Travel motivation and tourist satisfaction with wildlife tourism experiences in Gonarezhou and Matusadona National Parks, Zimbabwe
TL;DR: In this article, tourists' motivations for visiting two African state protected areas, tourists' wildlife tourism experiences, predictors of tourism experiences and overall satisfaction with the entire holiday or trip experience were investigated.
Abstract: We investigated tourist motivation for visiting two African state protected areas, tourists’ wildlife tourism experiences, predictors of wildlife tourism experiences and overall satisfaction with the entire holiday or trip experience. Data were collected in Gonarezhou and Matusadona National Parks, Zimbabwe, in December 2015 using 128 questionnaire surveys. Tourists’ push factors for visiting national parks were ‘recreation and knowledge seeking’, ‘appreciating wildlife’ and ‘feeling close to nature’. Pull factors for the two parks were largely similar with common factors being abundance of wildlife, availability of different animal species, availability of different plant species, wilderness, beautiful landscape and peaceful/quiet environment. We established that different motivation factors had different influences on wildlife tourism experiences. Satisfaction with wildlife tourism experiences was predicted by experiences with wildlife interaction and satisfaction with prices charged in the parks, while overall satisfaction with the entire holiday/trip experiences was predicted by satisfaction with wildlife tourism experiences, enhanced by interpretation and interaction with wild animals. The study highlights that while understanding tourist motivations is important, it is also beneficial for park planning and management to understand the predictors of good wildlife tourism experiences. We recommend that marketing for the two parks need to consider the tourist heterogeneity and demographic-based needs in the development of different travel products and promotional programs. Management implications While marketing for national parks needs to emphasise more on factors that motivate tourists to visit the parks, it is important to factor in the heterogeneity that exists among park tourists. Hence, in predicting variation in tourist motivation to travel, their demographic profiles should be considered. To enhance wildlife tourism experiences, park management can provide more opportunities for tourists to learn about nature and ensure the availability of wildlife species through enforcing mechanism to reduce poaching and habitat destruction. Park management also need to enhance tourists’ opportunities to learn more about nature. This is necessary to increase the level of tourist satisfaction.
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26 Oct 2010
TL;DR: In this article, the authors provide both a theoretical structure and practical guidelines for managers to ensure that tourism contributes to the purposes of protected areas and does not undermine them, and provide an understanding of protected area tourism, and its management.
Abstract: The link between protected areas and tourism is as old as the history of protected areas. Though the relationship is complex and sometimes adversarial, tourism is always a critical component to consider in the establishment and management of protected areas. These guidelines aim to build an understanding of protected area tourism, and its management. They provide both a theoretical structure and practical guidelines for managers. The underlying aim is to ensure that tourism contributes to the purposes of protected areas and does not undermine them.
688 citations
TL;DR: The authors explored the impacts of bicycle sharing and the satisfaction of tourists using such services using Hangzhou as a case study and data derived from 552 tourist surveys, a structural equat...
Abstract: This study explores the impacts of bicycle sharing and the satisfaction of tourists using such services. Using Hangzhou as a case study and data derived from 552 tourist surveys, a structural equat...
29 citations
TL;DR: In this paper, the authors identify the motivations that lead university students to choose mountain areas as tourism destinations for their studies, and identify the sub-group of the millennial generation that chooses mountain areas for travel.
Abstract: This study aimed to identify the motivations that lead a particular sub-group of the millennial generation, namely, university students, to choose mountain areas as tourism destinations bot...
28 citations
TL;DR: In this paper, a self-reporting structured questionnaire and Importance Performance Analysis (IPA) were employed to explore visitor satisfaction with 16 attributes of the popular nature-focused boat tours.
Abstract: Understanding visitor satisfaction is vital for ensuring the long-term sustainability of nature-based tourism experiences at high demand coastal destinations. The Ramsar listed Maduganga Mangrove Estuary (MME) on the west coast of Sri Lanka is such a destination. With the aim of enhancing the sustainability of tourism at MME, a self-reporting structured questionnaire and Importance Performance Analysis (IPA) were employed to explore visitor satisfaction with 16 attributes of the popular nature-focused boat tours. Respondents rated ‘to be in a natural setting’ as their main motivation for visiting this destination (73%), followed by ‘to use free time’ (60%), and ‘to be with family or friends’ (60%). Relaxing/fun/enjoyment (90%), enjoying boat rides (85%), and photography (73%) were the most popular activities reported by visitors. Respondents were generally satisfied with their boat tours, however, there was opportunity to enhance visitor satisfaction. Gap Analysis IPA identified nine instances where respondents ranked the performance of attributes as being significantly below their expectations (i.e. Performance Management implications IPA can help to inform the operation and management of nature-based tourism in coastal wetlands. Government agencies need to establish standards and monitor compliance regarding the quality of boat tour operations. Operators need to ensure delivery of interpretive information and conduct tours that minimise negative impacts on wildlife. This case study provides insights regarding delivery of tours and wildlife conservation at coastal wetlands in Tropical Asia.
27 citations
TL;DR: Investigation of the relationship between the number of visitors to national parks and five variables shows that increasing the economic benefits accruing from national parks regional policy could aim at a qualitative upgrading of tourist services, increased marketing of the unique national park label and the promotion of a diverse regional supply base.
Abstract: In the context of national-level strategies, the importance of tourism in national parks is on the rise. The objective of this study is to investigate the relationship between the number of visitors to national parks and five variables: area, number of employees, budget, average employee salary and number of researchers in 12 national parks in the Czech Republic, Germany and Austria. Analysis of factors influencing the number of visitors to national parks uses the method of retrospective analysis of the data contained in internal documents and questionnaires among managers of national parks. The number of candidate predictors is relatively high when compared with the number of observations. Due to this fact, the Gilmour method for statistical analysis is used. Statistical results represented by the parameter β2 for number of employees is −33,016 (95% CI, −50,592–−15,441) and by the parameter β3 for budget is 0.586 (95% CI, 0.295–0.878), showing that the number of visitors increases with budget, while it decreases with the number of employees. The results of this study are a useful starting point for managers in their efforts to focus on developing key areas in an appropriate way. In conclusion, results show that increasing the economic benefits accruing from national parks regional policy could aim at a qualitative upgrading of tourist services, increased marketing of the unique national park label and the promotion of a diverse regional supply base.
20 citations
Cites background from "Travel motivation and tourist satis..."
...Push factors for tourism in national parks are chiefly ‘recreation and knowledge seeking’, ‘appreciating wildlife’ and ‘feeling close to nature’ [22]....
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...These measures will enhance the experience of the visitors and ultimately increase their satisfaction [22]....
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References
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Book•
01 Jan 1983
TL;DR: In this Section: 1. Multivariate Statistics: Why? and 2. A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques.
Abstract: In this Section: 1. Brief Table of Contents 2. Full Table of Contents 1. BRIEF TABLE OF CONTENTS Chapter 1 Introduction Chapter 2 A Guide to Statistical Techniques: Using the Book Chapter 3 Review of Univariate and Bivariate Statistics Chapter 4 Cleaning Up Your Act: Screening Data Prior to Analysis Chapter 5 Multiple Regression Chapter 6 Analysis of Covariance Chapter 7 Multivariate Analysis of Variance and Covariance Chapter 8 Profile Analysis: The Multivariate Approach to Repeated Measures Chapter 9 Discriminant Analysis Chapter 10 Logistic Regression Chapter 11 Survival/Failure Analysis Chapter 12 Canonical Correlation Chapter 13 Principal Components and Factor Analysis Chapter 14 Structural Equation Modeling Chapter 15 Multilevel Linear Modeling Chapter 16 Multiway Frequency Analysis 2. FULL TABLE OF CONTENTS Chapter 1: Introduction Multivariate Statistics: Why? Some Useful Definitions Linear Combinations of Variables Number and Nature of Variables to Include Statistical Power Data Appropriate for Multivariate Statistics Organization of the Book Chapter 2: A Guide to Statistical Techniques: Using the Book Research Questions and Associated Techniques Some Further Comparisons A Decision Tree Technique Chapters Preliminary Check of the Data Chapter 3: Review of Univariate and Bivariate Statistics Hypothesis Testing Analysis of Variance Parameter Estimation Effect Size Bivariate Statistics: Correlation and Regression. Chi-Square Analysis Chapter 4: Cleaning Up Your Act: Screening Data Prior to Analysis Important Issues in Data Screening Complete Examples of Data Screening Chapter 5: Multiple Regression General Purpose and Description Kinds of Research Questions Limitations to Regression Analyses Fundamental Equations for Multiple Regression Major Types of Multiple Regression Some Important Issues. Complete Examples of Regression Analysis Comparison of Programs Chapter 6: Analysis of Covariance General Purpose and Description Kinds of Research Questions Limitations to Analysis of Covariance Fundamental Equations for Analysis of Covariance Some Important Issues Complete Example of Analysis of Covariance Comparison of Programs Chapter 7: Multivariate Analysis of Variance and Covariance General Purpose and Description Kinds of Research Questions Limitations to Multivariate Analysis of Variance and Covariance Fundamental Equations for Multivariate Analysis of Variance and Covariance Some Important Issues Complete Examples of Multivariate Analysis of Variance and Covariance Comparison of Programs Chapter 8: Profile Analysis: The Multivariate Approach to Repeated Measures General Purpose and Description Kinds of Research Questions Limitations to Profile Analysis Fundamental Equations for Profile Analysis Some Important Issues Complete Examples of Profile Analysis Comparison of Programs Chapter 9: Discriminant Analysis General Purpose and Description Kinds of Research Questions Limitations to Discriminant Analysis Fundamental Equations for Discriminant Analysis Types of Discriminant Analysis Some Important Issues Comparison of Programs Chapter 10: Logistic Regression General Purpose and Description Kinds of Research Questions Limitations to Logistic Regression Analysis Fundamental Equations for Logistic Regression Types of Logistic Regression Some Important Issues Complete Examples of Logistic Regression Comparison of Programs Chapter 11: Survival/Failure Analysis General Purpose and Description Kinds of Research Questions Limitations to Survival Analysis Fundamental Equations for Survival Analysis Types of Survival Analysis Some Important Issues Complete Example of Survival Analysis Comparison of Programs Chapter 12: Canonical Correlation General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Canonical Correlation Some Important Issues Complete Example of Canonical Correlation Comparison of Programs Chapter 13: Principal Components and Factor Analysis General Purpose and Description Kinds of Research Questions Limitations Fundamental Equations for Factor Analysis Major Types of Factor Analysis Some Important Issues Complete Example of FA Comparison of Programs Chapter 14: Structural Equation Modeling General Purpose and Description Kinds of Research Questions Limitations to Structural Equation Modeling Fundamental Equations for Structural Equations Modeling Some Important Issues Complete Examples of Structural Equation Modeling Analysis. Comparison of Programs Chapter 15: Multilevel Linear Modeling General Purpose and Description Kinds of Research Questions Limitations to Multilevel Linear Modeling Fundamental Equations Types of MLM Some Important Issues Complete Example of MLM Comparison of Programs Chapter 16: Multiway Frequency Analysis General Purpose and Description Kinds of Research Questions Limitations to Multiway Frequency Analysis Fundamental Equations for Multiway Frequency Analysis Some Important Issues Complete Example of Multiway Frequency Analysis Comparison of Programs
53,113 citations
01 Jan 1973
TL;DR: In this paper, a six-step framework for organizing and discussing multivariate data analysis techniques with flowcharts for each is presented, focusing on the use of each technique, rather than its mathematical derivation.
Abstract: Offers an applications-oriented approach to multivariate data analysis, focusing on the use of each technique, rather than its mathematical derivation. The text introduces a six-step framework for organizing and discussing techniques with flowcharts for each.
Well-suited for the non-statistician, this applications-oriented introduction to multivariate analysis focuses on the fundamental concepts that affect the use of specific techniques rather than the mathematical derivation of the technique. Provides an overview of several techniques and approaches that are available to analysts today - e.g., data warehousing and data mining, neural networks and resampling/bootstrapping. Chapters are organized to provide a practical, logical progression of the phases of analysis and to group similar types of techniques applicable to most situations.
Table of Contents
1. Introduction.
I. PREPARING FOR A MULTIVARIATE ANALYSIS.
2. Examining Your Data.
3. Factor Analysis.
II. DEPENDENCE TECHNIQUES.
4. Multiple Regression.
5. Multiple Discriminant Analysis and Logistic Regression.
6. Multivariate Analysis of Variance.
7. Conjoint Analysis.
8. Canonical Correlation Analysis.
III. INTERDEPENDENCE TECHNIQUES.
9. Cluster Analysis.
10. Multidimensional Scaling.
IV. ADVANCED AND EMERGING TECHNIQUES.
11. Structural Equation Modeling.
12. Emerging Techniques in Multivariate Analysis.
Appendix A: Applications of Multivariate Data Analysis.
Index.
37,124 citations
Book•
01 Jan 2000
TL;DR: Suitable for those new to statistics as well as students on intermediate and more advanced courses, the book walks students through from basic to advanced level concepts, all the while reinforcing knowledge through the use of SAS(R).
Abstract: Hot on the heels of the 3rd edition of Andy Field's award-winning Discovering Statistics Using SPSS comes this brand new version for students using SAS(R). Andy has teamed up with a co-author, Jeremy Miles, to adapt the book with all the most up-to-date commands and programming language from SAS(R) 9.2. If you're using SAS(R), this is the only book on statistics that you will need! The book provides a comprehensive collection of statistical methods, tests and procedures, covering everything you're likely to need to know for your course, all presented in Andy's accessible and humourous writing style. Suitable for those new to statistics as well as students on intermediate and more advanced courses, the book walks students through from basic to advanced level concepts, all the while reinforcing knowledge through the use of SAS(R). A 'cast of characters' supports the learning process throughout the book, from providing tips on how to enter data in SAS(R) properly to testing knowledge covered in chapters interactively, and 'real world' and invented examples illustrate the concepts and make the techniques come alive. The book's companion website (see link above) provides students with a wide range of invented and real published research datasets. Lecturers can find multiple choice questions and PowerPoint slides for each chapter to support their teaching.
25,020 citations
TL;DR: This chapter discusses Structural Equation Modeling: An Introduction, and SEM: Confirmatory Factor Analysis, and Testing A Structural Model, which shows how the model can be modified for different data types.
Abstract: I Introduction 1 Introduction II Preparing For a MV Analysis 2 Examining Your Data 3 Factor Analysis III Dependence Techniques 4 Multiple Regression Analysis 5 Multiple Discriminate Analysis and Logistic Regression 6 Multivariate Analysis of Variance 7 Conjoint Analysis IV Interdependence Techniques 8 Cluster Analysis 9 Multidimensional Scaling and Correspondence Analysis V Moving Beyond the Basic Techniques 10 Structural Equation Modeling: Overview 10a Appendix -- SEM 11 CFA: Confirmatory Factor Analysis 11a Appendix -- CFA 12 SEM: Testing A Structural Model 12a Appendix -- SEM APPENDIX A Basic Stats
23,353 citations
Book•
01 Jan 1991
TL;DR: In this paper, the authors discuss the role of measurement in the social sciences and propose guidelines for scale development in the context of scale-based measurement. But, the authors do not discuss the relationship between scale scores and scale length.
Abstract: Chapter 1: Overview General Perspectives on Measurement Historical Origins of Measurement in Social Science Later Developments in Measurement The Role of Measurement in the Social Sciences Summary and Preview Chapter 2: Understanding the "Latent Variable" Constructs Versus Measures Latent Variable as the Presumed Cause of Item Values Path Diagrams Further Elaboration of the Measurement Model Parallel "Tests" Alternative Models Exercises Chapter 3: Reliability Continuous Versus Dichotomous Items Internal Consistency Relability Based on Correlations Between Scale Scores Generalizability Theory Summary and Exercises Chapter 4: Validity Content Validity Criterion-related Validity Construct Validity What About Face Validity? Exercises Chapter 5: Guidelines in Scale Development Step 1: Determine Clearly What it Is You Want to Measure Step 2: Generate an Item Pool Step 3: Determine the Format for Measurement Step 4: Have Initial Item Pool Reviewed by Experts Step 5: Consider Inclusion of Validation Items Step 6: Administer Items to a Development Sample Step 7: Evaluate the Items Step 8: Optimize Scale Length Exercises Chapter 6: Factor Analysis Overview of Factor Analysis Conceptual Description of Factor Analysis Interpreting Factors Principal Components vs Common Factors Confirmatory Factor Analysis Using Factor Analysis in Scale Development Sample Size Conclusion Chapter 7: An Overview of Item Response Theory Item Difficulty Item Discrimination False Positives Item Characteristic Curves Complexities of IRT When to Use IRT Conclusions Chapter 8: Measurement in the Broader Research Context Before the Scale Development After the Scale Administration Final Thoughts References Index About the Author
11,710 citations