scispace - formally typeset
Search or ask a question

What are the determinants in net attractions of trips between places? 


Best insight from top research papers

The determinants of net attractions of trips between places vary depending on the specific context. In the study by Borevich and Vavilov, they focus on the determinants of net attractions in the context of commutative rings and group theory . Manrai, Manrai, and Friedeborn examine the determinants of destination competitiveness, specifically in the tourism industry . Ferrante, Abbruzzo, and De Cantis analyze the determinants of multi-destination trip behavior, considering factors such as length of stay, previous visits, and motivation for the trip . Bulut, Dünder, and Yücesoy investigate the determinants of net migration on a global scale, using regression modeling and variable selection techniques . Di Matteo explores the relationship between same day automobile trips by Canadians to the United States and the real exchange rate, highlighting the importance of considering nonparametric approaches in estimating tourism demand models .

Answers from top 5 papers

More filters
Papers (5)Insight
The determinants of cross-border trips between Canada and the United States are estimated using parametric and nonparametric techniques.
The determinants of net migration are investigated using regression modeling and variable selection techniques.
The determinants of multi-destination trips in Sicily include length of stay, number of previous visits, and motivation for the trip.
The determinants of net attractions of trips between places include environmental factors such as unique resources and tourism basics like hospitals and sanitation.
Journal ArticleDOI
Z. I. Borevich, N. A. Vavilov 
4 Citations
The given text does not provide any information about determinants in net attractions of trips between places.

Related Questions

What is the influence of distance in a decision for holiday destination?4 answersThe influence of distance in a decision for a holiday destination is significant. People are generally less willing to cover longer distances to go to a destination due to the effort and higher costs involved. Research has shown that the majority of tourists consider social distance as a factor in their choice of destination, indicating that it influences their decision-making process. Additionally, it has been observed that most international travel occurs to countries within 1,000 kilometers of the source market, with distant destinations struggling to attract a significant share of departures. However, it is important to note that factors such as market access, tourism development, relationship variables, and destination attributes also play a role in shaping travel patterns, regardless of distance.
What are some of the factors that influence travel motivation?4 answersFactors that influence travel motivation include economic ability, budget, age, marital status, travel duration, education, colonial impacts, global cultural interactions and communication. Additionally, cultural and tourism IP content, diversification of product carriers, and IP popularity also play a significant role in influencing travel motivation. Furthermore, queer identity, life-satisfaction, and travel motivation are interconnected, with travel motivation positively influencing psychological resilience, travel wellbeing, and tourist activity choice. Moreover, the desire to seek knowledge and innovation is a key motivational factor that drives international tourists to travel to Penang, along with the cultural and historical attractions offered by the destination. The concept of travel career pattern also highlights various factors that influence travel motivation, such as novelty-seeking, escaping/relaxing, relationship building, nature, self-development, kinship, self-actualization, self-enhancement, stimulation, isolation, nostalgia, autonomy, social status, and romance.
What are the determinants of tourism demand?5 answersThe determinants of tourism demand include factors such as exchange rate volatility, global income, demographic, social, economic, and education factors, income of both the destination and origin countries, domestic exchange rate, distance, common border, common language, similarity, membership to regional alliances, environmental quality, relative price, and visa restrictions. These factors have been found to have both positive and negative impacts on tourism demand. Factors such as stable currency rates and global income are particularly important in increasing tourist interest. Additionally, previous demand, terrorism, and Covid-19 have been found to influence tourist demand negatively. The findings also suggest that low environmental quality, distance, relative price, and visa restrictions have a negative impact on tourism demand, while factors such as tourist income, common language, and common border have a positive impact.
What are the factors of destination marketing?3 answersDestination marketing involves various factors that impact the marketing of tourist destinations. These factors can be categorized into internal and external environmental influences. The internal factors include capital structures, resources, capabilities, and marketing intermediaries, while the external factors encompass political, economic, social, and technological influences. Additionally, destination marketing involves the coordination, planning, and promotion of the destination by a public/public-private or private sector organization. The destination marketing organization (DMO) plays a crucial role in destination marketing, including activities such as destination branding, destination image development, and destination consumer-marketing communications. Other important aspects of destination marketing include the formulation of a destination product, the use of promotional tools, and the measurement of DMO performance. Furthermore, the concept of destination branding, the role of social media and digital marketing, and the use of events to attract visitors are also key factors in destination marketing.
What are digital marketing determinants affecting travel intention?4 answersDigital marketing determinants affecting travel intention include consumer motivation, parasocial relationships, trust, content quality, and user-generated content trust. These factors have been found to have a positive impact on travel intention among consumers. Specifically, consumer motivation, parasocial relationships, and trust are important factors that influence travel intention, as they lead to unique and different outcomes. Additionally, content quality and user-generated content trust have been found to positively affect social media followers' travel intentions. The economic and social perspectives also play a role in influencing user-generated content trust, which in turn affects inbound travel intention. Overall, these digital marketing determinants have important implications for destination marketers, tourism managers, and online travel agencies in attracting and retaining customers.
What are the determinants of bicycling?5 answersThe determinants of bicycling include factors such as perceptions of safety and quality of bicycle facilities, demographics (gender, age, education level), household characteristics (number of automobiles, number of bicycles, number of children), residential location, season, bicycle amenities at work (bicycle racks, showers), and overall perceptions of the quality of bicycle facilities. Other determinants include individual-level characteristics, social environment factors, and perceptions of physical environment factors at the neighborhood and metropolitan scales. Additionally, facilitators of bicycling include support for infrastructure enhancements, enjoyment from riding, riding to spend time with friends and family, and the number of children in the household. Environmental risk factors for bicycling injuries include factors such as traffic volume, intersections, retail establishments, and path obstructions. Bicycling behavior, bicycling constraints, individual cognitions (perceptions and normative beliefs), and the perception of safety when biking to various destinations also influence an individual's liking of bicycling.

See what other people are reading

Engression: Extrapolation for Nonlinear Regression?
5 answers
Engression is a novel nonlinear regression methodology designed to address the challenge of extrapolation in statistical and machine learning applications. It introduces a distributional regression technique for pre-additive noise models, where noise is incorporated into the covariates before applying a nonlinear transformation. This approach enables reliable predictions even beyond the training data boundaries, showcasing superiority over traditional regression methods like least-squares regression and quantile regression. By focusing on strictly monotone function classes, engression demonstrates successful extrapolation capabilities, outperforming existing techniques consistently. Additionally, the method's effectiveness is validated through empirical results on both simulated and real datasets, emphasizing its practical utility. The availability of engression implementations in R and Python further enhances its accessibility and applicability in various research and practical domains.
What is 9-scale hedonic sensory evaluation?
5 answers
A 9-point hedonic scale is a widely used method in sensory affective testing, particularly in food science. This scale involves participants rating their degree of liking or preference for a product on a scale with nine categories, typically ranging from extreme dislike to extreme like, with equal intervals between each point. The scale's numerical values are assigned to verbal cues, allowing for consistent measurement across participants and products. Research has shown that the scale's distribution of data is unique, often being multimodal and non-parametric, challenging traditional statistical power and sample size assessments. The scale's application extends to comparing products, identifying poor-performing items, and determining consumer preferences efficiently. Understanding the nuances of the 9-point hedonic scale is crucial for accurate sensory evaluations and product assessments.
What is statistical treatment?
5 answers
Statistical treatment refers to the application of statistical methods to analyze and interpret data. It involves processes such as scrutinizing data for normal distribution, calculating means and standard deviations, conducting significance tests, utilizing parametric or non-parametric tests based on data distribution, setting statistical significance levels, and applying corrections for multiple comparisons. In the context of photoluminescence quantum yield (PLQY) measurements, statistical treatment includes evaluating statistical uncertainties through multiple measurements and calculating weighted means to determine the standard deviation of the mean, aiding in the identification of outliers and time-dependent systematic errors. Overall, statistical treatment plays a crucial role in ensuring the accuracy and reliability of data analysis in various fields of research and experimentation.
The impact of rural urban migration in Limpopo?
5 answers
Rural-urban migration in Limpopo, particularly within the Great Limpopo Transfrontier Conservation Area, has led to significant consequences. The transboundary conservation initiatives in the region have not effectively alleviated poverty as expected, resulting in involuntary displacement of local people and their subsequent migration to urban areas across international borders for better opportunities. Studies in South Africa have shown that rural-urban migration can negatively impact subjective well-being, with a decrease of 8.3% attributed to false expectations and emotional costs. However, there are positive aspects as well, such as an improvement in economic well-being for sending households due to increased remittance inflows and higher monthly income per capita. Overall, the impact of rural-urban migration in Limpopo reflects a complex interplay of economic, social, and environmental factors.
What is the economic impact of food waste in bakeries and pastry shops in developing countries?
5 answers
Food waste in bakeries and pastry shops in developing countries has significant economic implications. Studies show that food wastage negatively affects economic growth, resulting in substantial economic damage and lost revenue. In the retail sector, losses of bakery and confectionery products (BBCP) account for a considerable portion of total losses, reaching up to 42% in some cases. The economic costs of food waste globally have reached millions of dollars annually, emphasizing the urgency to address this issue. Implementing strategies to reduce food wastage in bakeries and pastry shops can not only lead to economic benefits but also have positive environmental and social consequences. Therefore, tackling food waste in these establishments is crucial for sustainable economic development in developing countries.
Economic impact of food waste in bakeries and pastry Peru?
5 answers
Food waste in bakeries and pastry shops in Peru has significant economic implications. Research shows that inefficient waste management negatively affects economic growth. Implementing Lean Manufacturing methodologies, such as the Lean-Deming Model, can help optimize processes and reduce waste, leading to improved productivity. Additionally, reducing food loss and waste not only minimizes economic damage and lost revenue but also has positive environmental and social outcomes. By addressing the root causes of waste through tools like SLP, 5S, TPM, and Standard Work, bakeries can decrease waste below industry standards, potentially by around 1.65%. Therefore, enhancing waste management practices in bakeries and pastry shops in Peru can not only boost economic performance but also contribute to sustainable development and resource efficiency.
How does Japan's declining birth rates impact the country's economy?
4 answers
Japan's declining birth rates have profound implications for its economy. The country faces challenges due to a shrinking population, with estimates projecting a significant decrease by 2060. Factors contributing to the declining birth rate include a shift in marital and family values towards a Western European model, leading to a reduction in the number of newborns. Prime Minister Fumio Kishida's focus on supporting children and parents aims to address this demographic crisis by improving access to childcare and increasing female labor force participation. Despite efforts to boost fertility rates, such as implementing population maintenance policies, regional disparities persist, with higher Total Fertility Rates (TFR) in the west and lower rates in the east. The aging population necessitates more caregivers, potentially increasing the demand for foreign workers despite societal aversion to immigration.
What are the potential challenges and limitations associated with measuring the impact of trade openness on economic growth?
5 answers
Measuring the impact of trade openness on economic growth presents challenges and limitations. Studies on various countries like Morocco, Nigeria, Ghana, and Yemen reveal mixed results. While some show a positive short-term impact of trade openness on economic growth, others indicate non-linear relationships with income thresholds affecting the outcomes. In Nigeria, trade balance and openness were found to not significantly influence economic growth. Additionally, factors like inflation and investment were shown to have insignificant impacts on economic growth in Ghana and Nigeria, highlighting the complexity of measuring the effects of trade openness. Political and social factors were also found to influence the effectiveness of trade reforms on economic growth in Yemen, emphasizing the multifaceted nature of this relationship.
How do age-specific net migration patterns vary across countries and regions?
5 answers
Age-specific net migration patterns exhibit variations across countries and regions. Research in Russia highlights that young individuals predominantly migrate between peripheral areas and regional centers, with peak migration observed among 15-19 year-olds pursuing higher education. In South Korea, a novel analytical method utilizing dyadic matrices and PCA reveals age-specific migration patterns, capturing different life cycle stages. European Union studies emphasize the importance of migration dynamics in interpreting age structures, with variations linked to urbanization levels and territorial characteristics. Portugal's peripheral regions experience human desertification and aging, with migration models indicating different age groups responding to economic and demographic factors. These diverse findings underscore the significance of age-specific net migration patterns in shaping population dynamics globally.
How do researchers establish the criteria for determining the level of significance in Pearson's product moment correlations?
5 answers
Researchers establish the criteria for determining the level of significance in Pearson's product moment correlations by utilizing statistical methods and hypothesis testing. They test hypotheses using statistical criteria to divide the data into subsets, such as null and alternative, based on the divergence between empirical and theoretical distributions. Nonparametric tests like Pearson and Kolmogorov criteria are commonly employed for this purpose, especially in large sample sizes, to assess the subordination of sample distributions to the theory of the general population. Additionally, Fisher's transformation is utilized to determine confidence intervals for Pearson's correlation coefficient, particularly under conditions of normality and non-normality of residuals. These methods help researchers assess the significance of correlations and make informed inferences about the relationships between variables.
What is RWA (Regression Wavelet Analysis)?
5 answers
Regression Wavelet Analysis (RWA) is a method that utilizes wavelet functions for regression tasks. In the context of high-dimensional functions with low-dimensional variable interactions, hyperbolic wavelet regression is employed for fast and accurate function reconstruction. Additionally, a study introduces the concept of wavelet support vector censored regression, a novel approach in survival analysis, which outperformed traditional methods in terms of the C-index, showcasing the effectiveness of wavelet-based regression in complex data scenarios. Moreover, in the realm of spectral model calibration for measuring solution concentration in crystallization processes, wavelet function regression is proposed as an effective method due to its ability to handle nonlinear properties and high-dimensional variables, demonstrating superior measurement accuracy compared to traditional methods like partial least-squares (PLS).