Indeed, in e-commerce as an evolving industry, applying generative AI has been regarded as the key to the revolution of the entire pricing strategy. With the increase in the amount of available data and computational resources, the application of dynamic pricing strategy is more characteristic for e-commerce organizations. This article will concentrate on the comparison of the generative AI-based dynamic pricing and the traditional price techniques for instances specifically based on their effects on the efficiencies of revenue management and customer experience. The appearance of the term dynamic pricing in the sphere of e-commerce was possible due to the fact that large amount of data could be analyzed to find such sources of steady and predictable revenue and demand forecast was being made. However, in earlier research on these models, the most used variable was the Price Elasticity of Demand constructed from historical information; however, emerging complexities of consumers and markets required more studies on pricing models incorporating generative AI. This theoretical as well as practical research paper aims to underlie extended knowledge about the generative AI dynamic pricing strategies and assess their efficiency in comparison with the traditional price strategies. Such elements of costbenefit analysis as cost efficiencies, customer satisfaction levels, opportunities for increasing revenues etc for changing conditions are pinpointed. This research uses literature review and the analysis of cases to examine the key concepts and practical implementation of the generative AI-driven dynamic pricing in e-commerce. It analyses a critical set of factors specifically, personalization strategies, sensitivity to price changes, and perceived value regarding consumer behavior whilst making a purchase in dynamic price context. The study also compares the effectiveness of dynamic pricing systems based on generative AI with traditional models, including fixed, time-based, and competition-based pricing schemes. Further, in this work, it has been illustrated that e-commerce companies can gain significant benefits by integrating generative AI into dynamic pricing frameworks, such as higher revenue, enhanced customer experience.
Telematics, the integration of telecommunications and informatics, has emerged as a disruptive and innovative force in the automotive insurance sector. This paper presents a bibliometric analysis of the application of telematics within this domain. The analysis focuses on the co-occurrence and connections between telematics and automotive insurance, offering insights into trends, advancements, and future directions. By mapping the connections in the literature, the paper aims to provide conceptual clarity and further understanding of how telematics is shaping the automotive insurance landscape. The findings highlight the role of telematics in transforming traditional risk assessment models and customizing insurance premiums based on real-time driver behaviour.
Radial basis function (RBF) networks, deep neural networks (DNN) with Adam optimization, spline interpolation, polynomial approximation, and DNN with Levenberg- Marquardt (LM) optimization are five sophisticated techniques used in this work to build a novel universal linearization framework. Through adaptive mode selection of the most appropriate technique depending on the unique characteristics of the sensor data, the proposed system achieves higher accuracy and robustness in handling diverse nonlinearities. Experimental results demonstrate remarkable improvement in linearization performance for various kinds of thermocouple sensors, witnessing the usability and efficiency of the framework for real-time applications.
The present study examines the role of capital structure on financial performance of energy sector companies in a major emerging economy. By concentrating on the moderating effect of corporate governance practices on ownership structure and corporate performance, it closes a gap in the research. It uses energy companies financial data for a period of 10 years (2014-2023), employing panel regression approach using fixed-effect estimation. Debt-equity ratio, debt-asset ratio has been considered for measuring capital structure whereas return on assets has been taken as proxy of financial performance. The results were validated using GMM model (Generalized Method of Moments) to control for potential endogeneity. Corporate governance has been measured by size of Board, , CEO duality, board independence, and size of audit committee. Based on regression results, the study finds a substantial detrimental impact of capital structure on performance of business. Further, the moderation analysis has revealed mixed results as board size and independence positively moderates performance of firm and capital structure whereas CEO duality and audit committee have negative moderating effect. The current study provides significant implications for management and extends literature on debt financing and corporate performance particularly the underexplored corporate governance’s role.
It is important to model the probability distribution of solar power to draw conclusions for installing a solar plant. The amount of solar power is depending on solar radiation. In this paper, we deal with the statistical distribution of the power generated by a system of PV modules subject to failure. We obtain some characteristics of the system considering the reliability of modules. We model the mean power and provide the optimum number of PV modules that need to be installed on the power plant. We present a real data analysis for a specific location in Izmir, Türkiye.
It is unclear how to determine any unevenness in Spatiotemporal variability with onlya simple glance at datasets. Real-time annual average daily traffic (AADT) data continues to be the primary operations data needed to perfect the exactness of the performance of transportation systems valuation. Despite the enormous investment in data collection procedures (automated and manual), data collection seems sparse and has varying levels of accuracy. The disparity from unoptimized AADT data collection from low-volume roads has stimulated the end-users to close gaps through modeling. However, it is convenient to use statistical techniques to assess the dataset’s local/global trends and normality before optimization processes can conveniently be successful. Therefore, exploratory spatial data analysis (ESDA) is explored to identify statistically significant similarities and disparate within datasets collected from the various locations. In addition, the spatial clustering assessment is completed using Getis-Ord Gi statistics. In contrast, spatial autocorrelationis explored with Moran’s Index. AADT datasets (2009 to 2016) from low-volume roads in Montana, Minnesota, and Washington is explored. The ESDA results indicate that none of the datasets exhibits global trends of global spatial autocorrelation. Nonetheless, there were some indications of local spatial autocorrelation in the datasets. Additionally, the AADT datasets exhibit spatial heterogeneity. The Getis-Ord Gi statistics indicate that most data points are not statistically significant, and the minor hotspot regions are not statistically significant. Moran’s Index reveals perfect clustering in the datasets. Though values were dissimilar (alternating values), it expresses the clustering as perfect with spatial outliers or spatial heterogeneity. As a first step before any model is completed, the process will help resolve the variability and divergence within the dataset.
In response to rising environmental concerns, many corporations have adopted green branding strategies; however, not all such efforts are genuine. Greenwashing—the practice of misleading stakeholders about environmental practices—has emerged as a critical barrier to building public trust and promoting community engagement in sustainability. This study presents initial findings from a pilot survey conducted in Tamil Nadu, India, using a newly developed instrument to assess the impact of greenwashing on community engagement, with public trust as a mediating variable and environmental awareness as a moderator. A structured questionnaire with 77 items across four constructs—greenwashing, public trust, community engagement, and environmental awareness—was validated through expert review, exploratory factor analysis (KMO = 0.831), and reliability testing (Cronbach’s alpha > 0.87 for all constructs). Findings revealed a negative correlation between greenwashing and community engagement (r = –0.45), with public trust mediating this relationship. Moderation analysis confirmed that individuals with higher environmental awareness were less affected by greenwashing. Grounded in the Theory of Planned Behavior and ecological citizenship theory, the study highlights the deterrent effects of deceptive green claims on civic participation. Implications suggest the need for policy interventions, enhanced environmental education, and stricter regulations to counteract greenwashing and foster genuine community involvement.
In this study, we have introduced the Chen distribution as an underlying hazard rate function for a parametric proportional hazards model called the Chen proportional hazards model. The maximum likelihood estimation approach was considered for determining the model parameter values. The developed model’s use was demonstrated with lifetime data. Results of the model selection criteria and the graphs of Cox-Snell residuals from the study disclosed that the newly introduced model demonstrates superiority over the Weibull and Gompertz proportional hazards models and might be useful when examining different kinds of survival statistics.
The Lindley distribution is a significant statistical distribution due to its superior statistical and mathematical properties compared to the exponential distribution. Consequently, it is utilized in various fields. This paper aims to examine different goodnessof-FIT tests for the Lindley distribution using cumulative entropy. The classical estimation method is employed to estimate the distribution parameters. A Monte Carlo simulation study is conducted to analyze the power of the tests across different distributions and sample sizes. To facilitate this comparison, we utilized several established goodness-of-FIT tests based on empirical distribution. Finally, a numerical example is present to demonstrate the validity of our test.
In recent years, the global spread of COVID-19 has posed a significant challenge, impacting nearly every country. A newly identified virus variant, exhibiting significantly higher transmissibility, has recently emerged. A comprehensive understanding of the transmission dynamics and the application of focused control measures are necessary for managing and containing its spread. Non-Pharmacological Interventions (NPIs) remain a key component of the main strategies for stopping the spread. In the SEIARM model of COVID-19 transmission, this research presents a novel control strategy to limit the number of affected people. By integrating the concepts of PID and Sliding Mode Control (SMC), the suggested approach guarantees resilience to changes in model parameters brought on by the sliding mode control method. The effectiveness of this method in handling the newly mutated virus is examined, and its performance is compared against open-loop, PID, and SMC controllers.
As flood frequency analysis (FFA) is an exact and vigorous zone of analysis in statistical hydrology, so, in this regard, numerous probability models, parameters estimation procedures, regionalization problems, and other associated issues are being scrutinized in statistical hydrology. For this reason, research on FFA has been conducted with fluctuating passion over the past couple of decades. Therefore, there is a need to introduce flexible probability models and their parametric estimation techniques in statistical hydrology. However, for accurate probability modeling of upcoming flood events researchers try their best to study the heavy tailed distributions. In light of this, we intend to put forth a flexible probability model that not only addresses the problems with regional flood statistics but also illustrates the reasonable return periods for flood events brought on by melting glaciers and rising temperatures. We have described and examined some mathematical and statistical aspects of the suggested approach in light of the aforementioned scenario, and we have used it on actual flood data sets. To ensure realistic predictions using the proposed model, we have also analyzed it in terms of Mellin transformation.
This paper introduces a beta distribution (BD) of Kind-5 with its density function expressed in terms of Gauss hypergeometrric function. Key distributional properties including the cumulative distribution function and the mandatory constants such as central moments, non-central moments, skewness, kurtosis, log-geometric mean, Harmonic mean, Shannon’s differential entropy are thoroughly examined. Additionally, the moment, cumulant, characteristic functions are derived. Special cases are explored through transformations of beta kind-5 variable and parameter shifting. The paper also covers parameter estimation using the method of unconstrained maximum likelihood estimation, the construction of Fisher’s information matrix and a constrained maximum likelihood approach by using nonlinear programming with an illustrative application provided.
Modelling heterogeneous data sets is an emerging research area that is growing steadily. This paper introduces the Harmonic Mixture Gompertz distribution, a new mixture model derived from the weighted harmonic mean of the survival functions of two Gompertz distributions. Some statistical properties are obtained. A simulation study is conducted to evaluate the performance of some estimation techniques used. Two-lifetime data sets were applied to the proposed distribution to ascertain its versatility while comparing it with other modifications of the Gompertz distribution. A regression model from the proposed model is developed using logarithmic link functions.
This study aims to conduct a bibliometric analysis using VOSviewer to offer insights to researchers focusing on “brand Image”(BI) in brand management. It seeks to provide perspectives to managers and marketers for devising marketing strategies (MS) to enhance brand image. This study employs the Dimension.ai database to investigate the productivity of authors, co-authorship patterns, institutional affiliations, and the geographic distribution of scholarly output. Additionally, it examines the most frequently cited papers and citing
The purpose of this research is to find out whether Zepto’s quickest delivery service is more popular with customers than the standard one. A survey was administered to a subset of Zepto customers as part of the study approach. Specifically, we wanted to know how satisfied customers were with the quickest delivery options, what their delivery preferences were, and what demographics we might use for future surveys. Insights on e-commerce customers’ delivery service preferences are provided by this study. Many different types of businesses are under the umbrella of the technology industry. Electronics and software, computer hardware, artificial intelligence (AI), and IT services are just a few of the many areas in which these companies operate. In pursuit of greater future potential, technology businesses are known to engage substantially in R&D and to embark on riskier ventures. Computers, smartphones, wearable electronics, household appliances, televisions, and a host of other consumer goods are all made by them. Software development, logistics system management, database security, etc. are all areas where businesses rely on technological advancements. There are many different areas that rely on the fast-expanding technology sector. Thanks to its substantial exports of both money and technology, it has emerged as an important player in the country’s economy. Numerous employments have been generated and substantial amounts of foreign direct investment have been drawn to the industry. Cybersecurity, AI, and blockchain technology have been areas of focus for the government’s several programs that aim to bolster the sector
This paper presents the linear demand patterns prevalent in the textile industry, providing insights into their implications on inventory dynamics. Leveraging this understanding, a green inventory model is formulated that balances the ecological impact of production and distribution processes with the traditional goals of cost-effectiveness and service level optimization. The model takes into account various elements, including the
In this paper, the decision making in risk and decision making under uncertainty are studied, and some examples are presented. This paper is continuation of paper [29] in References.
This paper examines the role of reliability theory in system analysis, focusing on system representation, quantification, and uncertainty modeling. It explores various multiscenario reliability stress-strength models based on Topp-Leone and Generalized Rayleigh distributions, covering environments with multiple stresses, strength variability, and constrained stress conditions. The study addresses modeling complexity in stress-strength relationships for multi-component, n-standby, and cascade systems. Several parameter estimation methods are evaluated, including maximum likelihood estimation, the Jackknife, and Bayesian estimators, with a Monte Carlo simulation used to compare their performance. The results indicate that maximum likelihood estimation and Jackknife methods are superior due to their low bias and Mean Squared Error across various scenarios and sample sizes, demonstrating robustness and reliability. Bayesian methods offer flexibility but require careful management of priors and data volume, while non-parametric methods tend to have higher bias and MSE, particularly in complex scenarios or with smaller sample sizes.
Inventory management strategy always plays a very important part in supply chain management. The mathematical models proposed by most researchers consider that the production process to be dependable and assume that inventory would not be defective. Therefore, this paper presents a two-tier integrated production-inventory supply chain model that incorporates a defect rate while accounting for an unreliable production process. If defective items are identified, they will be returned to the upstream vendors for reprocessing, after which the repaired products will be shipped back to the buyers. We designed a linear regression equation which combines the concept of decision support system (DSS) to mitigate the uncertainty in ambiguous situations, and then based on the data of the past years to predict more precisely estimate buyer demand. The main goals of this paper are to minimize the expected joint total cost and to identify the best solution considering the presence of defective items, as illustrated by our proposed model. The predicted results based on numerical examples are provided to decision makers or managers for reference to help them make the right decisions and avoid corporate losses.
