International Journal of Innovation Studies
The International Journal of Innovation Studies (IJIS) is a peer-reviewed academic journal that focuses on enhancing the comprehension and application of innovation across multiple fields and disciplines. It provides a forum for researchers, scholars, practitioners, and policymakers to discuss groundbreaking research, theories, methodologies, and practical insights related to innovation. In addition to publishing high-quality research articles, IJIS also seeks to foster interdisciplinary collaboration and dialogue. By bringing together diverse perspectives and expertise, the journal aims to drive forward the frontiers of innovation studies, offering valuable contributions to both theoretical and practical advancements in the field.
DEVELOPMENT OF AN ENHANCED MULTI-HOP PROTOCOL FOR OPTIMIZATION OF NETWORK EFFICIENCY IN WIRELESS SENSOR NETWORK USING REINFORCEMENT LEARNING
The most prominent recent developments in distributed computing have utilized Wireless Sensor Networks (WSNs) for real-time data acquisition and monitoring of environmental, industrial, health, and urban environments. Despite such innovative applications, their continued use is hampered by limitations such as energy efficiency, maximization of network lifetime, and reliable multi-hop communication. A large part of existing research on WSN routing protocols is heuristic based and static, which, in turn, facilitates the wasteful use of energy in the network, quick depletion of the operating nodes of the network, and overall loss of network efficiency. This research proposes the development of a multi-hop routing protocol and network reinforcement learning WSN routing protocol. The new protocol uses Q-learning and energy-aware routing metrics to make decisions at each sensor node to create an optimal balance of energy, latency, and packet delivery ratio. The protocol was tested on the NS-3 network simulator in direct comparison to LEACH, PEGASIS, and AODV routing protocols, and demonstrated 35% network lifetime extension, 42% reduction in end-to-end delay, and 28% improvement in packet delivery ratio. Contributions of this study include the construction of a new state-space model for WSN routing, a distributed reinforcement learning framework that adapts to the resource limitations of the sensor nodes, and the development of an adaptive reward function that adjusts to the state of the network. The results confirm the validity of using AI techniques combined with standard networking protocols to tackle the complex problems of WSN deployments.
OKAFOR Anthony Chinedu (Ph.D) DAUDA, Umar Suleiman (Ph.D) OHIZE, Henry O. (Ph.D) KOLO, Jonathan G. (Ph.D) AJIBOYE, Johnson Adegbenga (Ph.D)
FIBRE-X DEPLOYMENT AND ITS IMPACT ON E-LEARNING AND TELEWORKING IN RURAL UNIVERSITIES IN SOUTHEAST NIGERIA
This study investigated the link between the provision of Fibre-X broadband and the use of e-learning and the productivity from teleworking amongst a cohort of faculty and students at four rural Nigerian universities in South East Nigeria. Using quantitative methods of a pre-post longitudinal quasi-experimental study design with matched participants (n=342) surveyed at two points in time (at baseline and 12 weeks post network provisioning), there were statistically significant improvements in both e-learning use (M=2.14 ± 31, p<0.001, d=2.08) and teleworking productivity (WPAI-adjusted score=41.26 ± 7.5, p<0.001). Fibre-X supplied a download speed of 48.3 mbps and 98.1% uptime using a single-mode fiber, XGS-PON architecture, up from 1.8 mbps and 61.2% uptime at pre-provisioning baseline. Hierarchical multiple-regression (including controls for demographics and device ownership; checked for multicollinearity with all VIF < 2.1; Harman single-factor bias test also passed) suggested bandwidth adequacy (=0.51, p<0.001) and network uptime (=0.38, p<0.001) were the best network predictors of engagement (controlling for age, digital skills, role and device possession). Analysis of differences in differences with two non-Fibre-X universities supported this finding (DiD=2.08 Likert scale points, p<0.001). In comparison to ten other related empirical studies from Africa, Asia and South America, there were no clear findings of policy or methodology difference which may have accounted for variation in results, with the study providing the strongest link between infrastructure and usage. Gaps, particularly with electricity (61.4% of the time there was a power cut in the previous 24 hours) and devices (38.6% of students reported that they did not own a device) will need addressing if all areas of the transformation agenda are to be met.
Okeke Ogochukwu C., Mgbeafulike Ike J., Ezenwegbu Nnamdi C., Okonkwo Chinonso J, Chieme Gabriel C., Anunwa Anthony Alexander & Onukwu Petrus Oge
EXAMINING HOW SELF-SERVICE TECHNOLOGY SHAPES CUSTOMER SATISFACTION AND LOYALTY IN ZAMBIA’S PAY-TV INDUSTRY
This study examines how self-service technology (SST) service quality shapes customer satisfaction and loyalty in Zambia’s pay-TV industry, a context where empirical evidence on digital service experiences remains limited. Grounded in Expectation Disconfirmation Theory and the Technology Readiness and Acceptance Model (TRAM), the study adopted a positivist approach and cross-sectional survey design, collecting data from 410 pay-TV subscribers. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) in Jamovi to evaluate both measurement and structural models. The findings revealed that SST service quality dimensions, functionality, security, design, assurance, enjoyment, and customisation significantly enhance customer satisfaction, which in turn strongly predicts customer loyalty. The model demonstrated substantial explanatory power, accounting for 68% of the variance in customer satisfaction (R² = 0.680) and 75.9% in customer loyalty (R² = 0.759), with acceptable model fit (RMSEA = 0.089; SRMR = 0.056). Customer satisfaction also played a mediating role in translating SST experiences into sustained loyalty. The study highlights the strategic importance of delivering reliable, user-friendly, and personalised digital platforms to strengthen retention and competitive advantage in subscription-based services.
Mwiya Bruce, Kabaso Mwenya, Chawala Moffat, Manchisi Roy, Kaulungombe Bernadette, Kasonde Timmy, Mufalali Simasiku Mwiya, Maureen Kanchebele Sinyangwe, Regina Muduli, Mundia Mukwalikuli, and Joseph Mate
A NEW INTELLIGENT DECISION-MAKING FRAMEWORK FOR THE DIAGNOSIS OF CHRONIC CANCER USING CUBIC BIPOLAR PYTHAGOREAN FUZZY TOPOLOGY AND MULTI-CRITERIA DECISION – MAKING
Abstract: This paper introduces the Cubic Bipolar Pythagorean Fuzzy Set (CBPFS), a novel framework for modelling uncertainty using fuzzy intervals and fuzzy numbers. The fundamental properties and operations of CBPFSs are presented, followed by the development of Cubic Bipolar Pythagorean Fuzzy Topology (CBPF Topology) based on P-order. Classical topological concepts, including open sets, closed sets, interior, exterior, frontier, neighborhoods, bases, and subspaces, are extended to the CBPF setting. The proposed topology provides an effective framework for handling uncertainty in complex decision-making problems. Furthermore, a CRITIC–MAIRCA multi-criteria group decision-making method is developed within the CBPF topological framework and applied to chronic cancer disease diagnosis. Comparative analysis demonstrates the effectiveness and reliability of the proposed approach over existing methods.
Nagalakshmi Palanisamy, Maragathavalli Shanmugasundaram
FREQUENCY BASED RANKING SUPPORT VECTOR MACHINE (FRSVM) ALGORITHM FOR FUNGAL DISEASE DETECTION IN WHEAT CROP BASED ON KCC DATASET
Wheat (Triticum Aestivum) is one of the most significant rabi (winter) crops in India, serving as a staple food for over 40% of the population. It is the second most-produced cereal after rice, contributing significantly to India’s food security and agricultural economy. Early detection of fungal diseases in wheat is critical for preventing yield loss and ensuring effective crop management. Farmers frequently describe crop symptoms through natural language queries submitted to agricultural advisory systems such as call centers or digital platforms. Manually analyzing these queries is time-consuming and inefficient when dealing with large volumes of farmer requests. This research proposes a Frequency based Ranking Support Vector Machine (FRSVM) algorithm for automatic classification of wheat fungal disease symptoms collected from Kisan Call Center (KCC) farmer queries in ICAR dataset. The dataset contains symptom-based farmer queries covering five major fungal diseases of wheat: Head Scab, Karnal Bunt, Loose Smut, Powdery Mildew, and Pink Stem Borer. The proposed approach utilizes text preprocessing and TF-IDF feature extraction to convert farmer symptom queries into numerical representations. A custom Symptom Scaler emphasizes disease-specific keywords such as “rust,” “powder,” and “blight,” while the ranking score prioritizes vital disease based on their frequency. The proposed model achieved 93.5% accuracy, with other Machine Learning baseline algorithms including Naive Bayes, Random Forest, Linear SVM and XG Boost classifiers. The results demonstrate that frequency based ranking mechanism with machine learning provides an effective solution for automatic fungal disease identification from farmer queries, enable faster decision-making for wheat crop disease management and also significantly improves automated agricultural advisory systems.
MASTER YOUR MIND: THE POWER OF THOUGHT ANALYSIS IN SMART DECISION-MAKING
Decision-making is a fundamental cognitive process that shapes the quality of human life. In adulthood, where responsibilities and complexities increase, the ability to make wise and timely decisions becomes crucial. Thought analysis—the systematic observation, evaluation, and refinement of one’s thinking patterns—emerges as a powerful tool to enhance decision-making capabilities. This article explores the concept of thought analysis, its psychological and cognitive foundations, and its role in improving decision-making among adults, with special reference to the thought analysis method proposed by Vethathiri Maharishi.
EXAMINING THE DETERMINANTS INFLUENCING PORTFOLIO DIVERSIFICATION DECISIONS OF RETAIL INVESTORS: EVIDENCE FROM ZERODHA INVESTORS
Portfolio diversification is an important investment strategy through which investors distribute their funds across different assets and securities to manage risk and achieve more stable returns. Although several studies have examined the determinants of investment decisions among retail investors, limited research has specifically focused on the factors influencing portfolio diversification decisions among investors using the Zerodha investment platform. Therefore, the present study aims to examine the influence of risk tolerance, investment horizon, and investment goals on the portfolio diversification decisions of retail investors using Zerodha. The study also seeks to assess the relationship between these factors and the level of portfolio diversification among retail investors. The study was conducted among 247 retail investors who use Zerodha for investment purposes. Pearson correlation, ANOVA, and Chi-square tests were employed to analyse the data and examine the relationships between the selected variables and portfolio diversification decisions. The findings of the Chi-square test indicate a statistically significant relationship between investors' risk tolerance level and their level of portfolio diversification. The correlation analysis revealed a coefficient of 0.207, indicating a weak positive relationship between investment horizon and portfolio diversification. The findings further indicate that investment goals influence the portfolio diversification decisions of retail investors. The scope of the study is limited to retail investors using the Zerodha investment platform and focuses specifically on risk tolerance, investment horizon, and investment goals as determinants of portfolio diversification. The study concludes that portfolio diversification decisions are not determined solely by these factors and may also be influenced by investors' preferences, financial conditions, investment knowledge, and prevailing market fluctuations. Further research incorporating additional behavioural, financial, and market-related variables and larger samples may provide a more comprehensive understanding of portfolio diversification decisions among retail investors.
A EMPERICAL STUDY ON “ CONSUMER PREFERENCE AND SATISFACTION LEVEL” WITH SPECIAL REFERENCE TO HIMALAYA AT DELTA REGION IN TAMIL NADU
The present study titled “An Empirical Study on Consumer Preference and Satisfaction Level with Special Reference to Himalaya at Delta Region in Tamil Nadu” aims to examine the buying behaviour, brand preference, and satisfaction level of consumers toward Himalaya products. With the growing awareness of herbal and natural healthcare products, consumer expectations and brand perceptions have undergone significant change, especially in semi-urban and rural regions. This research focuses on understanding the factors influencing consumer preference, such as product quality, price, availability, packaging, and promotional activities, and their impact on the overall satisfaction level of customers.The study adopts a descriptive and empirical research design, using both primary and secondary data. Primary data were collected through structured questionnaires from consumers residing in the Delta Region of Tamil Nadu. Appropriate statistical tools such as percentage analysis, chi-square test, and correlation analysis were used to interpret the findings. The results indicate that product quality, herbal ingredients, brand trust, and reasonable pricing play a significant role in shaping consumer preference and satisfaction. Moreover, the study highlights that continuous product innovation and effective communication strategies enhance consumer loyalty toward Himalaya products.The findings of this research provide valuable insights for marketers to develop appropriate strategies for sustaining customer satisfaction and improving brand equity in regional markets like the Delta Region of Tamil Nadu.
FACTORS INFLUENCING THE BRAND PREFERENCE AND BRAND LOYALTY OF RURAL CUSTOMERS TOWARDS AUTOMOBILE INDUSTRY IN THIRUVARUR DISTRICT
The survey has been interpreted to analyse the impact of component causing the brand preference and brand loyalty on selected brands of passenger cars among rural customers in Thiruvarur region in the state of Tamilnadu. The sample size of the study was 350 rural customers from the study area, they were selected using simple random sampling method. The researcher selected Maruthi Suzuki, Tata, Mahindra and Renault brands for the study. A structured questionnaire was utilized to collect primary data. The researcher applied mean, standard variation, constant of variation, Factor analysis and Regression analysis as statistical instrument to analyse the data. The survey found that among the six major factors considered for the study, the Comfortability factors, Financial Factors and Value Factors had high level of influence on rural customers’ brand preference and brand loyalty of passenger cars in the study area. Sales and service factors and Safety factors did not have significant influence on rural customers’ brand preference and brand loyalty of passenger cars in Thiruvarur district.
A HYBRID MACHINE LEARNING AND FEATURE OPTIMIZATION APPROACH FOR CARDIAC RISK PREDICTION
Cardiovascular disease (CVD) remains one of the major causes of mortality globally, emphasizing the importance of early prediction and effective risk detection. Recent breakthroughs in Machine Learning (ML) techniques have been increasingly applied to medical datasets to assist healthcare practitioners in clinical decision-making. Several studies have developed predictive architectures utilizing classification algorithms, feature selection techniques, hybrid learning models, and comparative analyses of multiple algorithms to identify bottlenecks and improve prediction capability. Despite these advancements, prior research often depends on relatively small or spatially specific datasets, lacks effective feature optimization, exhibits increased computational complexity, and provides reduced interpretability for clinicians. These limitations emphasize the need for a proficient integrated framework that productively reduces feature redundancy, enhances model generalization, and improves prediction accuracy. The proposed framework aims to develop an interpretable and accurate CVD prediction system by combining feature optimization techniques with ML classification models. The developed architecture focuses on identifying the most prominent clinical attributes, reducing redundant and correlated variables, and utilizing optimized features to train robust predictive models for early CVD detection. Primarily, data preprocessing and normalization are performed on the dataset. Dimensionality is subsequently reduced by applying Principal Component Analysis (PCA), followed by the Improved Dragonfly Optimization Algorithm (IDFOA) to select the most relevant features. The optimized features are then utilized to train deep learning-based predictive models and Gradient Boosting Machine (GBM) classifiers for disease prediction. Experimental analysis illustrates that the integrated optimization approach significantly enhances CVD prediction performance compared with baseline models. The combination of PCA and IDFOA effectively reduces redundant features while improving sensitivity and the Gini coefficient. Random Forest (RF) achieves the best predictive performance. Overall, the designed framework provides a proficient and reliable method for CVD prediction and supports intelligent clinical decision-support systems for early CVD risk assessment and improved healthcare outcomes.
Copyright (c) 2026 International Journal of Innovation Studies
SUSTAINABLE CLOSED-LOOP SUPPLY CHAIN MODELING WITH NEUTROSOPHIC PARAMETERS FOR THE HOME APPLIANCES INDUSTRY (REFRIGERATORS AND FREEZERS)
With growing concerns over environmental and social issues, the concept of sustainability has become a key pillar in the design and management of supply chains. In this context, closed-loop supply chains, aimed at reducing waste, recycling resources, and creating added value through circular processes, have gained special importance. This study aims to develop a mathematical model for designing a sustainable closed-loop supply chain in the home appliances industry (refrigerators and freezers). The proposed model considers three dimensions of sustainability, including cost, environmental impacts, and social indicators, and employs neutrosophic theory and trapezoidal neutrosophic fuzzy sets to manage uncertainties and ambiguities in the data. To solve the model, a genetic algorithm in the MATLAB environment is utilized. The results indicate that integrating the neutrosophic approach with closed-loop modeling can lead to more effective and sustainable decision-making in the supply chain of the home appliances industry. This model can serve as a practical tool for managers and decision-makers in achieving sustainable development goals and enhancing resource efficiency.
Samira Noorpoor, Farhad Hosseinzadeh Lotfi, Mohammad Fallah, Ghasem Tohidi
PRESENTING AN ONLINE CUSTOMER ORIENTATION TRANSFORMATION MODEL AMONG FOOTBALL BRAND FANS
The aim of this study was to develop a framework for presenting an online customer orientation transformation model among football fans with a mixed design approach. The research method was a two-stage exploratory mixed instrument-making type. The qualitative research method was a qualitative case study, and the potential participants included 19 fans of sports teams. The sampling approach was purposive and the sampling method was key experts. Data collection continued until theoretical saturation was reached. The research tool was a semi-structured interview. In the quantitative part, the research method was a descriptive-survey. Also, the statistical population of the present study in the quantitative part is all fans of sports teams in the country. And due to the uncertainty of the exact number of the statistical population and considering the Cochran sample size in uncertain conditions, 384 people were considered as a statistical sample. And the available sampling method was used. The data collection tools in the present study were in-depth interviews and a researcher-made questionnaire. Thematic analysis and grounded theory were used to examine the qualitative part of the research, and the structural equation method was used to examine the quantitative part. All analyses of the present research were conducted in maxqda, SPSS, and amos software. In this research, four categories of improving ideas, fan decision-making, improving communication, and improving knowledge are considered as causal conditions, empowering audiences, improving commitment, sociability, improving the platform, creating comfort, and creating ownership as background conditions, and two categories of individual and environmental turbulence factors are considered as intervening categories. The central category resulting from the causal conditions is considered to be creating self-confidence, psychological factors, incentives, and creating tangible income for the club. Also, the researcher named the subcategory creating information transactions based on the set of concepts extracted from the interviews and final codes. And the last section includes the consequences of social dynamics, creating social vitality, developing stereotypes and audience recognition, and developing growth orientation. Also, the overall evaluation indicators of the structural equation model, considering the desired range of these indicators, indicate that the proposed model is supported by the research data.
PURIFICATION AND MICROENCAPSULATION OF EUCALYPTOL: OPTIMIZING EVAPORATION AND ENHANCING PHARMACEUTICAL EFFICACY
Eucalyptol (1,8-cineole), a monoterpene with significant pharmacological properties, has garnered attention for its potential applications in pharmaceuticals. However, its clinical efficacy is constrained by challenges such as poor solubility, volatility, and instability. This study explores the optimization of purification and microencapsulation techniques to enhance the stability and controlled release of eucalyptol. Supercritical fluid extraction (SFE) was employed to achieve a high-purity eucalyptol extract, followed by further refinement using gas chromatography (GC). The microencapsulation process was conducted using biodegradable polymers, including polycaprolactone (PCL) and polyethylene glycol (PEG), through a solvent evaporation technique. The effects of various PEG/PCL ratios (1:1 to 4:1) on encapsulation efficiency, particle size distribution, and drug release were examined. Results indicated that the 4:1 PEG:PCL ratio optimized encapsulation efficiency and significantly reduced the evaporation rate by 50%, enhancing the stability of eucalyptol. Controlled release of eucalyptol was observed, with formulations achieving sustained release over 24 hours and maintaining over 80% stability after four months. The findings highlight the potential of advanced extraction and microencapsulation methods to improve the therapeutic efficacy and stability of eucalyptol, paving the way for its broader application in pharmaceutical and cosmetic formulations. This research contributes to the understanding of microencapsulation processes and presents strategies for refining drug delivery systems for volatile compounds like eucalyptol.
Eucalyptol, Microencapsulation, Supercritical Fluid Extraction (SFE), Purification, Pharmaceutical Applications
DIETARY PATTERNS AND NUTRITIONAL AWARENESS AMONG LATER ADOLESCENTS IN CHENNAI: A PILOT STUDY
Background: The period of adolescence is a time of growth and development that requires the adoption of healthy eating habits and adequate nutrition awareness for future health and well-being. The purpose of this pilot study was to evaluate the dietary pattern and nutritional awareness of the later adolescents (17–19 years old) in rural and urban areas of Chennai, India. Methods: The approach adopted was a cross-sectional pilot study involving 51 later adolescents by administering a structured questionnaire. The data obtained from the questionnaire included socio-demographic, eating habits and nutritional awareness information. Jamovi (version 2.6.26) was used to analyse data. The dietary behaviours were analysed by descriptive data, frequency distribution, reliability analysis (Cronbach's α) and Chi-square tests were conducted to explore the differences between nutritional awareness and place of residence Results: The findings showed that many of the participants consumed the breakfast regularly and consumed appropriate number of meals and fruits and vegetables daily. Unhealthy eating habits, however, were also noted including skipping meals, eating lots of snacks, eating late at night, and eating out at restaurants. The internal consistency of the dietary pattern scale was acceptable (Cronbach's α = 0.739). Some nutritional awareness variables were found to differ significantly between the teens in the rural and urban areas (p< 0.05) such as knowledge on the sources of vitamin C, protein and health risks from overconsumption of sugar.
DIFFERENCE SQUARE MEAN FUZZY LABELLING OF TREE RELATED GRAPHS
A graph is said to be a mean square fuzzy graph if it is characterized by the number of edges that are the least significant of any two neighboring vertices. In this study, we focus on the mean square fuzzy labeling technique for newly constructed graphs generated by replacing each edge of balloon trees, shrub trees, banana trees, and coconut trees. These are examples of tree graphs.
SYNTHESIS AND CHARACTERIZATION OF CHITOSAN BASED POLYPYRROLE COPOLYMER AND EVALUATION OF ITS OPTICAL PROPERTIES
Biopolymer-based conducting materials have emerged as attractive candidates for sustainable electronic applications. In this study, a chitosan–polypyrrole (CS–PPy) copolymer was synthesized through in situ chemical oxidative polymerization and characterized using FTIR, solid-state NMR, XRD, SEM–EDS and UV–visible spectroscopy. Structural analyses confirmed successful copolymer formation while XRD revealed a predominantly amorphous nature with reduced crystallinity. SEM images showed a rough- granular morphology with uniform polypyrrole distribution and EDS verified the presence of C, N, O and S elements. UV–visible studies demonstrated enhanced optical absorption and the optical band gap was determined to be 2.09 eV using Tauc’s method. The improved structural and optical properties indicate that the synthesized CS–PPy copolymer is a promising sustainable conducting material for applications in optoelectronics, sensors and advanced functional devices.
Prashant D. Yeram, Sandip B. Ghodile, and Bharadwaj S. Pahune
DRIVERS OF FINTECH ADOPTION IN BUSINESS OPERATIONS: EVIDENCE FROM MICRO-ENTREPRENEURS IN VILLUPURAM DISTRICT, TAMIL NADU
Financial Technology (FinTech) has transformed the financial services sector through innovations such as digital payments, mobile banking, internet banking and so on. Micro-level entrepreneurs increasingly depend on FinTech services for various activities of their business. However, the adoption of FinTech is influenced by factors such as awareness, perceived benefits, technological infrastructure, security concerns, and business needs. The present study examines the factors influencing the adoption and usage of FinTech services among micro-level entrepreneurs in Villupuram District, Tamil Nadu. The researchers selected a total of 450 entrepreneurs as sample and collected primary data through a well-structured questionnaire. The researchers applied the statistical tools of Cronbach Alpha, Factor Analysis, Descriptive statistics, Mann Whitney U Test and Kruskal Wallis Test. The study found that entrepreneurs have a moderate level of awareness of FinTech services and generally hold a positive perception towards their usage. Among the influencing factors, Convenience and Efficiency Factors emerged as the most important determinants of FinTech adoption, followed by Business and Customer-Oriented Factors and Economic Benefit Factors. The study also found that FinTech services help entrepreneurs improve business operations, financial transactions, and decision-making processes. The study concludes that FinTech plays a vital role in enhancing business efficiency and financial management among micro-level entrepreneurs. Therefore, improving awareness, digital infrastructure, security measures, and user support can further promote FinTech adoption and contribute to the growth and sustainability of micro-enterprises.
S. Jeno Benefit Raj and Dr. R. Narayana Gupta
IMPACT OF ELECTRONIC WORD-OF-MOUTH ON TRUST FORMATION FOR ONLINE FOOD BRANDS IN CHENNAI
Digital technologies and online food delivery platforms have reshaped consumer buying behaviour and intensified competition among online food brands. In this environment, Electronic Word-of-Mouth (eWOM) reviews, ratings, social media, food blogs, and online communities has become a key driver of consumer trust and purchase decisions. Positive eWOM boosts brand credibility, lowers perceived risk, and builds trust, while negative reviews can undermine confidence and damage reputation. This quantitative study examines how eWOM shapes trust toward online food brands among consumers in Chennai City, focusing on four dimensions-Online Review Quality, Review Credibility, Review Quantity, and eWOM Engagement and their influence on Consumer Trust, which in turn affects Brand Preference and Purchase Intention. A structured questionnaire was administered to 220 regular online food delivery users, with data analysed using descriptive statistics, reliability analysis, EFA, Pearson correlation, and multiple regression. The findings are expected to show that high-quality, credible, and informative reviews significantly build consumer trust, strengthening brand preference and purchase intention. The study contributes to digital consumer behaviour literature by linking eWOM and trust within a unified framework, offering practical guidance for online food brands, aggregators, digital marketers, and policymakers on building trust and sustainable growth through effective eWOM strategies.
Electronic Word-of-Mouth, Online Reviews, Consumer Trust, Online Food Brands, Brand Preference, Purchase Intention, Digital Marketing, Chennai City
ETHICAL PRINCIPLES OF ARTIFICIAL INTELLIGENCE IN SCIENTIFIC RESEARCH: A SYSTEMATIC REVIEW OF BIAS, PRIVACY, TRANSPARENCY, AND GOVERNANCE
The study addresses the ethical implications of artificial intelligence in scientific research and contributes to the achievement of Sustainable Development Goal 9: Industry, Innovation, and Infrastructure. Its objective was to systematically analyze the integration of ethical principles, algorithmic bias, data privacy, transparency, and governance, in the use of AI applied to scientific research, in order to build an integrative ethical framework to guide its responsible application. This qualitative, descriptive-analytical study systematically analyzed 15 studies selected from 65 scientific articles published between 2023 and 2025. The results show that the implementation of artificial intelligence optimizes processes and strengthens institutional trust, reveals significant risks in algorithmic biases that exacerbate inequalities affecting the validity of results; privacy is threatened by the misuse of sensitive data; transparency compromises scientific replicability and credibility; and the absence of governance creates regulatory gaps that limit the social legitimacy of productive knowledge. It is concluded that artificial intelligence is a strategic tool for digital transformation and organizational sustainability, provided it is managed under ethical principles, clear frameworks, and responsible practices that ensure equity, trust, and social acceptance.
Juan de Dios Aguilar Sánchez; César Augusto, Mendivel Gerónimo; Napoleón Cubas Irigoin; Yris Francisca Torres Baca; Edi Rojas Campos; Milord Idrogo Gálvez; Wilder Omar Vargas Campos; Jeiden Revilla Arce
ARTIFICIAL INTELLIGENCE PROTOCOLS APPLIED TO DIGITAL TRANSFORMATION, QUALITY, CONTINUOUS IMPROVEMENT, AND TECHNOLOGICAL INTEGRATION IN PUBLIC AND PRIVATE ADMINISTRATION
The study analyzes the influence of artificial intelligence protocols on digital transformation, quality, continuous improvement, and technological integration in public and private administration. It contributes to the achievement of Sustainable Development Goal 9: Industry, Innovation, and Infrastructure, and Goal 16: Peace, Justice, and Strong Institutions, by promoting ethical and technological frameworks that strengthen competitiveness and institutional sustainability. It started from the general objective of determining how artificial intelligence protocols impact administrative management, considering dimensions such as business competitiveness, ethical and regulatory governance, citizen data protection, and corporate social responsibility. The research was basic, with a quantitative approach and a non-experimental correlational design, developed with a population of officials and representatives of public and private institutions in Peru, Cajamarca region. The main results show that the implementation of AI protocols contributes significantly to strengthening operational efficiency, transparency, institutional trust, and improving service quality, although challenges related to data protection and ethical governance remain. It is concluded that the implementation of artificial intelligence protocols constitutes an administrative modernization tool for consolidating digital transformation and organizational sustainability, providing useful empirical evidence for public policies and business strategies.
Juan de Dios Aguilar Sánchez; Horna Zegarra, Indalecio Enrique; César Augusto, Mendivel Gerónimo; Napoleón Cubas Irigoin; Edi Rojas Campos; Milord Idrogo Gálvez; Wilder Omar Vargas Campos; Jeiden Revilla Arce
DETERMINANTS OF WORKING CAPITAL REQUIREMENTS: CASE OF VIETNAMESE CONGLOMERATES
Using financial data from the top seven conglomerates from various industries during the five years from 2018 to 2022, this analysis seeks to uncover the influences influencing the working capital requirements of Vietnamese conglomerates. The findings show that various factors, including financial leverage, profitability, tangible assets, and GDP, exert considerable force on working capital requirements in Vietnamese conglomerates. This study adds useful insights to previous research on the factors that impact conglomerates' working capital requirements, which might aid managers of finances, investors, and financial advisors alike.
Assoc. Prof. Ha Thi Nguyen, MSc. Dung Van Hoang, MSc. Linh My Hoang, Dr. Thuong Thu Nguyen, MSc. Hung Manh Nguyen, Dr. Tan Huu Nguyen
Drivers of FinTech Adoption in Business Operations: Evidence from Micro-Entrepreneurs in Villupuram District, Tamil Nadu
Financial Management Excellence, FinTech, Digital wallets, digital literacy and entrepreneurs.
S. Jeno Benefit Raj, Dr. R. Narayana Gupta
EXPLORING THE RELATIONSHIP BETWEEN ARTIFICIAL INTELLIGENCE INTEGRATION AND TPACK COMPETENCE AMONG PRE-SERVICE TEACHERS IN HIGHER EDUCATION
This research paper investigates strategies for harmonizing family dynamics and business governance to achieve success in family-owned businesses. Through interviews and case studies with a purposive sample of family business owners/managers, the study explores the challenges faced and the strategies employed in integrating family values with effective business governance. Findings reveal that successful harmonization involves navigating complex family dynamics, balancing business objectives with familial goals, and establishing clear communication channels. Moreover, the study identifies key factors contributing to successful integration, such as fostering trust, implementing structured governance mechanisms, and cultivating a shared vision among family members. The discussion contextualizes these findings within existing literature on family business management, highlighting implications for both theory and practice. Despite limitations in generalizability due to the qualitative nature of the study and the nature of the data, this research contributes to understanding the intricate dynamics of family businesses and provides actionable insights for practitioners seeking to achieve harmony between familial relationships and business governance structures.
Charlene C. Pita, Melona C. Deguma, Allan B. Canillo, Glenn I. Peñoza, Reylan G. Capuno
A SYSTEMATIC REVIEW OF GOVERNMENT STRATEGIC COMMUNICATION AND ITS IMPACT ON SMES DEVELOPMENT: A CROSS-CULTURAL PERSPECTIVE BETWEEN IRAN AND SPAIN
Effective strategic government communication can reduce information asymmetry, lower regulatory uncertainty, and enable SMEs to adopt innovative practices; yet, cross-country evidence on communication mechanisms and SME innovation outcomes remains limited. This study conducts a systematic review using the PRISMA 2020 protocol to compare strategic communication frameworks in Iran and Spain. We searched Scopus, Web of Science, and Google Scholar for studies published between 2000 and 2024, applying explicit inclusion and exclusion criteria to identify 14 relevant sources. Five recurrent dimensions emerged: transparency, policy consistency, digital communication, funding support communication, and stakeholder engagement. Comparative analysis shows that Spain’s institutional trust, digital transparency, and stable policy messaging support SME innovation ecosystems, while Iran’s fragmented communication structures and regulatory uncertainty constrain SMEs’ innovation capacity despite extensive policy initiatives. The study contributes a multidimensional model linking strategic communication mechanisms to SME innovation outcomes and highlights the importance of institutional quality in shaping policy effectiveness and innovation behavior among SMEs.
Shaho Heidari Gandoman, Elena Urquía Grande , Marie-Anne Lorain
ARTIFICIAL INTELLIGENCE IN HEALTHCARE MARKETING TRANSFORMING PATIENT ENGAGEMENT AND BUSINESS GROWTH
Artificial Intelligence (AI) has become an important technology in transforming healthcare marketing by enabling data-driven decision-making, personalised communication, improved patient engagement, and efficient healthcare service promotion. AI-based tools help healthcare organisations understand patient needs, optimise marketing strategies, and enhance customer experiences. However, the implementation of AI in healthcare marketing also presents several challenges, including data privacy concerns, high costs, lack of technical expertise, system integration issues, and resistance to technological changes. This study aims to analyse the benefits and challenges associated with implementing AI in healthcare marketing. The study adopted a quantitative research design to examine the role of AI in healthcare marketing. Data were collected from a total of 180 respondents using a structured questionnaire. The study was conducted in Chennai City. The respondents were selected using the stratified random sampling technique, where the population was divided into relevant groups to ensure adequate representation from different categories. The collected data were analysed using statistical tools such as descriptive statistics, ANOVA, and Pearson correlation analysis. ANOVA was used to identify differences in perceived AI benefits based on demographic variables such as age, gender, educational qualification, occupation, and monthly income. Pearson correlation analysis was used to examine the relationship between AI implementation challenges and overall adoption difficulties. The analysis showed that AI provides significant benefits in healthcare marketing, including improved customer interaction, personalisation, operational efficiency, and better decision-making. The ANOVA results indicated that age, educational qualification, occupation, and monthly income significantly influence respondents’ perceptions of AI benefits, whereas gender does not show a significant influence. The correlation analysis revealed significant positive relationships between AI implementation challenges and overall adoption difficulties. Factors such as data privacy concerns, implementation costs, system integration problems, lack of AI skills, poor data quality, and resistance from healthcare professionals were identified as major barriers to AI adoption.
IDENTITY CRISIS AND SOCIAL ALIENATION IN ABDULRAZAK GURNAH’S FICTION: MIGRATION, FAMILY DISRUPTION AND THE SEARCH FOR BELONGING
Abdul Razak Gurnah- the Nobel Prize winner of 2021 offers a profound exploration of migration, displacement and the psychological consequences of colonial and post- colonial transformations in East Africa. His novels frequently portray individuals caught between cultures, nations and memories, struggling to construct meaning full identities amid social fragmentation. This paper examines the interconnected themes of identity crisis, social alienation, migration and family disruption in selected novels by Abdulrazak Gurnah, particularly Memory of Departure, Admiring Silence, By the Sea and Gravel Heart. Drawing up on postcolonial theories of identity, diaspora and hybridity, the study investigates how migration destabilizes traditional notions of home and belonging while exposing characters to exclusion, cultural dislocation and emotional isolation. The analysis demonstrates that Gurnah presents identity as fluid rather than fixed , shaped by historical violences, colonial legacies and personal experiences of exile .Migration in his fiction functions both as means of escape and as a source of psychological conflict. Characters often leave their homelands seeking freedom, education or economic opportunities, yet encounter alienation and uncertainty in new environments. Family relationships further complicate this process, as separation, silence ,abandonment and generational conflict disrupt the continuity of personal and cultural identity. The paper argues that Gurnah’s narratives by challenging the simplistic understandings of migration by focussing its emotional and ethical dimensions. Rather than depicting exile solely as loss, he portrays it a s a complex condition in which individuals negotiate multiple affiliations and fragmented memories. In his novels, we can see nuanced characterizations and multi- layered narratives, Gurnah reveals how displaced individuals navigate the tensions between memory and forgetting, attachment and detachment, belonging and exclusion. Ultimately, his fiction illuminates the enduring struggle for selfhood in a postcolonial world marked by mobility, uncertainty and historical trauma.
Cinto Jose, Dr .F. Julian Xercies Rigley
AN EFFICIENT BERT-BASED FRAMEWORK FOR LARGE-SCALE TWITTER (X) SENTIMENT ANALYSIS USING ATTENTION AND ADAPTIVE THRESHOLDING
The massive growth of micro blogs such as Twitter has made their use of large-scale user generated data a crucial tool for real-time public opinion mining. However, traditional sentiment analysis pipelines treat data cleaning, feature representation and decision making as distinct tasks, resulting in sub-optimal overall pipeline performance, especially on data with highly noisy and linguistically variable characteristics like Twitter text. Existing approaches have three main drawbacks: (1) the use of pre-processing pipelines that perform the same cleaning operation regardless of the degree of noise in the individual samples, resulting in the under-cleaning of highly corrupted samples and in over-cleaning of informative informal language; (2) the use of feature extraction strategies that statically pool the contextual embedding representations of each token, discarding the differential contribution of each token to the category information; and (3) the use of classification stages with fixed decision thresholds that are not sensitive to the per-class probability distribution, a situation that leads to systematic misclassification of borderline and ambiguous instances. This paper proposes a novel three-phase approach that tackle each of these deficiencies by using a unique novel algorithm, namely the Noise-Adaptive Lexical Filter (NALF) for preprocessing in Phase 1, the Attention-Weighted Sentiment Pooling (AWSP) mechanism for feature extraction in Phase 2, and the Adaptive Threshold Classifier (ATC) for sentiment decision in Phase 3. The three components are tightly coupled using a well tuned bert-base-uncased backbone and tested on a Sentiment160 set of 1,600,000 tweets. The proposed framework's accuracy, macro-averaged F1 measure, and AUC-ROC on the held-out test partition exceed all the evaluated baselines with scores of 93.8%, 92.6%, and 0.971 respectively. Each novel algorithm contributes significantly to the overall improvement, with the ablation findings showing that the accuracy drops by 1.3%, 1.2%, and 1.2% for the NALF, AWSP, and ATC, respectively, when compared with the vanilla BERT baseline of 90.1%.
LINGUISTIC DUAL HESITANT SUPER HYPERSOFT TOPOLOGICAL SPACE IN MULTI CRITERIA DECISION MAKING
In this article presents a topology-based approach using linguistic dual hesitant super hypersoft sets to support medical diagnosis under conditions of vagueness and multiple interacting attributes. The method draws on dual hesitant membership and non-membership values along with topological notions, subbase, interior, and closure to structure and refine the evaluation process. A decision algorithm is proposed that merges the linguistic dual hesitant super hypersoft set assessments with these topological operations to rank alternatives and support decision-making.
Sathiyapriya Bangarusamy and Dr. V. Pankajam
LINGUISTIC PYTHAGOREAN SUPER HYPERSOFT TOPOLOGICAL SPACE IN MULTI CRITERIA DECISION MAKING
In this article topological framework of linguistic pythagorean super hypersoft set for medical diagnosis in uncertain multi-attribute settings. It uses pythagorean values and topological concepts such as subbase, interior, and closure. An algorithm integrating linguistic pythagorean super hypersoft set evaluation with topological operations such as interior and closure is developed for effective ranking and decision-making. A score- based ranking method is used to select optimal alternatives. The linguistic pythagorean super hypersoft topology method improves decision accuracy and reliability in medical diagnosis.
Lipschitz labelling on graph is a function from the vertex set and edge set of the graph to where and such that is bijective. for all for all There exists a positive integer such that for all and for . We study the problem of Lipschitz labelling within the context of structured graphs. Specifically, we establish the Lipschitz constants for three families of star-derived graphs: twinkling star graphs with ray of length , triangular graphs and bistar graphs .