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This paper aims to examine the dynamics of house prices in metropolitan cities in an emerging economy. The purpose of this study is to characterise the house price dynamics and the spatial heterogeneity in the dynamics.

The author explores spatial heterogeneity in house price dynamics, using data for 35 Indian cities with a million-plus population. The research methodology uses panel econometrics allowing for spatial heterogeneity, cross-sectional dependence and non-stationary data. The author tests for spatial differences and analyses the income elasticity of prices, the role of construction costs and lending to the real estate industry by commercial banks.

Long-term fundamentals drive the Indian housing markets, where wealth parameters are stronger than supply-side parameters such as construction costs or availability of financing for housing projects. The long-term elasticity of house prices to aggregate household deposits (wealth proxy) varies considerably across cities. However, the elasticity estimated at 0.39 is low. The highest coefficient is for Ludhiana (1.14), followed by Bhubaneswar (0.78). The short-term dynamics are robust and show spatial heterogeneity. Short-term momentum (lagged housing price changes) has a parameter value of 0.307. The momentum factor is the crucial dynamic in the short term. The second driver, the reversion rate to long-term equilibrium (estimated at −0.18), is higher than rates reported from developed markets.

This research applies to markets that require some home equity contributions from buyers of housing services.

Stakeholders can characterise stable housing markets based on long-term fundamental value and short-run house price dynamics. Because stable housing markets benefit all stakeholders, weak or non-existent mean reversion dynamics may prompt the intervention of policymakers. The role of urban planners, and local and regional governance, is essential to remove the bottlenecks from the demand side or supply side factors that can lead to runaway prices.

Existing literature is concerned about the risk of a housing bubble due to relaxed credit norms. To prevent housing market bubbles, some regulators require higher contributions from home buyers in the form of equity. The dynamics of house prices in markets with higher owner equity requirements vary from high-leverage markets. The influence of wealth effects is examined using novel data sets. This research, documents in an emerging market context, the observations cited in low-leverage developed markets such as Germany and Japan.

The purpose of this paper is to show the effect of Turkey's geopolitical risk on the number of international tourist arrivals to the country. When Turkish economy in 2019 is analyzed, it is seen that the share of tourism in national income is 11%. For this reason, national economy is significantly affected by changing of the number of international tourist arrivals. Security problems are an important variable affecting tourist arrivals.

The paper focused on secondary data for the period 2000–2019 for macroeconomic variables. Accordingly, the number of international tourist arrivals was added as a dependent variable, geopolitical risk as an independent variable, gross domestic product (GDP) and economic freedom index as control variables and inflations as an external variable to the model. The residual augmented least squares–the autoregressive distributive lag (RALS-ADL) cointegration test and the dynamic ordinary least squares (DOLS) coefficient estimator were used. It allows for more robust results to be obtained when the residues do not have a normal distribution.

The RALS-ADL cointegration test result shows that there is a cointegration relationship between variables at a 1% significance level. Moreover, the DOLS coefficient estimator results indicate that an increase in economic freedom and GDP increase the number of international tourists, whereas an increase in the Geopolitical Risk Index and inflation decreases the number of international tourism arrival. It can be said that tourists consider the security and economic stability of the host country when making tourism decisions.

Turkey is one of the most risky developing countries, as well as one of the most popular travel destinations. When the literature is examined, it has been found that studies for Turkey usually determine the relationship between the variables for a short period of time. However, to ensure sustainable growth and environment of confidence, the long-run relationship between variables should be determined so that policymakers can make more impactful decisions. Therefore, the aim of this study is to make a literature contribution, taking into account the long-term effects. In addition, unlike other studies, this study fills the gap in literature using the RALS-ADL cointegration test, which produces robust estimators.

We examine the aggregate price trend of the Greek housing market from a behavioral perspective.

We construct a behavioral real estate sentiment index, based on relevant real estate search terms from Google Trends and websites, and examine its association with real estate price distributions and trends. By employing EGARCH(1,1) on the New Apartments Index data from the Bank of Greece, we capture real estate price volatility and asymmetric effects resulting from changes in the real estate search index. Enhancing robustness, macroeconomic variables are added to the mean equation. Additionally, a run test assesses the efficiency of the Greek housing market.

The results show a significant relationship between the Greek housing market and our real estate sentiment index; an increase (decrease) in search activity, indicating a growing interest in the real estate market, is strongly linked to potential increases (decreases) in real estate prices. These results remain robust across various estimation procedures and control variables. These findings underscore the influential role of real estate sentiment on the Greek housing market and highlight the importance of considering behavioral factors when analyzing and predicting trends in the housing market.

To investigate the behavioral effect on the Greek housing market, we construct our behavioral pattern indexes using Google search-based sentiment data from Google Trends. Additionally, we incorporate the Google Trend index as an explanatory variable in the EGARCH mean equation to evaluate the influence of online search behavior on the dynamics and prices of the Greek housing market.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.

Various scholars suggest that there is a lack of research on the recruitment in small and medium-sized enterprises (SMEs) and also a scarcity of theoretical basis for the recruitment procedures used by these companies. As the vast majority of studies concentrate on larger organizations, they may not accurately reflect the challenges faced by smaller-sized entities to profoundly and accurately comprehend their recruitment procedures. In addition, the use of technology in recruitment has grown in importance in today’s quickly evolving business environment, particularly in light of the COVID-19 pandemic footprint. This study aims to examine the recruitment procedures used by SMEs and how they have been compelled to adjust to different extents to these technological improvements by the effects of the aforementioned epidemic.

With the aim to investigate the current recruitment practices in SMEs and the extent to which digital technologies are embraced by these companies within human resources (HR) procedures, this research relied on interviews with SMEs representatives. The qualitative methods used provided access to relevant data and insights, as they allowed close interactions with top managers and CEOs of ten companies from various sectors. Thus, the research results draw a vivid and reliable image of the procedures and practices used by small and medium-sized companies to attract, select and retain their staff.

This study’s findings are of increased interest to HR professionals, recruiters and managers in SMEs, who aim to attract and retain the best talent and optimize their recruitment strategies in a rapidly changing business environment, enabled by technological advancements. Effective HR recruitment procedures adapted to the specific needs of small and medium-sized companies can lead to several benefits for the organization, including improved employee selection, reduced turnover and increased organizational productivity.

Although the interviews examined here encompass recruitment techniques from SMEs in a variety of industries, the results’ generalizability is limited by the sample size and geography. Furthermore, the findings’ dependability is dependent on the accuracy of the data provided by the respondents.

This investigation confirms some of the theoretical underpinnings which point to the lack of formalized structures and procedures in the recruitment process in SMEs, which enjoy more flexibility in managing HR processes. In addition, the results reinforce the arguments indicating an adjustment between HR strategies or policies and organizational goals in smaller enterprises which adapt faster to changes in the market. Moreover, it becomes apparent that there is a relationship between the quality of job descriptions and the successful fit in attracting the right candidates for the open positions. Furthermore, digital technologies offer opportunities for expanding the recruiters’ reach to a wider audience and also support the selection stage, thus increasing the chances of finding suitable staff. As the need to shift from traditional recruitment to e-recruitment in SMEs has been highlighted in the literature, the qualitative research revealed that this need was driven on the one hand by the COVID-19 pandemic when these companies successfully adapted and implemented new online methods of recruiting, but also by the lack of skilled labor, leading to the expansion of recruitment to other parts of the country or even to other countries.

With regard to the proportion of men and women used in small and medium-sized companies, there is a clear need to involve and train more women in the predominantly male-dominated industrial and IT sectors. From this point of view, companies tend to devote more interest to integrating communities of women in these industries, as well as in key management positions. Another point of interest that the study highlights is the fact that SMEs have started to get creative with the benefits package they propose to candidates and focus on remote work, hybrid office–home working, or seasonal work to offer future employees a better work–life balance.

The added value of this investigation is filling the gaps in the current literature concerning recruitment procedures currently used by SMEs, the challenges they face and the solutions they advanced to solve them. Furthermore, SMEs often drive innovation and competition in the market and play a crucial role in the supply chain of larger companies, providing them with the goods and services they need to operate and supporting the availability and reliability of products from larger companies. They are often the driving force behind revitalizing local economies and creating new employment opportunities. Consequently, the underlying significance of this study is rooted in the need to modernize and simultaneously improve HR recruitment procedures through the integration of technology and a focus on innovation.

This paper explores how conflicting institutional logics shape the behaviors of macro- and micro-level actors in their use of a calculative practice. Thereby, this paper explains how quantification can undermine the intended purpose of a governance system based on a single number.

The study draws upon the literature on calculative practices and institutional logics to present the case of how a single number—specifically the conversion factor for Atlantic Cod, established by macro-level actors for the purposes of governance within the Norwegian fishing industry—is interpreted and used by micro-level actors in the industry. The study is based on documents, field observations and interviews with fishers, landing facilities, and control authorities.

The use of the conversion factor, while intended to protect fish stock and govern industry actions, does not always align with the institutional logics of micro-level actors. Especially during the winter season, these actors may seek to serve their interests, leading to potential system gaming. The reliance on a single number that overlooks seasonal nuances can motivate unintended behaviors, undermining the governance system’s intentions.

Integrating the literature on calculative practices with an institutional logics perspective, this study offers novel insights into the challenges of using quantification for the governance of complex industries. In particular, the paper reveals that when the logics of macro- and micro-level actors conflict in a single-number governance system, unintended outcomes arise due to a domination of the macro-level logics.

The paper identifies the influence of socio-economic factors and livelihood training in stimulating micro-entrepreneurship among women self-help group (SHG) members.

The study is based on a sample of 416 women SHG members drawn from all the four districts of Sikkim using cluster sampling procedure. A multivariate binary logistic model is used to find the impact of socio-economic factors, and a Poisson regression has been used to find the impact of training on fostering micro-entrepreneurship. The result is validated using a propensity score matching approach which corrects for the potential self-selection bias in the sample. Subsequently, a covariate adjustment estimator verifies the robustness of the approach.

The study finds that “size of landownership”, “amount of loan borrowed”, “member's age”, “number of earning and dependent members”, “number of years of SHG enrolment” as well as the “district to which the member belongs to” have a statistically significant influence on the graduation of SHG members to micro-entrepreneurs. Furthermore, it is found that members attending the livelihood training programmes had a significantly higher number of microenterprises.

The study differentiates itself by providing empirical evidence on how socio-economic factors and livelihood training stimulate micro-entrepreneurship among SHG women of Sikkim, which has so far remained unexplored. Moreover, advanced econometric method has been used to eliminate the possible self-selection bias involved with training participation and thereby provides reliable and robust results.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0070

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.

Currently, engineering project design lacks a design framework that fully combines subjective experience and objective data. This study develops an aided design decision-making framework to automatically output the optimal design alternative for engineering projects in a more efficient and objective mode, which synthesizes the design experience.

A database of design components is first constructed to facilitate the retrieval of data and the design alternative screening algorithm is proposed to automatically select all feasible design alternatives. Then back propagation (BP) neural network algorithm is introduced to predict the cost of all feasible design alternatives. Based on the gray relational degree-particle swarm optimization (GRD-PSO) algorithm, the optimal design alternative can be selected considering multiple objectives.

The case study shows that the BP neural network-cost prediction algorithm can well predict the cost of design alternatives, and the framework can be widely used at the design stage of most engineering projects. Design components with low sensitivity to design objectives have been obtained, allowing for the consideration of disregarding their impacts on design objectives in such situations requiring rapid decisions. Meanwhile, design components with high sensitivity to design objective weights have also been obtained, drawing special attention to the effects of changes in the importance of design objectives on the selection of these components. Simultaneously, the framework can be flexibly adjusted to different design objectives and identify key design components, providing decision reference for designers.

The framework proposed in this paper contributes to the knowledge of design decision-making by emphasizing the importance of combining objective data and subjective experience, whose significance is ignored in the existing literature.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.