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DECODING INTENTIONS TO PURCHASE ORGANIC FOOD PRODUCTS IN AN EMERGING ECONOMY VIA ARTIFICIAL NEURAL NETWORKS
Purpose. This study investigates the factors influencing consumers intentions to purchase organic food products in an emerging economy. It addresses the knowledge gap regarding the slower growth of the organic food market in these regions despite the global trend toward environmental sustainability. Methodology / approach. A survey approach involving 350 participants was used. Structural equation modeling (SEM) with SmartPLS 4 and Artificial Neural Network (ANN) with IBM SPSS 28 were used to analyse the impact of awareness of need, personal norms, environmental concern, and health consciousness on the intention to purchase organic food products. Results. The study found significant positive influences of awareness of need, personal norms, environmental concern, and health consciousness on the intention to purchase organic food products, explaining 63.1 % of the variance. Both the analysis approaches (PLS-SEM & ANN) revealed that, health consciousness, followed by awareness of need, emerged as the most important factor related to the intention to purchase organic food products. The results highlight the importance of awareness and personal values in driving pro-environmental behaviour. Originality / scientific novelty. This research offers essential insights into the determinants of organic food purchase intentions in an emerging economy. It emphasises the significance of awareness and personal values in fostering sustainable consumption behaviour, addressing a less explored area in existing literature. Practical value / implications. The findings have important implications for policymakers and marketers. Strategies focused on consumer education about the benefits of organic food can enhance awareness and appeal. Understanding core psychological needs and beliefs that shape consumer motivations can guide the development of effective marketing strategies. The study highlights the strong environmental consciousness among consumers and their desire to protect the environment. 2024, Institute of Eastern European Research and Consulting. All rights reserved. -
Decoding impermanent narratives: A study of transient migrants as digital influencers on YouTube
Students migrate from India annually for higher education in large numbers. Social media has become an essential network for disseminating information related to aspects of migration like student visas, college applications, residence and finances. YouTube engages vigorously in this dispersion of information. Many times, the sources of these kinds of information are found to be transient migrants themselves. YouTubers and influencers like Tushar Bareja, Nidhi Nagori, Gursahib Singh, Bani Singh and Saloni Verma, among others, have made a niche, creating content and sharing information about the experience of being a transient migrant. Much like the status of being transient, creating ones brand on social media is both dynamic and fleeting, which cannot be defined in a sense of permanence. The analysis of content created by YouTube influencers enables an insight into the definition of transient migrant identity. The topics that are covered in the content showcase the particular components of international student life that add to the concept of a transient migrant identity. The article attempts to ask the question of how the YouTube videos made by student migrants end up contributing to the transient migrant identity. It also attempts to decipher how the transient identity itself is packaged as a commodity to be monetized by these student migrant influencers on YouTube. Using theoretical frameworks of influencer culture, social media and migration, the article attempts to unravel the workings of YouTube in commodifying the transient migrant experience. 2025 Intellect Ltd. -
Decoding HERO: Predicting psychological capital with subjective well-being
The positive psychology movement has gained momentum in recent years and organizations have ascribed great importance to employee well-being in light of the favorable outcomes associated with it. The widely researched Psychological Capital (PsyCap) has been consistently linked to well-being across a variety of contexts but a gap still exists in literature about what lies to the 'left' of psychological capital. The present study attempts to fill this gap by examining subjective well being components- positive and negative affect and life satisfaction, as potential antecedents of PsyCap. The Academic PsyCap questionnaire, the Positive and Negative Affect Schedule (PANAS) and the Satisfaction with Life Scale (SWLS) were administered to participants. Results confirmed the expected associations between affect and PsyCap-positive affect positively predicted PsyCap and its four constituents whereas negative affect emerged as a negative predictor of PsyCap and its dimensions. Life satisfaction positively predicted only individuals' total hope scores. Thus, highlighting the role of subjective well-being components as antecedents of PsyCap, these findings suggest that promoting higher positive affect and lower negative affect can do more than just make individuals feel good, rather, it can bolster their reservoirs of crucial psychological resources as well. 2021 Ecological Society of India. All rights reserved. -
Decoding customer sentiments in quick commerce: comparative insights from BlinkIt, Zepto, and JioMart utilizing machine and deep learning models
The rapid expansion of quick commerce platforms like BlinkIt, Zepto, and JioMart has introduced unique challenges in understanding customer sentiments due to their operational focus on ultra-fast deliveries and hyper-local logistics. This study conducts a comprehensive analysis of sentiment classification methodologies, exploring both traditional ML techniques and advanced DL models to classify customer reviews into positive, negative, and neutral categories. Traditional models, while offering simplicity and interpretability, achieved moderate accuracy (83% with SVM) but struggled to capture the complexities of neutral sentiments. In contrast, DL models, particularly LSTM, achieved superior performance with an accuracy of 88.96% and a macro F1-score of 0.64, leveraging pre-trained embeddings like GloVe to enhance semantic understanding and contextual representation. Further experiments with optimizers, including Adam, RMSprop, SGD, and Nadam, revealed their limited impact on resolving class imbalance and improving neutral sentiment classification. To address these challenges, we integrated hybrid architectures combining GloVe and BERT embeddings, achieving a significant accuracy of 90.69% and demonstrating improved generalization across sentiment classes. However, the classification of neutral sentiments remained a persistent challenge, underscoring the need for advanced techniques like data augmentation and ensemble strategies. This research highlights the importance of adopting hybrid and deep learning-based approaches for sentiment analysis in quick commerce platforms. The findings provide actionable insights for enhancing customer satisfaction and service quality, while also paving the way for future research in domain-specific sentiment classification and scalable solutions for underrepresented sentiment categories. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2026. -
Decoding Customer Lifetime Value to Unlock Business Success with Predictive Machine Learning Approach
This study highlights how crucial customers are for a company's success who directly impacts revenue and overall business value. This study focuses on analysis of customer lifetime value, the research uses data from 5000 customers with 8 important features with the main goal of predicting customer lifetime value. Business leaders often face choices about where to invest in marketing, like loyalty programs, incentives and ads or nothing. The study suggests that customer lifetime value is a key metric for making smart decisions, which measures how much a customer spends over their time with a company. To predict this value, the research explored different machine learning models - linear regression, decision tree regressor, random forest, and AutoML regressor. Each model is checked for how well it predicts customer spending habits. The results show that AutoML regression stands out for its accuracy without overcomplicating things. This study offers insights for businesses looking to improve their customer-focused strategies and long-term success. 2024 IEEE. -
Decoding Cognitive Control and Cognitive Flexibility as Concomitants for Experiential Avoidance in Social Anxiety
Background and objectives: Avoidance is regarded as a central hallmark of social anxiety. Experiential avoidance is perilous for social anxiety, specifically among university students (young adults). Additionally, cognitive control and cognitive flexibility are crucial components of executive functions for a fulfilling and healthy lifestyle. The current research is a modest attempt to understand how cognitive flexibility and cognitive control affect the emergence of experiential avoidance in social anxiety in young adults. Methods: Using an ex-post facto design, the Social Phobia Inventory was employed to screen university students with social anxiety based on which one hundred and ninety-five were identified. Thereafter, participants completed the standardized measures on experiential avoidance, cognitive control and cognitive flexibility. Results: A stepwise multiple regression analysis was computed wherein the cognitive control predicts an amount of 5% of variance towards experiential avoidance, whereas a 10% of additional variance has been contributed by cognitive flexibility. Interpretation and Conclusions: The statistical outcome indicated that cognitive control is positively associated with experiential avoidance which is a negative correlate to cognitive flexibility among university students. Both also emerged as significant predictors of experiential avoidance and add a cumulative variance of 15% towards the same. This conclusion supports the need for improved and efficient management techniques in counseling and clinical settings. The Author(s) 2024 -
Decoding Cognitive Control and Cognitive Flexibility as Concomitants for Experiential Avoidance in Social Anxiety
Background and objectives: Avoidance is regarded as a central hallmark of social anxiety. Experiential avoidance is perilous for social anxiety, specifically among university students (young adults). Additionally, cognitive control and cognitive flexibility are crucial components of executive functions for a fulfilling and healthy lifestyle. The current research is a modest attempt to understand how cognitive flexibility and cognitive control affect the emergence of experiential avoidance in social anxiety in young adults. Methods: Using an ex-post facto design, the Social Phobia Inventory was employed to screen university students with social anxiety based on which one hundred and ninety-five were identified. Thereafter, participants completed the standardized measures on experiential avoidance, cognitive control and cognitive flexibility. Results: A stepwise multiple regression analysis was computed wherein the cognitive control predicts an amount of 5% of variance towards experiential avoidance, whereas a 10% of additional variance has been contributed by cognitive flexibility. Interpretation and Conclusions: The statistical outcome indicated that cognitive control is positively associated with experiential avoidance which is a negative correlate to cognitive flexibility among university students. Both also emerged as significant predictors of experiential avoidance and add a cumulative variance of 15% towards the same. This conclusion supports the need for improved and efficient management techniques in counseling and clinical settings. The Author(s) 2024. -
Decoding Breast Cancer Mutational Signatures: A Hybrid ElasticNetXGBoost Approach Using Gene Expression Data
TP53, PIK3CA, and MUC16 are somatic mutations that are useful in breast cancer progression and prognosis, but direct mutation profiling based on sequencing is not always practicable in practice. The data about gene expression can contain indirect transcriptomic patterns linked with mutational underlying states. This paper proposes an expression-based machine learning model to predict the status of mutations using METABRIC breast cancer cohort. Instead of directly estimating genetic changes, the suggested method estimates statistical relationships between transcriptomic phenotypes and binary somatic mutation states. A multi-stage gene features selection pipeline using variance filtering, mutual information ranking, and correlation pruning was used to reduce the number of genes (19,000). A hybrid predictive architecture was trained using these features that combined ElasticNet logistic regression and XGBoost that allowed balancing between linear regularization and nonlinear interaction modeling. The hybrid model with a combination of five-fold stratified cross validation yielded mean ROC-AUC of 0.94 (TP53), 0.92 (PIK3CA), and 0.90 (MUC16) with the stability of the calibration and equal error rates. Coefficient analysis and SHAP-based explanations were used to investigate the interpretability of the models to describe the expression patterns on mutation status. The suggested framework is a hypothesis-generating, complementary method of transcriptomic analysis, which must be reevaluated by external validation to determine the wider generalizability. 2026, International Journal of Prognostics and Health Management. All rights reserved. -
Decoding boomerang hiring: A suggestive framework to improve organizational efficiency
In an ever changing, volatile and dynamic business environment, efforts put by the human resources reflect the organizational efficiency. Organizations should always focus on maintaining smooth relations with the Alumni and Boomerangs as they play a crucial role in the expanding horizons of business. A positive word of mouth also helps in improving the goodwill and image of the company. It will encourage the prospective employees to view the organization in a positive light. Rehiring former employees is one of the mechanisms for recruitment used by a large number of corporations primarily because of the inherent advantage of added experience as well as savings in terms of cost of recruitment and training. The present study attempts to give an overview of Boomerang Hiring, the possible value additions being made in terms of Human Capital and Social Capital on basis of the type of respective organizations they are returning from. Additionally, the perspective of the rehired employee is also presented. The study is further enriched by quoting a few instances from the corporate world. The Rehiring Strategies tailored as per organizational requirements will lead towards holistic growth and development of the entity. 2020 SERSC. -
Decoding Big Data: The Essential Elements Shaping Business Intelligence
In today's Business Intelligence (BI) world, Big Data Analytics integration has become critical, transforming company strategy and decision-making processes. This study investigates the complex influence of Big Data on business intelligence, focusing on important drivers of this transition. It investigates how Big Data's improved data processing capabilities, integration of advanced analytics techniques such as machine learning, and real-time data insights enable businesses to make more informed decisions and achieve a competitive advantage. Furthermore, the paper emphasizes the importance of personalized consumer insights, operational savings, and strategic benefits obtained from predictive analytics when adopting Big Data for BI. 2024 IEEE. -
DECLINE OF TIBETAN MUSIC AND THE INVASION OF CHINA
This study explores the forms and features of Tibetan music that existed in the past and its gradual change to the present. It looks at the traditional music that Tibetans followed and the amalgamation of the same with other forms and genres of music as it altered during and after the Chinese invasion of 1949. The study will be working under the idea that the Tibetan music has lost its traditional trace as the prominence of commercial marketing of music have escalated and how this was brought on by the mentioned invasion: the political, social, cultural and economical aspects which directly or indirectly changed the musical culture of Tibet. It envelopes the idea that the musical tradition of Tibet has been lost due to the strong administrative control of the Chinese government and how that led to the failure of the native Tibetans to safeguard the Tibetan traditional forms and genres of music. -
Decision-making using regression analysis: a case study on Top Tier Holidays LLP
Research methodology: This study aims to investigate the factors that contribute to the overall tour experience and services provided by Top Tier Holidays. The study is mixed in nature, and the researchers have used analytical tools to analyse the data factually. Multiple regression using MS Excel is used in the study. Case overview/synopsis: This case is based on the experiences of a real-life travel and tour company located in New Delhi, India. The case helps understand regression analysis to identify independent variables significantly impacting the tour experience. The CEO of the company is focused on improving the overall customer experience. The CEO has identified six principal determinants (variables) applicable to tour companies success. These variables are hotel experience, transportation, cab driver, on-tour support, itinerary planning and pricing. Multiple regression analysis using Microsoft Excel is conducted on the above determinants (the independent variables) and the overall tour experience (the dependent variable). This analysis would help identify the relationship between the independent and dependent variables and find the variables that significantly impact the dependent variable. This case also helps us appreciate the importance of various parameters that affect the overall customer tour experience and the challenges a tour operator company faces in the current competitive business environment. Complexity academic level: This case is designed for discussion with the undergraduate courses in business management, commerce and tourism management programmes. The case will build up readers understanding of linear regression with multiple variables. It shows how multiple linear regression can help companies identify the significant variables affecting business outcomes. 2023, Emerald Publishing Limited. -
Decision-Making Models for Efficient Outbreak Response: A Management-Orientated Approach to Dengue Control in Andhra Pradesh, India
Dengue remains a serious health challenge across India, and Andhra Pradesh faces repeated outbreaks that put a heavy strain on hospitals, clinics, and communities. Combating this disease isnt just about tracking casesits about making quick, smart decisions to control its spread effectively. This study looks into different decision-making approaches that can help improve how Andhra Pradesh responds to dengue outbreaks, making actions faster and more targeted. Using a mix of existing epidemiological data, interviews with health officials and community leaders, and simulated scenarios, the research explores how tools like Multi-Criteria Decision Analysis (MCDA), the Analytic Hierarchy Process (AHP), and Decision Tree Analysis can assist in choosing the best strategies. These models help prioritise interventions such as resource distribution, vector control efforts, and public awareness campaigns, especially when dealing with uncertainties like limited resources or unpredictable case surges. The findings indicate that integrating these decision-making frameworks into public health planning can foster better coordination among policymakers, healthcare workers, and local authorities. This improved coordination can lead to quicker responses, more effective use of resources, and ultimately, a reduction in dengue cases and their impact on communities. The study emphasises that combining management science tools with traditional epidemiology isnt just helpfulits essential for strengthening outbreak preparedness. Plus, these approaches can be adapted to tackle other communicable diseases in India and similar settings worldwide, paving the way for smarter, more resilient public health systems. 2025, Indian Society for Malaria and Communicable Diseases. All rights reserved. -
Decision-Making Frameworks for Integrating Motion Control in Business Operations
This chapter includes some strategic plans of how to incorporate motions control technologies in the business to promote efficiency, agility, and to make data- driven decisions. It cogitates about the real- time motion tracking and the focus on optimization in the logistics sphere, production and asset management through automation and smart analytics. The chapter explains how AI, IoT, control algorithms help harmonize the processes of motion with the goals of organizations. They lay emphasis on decision theory, performance measures and on the flexibility of motion systems in dynamic environments. The chapter has shed light on the ability of motion control to become the engine of innovation and competition in the operation of the contemporary enterprise by offering scalable and practical integration models. 2026, IGI Global Scientific Publishing. All rights reserved. -
Decision Tree Based Routing Protocol (DTRP) for Reliable Path in MANET
In mobile ad hoc network due to node movements, there exists route failure in active data transmission which results in data loss and communication overheads. Hence, in such a dynamic network, routing through reliable path is one of the tedious tasks. In this paper, we propose a novel Decision Tree based Routing Protocol (DTRP) a data mining technique in route selection process from source to destination. The proposed DTRP protocol selects the one hop neighbors based on the parameters such as speed, Link Expiration Time, trip_time and node life time. Thus the performance of a route discovery mechanism is enhanced by selecting the stable one-hop neighbors along the path to reach the destination. The simulated results show that the lifetime of the route is increased and hence the data loss and end to end delay are minimized thereby increasing the throughput of the network using the proposed DTRP routing protocol compared to existing routing protocols. 2019, Springer Science+Business Media, LLC, part of Springer Nature. -
Decision making framework for foreign direct investment: Analytic hierarchy process and weighted aggregated sum product assessment integrated approach
Foreign direct investment (FDI) plays a paramount role in economic and social growth of every country. FDI acts as a source of external capital and helps in economic growth of the host country. Making decision for FDI during uncertain business environment is a challenge for all stakeholders. Therefore, in this study, we are proposing a decision making framework for FDI. Through literature review, we have identified the factors, on which FDI depends. A process-based, multi-criterion, integrated hierarchical approach for deciding about FDI, has been illustrated. In this study, five sectors are considered, that is, petroleum and natural resource, retailing and e-commerce, healthcare, information technology, and road and highways for illustrating the proposed framework. It is observed that information technology sector has got top priority for FDI followed by retailing and e-commerce and health care sector. Findings will help in taking appropriate decision by stakeholders for FDI. Ultimately it will also help in creating employment, economic growth, and welfare of society at large in the host country. 2021 John Wiley & Sons, Ltd. -
Decision Flow Tracing and Word Impact Analysis in Hybrid Transformer-Conditioned Diffusion Models for Text-to-Image Generation
Text-to-image diffusion models have become a cornerstone of modern generative AI, offering high-quality synthesis yet remaining constrained by their black-box nature, which limits controllability and interpretability. In this work, we propose a hybrid transformer-conditioned diffusion model that integrates UNet-based denoising with multi-head cross-attention transformer blocks at critical latent stages of the diffusion process. The architecture is trained on a curated set of 50,000 samples from DiffusionDB with a 200-step latent diffusion schedule. Text prompts are encoded using a 16-token BERT encoder and mapped into a 256-dimensional latent feature space. Cross-attention layers with eight heads are interlaced within the UNet bottleneck and decoder, enabling token-to-region correspondence and fine-grained semantic propagation. To ensure interpretability, we design an explainability framework that combines hierarchical token-level attention heat maps, temporal attention rollouts, and perceptual ablation studies based on learned image patch similarity. Analysis reveals that object tokens remain spatially and temporally consistent, while attribute tokens demonstrate sharper temporal volatility. JensenShannon divergence quantifies this redistribution of attention across diffusion steps. Experimental evaluation against a standard UNet diffusion baseline demonstrates clear improvements: Frhet Inception Distance decreases by 19.6, CLIP alignment score increases by 5.4, and Inception Score improves by 18.6. Moreover, attention coherence improves by 22%, underscoring the gains in explainability. The proposed framework establishes a pathway toward accountable, high-fidelity, and interpretable text-to-image synthesis. Beyond performance, it supports critical tasks such as bias evaluation, fairness auditing, and quality assurance, offering a robust foundation for the next generation of explainable generative AI systems. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Deciphering the properties of UV upturn galaxies in the Virgo cluster
The UV upturn refers to the increase in UV flux at wavelengths shorter than 3000 observed in quiescent early-type galaxies (ETGs), which still remains a puzzle. In this study, we aim to identify ETGs showing the UV upturn phenomenon within the Virgo galaxy cluster. We utilized a colourcolour diagram to identify all potential possible UV upturn galaxies. The spectral energy distributions (SED) of these galaxies were then analysed using the CIGALE software; we confirmed the presence of UV upturn in galaxies within the Virgo cluster. We found that the SED fitting method is the best tool to visualize and confirm the UV upturn phenomenon in ETGs. Our findings reveal that the population distributions regarding stellar mass and star formation rate properties are similar between UV upturn and red sequence galaxies. We suggest that the UV contribution originates from old stellar populations and can be modelled effectively without a burst model. Moreover, by estimating the temperature of the stellar population responsible for the UV emission, we determined it to be 13 000 K to 18 000 K. These temperature estimates support the notion that the UV upturn likely arises from the contribution of low mass evolved stellar populations (extreme horizontal branch stars). Furthermore, the Mg2 index, a metallicity indicator, in the confirmed upturn galaxies shows higher strength and follows a similar trend to previous studies. This study sheds light on the nature of UV upturn galaxies within the Virgo cluster and provides evidence that low-mass evolved stellar populations are the possible mechanisms driving the UV upturn phenomenon. 2024 The Author(s). -
Deciphering the plant growth-promoting traits of bacteria capable of sodium dodecyl sulfate removal from graywater: a sustainable approach for water reuse for irrigation
Sodium dodecyl sulfate (SDS), an anionic detergent found in cleaning products and cosmetics, is one of the chemical pollutants in waterways. SDS-utilizing bacteria were isolated from soil and water samples using 0.05% SDS basal medium. Three bacterial isolates were selected for 16S rRNA sequencing based on their ability to solubilize phosphate, potassium, and zinc, and they were identified as Pseudomonas putida MSK86 OR192890, Klebsiella pneumoniae NET12 OR345422, and Enterobacter sp. MSK86 OR398804. Enterobacter sp. MSK86 and K. pneumoniae NET12 lowered the SDS concentration in the sample 84.78% and 75.65%, respectively, while P. putida MSK86 reduced it 33.43% on the sixth day of incubation. A phosphate-potassium-zinc co-inoculum was prepared using Enterobacter and Pseudomonas species. Laundry wash water was added with the bacteria, individually and co-inoculum, and the fortified water was used to irrigate the Capsicum annuum L. seedlings. On the 45th day, the plants were harvested, and total glucose, protein, chlorophyll, and proline were checked by comparing control plants. Enterobacter sp. MSK86 increased carbohydrate and proline levels by 37.22mg/g ( 0.54 SE) and 2.44mg/g ( 0.1 SE), while K. pneumoniae NET12-treated plants showed an increase in chlorophyll by 1.95mg/g ( 0.02 SE) and total protein by 1.94mg/g ( 0.03 SE). The bacteria in this study showed they could lower SDS levels in graywater and improve farming by adding nutrients to the soil and plants, offering a sustainable way to tackle detergent pollution, fertilizer use, and water scarcity. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025. -
Deciphering the non-linear nexus between government size and inflation in MENA countries: an application of dynamic-panel threshold model
Contradictory to conventional economic theory, which foresees any increase in the size of government as inflationary, this article provides evidence that the reaction of price levels to changes in the size of government is nonlinear. The price levels do not necessarily increase in response to a rise in the size of the government but only up to a certain threshold or optimal level. Accordingly, this paper utilizes the dynamic panel threshold model to examine the threshold effects of government size (measured as government final consumption expenditure as a proportion of GDP) on inflation using a sample of 10 selected MENA countries from 1980 to 2019. The findings of this study stand out in several ways. First, the results support the nonlinear relationship between government size and inflation in the study area. Second, the government sizes estimated threshold level is equivalent to 12.46%. Third, government size negatively impacts inflation in the regime of small governments up to the threshold level. The impact turns positive once the government size goes beyond the threshold level in a regime of large size of government. These findings have ramifications for the conduct of fiscal policy. Policymakers in the MENA region can increase the size of government till it reaches the threshold level without exerting any upward pressure on price levels. The Author(s) 2024.