Group buying has become a prevalent selling method with the development of e-commerce and mobile internet. Since the success of group buying requires sufficient consumer participation, consumers are often uncertain about whether they can eventually obtain the low-priced products, resulting in potential post-purchase regret. Therefore, consumers may exhibit anticipated regret behaviour that influences their purchase decisions. In this paper, we examine how anticipated regret impacts a seller’s profit and selling strategy. We consider two types of regret: action regret (joining group buying but suffering failure) and forgone regret (purchasing directly but missing the cheaper price of group buying). Our analyses indicate that the seller should employ a mixed strategy, combining group buying and traditional individual buying, to induce consumers’ anticipated regret when they are more concerned about action regret. Alternatively, the seller should adopt a pure strategy, utilising only one selling method, when forgone regret prevails. We also find that anticipated regret may increase the seller’s profit. For direct sales, the action/forgone regret always benefits/hurts the seller. Then we extend our model to distribution sales, and find that the impact is reversed when the network effect margin of group buying is high due to double marginalisation. Finally, anticipated regret may also benefit consumers, suggesting that it may lead to a win-win situation for both the seller and consumers.

The relevance of the work. In the 21st century, humanity has faced global challenges, among which the issues of environmental degradation, depletion of natural resources and climate change have become particularly acute. Against this background, the traditional linear economic model, which involves excessive use of resources and the generation of a large amount of waste, has exhausted its potential. This is especially critical in the construction industry ‒ one of the most resource-intensive sectors of the world economy. In response to the challenges of modernity, more and more attention is paid to the concept of a circular economy, which allows creating closed production cycles, reducing the burden on the environment. In the context of restoring Ukraine's infrastructure after the war, the relevance of the transition to a circular construction model is growing exponentially. Purpose. The purpose of the article is to comprehensively analyze the possibilities and limitations of implementing a circular economy in the construction industry as a tool for implementing the principles of sustainable development. The research is aimed at studying theoretical foundations, practical experience, international cases, innovative materials and technologies that can transform approaches to the design, construction and operation of facilities. Methodology. The article uses an interdisciplinary approach that combines the concepts of ecological economics, urbanism, architecture, and systems analysis. A comparative analysis of linear and circular economic models is conducted, and examples of successful implementation of circular practices in construction in different countries, including Germany, Great Britain, the Scandinavian countries, and Japan, are studied. Statistical data on material consumption, energy costs, and waste generation in the construction sector are used. Special attention is paid to the analysis of regulatory, institutional, and behavioral barriers to circularity in Ukraine. The results. The author proves that the circular economy is not only an environmentally sound, but also an economically profitable strategy for construction. The use of secondary materials, organic insulation, energy-efficient technologies, and local raw materials allows for a reduction in carbon footprint, reduced waste disposal costs, and improved quality of the living environment. Key barriers to the implementation of a circular approach in Ukraine are identified – from the lack of a regulatory framework to the low level of environmental culture. At the same time, the country’s potential in implementing relevant solutions is highlighted, especially in the context of post-war reconstruction. The article calls for a revision of the paradigm of construction as a technical process and its transformation into a worldview model based on long-term responsibility to society, nature and future generations.

Investigating contract structures and logistics pricing strategies for profitability in the rose supply chain

Purpose This paper presents an exploratory analysis by studying the relationship between sustainability, marketing, and profit strategies from the perspective of Sustainable Supply Chain Management. Design/methodology/approach The research consists of two steps. The first is a multiple-case study involving six companies across diverse sectors. In this step, open questions and closed questions related to the two strategies (marketing and profit) are presented. In the second step, a survey is conducted across diverse sectors, where multiple-choice questions are asked of interviewees in multiple case studies, presented with yes/no answers and choices from the literature review. There are two kinds of analysis. For the multiple-case study analysis, the researchers used WordStat software version 9 (free) and QDA Miner Lite (free). For survey answers, a comparison was made with the literature review and the confidence interval to analyze the survey data regarding the questions were applied. Findings The results indicate that the social dimension in the Triple Bottom Line is not underrepresented; however, companies face challenges in selling green products through a green marketing strategy and increasing their profits, often being less aggressive in pursuing this latter aim. Research limitations/implications Companies must satisfy multiple and contradictory objectives, maximizing profits while reducing operating costs, minimizing environmental impacts, and maximizing social well-being. Despite the advances already reached by the studies in this subject, they face challenges to reconcile two different strategies (marketing and profit), sometimes opposite to sustainability. Furthermore, after a thorough search and literature analysis, no paper has been found that connects these strategies to sustainability. Practical implications For the companies in the survey, it is feasible to integrate the three dimensions of TBL, aligning profit with marketing strategies that seek environmental and social sustainable goals. These companies need to adopt sustainable practices aiming to reach legitimacy, rather than achieve financial gain. Hence, they should implement more sustainable initiatives and apply sustainable marketing strategies to cultivate customer loyalty. The field research demonstrated that it is possible to align the public and the private sustainable practices and interests in a developing country. Social implications Consumers and buyers could develop more conscious behavior, companies invest in sustainable consumption, production, and safer processes for their workers. Originality/value Companies must satisfy multiple and often contradictory objectives, maximizing profits while minimizing operating costs, reducing environmental impacts, and promoting social well-being. Despite the advances already achieved by studies in this subject, they face challenges in reconciling two different strategies (marketing and profit), which can sometimes be opposite to sustainability. Besides, after a deep search and literature analysis, there is no paper that connects these strategies with sustainability in the manufacturing industry.

Aim: Stock price prediction remains a highly challenging task due to the complex and nonlinear nature of financial time series data. While deep learning (DL) has shown promise in capturing these nonlinear patterns, its effectiveness is often hindered by the low signal-to-noise ratio inherent in market data. This study aims to enhance the stock predictive performance and trading outcomes by integrating Singular Spectrum Analysis (SSA) with deep learning models for stock price forecasting and strategy development on the Australian Securities Exchange (ASX)50 index. Method: The proposed framework begins by applying SSA to decompose raw stock price time series into interpretable components, effectively isolating meaningful trends and eliminating noise. The denoised sequences are then used to train a suite of deep learning architectures, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models. These models are evaluated based on their forecasting accuracy and the profitability of the trading strategies derived from their predictions. Results: Experimental results demonstrated that the SSA-DL framework significantly improved the prediction accuracy and trading performance compared to baseline DL models trained on raw data. The best-performing model, SSA-CNN-LSTM, achieved a Sharpe Ratio of 1.88 and a return on investment (ROI) of 67%, indicating robust risk-adjusted returns and effective exploitation of the underlying market conditions. Conclusions: The integration of Singular Spectrum Analysis with deep learning offers a powerful approach to stock price prediction in noisy financial environments. By denoising input data prior to model training, the SSA-DL framework enhanced signal clarity, improved forecast reliability, and enabled the construction of profitable trading strategies. These findings suggested a strong potential for SSA-based preprocessing in financial time series modeling.

Relevance. The development of the tourism industry in Russia is one of the priorities, while the unstable tourist flow due to the ongoing sanctions pressure from unfriendly countries and the ongoing special military operation requires the development of adaptive models of strategic profitability management of hotel enterprises. It is important to note the choice of the subject: the Kursk region is a border region of the Central Federal District, a counterterrorism operation is currently underway on its territory, and the hotel industry enterprises had to face a flood of internally displaced persons, and some of them became temporary accommodation centers, which undoubtedly affected their profitability. The purpose is to evaluate the dynamics of profitability of Kursk hotel enterprises in the context of a changing macroeconomic environment, in order to adapt the development strategy. Objectives: based on the analysis of available statistical data, to determine the dynamics of profitability of hotel enterprises in Kursk, to trace the impact of the transformation of the macroeconomic environment on the level of profitability, based on the data obtained, to determine promising areas of strategy for the development of hotel enterprises. Methodology. Methods of quantitative analysis, methods of comparing economic phenomena and processes, and generalization of analyzed economic information are used. Results. The growth in profitability of Kursk hotel enterprises of all star categories is confirmed by fairly convincing quantitative data, while the greatest increase in profitability is noted in organizations operating as a private company. Conclusions. The success of adaptation is determined by the company's ability to quickly reorient itself to government procurement. The high volatility of profitability associated with dependence on short-term contracts and the risks of a sharp increase in transaction costs has been revealed. Strategic adaptation methods based on priority partnership with government agencies and managed diversification are proposed.

This study examines the dynamics of US stock-specific news sentiment relative to aggregate market sentiment, leveraging a dataset of 2.7 million news articles. Focusing on negative tail events among S&P 100 constituents from 2003 to 2021, we identify a significant association between firm-specific sentiment changes and subsequent one-month stock performance. Specifically, a positive news sentiment score during adverse events suggests the absence of fundamental issues and correlates with superior post-event returns. These findings support the development of a profitable reversal trading strategy and indicate a potential regime shift in market behavior.

Cross-border e-commerce (CBEC) growth creates financial constraints for manufacturers, balancing limited production and pricing flexibility. E-commerce platforms evolve into coordinators of financing, risk control, and pricing, using digital tools like big data, blockchain, and AI credit scoring to enhance risk and capital management. This study uses a Stackelberg model, with the platform as leader, to analyze sales and financing mechanisms. Four financial scenarios compare pricing and profit strategies under different capital conditions. In capital-constrained settings, platform-led Digital Supply Chain Finance (DSCF) boosts profits by cutting prices, expanding supply, and raising commissions. In capital-sufficient cases, costs may limit gains, showing constraint-driven adaptation. Platform strategies shape manufacturer behavior, emphasizing their role in CBEC. This study offers insights for intelligent financing and sustainable development.

Personnel policy follows from the personnel development strategy, which is a complex work program for the selection and effective use of motivated and highly qualified personnel with the ability to adequately respond to the internal and external environment. Personnel management in an organization is a means ofrealizing personnel policy. This determines the relevance of researching the relationship between personnel policy and personnel development. The paper provides comparative characteristics of two types of open and closed personnel policies. It is believed that the formation and preparation of a reserve of personnel for promotion is more consistent with a closed personnel policy. The differentiation of personnel policy into open and closed policies is based on the organization's orientation towards its own personnel or external workforce, the degree of openness to the external environment when forming the personnel composition. A classification of personnel policy is given as passive, reactive (response), preventive and active policies. Which are implemented in accordance with the level of awareness of the rules and norms that underlie personnel measures and the influence of the management apparatus on the personnel situation of the organization due to this level. The preparation of a personnel reserve should be considered as a targeted complex program. We believe that such an approach corresponds to an active personnel policy. Personnel measures are actions that are aimed at achieving compliance with the goals and objectives of the organization and are carried out taking into account the specific objectives of the stage of organization development. The relationship between personnel development and the stages of the organization is discussed. The functions of personnel management change in accordance with the stages of organization development. Specialists distinguish the following stages of organizational development: formation, growth, stabilization, decline. Personnel development and organizational stages. The author believes that the formation and training of a reserve of personnel for promotion is most effective in the context of a dynamic growth and profitability strategy, in conditions of a closed personnel policy. The author conducted a study, the analysis of the results of which confirmed that it is possible to increase the efficiency of the institution's work through the selection and distribution of personnel, assessment and qualification improvement, reserve of replacements, and training of young specialists with leadership potential.

Abstract This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH). Traditional data sampling methods, such as time bars, often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time. To address this, we propose an alternative approach using information-driven sampling methods, including the CUSUM filter, range bars, volume bars, and dollar bars, and evaluate their performance using tick-level data from January 2018 to June 2023. Additionally, we introduce the Triple Barrier method for target labeling, which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction. We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies. The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction, achieving consistently positive trading performance even after accounting for transaction costs. Moreover, our system enables making trading decisions at any point in time on the basis of market conditions, providing an advantage over traditional methods that rely on fixed time intervals. Furthermore, the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architectures—including the vanilla Transformer encoder, FEDformer, and Autoformer—alongside other deep learning architectures and classical machine learning models, revealing insights into their relative performance.

Abstract This study evaluated the effect of organic (biofertilizers: Solorin and Orinmas) and inorganic (Novafol) liquid fertilizers on the production and nutritional quality of hydroponic green forage (HGF) from barley (Hordeum vulgare). The project was carried out at the Mache School of Agricultural Technology (I.E.S.T.P.) as an alternative to the forage shortage in high Andean regions. A completely randomized design (CRD) was used with four treatments and nine replicates, evaluating height, fresh biomass, dry matter (DM), and crude protein. The results demonstrated that the use of biofertilizers significantly increased the growth and quality of HGF compared to the chemical fertilizer and the control. The Orinmas treatment (T3) was the most efficient, achieving the highest percentage of crude protein (18.2%) and the highest dry matter content (15.1%). Solorin (T2) had the highest height (20.5 cm) and fresh biomass production (1,850 g/tray). It is concluded that hydroponics with biofertilizers is a sustainable and profitable strategy for mitigating forage shortages during critical periods. Keywords: Barley, Biofertilizers, Hydroponic green fodder

ABSTRACT Our study finds that firms that consider Environmental, Social, and Governance (ESG) aspects are positively associated with ambidextrous innovation, suggesting that firms simultaneously seek to implement both exploratory and exploitative innovations. Furthermore, we demonstrate that each component of the ESG score is positively associated with ambidextrous innovation. This implies that focusing on the E, S, and G aspects may also contribute to a firm’s ambidextrous innovation. Additionally, we find that the positive association between ESG scores and financial performance intensifies among firms exhibiting high levels of ambidextrous innovation. This finding indicates that firms that simultaneously consider ESG and innovation tend to be more profitable. Based on this evidence, we underscore the interrelated associations between ESG scores, ambidextrous innovation, and financial performance.

This study examines the effect of financial statement quality, corporate governance, and capital structure on firm value in Indonesia, with profitability serving as a mediating variable. Using a quantitative approach, data were collected from 165 companies across multiple industrial sectors through a structured Likert-scale questionnaire. The data were analyzed using Structural Equation Modeling–Partial Least Squares (SEM-PLS 3) to assess both direct and indirect relationships among variables. The findings reveal that financial statement quality and corporate governance have a positive and significant effect on firm value, whereas capital structure has a negative and significant effect. Profitability also plays a mediating role, strengthening the relationships between financial statement quality and corporate governance with firm value, but not between capital structure and firm value. These results confirm that transparent financial reporting, effective governance practices, and prudent capital management collectively enhance profitability and contribute to long-term value creation. The study provides empirical evidence that supports Agency Theory, Trade-Off Theory, and the Resource-Based View (RBV) in explaining firm value formation in emerging markets such as Indonesia. The practical implications emphasize the importance of improving governance mechanisms, financial reporting quality, and profitability strategies to increase investor trust and market valuation.

The increasing use of complex “black-box” models for financial news sentiment analysis presents a challenge in high-stakes settings where transparency and trust are paramount. This study introduces and validates a finance-focused, fully reproducible, open-source workflow for building, explaining, and evaluating sector-specific sentiment models mapped to standard market taxonomies and investable proxies. We benchmark interpretable and transformer-based models on public datasets and a newly constructed, manually annotated gold-standard corpus of 1500 U.S. sector-tagged financial headlines. While a zero-shot FinBERT establishes a reasonable baseline (macro F1 = 0.555), fine-tuning on our gold data yields a robust macro F1 = 0.707, a substantial uplift. We extend explainability to the fine-tuned FinBERT with Integrated Gradients (IG) and LIME and perform a quantitative faithfulness audit via deletion curves and AOPC; LIME is most faithful (AOPC = 0.365). We also quantify the risks of weak supervision: accuracy drops (−21.0%) and explanations diverge (SHAP rank ρ = 0.11) relative to gold-label training. Crucially, econometric tests show the sentiment signal is reactive, not predictive, of next-day returns; yet it still supports profitable sector strategies (e.g., Technology long-short Sharpe 1.88). Novelty lies in a finance-aligned, sector-aware, trustworthiness blueprint that pairs fine-tuned FinBERT with audited explanations and uncertainty checks, all end-to-end reproducible and tied to investable sector ETFs.

The article examines the impact of financial leverage on the financial performance of selected joint-stock banks listed on the Ghana Stock Exchange. The current state of Ghana’s banking sector is analyzed, revealing a contradictory situation regarding the impact of financial leverage on potential profitability levels, as its actual effect on profitability remains insignificant. It is concluded that this situation indicates the need for more effective financial management strategies. It is substantiated that, despite the theoretical advantages of using financial leverage for profit growth, no significant correlation was found in Ghanaian banks. This leads to the conclusion that a strategy of increasing profitability based on a reliance on borrowed capital is unreliable. The study demonstrated that bank size does not have a significant impact on return on equity, refuting the assumption regarding the significance of economies of scale in enhancing financial efficiency. A significant result was provided by the Hausman test, which indicated a close relationship between financial leverage and return on assets (ROA). Furthermore, random effects models confirmed a strong correlation between the level of financial leverage (Finley) and ROA. The results suggest that the growth of ROA in these banks is positively correlated with effective financial leverage management practices. It is substantiated that to increase share value and attract investment, bank management should ensure that debt levels generate sufficient return on capital through a thorough assessment of the debt-to-potential-income ratio. To this end, managers must focus on ensuring the efficient allocation of resources to generate stable profits while simultaneously controlling the debt burden. It is emphasized that regular monitoring of changing market conditions and their impact on profitability and leverage strategies is critical for achieving long-term success, minimizing risks, and maximizing benefits. Keywords: financial leverage, financial performance, banks, stock exchange, Ghana, return on assets, debt levels.

This study investigates the application of linear programming optimization, complemented by primal and dual linear programming, to improve raw material allocation and maximize profitability for the Haske Modern Bakery in Bauchi, Nigeria. A mathematical model was developed to optimize raw material usage, increase production efficiency, and enhance company returns. Linear programming was employed to identify the most profitable production strategy. Additionally, the Economic Order Quantity (EOQ) model was utilized to effectively manage inventory. EOQ calculations determined the optimal order quantities for flour, sugar, butter, milk, and yeast to be approximately 59 bags, 22 bags, 11 cartons, 12 bags, and 56 cartons, respectively. The total costs of ordering flour, sugar, butter, milk, and yeast are N19,349, N11,171, N5,586, N6,119, and N27,928, respectively. Reorder points were established with stock levels triggering reorders in 20 bags of flour, 4 bags of sugar, 2 cartons of butter, 2 bags of milk, and 30 cartons of yeast, assuming a constant lead time of seven days. The results showed that the optimal production strategy involved producing 319.8294 units of small-medium loaf (X₅) and 533.0490 units of another product (Y₃), with all other products (X₁, X₂, X₃, ..., X₁₃ and Y₁, Y₂, Y₃, ..., Y₉) being zeros units. This strategy is projected to yield a maximum profit of N15991.47. This study underscores the significance of utilizing linear programming and EOQ models to enhance operational efficiency and profitability in the bakery industry.

The increase in company value that does not align with the growth rate or the size of the company highlights the critical role of growth and company size in creating value, as well as the possibility that profitability becomes the key to this relationship. This study aims to analyze the role of profitability in moderating the effect of growth and company size on company value. This associative quantitative study uses a population of all cosmetic companies listed on the Indonesia Stock Exchange during the 2020-2024 period. The data were analyzed using MRA with the help of SPSS version 20 software. Research results show that growth has no effect, while size affects firm value. Profitability successfully moderates the influence of growth and firm size on firm value. In addition, the moderation effect on the influence of firm size occurs because high profitability signals that large companies are capable of managing their assets efficiently. These findings confirm the importance of a profitability strategy as a companion to asset growth in creating firm value.

Investment banks generate revenue through a range of complex and dynamic fee structures, including advisory fees, underwriting spreads, success fees, and retainers. Traditionally, fee determination and analysis have relied on historical averages, industry benchmarks, and linear models, often missing deeper relationships hidden within vast deal datasets. This study begins with a comprehensive analysis of investment bank fee structures, examining how fees vary by deal type, size, sector, region, and economic context.By extending the research to include Machine Learning (ML) and Deep Learning (DL) techniques, we aim to reveal non-linear patterns and identify the key drivers of fee variability that traditional methods overlook. ML models such as Random Forest and XGBoost are applied to predict fee percentages based on deal characteristics, client type, and market conditions, providing clearer insight into pricing strategies. In parallel, LSTM networks are used to forecast time-series trends in aggregate fee income, capturing seasonality and market cycles in a way that linear forecasting cannot.Our integrated approach demonstrates that AI models not only enhance forecasting accuracy but also uncover complex interactions between variables like market volatility, deal complexity, and client segment. The findings offer actionable insights for banks to design competitive yet profitable fee strategies and better align resources with market demand. Overall, this study illustrates the power of combining traditional financial analysis with modern AI tools to transform decision-making and strategy in investment banking.

ABSTRACT This article examines how the Black Manosphere develops through the ideology of misogynoir and the accumulation of racial capital via digital media, specifically podcasting. A critical analysis of three Black Manosphere podcasting channels on YouTube reveals the co-constitutive relationship between verbal and visual misogynoir in these spaces. The cumulative effect of linguistic choices, video titles, superchats, homepages, thumbnails, set interactions, and other intertextual media is the lucrative economy of the degradation of Black women. Ultimately, misogynoir emerges as a profitable strategy in the Black Manosphere in which content creators leverage platform affordances and affective currencies to exacerbate Black women’s marginalization.

AirBnb is a company with a platform of renting house and estate transactions. The website of AirBnb has the purpose of building a community based on trust between people, but neither party has provided substantial guarantees. Therefore, they are looking forward to the solutions of winning the belief of the customers. It has been shown in previous news by telling the situations of living in damaged house without any announcement. As a result, there are a lot of problems waiting to be solved and the income of the company keeps on the low level. Here we would like to assist landlords, travelers, and businesses in formulating profit strategies. We firstly analysized several factors like rooms, cancellation policy and so on. Then we picked three of them to do the research by building a linear regression model to predict the prices of Airbnb rental rooms, and generate a set of insights and recommendations that will help the company get higher income.