This study examines the impact of one-to-many parasocial relationships between social media influencers and consumers on product attitudes and referrals using social comparison theory. Focusing on beauty and fashion social media influencers on YouTube, this study employs a structural equation model to test how influencer credibility and the psychological dimensions of consumer envy affect product attitudes and referrals through parasocial relationships. The results of analyzing 120 samples of response data confirmed the validity of the components of influencer credibility, which include 5 factors (trustworthiness, expertise, social attractiveness, physical attractiveness, and homophily). Influencer credibility and envy were observed to positively influence product attitudes and referrals while enhancing parasocial relationships with consumers. In particular, trustworthiness, social attractiveness, and homophily strongly influence parasocial relationships. This study contributes to the literature by developing a comprehensive model of the parasocial relationships between influencers and consumers and their impact on product attitudes and referrals.
Mengfei Chen, Mohamed Kharbeche, Mohamed Haouari
et al.
Natural disasters like hurricanes, earthquakes, and floods devastate food supply chains and can threaten food security and public health. These disruptions, from production to consumption, lead to shortages, increased waste, and heightened vulnerability among food-insecure populations. This study addresses the need for effective emergency strategies to ensure food continuity and equity during crises. A Markov Decision Process (MDP)-based model is proposed to enhance food supply chain resilience under disaster conditions. The model involves a two-stage decision-making process: Stage 1 focuses on strategic decisions for immediate response, such as facility reconstruction, and Stage 2 handles tactical decisions during relief efforts, such as transportation routes and product flow. The objective functions of our model include minimizing response time and costs and ensuring equity of food accessibility. A resilience assessment approach is proposed to evaluate the performance of Pareto solutions. The proposed method is applied to the Qatar beef supply chain during a flooding scenario, demonstrating practical effectiveness. Sensitivity analysis is conducted to identify critical thresholds for establishing alternative distribution centers, which helps to optimize responses based on facility capacity. This research improves disaster preparedness and response, ensuring that food supply chains can adapt and recover quickly while enhancing the equity of people’s access to food and nutrition. A case study on Qatar’s beef supply chain under flood conditions shows that the proposed method achieves up to 95 % reduction in response time cost, a 9 % improvement in system resilience, and maintains over 99.5 % food accessibility under severe disruption scenarios.
Marketing. Distribution of products, Management. Industrial management
Many studies have aimed to broaden participation in computing (BPC) through extracurricular educational initiatives. When these initiatives are structured as open-enrollment extracurricular programs, their success often depends on their marketing approach. However, there is little in the computing education research literature about how to conduct effective marketing for these initiatives. We describe the changes made to the marketing strategy of one such program, an educational hackathon for middle school and high school students in the Pacific Northwest. These included reducing promotion to affluent families, using targeted school-based communication, and emphasizing cost supports during initial promotion. We then compare attendance and self-reported demographics before and after the intervention. Results indicate a higher proportion of students from marginalized and low-income communities and no reduction in overall attendance.
Marketing Mix Modeling (MMM) is a statistical technique used to estimate the impact of marketing activities on business outcomes such as sales, revenue, or customer visits. Traditional MMM approaches often rely on linear regression or Bayesian hierarchical models that assume independence between marketing channels and struggle to capture complex temporal dynamics and non-linear saturation effects [@Chan2017; @Hanssens2005; @Ng2021Bayesian]. **DeepCausalMMM** is a Python package that addresses these limitations by combining deep learning, causal inference, and advanced marketing science. The package uses Gated Recurrent Units (GRUs) to automatically learn temporal patterns such as adstock (carryover effects) and lag, while simultaneously learning statistical dependencies and potential causal structures between marketing channels through Directed Acyclic Graph (DAG) learning [@Zheng2018NOTEARS; @Gong2024CausalMMM]. Additionally, it implements Hill equation-based saturation curves to model diminishing returns and optimize budget allocation. Key features include: (1) a data-driven design where hyperparameters and transformations (e.g., adstock decay, saturation curves) are learned or estimated from data with sensible defaults, rather than requiring fixed heuristics or manual specification, (2) multi-region modeling with both shared and region-specific parameters, (3) robust statistical methods including Huber loss and advanced regularization, (4) comprehensive response curve analysis for understanding channel saturation.
Experiential focused on creating memorable and meaningful experiences for consumers, has emerged as a key strategy in promoting tourist destinations. particularly in destinations seeking to highlight their unique cultural characteristics. In this study, the influence of experiential marketing on the tourism demand of the floating islands Los Uros, Puno, an emblematic destination in Peru, is analyzed. The objective of the research was to evaluate how experiential marketing strategies impact tourism demand in this destination, focusing on the sensorial, affective and thought experiences that tourists perceive. The methodology adopted a quantitative approach, with a non experimental cross-sectional design, descriptive scope and a correlational analysis. The study population consisted of 158 tourists. To collect the data, a questionnaire based on a 5-point Likert scale was used, with a total of 21 items. Among the results, a significant positive correlation was found (r = 0.809, p < 0.001) between experiential marketing and tourism demand. Sensory (r = 0.780, p < 0.001), affective (r = 0.772, p < 0.001) and thought (r = 0.797, p < 0.001) were described as determining factors in tourists' decisions when visiting Los Uros. In addition, the multiple regression analysis indicated that experiential marketing explains 83% of the variability in tourism demand. In conclusion, experiential marketing strategies have a significant impact on tourism demand. It is recommended that marketing managers of Los Uros Island implement tactics that promote memorable experiences, prioritizing strategies that involve experiences that stimulate the thinking and emotions of tourists.
Vilma D'Souza, Shantha Kumari Kurumujji, Arjun K. Rathie
The study of probability distributions for random variables and their algebraic combinations has been a central focus driving the advancement of probability and statistics. Since the 1920s, the challenge of calculating the probability distributions of sums, differences, products, and quotients of independent random variables have drawn the attention of numerous statisticians and mathematicians who studied the algebraic properties and relationships of random variables. Statistical distributions are highly helpful in data science and machine learning, as they provide a range of possible values for the variables, aiding in the development of a deeper understanding of the underlying problem. In this paper, we have presented a new probability distribution based on the $\hat{I}$-function. Also, we have discussed the applications of the $\hat{I}$ function, particularly in deriving the distributions of product and the quotient involving two independent $\hat{I}$ function variates. Additionally, it has been shown that both the product and quotient of two independent $\hat{I}$-function variates also follow the $\hat{I}$-function distribution. Furthermore, the new distribution, known as the $\hat{I}$-function distribution, includes several well-known classical distributions such as the gamma, beta, exponential, normal H-function, and G-function distributions, among others, as special cases. Therefore, the $\hat{I}$-function distribution can be considered a characterization or generalization of the above-mentioned distributions.
A new 2 parameter unit Weibull distribution is defined on the unit interval (0,1). The methodology of deducing its PDF, some of its properties and related functions are discussed. The paper is supplied by many figures illustrating the new distribution and how this can make it illegible to fit a wide range of skewed data. The new distribution holds a name (Attia) as a nickname.
In decentralized finance, any individual can pool their assets into an automated market maker (AMM) -- herein we focus on the constant product market maker (CPMM) -- in exchange for a claim on a fraction of future pool assets and fees earned from the market making operations. This position is represented by a liquidity token, whose prevailing on-chain price is effectively the initial deposited assets. Though this price is well-defined, we treat the liquidity token as a derivative position in the prices of the underlying assets for the CPMM in order to deduce risk-neutral pricing and hedging formulas, not dissimilar to the Black-Scholes result. Adopting this perspective, in a frictionless environment, hedging the CPMM liquidity token under fair valuation should produce a riskless process, which therefore grows at the risk-free rate, something that is not seen in empirical case studies under the prevailing price. With our novel pricing formula, we construct a method to calibrate a volatility to data which provides an updated (non-market) valuation which is consistent with the (near-continuous) replication strategy out-of-sample. We conclude with a discussion of novel AMM design considerations motivated by this derivative-pricing perspective.
With the widespread popularity of internet celebrity marketing all over the world, short video production has gradually become a popular way of presenting products information. However, the traditional video production industry usually includes series of procedures as script writing, video filming in a professional studio, video clipping, special effects rendering, customized post-processing, and so forth. Not to mention that multilingual videos is not accessible for those who could not speak multilingual languages. These complicated procedures usually needs a professional team to complete, and this made short video production costly in both time and money. This paper presents an intelligent system that supports the automatic generation of talking avatar videos, namely Virbo. With simply a user-specified script, Virbo could use a deep generative model to generate a target talking videos. Meanwhile, the system also supports multimodal inputs to customize the video with specified face, specified voice and special effects. This system also integrated a multilingual customization module that supports generate multilingual talking avatar videos in a batch with hundreds of delicate templates and creative special effects. Through a series of user studies and demo tests, we found that Virbo can generate talking avatar videos that maintained a high quality of videos as those from a professional team while reducing the entire production costs significantly. This intelligent system will effectively promote the video production industry and facilitate the internet marketing neglecting of language barriers and cost challenges.
A systematic analysis of the advantages and challenges associated with the satellite-based implementation of the high dimensional extended B92 (HD-Ext-B92) and high-dimensional BB84 (HD-BB84) protocol is analyzed. The method used earlier for obtaining the key rate for the HD-Ext-B92 is modified here and subsequently the variations of the key rate, probability distribution of key rate (PDR), and quantum bit error rate (QBER) with respect to dimension and noise parameter of a depolarizing channel is studied using the modified key rate equation. Further, the variations of average key rate (per pulse) with zenith angle and link length in different weather conditions in day and night considering extremely low noise for dimension d=32 are investigated using elliptic beam approximation. The effectiveness of the HD-(extended) protocols used here in creating satellite-based quantum key distribution links (both up-link and down-link) is established by appropriately modeling the atmosphere and analyzing the variation of average key rates with the probability distribution of the transmittance (PDT). The analysis performed here has revealed that in higher dimensions, HD-BB84 outperforms HD-Ext-B92 in terms of both key rate and noise tolerance. However, HD-BB84 experiences a more pronounced saturation of QBER in high dimensions.
Online platforms often incentivize consumers to improve user engagement and platform revenue. Since different consumers might respond differently to incentives, individual-level budget allocation is an essential task in marketing campaigns. Recent advances in this field often address the budget allocation problem using a two-stage paradigm: the first stage estimates the individual-level treatment effects using causal inference algorithms, and the second stage invokes integer programming techniques to find the optimal budget allocation solution. Since the objectives of these two stages might not be perfectly aligned, such a two-stage paradigm could hurt the overall marketing effectiveness. In this paper, we propose a novel end-to-end framework to directly optimize the business goal under budget constraints. Our core idea is to construct a regularizer to represent the marketing goal and optimize it efficiently using gradient estimation techniques. As such, the obtained models can learn to maximize the marketing goal directly and precisely. We extensively evaluate our proposed method in both offline and online experiments, and experimental results demonstrate that our method outperforms current state-of-the-art methods. Our proposed method is currently deployed to allocate marketing budgets for hundreds of millions of users on a short video platform and achieves significant business goal improvements. Our code will be publicly available.
We design a coordination mechanism between a distribution system operator (DSO) and distributed energy resource aggregators (DERAs) participating directly in the wholesale electricity market. Aimed at ensuring system reliability while providing open access to DERAs, the coordination mechanism includes a forward auction that allocates access limits to aggregators based on aggregators' bids that represent their benefits of aggregation. The proposed coordination mechanism results in decoupled DSO and DERAs operations that satisfy the network constraints, independent of the stochasticity of the renewables, wholesale real-time locational marginal prices, and individual DERA's aggregation policy. Optimal bidding strategies by competitive aggregators are also derived. The efficiency of the coordination mechanism and the locational price distribution at buses of a radial distribution grid are demonstrated for a 141-bus radial network.
Out-of-distribution (OOD) detection has recently received much attention from the machine learning community due to its importance in deploying machine learning models in real-world applications. In this paper we propose an uncertainty quantification approach by modelling the distribution of features. We further incorporate an efficient ensemble mechanism, namely batch-ensemble, to construct the batch-ensemble stochastic neural networks (BE-SNNs) and overcome the feature collapse problem. We compare the performance of the proposed BE-SNNs with the other state-of-the-art approaches and show that BE-SNNs yield superior performance on several OOD benchmarks, such as the Two-Moons dataset, the FashionMNIST vs MNIST dataset, FashionMNIST vs NotMNIST dataset, and the CIFAR10 vs SVHN dataset.
Compared to other transportation modes, road, railroad and vessel, multiproduct pipelines are the safest and most economical way of conveying petroleum products over long distances day and night. During the last two decades, the operational multiproduct pipeline scheduling has gained increasing attention, where most of the contributions are based on continuous time representation. In this paper, we present a new discrete-time mixed integer linear programming model for the shortterm scheduling of multiproduct pipelines featuring multiple refineries and distribution centers. The model optimally determines the sequence and planning of product injections at input nodes, the sequence of product deliveries to output nodes, and traces the size and the products location along the pipeline at any time. The proposed model also allows to execute simultaneous injections at the input nodes and to tackle simultaneous injections and deliveries at an intermediate node that can act as both the input and output node. Solutions to two benchmark example problems illustrate that the proposed model presents significant reductions in the pipeline operational cost.
Old road signs, especially larger, sturdier ones whose support structures are still intact but whose vintage lettering and neon are either too decayed or too problematically specific to preserve offer public art programs an opportunity to rethink three-dimensional public artworks as sites more akin to galleries than stand-alone, immutable works of genius. Valued artworks are still assumed to be the product of unique, individual perspectives, and detractors of public art often consider the genre a lesser form because it necessarily involves legal limitations, institutional approval and revision processes, and thus multiple or communal perspectives that “dilute” the artwork. Empty road signs as sites for public art provide a unique opportunity to model an inclusive public art approach that also values historic continuity, a palimpsest of sorts that leaves traces of the past as well as room for new eruptions.
Having in mind that "film industry" generates significant long-term effect through improvements of tourism flow, many destinations are positioning toward international production houses. Creation of audiovisual content within a destination creates noteworthy current effect of the economy of the country, making researchers more interested in analysis of economic effects that fiscal incentives for production of movies and TV series for the national economy. Aim of the research in this Paper is to measure economic effect of the incentives for production of movies, commercials, series and postproduction that Serbia has been approving in analyzed period, as well as to determine contribution of the subject sector to the employment in Serbia. In addition, Paper analyses influence on employment of the sector in European countries, as well as economic impact. Paper allows understanding of the film sector for the country economy.
F. Javier Rondan-Cataluña, Bernabe Escobar-Perez, Manuel A. Moreno-Prada
Purpose – This research enables the authors to highlight the importance of proper pricing for retailers. The purpose of this paper is to demonstrate the importance of demand-based pricing, providing empirical results that reveal the validity of this pricing philosophy in the sport retailing industry. In particular, this study has identified the limits of acceptable prices for the products studied, selected the most appropriate method for pricing products suffering from high competition and compared the impact produced on price perceptions according to different retail environments to be able to relate changes in the acceptable prices ranges according to the geographical location of each point of sale, differentiating between rural or urban environment and type of client. Design/methodology/approach – The authors have carried out surveys of 350 customers in each of the three points of sale analysed. Therefore, there are a total of 1,050 interviewees, for the three products analysed. The direct method of acceptable prices setting is developed. In addition, ANOVA and t-test have been carried out to find differences between the three shops. Findings – One main finding is that the acceptable price range is not unique. Each point of sale has one that is distinct because it depends on many factors: the competition, the economic capacity of the closest residents, the location of the point of sale or the ability to attract customers. Originality/value – The foremost contribution of this paper is to demonstrate empirically how considering the local demand at setting prices would generate larger earnings, even for a small retail chain. The direct method of setting acceptable prices enables us to set the prices according to the demand. The best option is if these prices are above the costs. It can be noted that the prices should be set according to each shop, and a different price used in each point of sale to maximise profits and to adapt to what the typical customer of each shop is willing to pay, despite the products being the same and the points of sale belonging to the same retail chain.