Evaluation and identification of nature-based tourism (NBT) destinations is not a new practice; however, evaluating the variability of various inputs in the identification of NBT destinations have only recently gained special attention. This study highlights the importance of conducting sensitivity analysis of criteria weights in mapping NBT areas in the state of West Virginia. As an extension to the study by Dhami, Deng, Burns, and Pierskalla (2014, Identifying and mapping forest-based ecotourism areas in West Virginia incorporating visitors' preferences. Tourism Management, 42, 165-176) who examined and mapped NBT areas in West Virginia by incorporating visitors' perceptions as criteria weights into a spatial suitability model, this study focuses on how sensitive NBT areas are to the variations of visitors' and ecotourism experts' perceptions of selected criteria (i.e. remoteness, slope, vegetation, wildlife, mining, and logging) using a simple one-at-a-time method. Results indicate that visitors and ecotourism experts perceived the criteria in a similar manner with the presence of vegetation and remoteness being ranked as the most important criteria for NBT areas. The results also show that about one-third of West Virginia is highly suitable and least sensitive to variations in criteria weights, and therefore suitable for a wide range of NBT tourists, irrespective of their preferences for the selected NBT criteria. Research implications and limitations are discussed. ARTICLE HISTORY
Empirical data as well as scientific reports confirm that startups require diverse, in-house competencies in order to achieve market success. However, while technology-related competencies are usually brought to new tech ventures by their founders, there is a significant deficiency of market- and management-related competencies, which is reflected in the statistics of common startup failure reasons. In this context, a question arises: how interdisciplinarity of knowledge and competencies can be built into the very core of tech startups? The text addresses this question by zeroing in on the role of universities in this process. In the first part, the specificity of tech startups is analyzed. The next chapter overviews the hitherto academy-related startup education environment together with its shortcomings as far as interdisciplinarity is concerned. Finally, the case study of innovative and interdisciplinary academic ecosystem, which was built and tested within the project UniStartApp, is presented and discussed together with related lessons learned. The text is concluded with final remarks on challenges involved in embedding interdisciplinarity into startup education ecosystem.
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Given the increasing importance of electronic word-of-mouth (eWOM) in the global tourism market, the purpose of the study was to estimate weights customers assign to main attributes of tourist accommodations embodied in easily observed eWOM numerical ratings and subsequently to determine segments of customers with homogenous preferences. To this goal, the preferences tourists attach to price and seven other accommodation attributes rated by Internet users on Booking.com were revealed with the analytical hierarchy process (AHP). Next, a two-stage clustering procedure based on these preferences was undertaken followed by profiling of the clusters in terms of their socio-demographics and travel characteristics. The results show that even if the ranking of the attributes is roughly the same for all the segments (with cleanliness, value for money, and location always in top four), all eight attributes effectively segment tourists into three clusters: “quality-seekers” (45% of the market), “bargain-seekers” (35%), and “cleanliness-seekers” (20%). The segments differ in terms of tourists’ income and expenditures, type of accommodation, actual payer for accommodation, and trip purpose. In contrast, socio-demographics, and most tourists stay variables are alike across the segments. The proposed method of benefit segmentation provides a new perspective for an exploitation of eWOM data by accommodation providers in their marketing strategy.
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