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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 assess the efficiency of managing working capital in 1,388 Indian manufacturing firms from 2008 to 2019 and investigate the effects of firm-specific and macro-level determinants on working capital management (WCM) efficiency.

The current study accommodates a slack-based measure (SBM) in data envelopment analysis (DEA) for computing WCM efficiency. Further, we implement a panel data fixed-effects model that controls for heterogeneity across firms in determining the relationships of selected variables with WCM efficiency.

The results highlight that manufacturing firms operate at around 50 percent efficiency, which is constant throughout the study period. Furthermore, among the selected variables, yield, earnings, age, size, ability to create internal resources, interest rate and gross domestic product (GDP) significantly affect WCM efficiency.

Instead of the traditional models used for assessing efficiency, the SBM-DEA model is unit-invariant and monotone for slacks, implying that it can handle zero and negative data, which overcomes the incapability of prior DEA models. Hence, this provides accurate efficiency scores for robust analysis. Additionally, this paper provides a holistic working capital model recognizing firm-specific and macro-level determinants for a more explicit estimation of the relationship between WCM efficiency and the selected determinants.

There is widespread consensus that unchecked growth of e-waste is a major challenge to global sustainability transition. Current research has failed to connect e-waste with principles of circularity and sustainability from the consumption perspective. This paper aims to answer the following questions: What kind of environmental behaviors (EBs) exist among consumers in relation to e-waste?; In what ways are these consumers different from and similar to each other based on their EBs in relation to e-waste?; How do consumers and their EBs contribute to sustainable waste management?

Based on primary data from an Indian sample of information and communication technology consumers, EBs relevant to e-waste management are identified. In the next stage, a behavior-based segmentation and profiling of consumers is performed.

The first phase of analysis produced eight distinct EBs which were then used in the next phase to obtain a consumer typology of three segments. The three consumer segments differed significantly with each other on general environmental behavior and awareness about e-waste.

The paper develops a comprehensive conceptual framework for studying the demand-side circularity transition for sustainable e-waste management.

For business stakeholders, findings of the study and the proposed framework can inform behavior change interventions to customize offerings for different right consumer segments.

The paper adds new knowledge to the intersectional area of e-waste, consumer behavior and sustainability through the development of consumer typology and a conceptual framework.

Working capital efficiency (WCE) is crucial for the sustainability of both large and small firms. This study aims to use the sample of micro, small and medium-sized enterprises (MSMEs) in India and tries to understand the critical determinants of WCE.

Using a fixed effect panel data model on a sample of 578 MSMEs (59 micro, 226 medium and 296 small firms), this study explores the relationship between the predictors of WCE. Additionally, the study adopted two metrics for measuring WCE among each type of firm (micro, small and medium).

Several firm-specific variables, including leverage (lever), firm age (AGE), firm size (Fsiz), profitability (Prof), extended payment terms (EPT), human capital (HCap), asset turnover ratio (ATR), reverse factoring (RF) and firm growth (FG), have a significant effect on working capital management efficiency (WCE). In contrast, tangibility (Tangib) and salary expenses (Sal) had an insignificant effect on working capital management efficiency.

The study is based on secondary data. Future studies may incorporate some primary data, which will facilitate qualitative analysis.

The studies explore the relationship between WCE and expenses in HCap, EPT, RF and Sal as the predictors for WCE, which was not studied earlier in MSMEs scenario, especially in case of developing nation.

Using the extant literature review, this paper aims to explore the relationship between gender, entrepreneurial education (EE) and entrepreneurial intention (EI) in the Indian context, which the authors believe is a novel approach to this research stream. The authors also use career preparedness as a control variable to examine this relationship.

Data were collected from 368 undergraduate students across four Indian universities (one exclusively for female students) through a standard structured questionnaire. Additionally, rather than examining, EI has been treated as a monolithic construct; however, the authors conceptualize it as comprising three different dimensions that include grand vision and risk-taking ability; opportunity exploitation; and ability to persevere. An additional analysis was conducted for the students who reported higher scores for “being well prepared for their careers” through their institutes’ academic programs and communities of entrepreneurs. The authors also interviewed some entrepreneurship instructors, who confirmed the present findings through their observations.

The findings indicate that, essentially, there is a positive relationship between EE and EI. The authors find that male students scored higher for the first two dimensions of EI but not the third. Additionally, the authors used career preparedness as a control variable for additional analysis. The authors observed that students with higher “career preparedness” reported a positive relationship between EE and EI, independent of gender, for all three dimensions of EI. Thus, it may be assumed that if a community of entrepreneurs needs to be developed in India, a focus on career preparedness is critical.

First, given that the present survey reflected a single moment in linking EE to EI (which may be considered a limitation of the study), future researchers might focus on a longitudinal approach. Second, all the respondents are attending urban universities (and, as such, very likely belong to the upper middle class of Indian society). The financial divide between urban and rural India is well known; as such, the results might be different if the sample was drawn from rural and poor India.

The salience/value of this study lies in the conceptualization of EI comprising three sub-constructs to understand the impact of formal EE (with three sub-constructs) on EI. The focus on career preparedness for a female student is a new direction of inquiry with respect to entrepreneurial intention.

This study aims to integrate the theory of planned behavior (TPB) and the value-belief-norm (VBN) theory into a meta-analytic framework to synthesize green consumption literature.

By integrating the findings from 173 studies, a meta-analysis was performed adopting several analytical methods: bivariate analysis, moderation analysis and path analysis.

VBN- and TPB-based psychological factors (adverse consequences, ascribed responsibility, personal norms, subjective norms, attitude and perceived behavioral control) mediate the effects of altruistic, biospheric and egoistic values on green purchase intention. Further, inconsistencies in the proposed relationships are due to cultural factors (i.e. individualism-collectivism, power distance, uncertainty avoidance, masculinity–femininity, short- vs long-term orientation and indulgence-restraint) and countries’ human development status.

The authors selected papers published in English; hence, other relevant papers in this domain published in other languages might have been missed.

The findings are useful to marketers of green offerings in designing strategies, i.e. specific messages, targeting different customers based on countries’ cultural score and human development index, to harvest positive customer responses.

This study is the pioneering attempt to synthesize the TPB- and VBN-based quantitative literature on green consumer behavior to resolve the reported inconsistent findings.

This study aims to develop a robust theoretical framework to explain the impact of hotels’ green brand authenticity on guests’ perceptions of well-being, customer engagement and approach behaviors toward green brands.

In this study, the authors examined the effect of green brand authenticity on perceptions of well-being, customer engagement and approach behaviors toward green brands. For the quantitative empirical analysis, 352 samples were used. Green brand authenticity integrates quality commitment, heritage, uniqueness and symbolism as high-dimensional factors.

The study conceptualizes green brand authenticity as a multi-dimensional phenomenon with four dimensions: quality commitment, heritage, uniqueness and symbolism. The results showed that green brand authenticity has a positive effect on hotel guests’ perceived well-being and behavioral intentions. Interestingly, environmental values did not have a statistically significant regulatory role, while green behavior in everyday life had a partial regulatory role.

This study aims to develop and empirically test a conceptual model that depicts the function of green authenticity in explaining customer responses to green brands. The results and the theoretical framework proposed in this study provide significant insights for researchers and practitioners in the hotel industry.

Further than evaluating brand authenticity generally, this study evaluates the authenticity of a brand's environmental protection efforts. As a result of the empirical analysis conducted in this study, the green brand authenticity of a hotel had a positive effect on customers’ emotional and behavioral aspects. This finding provided valuable and meaningful insights for green hotels and hotel brand-related research.

The study investigates the influence of perceived security (PS) on behavioral intention (BI) via the trust attitude process and explores the moderating effects of gender. PS in mobile wallets enhances user trust (TR), attitude (ATT) and intention (INT). Using a multiple and serial mediation model, both TR and ATT were found to mediate the relationship between PS and BI.

Drawing on the stimulus-organism-response (S-O-R) theory, the proposed conceptual model comprises PS, TR, ATT and BI. An online survey was conducted with a cross-sectional sample of 744 mobile wallet users in India. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the hypothesized relationships and test the mediation effects.

Results show that the stimulus, PS, has a positive and significant influence on TR and ATT, which eventually has a positive influence on BI. The research model explains 64.4 percent of the variance in BI. Further, both TR and ATT independently and parallelly mediate the relationship PS and BI. Lastly, gender is found to moderate the relationship between TR and BI and ATT and BI.

The research showed the importance of PS, TR and ATT towards mobile wallet adoption INTs. Further, the findings support the idea that developing TR and ATT is essential for shaping INTs. This suggests that mobile wallet service providers should invest in methods that not just enhance user TR but also reinforce a positive ATT towards the platform. To demonstrate TR, mobile wallet providers must ensure the confidentiality and privacy of user data, keep customer interests in mind and fulfill commitments. Lastly, for strengthening customer TR, excellent customer support is extremely important.

While prior researchers have majorly used technology acceptance model (TAM) and unified theory of acceptance and use of technology (UTAUT) models to explain adoption INTs, this study examines the relationship between PS, TR, ATT and BI through the lens of the SOR framework.

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.

Grounded in the stakeholder theory and signaling theory, this study aims to broaden the research agenda on environmental, social and governance (ESG) investing by uncovering public sentiments and key themes using Twitter data spanning from 2009 to 2022.

Using various machine learning models for text tonality analysis and topic modeling, this research scrutinizes 1,842,985 Twitter texts to extract prevalent ESG investing trends and gauge their sentiment.

Gibbs Sampling Dirichlet Multinomial Mixture emerges as the optimal topic modeling method, unveiling significant topics such as “Physical risk of climate change,” “Employee Health, Safety and well-being” and “Water management and Scarcity.” RoBERTa, an attention-based model, outperforms other machine learning models in sentiment analysis, revealing a predominantly positive shift in public sentiment toward ESG investing over the past five years.

This study establishes a framework for sentiment analysis and topic modeling on alternative data, offering a foundation for future research. Prospective studies can enhance insights by incorporating data from additional social media platforms like LinkedIn and Facebook.

Leveraging unstructured data on ESG from platforms like Twitter provides a novel avenue to capture company-related information, supplementing traditional self-reported sustainability disclosures. This approach opens new possibilities for understanding a company’s ESG standing.

By shedding light on public perceptions of ESG investing, this research uncovers influential factors that often elude traditional corporate reporting. The findings empower both investors and the general public, aiding managers in refining ESG and management strategies.

This study marks a groundbreaking contribution to scholarly exploration, to the best of the authors’ knowledge, by being the first to analyze unstructured Twitter data in the context of ESG investing, offering unique insights and advancing the understanding of this emerging field.