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Investigating the factors influencing health equity
This chapter determines various determinants of global health and wellbeing in relation to the Third Sustainable Development Goal of ensuring "Good Health and Well-being." It investigates the contribution of cultural, environmental, systemic, and socio-economic factors to health outcomes and the expected international cooperation towards closing the gaps in the health of nations. In the chapter, a qualitative methodology has been followed based on secondary data in the form of two peer-reviewed literature pieces. From there, the synthesis has been done to find an overview of global healthcare governance structures, their effectiveness, and major challenges.The study shows that while mechanisms of global health care governance are rated as being moderately effective, wide gaps in funding, transparency, and equitable access to services persist. Respondents also showed the need for increased international collaboration, especially on social determinants of health and strengthening health systems in low-and middle-income countries. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Investigating the heterogeneity of ESG investors: evidence from emerging economies
Purpose This study aims to examine the heterogeneity in behavioural characteristics of retail investors regarding sustainable investments, identifying patterns of convergence and divergence in sustainability-oriented market behaviours. By developing and validating specialized indices for environmental, social and governance (ESG) preferences, investor sentiments, performance perceptions, investment intentions, subjective norms, cognitive biases and greenwashing concerns, this research investigates how socio-demographic factors influence these indices through assessing heterogeneity across investor segments. Design/methodology/approach The authors develop and validate five ESG behavioural indices capturing multiple dimensions of sustainable investment behaviour. Data were collected through a comprehensive survey of 511 active retail investors in the Indian stock market. Heterogeneity analysis was conducted to identify variations in behavioural characteristics across the sample. The authors use quantile regression analysis to assess heterogeneity across demographic segments (age, income, gender, employment, education and investment experience), examining how relationships vary across the conditional distribution of ESG behavioural dimensions. Findings The analysis reveals heterogeneity in ESG investment behaviour across demographic segments. Age consistently reduces ESG engagement across all dimensions, while higher income enables selective sustainability preferences but increases investment irrationality. Gender creates divergent ESG orientations, with distinct patterns in environmental versus social priorities. Employment status and education facilitate ESG adoption through stability and social learning mechanisms, whereas investment experience paradoxically generates both sophisticated awareness and fundamental skepticism. Critically, performance perceptions emerge as the primary determinant mediating demographic influences on ESG preferences, establishing that sustainability investment behaviour is instrumentally rational rather than value-expressive in emerging markets. Practical implications The findings provide insights for enhancing sustainable investment participation. Financial institutions should develop targeted educational programmes to address knowledge gaps, as awareness significantly influences ESG preferences. Recognizing investor heterogeneity is essential younger, high-income investors respond to performance narratives, while older investors seek transparency. Addressing greenwashing concerns through standardized reporting and third-party certifications builds trust. Leveraging social influence through choice architecture and behavioural nudges can overcome decision-making barriers. Income-based strategies should include structured ESG portfolios for high-income investors prone to impulsivity, while providing educational support on stable returns for price-sensitive retail investors in emerging markets. Social implications The identified behavioural market failure in sustainable investing has important implications for the development of sustainable finance policies in emerging markets. Addressing the divergence in sustainability views could accelerate the transition towards more sustainable capital markets and contribute to broader sustainability goals. The findings highlight the need for targeted initiatives and policy interventions to bridge the gap between ESG preferences and actual investment behaviour. Originality/value This study advances sustainable finance through three contributions. First, the authors develop and validate multidimensional ESG behavioural indices capturing preferences, sentiments, perceptions, intentions and irrationality among retail investors. Second, the authors establish demographic heterogeneity as a structural market characteristic challenging the homogeneous investor assumption. Third, the authors theorize performance primacy as the fundamental mechanism driving ESG preference formation, demonstrating instrumental rationality rather than value-expression. These frameworks, validated through quantile regression analysis, provide actionable insights for policymakers and practitioners designing targeted interventions across demographically diverse investor segments in emerging markets. 2026 Emerald Publishing Limited -
Investigating the Impact of Emotional Contagion on Customer Attitude, Trust and Brand Engagement: A Social Commerce Perspective
Social Commerce networks are a powerful platform for spreading positive and negative emotional contagion, which is affecting users from different perspectives, i.e., psychology, attitude, buying decision. Emotional contagion is the phenomenon of having a person's emotions and behaviours directly trigger similar emotions or behaviour in other people. This research proposes a model to analyze the factors influencing emotional contagion that, in turn, impact consumer's attitudes, trust, and brand engagement. This study used a survey approach using a structured questionnaire. Primary data was collected from 174 social media users who shop online. The proposed model was tested using multiple regression analysis. The results demonstrated that effective content, visual or text, triggers customers' emotional contagion, influencing customer attitude and trust leading to brand engagement. The research study's findings can be used for deciding on content strategies of advertisements pertaining to social commerce. 2022 Academy of Taiwan Information Systems Research. All rights reserved. -
Investigating the in-flight performance of the UVIT payload on AstroSat
We have studied the performance of the Ultraviolet Imaging Telescope payload on AstroSat and derived a calibration of the far-ultraviolet (FUV) and near-ultraviolet (NUV) instruments on board. We find that the sensitivity of both the FUV and NUV channels is as expected from ground calibrations, with the FUV effective area about 35 per cent and the NUV effective area about the same as that of GALEX. The point spread function of the instrument is on the order of 1.2-1.6". We have found that pixel-to-pixel variations in the sensitivity are less than 10 per cent with spacecraft motion compensating for most of the flat-field variations. We derived a distortion correction but recommend that it be applied post-processing as part of an astrometric solution. 2018 The Author(s). -
Investigating the Interaction of Digital Capabilities, Sustainable Practices, Product Quality, and Customer Satisfaction in Perishable Food Supply Chains
To ensure efficient delivery of perishable food products, food supply chains (FSCs) have advanced the usage of recent technologies and started integrating them into logistical systems. This study examines the interplay between digital capabilities, sustainable practices, logistical networks, and customer satisfaction in perishable FSCs through a cross-sectional survey of 416 Indian consumers. It draws on a comprehensive literature review that highlights the potential variables and their impacts on the perishable FSCs. The data was collected using a five-point Likert-scale questionnaire, analyzed using partial least squares-structural equation modeling (PLS-SEM), and robustness was ensured by Harman's test. The study integrates value percept theory (VPT) to develop a comprehensive conceptual framework that explains how digital capabilities and sustainable practices enhance product quality and customer satisfaction in perishable FSCs. The findings from the study explicitly support the positive moderating role of logistical networks in the relationship between digital capabilities and product quality. The findings can assist management professionals operating in the perishable food sector in enhancing their theoretical and practical comprehension of the profound influence exerted by digital capabilities, sustainable practices, and logistical networks on the crucial nexus between product quality and customer satisfaction. 2026 ERP Environment and John Wiley & Sons Ltd. -
Investigating the Relationship Among Demographic Profiles and Customer Engagement Cluster Groups on Mobile E-Commerce Applications
Recently, the world is moving towards a new future where most of the communications is happening through the Internet. The use of smartphones has rapidly increased for the past two decades. People show high interest in adopting new technologies in their day to day lives. In order to connect very closely with the customers, many retailers started to expand their businesses with the help of mobile e-commerce applications. The extensive use of mobile phones and the favorable independence of time and location promised the future of mobile e-commerce applications. This created a necessity for the online businesses to understand the factors that influence customer engagement in mobile e-commerce applications. This research was aimed to provide a clear understanding on the factors that affect customer engagement in mobile e-commerce applications with respect to the age, frequency and experience of consumers. The factors like customer satisfaction, utilitarian value, ease of use, hedonic value, trust and personalized recommendations were taken into account to understand their influence on the customer engagement in mobile e-commerce applications. These factors were studied with respect to the age group of consumers and they were classified into four groups as follows: below 20years, 21 to 35years, 36 to 50years, 51years and above. Also, the effect of frequency and experience in using mobile applications towards customer engagement in mobile e-commerce applications were studied in this research. The questionnaire with 29 items were sent to 250 respondents through e-mail and other social media platforms and out of which 151 responses were taken into account for the analysis part. ANOVA tests were performed to analyze the influence of age, frequency and experience in using mobile e-commerce applications on the factors like customer satisfaction, utilitarian value, ease of use, hedonic value, trust and personalised recommendations that affect customer engagement in mobile e-commerce applications. To further analyze and group the respondents engagement in mobile e-commerce applications, k-means cluster analysis was performed. Later, ANOVA tests were performed to understand the differences among demographic profiles and cluster groups in customer engagement. From these tests, the moderating effect of age, frequency and experience factors on customer engagement were analyzed. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Investigating the Role of Intelligent HR Systems in Enhancing the Relationship Between Employee Engagement and Performance: A Computational Perspective for Economic Development
This study examines the impact of Human Capital Management Practices (HCMPs), specifically employment opportunities, training, and rewards, on employee performance. Work engagement is a mediating component in this analysis. The results indicate that HCMPs enhance employee performance by increasing work engagement. This demonstrates the strategic role that HR plays in developing a motivated workforce that propels business success. Engaging employees is essential for every business to succeed in providing its clients with high-quality services; engaged employees treat customers royally and talk about the success of their organization. According to research, engaged workers are 87% less likely to quit than disengaged ones. They tend to maintain regular attendance without absenteeism, treat their work with care, and contribute to the business's growth. As a result, they are often rewarded for their dedication and commitment. The manager must communicate with employees, listen to their concerns, and reward them for improved work. Employee engagement, on the other hand, improves performance and helps the company expand. It is bothering because employees who are less engaged are more likely to leave than those who are more engaged. When workers are engaged, they work hard, do better at their jobs, and stick with the company for years. To measure the level of engagement, research indicates that companies with poor employee engagement experienced an average operational income decrease of almost 32%, created a foundation for engagement to occur easily, set goals and tracked employee engagement, and encouraged continued high engagement levels with routine public recognition for employees who are engaged. The primary task is to break the culture of dependency on leaders and develop teamwork with visibility to accountability and engagement. This empowers employees to solve their issues and provides opportunities for coaching and mentoring. High employee engagement results in high organizational performance. The Research Publication. -
INVESTIGATING THE ROLE OF UTAUT2 IN THE USER SATISFACTION AND CONTINUED USAGE OF MOBILE FITNESS APPS: EVIDENCES FROM INDIA
This study examines mobile fitness app (MFA) adoption and use using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). CB-SEM analysis of 445 respondents through IBM-AMOS was conducted. UTAUT2 constructs were examined including, consumer satisfaction, e-loyalty, and mobile fitness app reuse. Also, the roles of socio-demographic factors as control variables were examined. The results indicated that UTAUT2 significantly influenced user perception of MFAs. UTAUT2 constructs increased user satisfaction. User satisfaction positively and significantly influenced e-loyalty and app reuse. The study also suggests future research on UTAUT2's role on MFAs. The study highlights UTAUT2 by revealing the constructs that influence MFA adoption, and selected consequences. (2024), (Amity University). All rights reserved. -
Investigating the transport flexibility measures for freight transportation: a fuzzy best-worst method approach
Unpredicted disruptions force organisations to ensure flexibility for fulfilling customer demand. Enabling flexibility along the transportation system is the most suitable solution for unpredictable disruptions. Flexibility, being a potential element, requires more attention to gain competitive advantages. In this study, an effort has been made to investigate different transport flexibility measures (TFMs) related to freight transportation. Initially, an extensive literature survey is performed to identify different TFMs linked with the supply chain and logistics domain. Further, an integrated fuzzy best-worst method (FBWM) has been adopted to prioritise the identified TFMs and sensitivity analysis is performed to ensure robustness of the model. The findings of the study reflect mode, fleet, vehicle and speed flexibility as the significant flexibility measures for freight transportation. This study will help practitioners, managers and decision-makers associated with freight transportation to make better decisions to ensure flexibility in the freight transportation system. Copyright 2022 Inderscience Enterprises Ltd. -
Investigating the Use of Natural Language Processing in Electronic Medical Record
Natural language processing (NLP) implemented in digital scientific records (EMRs) can substantially enhance the nice and efficiency of affected person care. The purpose of NLP implemented in EMRs is to extract applicable facts from affected persons' notes written in a human language together with English. This information can then be stored in a suitable structured form for further evaluation and records mining. NLP has been carried out in the clinical field for the reason that Fifties as a green approach for retrieving textual content-based data and reading interactions among affected persons and healthcare professionals. With the arrival of electronic facts, NLP has come to be extra extensively applied for the diffusion of purposes, inclusive of automatic coding, scientific choice aid, and medical doctor order access. This summary makes a of exploring the usage of NLP in EMRs. The scope of this research consists of an evaluate of present NLP technologies and their software in EMRs. It additionally outlines a number of the present-day demanding situations inside the use of NLP for clinical information and shows capability answers. Finally, the potential applications of NLP-driven EMRs are discussed, inclusive of making use of in-health practitioner order entry, scientific choice assistance, and population health control. 2024 IEEE. -
Investigating Various Meshing Techniques in Computational Fluid Dynamics (CFD) for their Impact on Heat Transfer Parameters of Fins
The study explores the effects of different meshing techniques on the accuracy and efficiency of heat transfer and fluid dynamics simulations in a finned heat exchanger. A 3D-CAD model, developed in Autodesk Fusion, analysed aluminium fins subjected to a heat flux of 5903 W/m under flow conditions with Reynolds numbers from 8490 to 23300. Four mesh types Tetrahedral, Polyhedral, Hexacore and Poly-Hexacore were compared. Mesh independence analysis showed that Hexacore meshes, especially Mesh Set-F (4,568,602 elements), delivered high accuracy in predicting Nusselt numbers and pressure drops, making them suitable for detailed simulations. Polyhedral meshes, particularly Mesh Set-E (498,044 elements), exhibited the best computational efficiency, ideal for resource-conscious analyses. The study underscores the trade-offs between accuracy and computational cost. Hexacore meshes are recommended for precise evaluations, while Polyhedral meshes are better suited for preliminary designs or time-sensitive applications. However, Hexacore meshes require higher computational resources, limiting their practicality for large-scale or real-time simulations. Major Findings: The findings are valuable for industries such as Heating Ventilation Air Conditioning (HVAC), automotive and electronics cooling, where heat exchanger performance predictions are crucial. By tailoring mesh configurations to specific project needs, engineers can optimise simulation accuracy and computational efficiency, enhancing design workflows and outcomes. The study provides a framework for balancing accuracy and cost, offering insights into mesh selection for effective thermal and fluid performance evaluations. 2025, Informatics Publishing Limited. All rights reserved. -
Investigating wave propagation across loosely bonded interfaces in visco-piezo composites with flexoelectricity in LiNbo3 and AlN
This study compares the transference of surface seismic waves at the loosely bonded interface of a visco-piezo composite structure using two materials, lanthanum niobate (LiNbO 3) and aluminium nitride (AIN). The structure comprises a viscoelastic layer bonded to a piezoelectric substrate, incorporating the flexoelectric effect. The shear response of the upper layer is modelled using three rheological models: Kelvin-Voigt, Maxwell and Newton. An analytical separable variable method is employed to derive complex dispersion relations for both electrically open- and short-circuit conditions. The numerical analysis focuses on the influence of key parameters, such as bonding conditions and interfacial parameters, on phase velocity and attenuation coefficients in both materials. Results indicate that AIN shows higher phase velocities, while LiNbO 3 demonstrates a stronger impact on attenuation, particularly in the Kelvin-Voigt model. In addition, the flexoelectric effect significantly alters the wave behaviour in both materials, impacting both phase velocity and attenuation. This comparison reveals important differences in wave propagation behaviour, which is crucial for the development of devices like sensors, actuators and energy harvesters. The study offers new insights into piezo-flexo coupling and its potential applications in advanced piezoelectric systems. 2025 The Author(s). -
Investigation into the Mechanical, Fatigue and Superplastic Characteristics of Shape Memory Alloys (SMA) in CuAlMn, CuAlBeMn, and CuAlFeMn Compositions and Their Composite Variants
Shape memory alloys (SMAs) exhibit high sensitivity to compositional changes in terms of their super elasticity, shape memory effect, and transition temperatures. A deeper comprehension of SMA composition and its impact on mechanical properties can be attained by differential scanning calorimetry. The current study uses experimental work to assess the energy absorption capacity, mean fracture width, residual strength, and cracking strength of samples made of short shape memory alloy (SMA) fibers that are randomly distributed on the specimens tensile side. In this investigation, three samples were synthesized based on the Cu, Al, and Mn proportions found in CuAlMn shape memory alloys (SMA1, SMA2, and SMA3). Moreover, three samples with different ratios of Cu, Al, Mn, Be, and Fe were synthesized for the shape memory alloys CuAlBeMn and CuAlFeMn (SMA2, and SMA3). The synthesized CuAlMn, CuAlBeMn, and CuAlFeMn SMA alloys showed good strain recovery, ranging from 90 to 95%. The martensite that forms and changes when the alloys are heated and quenched mostly controls the strain recovery by the corresponding SMAs. SMA 2 of the CuAlBeMn has a greater strain recovery rate, rising by 8.5% and 44.38%, respectively, in comparison to SMA 1 and SMA 3. CuAlBiMn shape memory alloys demonstrated superior super elasticity and martensite stability in comparison to SMA 1 and SMA 2 respectively. SMA 1 and SMA 2 demonstrated greater residual strength, cracking strength, and energy absorption capacity for all fiber volume fractions. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Investigation of 1,1?-Binaphthalene-2,2?-diamine as an organic electrode for High-Performance aqueous rechargeable Lithium-Ion batteries
Aqueous rechargeable lithium-ion batteries (ARLIBs) are the most remarkable energy storage devices currently available for various applications with a growing demand for high-performance batteries. The role of electrochemical analysis for lithium-ion batteries, especially electrode reactions, is widely observed in many fields of electrochemical techniques, such as cyclic voltammetry (CV), which is one of the methods that is possible to know the electrochemical factors affecting the reaction voltage and reversibility. This study contributes to the ongoing development of ARLIBs by investigating 1,1?-binaphthalene-2,2?-diamine (BINAM) as a potential organic electrode material. The comprehensive structural and electrochemical characterization is emphasized by the principle of CV and its applications to better understand the electrochemical reactions and the battery performance results, highlighting the viability of BINAM for future ARLIB applications. The cell BINAM | Sat.Li2SO4 | LiMn2O4 delivered its specific 325/155 mAhg?1 capacity and columbic efficiency of ? 9285 %. These findings underscore the importance of considering organic electrode materials and their unique advantages in enhancing the efficiency, sustainability, and cost-effectiveness of lithium-ion battery technology. 2025 Elsevier B.V. -
INVESTIGATION OF ANTECEDENTS AND CONSEQUENCES OF USEFULNESS IN ONLINE TRAVEL COMMUNITIES: THE MODERATING ROLE OF DECISION MAKING STAGE
This study examines the perceived usefulness (PU) of online discourse and the decision-making behavior of users in Online Travel Communities (OTCs). Partial least squares structural equation modeling (PLS-SEM) was used on secondary data available in OTCs in the form of 852 threads to empirically test the proposed integrated model. The antecedents of the perceived usefulness of online travel communities were found to be the argument quality and credibility. These influence the PU of user-generated content significantly and are helpful in information adoption in OTCs. The PU of OTC discourse positively impacts travelers' information adoption and decision-making. The current study offers implications for OTCs and online service providers for enhancing the usefulness of user-generated content in OTCs and social media sites, leading to online information use and travel decision-making. Prior literature has explored the nature and magnitude of the influence of electronic word-of-mouth (eWOM) on information adoption and intention to use information for travel purchases from users' perspectives and has investigated the PU of third-party travel sites. This paper is an effort to examine PU and decision-making by analyzing the User-Generated-Content (UGC) posted by the actual users. 2023 Akdeniz University Publishing House. All rights reserved. -
Investigation of Brain Tumor Recognition and Classification using Deep Learning in Medical Image Processing
A brain tumour is the growth of brain cells that are abnormal, some of which may progress into cancer. Magnetic Resonance Imaging (MRI) scans are the method used most frequently to detect brain tumours. The brain's abnormal tissue growth can be seen on the MRI images, which reveal. Deep learning and machine learning techniques are employed to identify brain tumours in a number of research publications. It only takes a very short amount of time to predict a brain tumour when these algorithms are applied to MRI images, and the increased accuracy makes patient treatment simpler. Thanks to these forecasts, the radiologist can make quick decisions. The suggested approach employs deep learning, a convolution neural network (CNN), an artificial neural network (ANN), a self-defined neural network, andthe existence of brain tumor. 2022 IEEE. -
Investigation of Cervical Cancer Detection from Whole Slide Imaging
Early cancer detection is critical in enhancing a patient's clinical results. Cervical cancer detection from a large number of whole slide images generated regularly in a clinical setting is a complex and time-consuming task. As a result, we require an efficient and accurate model for early cancer diagnosis, especially cervical cancer as it can be fully prevented if detected in an early stage. This study focuses on in-depth writing on current methodologies for cervical cancer segmentation and characterization from the whole cervical slide. It combines the state of their specialty's performance measurement with the quantitative evaluation of cutting-edge techniques. Numerous publications over the last eleven years (2011-2022) clearly outline various cervical imaging methods over multiple blocks. And this review shows different types of algorithms used in each processing stage of detection. The study clearly indicates the advancements in the automation field and the necessity of the same. Published under licence by IOP Publishing Ltd. -
Investigation of corrosion behavior of Cenosphere reinforced iron based composite coatings
In the present study cenopshere was reinforced with FeCrNiC (Metco 42C) as matrix material and prepared four different feedstock powders such as FeCrNiC+0%Cenosphere, FeCrNiC+5%Cenosphere, FeCrNiC+10%Cenosphere and FeCrNiC+15%Cenosphere were coated by plasma spray technique on T22 substrate. Evaluation of the substrate and coatings potential under salt spray test was performed. Dense fog of 5% NaCl salt water was used to create a corrosive atmosphere within the chamber. The salt water's pH was kept constant at 6.57. The materials that underwent corrosion were examined using X-ray diffraction (XRD), and scanning electron microscopy (SEM). The FeCrNiC+15%Cenosphere and FeCrNiC+10%Cenosphere coatings exhibited reduced weight loss during a 168-h corrosion test compared to the FeCrNiC+5%Cenosphere, FeCrNiC coatings, and substrate. The excellent chemical stability and corrosion resistance of Cr23C6, SiO2, NiO, and Cr2O particles contribute to gradually avoid the formation of red rust on Fe-based coated samples with exposure approaches to 52 and 130 h. 2024 The Authors -
Investigation of detoxification nature of activated carbons developed from Manilkara zapota and de oiled soya
Heavy metals are poisonous and detrimental water contaminant. Their existence affects human beings, animals and vegetation as a outcome of their mobility in aqueous ecosystem, toxicity and nonbiodegradability. This work aimed at the development of new adsorbent in the detoxification of heavy metals using Manilkara zapota tree wood and de oiled soya. The study completely focused on the characterization of the developed activation in the view of using it as a adsorbent. The characterization of activated carbon was effected SEM analysis, FTIR, XRD analysis and surface area determination. Both the activation carbon have showed a tremendous characterization in their employability as adsorbent in adsorption of heavy metals in aqueous solution. 2019 Elsevier Ltd. All rights reserved. -
Investigation of dielectric properties of indigenous blended ester oil for electric system applications
The insulation condition of a transformer decides the longevity of the equipment. The unpredicted failure of power transformer will lead to major disaster in the distribution network and it affects both environment and public safety. Nowadays synthetic oil and natural esters are alternatives to transformer oil because of the biodegradable nature. In this paper, investigations were carried out to study the performance of the blended ester. The different properties investigated were viscosity, breakdown voltage, flash point, dielectric dissipation factor and moisture content. Comparisons of the properties were made between mineral oil, vegetable oil without additives and with additives. Further Investigation was carried out to study the impact of antioxidants and degasification. The results indicated that the addition of antioxidants and degasification of the vegetable oil improve significantly its voltage withstanding capacity. The Indigenous oil is code named as DM; Indigenous oil with DBPC is codenamed as DM1, Indigenous oil with BHA is codenamed as DM2. The results have been tabulated and found to be satisfactory. 2020 ASTES Publishers. All rights reserved.