Social media influencer marketing has emerged as an essential marketing strategy in the online interactive environment. This study investigates the impact of influencer-consumer fit (ICF) on behavioral intentions; intention to co-create brand value (ICC) and purchase intention (PI), with the serial mediation of influencer authenticity (IA) and attitude toward brand (ATB). A self-administered questionnaire was distributed to followers of social media influencers in Pakistan. The data were collected from 421 female followers of social media influencers through survey and partial least squares—structural equation modeling was used for data analysis. The findings reveal that ICF impacts IA, while the latter impacts ATB. ATB in turn impacts behavioral intentions. The direct effects suggest that ICF impacts consumers’ PI but not the ICC. However, with the serial mediation of IA and ATB, the relationship becomes significant. The findings of this study may assist managers in building brand strategies to achieve excellence in a highly dynamic and competitive market by leveraging the power of influencer marketing.

The study’s purpose is to evaluate the influence of some factors of the model of planned behavior (TPB) and the perceived academic support of the university on the attitude toward entrepreneurship and entrepreneurial intention of students. The results of Structural Equation Modeling (SEM) linear structural model analysis with primary data collected from 1162 students indicated that entrepreneurial intention is influenced by attitude toward entrepreneurship, subjective norm, perceived educational support, and perceived concept development support. In addition, this study also found the positive influence of perceived educational support, concept development support, and business development support on attitude towards entrepreneurship. Interestingly, the influence of perceived business development support on entrepreneurial intention was rejected, and personal innovativeness is demonstrated to promote an attitude toward entrepreneurship. Notably, this study also highlights the moderating role of personal innovativeness on the relationship between attitude toward entrepreneurship and entrepreneurial intention. Based on these findings, several implications were suggested to researchers, universities, and policymakers.

In light of swift urbanization and the lack of precise land use maps in urban regions, comprehending land use patterns becomes vital for efficient planning and promoting sustainable development. The objective of this study is to assess the land use pattern in order to catalyze sustainable township development in the study area. The procedure adopted involved acquiring the cadastral layout plan of the study area, scanning, and digitizing it. Additionally, satellite imagery of the area was obtained, and both the cadastral plan and satellite imagery were geo-referenced and digitized using ArcGIS 9.2 software. These processes resulted in reasonable accuracy, with a root mean square (RMS) error of 0.002 inches, surpassing the standard of 0.004 inches. The digitized cadastral plan and satellite imagery were overlaid to produce a layered digital map of the area. A social survey of the area was conducted to identify the specific use of individual plots. Furthermore, a relational database system was created in ArcCatalog to facilitate data management and querying. The research findings demonstrated the approach's effectiveness in enabling queries for the use of any particular plot, making it adaptable to a wide range of inquiries. Notably, the study revealed the diverse purposes for which different plots were utilized, including residential, commercial, educational, and lodging. An essential aspect of land use mapping is identifying areas prone to risks and hazards, such as rising sea levels, flooding, drought, and fire. The research contributes to sustainable township development by pinpointing these vulnerable zones and providing valuable insights for urban planning and risk mitigation strategies. This is a valuable resource for urban planners, policymakers, and stakeholders, enabling them to make informed decisions to optimize land use and promote sustainable development in the study area.

In this study, optical and microwave satellite observations are integrated to estimate soil moisture at the same spatial resolution as the optical sensors (5km here) and applied for drought analysis in the continental United States. A new refined model is proposed to include auxiliary data like soil texture, topography, surface types, accumulated precipitation, in addition to Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) used in the traditional universal triangle method. It is found the new proposed soil moisture model using accumulated precipitation demonstrated close agreements with the U.S. Drought Monitor (USDM) spatial patterns. Currently, the USDM is providing a weekly map. Recently, “flash” drought concept appears. To obtain drought map on daily basis, LST is derived from microwave observations and downscaled to the same resolution as the thermal infrared LST product and used to fill the gaps due to clouds in optical LST data. With the integrated daily LST available under nearly all weather conditions, daily soil moisture can be estimated at relatively higher spatial resolution than those traditionally derived from passive microwave sensors, thus drought maps based on soil moisture anomalies can be obtained on daily basis and made the flash drought analysis and monitoring become possible.

The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI's capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.