We demonstrate market inefficiency in cryptoasset markets. Our approach examines investments that share a dominant risk factor but differ in their exposure to a secondary risk. We derive equilibrium restrictions that must hold regardless of how investors price either risk. Our empirical results strongly reject these necessary equilibrium restrictions. The rejection implies market inefficiency that cannot be attributed to mispriced risk, suggesting the presence of frictions that impede capital reallocation.
This research examines how consumers judge a product's effectiveness based on its legal status. Across eight preregistered experiments, the authors find that consumers tend to believe legal products are less effective than illegal ones. Even when observing identical, objective product outcomes (e.g., equal weight loss from a drug), consumers perceive reduced product benefits from a product described as legal (vs. illegal). The authors test an account of why this belief occurs. When a product is legal, consumers infer that the government allows broad access to it, which they associate with lower product strength. In contrast, illegal products, which consumers presume are harder to access, are viewed as higher in product strength. This strength inference leads consumers to believe a legal product produces both smaller positive effects (lower efficacy) and smaller negative effects (lower harm) than an illegal product. Supporting this theory, the impact of legality on perceived efficacy is eliminated if legal and illegal products are described as equally accessible or equally strong. The authors further demonstrate that these beliefs influence consumer choice. Given the significant health and economic consequences of illegal product consumption, this research has important implications for consumers, marketers, public health professionals, and policy makers.
This study aims to explore how pricing can be optimized using artificial intelligence techniques, including machine learning and deep learning.
Traditional methods of setting price for goods and services are some of the main methods that organizations have relied on for long time. The most notably method is cost-based pricing, where the price is calculated by adding direct and indirect costs and adding a specific profit margin that ensures sufficient returns for the organization. Competition-based pricing is also used, where the price is determined based on market and competitor prices without placing significant emphasis on actual costs. This is appropriate in markets characterized by product convergence and multiple competitors. Another traditional method is perceived value-based pricing, which relies on the customer's assessment of the value of a product or service. Psychological pricing is used, where product pricing is based on methods that influence consumer perceptions. Traditional methods also include promotional pricing, which is used for temporary offers to stimulate demand, especially during recessions or when launching new products. Geographic pricing, which takes into account differences in costs, taxes, and market conditions across different geographic regions, allowing for flexibility in distribution and marketing. Although these methods were effective in previous periods, technological and behavioral developments is driving many organizations to adopt more sophisticated pricing methods that respond to changing market conditions.
Facial expression recognition plays an important role in several fields, among them optimizing the prices of goods and services. Facial recognition systems generally consist of three main stages: preprocessing, feature extraction, and classification.
Preprocessing is a vital step in improving face recognition performance. It involves enhancing image quality through operations like clarity adjustment, scaling, and noise removal. This step also eliminates irrelevant details (e.g., ears) and prepares the image for accurate recognition by applying techniques such as alignment, normalization, binarization, and standardization.
Feature extraction focuses on extracting key facial features — like the eyes, nose, and mouth—and their geometric arrangement to classify expressions. Each face has a distinct structure that enables recognition. Techniques such as eigenfaces and scale-invariant feature transform are used for accurate feature extraction. Facial emotions are conveyed through the activation of specific muscle groups, revealing complex information about a person’s mental state. Machine learning and deep learning techniques are used to recognize and classify these expressions by training models on labeled facial images.
The goal of price optimization is to find the best pricing strategy that leads to setting the appropriate price, maximizing profit, and meeting customer needs. This can be achieved by relying on customer behavior through facial expressions, as the face is a key feature in expressing emotions. By analyzing customer facial expressions relative to the prices of goods and services offered in retail stores (supermarkets), the store owners understand better the customer reactions to prices.
The results of analyzing customer facial expressions, both positive and negative (such as happiness, sadness, anger, surprise, fear, and disgust), provide store owners with accurate insights into customers' emotional feelings as they interact with products or services. These analysis enable store owners to precisely meet customer needs, design personalized offers and services, and enhance the shopping experience. This, in turn, leads to increased sales, builds trust between the store and customers, enhances customer loyalty and satisfaction, and increases profits. It also enables store owners to make accurate decisions based on available data, creating a competitive advantage in a market characterized by constantly changing customer tastes.
Financial technology (fintech) is a growing industry in Indonesia, supported by advances in the technological infrastructure. At the end of 2019, the Financial Services Authority (OJK), the financial authority in Indonesia, recorded 164 registered and licensed fintech (P2P lending) companies. However, since early 2018, the Investment Alert Task Force (SWI) and the Ministry of Communication and Information Technology have blocked 1,350 illegal fintech platforms. Illegal fintech lending practices have mechanisms beyond the responsibility and authority of the OJK, including the risk of collection and distribution of personal data. The essence of this study is to discuss the landscape of fintech P2P lending in Indonesia from Indonesian Online News data, explore cases of fintech p2p lending in Indonesia, and understand the rules and policies. Qualitative research with a case study approach and Focus Group Discussion techniques were used to obtain data from 4 stakeholders in the Fintech P2P Lending Industry in Indonesia. VOS Viewer software is used to build keywords from Indonesian Online News collections, NVIVO 12 qualitative software is used to assist data analysis. The research found the keyword clusters most frequently discussed in the Indonesian Online News collection and five case themes such as public awareness about P2P lending (user understanding), data leakage, and restriction of data access, including personal data protection, personal data fraud, illegal fintech lending, and Product marketing ethics.
Umme Rubab, Muhammad Rahies Khan, Muhammad Mutasim Billah Tufai
Achieving sustainable performance is a challenging but useful tool for firms in this competitive environment. The literature highlights several avenues to address sustainable performance, but there is alack of emphasis on common practices and strategies. This study examines the role of green initiatives, specifically eco-design, green purchasing and reverse logistics, in addressing environmental performance. Practice-based view theory is used to evaluate the influence of these common green practices on a firm's environmental performance. A total of 214 participants were approached to participate in this study and data analysis was conducted using AMOS. The findings reveal a significant and positive impact of eco-design, green purchasing and reverse logistics on environmental performance. This study provides implications for practitioners, policymakers and academics regarding environmentally oriented business operations that could better serve manufacturing firms. Additionally, firms are encouraged to focus on easily imitable, easy-to-transfer and easy-to-understand practices to address sustainable performance.
Organizational behaviour, change and effectiveness. Corporate culture, Marketing. Distribution of products
Romy Jake G. Pagador, Anthony G. Peñas, Cezar S. Obias Jr.
et al.
The contribution of agriculture in the socio-economic development is undeniable and is truly an important part of the Philippine economy. It is also a major source of livelihood and employment of most Filipinos especially in the rural areas. The general intent of this study is to evaluate the impact of the government agricultural intervention on selected upland communities in Goa, Camarines Sur especially on the far-flung barangays of the municipality using Regression Discontinuity Design. This study assessed the socio-economic and poverty status of the local farmers through the agricultural interventions received. The results showed that the distribution of seeds, fertilizers and cash assistance to the upland farmers could improve the overall outputs of the farmers, which can help alleviate their lives. Similarly, the agricultural interventions of the government have great and positive impact to alleviate poverty and improve the quality of life of the local farmers. However, irrigation and farm-to-market roads need to be prioritized to ensure that agricultural production output increase.
Industries. Land use. Labor, Marketing. Distribution of products
Objective: Environmental issues are a concern, especially global warming. One of the consequences of global warming is a significant increase in carbon emissions each year. However, investors are trying to understand whether increased carbon also improves companies' financial performance. This study seeks to investigate the influence of carbon productivity on the company’s financial performance (case study of companies listed on the IDX80 index). Research Design & Methods: This research uses a quantitative method with secondary data taken from the company's annual and sustainability reports from 2020 to 2023. The sampling method used is the purposive sampling method. The sample used in this research was 80 companies listed on the IDX80 Index. Panel Data Regression Analysis is used to analyze the data. Findings: The findings of the study indicated carbon productivity has no significant effect on company financial performance, whether measured through ROA or MBR On the other hand, when control variables are added, they have a significant effect on the company's financial performance as measured through ROA. Implications and Recommendations: From these findings, stakeholders, investors and financial managers in the Indonesian capital market can help in making investment decisions, especially regarding the influence of carbon productivity on financial performance and for stakeholders. Contribution & Value Added: This study adds value to the practice of finance that seeks to see that companies that disclose higher carbon emissions will affect the company's financial performance in the context of developing countries, especially Indonesia.
The transformational era has given rise to a new generation that is presumed to have different preferences than the previous generation. The proliferation of coffee shops has expanded consumer alternatives for purchasing decisions. This research aims to identify and segment Millennial and Zoomer consumers based on demographic, behavioral, and psychographic characteristics in the purchase of coffee beverages at coffee shops. The research employed a non-probability sampling method, specifically judgmental sampling, with a sample size of 180 coffee shop consumers in Madiun City. The data were analyzed using descriptive statistics, independent sample t-tests, and K-means clustering. The research findings indicate that millennials and Zoomer consumers tend to be dominated by females, mostly visiting once a week for leisure and prioritizing taste quality. There are significant differences between Millennial and Zoomer consumers in terms of promotional media attributes, WiFi/power outlet availability, live music entertainment, ordering process, and payment options. Based on consumer segmentation analysis, three segments were identified: coffee enthusiasts, adventurous coffee connoisseurs, and consumers who prioritize coffee beverage quality. The managerial implications for coffee shops involve directing marketing strategies specifically towards the adventurous taste segment and emphasizing quality by prioritizing innovation and differentiation in serving high-quality coffee beverages.
The family. Marriage. Woman, Marketing. Distribution of products
Users post numerous product-related questions on e-commerce platforms, affecting their purchase decisions. Product-related question answering (PQA) entails utilizing product-related resources to provide precise responses to users. We propose a novel task of Multilingual Cross-market Product-based Question Answering (MCPQA) and define the task as providing answers to product-related questions in a main marketplace by utilizing information from another resource-rich auxiliary marketplace in a multilingual context. We introduce a large-scale dataset comprising over 7 million questions from 17 marketplaces across 11 languages. We then perform automatic translation on the Electronics category of our dataset, naming it as McMarket. We focus on two subtasks: review-based answer generation and product-related question ranking. For each subtask, we label a subset of McMarket using an LLM and further evaluate the quality of the annotations via human assessment. We then conduct experiments to benchmark our dataset, using models ranging from traditional lexical models to LLMs in both single-market and cross-market scenarios across McMarket and the corresponding LLM subset. Results show that incorporating cross-market information significantly enhances performance in both tasks.
Artificial intelligence (AI) is widely deployed to solve problems related to marketing attribution and budget optimization. However, AI models can be quite complex, and it can be difficult to understand model workings and insights without extensive implementation teams. In principle, recently developed large language models (LLMs), like GPT-4, can be deployed to provide marketing insights, reducing the time and effort required to make critical decisions. In practice, there are substantial challenges that need to be overcome to reliably use such models. We focus on domain-specific question-answering, SQL generation needed for data retrieval, and tabular analysis and show how a combination of semantic search, prompt engineering, and fine-tuning can be applied to dramatically improve the ability of LLMs to execute these tasks accurately. We compare both proprietary models, like GPT-4, and open-source models, like Llama-2-70b, as well as various embedding methods. These models are tested on sample use cases specific to marketing mix modeling and attribution.
Ahmad Nazari Torshizi, Zahra Hemmat Yar, Javad Gholamian
et al.
Purpose: Today, there are sizable chunk of cities in the world, trying to introduce themselves as destination brands in various areas. Given that, this study aimed to explain the effective factors on destination branding in the field of sports tourism in Mashhad.
Method: This study was of applied research, from the view point of purpose, and also, in terms of its nature, it was in the field of exploratory research, furthermore, in terms of data collection, it was of a descriptive-survey type. Also, due to the use of Q methodology, it was a type of mixed research (quantitative-qualitative). The statistical population included marketing and branding specialists, sports tourism experts, sports management professors and people active in the field of tourism who had complete knowledge of Mashhad. For this purpose, theoretical saturation was achieved after in-depth and semi-structured interviewing 16 experts selected purposefully and finally 26 people completed the Q questionnaire.
Results: The data obtained from sorting the Q statements were entered into SPSS software version 26 and analyzed through varimax rotation (exploratory factor analysis). The priority of these models were infrastructural factors, inner city situation, cultural activities, commercial creativities, urban essence, and transportation, respectively.
Conclusion: The results of this research emphasize the strengthening of indicators such as suitable access to the Internet, managerial decisions, cultural diversity, the existence of suitable places and markets, the potential of the region and the smoothness of traffic and roads in order to make the destination as a brand in Mashhad's sports tourism.
Olivier Lindamulage De Silva, Vineeth Satheeskumar Varma, Ming Cao
et al.
A Stackelberg duopoly model in which two firms compete to maximize their market share is considered. The firms offer a service/product to customers that are spread over several geographical regions (e.g., countries, provinces, or states). Each region has its own characteristics (spreading and recovery rates) of each service propagation. We consider that the spreading rate can be controlled by each firm and is subject to some investment that the firm does in each region. One of the main objectives of this work is to characterize the advertising budget allocation strategy for each firm across regions to maximize its market share when competing. To achieve this goal we propose a Stackelberg game model that is relatively simple while capturing the main effects of the competition for market share. {By characterizing the strong/weak Stackelberg equilibria of the game, we provide the associated budget allocation strategy.} In this setting, it is established under which conditions the solution of the game is the so-called ``winner takes all". Numerical results expand upon our theoretical findings and we provide the equilibrium characterization for an example.
We describe how the market-based average and volatility of the "actual" return, which the investors gain within their market sales, depend on the statistical moments, volatilities, and correlations of the current and past market trade values. We describe three successive approximations. First, we derive the dependence of the market-based average and volatility of a single sale return on market trade statistical moments determined by multiple purchases in the past. Then, we describe the dependence of average and volatility of return that a single investor gains during the "trading day." Finally, we derive the market-based average and volatility of return of different investors during the "trading day" as a function of volatilities and correlations of market trade values. That highlights the distribution of the "actual" return of market trade and can serve as a benchmark for "purchasing" investors.
Md Sabbir Hossain, Nishat Nayla, Annajiat Alim Rasel
Product market demand analysis plays a significant role for originating business strategies due to its noticeable impact on the competitive business field. Furthermore, there are roughly 228 million native Bengali speakers, the majority of whom use Banglish text to interact with one another on social media. Consumers are buying and evaluating items on social media with Banglish text as social media emerges as an online marketplace for entrepreneurs. People use social media to find preferred smartphone brands and models by sharing their positive and bad experiences with them. For this reason, our goal is to gather Banglish text data and use sentiment analysis and named entity identification to assess Bangladeshi market demand for smartphones in order to determine the most popular smartphones by gender. We scraped product related data from social media with instant data scrapers and crawled data from Wikipedia and other sites for product information with python web scrapers. Using Python's Pandas and Seaborn libraries, the raw data is filtered using NLP methods. To train our datasets for named entity recognition, we utilized Spacey's custom NER model, Amazon Comprehend Custom NER. A tensorflow sequential model was deployed with parameter tweaking for sentiment analysis. Meanwhile, we used the Google Cloud Translation API to estimate the gender of the reviewers using the BanglaLinga library. In this article, we use natural language processing (NLP) approaches and several machine learning models to identify the most in-demand items and services in the Bangladeshi market. Our model has an accuracy of 87.99% in Spacy Custom Named Entity recognition, 95.51% in Amazon Comprehend Custom NER, and 87.02% in the Sequential model for demand analysis. After Spacy's study, we were able to manage 80% of mistakes related to misspelled words using a mix of Levenshtein distance and ratio algorithms.
Salem F. Hegazy, Salah S. A. Obayya, Bahaa E. A. Saleh
Practical implementations of quantum key distribution (QKD) have been shown to be subject to various detector side-channel attacks that compromise the promised unconditional security. Most notable is a general class of attacks adopting the use of faked-state photons as in the detector-control and, more broadly, the intercept-resend attacks. In this paper, we present a simple scheme to overcome such class of attacks: A legitimate user, Bob, uses a polarization randomizer at his gateway to distort an ancillary polarization of a phase-encoded photon in a bidirectional QKD configuration. Passing through the randomizer once on the way to his partner, Alice, and again in the opposite direction, the polarization qubit of the genuine photon is immune to randomization. However, the polarization state of a photon from an intruder, Eve, to Bob is randomized and hence directed to a detector in a different path, whereupon it triggers an alert. We demonstrate theoretically and experimentally that, using commercial off-the-shelf detectors, it can be made impossible for Eve to avoid triggering the alert, no matter what faked-state of light she uses.
We compute the total cross-section and invariant mass distribution for heavy-quark pair production in $e^+e^-$ annihilation at the next-to-next-to-next-to-leading order in QCD. The obtained results are expressed as piecewise functions defined by several deeply expanded power series, facilitating a rapid numerical evaluation. Utilizing top-pair production at a collision energy of 500 GeV as a benchmark, we observe a correction of approximately $0.1\%$ for the total cross-section and around $10\%$ for the majority of the invariant mass distribution range. These results play a crucial role in significantly reducing theoretical uncertainty: the scale dependence has been diminished to $0.06\%$ for the total cross-section and to $5\%$ for the invariant mass distribution. This reduction of uncertainty meets the stringent requirements of future lepton colliders.
Demirović-Bajrami Dunja, Simat Karolina, Vuksanović Nikola D.
et al.
The purpose of the paper was to investigate the attitudes and perceptions of young people about the fast food restaurants in Serbia, with the special emphasis to the elements of corporate social responsibility (brand, nutritional values, ethical values and the quality of food), and to show the extent to which these products are represented in their daily diet. Data were collected from February to June 2019 between students of the University of Belgrade and Novi Sad, and between young people at high schools in Belgrade and Novi Sad (Serbia). A total sample consisted of 1145 young consumers. It was evident that the values and preferences of the target group of the leading fast food restaurants have changed, in the already developed market as well as in the developing ones, such as Serbia. The paper presents empirical results of using the services of fast food restaurants in Serbia by the younger population, as well as their perception of corporate social responsibility, with the special emphasis to the restaurants of McDonald's and KFC.
An open market is a subset of an entire equity market composed of a certain fixed number of top capitalization stocks. Though the number of stocks in the open market is fixed, the constituents of the market change over time as each company's rank by its market capitalization fluctuates. When one is allowed to invest also in the money market, the open market resembles the entire 'closed' equity market in the sense that the equivalence of market viability (lack of arbitrage) and the existence of numeraire portfolio (portfolio which cannot be outperformed) holds. When access to the money market is prohibited, some topics such as Capital Asset Pricing Model (CAPM), construction of functionally generated portfolios, and the concept of the universal portfolio are presented in the open market setting.
Adrianna Damiana Mastalerz-Kodzis, Ewa Katarzyna Pośpiech
The development of methods describing time series using stochastic processes took place in the 20th century. Among others, stationary processes were modelled with Hurst exponent, whereas non‑stationary processes with Hölder function. The characteristic feature of this type of processes is the analysis of the memory present in the time series. At the turn of the 21st century interest in statistics and spatial econometrics, as well as analyses carried out within the new economic geography arose. In this article, we have proposed the implementation of methods taken from the analysis of time series in the modelling of spatial data and the application of selected measures in studying the intensity of expansion in spatial phenomena. As the intensity measure we use Hölder point exponents. The article is composed of two parts. The first one contains the description of study methodology, the second – examples of application.
In a competitive marketing, there are a large number of players which produce the same product. Each firm aims to diffuse its product information widely so that it's product will become popular among potential buyers. The more popular is a product of a firm, the higher is the revenue for the firm. A model is developed in which two players compete to spread information in the large network. Players choose their initial seed nodes simultaneously and the information is diffused according to Independent Cascade model (ICM). The main aim of the player is to choose the seed nodes such that they will spread its information to as many nodes as possible in a social network. The rate of spreading of information also plays a very important role in information diffusion process. Any node in a social network will get influenced by none or one or more than one information. We also analyzed how much fraction of nodes in different compartment changes by changing the rate of spreading of information. Finally, a game theory model is developed to obtain the Nash equilibrium based on best response function of the players. This model is based on Hotelling's model of electoral competition.