Brand equity and smartphone choice among Ghanaian university students

A temporal fusion transformer for multi-step forecasting of Indonesia’s strategic food commodity prices

Deep neuro-stochastic ensemble for lighting energy consumption forecasting and cost estimation under dynamic pricing schemes

Dynamic tariffs strategy in the vehicle-to-grid pricing scheme of microgrid clusters

Comparing delay-, distance-, and cordon-based congestion pricing strategies via large-scale simulation

Behavior-Based Pricing strategy of quality-differentiated products with imperfect customer recognition capability

Artificial intelligence (AI) is increasingly reshaping the operational landscape of quick-service restaurants (QSRs) by improving service efficiency and strengthening customer interactions. This study aims to examine the key factors influencing AI-enabled service quality and their impact on customer satisfaction, with particular emphasis on the mediating role of perceived value within the SERVQUAL framework. The research is based on data collected from 459 millennial respondents using a structured questionnaire, with participants selected through simple random sampling. The findings indicate that dimensions such as tangibility, reliability and responsiveness significantly contribute to customer satisfaction, while perceived value serves as an important mediating variable in this relationship. The results further demonstrate that AI-driven service quality positively influences customers’ perceived value, which, in turn, enhances their overall satisfaction. This highlights the critical role of value perception in translating technological advancements into meaningful customer experiences. The study provides practical implications for QSR operators, suggesting that effective integration of AI technologies can streamline service delivery, optimise pricing strategies and improve brand communication, ultimately leading to higher customer satisfaction and loyalty. Additionally, future research may expand the scope by incorporating other SERVQUAL dimensions, such as empathy and assurance, to gain a more comprehensive understanding of AI-driven service quality in the QSR sector.

Abstract Following the development of e-commerce, the manufacturer can sell products via direct or livestream modes. To meet market demand, the manufacturer will strategically adjust product prices, and consumers weigh whether to make early- or later-stage purchases based on their needs. In the current market, we assume two types of consumers including myopic and strategic consumers. Meanwhile, the tolerance level of each consumer regarding whether to delay purchase is heterogeneous. In our work, we built a two-stage game model to analyze the impact of consumers’ strategic behavior and their patience to delay purchases on manufacturer’s choice of the stage to adopt the livestream selling strategy under three scenarios including the manufacturer chooses a direct format to sell products in both stages (NN), the manufacturer chooses a direct format in the first stage and a livestream format to sell products in the second stage (NL), the manufacturer chooses a livestream format in the first stage and a direct format to sell products in the second stage (LN). Our main findings show that when strategic consumers dominate the market and consumers are more willing to postpone their purchasing, the manufacturer should adopt a penetration pricing strategy under NL scenario; otherwise, a skimming pricing strategy can be adopted. Under LN scenario, the manufacturer should directly employ a skimming pricing strategy. In addition, when the discount factor and the proportion of myopic consumers are small, and the commission rate is high, the manufacturer is more inclined toward the NL scenario; otherwise, it prefers to the LN scenario. In the extended models, we discuss the influence of different parameters, including the changed popularities of live-streamer, commission rates, and pit fees for the profit of the manufacturer. Moreover, we explore how the manufacturer selects livestream selling in both stages, and compare it with three main scenarios by using numerical studies. The result shows that the LN scenario is optimal in most cases.

Super Smart Grids (SSG) aim to provide large-scale, multi-zonal electricity access while dynamically balancing supply and demand. However, their implementation faces multidisciplinary challenges that range from ensuring grid stability to avoiding structural injustices in their design. Existing load forecasting approaches are unsuitable for SSG planning or deployment due to an over-reliance on regional specificity, time-series data, and a lack of social understanding. This paper proposes a hybrid approach to load forecasting that combines time-series power consumption and socioeconomic metrics, coupled with a novel deep learning algorithm that integrates Artificial Neural Network (ANN) and Luong's Attention Mechanism (LAM). First, two parallel ANNs are used to extract the main features of load demand behavior. Then, LAM fuses both ANNs using an attention score function that dynamically selects the most relevant characteristics per sample to improve the generalization abilities of the model. The SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) frameworks then interpret this algorithm to thoroughly comprehend its load forecast decision-making. Evaluated on 92 suburban zones in Australia, the proposed method achieves a Mean Absolute Percentage Error (MAPE) of 1.78%, outperforming Bidirectional Long-Short Term Memory (BiLSTM), Long-Short Term Memory (LSTM) and Recurrent Neural Network (RNN) when socioeconomic metrics are present during training. By merging high-resolution forecasting with socioeconomic awareness, this approach enhances demand and supply management, optimizes pricing strategies, and ensures equitable energy distribution-critical requirements for SSG deployment.

Transitioning from landfill to cross-regional centralized recycling: Uncovering the mechanisms and potentials of hard-to-treat hazardous waste through medical waste incineration fly ash recycling.

Rapid urbanization in Dhaka has driven significant shifts in food consumption patterns among its residents. Understanding the factors influencing food choice is crucial in making informed decisions by policy makers and enabling the development of targeted interventions to promote healthier food options. This study aims to explore the factors influencing the food choices of consumers in Dhaka city, with a focus on underlying food choice motives and pricing strategies. A cross-sectional study was conducted between August 2023 to February 2025 using written questionnaires. Purposive sampling technique was employed to obtain the sample. The questionnaire was administered in person at various points of food purchase, including grocery stores, local bazars, and supermarkets in Dhaka city. The study evaluated key food choice motives and pricing strategies using validated questionnaires. One-way ANOVA was used for mean comparisons. Tukey's test was employed for post-hoc mean comparisons between income groups. Health emerged as the most significant food choice motive. The middle-income group prioritized health and sensory appeal when making food choices, with mean (SD) scores of 4.4 (0.5) and 4.3 (0.8), respectively. In terms of attractiveness of pricing strategies, this group found healthy products on sale at a cheap rate more attractive. Conversely, the strategy of subsidized prices through TCB-operated open market sales was more attractive to the low-income group, with a mean (SD) score of 4.2 (0.9) than to the middle-income group 3.8 (1.2). To increase the consumption of healthy foods, the middle-income group preferred strategies such as healthy food options at a low VAT rate and healthy products on sale at a cheap rate. This study suggests that health is the most important factor among all food choice motives. The findings offer insights for designing interventions and guiding policymakers in promoting a healthier food environment.

ABSTRACT Various factors, such as demographic characteristics, medical history, and claims history influence health insurance pricing. Predicting pricing strategies and maintaining data security are important aspects of health insurance. This study proposes the factors influencing health insurance data and develops predictive models using Artificial Intelligence (AI) techniques to understand pricing strategies in the health insurance sector. This predicted AI model is encrypted and stored in a cloud database to ensure data security and management. Initially, the US Health Insurance Dataset is utilized, Hot Deck Imputation is applied for missing values, and Cook’s distance for outlier detection to increase the reliability of the data. The discrete cosine transform key features are extracted for prediction using the transform. Attention in an Echo State Network (ESN) increases the accuracy of pricing. Brakerski-Fan-Vercauteren encryption ensures sensitive insurance data is protected, and private cloud deployment is made possible by encryption, which secures the AI model. The experimental results proved the efficiency of the method for price predictions with a 0.07235 Mean Absolute Error and R2 score of 99.57%, while imposing security with an encryption time of 26.65 seconds for 1000MB of model data, thus providing a robust and scalable solution for health insurance pricing.

Purpose This study aims to investigate the transformative impact of the COVID-19 pandemic on over-the-top (OTT) platform adoption and digital entertainment consumption among Indian consumers. By integrating the technology acceptance model (TAM), uses and gratifications (UG) theory and crisis-driven behavioural frameworks, the research seeks to identify core determinants, mediators and moderators influencing user adoption and to quantify the relative contribution of technological, motivational and contextual drivers in shaping digital media behaviour during periods of acute disruption. Design/methodology/approach A cross-sectional survey was conducted with 412 respondents selected through multi-stage stratified sampling to ensure representativeness across age, income, gender and region. The 64-item instrument, pilot-tested and validated for reliability, measured TAM, UG and crisis constructs. Data were analysed using descriptive and inferential statistics, multivariate regression, multinomial logistic regression and structural equation modelling (SEM) to test direct, mediated and moderated pathways among key variables, providing a robust empirical assessment of the integrated conceptual framework. Findings Results reveal a 180% increase in OTT subscriptions and a dramatic rise in streaming hours post-pandemic. SEM analysis confirms perceived usefulness (ß = 0.54, p < 0.001) and UG motives (ß = 0.37, p < 0.001) as significant predictors of behavioural intention, with crisis exposure moderating the intention–usage relationship. The combined model explained 69% of behavioural variance. Distinct adoption and usage patterns emerge for urban youth and high-income groups, while platform choice is shaped by content, pricing and regional offerings. Research limitations/implications The cross-sectional design limits causal inference, and urban, digitally savvy youth are overrepresented, constraining generalizability to rural or older populations. Self-reporting introduces potential for recall and social desirability bias, and online sampling may exclude less-connected demographics. The study focuses on major OTT providers, omitting niche and/or regional platforms. Future research should employ longitudinal, probability-based and mixed-method designs to capture behavioural persistence and deeper motivational dynamics. Practical implications OTT providers can optimize segmentation by targeting high-intention, crisis-sensitive users with tailored content and pricing strategies. Investments in localized, diverse content and data-driven hybrid subscription models will enhance user retention. Policymakers should prioritize digital infrastructure expansion and actively promote regional content to strengthen industry resilience. Platform developers should implement adaptive interface and recommendation systems responsive to both technological and motivational drivers illuminated here. Social implications Widespread OTT adoption during crisis deepens digital inclusion for urban and youth segments but risks widening access gaps for marginalized groups. Enhanced content diversity and regional programming have the potential to foster cultural expression and cross-demographic engagement. Policy support for affordable access and digital literacy is vital to equitable benefits from accelerated media digitalization post-pandemic. Originality/value This paper advances knowledge by empirically validating an integrated TAM–UG–crisis framework for digital platform adoption in an emerging market under pandemic-induced upheaval. The multi-theoretical approach demonstrates the value of fusing technological, psychological and contextual perspectives to model adoption behaviour, offering actionable insights for researchers, industry practitioners and policymakers seeking to understand and shape digital transformation in volatile environments.

Purpose As e-commerce continues to proliferate, an increasing number of retailers are using online coupons to attract consumers. Additionally, dual-channel retailers are adopting the Buy-online-and-pickup-in-store (BOPS) strategy, transitioning to an omni-channel strategy to enhance consumers' shopping experience. This article aims to analyse how retailers' online coupon distribution and BOPS strategy implementation jointly influence their decision-making in channel management. Design/methodology/approach We construct two decision models, dual-channel and omni-channel for an e-commerce supply chain consisting of a single retailer and a single e-commerce platform. The online-only coupon distribution strategies of the retailers under these two models are investigated separately. We develop a Stackelberg game model and utilize the Karush–Kuhn–Tucker (KKT) method along with backwards induction to determine the optimal decisions for retailers. Findings In a dual-channel decision-making model, whether a retailer distributes coupons online depends on the relationship between the consumers' preference for online shopping and travel costs. After implementing BOPS, retailers always distribute coupons online in the omni-channel. Finally, we find that the conditions promoting retailers to implement BOPS vary depending on the coupon distribution strategies employed. Furthermore, we extend the base model in multiple ways, including considering the coupon distribution across all channels with differentiated pricing strategies, the retailers' logistics and inventory costs, endogenizing commission rates and the platform responsible for coupon distribution. The results remain qualitatively unchanged. Originality/value This study provides insights into the consideration that under different coupon strategies for dual-channel and omni-channel, and how the firms can achieve maximum by opening BOPS.

ABSTRACT The improvement of the electric vehicle (EV) industry is a core strategic initiative for China to advance the green economy and enhance domestic automotive competitiveness. However, China’s EV sector still faces two critical challenges: a gap in core technologies compared to developed countries and the risk of subsidy fraud amid equal foreign market access, both hindering its high-quality development. To address these issues, this study contributes a novel quality-oriented subsidy policy (QOSP) for domestic EV manufacturers (DEVMs), designed to fundamentally drive technological innovation while mitigating moral hazard. Methodologically, we constructed sequential game models to investigate the dynamic optimal pricing strategies of DEVMs and imported EV manufacturers (IEVMs), and analyzed the policy’s impact on domestic EV (DEV) quality improvement. The results reveal three key findings: (1) The QOSP effectively incentivizes DEVMs to enhance product quality, directly addressing the core technology gap; (2) Sufficient market demand potential and technical innovation capacity are prerequisites for the QOSP’s effective implementation; (3) An optimal subsidy intensity balances national financial burden and urgent DEV quality improvement, ensuring the steady development of the DEV market. This study provides a practical policy framework for governments to optimize EV industry support, balancing innovation, market fairness and financial sustainability.

Between 2022 and 2025, several ultra-high-cost, one-time therapies were approved in the United States, offering potentially curative options for conditions such as spinal muscular atrophy, hemophilia, and sickle cell disease. Despite their clinical value, upfront costs often exceeding $2-4 million create major challenges for payers, especially Medicaid programs with fixed budgets. Traditional reimbursement models, designed for chronic therapies, may not align with the long-term benefits and financial risks of these treatments. This has led to growing interest in alternative payment models, including outcomes-based agreements, installment payments, and risk-sharing contracts. However, adoption remains inconsistent due to regulatory barriers, administrative complexity, and limited long-term data. A narrative literature review was conducted to evaluate U.S. pricing strategies and manufacturer-payer payment models for ultra-high-cost, one-time therapies. Peer-reviewed and grey literature were identified through searches of PubMed, Embase, and Google Scholar, along with policy and regulatory sources. Publications from 2010 to 2025 were included and synthesized to categorize payment models and identify implementation challenges. Manufacturers used diverse pricing approaches rather than a single model. Outcomes-based agreements were more common when clinical endpoints were measurable, while other therapies relied on lump-sum payments with support programs. Adoption varied based on therapy characteristics and payer preferences, with limited standardization across stakeholders. These therapies are financed through a mix of traditional and alternative models, but regulatory and operational barriers continue to limit widespread adoption.

Guar or Cluster bean (Cyamopsis tetragonoloba L.) has been cultivated for grains as well as for green vegetable purposes since ancient times in India. This study examines the trends and growth performance of guar gum exports from India along with its major importing countries during the period 2013–2022. The present study was based on secondary data pertaining to guar gum exports from India and its major importing countries. The data were collected for the period 2013–14 to 2022–23 from published sources such as APEDA and FAOSTAT. The analysis focuses on annual growth rates in export quantity and value to assess the stability and dynamics of India’s export performance in the global market. The results reveal that guar gum exports exhibited a highly fluctuating trend throughout the study period, indicating significant instability in both quantity and value. The growth pattern of export quantity showed alternating phases of expansion and contraction. Positive growth was observed in selected years such as 2014, 2018, 2021, and 2022, with the highest increase recorded in 2018. However, several years experienced substantial negative growth, particularly during 2015–2017 and 2019–2020, reflecting instability in export performance. A similar pattern was observed in export value, which declined sharply during 2014–2016, followed by recovery phases in subsequent years, especially in 2017, 2021, and 2022. The strong growth in recent years indicates improved price realisation and revival in global demand. The analysis of major importing countries, including the USA, China, Germany, Italy, Canada, and Australia, also showed synchronised fluctuations with India’s export trends. Periods of global expansion, such as 2014, 2018, 2021, and 2022, were characterised by positive growth across most countries, while contraction phases during 2015–2017 and 2019–2020 reflected widespread decline in international demand. These patterns suggest a strong linkage between India’s export performance and global market conditions. Overall, the results indicate that guar gum exports are highly sensitive to external economic conditions and energy market dynamics. The study suggests the need for diversification of export destinations, promotion of value addition, and supportive policy measures to stabilise export performance and reduce volatility in the future.

The rapid growth of the halal skincare industry in Indonesia has triggered increasingly fierce competition among brands. This phenomenon pressures companies to develop competitive pricing strategies while maintaining superior product quality, with the aim of achieving optimal consumer satisfaction. Consumer satisfaction is a crucial factor because it directly influences customer loyalty and continued product usage. This study focuses on students at the Kemandiri Campus in Sidoarjo as research subjects. This group represents young, critical, rational consumers with a high awareness of Islamic values. As members of an Islamic educational institution, they tend to prioritize halal aspects, superior quality, and affordable prices when choosing skincare products. Furthermore, the increasing demand among students for safe, economical, and sharia-compliant skincare products makes halal skincare a contextually relevant research topic. This study aims to examine the influence of price and product quality on consumer satisfaction with halal skincare products among students at the Kemandiri Campus in Sidoarjo. A quantitative approach was applied, involving 100 respondents selected through purposive sampling. Data were collected through a questionnaire, followed by validity, reliability, and classical assumption tests, with hypothesis testing using the F test and t test. The results showed a t probability value of 0.001 for price and 0.000 for quality (both <5%), thus proving a significant positive influence.

Electric vehicles (EVs) are essential for sustainable urban mobility, coordinating transportation demands with energy distribution networks. However, uncoordinated EV charging neglects trip chain continuity, inducing spatial–temporal congestion and overloading local charging capacities. Thus, effectively guiding EVs is a key problem in mitigating traffic emissions and preventing power grid-side stress. In this paper, a two-stage dynamic routing framework within a traffic–energy coordination architecture is proposed, integrating an AHP–Entropy–TOPSIS model for station selection and an Improved Ant Colony Optimization algorithm for trajectory execution. Using this framework, a series of macro–micro simulations on the Sioux Falls network was conducted alongside a congestion-driven dynamic pricing mechanism. The results indicate that the pricing strategy facilitates spatial load balancing through peak shaving at core nodes. Compared to conventional standard meta-heuristic baselines, this framework reduces average economic costs by 28.9% while ensuring battery safety and limiting indirect carbon emissions. The proposed framework provides a multi-objective navigation solution that prevents cross-layer decision fragmentation, supporting the sustainable development of smart city infrastructure.

The rapid growth of digital food delivery services has transformed consumer behavior and the restaurant industry in India. This study examines the comparative effectiveness of customer service provided through food brands’ own applications and third-party delivery platforms. The research focuses on key parameters such as user experience, pricing strategies, delivery efficiency, customer satisfaction, and data privacy concerns. Primary data was collected from 75 respondents using structured questionnaires, and analyzed using percentage analysis, correlation, and regression techniques. The findings reveal that third-party platforms outperform brand-owned apps in convenience, variety, and pricing benefits, while brand apps excel in reliability, quality control, and personalized engagement. A strong positive correlation (r ≈ 0.91) was identified between usage frequency and privacy concerns. The study concludes that a hybrid approach combining operational efficiency and direct customer engagement can significantly enhance service quality. These insights are valuable for businesses, marketers, and platform developers in improving digital service delivery.