International Journal of Human Capital in Urban Management

Abstract BACKGROUND AND OBJECTIVE: Krishnagiri is a significant hub for mango cultivation, primarily producing varieties like 'Banginapalli,' 'Alphonso,' and 'Thothapuri.' Despite the region’s favorable agro-climatic conditions, mango farming faces several challenges, including climate variability, inadequate infrastructure, and limited technology adoption. This study aims to analyze the dynamics of the mango supply chain in Krishnagiri, focusing on technology adoption, market access, infrastructure challenges, and government policies, with the primary objectives of identifying key barriers, assessing technology usage, and recommending interventions to enhance supply chain efficiency.
METHODS: A mixed-methods methodology was employed, combining quantitative, qualitative, and mixed-methods analyses. Quantitative data were collected through structured surveys with 150 mango farmers, 80 traders, and 30 government officials. The survey included Likert-scale and multiple-choice questions covering technology adoption, market access, and infrastructure. Qualitative data were gathered through in-depth interviews, focus group discussions, and field visits, providing deeper insights into stakeholder perceptions and challenges. Statistical tools like the Statistical Package for the Social Sciences and Excel were used for data analysis.
RESULTS: The results show that 60% of farmers sell locally, 25% engage in export, and 15% rely on intermediaries. Technology use is low, with 68% reporting minimal digital tool adoption and only 12% using the Internet of Things or blockchain. Barriers to adoption affect 58%, with cost being the main issue for 40%. Infrastructure gaps are significant: 70% report inadequate cold storage, and 65% face transport issues like delays and poor roads, contributing to post-harvest losses. Water scarcity affects 62% due to groundwater overuse, and 45% report pest issues, mainly fruit flies and powdery mildew. Policy feedback is mixed, with 52% viewing government policies as effective while 48% cite poor local implementation. Encouragingly, 78% are willing to adopt new technologies if subsidized. Smart tech adoption remains low (mean 33.6%, benefit 27.4%, variance 32.64 and 31.04), and 55.6% are affected by logistics issues (severe impact variance 69.04). Farmer engagement shows high inconsistency (variance 356.9 vs. traders’ 236.97), with positively skewed data emphasizing the need for targeted support.
CONCLUSION: The conclusion should highlight the importance of promoting digital tools and smart technologies, increasing Internet of Things and blockchain adoption (12%) through subsidies and digital literacy. By deploying smart logistics, it must address infrastructure gaps, with 70% lacking cold storage and 65% facing transport delays. Enhancing market access beyond local sales (60%) via Information and Communication Technology linkages is vital. Policy support through subsidies, awareness, and local implementation is needed. Future research should assess long-term tech adoption and policy impacts on urban resilience and smallholder sustainability.

Abstract BACKGROUND AND OBJECTIVES: The Mediterranean city of Oran, located in Algerian coastal territory, experienced extensive morphological modifications through the sustained influence of Spanish and French colonial activities. Its strategic position attracted successive occupations that left visible traces in the urban form. This study analyzed how spatial structures evolved under these colonial regimes and how political and strategic decisions shaped Oran’s urban morphology. It also sought to explain the mechanisms through which these transformations occurred and how they continued to affect the city's organization.
METHODS: Historical maps from 1736 and 1927 were used to analyze space syntax, measuring global and local network integration, choice, and intelligibility. The syntactic results were combined with the urban decoding principles developed by Caniggia and Maffei to provide historical context to the morphological shifts. These years were chosen because they represent turning points in Spanish and French urban planning strategies.
FINDINGS: The integration value of Plaza Mayor in 1736 was 0.61, confirming its role as the central hub under Spanish rule. In 1927, the highest integration value shifted to Place d’Armes (0.66), reflecting the French strategic repositioning of the city’s core. This shift led to urban fragmentation and a reduction in intelligibility in certain areas. Despite modernization efforts, peripheral zones maintained low integration values, confirming persistent spatial segregation.
CONCLUSION: The research established how colonial rule directly influenced the spatial layout of the city and demonstrated that spatial syntax is an effective tool for analyzing historical urban development patterns. The results contribute to urban heritage planning by showing how spatial dynamics reflect broader socio-political contexts.

Abstract BACKGROUND AND OBJECTIVES: The roots of Performance Management can be traced to ancient civilizations; however, the industrial revolution marked a turning point in this field, introducing concepts such as systematic evaluation, management by objectives, and excellence models. In modern times, PM has evolved to emphasize process analysis, self-assessment, benchmarking, and workforce development as core components. Despite these advancements, there is still a notable gap in the availability of a comprehensive, visually intuitive, and step-by-step framework to guide managers in implementing a holistic PM system. This manuscript aims to address this gap by introducing a detailed, graphical, and systematic PM model that provides clear guidance for practitioners.
METHODS: This study reviews various performance and excellence models to propose a novel conceptual framework. The proposed model is evaluated using the Analytic Hierarchy Process, Data Envelopment Analysis, and the Technique for Order of Preference by Similarity to Ideal Solution.
FINDINGS: The study constructs a new PM system by integrating an organization’s mission, vision, strategies, processes, and stakeholder perspectives into Key Performance Indicators. It involves collecting relevant data, applying a scoring mechanism, calculating departmental efficiency, and ranking organizations to establish a data-driven decision-making framework. A case study is presented to illustrate the model’s application, revealing that while DEA assigned 100% efficiency to two distinct organizations, TOPSIS yielded scores of 94.67% and 46.86%, with different rankings. The reasons for these discrepancies are thoroughly examined and discussed.
CONCLUSION: This manuscript introduces a conceptual model for PM, structured around eight key steps. These steps include team formation, indicator development, Balanced Scorecard development, weight calculation, scoring system design, data collection, data analysis, and feedback and continuous improvement. The model classifies KPIs into three main categories: specialized KPIs that are derived from missions, visions, strategies, critical success factors, and core processes. Self-assessment KPIs, developed based on established excellence models, and customer survey KPIs, designed to capture external stakeholder feedback. AHP is employed to determine the weights of the KPIs and BSC aspects, ensuring a systematic and objective prioritization. DEA is utilized for efficiency calculations, while the TOPSIS method is applied to analyze the results and derive actionable managerial insights. To demonstrate the model's applicability, it is implemented in a hypothetical municipality using arbitrary data, showcasing its capability to provide a comprehensive and structured approach to performance management.

Abstract BACKGROUND AND OBJECTIVES: As artificial intelligence increasingly transforms business potential, its adoption in small businesses has become a growing interest. This study explores college students’ perceptions of artificial intelligence in the context of small businesses, comparing insights from Kuwait and Malaysia. With small businesses playing a pivotal role in the economic growth of both countries, understanding how future employees perceive artificial intelligence can provide valuable guidance for educators, entrepreneurs, and policymakers. The research aims to investigate key factors such as artificial intelligence awareness, perceived benefits and challenges, willingness to adopt artificial intelligence technologies, and the perceived effectiveness of artificial intelligence in improving business operations. In addition, a bibliometric analysis was conducted to compare the research trends and national emphasis on artificial intelligence in business within the two countries.
METHODS: A quantitative research approach was employed using a structured survey distributed to a total of 834 university students from Kuwait and Malaysia. The survey collected data on various dimensions, including artificial intelligence awareness, perceived utility, anticipated challenges, and readiness to engage with artificial intelligence in small business settings. The data were statistically analyzed to determine significant differences and similarities across the two national contexts. Also, a bibliometric analysis was carried out to explore the scholarly output and collaboration trends related to artificial intelligence and business in both countries.
FINDINGS: Statistical analysis of the survey data from 834 students (433 from Malaysia and 401 from Kuwait) revealed high mean scores across all constructs, ranging from 3.90 to 4.08 on a 5-point Likert scale, indicating overall positive perceptions of AI’s role in small businesses. Independent-samples t-tests showed no significant differences (p > 0.05) between the two countries regarding AI awareness, perceived benefits, anticipated challenges, willingness to adopt AI, and perceived impact on SMEs. Reliability coefficients (Cronbach’s α) for the constructs ranged from 0.73 to 0.86, confirming internal consistency. Additionally, bibliometric analysis showed that Malaysia produced 105 relevant publications from 2010–2024, with 1,872 total citations, while Kuwait produced 10 publications from 2018–2024, with 234 citations. Despite fewer publications, Kuwait demonstrated higher average citations per paper (23.4 vs. 17.83), indicating a high-impact research contribution.
CONCLUSION: This study underscores the universal recognition of artificial intelligence's value in small businesses among young professionals in Kuwait and Malaysia. The findings highlight the need for context-sensitive policies, focused artificial intelligence education, and hands-on support mechanisms for small enterprises. Cross-cultural collaboration and targeted research initiatives are recommended to responsibly harness artificial intelligence’s potential and drive sustainable innovation in the small business sector.

Abstract BACKGROUND AND OBJECTIVES: Globally, public housing delivery faces persistent challenges in meeting the growing demand for affordable and accessible housing. Despite several national interventions, the gap between policy intent and housing outcomes remains significant. This study aimed to evaluate the effectiveness of government-funded housing delivery by examining levels of awareness, accessibility, affordability, and associated financial burdens among residents of public housing estates in Ogun State, Nigeria.
METHODS: An evaluative research design incorporated both primary and secondary data. The study population comprised residents of fully completed and occupied federal housing estates. A systematic sampling technique selected 134 housing units from a sampling frame of 1,337. Data were gathered through structured questionnaires and direct observations and were analysed using descriptive statistics and Pearson correlation at a 0.05 significance level.
FINDINGS: Among the respondents, 67% reported awareness of the housing delivery initiative, while 52% indicated that housing units were difficult to access. 47.5% perceived the housing units as expensive, and 22.5% rated them as very expensive. The financial burden was notably high, with power supply (AMS = 4.47), housing costs (AMS = 4.25), and water provision (AMS = 4.23) ranked as top cost concerns. Significant correlations were observed between housing costs and water provision (r = .994, p = .001), power supply (r = .992, p = .001), and transport to work (r = .970, p = .006). Furthermore, 38.5% of residents rated the housing environment as poor, while 46.8% described internal road infrastructure as poor.
CONCLUSION: The findings revealed substantial gaps in the design and implementation of public housing delivery programs, with issues of affordability, accessibility, and infrastructure maintenance contributing to financial stress among residents. Stronger political will, targeted subsidies, improved infrastructure, and inclusive publicity strategies are required to enhance housing outcomes and ensure that housing delivery is equitable, affordable, and sustainable.

Abstract BACKGROUND AND OBJECTIVES: Land use change is a pressing global environmental crisis requiring scientific study for sustainable regional decisions. This study analyzes the spatial-temporal dynamics of land use in the Hyrcanian forests of western Mazandaran province from 2013-2023 using remote sensing data. Image classification was based on six land use classes: vegetation, built-up, agriculture, water bodies, forest, and bare land.
METHODS: An Artificial Neural Network was employed to predict land use changes over ten years. The model was validated by comparing the simulated 2023 map with the actual map, resulting in a Kappa coefficient of 92%.
FINDINGS: Land use change maps from 2013-2023 show that built-up areas increased by 26.5517 km2, while forest and other vegetation decreased by 43.6353 km2 and 85.1967 km2, respectively. Projections for 2023-2033 indicate similar trends: an increase in built-up areas by 31.3106 km2 and a decrease in forest and other natural areas by 8.875 km2 and 16.6104 km2, respectively.
CONCLUSION: This research offers a valuable tool for the sustainable management of Hyrcanian forests, aiding informed decision-making for environmental improvement, identifying threats, optimal resource management, and predicting the effects of climate change. It offers valuable insights for sustainable planning, management, and improved environmental outcomes.

Abstract BACKGROUND AND OBJECTIVES: Human Resource Analytics has become a strategic enabler in modern workforce management, particularly amid digital transformation. However, scholarly research in Human Resource Analytics remains fragmented across regions and disciplines. This study employs bibliometric analysis to map global research output in Human Resource Analytics from 2012 to 2024, identifying trends, influential contributors, thematic concentrations, and underexplored areas.
METHODS: A bibliometric analysis approach was applied using data retrieved from the Scopus database. The dataset covered publication metadata, including authorship, institutional affiliation, geographic origin, keywords, sources, and citation counts. VOSviewer software was used to construct visual maps illustrating keyword co-occurrence, co-authorship networks, and citation clusters, enabling a comprehensive overview of research activity and intellectual structure in the Human Resource Analytics.
FINDINGS: A total of 211 publications are identified and analysed over 13 years, involving 602 contributors. The analysis highlights 2,674 citations, with an average of 12.67 citations per paper. The corresponding h-index and g-index are 24 and 47, respectively. The results reveal three research themes. They present an increasing global interest, with leading contributions from high-income countries. The three core themes are workforce analytics, Artificial Intelligence in Human Resource Management, and strategic decision-making. Notably, low representation from developing regions and limited use of advanced predictive analytics highlight critical research gaps.
CONCLUSION: This study provides a comprehensive quantitative overview of the evolution of Human Resource Analytics scholarship. It guides future research by identifying emerging priorities, regional disparities, and methodological developments, offering valuable insights for advancing data-driven human resource practices and fostering more strategic, evidence-based decision-making in HR management and policy development globally.

Abstract BACKGROUND AND OBJECTIVES: Batna city's road network has evolved due to various factors, particularly residents’ practices and movements, which have shaped the city’s routes and spatial hierarchies. However, the logic of urban production has been unexplored through the prism of user experience. This study aimed to analyze the spatial configuration of the city’s road network, to identify the mechanisms driving the city’s evolution, highlighting the users’ contribution, providing guidelines for future, thoughtful urban planning, and enhancing the quality of urban space by reconciling city users' perception with different approaches to planning.
METHODS: The study applied space syntax Analysis, using depthmap 10 software. It calculated the following parameters: Connectivity, integration, and entropy, and analyzed 25753 axes of Batna’s road network. These measures allowed understanding users’ perception of accessibility, movement, and route choices. This analysis was complemented by a sociological study and field observations. To assess the robustness of the relationship between these parameters, statistical tests were performed, including Pearson and Spearman correlations, as well as a linear regression test analyzed through the ANOVA table, to examine the relationships between two dualities: connectivity/ integration and connectivity/ entropy.
FINDINGS: Connectivity values in Batna city ranged from 0 to 7, with higher values (4-7) found in formal districts. The average integration value was 1.17, peaking at 2.63. Pearson and Spearman confirmed strong correlations between these attributes (p-value less than 0.001), while the ANOVA table from linear regression predicted 14% of the variance in Integration. These values revealed areas with high centrality and accessibility, aligning with the questionnaire responses on urban dynamics and frequency. The analysis also recorded high spatial choice and complexity, revealed by a maximum entropy value of 1.057, observed in more than 25753 spatial units analyzed in this study.
CONCLUSION: The study was distinguished by the inclusive approach, based on the exhaustive analysis of the road structure of Batna city. It highlighted that urban quality and perception, although immaterial and subjective concepts, can be objectified and analyzed rigorously. It advocated for urban planning that is more sensitive to spatial configuration. This study was unique in that it analyzed the entire road network of the city, thus providing a solid basis for testing, in future research, other parameters derived from the Space Syntax method.

Abstract BACKGROUND AND OBJECTIVES: This study explored the complex relationships among economic growth, environmental policies, renewable energy adoption, and carbon emissions in various economic environments, specifically focusing on Brazil, Russia, India, China, and South Africa. These factors are investigated through the lens of the Environmental Kuznets Curve framework to discover if these emerging economies adhere to, vary from, or demonstrate distinctive variants. This study has established a framework for analyzing how development phases impact environmental degradation and evaluates the sustainability initiatives, particularly focusing on renewable energy adoption and policy change, to help lessen carbon emissions in different economic contexts.
METHODS: The study employed a range of statistical and econometric techniques to examine the relationship among economic growth, environmental policies, renewable energy adoption, and carbon emissions in Brazil, Russia, India, China, and South Africa from 1990 to 2022. This study used statistical analysis through descriptive statistics and correlation analysis. Furthermore, stationarity of the data is tested by using the Augmented Dickey-Fuller and Phillips-Perron tests. Moreover, the bounds testing is utilized to examine the cointegration among the estimated variables, whereas the Autoregressive Distributed Lag method is applied to investigate both short and long-run elasticities. By utilizing these methods collectively, a detailed investigation of whether panels of these economies align with, deviate from, or exhibit variations in the Environmental Kuznets Curve framework.
FINDINGS: The findings revealed that an inverted U-shaped trajectory between economic development and environmental pollution is evident in Russia, South Africa, Brazil, and China, suggesting that as economic development increases in these countries, environmental degradation worsens because of escalating carbon emissions. Four key points of particular interest from energy, environmental, and economic perspectives are: (1) realigning panel of these countries’ industrialization and economic development policies with environment management initiatives, acknowledging that (2) an all-size-fits-all renewable energy consumption policy may not be equally effective in abating carbon emissions across all economies, (3) the incomplete coverage of consumptions sources and a subset of industries limiting a higher carbon tax’s effective reduction in carbon emissions (4) financial regulations and incentives related to carbon taxes, green financing, or sustainable investments and emission reductions providing an impactful and novel avenue for enhancing financial development’s carbon emissions inhibiting role.
CONCLUSION: These findings may offer policy-makers or organizations key information for sustainable development policies, which help address the conflict between economic growth and environmental quality, and may be applicable in all selected countries as well as other developing countries.

Abstract BACKGROUND AND OBJECTIVES: The management of high-level nuclear waste is a pressing global challenge, with over 400,000 metric tons in temporary storage worldwide. International disputes frequently arise due to disagreements over site selection, cost allocation, environmental risks, and long-term liability, often leading to negotiation deadlocks. Existing governance frameworks lack structured mechanisms to balance the competing objectives of multiple stakeholders, including waste-producing ("Disposer") nations and potentially affected ("Affected") nations. This paper aims to resolve these transboundary nuclear waste treatment conflicts by developing a hybrid analytical model that integrates Game Theory to model strategic interactions and the ε-Multi-Objective Evolutionary Algorithm (ε-MOEA) to find optimal solutions that balance competing goals such as cost, environmental safety, and economic benefits.
METHODS: This manuscript employs a combined Game Theory and computational algorithm approach to resolve international nuclear waste disputes. Game Theory models the conflict between two player types: disposer countries (waste producers) and affected countries (those impacted by disposal). Each player has specific goals, strategies, and costs, with the model seeking a Nash Equilibrium-a stable agreement where no country can unilaterally improve its outcome. Due to the problem's high complexity with multiple competing objectives, the study utilizes the ε-MOEA optimization algorithm. This algorithm efficiently explores millions of possible strategy combinations to identify optimal compromises, balancing outcomes to ensure fair and practical solutions for all involved countries.
FINDINGS: Computational experiments compared ε-MOEA against other multi-objective algorithms (NSGA-II, NSGA-III, PESA2, VEGA). The key finding was that all algorithms converged to the same optimal fitness value (-5280.33), demonstrating the model's robustness in identifying a stable equilibrium. However, ε-MOEA achieved this result with the shortest and most stable runtime (approximately 5.00 seconds per iteration), significantly outperforming other algorithms in computational efficiency. This indicates that ε-MOEA is particularly well-suited for solving this complex, high-dimensional problem efficiently, providing a diverse set of Pareto-optimal solutions for policymakers to evaluate trade-offs.
CONCLUSION: A hybrid Game theory and ε-MOEA framework effectively resolves international nuclear waste conflicts by modeling strategic interactions and optimizing for multiple objectives. This scalable approach identifies stable, fair agreements that balance the interests of both producing and affected nations, supporting sustainable international governance. Future work should focus on improving computational efficiency for larger numbers of players.

Abstract BACKGROUND AND OBJECTIVES
In the manufacturing sector, selecting the most suitable supplier is a critical strategic decision. In today's context, where sustainability has become a key performance indicator, the automotive industry emphasises supplier selection strategies that align with traditional economic criteria, as well as environmental and social sustainability. Sustainable supplier selection is a complex decision-making process. The objective of this article is to simplify the selection of a supplier, considering all three sustainability factors as important through the expertise of experts.
METHODS: In this study, a grey-based decision-making approach is employed. To address ambiguity and capture subjective judgments effectively, a linguistic scale-based questionnaire is utilized for both supplier evaluation and criteria weight determination. The model converts the expert rating into a grey number-based rating for the criteria weight and the supplier performance rating on each of the identified criteria. The proposed method computes the relative closeness index. The evaluated relative closeness index ranks the best suppliers that are closest to the ideal positive supplier. To demonstrate the applicability and effectiveness of the proposed methodology, a case study from the automotive industry is presented.
FINDINGS: The proposed method employs grey numbers to evaluate the criteria weights and grey numbers for supplier rating. Using modified grey relational analysis, the suppliers are ranked. The criteria identified by the experts were both quantitative and qualitative. The best sustainable supplier is supplier 4, with the relative closeness index farthest from the possibility degree 0.5, with a value of 0.7622. A comparative analysis was conducted, revealing that the top three ranked suppliers demonstrated consistent positions across the evaluated methods
CONCLUSION: Suppliers from the automobile sector were evaluated using distinct criteria. Industry experts prioritized traditional operational factors such as technical capability, product quality, delivery reliability, workplace safety, and employee health. Furthermore, environmental performance and sustainability were also rated favourably, highlighting the growing importance of environmentally sustainable practices. The study emphasizes the reduction in computational complexity associated with making informed decisions in complex scenarios.

Abstract Floods in urban areas remain a critical threat to human life, health, and economic stability. This study focused on Nigeria's urban flooding, which has become a yearly occurrence. Recently, floods have been deepened by the climate change situation, urban planning lapses, and overwhelmed drainage infrastructures. Other factors intensifying urban flooding in Nigeria include rapid urbanization, choking off the natural spaces and vegetation with impervious surfaces, and accelerating stormwater runoff. The Nigerian government has implemented various flood risk management strategies, including the National Disaster Response Plan and flood control measures such as flood warning, preparedness, and responses. However, studies from the literature confirmed the insufficient understanding of flood events, such as the driving variables and uncertainties about watershed characteristics and climatic variability that impede flood risk management and prediction skills. Therefore, more proactive, sustainable strategies to handle floods are desperately needed in light of the numerous recent climate and flooding-related calamities ravaging the low-lying regions. Similarly, there is a paucity of empirical research on sustainable solutions for attenuating the volume of runoff that is seemingly resulting in flooding. This review fills this gap in the literature. More so, aligning with the United Nations’ 2030 Agenda for Sustainable Development, the Sustainable Development Goals. Sustainable flood risk solutions touch on several SDGs, targeting all sustainable practices, resilient infrastructure, water management, sustainable cities and communities, and the sustainable use of terrestrial ecosystems. This review equally focuses on harnessing the potential embedded in the sustainable practices that can fit into other purposes.