Hasil untuk "Marketing. Distribution of products"

Eugenia Diva Widodo, Syafira Isna Syalsabila, Julius Poerwanto et al.

Mai Kikumori, Ryuta Ishii

One of the common strategies in the content business is book-to-movie adaptation, in which the textual content in the book medium is presented as visual and sound content in the film medium. While books include a variety of genres, the novel is most widely used as the basis for a movie. A unique feature of the novel-based movie is the presence of two types of movie consumers: “original novel fans,” who read the original novel and watch the movie because they liked the book, and “no-novel readers,” who watch the movie without having read the original novel. Given this unique consumer feature and the fact that electronic word-of-mouth (e-WOM) is a key factor in film success, it is important to address the following questions: “Are original novel fans or non-novel readers more active in e-WOM generation?” and “What is their mechanism of e-WOM generation?”. We aim to address these questions through two studies. In Study 1, we examine which of the two types of movie consumers generate e-WOM, and we show that original novel fans are more likely to generate e-WOM. In Study 2, we focus on original novel fans, who are key consumers of e-WOM, and examine their e-WOM generation mechanisms. The results show that original novel fans generate e-WOM through social pathways. This study provides a better understanding of consumer behaviors regarding e-WOM generation. Moreover, we provide useful insights for business managers involved in novel-to-movie adaptation.

Rosa TITOUCHE , Lounas HADDADI

Les nouvelles technologies ont permis de nombreuses avancées et ce dans tous les domaines, le confinement qu’a subi le monde a mis en exergue ce fabuleux outils. En effet, les NTIC ont permis à l’économie mondiale de ne pas s‘effondrer, grâce notamment au télétravail et à l’enseignement à distance. L’Algérie n’est pas en reste, puisque le télé-enseignement est mis en place et s’est généralisé malgré la fin du confinement. La question qui se pose est la suivante : comment les enseignants et les étudiants perçoivent ces changements imposés à toutes les universités algériennes dans des délais assez courts sans laisser aux concernés (les enseignants, les étudiants, le staff admiratif) le temps de s’y adapter, d’apprendre à les utiliser sans formations dispensées au préalable ? Pour répondre à cette question, nous avons décidé de mener une enquête auprès des étudiants et des enseignants

Mingsheng Qiu, Liping Gao, Zhouyong Lin et al.

This study addresses the coal transportation and flow distribution challenges within a “port-before-plant” type thermal power plant, with a strong emphasis on green logistics principles. Despite significant advancements in clean energy, coal remains a dominant energy source in China, necessitating optimization in its usage and management to mitigate environmental impacts. This paper introduces an integrated optimization model grounded in green logistics and employs an Improved Particle Swarm Optimization (IPSO) algorithm to efficiently manage the coal supply chain from unloading at ports to loading into generators. The model incorporates new parameters and constraints that not only reflect the operational realities of coal logistics but also emphasize minimizing carbon emissions and energy consumption. Numerical experiments demonstrate the algorithm’s superior performance compared to traditional solvers like Gurobi, particularly in handling large-scale instances. Sensitivity analysis reveals the importance of prioritizing efficient and environmentally sustainable unloading and loading equipment, suggesting strategies for optimizing green coal transportation routes. Overall, this research provides valuable insights for policymakers and industry operators to enhance operational efficiency while ensuring environmental sustainability through the implementation of green logistics.

Aleksandr Farseev, Qi Yang, Marlo Ongpin et al.

Online marketing faces formidable challenges in managing and interpreting immense volumes of data necessary for competitor analysis, content research, and strategic branding. It is impossible to review hundreds to thousands of transient online content items by hand, and partial analysis often leads to suboptimal outcomes and poorly performing campaigns. We introduce an explainable AI framework SOMONITOR that aims to synergize human intuition with AI-based efficiency, helping marketers across all stages of the marketing funnel, from strategic planning to content creation and campaign execution. SOMONITOR incorporates a CTR prediction and ranking model for advertising content and uses large language models (LLMs) to process high-performing competitor content, identifying core content pillars such as target audiences, customer needs, and product features. These pillars are then organized into broader categories, including communication themes and targeted customer personas. By integrating these insights with data from the brand's own advertising campaigns, SOMONITOR constructs a narrative for addressing new customer personas and simultaneously generates detailed content briefs in the form of user stories that, as shown in the conducted case study, can be directly applied by marketing teams to streamline content production and campaign execution. The adoption of SOMONITOR in daily operations allows digital marketers to quickly parse through extensive datasets, offering actionable insights that significantly enhance campaign effectiveness and overall job satisfaction.

Ryan Dew, Nicolas Padilla, Anya Shchetkina

Recent years have seen a resurgence in interest in marketing mix models (MMMs), which are aggregate-level models of marketing effectiveness. Often these models incorporate nonlinear effects, and either implicitly or explicitly assume that marketing effectiveness varies over time. In this paper, we show that nonlinear and time-varying effects are often not identifiable from standard marketing mix data: while certain data patterns may be suggestive of nonlinear effects, such patterns may also emerge under simpler models that incorporate dynamics in marketing effectiveness. This lack of identification is problematic because nonlinearities and dynamics suggest fundamentally different optimal marketing allocations. We examine this identification issue through theory and simulations, wherein we explore the exact conditions under which conflation between the two types of models is likely to occur. In doing so, we introduce a flexible Bayesian nonparametric model that allows us to both flexibly simulate and estimate different data-generating processes. We show that conflating the two types of effects is especially likely in the presence of autocorrelated marketing variables, which are common in practice, especially given the widespread use of stock variables to capture long-run effects of advertising. We illustrate these ideas through numerous empirical applications to real-world marketing mix data, showing the prevalence of the conflation issue in practice. Finally, we show how marketers can avoid this conflation, by designing experiments that strategically manipulate spending in ways that pin down model form.

Chang Gong, Di Yao, Lei Zhang et al.

In online advertising, marketing mix modeling (MMM) is employed to predict the gross merchandise volume (GMV) of brand shops and help decision-makers to adjust the budget allocation of various advertising channels. Traditional MMM methods leveraging regression techniques can fail in handling the complexity of marketing. Although some efforts try to encode the causal structures for better prediction, they have the strict restriction that causal structures are prior-known and unchangeable. In this paper, we define a new causal MMM problem that automatically discovers the interpretable causal structures from data and yields better GMV predictions. To achieve causal MMM, two essential challenges should be addressed: (1) Causal Heterogeneity. The causal structures of different kinds of shops vary a lot. (2) Marketing Response Patterns. Various marketing response patterns i.e., carryover effect and shape effect, have been validated in practice. We argue that causal MMM needs dynamically discover specific causal structures for different shops and the predictions should comply with the prior known marketing response patterns. Thus, we propose CausalMMM that integrates Granger causality in a variational inference framework to measure the causal relationships between different channels and predict the GMV with the regularization of both temporal and saturation marketing response patterns. Extensive experiments show that CausalMMM can not only achieve superior performance of causal structure learning on synthetic datasets with improvements of 5.7%\sim 7.1%, but also enhance the GMV prediction results on a representative E-commerce platform.

Elham Khamoushi

Artificial Intelligence (AI) has revolutionized food marketing by providing advanced techniques for personalized recommendations, consumer behavior prediction, and campaign optimization. This paper explores the shift from traditional advertising methods, such as TV, radio, and print, to AI-driven strategies. Traditional approaches were successful in building brand awareness but lacked the level of personalization that modern consumers demand. AI leverages data from consumer purchase histories, browsing behaviors, and social media activity to create highly tailored marketing campaigns. These strategies allow for more accurate product recommendations, prediction of consumer needs, and ultimately improve customer satisfaction and user experience. AI enhances marketing efforts by automating labor-intensive processes, leading to greater efficiency and cost savings. It also enables the continuous adaptation of marketing messages, ensuring they remain relevant and engaging over time. While AI presents significant benefits in terms of personalization and efficiency, it also comes with challenges, particularly the substantial investment required for technology and skilled expertise. This paper compares the strengths and weaknesses of traditional and AI-driven food marketing techniques, offering valuable insights into how marketers can leverage AI to create more effective and targeted marketing strategies in the evolving digital landscape.

ACHNID JAOUAD, OUKASSI MUSTAPHA

L’instauration d’un système de prime au rendement des enseignants présente un défi théorique et pratique. Quelles sont alors les opportunités de la mise en œuvre d’un tel système au secteur de l’éducation? Par quels moyens évaluer ce rendement? Et comment ce système inciterait les enseignants au rendement? Telles sont les questions qui pourraient atteindre l’objectif principal de cette recherche qui est de donner quelques éléments de réponses quant aux axes prioritaires à adopter dans l’instauration de ce système au Maroc. En général, plusieurs théories telle que la théorie de l’agence démontrent que la prime de rendement est un élément essentiel de la motivation des collaborateurs. L’étude benchmark montre que plusieurs pays qui ont mis des systèmes de prime au rendement des enseignants réalisent de bonnes performances dans les programmes internationaux d’Évaluation des Systèmes Éducatifs. Nous avons adopté dans cette recherche la méthode d’analyse du contenue via un guide d’entretien administré à plusieurs experts et responsables de l’Académie Régionale de l'Éducation et de la Formation de Béni Mellal Khénifra. L’analyse effectuée montre que la mise en œuvre d’un système de prime au rendement aux enseignants au Maroc contribuera à l’amélioration de quelques éléments visibles de leurs activités, et que les notes obtenues par les élèves aux tests est l’outil le plus approprié pour évaluer ce rendement. Parmi les limites de ce type de recherche l’absence d’enquêtes institutionnelles marocaines qui génèrent des données probantes permettant des analyses quantitatives poussées. Adopter les connaissances managériales pourrait ouvrir des perspectives d’amélioration importantes au système éducatif marocain.

L. Yespergenova, Zh. Zhakusheva, A. Faizulayev

The banking industry plays a significant role in the development of the economic growth of the country. The purpose of this study is to determine the key factors that influence the profitability of the banking industry in Kazakhstan from 2012 to 2020 using firm-specific and macroeconomic variables.  For this research, 8 banks and 9 years were selected and the data were analyzed according to the feasible generalized least squares (FGLS) method. Findings demonstrate that political stability, liquidity risk, and interest rate have negative, and GDP growth, inflation, and NPL have a positive, but insignificant impact on profitability. Capital adequacy and bank size resulted in a positive and significant effect on ROA.  As a recommendation, the banks should emphasize TETA and size to be profitable. To the best of our knowledge, this paper contributes to the existing literature is twofold. First of all, it is the first study that conducted empirical analysis on the 8 largest banks of Kazakhstan by employing the FGLS method determining the financial performance. Secondly, the number of variables and years were broadened compared to previous researchers and a political stability indicator was added to the study. The practical significance of this paper recommends to policymakers, managers, and government officials should pay more attention to internal factors rather than external factors, because the expansion of the size of banks will improve the financial performance of banks, and eventually, this will be incorporated to into the development of the financial market of the country. Keywords: profitability; largest banks; economic growth; political stability.

Silvio M. Brondoni, Fabio Musso

The continuation of the war in Ukraine and the appearance of the BRICS+ have consolidated some critical issues that appeared at the beginning of the war, including the rise in the prices of energy and food raw materials. In global markets, companies are nowadays exposed to a fierce competition and new socio-environmental forces on a vast scale. For global companies, the ongoing geopolitical changes increase corporate and network profit risks, but also represent new, great opportunities for corporate management on long-term trends.

Jing Yuan, Songyu Jiang, Bethzaida Mary Joy Dela Cruz

Mobile payment has a pronounced impact on the consumption mode of various industries and provides new clues for sustainable consumption. This study aims to explore the role of perceived risk and perceived technology on sustainable consumption intention and behavior. Moreover, it proposes the structural equation model of mobile payment for sustainable consumption behavior. 574 participants from Chinese higher education institutions filled in the questionnaire. The bootstrapping method was used to solve the problem of mediating factors. Amos 26.0 helped to construct structural equation models. The study determined the negative effect of the perceived mobile payment risk on the perceived mobile payment usefulness, perceived mobile payment ease of use, and sustainable consumption intention. Moreover, the three variables have a particular buffer in the relationship between perceived mobile payment risk and sustainable consumption behavior. Furthermore, perceived mobile payment usefulness positively impacts sustainable consumption intention, and they have a chain-mediated effect on the relationship between perceived mobile payment risk and sustainable consumption behavior. The same effect also occurs in the relationship between perceived mobile payment ease of use and sustainable consumption intention.

Marko Vidrih, Shiva Mayahi

This paper delves into the transformative power of Generative AI-driven storytelling in the realm of marketing. Generative AI, distinct from traditional machine learning, offers the capability to craft narratives that resonate with consumers on a deeply personal level. Through real-world examples from industry leaders like Google, Netflix and Stitch Fix, we elucidate how this technology shapes marketing strategies, personalizes consumer experiences, and navigates the challenges it presents. The paper also explores future directions and recommendations for generative AI-driven storytelling, including prospective applications such as real-time personalized storytelling, immersive storytelling experiences, and social media storytelling. By shedding light on the potential and impact of generative AI-driven storytelling in marketing, this paper contributes to the understanding of this cutting-edge approach and its transformative power in the field of marketing.

David Fellner, Thomas I. Strasser, Wolfgang Kastner et al.

The changes in the electric energy system toward a sustainable future are inevitable and already on the way today. This often entails a change of paradigm for the electric energy grid, for example, the switch from central to decentralized power generation which also has to provide grid-supporting functionalities. However, due to the scarcity of distributed sensors, new solutions for grid operators for monitoring these functionalities are needed. The framework presented in this work allows to apply and assess data-driven detection methods in order to implement such monitoring capabilities. Furthermore, an approach to a multi-stage detection of misconfigurations is introduced. Details on implementations of the single stages as well as their requirements are also presented. Furthermore, testing and validation results are discussed. Due to its feature of being seamlessly integrable into system operators' current metering infrastructure, clear benefits of the proposed solution are pointed out.

Tianchi Cai, Jiyan Jiang, Wenpeng Zhang et al.

We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the challenge, we propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies. The proposed method reduces the need to store infinitely many policies in previous methods to only constantly many policies, which achieves nearly optimal policy efficiency, making it practical and favorable for industrial usage. We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation. Our experiments on a large-scale marketing campaign with tens-of-millions users and more than one billion budget verify the theoretical results and show that the proposed method outperforms various baseline methods. The proposed method has been successfully deployed to serve all the traffic of this marketing campaign.

Dwi Agustina Kurniawati, Asfin Handoko, Rajesh Piplani et al.

Purpose This paper aims to optimize the halal product distribution by minimizing the transportation cost while ensuring halal integrity of the product. The problem is considered as a capacitated vehicle routing problem (CVRP), based on the assumption that two different types of vehicles are used for distribution: vehicles dedicated for halal product distribution and vehicles dedicated for nonhalal products distribution. The problem is modeled as an integer linear program (ILP), termed CVRP-halal and nonhalal products distribution (CVRP-HNPD). It is solved using tabu-search (TS)-based algorithm and is suitable for application to real-life sized halal product distribution. Design/methodology/approach Two approaches are used in solving the problem: exact approach (integer-linear program) and approximate approach (TS). First, the problem is modeled as ILP and solved using CPLEX Solver. To solve life-sized problems, a TS-based algorithm is developed and run using MATLAB. Findings The experiments on numerical data and life-sized instances validate the proposed model and algorithm and show that cost-minimizing routes for HNPD are developed while ensuring the halal integrity of the products. Practical implications The proposed model and algorithm are suitable as decision support tools for managers responsible for distribution of halal products as they facilitate the development of minimum cost distribution routes for halal and nonhalal products while maintaining the integrity of halal products. The model and algorithm provide a low transportation cost strategy at the operational level of halal products distribution while fulfilling the halal logistics requirement. Originality/value To the best of the author’s knowledge, this is the first study that specifically deals with the CVRP of halal products distribution by proposing CVRP-HNPD model and TS-CVRP-HNPD algorithm. The proposed model and algorithm ensure the integrity of halal products along the distribution chain, from the warehouse (distribution center) to the retailer, while achieving lowest transportation cost.

Vianney Taquet, Vincent Blot, Thomas Morzadec et al.

Estimating uncertainties associated with the predictions of Machine Learning (ML) models is of crucial importance to assess their robustness and predictive power. In this submission, we introduce MAPIE (Model Agnostic Prediction Interval Estimator), an open-source Python library that quantifies the uncertainties of ML models for single-output regression and multi-class classification tasks. MAPIE implements conformal prediction methods, allowing the user to easily compute uncertainties with strong theoretical guarantees on the marginal coverages and with mild assumptions on the model or on the underlying data distribution. MAPIE is hosted on scikit-learn-contrib and is fully "scikit-learn-compatible". As such, it accepts any type of regressor or classifier coming with a scikit-learn API. The library is available at: https://github.com/scikit-learn-contrib/MAPIE/.

Lu Wang, Mokhtar Bozorg, Mohammad Rayati et al.

The concept of Energy Hub (EH) has been emerged to accommodate renewable energy sources in a multi-energy system to deploy the synergies between electricity and other energy sources. However, the market mechanisms for the integration of the EHs into the energy markets are not sufficiently elaborated. This paper proposes a flow-based two-level distributed trading mechanism in the regional electricity market with EH. At the lower level, the regional system operator coordinates the regional grids transactions in two markets, the local energy market with EH and the wholesale market of the upstream grid. Every nodal agent as an independent stakeholder leverages price discrepancy to cross arbitrage from different markets. At the upper level, the EH is a third player intending to maximize profit from trading in the regional electricity market and gas market. The regional electricity market clearing problem is formulated as a mathematical program with equilibrium constraints, for which we develop an ADMM-based distributed algorithm to obtain the equilibrium solution. The DC power flow is decomposed into optimization problems for the regional system operator and agents at different nodes, which can be solved in a distributed manner to achieve global optimality without violating the privacy of players. Case studies based on a realistic regional grid verify the effectiveness of the proposed algorithm and show that the mechanism is effective in decomposing power flow and increasing energy efficiency.

Leo Ardon, Dario Morelli, Francesco Villani et al.

Surfing on the internet boom, the digital marketing industry has seen an exponential growth in the recent years and is often at the origin of the financial success of the biggest tech firms. In this paper we study the current landscape of this industry and comment on the monopoly that Google has managed to gain over the years through technical innovations and intelligent acquisitions. We then propose potential avenues to explore in an effort to help moving the digital marketing industry towards a fairer model.

Khosro Pakmanesh, Mehdi Mojaradi

Context: In recent decades, many financial markets and their participants have changed their working method from a completely manual and traditional one to an automatic one, benefiting from complex software systems. There are different approaches to the development of such software systems. Objective: In this paper, we study the application of the Multi Product Line (MPL) approach in the software ecosystem (SECO) of the equity market. By profiting from the concepts and practices of the MPL approach, we want to design a SECO that makes the real-time and automated flow of financial transaction data between market participants' software pieces possible. Method: We first provide some background information about the equity market, its participants and their relations, and two primary order life-cycles in which these players cooperate. After that, we analyze the variability in each market participant's software. Next, we describe the employed architecture and the implementation approach. Finally, we discuss three scenarios by which the whole proposed SECO is tested and validated. Results: To implement the mentioned working method, named Straight-through Processing (STP), different technical and non-technical elements' contribution is essential. Attaining success in developing the equity market's SECO addresses the technical aspect and prepares the technical infrastructure for the rest of the work. Conclusion: The successful validation of the equity market's SECO indicates that the adoption of the MPL approach is a viable strategy for the development of equity market SECOs. It also suggests that this approach is worthy of more attention and investment.