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There is an increasing interest in the international management literature in cultural differences between in-country regions. Yet, the regions of any country may be merely political products and not necessarily cultural units. The goal of this article is to propose clear empirical criteria for deciding if a set of entities, such as a country's administrative regions, can be legitimate units of cross-cultural analysis and to test these criteria in an empirical study.

The authors review the literature on what constitutes a unit of cross-cultural analysis and propose empirical criteria. For instance, the regions of a given country are meaningful units of cross-cultural analysis if one can replicate (an) established dimension(s) of culture at the regional level, including some of the dimension(s)' antecedents and predictive properties. The authors apply this test in the context of the Russian Federation (RF), using an RF database (18,768 respondents from 60 administrative regions) with items borrowed from the World Values Survey.

The RF regions pass the authors’ test. At the regional level, the selected items yield an individualism-collectivism (IDV-COLL) dimension that is similar to its nation-level counterpart in the revised Minkov-Hofstede model in terms of concept and antecedents (wealth differences and geographic latitude) and outcomes that are relevant in business (innovation rates and quality of governance). The authors also find other patterns that confirm the properties of RF regions as meaningful units of cultural analysis.

The authors’ criteria and the test based on them are suitable for large countries, with significant geo-climatic variety and ethnic diversity, but may be inapplicable in small countries with less diversity. It is questionable if the latter countries contain enough cultural variation to justify a cross-cultural analysis of their sub-national regions.

The authors’ criteria can be used in future research in any large country to decide if its regions justify a cross-cultural analysis in the field of management and business or any other field.

Cultural differences within a country are important as they may inform political and management decisions. Yet, to demonstrate that those differences are real, and not imaginary, one needs a methodology like the authors’.

The study contributes to the discussion of the meaningfulness of in-country regions as cultural units for cross-cultural analysis in international business by focusing on the RF.

The paper investigates the prevalence of extreme poverty in a panel of 39 sub-Saharan African (SSA) countries over the period 2000–2018 while accounting for spillover effects.

The study adopts the recently developed spatial dependence-consistent, bias-corrected quasi-maximum likelihood (QML) estimators and the linear dynamic panel regression to control for the potential endogeneity in poverty and corruption spillovers.

The spatial model shows. consistently across all the specifications, that there is a substantial spillover effect of corruption and poverty across the region. Additionally, the study also found that investment in health and education is a significant determinant of poverty in the region. However, the effectiveness of these policy variables to reduce poverty declines in the face of corruption spillovers. More importantly, the empirical analysis shows that poverty does not only exhibit spatial spillovers but also has a persistent effect over time. The results, therefore, suggest that to reduce poverty in the region, sub-Saharan African governments must adopt spatially differentiated policies and programmes by working together to reduce unemployment and corruption in the region, and not the widely adopted spatially mute designs currently in place. The research and policy implications are discussed.

The study accounts for spatial dependency and spillover effects in the analysis of poverty and corruption in SSA

In light of the shift in focus from information communication technology (ICT) access (access divide) and skills (skills divide) to the tangible impacts of ICT use (impact divide), a growing number of scholars have called for further investigation into the inter-territorial and multi-dimensional aspects of the digital divide in China. This study aims to address these gaps by examining the disparities across 31 provinces, particularly emphasizing the transition from the traditional access and skills divides to the impact divide.

Multivariate regression analysis extensively investigates the transition from the access and skills divides to the impact divide across 31 provinces. Additionally, ArcGIS software is used to analyze spatial agglomeration and the auto-correlation (Moran-i) and predict mapping patterns in the data corresponding to all three levels of the digital divide.

According to the study's findings, poverty is a significant factor in the digital divide between different regions in China. The research shows that provinces with advanced administrative systems, such as Guangdong, Shanghai, Beijing, Jiangsu, Shandon and Zhejiang, have high scores on the digital development index (DDI). However, regions with poverty-ridden and rural areas, primarily located in southwest, central and western China, tend to have lower DDI scores.

This study offers a novel contribution to the literature by presenting an innovative conceptual framework that explores the impact divide within China's provinces. The authors also address this lacuna in the literature by developing and testing two dimensions to examine the relationships statistically under a wide range of socioeconomic and ICT indicators.

To offer a realistic foundation for urban cultural construction planning, we want to investigate the distribution features of Shanghai's cultural functional elements and examine the distribution patterns in urban space.

In this research, we managed to gather POI geographic data, refined and categorized them to integrate eight categories of cultural functional elements, observed the density and agglomeration, distribution direction and hot and cold spots of overall and each type of cultural functional elements using geospatial analysis methods and then investigated the factors influencing cultural functional elements using geographic detectors.

Our research shows apparent differences between regions and most cultural functional elements are found in the inner city. Second, there are hot and cold spots in the way different cultural functional elements are spread out. Its geographic structure is primarily influenced by third-party traffic service capacity and available time.

This work provides a benchmark for cultural planning in Shanghai by establishing the spatial aggregation impact of cultural functional elements.

This chapter investigates the geographical variations and approaches that shape the implementation of smart city projects on a global scale. Recognizing the significant influence of contextual factors on smart city initiatives, this chapter aims to comprehend the dynamics that drive the objectives and approaches of smart city projects across different countries. This chapter provides an overview of the strategic and geographical factors that impact smart city implementation, considering organizational drivers and practices, such as the Hofstede model in context. It explores the role of citizen-based, technology-based, and decision-making-based development in smart city implementation. Moreover, this chapter examines the strategic, cultural, socioeconomic, and geographical differences that influence smart city projects worldwide. It analyzes the geographical influence and internal drivers of smart city projects, focusing on Europe, North America, Latin America, and East and South Asia. This analysis offers insights into diverse approaches to smart city development, encompassing top-down and bottom-up approaches. By examining various perspectives and experiences from smart city initiatives worldwide, this chapter sheds light on the challenges and opportunities associated with implementing smart city strategies in different contexts.

To test the applicability of the user-generated content (UGC) derived from social travel network sites for online reputation management, the purpose of this study is to analyze the spatial clustering of the reputable hotels (based on the TripAdvisor Best-Value indicator) and reputable outdoor seating restaurants (based on ranking indicator).

This study used data mining techniques to obtain the UGC from TripAdvisor. The Hierarchical Density-Based Spatial Clustering method based on algorithm (HDBSCAN) was used for robust cluster analysis.

The findings of this study revealed that best value (BV) hotels and reputable outdoor seating restaurants are most likely to be located in and around the central districts of the urban tourist destinations where population and economic activities are denser. BV hotels' spatiotemporal cluster analysis formed clusters of different sizes, densities and shape patterns.

This study showed that reputable hotels and restaurants (H&Rs) are concentrated within districts near historic city centers. This should be an impetus for applied research on urban investment environments.

The findings would be rational guidance for entrepreneurs and potential investors on the most attractive tourism investment environments.

There has been a lack of studies focusing on analyzing the spatial clustering of the H&Rs using UGC. Therefore, to the best of the authors’ knowledge, this study is the first to map and analyze the spatiotemporal clustering patterns of reputable hotels (TripAdvisor BV indicator) and restaurants (ranking indicator). As such, this study makes a significant methodological contribution to urban tourism research by showing pattern change in H&Rs clustering using data mining and the HDBSCAN algorithm.

为了测试社交旅游网站 (STNS) 的用户生成内容 (UGC) 对在线声誉管理 (ORM) 的适用性, 本研究分析了知名酒店的空间聚类（基于 TripAdvisor 最佳价值指标) 和信誉良好的户外座位 (ODS) 餐厅(基于排名指标)。

该研究使用数据挖掘技术从 TripAdvisor 获取 UGC。 基于（HDBSCAN）算法的分层基于密度的空间聚类方法用于鲁棒聚类分析。

调查结果显示, 最具价值 (BV) 酒店和信誉良好的 ODS 餐厅最有可能位于人口和经济活动较为密集的城市旅游目的地的中心区及其周边地区。 BV 酒店的时空聚类分析形成了不同大小、密度和形状模式的聚类。

目前的文献扔缺乏专注于分析利用 UGC 的酒店和餐厅 (H&R) 空间聚类的研究。 因此, 本研究首次绘制并分析了知名酒店（TripAdvisor BV 指标）和餐厅（排名指标）的时空聚类模式。 因此, 本研究通过利用数据挖掘和 HDBSCAN 算法显示 H&Rs 聚类的模式变化, 为城市旅游研究做出了重要的方法论贡献。

这项研究表明, 著名的 H&R 集中在历史悠久的市中心附近的地区。 这应该是对城市投资环境的应用研究的推动力。

研究结果将为企业家和潜在投资者提供最具吸引力的旅游投资环境的理性指导。

This research aimed to maximize the value of date exports for the Kingdom of Saudi Arabia.

To achieve its objective, this study relied on secondary data and quantitative economic analysis represented by the Linear programming model.

This study showed that Saudi Arabia exports dates to the United Arab Emirates, Yemen, Kuwait, Turkey, Somalia, Jordan, Oman, India, Indonesia, Bangladesh Morocco, Lebanon, and others. The geographical concentration coefficient for the quantity and value of date exports was 35.05% and 34.74%, respectively, during the study period. Saudi Arabia exported a quantity of dates amounting to 83.08 thousand tons, representing 40.57% of the average total amount of Saudi dates exports during the study period, to Yemen, Somalia, India, Indonesia, Bangladesh, Egypt, China, Djibouti, Bahrain, and Ethiopia, at prices lower than the average export price of 1200.31 dollars/ton, and therefore the export policy needs to restructure the geographical distribution of date exports. Based on the models of geographical distribution, Saudi date exports value can be increased by 32.76–127.12 million dollars, meaning can be increased by 13.77% – 53.44%. In light of the results of the proposed models, this study recommends the need to restructure the geographical distribution of Saudi date exports so that the value of Saudi date exports can be increased by 127.12 million dollars from the current situation for the period 2017–2021.

The paper’s original contribution lies in its proposal to restructure the geographical distribution of Saudi date exports to increase the value of exports.

This study aims to explain how the impact of COVID-19 on human mobility is affected by the perceived risk of the pandemic.

Using a statistical analysis and a geographic visualization technique, we investigate whether and how changes in people’s restaurant visiting patterns during COVID-19 vary with their level of risk perception.

The changes in people’s restaurant visiting patterns vary with their risk perception: the tendency to increase the number of visits to restaurants located in non-popular areas is related to the level of perceived risk.

This research confirms the importance of risk perception when examining the pandemic’s multi-dimensional impacts.

这项研究旨在解释 COVID-19 对人类流动性的影响如何受到大流行的感知风险的影响。

使用统计分析和地理可视化技术, 我们调查人们的餐厅是否以及如何变化COVID-19 期间的访问模式因他们的风险感知水平而异。

人们的餐厅光顾模式的变化随着他们的风险感知而变化：去位于非热门区域的餐厅的光顾次数增加的趋势与感知的风险水平有关。

这项研究证实了在检查大流行的多维影响时风险认知的重要性。

Mediante un análisis estadístico y una técnica de visualización geográfica, investigamos si los cambios en los patrones de visita a restaurantes de las personas durante COVID-19 varían en función de su nivel de percepción del riesgo y cómo lo hacen.

Esta investigación pretende explicar cómo el impacto de COVID-19 en la movilidad humana se ve afectado por el riesgo percibido de la pandemia.

Los cambios en las pautas de visita a restaurantes de las personas varían en función de su percepción del riesgo: la tendencia a aumentar el número de visitas a restaurantes situados en zonas no populares está relacionada con el nivel de riesgo percibido.

Esta investigación confirma la importancia de la percepción del riesgo a la hora de examinar los impactos multidimensionales de la pandemia.

This study aims to explore the emotional landscapes and spatial preferences of digital nomads, focusing on how sentiments expressed in destination reviews influence their mobility and destination choices.

Employing a lexicon-based sentiment analysis of social media comments and reviews, alongside advanced geographical information systems (GIS) mapping techniques, the study analyzes the emotional tones that digital nomads associate with various destinations worldwide.

The analysis reveals significant patterns of emotional sentiments, with trust and joy being predominant in preferred destinations. Spatial patterns identified through GIS mapping highlight the global distribution of these sentiments, underscoring the importance of emotional well-being in destination choice.

Insights from this study offer valuable guidance for Destination Management Organizations (DMOs) in strategic planning, enhancing destination appeal through targeted marketing strategies that resonate with the emotional preferences of digital nomads.

This research introduces a novel approach by integrating sentiment analysis with GIS to map the emotional and spatial dynamics of digital nomadism, contributing a new perspective to the literature on tourism and mobility.

Fingerprints and blood samples are important for the identification of individuals and criminals. The present study aims to identify the predominant fingerprint patterns and the association between the fingerprint patterns and ABO–Rh blood groups in Omani population.

A cross-sectional study was conducted on 200 Omani individuals aged 18 years (104 males and 96 females). The imprints of all right and left-hand fingers were taken, and the types of the fingerprints were determined using a standard protocol. The blood group of all the subjects was recorded. Chi-square test was performed to identify the association between the fingerprint patterns and the ABO and Rh blood groups.

The loop fingerprint pattern was the most common in Omani subjects (49.4%), followed by whorl (44.9%) and arch (5.7%) pattern. A significant association (p < 0.001) was found between gender and fingerprint pattern. The loop was the highest occurring pattern in the females (54.6%), while the whorl was more in males (50.0%). The whorl pattern was the most common in the AB+ and O− groups. The loop was a predominant pattern in the A+, A−, B+, B− and O+ groups. The Chi-square test also revealed a significant correlation between different fingerprint patterns and blood groups of the subjects (p < 0.001).

The present study is an outcome of undergraduate student's research project thesis for the Doctor of Medicine (MD) program. The results of the present study may help in creating a data bank for biometrics, which can be useful for diagnosing associated diseases and also help in identification of individuals.