North Bengal University

The rapid growth of Artificial Intelligence (AI) has significantly disrupted the traditional finance domain. The application of AI in finance is referred to as financial intelligence. However, the theoretical understanding of why and how firms adopt financial intelligence remains underdeveloped. To address these research gaps, this study examines the key factors influencing the intention to adopt financial intelligence systems in the finance domain and investigates perceived firm performance associated with their adoption. We have developed a theoretical framework based on the Technology Acceptance Model (TAM) and formulated research hypotheses. To test these hypotheses, we used a structured, pre-tested instrument and collected 463 valid responses from finance professionals regarding their perceptions of financial intelligence systems adoption. We analysed the data using Partial Least Squares Structural Equation Modelling (PLS-SEM). Our findings indicate that effort expectancy, performance expectancy, attitude, perceived trust, organisational competency, and organisational innovativeness are significant predictors of the intention to adopt financial intelligence systems. In contrast, neither social influence nor perceived risk was found to be statistically significant. Notably, performance expectancy has a strong impact on the development of a positive attitude toward financial intelligence systems. We also discovered a partially significant mediating effect of attitude on the relationship between effort expectancy and performance expectancy concerning the intention to adopt financial intelligence systems. The adoption of financial intelligence systems has a positive impact on firm performance. These insights will help firms identify the key factors that influence the adoption of financial intelligence systems and develop effective strategies for their implementation. Additionally, they will encourage future research into governance mechanisms that facilitate the responsible integration of these systems. This study serves as a valuable guide to the effective adoption of financial intelligence systems and represents the first comprehensive research on this topic among Indian firms, utilising the adapted UTAUT model, an extension of the TAM.

Livelihood vulnerability arises not only from climatic stressors but also from entrenched socioeconomic inequalities and generational marginalization shaping household resilience. Moving beyond climate-centric models, this study examines livelihood vulnerability among an Indigenous community in India through a socioeconomic-institutional lens, operationalized via exposure, sensitivity, and adaptive capacity. Applying a tridimensional framework with thirty-four indicators, the study assesses microscale socioeconomic vulnerability. Findings reveal strong spatial clustering, reflecting localized marginalization and unequal service access. Over one-third of households experience moderate exposure, and nearly one-third face high to extreme exposure, particularly in central, western, and southern areas marked by isolation and poor infrastructure. Nearly one-third of households fall into high and extreme sensitivity categories due to low education and limited services. Importantly, adaptive capacity is uneven; about four-fifths of households fall within moderate to high vulnerability levels. Key drivers include chronic illness, food insecurity, inadequate housing, limited vocational training, educational deprivation, poor service access, and caregiving burdens. Although community trust and public distribution systems offer partial support, adaptive capacity remains constrained by low asset ownership, weak digital access, and fragile institutional linkages. The study draws attention to the need for context-specific interventions that address structural barriers and spatial inequalities. It also highlights the importance of integrating socioeconomic and spatial dimensions into vulnerability assessments to promote more equitable development policies for the left-behind populations of the Global South.

Dendrocalamus strictus popularly known as ‘Male bamboo’ is a multipurpose bamboo which is extensively utilized in pharmaceutical, paper, agricultural and other industrial implements. In this study, in vitro regeneration of D. strictus through nodal culture has been attempted. Murashige and Skoog’s medium supplemented with 4 mg/l BAP was found to be most effective in shoot regeneration with 3.68 ± 0.37 shoots per explant. The effect of Kn was found to be moderate. These hormones also had considerable effect on the shoot length. The highest shoot length after 6 weeks (3.11 ± 0.41 cm) was noted with 5 mg/l BAP followed by 3.07 ± 0.28 cm with 5 mg/l Kn, while decrease in the shoot length was noted with other treatments. The effect of IBA and NAA individually or in combination at different concentrations on rooting was evaluated. The highest number of root (1.36 ± 0.04) was regenerated on full-strength MS medium supplemented with 3 mg/l NAA, while maximum length of 1.64 ± 0.03 cm of roots was recorded with combination of 1 mg/l IBA and 3 mg/l NAA. Tissue-cultured plants thus obtained were successfully transferred to the soil. The clonal fidelity among the in vitro-regenerated plantlets was assessed by RAPD and ISSR markers. The ten RAPD decamers produced 58 amplicons, while nine ISSR primers generated a total of 66 bands. All the bands generated were monomorphic. These results confirmed the clonal fidelity of the tissue culture-raised D. strictus plantlets and corroborated the fact that nodal culture is perhaps the safest mode for multiplication of true to type plants.

Rapid urbanization significantly influences the quantity and quality of natural water resources. This study investigates the impact of urban growth on the water quality of the Balason and Mahananda rivers in Siliguri. To evaluate the Water Quality Index (WQI), water samples for eleven physicochemical parameters were collected from seventeen selected sites. An upper - urban - downstream gradient approach was adopted, integrating statistical and GIS-based techniques for comprehensive analysis. WQI results indicated a marked decline in water quality during the lean season, particularly in densely populated urban stretches. Average WQI scores ranged from good to poor, with the most degraded sites located within the central urban zone. Statistical assessments revealed significant spatial variation in water quality across upstream, urban, and downstream segments. This research highlights that improving urban water quality aligns directly with SDG 6 (clean water and sanitation), suggesting the urgent need for integrated water resource management and sustainable urban planning.

The present manuscript is aimed at studying the thermal shock response in a nonlocal orthotropic double-porous thermoelastic medium under the purview of the three-phase-lag (TPL) model, the Green-Naghdi model of type III (GN-III), and the Lord-Shulman (LS) model of hyperbolic thermoelasticity based on Eringen's nonlocal elasticity theory. To solve the current problem, a vector matrix equation is created, and the eigenvalue approach technique is used. To illustrate and validate the theoretical results, numerical simulations have been performed, and various physical quantities for time and distance are presented graphically. It also describes how various physical quantities are affected by nonlocal, phase-lag, and void parameters. The features of various physical quantities for nonlocal versus local, double void versus without void, and first kind of void versus second kind of void are discussed. Some 3D surface curves are also presented, and it can be observed that they satisfy the surface boundary conditions.