Tourist Satisfaction Level: Evaluation in a Peruvian Adventure Tour Operator

Cordova-Buiza, Franklin; CALDERON-SANCHEZ, Jennifer; CHUZON-CANICELA, Lucero

doi:10.5171/2022.773904

Cited by 9 publications

(6 citation statements)

References 1 publication

(1 reference statement)

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“…It contains the following keywords: Mountain, Adventure, Hiking, Trail, Alpine, Tourism, Area, Mountain Tourism, Landscape, Climbing, Hiker, Bike, Adventure Tourism, Destinations, Mountain Area, Biking, Activity, Management, Natural, and Recreation. The dimensions in this parameter include creating an Adventure Tourism package, assisting in the improvement of product offerings in areas with natural resources [118], analyzing how visitors and tour guides behave in terms of sustainability during Adventure Tourism trips in natural places [119], evaluating the level of customer satisfaction of an adventure trip provider [120], risk management in Adventure Tourism by assisting tourism organizers and adventure tour guides in identifying risk and developing risk mitigation and risk reduction techniques [121], and investigating how Adventure Tourism may help the destination's tourist growth [122].…”

Section: Adventure Tourismmentioning

confidence: 99%

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

2023

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Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to discover holistic, multi-perspective (e.g., local, cultural, national, and international), and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from the academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media and Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months from March 2021 to August 2022 to showcase the public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles. Discovering system parameters are required to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The work presented in this paper has significant theoretical and practical implications in that it improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, holistic, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies.

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Section: Adventure Tourismmentioning

confidence: 99%

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

2023

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“…It contains the following keywords, Mountain, Adventure, Hiking, Trail, Alpine, Tourism, Area, Mountain Tourism, Landscape, Climbing, Hiker, Bike, Adventure Tourism, Destinations, Mountain Area, Biking, Activity, Management, Natural, and Recreation. The dimension in this parameter include creating an Adventure Tourism package, assisting in the improvement of product offerings in areas with natural resources [106], analyzing how visitors and tour guides behave in terms of sustainability during Adventure Tourism trips in natural places [107], evaluating the level of customer satisfaction of an adventure trip provider [108], risk management in Adventure Tourism by assisting tourism organizers and adventure tour guides in identifying risk and developing risk mitigation and risk reduction techniques [109], and investigating how Adventure Tourism may help the destination's tourist growth [110].…”

Section: 36mentioning

confidence: 99%

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Alsahafi¹,

Alzahrani²,

Mehmood³

2023

Preprint

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The Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to dis-cover holistic, multi-perspective (e.g., local, cultural, national, and international) and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media &amp; Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months starting March 2021 to August 2022 to showcase public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. Discovering system parameters are re-quired to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The proposed approach improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles.

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“…Therefore, the absence reflects little public interest in entrepreneurial projects. In addition to this, it is important to analyse the frameworks developed by early stage companies in order to develop strategies and educational programs that encourage entrepreneurship (Goyanes, 2015;Cordova-Buiza et al, 2022b;Valencia et al, 2016). From this perspective, Vélez et al (2020) and Friedland &amp Jain (2022) confirmed that entrepreneurship education is related to the development and empowerment of skills, attitudes and values related to entrepreneurship.…”

Section: Literature Reviewmentioning

confidence: 99%

Factors Influencing the Entrepreneurial Intention of University Business Students: Evidence from Lima-Peru

Cordova-Buiza,

Toribio-Tamayo,

Garcia-Portuguez

et al. 2023

ECIE

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Entrepreneurial intention has become a question, and in order to investigate its study, this research was based on the Theory of Planned Behaviour, which takes as dimensions the entrepreneurial attitude, the subjective norm and self-efficacy. The purpose of this research is to identify the factors that influence the entrepreneurial will of university business students in Peru. For this purpose, the study method used is mixed, cross-sectional and non-experimental; it comprises a quantitative analysis with a sample of 400 university students, to whom a virtual questionnaire was applied; and it is complemented with a qualitative analysis through videoconference interviews with 11 experts in entrepreneurship in the Peruvian and international context, made up of business career coordinators, university business incubator coordinators and student entrepreneurs. The results highlight as factors in entrepreneurial intention the desire to be independent (65%), the challenge in practice of creativity and innovation (62%), the enjoyment of knowledge about business genesis strategies (55%), also the development of soft skills such as leadership and resilience, as the merit of attributing technological tools within the business, notions highlighted by the qualitative analysis. It is concluded that universities should take these factors into account in the design of competencies in their student curricula, creating programs that generate an entrepreneurial ecosystem, and with it, the formation of companies, new jobs and socioeconomic improvement.

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Tourist Satisfaction Level: Evaluation in a Peruvian Adventure Tour Operator

Abstract: Tourism is an activity that allows economic development by generating employment, promoting cultural manifestations, improving infrastructure, integrating local communities, preserving attractions, and, ultimately, improving people's quality of life.

Cited by 9 publications

References 1 publication

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Smarter Sustainable Tourism: Data-Driven Multi-Perspective Parameter Discovery for Autonomous Design and Operations

Factors Influencing the Entrepreneurial Intention of University Business Students: Evidence from Lima-Peru

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