Hasil untuk "Marketing. Distribution of products"

Manuel Machado , Álvaro Dias, Mafalda Patuleia et al.

Purpose: The research investigates the direct and indirect effects of marketing capabilities on innovation in lifestyle tourism businesses. It also explores the mediating effects of entrepreneurial self-efficacy, intense positive emotions, and proactivity affect lifestyle entrepreneurship. Methods: To test the conceptual model survey data were used with a sample of 187 entrepreneurs operating in lifestyle tourism in Portugal. The model was examined using a Partial Least Squares Structural Equation modelling (PLS-SEM). Results: It was found that marketing capabilities’s impact on innovation is direct and indirect, showing that entrepreneurial self-efficacy, positive emotions, and proactivity play a key part in the link between marketing capabilities and innovation. Implications: By adding self-efficacy, emotions, and proactivity to the effects of marketing on innovation, the study advances the study on lifestyle entrepreneurship in tourism. In particular, the study highlights the key role of self-efficacy, emotions, and proactivity. This research describes important characteristics of lifestyle tourism entrepreneurs, providing important insights regarding psychological and behavioural factors mediating the link between marketing capabilities and innovation.

Musa Mbago, Marcia Mkansi, Joseph M. Ntayi et al.

PurposeThis study aims to examine the role of reverse supply networks (RSN) and innovative technologies in enhancing reverse logistics (RL) implementation, underpinned by the Complex Adaptive Systems theoretical lens.Design/methodology/approachA cross-sectional quantitative research design was adopted, using data from 65 plastics recycling focal firms, representing 264 responses from RL practitioners. Data analysis used Partial Least Squares Structural Equation Modeling through SmartPLS to test established hypotheses.FindingsThe results reveal the multidimensional nature of the RSN and its significant role in RL implementation in plastics recycling industries. Specifically, supply base and formalization significantly influence RL implementation, while relations did not prove to be a significant predictor. In addition, innovative technologies mediate the relationship between the RSN and RL implementation.Research limitations/implicationsThis study, focused on Uganda’s plastics recycling industry, may limit generalizability to other sectors or regions. Future research should explore paper, electronics or textile recycling to assess RSNs across industries and developing countries. The cross-sectional design restricts observing long-term dynamics, limiting insights into changes over time.Originality/valueThis study advances the application of complex adaptive systems theory in RL operations, offering a novel perspective on the structural dynamics of RSNs. By empirically examining their influence on RL implementation, this study contributes to global discourse by enriching theoretical understanding and offering practical implications for enhancing RL efficiency.

Aleksandr Farseev, Marlo Ongpin, Qi Yang et al.

The future of digital marketing lies in the convergence of human creativity and generative AI, where insight, strategy, and storytelling are co-authored by intelligent systems. We present MindFuse, a brave new explainable generative AI framework designed to act as a strategic partner in the marketing process. Unlike conventional LLM applications that stop at content generation, MindFuse fuses CTR-based content AI-guided co-creation with large language models to extract, interpret, and iterate on communication narratives grounded in real advertising data. MindFuse operates across the full marketing lifecycle: from distilling content pillars and customer personas from competitor campaigns to recommending in-flight optimizations based on live performance telemetry. It uses attention-based explainability to diagnose ad effectiveness and guide content iteration, while aligning messaging with strategic goals through dynamic narrative construction and storytelling. We introduce a new paradigm in GenAI for marketing, where LLMs not only generate content but reason through it, adapt campaigns in real time, and learn from audience engagement patterns. Our results, validated in agency deployments, demonstrate up to 12 times efficiency gains, setting the stage for future integration with empirical audience data (e.g., GWI, Nielsen) and full-funnel attribution modeling. MindFuse redefines AI not just as a tool, but as a collaborative agent in the creative and strategic fabric of modern marketing.

Saurabh Sharma, Ambuj Singh

The problem of maximizing the adoption of a product through viral marketing in social networks has been studied heavily through postulated network models. We present a novel data-driven formulation of the problem. We use Graph Neural Networks (GNNs) to model the adoption of products by utilizing both topological and attribute information. The resulting Dynamic Viral Marketing (DVM) problem seeks to find the minimum budget and minimal set of dynamic topological and attribute changes in order to attain a specified adoption goal. We show that DVM is NP-Hard and is related to the existing influence maximization problem. Motivated by this connection, we develop the idea of Dynamic Gradient Influencing (DGI) that uses gradient ranking to find optimal perturbations and targets low-budget and high influence non-adopters in discrete steps. We use an efficient strategy for computing node budgets and develop the ''Meta-Influence'' heuristic for assessing a node's downstream influence. We evaluate DGI against multiple baselines and demonstrate gains on average of 24% on budget and 37% on AUC on real-world attributed networks. Our code is publicly available at https://github.com/saurabhsharma1993/dynamic_viral_marketing.

Anirban Mukherjee

Generative AI has ushered in the ability to generate content that closely mimics human contributions, introducing an unprecedented threat: Deployed en masse, these models can be used to manipulate public opinion and distort perceptions, resulting in a decline in trust towards digital platforms. This study contributes to marketing literature and practice in three ways. First, it demonstrates the proficiency of AI in fabricating disinformative user-generated content (UGC) that mimics the form of authentic content. Second, it quantifies the disruptive impact of such UGC on marketing research, highlighting the susceptibility of analytics frameworks to even minimal levels of disinformation. Third, it proposes and evaluates advanced detection frameworks, revealing that standard techniques are insufficient for filtering out AI-generated disinformation. We advocate for a comprehensive approach to safeguarding marketing research that integrates advanced algorithmic solutions, enhanced human oversight, and a reevaluation of regulatory and ethical frameworks. Our study seeks to serve as a catalyst, providing a foundation for future research and policy-making aimed at navigating the intricate challenges at the nexus of technology, ethics, and marketing.

Jiayao Zhang, Yuran Bi, Mengye Cheng et al.

Data is the new oil of the 21st century. The growing trend of trading data for greater welfare has led to the emergence of data markets. A data market is any mechanism whereby the exchange of data products including datasets and data derivatives takes place as a result of data buyers and data sellers being in contact with one another, either directly or through mediating agents. It serves as a coordinating mechanism by which several functions, including the pricing and the distribution of data as the most important ones, interact to make the value of data fully exploited and enhanced. In this article, we present a comprehensive survey of this important and emerging direction from the aspects of data search, data productization, data transaction, data pricing, revenue allocation as well as privacy, security, and trust issues. We also investigate the government policies and industry status of data markets across different countries and different domains. Finally, we identify the unresolved challenges and discuss possible future directions for the development of data markets.

Dugang Liu, Xing Tang, Han Gao et al.

As a key component in online marketing, uplift modeling aims to accurately capture the degree to which different treatments motivate different users, such as coupons or discounts, also known as the estimation of individual treatment effect (ITE). In an actual business scenario, the options for treatment may be numerous and complex, and there may be correlations between different treatments. In addition, each marketing instance may also have rich user and contextual features. However, existing methods still fall short in both fully exploiting treatment information and mining features that are sensitive to a particular treatment. In this paper, we propose an explicit feature interaction-aware uplift network (EFIN) to address these two problems. Our EFIN includes four customized modules: 1) a feature encoding module encodes not only the user and contextual features, but also the treatment features; 2) a self-interaction module aims to accurately model the user's natural response with all but the treatment features; 3) a treatment-aware interaction module accurately models the degree to which a particular treatment motivates a user through interactions between the treatment features and other features, i.e., ITE; and 4) an intervention constraint module is used to balance the ITE distribution of users between the control and treatment groups so that the model would still achieve a accurate uplift ranking on data collected from a non-random intervention marketing scenario. We conduct extensive experiments on two public datasets and one product dataset to verify the effectiveness of our EFIN. In addition, our EFIN has been deployed in a credit card bill payment scenario of a large online financial platform with a significant improvement.

Anna Milewska, Daniel Błażejczyk

The goal of this article was to identify and characterize the socio-economic effects of the introduction of the “Family 500+” program in Poland. The research part focused on checking the implementation of the assumptions, which were placed on the introduction of the "Family 500+" program. The main focus of the study was fertility, the improvement of which is a key goal of the program. The research methods used in the article were: data analysis method - data on the number of births of children in Poland, the fertility rate as well as the method of synthesis, inference and interpretation were analyzed. On the basis of the data analysis, conclusions were drawn and their nature explained. This was done in order to reflect on the effectiveness of public spending on the "Family 500+" program. To achieve the intended objective, research and analysis were carried out using selected indicators and statistical data. Their results allowed to verify the effectiveness of public funds spent on the indicated program.

Shaiban Ahmed, David Le, Taeyoon Son et al.

Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a redesigned UNet architecture which employs an encoder-decoder pipeline. The input section encompasses partially compensated OCT B-scans with individual retinal layers optimized. Corresponding output is a fully compensated OCT B-scans with all retinal layers optimized. Two numeric parameters, i.e., peak signal to noise ratio (PSNR) and structural similarity index metric computed at multiple scales (MS-SSIM), were used for objective assessment of the ADC-Net performance. Comparative analysis of training models, including single, three, five, seven and nine input channels were implemented. The five-input channels implementation was observed as the optimal mode for ADC-Net training to achieve robust dispersion compensation in OCT

Klaudia Macias

The world is constantly undergoing socio-economic and cultural changes, but the existence of patriarchy and the related gender inequality remain unchanged. In response to this state of aff airs, a feminist movement emerged and infl uenced society. Companies recognize the changes taking place in society and decide to include the feminist movement in their cause-related marketing campaigns. This is how a relatively new form of marketing communication, called femvertising, was born. This paper aims to analyze this form of marketing communication and the eff ect of both hostile and benevolent sexism on the perception of advertising. The research method employed in this study was a quantitative survey on a sample of 321 Polish women. The results of the survey showed that women rate advertisements portraying females in an unconventional way higher than traditional advertisements. Moreover, the perception of advertisements is infl uenced by the level of both benevolent and hostile sexism. The higher the level of sexism, the more negative the evaluation of pro-women advertising becomes. This is a suggestion for advertisers that diversifying an advertising message can be an eff ective marketing strategy.

Cheng Jie, Da Xu, Zigeng Wang et al.

With the increasing scale of search engine marketing, designing an efficient bidding system is becoming paramount for the success of e-commerce companies. The critical challenges faced by a modern industrial-level bidding system include: 1. the catalog is enormous, and the relevant bidding features are of high sparsity; 2. the large volume of bidding requests induces significant computation burden to both the offline and online serving. Leveraging extraneous user-item information proves essential to mitigate the sparsity issue, for which we exploit the natural language signals from the users' query and the contextual knowledge from the products. In particular, we extract the vector representations of ads via the Transformer model and leverage their geometric relation to building collaborative bidding predictions via clustering. The two-step procedure also significantly reduces the computation stress of bid evaluation and optimization. In this paper, we introduce the end-to-end structure of the bidding system for search engine marketing for Walmart e-commerce, which successfully handles tens of millions of bids each day. We analyze the online and offline performances of our approach and discuss how we find it as a production-efficient solution.

Piotr Pietraszewski

The paper discusses the links between stock market performance and real economic activity and presents results of an empirical inquiry into dynamic relationships between the main stock index quoted on the Warsaw Stock Exchange (WIG) and GDP in Poland over the years 1995–2019. In many empirical studies for highly developed countries not only short‑run dynamic interactions but also a long‑run cointegrating relationship between the stock index and output have been found. Previous studies for Poland reported mainly short‑run linkages between stock returns and changes of economic activity whereas the evidence for a long‑run cointegrating relationship is still quite scarce. In this paper, the VAR‑VECM methodology with the Johansen tests for cointegration is used to study a substantially longer quarterly data interval than has been investigated so far. Research results show that stock returns Granger‑cause GDP growth with up to three‑quarters lead. The evidence for the existence of a long‑term cointegrating relationship has also been found.

Ahmad Al Adwan

The study aimed to investigate the impact of motivation factors on an individual’s decision to choose Jordan as their primary tourism destination in the Middle East. The decision to choose Jordan as a medical destination will be analyzed based on factors, including government support; push engagement, and image perception. To this end, the study will gather data from 300 online individuals who have traveled to the Middle East for medical purposes. A qualitative approach will be adopted to provide insight into an individual’s preference for Jordan as the primary medical destination. A Partial Least Squares Structural Equation Modeling method was adopted, which allows for the creation of relations with different variables. The study’s findings indicate that people from rural areas in the Middle East preferred Jordan as a medical destination. Also, more women than men traveled to Jordan for medical purposes. Finally, more single people than married persons choose medical assistance in Jordan. Future studies are needed to ascertain how factors such as quality and cost influenced medical tourism into Jordan. AcknowledgmentsI would like to thank the Business School at Al Ahliyya Amman University, Jordan. Specifically, many thanks go to the departments of marketing.

Iaroslav Gadzalo, Mykola Sychevskiy, Olha Kovalenko et al.

The methodological approach for assessing the formation of food demand in unforeseen situations using digital Internet-technologies and the assessment itself, is substantiated in the paper (in the context of the COVID-19 pandemic of 2020). Comparison and theoretical generalization, as well as statistical test-assessment of hypotheses and structural regularities based on the data of Google Trends Internet platform, is used to analyze consumer preferences and intensity of demand changes for meat, milk, sugar, bread, and flour during the pandemic and quarantine, both in developed and developing countries. It is discovered that the biggest changes can be observed in the developed countries: consumer preferences shifted from rather expensive food products (milk and meat) to much cheaper ones (flour and bread). It is asserted that a decrease in consumer demand for basic food products will have a negative impact on the global economy. In 2020, a considerable decrease in GDP is expected for the developed countries; in the developing countries, GDP decline will not be as large, but prices are expected to rise much more noticeably. The following anti-crisis measures are proposed: support of the most vulnerable population and increase of food accessibility; temporary reduction of the VAT and other taxes influencing the price of food; reduction of central banks’ lending rates, etc. With the correct measures applied, the stabilization of consumer demand for food and gradual growth of the global economy is expected by the end of 2021.

Dubravka Vlasic, Katarina Poldrugovac , Sandra Jankovic

Competitive pricing is considered to be a very important part of revenue management, a management instrument that enables selling right products and services to the customers at the prices that will produce highest revenues. Marina business is supposed to be a business whose products or services are perishable (similar to hotels, airlines, campsites, hostels etc.) and tracking prices of competitors is very important part of managing its business. The purpose of this paper is to address the problem of relative price position and relative price fluctuation performance in marina business and seeks to complement existing research in the domain of strategic price positioning. The research results reveal that marinas who set their prices higher than their competition achieve lower level of berth occupancy and at the same time succeed higher RevPAB. Marinas with lower prices than their competitors achieve higher level of berth occupancy and lower RevPAB.

Eric E. Mang’unyi, Krishna K. Govender

While consumers play a very crucial role in the marketing strategies of companies, effective development of strategies must satisfy their needs and wants. Therefore, an evaluation and understanding of the underlying factors and/or dimensions influencing consumer buying behavior are critical for supermarkets to both retain and acquire new customers. The article reports on factors impacting the consumer buying behavior and the relationship among the factors. The study uses data from a cross-sectional survey conducted within a random sample of 699 customers at 17 supermarkets in Nairobi, Kenya. Reliability and factorial validity of the self-administered questionnaire were evaluated and considered satisfactory, while structural equation modelling (SEM) was used to test several hypotheses. Social characteristics were a good predictor of the consumers’ inclination to patronize a supermarket, thus directly influencing the buying behavior. A strong positive connection between psychological factors and buying behavior was ascertained based on income, which suggests that although psychological characteristics impact consumer attitudes towards the supermarket, income and education levels may well play a determining role in this regard. Retail marketers in general and in Kenya in particular are encouraged to be cognizant of the above when developing strategic marketing programs to increase the level of patronage. As a research paper, the study is limited to the data and prior empirical research. It offers the benefit of new research directions for marketing managers in understanding and satisfying the consumers. The main contribution of the present research, interdisciplinary in nature due to combining elements linked to both marketing and psychology, is its focus on consumer buying behavior towards supermarkets in a developing country, thus producing revealing insights.

Robert E. Gaunt, Guillaume Mijoule, Yvik Swan

We provide a general result for finding Stein operators for the product of two independent random variables whose Stein operators satisfy a certain assumption, extending a recent result of Gaunt, Mijoule and Swan \cite{gms18}. This framework applies to non-centered normal and non-centered gamma random variables, as well as a general sub-family of the variance-gamma distributions. Curiously, there is an increase in complexity in the Stein operators for products of independent normals as one moves, for example, from centered to non-centered normals. As applications, we give a simple derivation of the characteristic function of the product of independent normals, and provide insight into why the probability density function of this distribution is much more complicated in the non-centered case than the centered case.

Farid Rahem

تمثل المقاولاتية خيار استراتيجي للتشغيل خاصة بالنسبة للطلبة، وهذه الدراسة مساهمة إضافية هدفت إلى التعرف على توجهات طلبة جامعة العربي التبسي ورغباتهم نحو تحقيق مشاريعهم الخاصة مع تحديد الإمكانات والتحديات التي سيواجهونها عند مباشرة مشاريعهم، ولقد توصلت إلى أن هناك رغبة لدى الطلبة في إنشاء مشاريع ريادية، كما بينت أنهم يتمتعون بإمكانات تسهل لهم ذلك رغم وجود مجموعة من التحديات أهمها ضعف المناخ الاستثماري بشكل عام.  

Alexander Il'inskii, Sofiya Ostrovska

Lin's condition is used to establish the moment determinacy/indeterminacy of absolutely continuous probability distributions. Recently, a number of papers related to Lin's condition for functions of random variables have emerged. In this work, Lin's condition is studied for the product of random variables with given densities in the case when their joint distribution is singular.

José Horta, Daniel Kofman, David Menga et al.

The limited capacity of distribution grids for hosting renewable generation is one of the main challenges towards the energy transition. Local energy markets, enabling direct exchange of energy between prosumers, help to integrate the growing number of residential photovoltaic panels by scheduling flexible demand for balancing renewable energy locally. Nevertheless, existing scheduling mechanisms do not take into account the phases to which households are connected, increasing network unbalance and favoring bigger voltage rises/drops and higher losses. In this paper, we reduce network unbalance by leveraging market transactions information to dynamically allocate houses to phases using solid state switches. We propose cost effective mechanisms for the selection of households to switch and for their optimal allocation to phases. Using load flow analysis we show that only 6% of houses in our case studies need to be equipped with dynamic switches to counteract the negative impact of local energy markets while maintaining all the benefits. Combining local energy markets and dynamic phase switching we improve both overall load balancing and network unbalance, effectively augmenting DER hosting capacity of distribution grids.