Vol7No1Paper2 Salguero To cite this article: Salguero, G.C, Resende, P.C. Jr. and Fernández, I.A. (2017) Proposal of an assessment scale in competitive intelligence applied to the tourism sector. Journal of Intelligence Studies in Business. 7 (2) 38-47. Article URL: https://ojs.hh.se/index.php/JISIB/article/view/199 This article is Open Access, in compliance with Strategy 2 of the 2002 Budapest Open Access Initiative, which states: Scholars need the means to launch a new generation of journals committed to open access, and to help existing journals that elect to make the transition to open access. Because journal articles should be disseminated as widely as possible, these new journals will no longer invoke copyright to restrict access to and use of the material they publish. Instead they will use copyright and other tools to ensure permanent open access to all the articles they publish. 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Journal of Intelligence Studies in Business Publication details, including instructions for authors and subscription information: https://ojs.hh.se/index.php/JISIB/index Proposal of an assessment scale in competitive intelligence applied to the tourism sector Gisela Casado Salgueroa, Pedro Carlos Resende Jr.b and Ignacio Aldeanueva Fernándeza aDepartment of Business Economy and Administration, Faculty of Economics and Business Studies, University of Malaga, Malaga, Spain; bDepartment of Administration, Faculty of Economics, Administration, Accounting and Information Science, University of Brasilia, Brasilia, Brazil; Corresponding authors: gcasado@uma.es, pcrj73@gmail.com and ialdeanuevaf@uma.es Journal of Intelligence Studies in Business PLEASE SCROLL DOWN FOR ARTICLE Editor-in-chief: Klaus Solberg Søilen Included in this printed copy: Why the social sciences should be based in evolutionary theory: the example of geoeconomics and intelligence studies Proposal of an assessment scale in competitive intelligence applied to the tourism sector Gisela Casado Salguero, Pedro Carlos pp. 38-47 Resende Jr. and Ignacio Aldeanueva Fernández Key success factors to business intelligence solution implementation Journal of Intelligence Studies in Business V o l 7 , N o 1 , 2 0 1 7 J o u r n a l o f I n t e llig e n c e S t u d ie s in B u s in e s s ISSN: 2001-015X Vol. 7, No. 1 2017 José Manuel Villamarín García and pp. 48-69 Beatriz Helena Díaz Pinzón Business intelligence and SMEs: Bridging the gap Ekavi Papachristodoulou, Margarita pp. 70-78 Koutsaki and Efstathios Kirkos Klaus Solberg Søilen pp. 5-37 A new model for identifying emerging technologies Klaus Solberg Søilen pp. 79-86 Proposal of an assessment scale in competitive intelligence applied to the tourism sector Gisela Casado Salgueroa*, Pedro Carlos Resende Jr.b* and Ignacio Aldeanueva Fernándeza* aDepartment of Business Economy and Administration, Faculty of Economics and Business Studies, University of Malaga, Malaga, Spain bDepartment of Administration, Faculty of Economics, Administration, Accounting and Information Science, University of Brasilia, Brasilia, Brazil *Corresponding authors: gcasado@uma.es, pcrj73@gmail.com and ialdeanuevaf@uma.es Received 27 January 2017; accepted 20 February 2017 ABSTRACT Companies operate in uncertain environments, where decision-making is a complex task. Thus, one of the key elements to take into account in the aforementioned decision-making is the environment in which the business operates. This is where competitive intelligence (CI) makes sense, understood as the process of establishing the environmental information needs, information acquisition and its analysis, transforming it into intelligence and putting it at the service of decision-makers in the company. This paper focuses on the proposal of a CI model that can be applied in the tourism sector, specifically in hotels, due to the relevance of this sector in many economies worldwide. In order to build the model a deep review of the CI literature was made and subsequently the content validation method was applied, for the purpose of identifying the most important items in the two first stages of the CI cycle: planning and collection. KEYWORDS Competitive intelligence cycle, decision-making, hotel management, tourism 1. INTRODUCTION Nowadays, companies develop their activity in more and more uncertain and complex everyday environments (Zhang et al., 2010). The nature of that environment makes it a difficult task for the companies to maintain a competitive advantage (Shih et al., 2010), as well as carry out decision-making. According to Jiménez-Quintero & Aldeanueva-Fernández (2016), a country’s political situation, together with their way of visualizing international business, has an important impact on decision- making. Consequently, management systems are now becoming more dynamic and less predictable, i.e., more sophisticated. In order to make decisions that guarantee the maintenance of a competitive advantage and business survival, companies not only have to take into account their internal environment, but also what has happened, is happening or could happen in their external environment. It is the latter point that competitive intelligence (CI) processes are focused on. CI can be defined as the art of collecting, processing and storing information to be made available to people at all levels of a firm to help shape its future and protect it against current competitive threats. It should be legal and respect codes of ethics. It involves a transfer of knowledge from the environment to the organisation within established rules (Rouach & Santi, 2001). As Søilen (2015) states, the growing importance of CI in academics brings it closer to become a relevant discipline in the social sciences. The tourism sector is a key element for socioeconomic progress, because of the enterprise and job creation that comes with it. Its growth has been practically uninterrupted and it is expected to continue with this trend Journal of Intelligence Studies in Business Vol. 7, No. 1 (2017) pp. 38-47 Open Access: Freely available at: https://ojs.hh.se/ 39 until 2030, according to the World Tourism Organization (2015). Tourism growth is, therefore, essential to achieve GDP increments. This has been proved by extensive research undertaken in several countries: Sweden, Norwegian, Denmark and more (Lee & Chang, 2008); United Kingdom, Croatia and Spain (Pérez-Rodríguez et al., 2015); Hungary, Romania, France and Spain (Zurub et al., 2015); Brazil, Chile, Colombia, Ecuador, Peru and other Latin-American countries (Eugenio-Martín et al., 2004); Taiwan and South Korea (Chen & Chiou-Wei, 2009); China, Pakistan, Russia and India (Tiwari, 2011). Given the importance that tourism has acquired worldwide, and its prominent role in the GDP of many economies, we decided to conduct research that linked, on the one hand, one of the key agents in tourism: hotels; and on the other hand, CI, understood as a tool for decision-making, and hence, business survival. In this context, the main objective of this paper is to elaborate on an assessment scale of the CI process applied to the tourism sector using a content validation method. This is because, apart from the aforementioned, a literature review about CI between 2011 and 2016 has been conducted, finding a lack of CI research in tourism. 2. LITERATURE REVIEW ON COMPETITIVE INTELLIGENCE CI is based on the environmental school of strategic management (Casado-Salguero & Jiménez-Quintero, 2016) and plays a very important role in the development and deployment of corporate strategies (Dishman & Calof, 2008). The proof of this is in the significant number of proposals present in specialised literature that incorporate CI in several countries and fields. For instance, Šperková et al. (2015) in the banking sector of Czech Republic, Bisson (2014) in public agricultural organisations in France, and Fatti & du Toit (2013) in the pharmaceutical industry in South Africa, etc. A traditional definition of CI is the one that Porter (1980) presented in his book “Competitive Strategy: Techniques for Analysing Industries and Competitors”, where he explains that CI includes the early recognition of threats and opportunities through gathering and analysing information related to the environment of the company to support managers in the business decision- making process. According to Calof (2008), CI helps the company maintain and create competitive advantages by using information from the environment about clients, competitors, and technologies. Fleisher & Bensoussan (2007) define this term as the process whereby a company legally gathers and interprets the environmental information, to make it available to decision-makers. In this case Søilen (2016) shows that the internet and mobile telephones allow access to a wider range of knowledge about companies’ and people’s activities. Therefore, it is necessary to enable secure encryption to preserve confidential electronic information. Casado-Salguero & Jiménez-Quintero (2016) explain that CI in the organisation is the set of practises aimed at gathering information from the business environment ethically and legally, in order to transform it into intelligent information useful for strategic decision-making and, therefore, leading to business success and survival. Table 1 Concepts under which CI has been studied, based on Dishman & Calof (2008) Concept Authors Environmental scanning Aguilar, 1967; Fahey & King, 1977; Fahey & Narayanan, 1982; Hambrick, 1982; Sashittal & Jassawalla, 2001; Saxby et al., 2002 Business intelligence Cleland & King, 1975; Benjamin, 1979; Pearce, 1976 Strategic intelligence Aaker, 1983; Montgomery & Weinberg, 1979 Competitor analysis Ghoshal & Westney, 1991; Rothschild, 1979 Competitive technical intelligence Albagli et al., 1996; Brockhoff, 1991 Market intelligence Chonko et al., 1991; Maltz & Kohli, 1996 Peripheral vision Day & Schoemaker, 2006 Competitive analytics concept Davenport, 2006 40 Table 2 Empirical research on CI by industries (2011-2016) Sector of Activity Authors Exploitation of natural resources Rothberg & Erickson (2013); Sewdass & du Toit (2014); Ramírez et al. (2013); Guimaraes (2011); Jin & Ju (2014); Johns & Van Doren (2010) Public sector Sewdass (2012) Technology-based companies Adidam et al. (2012); Dos Reis et al. (2013); Yap et al. (2013); Ramírez et al. (2013); Mariadoss et al. (2014); De Carvalho & Janissek (2014); Sewdass & du Toit (2014); Guimaraes (2011); Samtani & Capatina (2012); Capatina et al. (2013); Nemutanzhela & Iyamu (2011); Sun & Wang (2015); Jin & Ju (2014); Opait et al. (2016); Ahearne et al. (2013); Xu et al. (2011); Johns & Van Doren (2010) Manufacturing Adidam et al. (2012); Pellissier & Nenzhelele (2013); Yap et al. (2013); Dos Reis et al. (2013); Sewdass & du Toit (2014); Guimaraes (2011); Jin & Ju (2014); Shih et al. (2010) Service sector Faust & Gadotti (2011); Nemutanzhela & Iyamu (2011); Adidam et al. (2012); Zheng et al. (2012); Pellissier & Nenzhelele (2013); Dos Reis et al. (2013); Yap et al. (2013); Tuţă et al. (2014); De Carvalho & Janissek (2014); Sewdass & du Toit (2014); Trong (2013); Guimaraes (2011); García- Alsina et al. (2013); Fernández & Tañski (2011); Rapp et al. (2015); Zambon & Anunciação (2014); Ahearne et al. (2013); Hughes et al. (2013) ; Guarrochena & Paul (2013); Erickson & Rothberg (2013) ; Gatsoris (2012) ; Pelissari et al. (2012); Safarnia et al. (2011); Fernández & Tañski (2011) Hotel sector Faust & Gadotti (2011); Rapp et al. (2015); Calero et al. (2010) Despite its presence both in academic and professional areas, a single generally accepted definition of CI does not exist (Fleisher & Wright, 2009). In fact, as can be seen in Table 1, there are different CI perspectives approached by several authors in the literature. Examining the literature, in last five years we can find empirical research about CI in different sectors, including tourism. However, studies about CI in hotels are scarce, which is a strong argument to conduct this paper. To reach that conclusion a literature review in two databases was undertaken: Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and Web of Science (WOS). These databases contain the highest impact journals in the indexed literature. The keyword in the search was “competitive intelligence”, and in order to select the sample of papers the following criteria where established: a) papers had to include the keyword “competitive intelligence”, either in the title or in the keywords; b) they had to follow empirical research; c) they had to belong to the field of business and economics; and d) they had to be published between 2011 and 2016. Thirty-six papers were obtained in all, which constitute the analysis base used in this paper. The analysed papers were classified, as shown in Table 2, within the following industries: exploitation of natural resources, public sector, technology-based companies, manufacturing, service sector, and finally, hotel sector. Research in CI during the last five years can be summed up as follows: research is mostly focused on service sector companies (33% of the selected papers), followed by technology-based companies (31%) and manufacturing (16.7%). Note the scarce research on the public sector (2.4%) and natural resources exploitation (11.9%), but it is especially important to highlight the poor participation of CI research in hotels, which comprises only 4.8% of selected papers. 3. PROPOSAL OF A COMPETITIVE INTELLIGENCE MODEL Many multinational companies are aware of the fact that, thanks to CI, a competitive advantage can be achieved, so some of them, such as Procter and Gamble, General Motors and British Petroleum, have established formal CI units within the organisation or have 41 adopted structured processes to gather and analyse information from the environment (Bose 2008; Groom & David, 2001; Pepper 1999; Vedder et al., 1999; cited by Hughes et al., 2013). Furthermore, companies with a higher standard in CI activities also show better financial performance (Adidam et al., 2012). Due to the above, and the relevance of the tourism sector in many economies, we propose a CI model that can be applied by hotels. We focus on the first two stages of the CI cycle, based on a comprehensive literature review to establish the items belonging to each stage, items that have subsequently been validated by a committee of experts in the tourism sector. The result is a CI model applicable to hotels (and even to other sectors, with the appropriate modifications), which will allow to organisations to implement a CI process. But in practice, how is a CI process applied? There are several authors that talk about the so-called “CI cycle”, in other words, a set of successive phases that help us obtain the necessary intelligence for decision-making. In the various CI cycles that we can find in the literature, there are common elements among them. However, the name and number of the stages can be different (Cloutier, 2013). Figure 1 shows one of the most widely accepted CI cycles among professionals and academics. We are referring to the one proposed by the Strategic and Competitive Intelligence Professionals (2014). This cycle consists of the following five stages: a) planning (recognition of the information needs); b) gathering (needed information and choosing a source to obtain it); c) analysis (turning information into intelligence); d) dissemination (making the obtained intelligence available for decision-makers); and e) feedback (setting a mechanism to validate the reliability of the obtained intelligence to determine the potential variances in any stages of the cycle). 4. METHODOLOGY To build the assessment scale of the CI practices, a content validation by a panel of judges was applied, according to Hernández- Nieto (2002) and Pasquali (2010). To measure the content validation coefficient (CVC) of each item in the questionnaire, the following criteria were adopted: a) clarity of language; b) practical relevance; and c) theoretical relevance. The aim of the validation through a panel of judges is to confirm, theoretically, the hypothesis that items properly represent the Figure 1 . Stages in the CI cycle, based on Strategic and Competitive Intelligence Professionals (2014) and Cloutier (2013). 42 construct, by asking people who don’t constitute a representative sample of the population to build that instrument (Pasquali, 2010). Nine judges were selected to be part of the production of the questionnaire content validation coefficient (CVC). CVC calculation was made through the following steps: a) Each item mean score (Mx) is calculated from the judges’ score: 𝑀" = 𝑋%& ' %() 𝑗 where i=1 represents the total judges’ score and j the number of judges. b) The initial CVC is obtained from: 𝐶𝑉𝐶% = 𝑀" 𝑉-." c) Error is the same for each item, and it is calculated as follows: 𝑃01 = 1 𝑗 & d) Then, the final CVC is obtained for each item: 𝐶𝑉𝐶3 = 𝐶𝑉𝐶% − 𝑃01 e) Finally, the total CVC of the questionnaire is calculated for each assessment criteriaon (clarity of language, practical relevance and theoretical relevance): 𝐶𝑉𝐶5 = 𝑀6761 − 𝑀891 where M;<;= is the mean of content validation coefficient items and M>?=, the mean of error of the items in the questionnaire. After the calculation, it is recommended that only items with CVCB > 0,8areaccepted. are accepted. 5. ANALYSIS AND RESULTS The instrument consists of two blocks of items that assess some of the main CI activities found in the literature: planning of the CI needs and information gathering. For the planning block, 51 items were proposed, although 30 didn’t reach the minimum coefficient required in the literature after the panel of judges’ evaluation and those items had to be removed from the scale. Items with a content validation coefficient below 0.8 were excluded from the proposed scale. Therefore, the block was only composed of 21 items. Table 3 Items referring to planning of CI activities. Items referring to planning of CI activities 𝐶𝑉𝐶5 1. Competitor price is decisive to fix my price 0.90 2. There is a management practise to monitor competitor strategy 0.87 3. There is a management practise to monitor competitor price 0.87 11. Takes into account guest satisfaction with each department to manage it 0.83 12. Takes into account guest opinion on the state of the premises 0.92 13. Provider prices determine if we continue working with them in the future 0.84 15. We know other existing providers’ prices 0.93 17. Issuing country’s political stability in long-term concerning decisions is taken into account 0.83 18. The economical stability of the country in long-term concerning decisions is taken into account 0.81 20. Our country’s threat of terrorism impacts long-term decisions 0.83 28. Level of crime and public security affect tourists arrival 0.86 29. Our country’s infrastructure affects tourist arrival 0.86 30. Issuing countries’ infrastructure affects tourist arrival 0.81 33. Unemployment rate affects domestic tourism 0.91 34. Issuing countries’ unemployment rate affects tourist arrival 0.87 36. The standard of living in our country impacts domestic tourism 0.93 37. Issuing countries’ standard of living impacts tourist arrival 0.90 43. Process automation affects way of working 0.84 45. Issuing countries’ culture in its management is taken into account 0.87 46. Countries’ culture in its management is taken into account 0.85 50. Energy costs affects management 0.87 43 This way, there is alignment between the assessed dimensions and a coefficient whose extent is 0.13, the accepted rate in the literature. For the gathering block, 41 items were proposed, although 24 didn’t reach the minimum coefficient required in the literature after the panel of judges’ evaluation and those items had to be removed from the scale. Items with a content validation coefficient below 0.8 were excluded from the proposed scale. Therefore, the block was only composed of 17 items. For items referring to information gathering, the extent of the CVCt was 0.18, as recommended in the literature. That was possible due to the fact that in about 35% of the items, the CVCt was between 0.9 and 1. At least one item, one out of the three assessed dimensions, had a maximum concordance qualification among the panel of judges. Table 4 Items referring to gathering of the information. Items referring to gathering of the information 𝐶𝑉𝐶5 1. There is a management practise to identify main competitors 0.97 2. There is a management practise to monitor competitor strategy 0.84 3. There is a management practise to monitor competitor price 0.96 4. There is a management practise to monitor new competitor services 0.84 5. There is a management practise to monitor competitor scores on search engines 0.88 7. There is a management practise to segment the market 0.93 8. There is a management practise to determine each segment’s characteristics 0.87 9. There is a management practise to monitor guests’ suggestions 0.93 11. There is a management practise to monitor the information obtained about guests in each department 0.99 12. There is a management practise to monitor the information obtained about competitors in each department 0.84 20. There is a management practise to monitor environmental legislation 0.84 21. There is a management practise to monitor the level of crime and public security 0.81 31. There is a management practise to monitor labour qualification 0.84 33. There is a management practise to monitor new ICTs 0.87 34. There is a management practise to monitor the life cycle of the products 0.86 39. There is a management practise to monitor energy costs 0.90 41. There is a management practise to cooperate with strategic alliances to develop new products and services 0.84 6. CONCLUSIONS AND FUTURE RESEARCH If a company wants to survive and be successful, it has to be accomplished by means of least bias decision-making. Nowadays, it is a difficult duty as far as the environment is concerned due to its instability and turbulence, consequently it is indispensable to have a wide knowledge of it, in such a way that the environment information can be incorporated into decision-making. For decades, companies have understood this, and many well-known companies, even the most limited in size and resources, are applying CI processes that allow them to have a better understanding of their environment. In this paper we present a study of how CI processes should be applied in one of the most important sectors in many economies: tourism. Specifically, this work focuses on the proposal of a CI assessment model for hotels, on the basis of the two first stages of the CI cycle described by the Strategic and Competitive Intelligence Professionals (2014). After a comprehensive literature review, in the first stage (planning), 51 items were obtained, and in the second one (gathering), 41. In each stage, Hernández-Nieto (2002) and Pasquali’s (2010) content validation method was applied, and items were reduced to 21 in the first stage and 17 in the second. The aim of this research is that the first two assessed stages of the CI cycle can be of used for hotels’ decision-makers to get to know how their company applies CI processes, or to help them to establish structured CI process. Furthermore, we pursue an increase in scientific knowledge in business management. In this line of research, and in accordance with the topic, we keep the possibility of completing the assessment of the CI cycle open, 44 following the methodology applied herein. Once the cycle has been completed, one may analyse its degree of implementation in hotels and examine their profitability, to be able to determine (as has already been done in the literature) the relationship between CI and profitability, but focusing exclusively on the hotel sector. 7. REFERENCES Aaker, D. A. (1983). Organizing a strategic information scanning system. California Management Review, 25(2), 76-83. Adidam, P. T., Banerjee, M. & Shukla, P. (2012). Competitive intelligence and firm's performance in emerging markets: an exploratory study in India. Journal of Business & Industrial Marketing, 27(3), 242- 254. Aguilar, F. J. (1967). Environmental scanning. New York: Macmillan. Ahearne, M., Lam, S. K., Hayati, B. & Kraus, F. (2013). Intrafunctional competitive intelligence and sales performance: a social network perspective. Journal of Marketing, 77(5), 37-56. Albagli, A., Dawson, P. & Hasnain, S. (1996). Competitive science and technology intelligence. International Journal of Technology Management, 12(3), 320-328. Benjamin, W. A. (1979). Management of business information. Industrial Marketing Management, 8(1), 51-56. Bisson, C. (2014). Exploring competitive intelligence pratices of french local public agricultural organisations. Journal of Intelligence Studies in Business, 4(2), 5-29. Bose, R. (2008). Competitive intelligence process and tools for intelligence analysis. Industrial Management & Data Systems, 108(4), 510- 528. Brockhoff, K. (1991). Competitor technology intelligence in german companies. Industrial Marketing Management, 20(2), 91-98. Calero, F. J., Parra, E. & Santana, A. (2010). Vigilancia tecnológica e inteligencia competitiva: un análisis de la demanda tecnológica en alojamientos turísticos en Canarias. Revista de Análisis Turístico, 9, 30- 41. Calof, J. L. (2008). Selling competitive intelligence. Competitive Intelligence Magazine, 11(1), 39-42. Capatina, A., Nistor, R. & Bleoju, G. (2013). Empirical evidence on cultural dimensions related to competitive intelligence strategies adopted by the romanian software companies. Business Management Dynamics, 2(7), 20-27. Casado-Salguero, G. & Jiménez-Quintero, J. A. (2016). Competitive intelligence in the tourism sector, with special focus on Southern Europe. Tourism & Management Studies, 12(1), 136- 144. Chen, C. F. & Chiou-Wei, S. Z. (2009). Tourism expansion, tourism uncertainty and economic growth: new evidence from Taiwan and Korea. Tourism Management, 30(6), 812-818. Chonko, L. B., Tanner, J. F. & Smith, E. R. (1991). Selling and sales management in action: The sales force's role in international marketing research and marketing information systems. Journal of Personal Selling & Sales Management, 11(1), 69-80. Cleland, D. I. & King, W. R. (1975). Competitive business intelligence systems. Business Horizons, 18(6), 19-28. Cloutier, A. (2013). Competitive intelligence process integrative model based on a scoping review of the literature. International Journal of Strategic Management, 13(1), 57-72. Davenport, T. H. (2006). Competing on analytics. Harvard Business Review, 84(1), 98-107. Day, G. S. & Schoemaker, P. J. H. (2006). Peripheral vision: detecting the weak signals that will make or break your company. Boston: Harvard Business School Press. De Carvalho, F. L. & Janissek, R. (2014). Uma proposta de relação de requisitos funcionais para um software de apoio ao processo de inteligência. REAd: Revista Eletrônica de Administração, 20(2), 425-460. Dishman, P. L. & Calof, J. L. (2008). Competitive intelligence: a multiphasic precedent to marketing strategy. European Journal of Marketing, 42(7-8), 766-785. Dos Reis, C., de Abreu, A. F. & Agrasso, M. A. (2013). Best practices in brazilian companies. Journal of Technology Management & Innovation, 8, 79-91. Erickson, G. S. & Rothberg, H. N. (2013). A strategic approach to knowledge development 45 and protection. The Service Industries Journal, 33(13/14), 1402-1416. Eugenio-Martín, J. L., Martín-Morales, N. & Scarpa, R. (2004). Tourism and economic growth in latin american countries: a panel data approach (working paper 26). Milán: Fondazione Eni Enrico Mattei. Fahey, L. & King, W. R. (1977). Environmental scanning for corporate planning. Business Horizons, 20(4), 61-71. Fahey, L. & Narayanan, V. K. (1982). The micro- politics of strategy formulation 1. Academy of Management Review, 7(1), 25-34. Fatti, A. & du Toit, A. S. A. (2013). Competitive intelligence in the South African pharmaceutical industry. Journal of Intelligence Studies in Business, 3(1), 5-14. Faust, D. & Gadotti, S. J. (2011). La inteligencia competitiva aplicada a las redes hoteleras brasileñas. Estudios y Perspectivas en Turismo, 20(2), 478-498. Fernández, M. J. & Tañski, N. C. (2011). Inteligencia competitiva: propuesta de modelo sistémico como cambio organizacional para los hospitales del sur de Brasil. Visión de futuro, 15(2), 1-24. Fleisher, C. S. & Bensoussan, B. E. (2007). Business and competitive analysis: effective application of new and classic methods. New Jersey: Pearson Education. Fleisher, C. S. & Wright, S. (2009, June). Causes of competitive analysis failure: understanding and responding to problems at the individual level. In Third European Competitive Intelligence Symposium. Stockholm. García-Alsina, M., Ortoll, E. & Cobarsí-Morales, J. (2013). Enabler and inhibitor factors influencing competitive intelligence practices. Aslib Proceedings, 65(3), 262-288. Gatsoris, L. (2012). Competitive intelligence in greek furniture retailing: a qualitative approach. EuroMed Journal of Business, 7(3), 224-242. Ghoshal, S. & Westney, D. E. (1991). Organizing competitor analysis systems. Strategic Management Journal, 12(1), 17-31. Groom, J. R. & David, F. R. (2001). Competitive intelligence activity among small firms. SAM Advanced Management Journal, 66(1), 12-20. Guarrochena, M. & Paul, L. M. (2013). Estrategias de gestión de la información asociadas a la inteligencia competitiva: apropiación práctica en organizaciones de apoyo a empresas exportadoras. Visión de futuro, 17(2), 148-167. Guimaraes, T. (2011). Industry clockspeed's impact on business innovation success factors. European Journal of Innovation Management, 14(3), 322-344. Hambrick, D. C. (1982). Environmental scanning and organizational strategy. Strategic Management Journal, 3(2), 159-174. Hernández-Nieto, R. A. (2002). Contributions to statistical analysis. Mérida: Universidad de Los Andes. Hughes, D. E., Le Bon, J. & Rapp, A. (2013). Gaining and leveraging customer-based competitive intelligence: the pivotal role of social capital and salesperson adaptive selling skills. Journal of the Academy of Marketing Science, 41(1), 91-110. Jiménez-Quintero, J. A. & Aldeanueva- Fernández, I. (2016). Dirección estratégica internacional. Madrid: Ediciones Pirámide. Jin, T. & Ju, B. (2014). The corporate information agency: do competitive intelligence practitioners utilize it? Journal of the Association for Information Science and Technology, 65(3), 589-608. Johns, P. & Van Doren, D. C. (2010). Competitive intelligence in service marketing: a new approach with practical application. Marketing Intelligence & Planning, 28(5), 551- 570. Lee, C. C. & Chang, C. P. (2008). Tourism development and economic growth: a closer look at panels. Tourism management, 29(1), 180-192. Maltz, E. & Kohli, A. K. (1996). Market intelligence dissemination across functional boundaries. Journal of Marketing Research, 33(1), 47-61. Mariadoss, B. J., Milewicz, C., Lee, S. & Sahaym, A. (2014). Salesperson competitive intelligence and performance: the role of product knowledge and sales force automation usage. Industrial Marketing Management, 43(1), 136-145. Montgomery, D. B. & Weinberg, C. B. (1979). Toward strategic intelligence systems. Journal of Marketing, 43(4), 41-52. Nemutanzhela, P. & Iyamu, T. (2011). The impact of competitive intelligence on products and services innovation in organizations. 46 International Journal of Advanced Computer Science and Applications, 2(11), 38-44. Opait, G., Bleoju, G., Nistor, R. & Capatina, A. (2016). The influences of competitive intelligence budgets on informational energy dynamics. Journal of Business Research, 69(5), 1682-1689. Pasquali, L. (2010). Instrumentação psicológica: fundamentos e práticas. Porto Alegre: Artmed Editora. Pearce, F. T. (1976). Business intelligence systems: the need, development, and integration. Industrial Marketing Management, 5(2-3), 115-138. Pelissari, A. S., Defreitas, I. V., Vanalle, R. M. & Soares, M. L. (2012). Diagnóstico do uso da inteligência competitiva empreendedora em pequenas empresas da indústria de confecções da cidade de Vila Velha–Es. Revista de Administração da UFSM, 5(2), 183-203. Pellissier, R. & Nenzhelele, T. E. (2013). The impact of work experience of small and medium-sized enterprises owners or managers on their competitive intelligence awareness and practices. South African Journal of Information Management, 15(1), 1- 6. Pepper, J. E. (1999). Competitive intelligence at Procter & Gamble. Competitive Intelligence Review, 10(4), 4-9. Pérez-Rodríguez, J. V., Ledesma-Rodríguez, F. & Santana-Gallego, M. (2015). Testing dependence between GDP and tourism's growth rates. Tourism Management, 48, 268- 282. Porter, M. E. (1980). Competitive strategy: techniques for analyzing industries and competitors. New York: Free Press. Ramírez, R., Österman, R. & Grönquist, D. (2013). Scenarios and early warnings as dynamic capabilities to frame managerial attention. Technological Forecasting and Social Change, 80(4), 825-838. Rapp, A., Agnihotri, R., Baker, T. L. & Andzulis, J. M. (2015). Competitive intelligence collection and use by sales and service representatives: how managers’ recognition and autonomy moderate individual performance. Journal of the Academy of Marketing Science, 43(3), 357-374. Rothberg, H. N. & Erickson, G. S. (2013). Intelligence in the oil patch: knowledge management and competitive intelligence insights. Journal of Intelligence Studies in Business, 3(3), 29-36. Rothschild, W. E. (1979). Competitor analysis: the missing link in strategy. Management Review, 68(7), 22-8. Rouach, D. & Santi, P. (2001). Competitive intelligence adds value: five intelligence attitudes. European Management Journal, 19(5), 552-559. Safarnia, H., Akbari, Z. & Abbasi, A. (2011). Review of competitive intelligence & competitive advantage in the industrial estates companies in the Kerman city: appraisal and testing of model by Amos graphics. International Business and Management, 2(2), 47-61. Samtani, M. & Capatina, A. (2012). Achieving the next level of growth through competitive intelligence practices: an exploratory study of romanian offshore technology service providers. Economics and Applied Informatics, 3, 15-20. Sashittal, H. C. & Jassawalla, A. R. (2001). Marketing implementation in smaller organizations: definition, framework, and propositional inventory. Journal of the Academy of Marketing Science, 29(1), 50-69. Saxby, C. L., Parker, K. R., Nitse, P. S. & Dishman, P. L. (2002). Environmental scanning and organizational culture. Marketing Intelligence & Planning, 20(1), 28- 34. Sewdass, N. (2012). Proposing a competitive intelligence (CI) framework for Public Service departments to enhance service delivery. South African Journal of Information Management, 14(1), 1-13. Sewdass, N. & du Toit, A. (2014). Current state of competitive intelligence in South Africa. International Journal of Information Management, 34(2), 185-190. Shih, M. J., Liu, D. R. & Hsu, M. L. (2010). Discovering competitive intelligence by mining changes in patent trends. Expert Systems with Applications, 37(4), 2882-2890. Søilen, K. S. (2015). A place for intelligence studies as a scientific discipline. Journal of Intelligence Studies in Business, 5(3), 35-46. 47 Søilen, K. S. (2016). Economic and industrial espionage at the start of the 21st century: status quaestionis. Journal of Intelligence Studies in Business, 6(3), 51-64. Šperková, L., Škola, P. & Bruckner, T. (2015). Evaluation of e-word-of-mouth through business intelligence processes in banking domain. Journal of Intelligence Studies in Business, 5(2), 36-47. Strategic and Competitive Intelligence Professionals (2014). SCIP. Retrieved from https://www.scip.org/ Sun, L. & Wang, Y. Z. (2015). Identifying the core competitive intelligence based on enterprise strategic factors. Journal of Shanghai Jiaotong University (Science), 20(1), 118-123. Tiwari, V. (2011). Software engineering issues in development models of open source software. International Journal of Computer Science and Technology, 2(2), 38-44. Trong, L. (2013). Corporate social responsibility, upward influence behavior, team processes and competitive intelligence. Team Performance Management: An International Journal, 19(1/2), 6-33. Tuţă, M., Zara, I. A., Orzan, G., Purcarea, V. L. & Orzan, O. A. (2014). Competitive intelligence: an enhancement to business intelligence. Economic Computation & Economic Cybernetics Studies & Research, 48(2), 1-11. Vedder, R. G., Vanecek, M. T., Guynes, C. S. & Cappel, J. J. (1999). CEO and CIO perspectives on competitive intelligence. Communications of the ACM, 42(8), 108-116. World Tourism Organization (2015). Panorama OMT del turismo internacional. Madrid: WTO. Xu, K., Liao, S. S., Li, J. & Song, Y. (2011). Mining comparative opinions from customer reviews for competitive intelligence. Decision Support Systems, 50(4), 743-754. Yap, C. S. Rashid, M. Z. A. & Sapuan, D. A. (2013). Perceived environmental uncertainty and competitive intelligence practices. VINE: The Journal of Information and Knowledge Management Systems, 43(4), 462-481. Zambon, A. & Anunciação, P. (2014). Inteligência competitiva: percepções de valor no setor da bijuteria. Revista Portuguesa e Brasileira de Gestão, 13(2), 41-60. Zhang, X., Majid, S. & Foo, S. (2010). Environmental scanning: an application of information literacy skills at the workplace. Journal of Information Science, 36(6), 719- 732. Zheng, Z., Fader, P. & Padmanabhan, B. (2012). From business intelligence to competitive intelligence: inferring competitive measures using augmented site-centric data. Information Systems Research, 23(3), 698-720. Zurub, H. H., Ionescu, A. & Constantin, V. D. (2015). Measuring the economic impact of tourism in european emerging markets. Procedia Economics and Finance, 32, 95-102.