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The purpose of this study is to analyze the horizontal expansion of Burayu Town between 1990 and 2020. The study typically acts as a baseline for integrated spatial planning in small- and medium-sized towns, which will help to plan sustainable utilization of land.

Landsat5-TM, Landsat7 ETM+, Landsat5 TM and Landsat8 OLI were used in the study, along with other auxiliary data. The LULC map classifications were generated using the Random Forest Package from the Comprehensive R Archive Network. Post-classification, spatial metrics, and per capita land consumption rate were used to understand the manner and rate of expansion of Burayu Town. Focus group discussions and key informant interviews were also used to validate land use classes through triangulation.

The study found that the built-up area was the most dynamic LULC category (85.1%) as it increased by over 4,000 ha between 1990 and 2020. Furthermore, population increase did not result in density increase as per capita land consumption increased from 0.024 to 0.040 during the same period.

As a result of financial limitations, there were no high-resolution satellite images available, making it challenging to pinpoint the truth as it is on the ground. Including senior citizens in the study region allowed this study to overcome these restrictions and detect every type of land use and cover.

Data on urban growth are useful for planning land uses, estimating growth rates and advising the government on how best to use land. This can be achieved by monitoring and reviewing development plans using satellite imaging data and GIS tools.

The use of Random Forest for image classification and the employment of local knowledge to validate the accuracy of land cover classification is a novel approach to properly customize remote sensing applications.

The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

The continuous increase of energy demands is a critical worldwide matter. Jordan’s household sector accounts for 44% of overall electricity usage annually. This study aims to use artificial neural networks (ANNs) to assess and forecast electricity usage and demands in Jordan’s residential sector.

Four parameters are evaluated throughout the analysis, namely, population (P), income level (IL), electricity unit price (E$) and fuel unit price (F$). Data on electricity usage and independent factors are gathered from government and literature sources from 1985 to 2020. Several networks are analyzed and optimized for the ANN in terms of root mean square error, mean absolute percentage error and coefficient of determination (R²).

The predictions of this model are validated and compared with literature-reported models. The results of this investigation showed that the electricity demand of the Jordanian household sector is mainly driven by the population and the fuel price. Finally, time series analysis approach is incorporated to forecast the electricity demands in Jordan’s residential sector for the next decade.

The paper provides useful recommendations and suggestions for the decision-makers in the country for dynamic planning for future resource policies in the household sector.

The COVID-19 pandemic has significantly impacted global tourism, with international travel bearing the burden of restrictions. Domestic tourism has also faced substantial challenges. This paper aims to analyse the impact of the COVID-19 pandemic on domestic tourism in Spain, focusing on travel from Madrid (the country’s capital) to other tourist destinations.

Mobile phone data has been used to study the evolution of tourist trips over the summers of 2019, 2020 and 2021. Regression models are used to explain the number of visitors at destinations.

The pandemic not only caused a drastic drop in tourist flows but also disrupted the overall pattern of the domestic flow system. Winning destinations were typically areas in proximity to Madrid and less densely populated destinations, while urban destinations were major losers. The preferences of domestic tourists varied notably by income group, but the decrease in trip volumes showed only marginal differences.

The paper demonstrates the potential of mobile phone data analysis to study the uneven impact of external shocks, such as the COVID-19 pandemic, on tourist destinations. This approach considers spatial resilience heterogeneity within regions or provinces. By incorporating income information, the analysis introduces a social dimension to highly detailed spatial data, surpassing traditional studies conducted at the regional or national levels.

COVID-19大流行对全球旅游业产生了重大影响，国际旅行受到了限制的影响最为严重。国内旅游也面临着重大挑战。本文分析了COVID-19大流行对西班牙国内旅游的影响，重点关注从马德里（该国首都）到其他旅游目的地的旅行。

本研究使用移动电话数据研究了2019年、2020年和2021年夏季旅游出行的演变。采用回归模型解释了各目的地游客数量。

大流行不仅导致了旅游流量急剧下降，还扰乱了国内流动系统的总体模式。获胜的目的地通常是马德里附近的地区和人口较稀少的目的地，而城市目的地是主要的输家。国内游客的偏好在收入群体之间有明显差异，但旅行量的减少只显示出边际差异。

本文展示了使用移动电话数据分析研究外部冲击（如COVID-19大流行）对旅游目的地的不均匀影响的潜力。该方法考虑了区域或省份内的空间弹性异质性。通过整合收入信息，该分析为高度详细的空间数据引入了社会维度，超越了传统在区域或国家水平进行的研究。

Data storytelling courses position students as agents in creating stories interpreted from data about a social problem or social justice issue. The purpose of this study is to explore two research questions: What themes characterized students’ iterative development of data story topics? Looking back at six years of iterative feedback, what categories of data literacy pedagogy did instructors engage for these themes?.

This project examines six years of data storytelling final projects using thematic analysis and three years of instructor feedback. Ten themes in final projects align with patterns in feedback. Reflections on pedagogical approaches to students’ topic development suggest extending data literacy pedagogy categories – formal, personal and folk (Pangrazio and Sefton-Green, 2020).

Data storytelling can develop students’ abilities to move from being consumers to creators of data and interpretations. The specific topic of personal data exposure or risk has presented some challenges for data literacy instruction (Bowler et al., 2017). What “personal” means in terms of data should be defined more broadly. Extending the data literacy pedagogy categories of formal, personal and folk (Pangrazio and Sefton-Green, 2020) could more effectively center social justice in data literacy instruction.

Implications for practice include positioning students as producers of data interpretation, such as role-playing data analysis or decision-making scenarios.

Data storytelling has the potential to address current challenges in data literacy pedagogy and in teaching critical data literacy.

Course descriptions provide a template for future data literacy pedagogy involving data storytelling, and findings suggest implications for expanding definitions and applications of personal and folk data literacies.

Framed by nostalgia marketing, this research draws upon lessons from ParticipACTION, a Canadian non-profit health promotion organization, to examine one of their most well-known campaigns, Body Break with ParticipACTION, in order to assess the potential role for nostalgia-based marketing campaigns in sport participation across generational cohorts.

Exploratory sequential mixed methods involving two studies were completed on behalf of ParticipACTION, with the authors developing the research instruments and the collection of the data undertaken by research agencies. Study 1 was the secondary analysis of qualitative data from five focus groups with different demographic compositions that followed a common question guide. Study 2 was a secondary data analysis of a pan-Canadian online survey with a sample (n = 1,475) representative of the overall adult population that assessed awareness of, and attitudes toward, ParticipACTION, Body Break, physical activity and sport participation. Path analysis tested a proposed model that was based on previous research on attitudes, brand and loyalty. Further, multi-group path analyses were conducted to compare younger generations with older ones.

The results provide direction and understanding of the importance of nostalgia in marketing sport participation programs across generational cohorts. For instance, in the four parent-adult focus groups, unaided references as well as frequent and detailed comments regarding Body Break were observed. Similarly, Millennials reported that Body Break was memorable, Canadian and nostalgic, with a mix of positive and negative comments. The importance of nostalgia was supported sequentially via results from the national survey. For example, while 54.1% of the 40–54 age-group associated ParticipACTION positively with Body Break, so did 49.8% of the 25–39-year age group, most of whom were not born when the promotion ran. Further, brand resonance was found to explain 4% more variance in moderate-to-vigorous physical activity (MVPA), the proxy for sport participation, for younger people compared to older people.

Results provide direction to brands, properties and agencies around the use of nostalgia in sport marketing campaigns and sponsorship efforts. For brands seeking to sponsor sport properties to alter their image with potential consumers in a new market, associating with a sport property that many view as nostalgic could improve the impact of the campaign. On the sport property side, event managers and marketers should both identify existing assets that members or fans are nostalgic about, as well as consider building nostalgia into current and new properties they develop.

This research is valuable to the sport marketing and sponsorship literature through several contributions. First, the use of nostalgia marketing, and nostalgia in general, is novel in the sport marketing and sponsorship literature, with future research in nostalgia and sponsorship recommended. Second, the potential to adopt or adapt Body Break to other sport participation and physical activity properties is empirically supported. Finally, the finding that very effective promotions can have a long-lasting effect, both on those who experienced the campaigns as well as younger populations who only heard about it, is notable.

This paper investigates the ways digital media applications in rural areas have transformed the influence of social networks (SN) on farmers' adoption of various climate change mitigation measures (CCMM), and explores the key mechanisms behind this transformation.

The study analyzes data from 1,002 farmers’ surveys. First, a logit model is used to measure the impact of SN on the adoption of different types of CCMM. Then, the interaction term between digital media usage (DMU) and SN is introduced to analyze the moderating effect of digital media on the impact of SN. Finally, a conditional process model is used to explore the mediating mechanism of agricultural socialization services (ASS) and the validity of information acquisition (VIA).

The results reveal that: (1) SN significantly promotes the adoption of CCMM and the marginal effect of this impact varies with different kinds of technologies. (2) DMU reinforces the effectiveness of SN in promoting farmers' adoption of CCMM. (3) The key mechanisms of the process in (2) are the ASS and the VIA.

This study shows that in the context of DMU, SN’s promotion effect on farmers' adoption of CCMM is strengthened.

Tourist confidence plays an important role in people’s decision-making, including decisions related to consuming tourism products. However, little research has explored this concept in depth to provide proper guidance on its operation and measurement. This paper aims to explore the concept of tourist confidence, develop and test a measurement and test the effects of tourist confidence on people’s travel intentions at different periods of time.

This study used repeated cross-sectional data and collected responses from three waves of national surveys of U.S. residents in 2021.

The results revealed three underlying dimensions of tourist confidence: (1) confidence in tourism/hospitality service providers, (2) self-confidence in tourist competency and (3) confidence in travel safety. The validity and reliability of the instrument were deemed satisfactory. The results also indicate the positive associations between tourist confidence and people’s travel decisions.

This study developed a measurement that is specific to the domain of tourist confidence. The findings of this paper reveal the multidimensional nature of tourist confidence. When translated into practice, the findings of this study can help practitioners understand how to effectively enhance tourist confidence in challenging, difficult and uncertain times.

The paper provides a detailed historical account of Douglass C. North's early intellectual contributions and analytical developments in pursuing a Grand Theory for why some countries are rich and others poor.

The author approaches the discussion using a theoretical and historical reconstruction based on published and unpublished materials.

The systematic, continuous and profound attempt to answer the Smithian social coordination problem shaped North's journey from being a young serious Marxist to becoming one of the founders of New Institutional Economics. In the process, he was converted in the early 1950s into a rigid neoclassical economist, being one of the leaders in promoting New Economic History. The success of the cliometric revolution exposed the frailties of the movement itself, namely, the limitations of neoclassical economic theory to explain economic growth and social change. Incorporating transaction costs, the institutional framework in which property rights and contracts are measured, defined and enforced assumes a prominent role in explaining economic performance.

In the early 1970s, North adopted a naive theory of institutions and property rights still grounded in neoclassical assumptions. Institutional and organizational analysis is modeled as a social maximizing efficient equilibrium outcome. However, the increasing tension between the neoclassical theoretical apparatus and its failure to account for contrasting political and institutional structures, diverging economic paths and social change propelled the modification of its assumptions and progressive conceptual innovation. In the later 1970s and early 1980s, North abandoned the efficiency view and gradually became more critical of the objective rationality postulate. In this intellectual movement, North's avant-garde research program contributed significantly to the creation of New Institutional Economics.

This study aims to study the moderating effects of private ownership and government support on the relationship between outsourcing and product innovation in entrepreneurial ventures in a transition economy.

The data of 10,296 Vietnamese entrepreneurial ventures from the four rounds of the survey conducted by the General Statistics Office (GSO) of Vietnam to investigate the moderating effects of private ownership and government support on the association between outsourcing and entrepreneurial ventures’ product innovation performance. The Probit regression model is employed to estimate such associations.

Our research uncovered that the impact of outsourcing on the likelihood of product innovation is more significant for entrepreneurial operations characterized by a substantial degree of private ownership and government backing as opposed to those without.

The results of our research indicated that the resource-based perspective and extended resource-based view (ERBV) are essential in examining the impact of gaining resources or skills from external sources on the growth of entrepreneurial enterprises. These ideas have significance and importance not just in industrialized economies but also in countries undergoing transition. Our findings suggest that entrepreneurial enterprises should have the ability to manage a wide range of resources and make decisions about which activities should be handled internally and which should be delegated to other parties.

Our findings also imply that entrepreneurial ventures should be able to control many resources and choose which tasks should be performed in-house and which should be outsourced to third parties.

By adopting and leveraging the resource-based view (RBV) and extended resource-based views (ERBV), our study developed a theoretical model about private ownership and government support for moderate outsourcing’s impact on entrepreneurial innovation in a transition economy.