many viewpoints, which have to be taken into consideration to gain overall insight into innovation phenomena in general. Following this argument, Camisón and Monfort‐Mir (2012) claim that innovation at the individual firm level might not be appropriately evaluated with indicators intended for measuring innovation at national or regional levels. Second, the authors question whether standard measurement tools for general innovative activities capture the great variety of characteristics of all services included in multidisciplinary tourism (Tribe, 1997; Liburd, 2012). Further: can innovations in services and tourism that are passing through a period of rapid change of pace (Hall, 1998; Liburd, 2012; Weaver, 2012) be effectively evaluated with indicators that were – already some time ago – developed mainly to capture technical innovation in manufacturing (Sundbo, 1998a; Drejer, 2004)? A very large part of the tourism industry is represented by small‐sized and medium‐sized firms with figures that Int. J. Tourism Res., 16: 113–125 (2014) DOI: 10.1002/jtr

A Tool for Measurement of Innovation Newness and Adoption can (on a national level) cover over a 90% share of companies employing up to 100 employees and over 70% employing up to 20 employees (Smith, 2006; Thomas et al., 2011). Many smaller tourism firms have limited innovative capacities and are reasonably dependent on public policies supporting their progress, so it is significant to have appropriate insight into the target organizations’ situation and evolution (Camisón and Monfort‐Mir, 2012). Such insights help improve the policies to better serve the firms and advance their competitiveness. In practice, it can be quite the opposite. For example, European Community innovation surveys, as the information source of innovation activity, drivers and company behavior in Europe, cover only firms with more than 10 employees. That means that an extensive share of small firms that are dependent on public policies might be excluded from the national research projects’ reach. There are not many real innovators in the tourism field where imitators and adapters prevail and which mostly develop incremental innovations out of previous adoptions and knowledge (Hjalager, 2010; Camisón and Monfort‐Mir, 2012). Tourism innovations are usually not linearly developed from market research, design, R&D and so on but occur more stochastically or in other ways as in the manufacturing sector (Sundbo et al., 2007). Camisón and Monfort‐Mir (2012) argue that tourism innovations still do occur often but are not always spotted by the official instruments that are focused on some other sectors and scales. So, according to their suggestions, there might actually be more innovations detected in tourism, but that due to its multidisciplinary and highly structured characteristics, they might be ‘hidden’. Several primary empirical studies, which could reveal some of the ‘hidden’ tourism innovation issues are shown in Table1. In some of the surveys, all tourism‐related firms were observed, whereas other surveys focused on specific types of hotels (alpine, sun and sand, ski), ranging from 20 to 392 research units. Regions involved include Spain, Italy, Austria and North America. In all cases, firm managers were addressed through personal open‐structured interviews or questionnaires. Most surveys focused on the managers’ point of view about their own firm's innovations and related activities. One study included the tourists’ perspective (as interpreted by managers). Two surveys additionally involved the researchers’ assessment of innovations impacts/extents. Basic research approaches mostly consisted of a gathered record of adopted innovations together with certain description of the innovations and related innovation activities. Additionally, some focused on effects, objectives, sources, obstacles and technological bases of innovations or their diffusion characteristics (relative advantage, compatibility, simplicity and ‘trialability’). Most of them also estimated customer impact, innovativeness, and/or profitability. At the innovation category level, the terms differed notably. Most often, product, process, delivery, organization and market innovation categories were applied. Apart from that, other categories or terms were used: quality assurance, gastronomy, marketing, human resources, product bundling, wellness, information technology, operational procedures, strategy development, animation, ICT, productive process, Copyright © 2012 John Wiley & Sons, Ltd.

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management process, total innovations, equipment types, sustainable innovation systems and technologies, and so on. Most of these additional terms could be subcategories of the most often used innovation categories and, as such, could fit well in a more elaborated and standardized tourism innovation (sub)categorization. Findings of the studies were similarly quite diverse. One common and already mentioned conclusion is that tourism is not very innovative in general. In the Balearic Islands, Jacob et al. (2003) highlight the prevalence of technological innovations and the positive tourism innovation effects on a firm's image, profitability and customer satisfaction. Sicilian small and medium entrepreneurs lack understanding of the innovation concept; they are also not very active in marketing innovations, although their process and delivery improvements are common (Volo, 2006). Austrian Alpine hotels are more adoptive than inventive, richer with process innovations; there is no correlation between innovation and manager age or education. It also seems that clearly defined target markets are positively connected with hotels’ innovation activities and success (Pikkemaat and Peters, 2005). Spanish general hotel research has shown that innovation positively correlates with size, specialization and a greater destination diversity of hotels and negatively correlates with a higher destination density of hotels (Perez et al., 2006). Research of sun and sand hotels in Spain confirms the positive size correlation and adds that hotels owned by a chain, hotels under management contract or hotels managed as leased properties tend to be more innovative; as hotels belonging to a chain are also highly inclined to human capital skills and abilities adjustments (Orfila‐Sintes et al., 2005; Martínez‐Ros and Orfila‐Sintes, 2009). When sustainable innovations are concerned, opinion leadership and innovations’ simplicity correlate with innovation adoption rate (Smerecnik and Andersen, 2011). The most common and acute research limitations and future implications include the following: low rates of general and/or specific question response, the over‐reliance on managers’ perceptions of innovations and need for more intensive multivariate analysis and analysis of gaps between entrepreneurs’ and customers’ perceptions of innovation. Similar diverse conclusions, limitations and implications have already been noted in Hjalager's tourism innovation literature review. The basic reason for the situation is not hidden in the researchers’ misconceptions but might be found in the lack of common tourism innovation research guidelines (Hjalager, 2010; Liburd, 2012). An important step toward designing such guidelines has been recently taken by Camisón and Monfort‐Mir (2012) with their discussion of ‘what information is needed to involve all dimensions of innovation in tourism firms and how such information could be collected’. The authors argue that innovation specifics in every tourism activity on the individual firm level and for firms of all sizes should be taken into account in such a way that they may be compared with innovation levels in tourism among countries. For this reason, sector‐specific metrics of innovation are to be developed, which could then allow gathering the most relevant forms of innovation for each activity and permit the comparative analysis of Int. J. Tourism Res., 16: 113–125 (2014) DOI: 10.1002/jtr

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Innovation categories

Approach

Point of view

Firm type (+size) Survey type

Location

Pilot study. Manager defines: – list and description of any introduced changes or improvements – characteristics of firm's innovation activity (effects, objectives, sources, obstacles, technological bases). Product, process, delivery, internal organization, external organization, market.

Personal open‐structured interviews with managers Managers

Balearic Islands, Spain Tourism (20)

Jacob et al. (2003)

(Volo, 2006)

Investigating the impact that each innovation (product, process, delivery, organization, markets and marketing) had on four dimensions of the ‘tourism experience’: (i) accessibility; (ii) effective transformation; (iii) convenience; and (iv) value.

Personal open‐structured interviews with managers Managers and tourists (as seen by managers) Manager and/or researcher define: – number of novel initiations – customer impact – innovation level.

Tourism (NA)

Sicily, Italy

Table 1. Characteristics of tourism innovation surveys

Innovation activities in diverse functional areas, such as quality assurance, gastronomy, marketing, human resources, product bundling, wellness, information technology, operational procedures, strategy development, and animation.

Manager and/or researcher define: – self‐evaluation of innovation activities – rank innovativeness from 0 to 3.

Personal open‐structured interviews with managers Managers and researchers

6 alpine destinations, Austria Alpine hotels (107)

(Pikkemaat and Peters, 2005)

Technological innovation in areas: Quality management, environmental quality management, hardware and computer systems, ICT in external management, ICT in internal management, kitchen equipment, restaurant equipment, rooms facilities, maintenance and saving in supplies, security systems, laundry and cleaning.

Manager rates every activity area likely to incorporate innovation with: (i) no change‐ (ii) radical‐ (iii) incremental‐ or (iv) outsourced activity.

Manager defines adopted innovations and their relative profitability.

ICT, productive process, management process, total innovations.

Managers

Balearic Islands, Spain Sun and sand hotels (331) Questionnaire answered by managers

(Orfila‐Sintes et al., 2005)

Managers

Questionnaire answered by managers

Hotels (392)

Spain

(Perez et al., 2006)

(Martínez‐Ros and Orfila‐Sintes, 2009)

Manager gives information about adoption of various sustainable innovations and their perceived innovation characteristics (relative advantage, compatibility, simplicity and trialability) and characteristics of adopters. Sustainable innovations types: environmental management systems, renewable energy technologies, energy‐efficient building design, community environmental advocacy, purchasing reusable products.

Managers

Hotels and ski resorts (49) Questionnaire answered by managers

North America

(Smerecnik and Andersen, 2011)

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Int. J. Tourism Res., 16: 113–125 (2014)

DOI: 10.1002/jtr

A Tool for Measurement of Innovation Newness and Adoption innovation between sectors. Additionally, Camisón and Monfort‐Mir suggest that apart from research into the high diversity among firms in tourism or any other sector(s), research should also focus on classifying firms that share similar innovative features. In their service specifics chapter, Coombs and Miles (2000) discuss four approaches or stages of studying innovation in services (indifference, assimilation, autonomy and synthesis). The newest approach, synthesis, proposes the possibility of developing an innovation model typology that would be appropriate for all economic sectors, including diverse activities within the tourism sector. This means decreasing the domination of the manufacturing approach in the innovation measurement and broadening the innovation concept beyond the Schumpeterian legacy foundations (ibid.; Drejer, 2004; Camisón and Monfort‐Mir, 2012). Camisón and Monfort‐Mir emphasize the importance of knowledge production and dissemination processes in tourism innovation research apart from commonly accepted innovation performance measurements. According to their guidelines, a complete ‘synthesis’ innovation measurement approach would therefore include indicators of hidden dimensions and indicators of innovative performance and capabilities. The latter includes firms’ organizational learning capabilities and their abilities to reinforce the resources together with fostering knowledge development (ibid.). Such additional measurements should especially comprise diverse methods of knowledge diffusion: embodied knowledge (included in the acquired and accessed equipment), disembodied knowledge (available through open and free sources) and knowledge achieved directly from other people together with staff training and individual and organizational learning. All in all, two main additional ‘knowledge’ focuses should be considered: stocks and flows of internal and external knowledge (OECD, 2005; Camisón and Monfort‐ Mir, 2012). The search for predicted hidden innovations, the synthesis approach and additional innovation focuses might begin with investigating the basic innovation categorization schemes and models. Mostly, innovation categories include product, process, marketing, organization, technology, social, human resource and so on. (Pikkemaat and Peters, 2005; Bieger and Weinert, 2006; Fagerberg et al., 2006; Hall and Williams, 2008). Apart from that, quite a clear distinction of only product, process and market innovations is used in Bieger and Weinert (2006) and their three‐axis innovation co‐ordination system. In her thorough tourism innovation literature review, Hjalager (2010) summarizes five tourism innovation categories: product or service, process, managerial, marketing and institutional. Hjalager also concludes that: ‘There is a lack of consistency in definition and measurement of rates of innovation, which could facilitate comparisons across industry sectors and national borders’. Instead of categories, the dual‐core innovation model (Daft, 1978; OECD, 2005; Camisón and Monfort‐Mir, 2012) identifies two general innovation types: technological/ technical innovation and organizational/administrative innovation. Each changes one of a firm's two cores: the technological core (firm's technical system) and the administrative core Copyright © 2012 John Wiley & Sons, Ltd.

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(firm's social system). Technological/technical innovation is, as mentioned by Camisón and Monfort‐Mir, further divided into two general classifications types: product–process innovation (Abernathy and Utterback, 1978) and incremental–radical degree of novelty (Abernathy and Clark, 1985; Damanpour, 1991; Hjalager 2002). On the non‐technological – i.e. organizational and marketing – side, innovations change the social structure of the firm: management of human resources, structure and organization of the work, and the interactions with customers, suppliers, markets and competitors (Damanpour et al., 2009). Another innovation description methodology related to the dual‐core model approach is used by Hall and Williams (2008), distinguishing between the form of the innovation and its impact range (Sundbo 1998b; Camisón and Monfort‐Mir, 2012). The form of innovation describes its configuration characteristics, whereas impact range defines whether effects of the innovation are recognizable at a world, national, regional or sectoral level. Taking into account Daft's dual‐core model and Hall and Williams’ form/innovation approaches, their basic distinguishing innovation characteristics can be additionally re‐arranged in two groups: content and appearance. ‘Content’ characteristics include product, process, organizational, marketing and form types. Characteristics like incremental or radical degree of novelty and innovation's impact range define its ‘appearance’ (Mitsufuji, 2003; Johannessen and Olsen, 2010; Yücel & van Daalen, 2011; Chang & Hughes, 2012; Jantunen et al., 2012). Whereas innovation ‘content’ can be described through different means of innovation categorization (Pikkemaat and Peters, 2005; Bieger and Weinert, 2006; Sipe and Testa, 2009; Hjalager, 2010), innovation ‘appearance’ has at least two perspectives. The first includes questions regarding how innovation appeared inside the firm's system boundaries; i.e. how the innovation was perceived internally in the firm and what improved because of the adoption of the innovation (Perez et al., 2006, Martínez‐Ros and Orfila‐Sintes, 2009; Monica Hu et al., 2009). The second perspective looks outside the firm's system boundaries: how was the innovation perceived by customers, suppliers, markets and competitors and what was improved because of that (Hoegl and Wagner, 2005; Aldebert et al., 2011). Another systematic suggestion of classifying the innovation appearance characteristics is included in this paper's introductory section by Hjalager's output innovation process groups of variables: innovations’ extents, effects, implications and their impacts. Diverse approaches to evaluating those innovation process outputs suggest that appearance factors of innovation are fragmentally dealt with through diverse quantitative and qualitative methods and geographical, sociological, managerial and other perspectives (Rogers, 2003; Fagerberg et al., 2006; Sundbo et al., 2007) through which ‘tourism innovation research must reach out in a cross‐disciplinary manner’ (Hjalager, 2010). One promising fragmental way of dealing with the innovation appearance factors is focusing on innovations’ newness characteristics (Barnett and Clark, 1996; Johannessen et al., 2001; van Trijp and van Kleef, 2008; Frankelius, 2009; Witt, 2009; Beaugé, 2012). Newness factors alone Int. J. Tourism Res., 16: 113–125 (2014) DOI: 10.1002/jtr

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are not enough to make the innovation a successful one, but when taken into account together with other ‘content’ and ‘appearance’ innovation categories inside the firm's strategic plan, they can contribute to greater competitiveness (Bianchi, 2002; Cozzarin, 2006, Mugge and Schoormans, 2012). Appropriate knowledge about actual technological change and existent competition ecosystems can improve managers’ strategic decisions and engagements for which state‐of‐the‐art insights into the newest trends are crucial (Johne and Snelson, 1988; Amason et al., 2006; Wiklund et al., 2010; Camisón and Monfort‐Mir, 2012; Dahlqvist and Wiklund, 2012). Throughout innovation, research history novelty and newness have been in the focus of innovation definitions (Johannessen et al., 2001; Hall and Williams, 2008; Hjalager, 2010). Although theorizing about innovation's newness characteristics Johannessen et al. (2001) introduce three important questions: (i) what is new; (ii) how new it is; and (iii) to whom it is new. They claim that only after these questions are thoroughly answered and these answers declared at the beginning of each study can one compare different results and talk about a systematic approach to the plethora of possible innovation adopters and answers to what is new for them and how new it is in broader terms. When focusing on its newness characteristics, innovation can be described as the intermediate stage on the continuum between invention and adoption (Marchetti, 1980; Sahal, 1983; Schmiemann, 1999; Volo, 2006; Ahn et al., 2010). Invention represents major developments in science or technology without already known implications. Adoption characterizes a firm's first introduction of existing, already known solutions. Tourism firms operate in a highly interdependent business environment and their offer in most cases depends on several non‐tourism firms and industries, including those in food, beverage, agriculture, architecture, culture, entertainment, health care, finance, information technology, education, safety and so on (Pikkemaat and Peters, 2005; Goeldner and Ritchie, 2008; Križaj and Črnigoj, 2008; Hjalager, 2010; Mekinc et al., 2010). Similarly, based on Barras’ reverse product cycle framework, several authors assert that a supplier‐driven process is one of the basic tourism phenomenon characteristic whereby firms mostly innovate with purchased products and services from their suppliers (Sundbo and Gallouj, 2000; Hjalager, 2002; De Jong and Vermeulen, 2003; Orfila‐Sintes et al., 2005). Logically, there is not much ‘new to the world’ products found in tourism where adoptions on the invention‐adoption continuum are the more preferred type of innovation activity (Pikkemaat and Peters, 2005) and, as such, are not science based (J. Sundbo, 1997). Each invention starts its ‘first in the world’ appearance somewhere on the globe. After that, it is gradually diffused through different social systems at different diffusion rates (Rogers, 2003; Lim, 2009; Kvam and Straete, 2010; Smerecnik and Andersen, 2011; El‐Gohary, 2012; Spencer et al., 2012) and adapted to local needs and environments in different ways. Although such diffused tourism adoptions are not generally perceived as innovations any more, they can play a substantial role in the further development of the Copyright © 2012 John Wiley & Sons, Ltd.

destinations where faster or slower diffusion of ‘already world known’ innovations can still help to differentiate between otherwise not so different tourism destinations (Keller, 2006). This ‘diffusion logic’ of the invention‐ innovation‐adoption continuum is manifested in the European Community innovation surveys (CIS). The data are collected every twoyears, and its regularly updated methodology originates from the Oslo Manual (OECD, 2005), which, in general, does not cover tourism as a standard industry classification but still offers applicable research guidelines (Hall, 2009). The Oslo Manual's defined minimum requirement for an innovation is that it must be new or significantly improved in regard to the firm. Aside from new products, processes and so on that firms are the first to develop, innovations can also be adopted from other firms/organizations and are still treated as innovations for the firm. Firms are identified as innovative if they have introduced an innovation during the period of observation. So adoption in Volo's invention‐adoption continuum (Volo, 2006) is already classified as innovation in the OECD's terms. The authors of the Oslo Manual state that such broad definition of an innovative company may not be appropriate for all policy/research needs and permit more narrow research definitions. This paper's research needs and approaches are summed up according to the above described literature in the following section.

METHODOLOGY For the purpose of the presented investigation of innovations’ newness and adoption characteristics, the OECD minimum requirement is employed. Following this basic foundation, the research is focused on several innovation measurement issues presented in the previous section: • Hidden nature of tourism innovation • Innovation specifics in every tourist activity on the individual firm level • Measurement of firms of all sizes • Possibility to compare innovation levels in tourism among countries • Record creation, stock and flow of internal and external knowledge • Extract information for strategic decision making • Classification of firms with similar innovative features The research quest is open for anything that, in the innovation co‐ordination system (see Figure1), changes a tourism firm's stage from p0 to p1, from a less to more developed phase. If following this basic rule, one records everything that is perceived as new in a firm and could potentially be an innovation, and if the recorded database would be properly processed and evaluated later, the research approach could identify the commonly expected innovations and possible hidden innovations that are not pre‐assumed in existent innovation models. At the same time, the database should allow for the measurements and comparisons listed above. Int. J. Tourism Res., 16: 113–125 (2014) DOI: 10.1002/jtr

A Tool for Measurement of Innovation Newness and Adoption The paper's introductory problem is measuring the newness level of adopted tourism innovations in an observed tourism firm. The Oslo Manual's minimum requirement to treat something as an innovation states that innovation must be at least new or provide a significant improvement to the firm. Apart from a firm's newness level, an innovation can be new at greater levels: region, country, union (EU, USA, etc.) and continent. Ultimately, we can talk about the world innovation level where the innovation is, for the first time, introduced to the whole world (e.g. the first occurrence of the low‐cost airline business model). Firms to be analyzed can be collected just for certain tourism segments, such as a whole accommodation sector or providers of bicycle city tours, and even further subdivided according to specific criteria: size, age and type. A firm can introduce an innovation that is not new compared with all industries in its region (for instance, interactive mobile phone application), but its innovation could be the first one (and thus more competitive) in its region among small tourist agencies (first such agency that has introduced that kind of business‐to‐customer communication channel). The suggested approach is to input data about diverse tourism firms and their adopted innovations, which are new at different levels and (sub)segments in a common database structure. With appropriate algorithms the newness level can be identified for each firm's innovations and, consequently, the most innovative firms in different sectors and regions would show up. This would represent a useful upgrade of the suggestion (Volo, 2006) that surveyed managers should estimate each introduced innovation's position along an invention‐adoption continuum. Namely, automated calculations can more objectively and thoroughly allocate all innovations and firms in the continuum. A special application was developed to test the outcomes of the proposed approach. The basic mechanism originates

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from the introduction of all necessary descriptive attributes of adopted innovations in tourism firms. Through the usage of a proper database structure and taxonomy, it is possible to calculate similarity distances between different firms’ innovations and generate clusters of similar/different adopting innovation types of tourism firms (Shenhar et al., 1995; Garcia and Calantone, 2002; Everitt et al., 2011). When presented in a dendrogram and/or additive tree (see Figure3), the clusters show up, filtering individual firms that may not be near any of the created clusters. Among those filtered individuals, one should be able to recognize highly innovative firms with uncommon characteristics. There are no such information sources readily available from which we would be able to extract the data about adopted innovations in all tourism firms. Our aim was to define a survey pilot set, analyze the possibilities of calculations with the proposed methodology and disclose a first brief picture of the situation in a chosen region or country. For the pilot study, we have concentrated on Slovenia (population 2.05 mil., 20273km2, see Figure2), European Union member from 2004, with approximately 7000 registered tourism‐related firms (estimate obtained from Slovenian Tourism Board representatives). As an information resource, we have chosen main national press and Web media covering news about Slovenian tourism: two most widespread national daily newspapers, the biggest daily business newspaper, five professional tourism journals published by diverse tourism associations, three Web portals managed by the national tourism board and 1 Web portal managed by a national TV operator. Listed sources were selected on the basis of their reputation and for being traditionally used by a majority of local professional readers. Still, some data could be potentially unclear or exaggerated. In such cases, the research team has used a firm's Web page, their contacts or personal correspondence with local experts who know the

Figure 2. Slovenia on the EU members map (a) and country's statistical regions (b). Sources: ec.europa.eu (a) and www.stat.si (b), accessed 5 January 2011.

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background of the particular news. Media information was gathered for the period 1 January 2007 to 1 June 2010. There were 351 tourism related firms identified through news coverage or announcements of the adoption of innovations new at least at the firm newness level. Three steps were introduced during the development of the application. Based on the available data, we developed the following: (1) a proper description of tourism firms in the application. Next, based on the prepared description, we (2) defined the difference between two firms. Finally, we (3) found the proper approach to calculate and show differences between firms. These differences are used in the application to cluster similar firms and to find the ‘most different’ ones from all others. The latter should present firms with more unusual and apparently newer innovations. The steps (1‐2‐3) are described in detail as follows. In step (1), we described each firm with a set of attributes. The attributes were defined in a recursive fashion, which resulted in a tree‐like structure defining a firm. The root of the tree was the firm's ID, followed by several attribute levels of tree branches and sub‐branches, describing the firm's demographics, characteristics and adopted innovations. Next, we inserted descriptions of all firms in a tree‐structured database that permitted us to compare firms – calculate their structural similarity (Zhang and Shasha, 1989). In more detail, at the first tree level, we used the attributes describing the following: firm demographics, firm tourism types, product innovations, process innovations, marketing innovations and institutional innovations. Basic innovation taxonomy followed the Bieger and Weinert (2006) and Hjalager (2010) categorization proposal; further subdivision of demographics and tourism innovation branches was

performed in accordance to the ‘Thesaurus on Tourism and Leisure Activities’, which provides structured lists of descriptors for indexing and retrieving information on tourism and leisure activities (UNWTO, 2002). The tree database structure was presented by using the taxonomy and attributes shown in Table2. The symbol ‘*’ in the table indicates an eventual further attribute's branch subdivision. As an example, the product innovations attribute branch is further subdivided in the lower part of Table2. In step (2), based on the presented firms' description, we calculated the distance between two firms by using an algorithm, which calculates how similar any two trees are (i.e. how many leaves are identical in two compared trees). The algorithm was used in a pilot set to create the similarity matrix of all 351 trees in the database representing 351 firms covered by news of adoption of innovations new at least at the firm newness level. The similarity algorithm uses Jaccard's measure for calculating similarities in terms of a ratio between the number of common tree leaves divided by the total number of all common and all different leaves in two trees (Everitt et al., 2011). Let us use a simple example for a pair of trees shown in Table3. Tree A is a hotel that has introduced new eco‐friendly beds in rooms and the possibility of self‐check‐in at their reception. Tree B shows a hotel, which introduced eco‐friendly beds and the possibility of gathering guest suggestions for the hotel restaurant's daily meals. There are four identical (in bold) attributes and eight types of attributes in the two trees. Tree similarity in the example is calculated as a distance or distinction between tree attributes D(A,B)=4 / (4+2+2)=0.5. By computing the distances between all pairs of firms, we derived a matrix of distances in step (3). We used the matrix

Table 2. Tree database structure Tree database structure

PRODUCT branch subdivision

Copyright © 2012 John Wiley & Sons, Ltd.

FIRM_ID FIRM_TYPE (Organizational form*, No. employees, Firm age, Regional data*) PROVIDER_TYPE (Hospitality*, Food&Beverage*, Spa*, Tourist Agency*, Natural and Cultural Heritage*, Transport*, Sport and Recreation*, …) PRODUCT innovations* PROCESS innovations* MARKET innovations* INSTITUTIONAL innovations* PRODUCT innovations Hospitality New objects Hotel* Theme* Nudist hotel … Reception* Room Eco‐friendly bed … … Food and beverage* Spa* Tourist agency* Natural heritage* Cultural heritage* Transport* Sport and recreation* …

Int. J. Tourism Res., 16: 113–125 (2014) DOI: 10.1002/jtr

A Tool for Measurement of Innovation Newness and Adoption Table 3. Trees A and B Tree A

Tree B

Firm A PRODUCT innovations Hospitality Room Eco‐friendly bed Reception Self‐check‐in Firm B PRODUCT innovations Hospitality Room Eco‐friendly bed Food and beverage Guest‐suggested menus

to cluster firms based on their mutual differences. In doing so, the clustering algorithms actually use the presented taxonomy and database structure for measuring the newness level of tourism innovations by invention‐adoption allocation of the recorded trees’ data (i.e. whether the firm's combination of innovations is presented more or less often). In these algorithms, data clustering techniques were employed based on calculating similarities between recorded tourism firms’ trees. In our pilot research, the calculated 351×351 matrix was processed with a basic unweighted average hierarchical clustering method to present similarities of tourism firms in dendrograms and additive trees (ibid); an example of the latter is shown in section 4. The final result of the clustering calculation is the identification of groups of similar trees (firms) and the most unusual trees (firms) that have the most unusual leaves (characteristics and adopted innovations) in their description. The employed database and algorithms, based on tree difference calculations, can be used on a (filtered) set of local, regional, national, branch‐specific or world‐wide tourism firms. Consequently, the most similar and the most unusual firms can be identified where firms recognized as the most unusual are presumably introducing the most unique and newest innovations from a branch‐specific or overall and global or local ‘innovative point of view’. Apart from invention‐adoption allocation of the recorded trees’ data, the developed taxonomy and database structure provide easy access to statistical analysis of innovations’ adoption by tourism destinations. The first part of the next section presents statistical information about tourism innovation activity in examined regions that is available for analysis and calculation in the developed database.

PILOT RESULTS AND TOOL VALIDATION One of the resulting examples of statistical information that we were able to extract from the created pilot database set was a regional distribution of innovations. The region with the most registered number of innovations was the Osrednjeslovenska region, which is already known as one of the most developed Slovenian tourism regions – the country's capital is located here (all Slovenian statistical regions are shown in Figure2). Regional type of distribution Copyright © 2012 John Wiley & Sons, Ltd.

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was not used in the similar surveys presented in the literature review – most often, they concentrated only on certain regions and/or segments. The number of innovations per organization is a result not presented in most mentioned similar surveys, but for instance, in Jacob et al. (2003), it is stated that firms introduced 1.4 innovations per year, whereas in our case, it was 1.08; this seems to coincide with perceptions of Slovenia as not yet highly developed in exploiting innovation potentials in general (Markič et al., 2011). Furthermore, the most active identified regions were Pomurska (1.66 innovations per year) and Obalnokraška (1.17 innovations per year), known for their rapid development in recent years. Other information available through developed filtering algorithms include distribution of innovations per type of organization where the most active were the following segments: accommodation, tourist agencies and spas; however, this is harder to compare with other studies since they either did not analyze different segments, or they did not provide these numbers. Two additional trends that we were able to identify with the proposed methodology are size and age trends. As confirmed in all studies in the literature review, the analyzed firms with more employees introduced more innovations. Another interesting trend that needs further verification was that in the pilot data set firms up to threeyears old and older than 20years introduced more innovations than those in between (up to threeyears 1.48, in‐between 0.94, older than 20years 1.45 innovations per year). Information about the distribution of innovation categories presents an interesting distribution ratio. The approximate frequency ratio of product, process and market innovation categories was 4:2:1, whereas institutional innovations, as the fourth category we used, were hardly present. Additionally, utilized identification and classification of innovation subcategories demonstrated that the most common innovations in our pilot set were as follows: new objects or their renovation, promotional literature and websites, hiking tours and events, culinary festivals and entrance into the young families market segment. The surveyed pilot set was not intended to be comprehensive enough to obtain the statistically significant information for all tourism segments on the national level. The main aim was to validate the developed tool: to test the possibilities of calculations with the proposed taxonomy and instantly draw an introductory picture of the tourism innovation situation as covered in the country's national media. The pilot results presented up to here show the regular possibilities of data processing for firms and innovation categories. The central added value of the proposed methodology is the means to assess tourism innovations’ newness levels (innovations’ uniqueness and firms’ benchmarking). The first such developed newness level functionality are dendrograms and additive trees of firms’ similarities in certain regions or tourism segments. As mentioned in section 3, using calculated similarities between firms’ trees together with the basic unweighted average hierarchical clustering method groups similar firms and distinguishes those most unusual. An example for information gathered Int. J. Tourism Res., 16: 113–125 (2014) DOI: 10.1002/jtr

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Figure 3. Additive tree for Jugovzhodna Slovenija statistical region.

from available media about one of Slovenia's regions (Jugovzhodna Slovenija) is shown in Figure3, where the firm LL_027 stands out. The layout of the depicted additive tree distinguishes this firm, which is widely recognized in the country and has received, by far, the most tourism innovation awards at home and abroad. By the same logic of comparison, other firms are also scattered in additive trees according to their resemblance. In that way, apart from extracting the most unusual firms (i.e. the trees that are most different among the whole set), we can identify several segments within the analyzed group. For instance, DE_034, DN_002, LL_102 and LL_039 represent four similar tourist attraction managing firms (two professional, two volunteer), which have improved their attraction infrastructure and/or their promotional methods. The clustering methodology can be, as shown, used by researchers to identify diverse segments or types of tourism innovators. On the other hand, the same methodology can serve tourism firms themselves with a given set of feasible further innovating steps to enhance their business prospects. For each surveyed company, a summary sub‐tree of the most similar firms and their adopted innovations can also be generated in the application so that firm managers can check what innovations their direct competition is adopting. The result is a merged list of all adopted innovations in all similar firms (the number of similar firms used in the merged list can be from 5 to 50). A test example – for a small hotel from the capital city of Ljubljana and an executed search for the nine most similar firms – generated suggested new innovations to be considered by hotel management: guests involvement in planning of the hotel restaurant's daily menu, passion packages for couples (romantic food and books in the room), free weekend car rentals for frequent guests and so on.

CONCLUSIONS The main aim of the research was to develop and validate the tool used for measurement of tourism innovations’ newness level and their adoption. The tool should help researchers and managers in tracking and benchmarking how innovative tourism firms are. Three research problems and their solutions were presented for that purpose. The first research problem was solved by discovering and implementing a new way of measuring newness levels and Copyright © 2012 John Wiley & Sons, Ltd.

the adoption of tourism innovations. The concept of similarity calculation between tourism firms was introduced and relative newness levels were calculated throughout the database. The calculation grouped similar firms and distinguished those most unusual. As presented in the example in Figure3, one of the most awarded and innovative firms stood out in the regional pilot set. The research approach consequently provided additional functionality: the possibility of listing innovations adopted by the most similar firms generated on demand for any firm that is recorded in the database. The generated list of all adopted innovations in the most similar (and competing) firms can offer a company direct answers as to how it should (at least) innovate in the near future to remain competitive. The second research problem focused on an appropriate taxonomy, one that would allow for the calculation of correct innovation newness levels. The taxonomy was developed according to recommendations found in the literature and is flexible enough to follow the different available innovation classifications to easily cross‐compare the results and at a certain point converge to form a common taxonomy. Flexibility is gained through the so‐called all‐ encompassing tree (or ‘tree of all trees’) that contains all used leaves in all database trees. By moving or changing leaves in the all‐encompassing tree, all changes are disseminated through all recorded firms’ trees, thus allowing instant reorganization or reclassification of innovation attributes used in the taxonomy. The data organized by the taxonomy also provided solutions for the third research problem – statistical analysis of innovation adoption in tourism destinations. If proper innovation attributes are used and suitably classified in the taxonomy, the results are evident. Algorithms are capable of analyzing either the whole database or just its segments – to examine and take into account different newness levels (as explained earlier using the low cost airline and the mobile application examples). At the same time, statistical results are adaptable for the purpose of comparison with diverse formats of the existent and future tourism innovation surveys. The paper suggests a new, interoperable and generic approach, based on suggestions derived from the literature review as follows. Overall scanning and processing of available tourism innovation information sources can support the quest for (hidden) innovation specifics in every tourist activity on the individual firm level and for firms of all sizes. Creation, stock and flow of internal and external knowledge are included in the records of all new achievements and activities that firms are reporting about and are included in the research input data. The developed database and algorithms offer a straightforward possibility to compare innovation levels in tourism among regions, countries and sub(sectors) and, at the same time, accomplish the literature's proposition to classify firms with similar innovative features. Using the results of the introduced clustering approach, all the gathered information can also be available to firms and other stakeholders for improving strategic decision making. With a big enough sample, one could provide important comparable insight into how countries and their regions are developing at each tourism segment and innovation category Int. J. Tourism Res., 16: 113–125 (2014) DOI: 10.1002/jtr

A Tool for Measurement of Innovation Newness and Adoption direction, possibly focusing only on those categories or segments (like green innovation) that are of special interest to researchers, policy‐makers or change agents. Finally, an additional unique contribution of the paper is a newness level calculation not previously introduced in such a way. The proposed methodology of course has the potential to be improved through the fine‐tuning of the taxonomy and algorithms (for instance optimizing similarity and clustering calculations). At the same time, the methodology should be tested on extensive data sets and for diverse tourism regions. Further upgrades call for using semantics in the automated data gathering of tourism innovations over the Web, extensive multivariate analyses and including attributes and calculations of added values (appearance factors) of implemented innovations. For more statistically significant results, additional data gatherings are necessary, including the possibility of transferring the already accomplished survey data from other countries in the emerging database. Its tree structure can easily adapt to researchers’ approaches and possible different innovation classifications. There is only one basic assumption: the definition of innovation should follow the Oslo Manual's minimum requirement for treating something as an innovation. Already, the proposed methodology offers straightforward, instant innovation insight. The necessary information can be instantly gathered for a specific tourism segment or geographical unit to obtain quick results, which can be reused in a later, thorough scan. Even before tourism managers are asked in detail what their firms are innovating, publicly accessible, gathered and adequately processed news about implemented tourism innovations can provide reliable first and newest impressions of the state of the art in local or global tourism and can suggest to tourism managers possible and already introduced innovations in their fields of interest.

ACKNOWLEDGEMENT The presented work was co‐financed by the Slovenian Research Agency and the Slovenian Ministry of Economic Development and Technology through funding of the national research project ‘Innovation in tourism’, No. V5‐ 0449, for which the authors wish to express their appreciation.

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[Note: Correction added on 31 August 2013 following initial online publication on 2 August 2012. References to Sundbo et al. (2007b) have been corrected to Sundbo et al. (2007); references to Sundbo et al. (2007a) have been corrected to Orfila‐Sintes and Mattsson (2009). The reference list has been amended accordingly.]

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Int. J. Tourism Res., 16: 113–125 (2014) DOI: 10.1002/jtr