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Land value in urban areas in India is influenced by regulations, bylaws and the amenities associated with them. Planning interventions play a significant role in enhancing the quality of the neighbourhood, thereby resulting in a change in its value. Land is a distinct commodity due to its fixed location, and planning interventions are also specific to certain locations. Consequently, the factors influencing land value will vary across different areas. While recent literature has explored some determinants of land value individually, conducting a comprehensive study specific to each location would be more beneficial for making informed policy decisions. Therefore, this article aims to examine and identify the critical factors that impact the value of residential land in the National Capital Territory of Delhi, India.

The study employed a combination of semi-structured and structured interview methods to construct a Relative Importance Index (RII) and ascertain the critical determinants affecting residential land value. A sample of 36 experts, comprising property valuers, urban planners and real estate professionals operating within the National Capital Territory of Delhi, India, were selected using snowball sampling techniques. Subsequently, rank correlation and ANOVA methods were employed to evaluate the obtained results.

Location and stage of urban development are the most critical determinants in determining residential land values in the National Capital Territory of Delhi, India. The study identifies a total of 13 critical determinants.

A scenario planning approach can be developed to achieve an equitable distribution of values and land use entropy. A land value assessment model can also be developed to assist professional valuers.

There has been a lack of emphasis on assessing the impact of planning interventions and territorial regulation on land values in the context of Delhi. This study will contribute to policy decision-making by developing a rank list of planning-based determinants of land value.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

Building on Stimulus-Organism-Response theory, the current study examines the influence of pickup service quality in buy-online pickup in-store service (BOPIS) on users' perceived relationship investment with the mediating role of users' perceived experience quality and relationship proneness. This research also demonstrates the subsequent impact of BOPIS users' perceived relationship investment on their relationship performance indicators, like their cross-buying behaviors (breadth), frequency of their purchase (depth) and longevity of their relationship (length) with the store. The moderating role of BOPIS users' service experience consciousness in a few proposed relationships was tested.

The research is descriptive, quantitative and cross-sectional investigation. The study employed a purposive sampling technique. It was conducted using data collected using a validated self-administered questionnaire from 786 Indian omnichannel shoppers who have used BOPIS services in the past. The proposed conceptual model was tested using Partial Least Squares-Structural Equation Modeling.

The results indicate that BOPIS users' perceived experience quality and relationship proneness positively mediate pickup service quality and perceived relationship investment. The users' perceived relationship investment subsequently significantly positively impacts different dimensions of their relationship performance with the store (breadth, depth and length). Additionally, BOPIS users' service experience consciousness has a significant negative moderating effect on the direct relationship between pickup service quality and different dimensions of relationship performance.

The study is conducted in the Indian population, where omnichannel retailing is still nascent.

This study addresses the need to investigate the relationship performance indicators of BOPIS users, like their cross-buying behaviors(breadth), frequency of their purchase(depth) and longevity of their relationship(length) with the store. This study is the first to show that pickup service quality might explain the relationship performance of BOPIS users through their perceived experience quality, relationship proneness and relationship investments. The moderating role of BOPIS users' service experience consciousness in a few proposed relationships was also tested for the first time.

This study’s fundamental objective is to assess climate change impact on reference evapotranspiration (ET_o) patterns in Egypt under the latest shared socioeconomic pathways (SSPs) of climate change scenarios. Additionally, the study considered the change in the future solar radiation and actual vapor pressure and predicted them from historical data, as these factors significantly impact changes in the ET_o.

The study utilizes data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze reference ET_o. Six models are used, and an ArcGIS tool is created to calculate the monthly average ET_o for historical and future periods. The tool considers changes in actual vapor pressure and solar radiation, which are the primary factors influencing ET_o.

The research reveals that monthly reference ET_o in Egypt follows a distinct pattern, with the highest values concentrated in the southern region during summer and the lowest values in the northern part during winter. This disparity is primarily driven by mean air temperature, which is significantly higher in the southern areas. Looking ahead to the near future (2020–2040), the data shows that Aswan, in the south, continues to have the highest annual ET_o, while Kafr ash Shaykh, in the north, maintains the lowest. This pattern remains consistent in the subsequent period (2040–2060). Additionally, the study identifies variations in ET_o , with the most significant variability occurring in Shamal Sina under the SSP585 scenario and the least variability in Aswan under the SSP370 scenario for the 2020–2040 time frame.

This study’s originality lies in its focused analysis of climate change effects on ET_o, incorporating crucial factors like actual vapor pressure and solar radiation. Its significance becomes evident as it projects ET_o patterns into the near and distant future, providing indispensable insights for long-term planning and tailored adaptation strategies. As a result, this research serves as a valuable resource for policymakers and researchers in need of in-depth, region-specific climate change impact assessments.

The global construction industry faces both challenges and opportunities from electronic waste (e-waste). This study aims to present a bibliometric analysis and comprehensive literature assessment on e-waste in concrete construction materials.

This study studies 4,122 Scopus documents to examine garbage generation in different countries and inventive ways to integrate e-waste into construction as a sustainable strategy. This study lists famous researchers and their cooperation networks, demonstrating a robust and dynamic area with a surge in research output, notably from 2018 to 2022. Data is visually represented using VOS Viewer to show trends, patterns and study interests throughout time.

The findings imply that e-waste can improve construction materials’ mechanical characteristics and sustainability. The results are inconsistent and suggest further optimization. e-Waste into construction has garnered scientific interest for its environmental, life cycle, and economic impacts. This field has great potential for improving e-waste material use, developing sophisticated prediction models, studying environmental implications, economic analysis, policy formulation, novel construction methods, global cooperation and public awareness. This study shows that e-waste can be used in sustainable building. It stresses this area’s need for research and innovation. This lays the groundwork for using electronic trash in buildings, which promotes a circular economy and environmental sustainability.

The findings underscore the critical role of ongoing research and innovation in leveraging e-waste for sustainable building practices. This study lays the groundwork for integrating e-waste into construction, contributing to the advancement of a circular economy and environmental sustainability.

The social implications of integrating e-waste into construction are significant. Using e-waste not only addresses environmental concerns but also promotes social sustainability by creating new job opportunities in the recycling and construction sectors. It fosters community awareness and responsibility towards sustainable practices and waste management. Additionally, this approach can reduce construction costs, making building projects more accessible and potentially lowering housing prices.

This research contributes to the field by offering a bibliometric analysis and comprehensive assessment of e-waste in concrete construction materials, highlighting its global significance.

The different dimensions of the online engagement behaviors exhibited by omnichannel shoppers, who mainly rely on the online channel for information search, are still understudied. This study aims to investigate how service journey quality (SJQ) has an impact on the overall omnichannel customer experience leading to customer identification (CI) with the store, subsequently leading to their exhibition of online engagement behaviors (writing online reviews, blogging, rating products and service online and indulging in customer-to-customer online interactions.

The research is cross-sectional, quantitative and descriptive. Purposive sampling was used to choose the research's participants. Data were collected from 591 Indian omnichannel customers who had previously made an omnichannel purchase that included the concurrent usage of various channels of a retailer using a verified self-administered survey. Using the Smart PLS 4.0 software, the proposed conceptual model has been evaluated.

The results indicate that omnichannel customer experience mediates the relationship between SJQ and CI with the store, subsequently leading to their exhibition of online engagement behaviors (writing online reviews, blogging, rating products and service online and indulging in customer-to-customer online interactions). The perceived customer gratitude toward the store significantly and positively moderated the direct relationship between SJQ and different online engagement behaviors (writing online reviews, blogging, rating products and service online and indulging in customer-to-customer online interactions).

The study relied upon the omnichannel shoppers of only Indian population and relied on a cross-sectional data collection procedure for this research.

Post-pandemic, with highly dynamic shifts in customer preferences, the need for channel-agnostic shopping leading to the unpredictability of purchase patterns has made SJQ the only dimension to achieve sustainable loyalty intentions through value co-creation in an omnichannel retail context. Emphasizing post-purchase behaviors like different online engagement behaviors (writing online reviews, blogging, rating products and services online and indulging in customer-to-customer online interactions), this study is the first to show that SJQ might affect four different online customer engagement behaviors through omnichannel shopping experience and CI with the store. The moderating effect of customer-perceived gratitude toward the retailer on a few proposed hypotheses was also tested to give managerial recommendations. The study also answers the call to investigate the moderating role of customer gratitude in determining service quality-driven engagement behaviors.

The online brand advocacy behaviors of omnichannel shoppers, who mainly rely on integrated brick-and-mortar stores (recognized as a vital channel for delivering a seamless shopping experience and meeting the dynamic needs of the shoppers), are still understudied. This study aims to investigate how integrated store service quality (ISSQ) may elicit both positive and negative emotions that contribute to a memorable omnichannel shopping experience and have an impact on shoppers' attachment to the store, leading to their exhibition of online brand advocacy behaviors (brand defense, brand positivity, brand knowledge sharing and virtual positive expression).

The study is a cross-sectional, descriptive and quantitative investigation. The research participants were chosen using a purposive sampling technique. Using a validated self-administered questionnaire, data were gathered from 886 Indian omnichannel shoppers who often purchase at the integrated brick-and-mortar store. The proposed conceptual model was tested using Smart PLS software for partial least squares-structural equation modeling.

The results indicate that positive and negative emotions mediate the relationship between ISSQ and memorable omnichannel shopping experiences, subsequently impacting omnichannel shoppers' attachment to the store and leading to online brand advocacy behaviors. The relationship strength perceived by shoppers significantly positively moderated the relationship between store attachment and different online brand advocacy behaviors (brand defense, brand positivity, brand knowledge sharing and virtual positive expression).

The study relied upon single cross-sectional data from the Indian population, where omnichannel retailing is still nascent.

This study addresses the need to investigate the different emotions that arise while evaluating service quality in omnichannel retail purchase journeys leading to memorable shopping experiences. Emphasizing post-purchase behaviors like different online brand advocacy behaviors (brand defense, brand positivity, brand knowledge sharing and virtual positive expression), this study is the first to show that ISSQ might affect four different OBAs through memorable omnichannel shopping experience and the shopper's sense of attachment to the store. The moderating effect of relationship strength perceived by shoppers with the retailer on a few proposed hypotheses was also tested to give managerial recommendations.

The purpose of this paper is to examine the mechanical characteristics and optimization of wear parameters of hybrid (TiO₂ + Y₂O₃) nanoparticles with Al matrix using squeeze casting technique.

The hybrid aluminium matrix nanocomposites (HAMNCs) were fabricated with varying concentrations of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃), from 2.5 to 10 Wt.% in 2.5 Wt.% increments. Dry sliding wear test variables were optimized using the Taguchi method.

The introduction of hybrid nanoparticles in the aluminium (Al) matrix was evenly distributed in contrast to the base matrix. HAMNC6 (5 Wt.% TiO₂ + 5 Wt.% Y₂O₃) reported the maximum enhancement in mechanical properties (tensile strength, flexural strength, impact strength and density) and decrease in porosity% and elongation% among other HAMNCs. The results showed that the optimal combination of parameters to achieve the lowest wear rate was A3B3C1, or 15 N load, 1.5 m/s sliding velocity and 200 m sliding distance. The sliding distance showed the greatest effect on the dry sliding wear rate of HAMNC6 followed by applied load and sliding velocity. The fractured surfaces of the tensile sample showed traces of cracking as well as substantial craters with fine dimples and the wear worn surfaces were caused by abrasion, cracks and delamination of HAMNC6.

Squeeze-cast Al-reinforced hybrid (TiO₂+Y₂O₃) nanoparticles have been investigated for their impact on mechanical properties and optimization of wear parameters.

This paper examines the integration of artificial intelligence (AI) within family businesses, focusing on how AI can enhance their competitiveness, resilience and sustainability. The study seeks to provide insights into AI’s application in family business contexts, addressing the unique strengths and challenges these businesses face.

A systematic literature review was conducted to synthesize existing research on the adoption and integration of AI in family businesses. The review involved a comprehensive analysis of relevant academic literature to identify key trends, opportunities, challenges and factors influencing AI adoption in family-owned enterprises.

The review highlights the significant potential of AI for family businesses, particularly in improving operations, decision-making and customer engagement. It identifies opportunities such as analysing customer data, enhancing brand building, streamlining operations and improving customer experiences through technologies like Generative AI, Machine Learning, AI Chatbots and NLP. However, challenges like resource constraints, inadequate infrastructure, low customization and AI knowledge gaps inhibit AI adoption in family firms. The study proposes an AI adoption roadmap tailored for family businesses and outlines future research directions based on emerging themes in AI use within these enterprises.

This paper addresses the underexplored area of AI integration in family businesses, contributing to the academic understanding of the intersection between AI and family-owned enterprises. The study offers a comprehensive synthesis of existing research, providing valuable insights and practical recommendations for enhancing the competitiveness and sustainability of family businesses through AI adoption.

Agriculture is a vital sector for every country, especially for a country like India, where the majority of the population is dependent on agriculture as their earning source. Technological improvements in agriculture will increase output with proper forecasting of input resources. In this study, the author tries to investigate the attitude of end users (farmers) about the use of Industry 4.0 (I4.0) technologies.

The unified theory of acceptance and use of technology (UTAUT) model is used to assess the behavioral aspects. The significance of socioeconomic and technological factors is highlighted, providing the study with a thorough understanding of farmers' decision-making processes. A research questionnaire was developed for data collection, and descriptive and inferential statistics were used to analyse the results using AMOS and SPSS software.

A total of 371 survey responses were collected. The results demonstrate that the hypothesis regarding UTAUT model components is validated, while several mediating hypotheses are not supported, indicating that they are not significant in farmers' decision-making.

In this study, socioeconomic and technological factors are considered to be mediating and moderating elements between the constructs of the UTAUT model. Increasing the accuracy and reliability of our study by integrating mediating and moderating variables. This study assists industry specialists in understanding the elements that farmers consider while switching toward new technologies.