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Conscious consumers have been influenced to either cut back on their fashion consumption or switch to ethical clothing (EC) as a result of the detrimental social effects of fast fashion that have been seen over the past 10 years. It also reflects how the ethical belief of the young generation influences them to be conscious of the ill effects of their fashion choices or behave like an ignorant irresponsible buyer. This study aims to examine this issue in detail to find out the prevalence and impact of such beliefs on consumption choices over a period of time.

This study uses the cross-sectional data of 525 respondents from India to explore and unearth the EC phenomenon in emerging markets. It follows a two-step approach consisting of confirmatory factor analysis and structural equation modeling to examine the proposed hypotheses using AMOS 22 software.

It was found that consumers in developing economies are concerned about the ethical standards followed by the fashion industry (FI), which is reflected in the form of inhuman working conditions for FI workers.

This study emphasizes understanding attitude, subjective norms, behavioral control and EC related to ethical buying behavior and their interaction mechanisms that transform it into the actual buying intention of EC.

It was an eye-opener that collective societal culture and standards do not influence ethical purchase decisions but it is rather the individual’s own ethical rules which is a result of established core family values that significantly shape fashion consumption. This study advances existing literature by empirically verifying the relationship between consumer attitude, consumers’ subjective norms, perceived behavioral control, environmental concern with ethical buying behavior and ethical purchase intention. It could provide insightful information and support academic research as well as real-world marketing and environmental initiatives.

Even though the pandemic years resulted in a significant increase in massive open online courses (MOOCs), there are still countries where penetration is low. The rejection of MOOCs can inhibit individual and societal advancements. The purpose of this study is to explore what is behind the resistance to MOOCs in these regions. Using the theoretical framework of innovation resistance theory, it defines the factors that inhibit the adoption of MOOCs.

The research is based on two studies. In the first study, in-depth interviews were used to explore factors that may cause barriers to adoption. Following the results of the first phase, a survey was conducted to investigate resistance to MOOCs, including both users and nonusers of such platforms.

Structural equation modeling revealed the presence of functional and psychological barriers, with the most significant being usage and value-related barriers. The lack of information and the need for interaction were identified as the main factors contributing to these barriers.

The results help increase the acceptance and effective integration of MOOCs into different educational environments, especially in countries with high resistance.

This study aims to assess housing affordability and estimate demand using a hedonic regression model in the context of Lucknow city, India. This study assesses housing affordability by considering various housing and household-related variables. This study focuses on the impoverished urban population, as they experience the most severe housing scarcity. This study’s primary objective is to understand the demand dynamics within the market comprehensively. An understanding of housing demand can be achieved through an examination of its characteristics and components. Individuals consider the implicit values associated with various components when deciding to purchase or rent a home. The components and characteristics have been obtained from variables relating to housing and households.

A socioeconomic survey was conducted for 450 households from slums in Lucknow city. Two-stage regression models were developed for this research paper. A hedonic price index was prepared for the first model to understand the relationship between housing expenditure and various housing characteristics. The housing characteristics considered for the hedonic model are dwelling unit size, typology, condition, amenities and infrastructure. In the second stage, a regression model is created between household characteristics. The household characteristics considered for the demand estimation model are household size, age, education, social category, income, nonhousing expenditure, migration and overcrowding.

Based on the findings of regression model results, it is evident that the hedonic model is an effective tool for the estimation of housing affordability and housing demand for urban poor. Various housing and household-related variables affect housing expenditure positively or negatively. The two-stage hedonic regression model can define willingness to pay for a particular set of housing with various attributes of a particular household. The results show the significance of dwelling unit size, quality and amenities (R² > 0.9, p < 0.05) for rent/imputed rent. The demand function shows that income has a direct effect, whereas other variables have mixed effects.

This study is case-specific and uses a data set generated from a primary survey. Although household surveys for a large sample size are resource-intensive exercises, they provide an opportunity to exploit microdata for a better understanding of the complex housing situation in slums.

All the stakeholders can use the findings to create an effective housing policy. The variables that are statistically significant and have a positive relationship with housing costs should be deliberated upon to provide the basic standard of living for the urban poor. The formulation of policies should duly include the housing preferences of the economically disadvantaged population residing in slum areas.

This paper uses primary survey data (collected by the authors) to assess housing affordability for the urban poor of Lucknow city. It makes the results of the study credible and useful for further applications.

India’s rapid economic growth has triggered a significant transformation in its logistics sector, fueled by comprehensive reforms and digital initiatives outlined in the National Logistics Policy. Smart warehouses, equipped with cutting-edge technologies such as IoT, AI and automation, have taken center stage in this evolution. They play a pivotal role in India’s digital journey, revolutionizing supply chains, reducing costs and boosting productivity. This AI-driven transformation, in alignment with the “Digital India” campaign, positions India as a global logistics leader poised for success in the industry 4.0 era. In this context, this study highlights the significance of smart warehouses and their enablers in the broader context of supply chain and logistics.

This paper utilized the ISM technique to suggest a multi-tiered model for smart warehouse ecosystem enablers in India. Enablers are also graphically categorized by their influence and dependence via MICMAC analysis.

The study not only identifies the 17 key enablers fostering a viable ecosystem for smart warehouses in India but also categorizes them as linkage, autonomous, dependent and independent enablers.

This research provides valuable insights for practitioners aiming to enhance technological infrastructure, reduce costs, minimize wastage and enhance productivity. Moreover, it addresses critical academic and research gaps contributing to the advancement of knowledge in this domain, thus paving the way forward for more research and learning in the field of smart warehouses.

The qualitative modeling is done by collecting experts' opinions using the ISM technique solicits substantial value to this research.

This study aims to optimize capital allocation across several stocks to maximize expected returns while minimizing risk. By enhancing the portfolio selection model with new constraints and a unique objective function, the research introduces improved strategies for achieving these financial objectives.

This research uses a Sharpe ratio index for portfolio comparison and introduces a self-adjusting algorithm based on a genetic algorithm, eliminating the need for manual parameter adjustments. The effectiveness of this methodology is assessed across various test cases, demonstrating its applicability and robustness in dynamic financial market conditions.

This study confirms that the proposed algorithm consistently outperforms traditional models, offering robustness across different market conditions. Results indicate significant risk management and return maximization, and improvements have been attributed to the innovative model enhancements and algorithmic adjustments.

Incorporating a new objective function prioritizing the price-to-earnings ratio and introducing technical analysis constraints significantly enhance portfolio profitability and risk management. Another key contribution is the self-adjusting algorithm streamlining parameter adjustments, fostering more dynamic and accurate responses to market changes. These contributions have not been observed in prior research, providing a novel approach to the portfolio selection problem.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

The purpose of this study is to investigate the structural, surface roughness and corrosion properties of the zirconium oxide thin films deposited onto SS304 substrates using the direct current (DC) magnetron sputtering technique.

DC sputtering at different powers – 80, 100 and 120 W – was used to deposit ZrO₂ thin films onto different substrates (Si/SS304) without annealing of the substrate. Atomic force microscope (AFM), energy-dispersive X-ray spectroscopy (EDS), Tafel extrapolation and contact angle techniques were applied to investigate the surface roughness, chemical compositions, corrosion behavior and hydrophobicity of these films.

Results showed that the thickness of the deposited film increased with power increase, while the corrosion current decreased with power increase. AFM images indicated that the surface roughness decreased with an increase in DC power. EDS analysis showed that the thin film has a stoichiometric ZrO₂ (Zr:O 1:2) composition with basic uniformity. Water contact angle measurements indicated that the hydrophobicity of the synthesized films decreased with an increase in surface roughness.

DC magnetron sputtering technique is infrequently used to deposition thin films. The obtained thin films showed good hydrophobic and anticorrosion properties. Finally, results are compared with other deposition techniques.