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A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

Waiting time, as an important predictor of queue abandonment and patient satisfaction, is important for resource utilization and patient experience management. Medical institutions have given top priority to reforming the appointment system for many years; however, whether the increased information transparency brought about by the appointment scheduling mechanism could improve patient waiting time is not well understood. In this study, the authors examine the effects of information transparency in reducing patient waiting time from an uncertainty perspective.

Leveraging a quasi-natural experiment in a tertiary academic hospital, the authors analyze over one million observational patient visit records and design the propensity score matching plus the difference in difference (PSM-DID) model and hierarchical linear modeling (HLM) to address this issue.

The authors confirm that, on average, improved information transparency significantly reduces the waiting time for patients by approximately 6.43 min, a 4.90% reduction. The authors identify three types of uncertainties (resource, process and outcome uncertainty) in the patient visit process that affect patients' waiting time. Moreover, information transparency moderates the relationship between three sources of uncertainties and waiting time.

The authors’ work not only provides important theoretical explanations for the patient-level factors of in-clinic waiting time and the reasons for information technology (IT)-enabled appointment scheduling by time slot (ITASS) to shorten patient waiting time and improve patient experience but also provides potential solutions for further exploration of measures to reduce patient waiting time.

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.

The purpose of this paper is to develop a conceptual framework that examines the impacts of changing seasons on tourism.

The paper presents a conceptual process model of the impact of seasons on all aspects of in-destination tourist behaviour. The model is developed from the literature and is then tested using Cairns, Australia as a case study.

Seasons influence the actual and perceived range of products/experiences available, which dictate the pull features of a destination, that in turn, influence who comes and why they come. Combined the activity sets and visitor profile define in-destination behaviour and, ultimately, satisfaction.

The study fills a needed research gap in two ways. Firstly, it explains conceptually and then tests empirically how changes in seasons affect the delivery of tourism products and experiences. Secondly, it adds significantly to our understanding of the factors that influence in-destination behaviour.

Managerial implications for destination management organisations are identified.

This paper presents a new conceptual process model for a previously unexamined issue.

本文提出了一个季节影响目的地游客行为的过程的理论模型。该模型基于文献开发而成, 然后以澳大利亚凯恩斯作为案例进行测试。

本文的目的是开发一个研究季节变化影响旅游的理论框架。

研究发现季节会影响实际和感知的产品或体验的范围, 从而决定一个目的地的吸引力特征。他们反过来可以影响谁来旅游以及他们来旅游的原因。 结合活动和旅游者画像来定义其目的地行为。

这项研究从两个方面填补了理论空白。 首先, 它从概念上解释, 然后实证检验了季节的变化如何影响旅游产品和体验的供应。 其次, 它极大地增强了我们对影响目的地行为的因素的理解。

本文指出了对目的地管理的实践意义。

本文针对先前未经考察的问题提出了一种新的理论过程模型。

El artículo presenta un modelo de proceso conceptual del impacto de las estaciones en todos los aspectos del comportamiento en en destino turístico. El modelo se desarrolla a partir de la literatura y luego se pone a prueba usando Cairns, Australia como estudio de caso.

El propósito de este artículo es desarrollar un marco conceptual que examine los impactos de los cambios de estación en el turismo.

Las estaciones influyen en la gama, real y percibida, de productos/experiencias disponibles que condicionan las características de atracción de un destino. Las estaciones, a su vez, influyen en quién viene y por qué viene. Los conjuntos de actividades combinadas y el perfil del visitante definen el comportamiento en el destino.

El estudio llena un vacío de investigación necesario de dos maneras. Primero, explica conceptualmente y luego demuestra empíricamente cómo los cambios en las estaciones afectan la oferta de productos y experiencias turísticas. En segundo lugar, contribuye significativamente a la comprensión de los factores que influyen en el comportamiento en el destino.

Se identifican las implicaciones de gestión para las organizaciones de gestión de destinos.

Este artículo presenta un nuevo modelo de proceso conceptual para un tema no examinado previamente.

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.

This study aims to develop an econometric analysis of how modern agriculture can be a fundamental instrument for reducing the levels of multidimensional poverty in Uganda. It demonstrates the importance of agriculture in reducing inequalities amongst the poor while focusing on the relationship between increasing productions from modern agricultural practices and the poverty level across the country.

The study explores Box–Jenkins approach to cereal production data with the use of econometric analysis as the main tool to determine the implications of modern agricultural practices in Uganda. Most poor people around the world are in marginalized rural environments, and agriculture provides for their livelihoods. This makes agricultural development crucial for reducing multidimensional poverty on a large scale and needs development within agriculture to be enhanced. Education, health and standard of living are the three dimensions considered from the weighted indicators, amounting to 30%, to be categorized poor in the three dimensions.

Modernization of agriculture is an ultimate solution to multidimensional poverty reduction in Uganda through employment generation and the effects of food prices. Shreds of evidence support the theories that agricultural incomes together with the actual wages increase with a general rise in the rural non-agricultural economy. Results depict a close correlation between national income and GDP per capita which is a very significant indication that more application of agricultural technology would lead to a sub sequential improvement of livelihoods engaged in agricultural practices.

Agriculture remains a vital sector that employs a greater portion of the population in Uganda’s economy. Major roles have been played by the sector in the economy including employment opportunities, rural household incomes, food supplies and a reduction in poverty from a multidimensional front. Exploring the behavior of poverty level using modern agriculture as an indicator and its relationship with the poverty level arising from improved agricultural practices could provide a meaningful display of variation in poverty across the regions at the country level.

The study aims to investigate how attribution theory in the context of pricing strategies can help tourism destinations recover from the negative impacts of the COVID-19 pandemic.

The study adopted a qualitative research design using semi-structured interviews to address the lack of research in this area. Interview participants included tourists and tourism customers. The interview responses were then analyzed using “Nvivo” qualitative data analysis software to identify critical themes regarding applying attribution theory to pricing strategies.

The findings revealed that tourists prefer bundled and hedonic pricing strategies that integrate the service providers' pricing strategies' locus of control, stability and controllability. Tourists do not favor dual pricing strategies unless the reasons for price controllability or stability are justified. Tourists also prefer the controllable pay-what-you-want pricing strategy. Although tourists accept dynamic pricing, certain conditions related to price locus, stability and controllability must be met.

The research shows tourists prefer pricing strategies that give them control and flexibility, like bundled packages and pay-what-you-want models. Service providers should integrate pricing strategies that transparent costs and justify price fluctuations. While dynamic pricing is accepted if necessitated by external factors, tourists are wary of unnecessary price changes. Providers can build trust and satisfaction by explaining pricing rationale and offering controllable options like bundles.

The study contributes to the theory by applying attribution theory to the context of pricing strategies in tourism. It also provides innovative recommendations for tourism managers on how to use pricing strategies after the COVID-19 pandemic. The findings offer new insights that extend beyond previous research.

The purpose of this study is to investigate environmental factors impacting Ajanta mural deterioration, assessing global tourism effects and visitor conduct on cave environment and murals. This study recognizes stakeholder roles in conservation, providing data-driven insights to guide institutions like the Archaeological Survey of India. The objective is sustainable tourism practices to balance public access with mural preservation for future generations.

Over 25 years, Ajanta Caves' visitors doubled, impacting microclimatic conditions for ancient murals. This study assesses visitor impact to establish a regime and determine carrying capacity, considering temperature, humidity and pollution. Challenges arise from quantifying capacity because of variables. This research informs global tourism and heritage conservation, offering methodologies applicable to cultural sites worldwide.

This study examines environmental impacts on mural preservation in Ajanta Caves, including humidity, microbial growth, sunlight exposure, air quality and tourist presence. Tourist influx escalates CO₂ levels, directly endangering murals. Concerns about particulate matter, especially during visits, emphasize the need for data-driven decision-making and modern technology use to protect Ajanta Caves' artwork, crucial because of its global significance and tourism-related vulnerabilities.

This study carries substantial social implications with a global resonance. The active engagement of the local community and tourism stakeholders in conserving and promoting the Ajanta Caves fosters empowerment, igniting a sense of pride, ownership and responsibility among residents and ensuring sustainable enjoyment of cultural heritage while safeguarding it for future generations. In addition, there will be socioeconomic benefits to local residents such as employment opportunities as tour operators, tour guides, hospitality staff, artisans and souvenir shopkeepers.

This study integrates art conservation, environmental science, cultural heritage preservation and social aspects to address global tourism challenges. Focusing on a site of worldwide significance, this study offers practical strategies for artwork preservation, tourism management and environmental concerns. These recommendations provide real-world solutions applicable to heritage sites globally, bridging scientific analysis with social and cultural insights.

本研究调查了影响阿詹塔壁画退化的环境因素, 评估了全球旅游业和游客行为对洞穴环境和壁画的影响。它承认利益相关者在保护中的作用, 为印度考古调查等机构提供数据支撑的见解。目的是实践可持续旅游, 平衡公众进入和供子孙后代使用的壁画保护。

25年来, 阿詹塔洞穴的游客增加了一倍, 影响了古壁画的小气候条件。该研究评估了游客的影响, 考虑温度、湿度和污染, 建立了一个制度并确定承载能力。量化承载能力的指标面临着挑战。这项研究为全球旅游业和遗产保护提供了信息, 提供了适用于世界各地文化遗址的方法。

这项研究考察了环境对阿詹塔洞穴壁画保护的影响, 包括湿度、微生物生长、阳光照射、空气质量和游客的存在。游客的涌入使二氧化碳含量上升, 直接危及壁画。对颗粒物的考虑, 尤其是在参观期间, 强调了数据驱动决策和现代技术应用对保护阿詹塔洞穴的艺术品的必要性。这一点至关重要, 因为它具有全球意义和旅游相关的脆弱性。

这项研究具有重大的社会影响并引起全球共鸣。当地社区和旅游利益相关者积极参与阿旃陀石窟的保护和推广, 可以增强居民的赋权, 激发居民的自豪感、主人翁意识和责任感, 确保可持续享受文化遗产, 同时为子孙后代保护文化遗产。此外, 还将为当地居民带来社会经济效益, 例如旅游经营者、导游、接待人员、工匠、纪念品店主等的就业机会。

这项研究综合了艺术保护、环境科学、文化遗产保护和社会方面以应对全球旅游业的挑战。它关注一个具有世界意义的遗址, 为艺术品保护、旅游管理和环境问题提供了实用的策略。这些建议提供了适用于全球遗产地的现实世界解决方案, 将科学分析与社会和文化见解联系起来。

El estudio investiga los factores medioambientales que influyen en el deterioro de los murales de Ajanta, evaluando los efectos globales del turismo y el comportamiento de los visitantes sobre el entorno de las cuevas y los murales. Se examina el papel que desempeñan las partes interesadas en la conservación y aporta datos para orientar a instituciones como la encuesta arqueológica de India. El objetivo son las prácticas turísticas sostenibles para equilibrar el acceso del público con la conservación de los murales para las generaciones futuras.

A lo largo de 25 años, los visitantes de las cuevas de Ajanta se han duplicado, con un impacto en las condiciones microclimáticas de los murales antiguos. El estudio evalúa el impacto de los visitantes para establecer una regulación y determinar la capacidad de carga, teniendo en cuenta la temperatura, la humedad y la contaminación. La cuantificación de la capacidad plantea problemas debido a las variables. La investigación aporta información al turismo mundial y a la conservación del patrimonio, ofreciendo metodologías aplicables a sitios culturales de todo el mundo.

Este estudio examina los impactos ambientales en la conservación de los murales de las cuevas de Ajanta, incluyendo la humedad, el crecimiento microbiano, la exposición a la luz solar, la calidad del aire y la presencia de turistas. La afluencia de turistas aumenta los niveles de CO2, poniendo directamente en peligro los murales. La preocupación por las partículas, especialmente durante las visitas, pone de relieve la necesidad de tomar decisiones basadas en datos y de utilizar tecnología actual para proteger las obras de arte de las cuevas de Ajanta, algo crucial debido a su importancia mundial y a las vulnerabilidades relacionadas con el turismo.

Este estudio conlleva importantes implicaciones sociales con una resonancia global. La participación activa de la comunidad local y las partes interesadas del turismo en la conservación y promoción de las Cuevas de Ajanta fomenta el empoderamiento, generando un sentido de orgullo, propiedad y responsabilidad entre los residentes y garantiza el disfrute sostenible del patrimonio cultural al mismo tiempo que lo salvaguarda para las generaciones futuras. Además, habrá beneficios socioeconómicos para los residentes locales, como oportunidades de empleo como operadores turísticos, guías turísticos, personal de hostelería, artesanos, comerciantes de souvenirs, etc.

Este estudio integra la conservación del arte, las ciencias medioambientales, la preservación del patrimonio cultural y los aspectos sociales para abordar los retos del turismo mundial. Centrándose en un sitio de importancia mundial, ofrece estrategias prácticas para la conservación de las obras de arte, la gestión del turismo y los problemas medioambientales. Estas recomendaciones aportan soluciones reales aplicables a lugares patrimoniales de todo el mundo, tendiendo puentes entre el análisis científico y las percepciones sociales y culturales.

As climate change increasingly affects rural food production, there is an urgent need to adopt agricultural adaptation strategies. Because the agricultural sector in Nepal is one of the most vulnerable to the effects of climate change, the adaptation strategies of household farmers in rural areas are crucial. This study aims to address the impacts of agricultural climate change adaptation strategies in Nepal. The research empirically analyzed climate hazards, adaptation strategies and local adaptation plans in Mangalsen Municipality, Achham District, Sudurpashchim Province, Nepal.

This study used a purposive sampling of household lists, categorized as resource-rich, resource-poor and intermediate households. The analysis used primary data from 110 household surveys conducted among six focus groups and 30 informants were selected for interviews through purposive random sampling.

Climate change significantly impacts rainfall patterns and temperature, decreasing agriculture productivity and increasing household vulnerability. To overcome these negative impacts, it is crucial to implement measures such as efficient management of farms and livestock. A comprehensive analysis of Nepalese farmers' adaptation strategies to climate change has been conducted, revealing important insights into their coping mechanisms. By examining the correlation between farmers' strategies and the role of the local government, practical policies can be developed for farmers at the local level.

This study represents a significant breakthrough in the authors' understanding of this issue within the context of Nepal. It has been conclusively demonstrated that securing land tenure or land security and adopting appropriate agricultural methods, such as agroforestry, can be instrumental in enabling Nepalese households to cope with the effects of climate change effectively.

This study aims to investigate the impact of earnings predictability and truthfulness on nonprofessional investors’ investment willingness.

Earnings predictability is captured by quarterly earnings autocorrelation, and earnings truthfulness is indicated by real earnings management (REM). The average of investment attractiveness and willingness measures investment willingness. The authors use experiments to isolate the impact of quarterly earnings autocorrelation and REM on investors’ investment behaviors.

From the 2 × 2 design, the authors observe that investors weight more on earnings predictability than earnings truthfulness.

The generalization of the findings may be constrained for the following reasons. First, the authors use only one proxy, REM, to measure earnings truthfulness. In addition, the authors provide the participants, Amazon Mechanical Turk, with earnings predictability. Results may no longer hold if each participant has different understanding and analysis of earnings predictability.

In periods of unprecedented and severe financial uncertainty (i.e. the COVID-19 pandemic), investors rely more on earnings predictability than on earnings truthfulness. The study assists managers to strategically emphasize the predictability of earnings to attract investors, especially when firms face financial challenges or uncertainty.

This study contributes to understanding investor behavior and the critical role of earnings predictability and truthfulness in shaping investment decisions.

This paper contributes to the literature of earnings properties in financial reporting, particularly by shedding light on the nuanced interplay between earnings predictability and earnings truthfulness. The research also demonstrates that elevated earnings autocorrelation indirectly stimulates investment willingness by enhancing the investors’ perception of earnings persistence of targeted firms.