Hasil untuk "Revenue. Taxation. Internal revenue"

Oleh Blazhko, Olena Churikanova, Aygun Aliyeva

Growing economic volatility has renewed interest in whether EU cohesion instruments are associated with measurable improvements in macro-level competitiveness. This article examines the relationship between European Structural and Investment Funds (ESF) and EU competitiveness indicators, focusing on GDP, inflation, unemployment, imports, and exports. Using annual EU-level data from 2007 to 2022, the study employs descriptive statistics, Pearson correlation with Student’s t-tests, and pairwise and multivariate OLS regressions, all implemented in MS Excel. The results show strong correlations between ESF and GDP (r = 0.88), imports (r = 0.80), and exports (r = 0.79), a moderate correlation with inflation (r = 0.61), and a strong negative correlation with unemployment (r = –0.72), with all coefficients statistically significant (e.g., t = 9.98 for GDP and t = –5.6 for unemployment against a critical value of 2.145). Pairwise regressions indicate substantial explanatory power for GDP (R² = 0.78; slope +7,822.15 per ESF unit), imports (R² = 0.65; slope +1.85), and exports (R² = 0.64; slope +1.59), while inflation remains weaker but significant (R² = 0.38; slope +0.0077) and unemployment declines with higher ESF (R² = 0.53; slope –0.0072). In the multivariate model, overall fit is high (R² = 0.927; Significance F = 0.000022), and GDP (p = 0.0091) and inflation (p = 0.0036) remain significant predictors of ESF; a reduced specification including GDP, inflation, and imports retains strong fit (R² = 0.922; Significance F = 0.00000063) with all included predictors statistically significant. Policy implications suggest prioritising ESF allocations towards productivity-enhancing and trade-oriented investments (skills linked to labour demand, innovation diffusion, and export readiness) that align with the strongest GDP and trade associations observed. Given the positive co-movement with inflation (r = 0.61; p = 0.0036 in the multivariate model), programme implementation should also incorporate macro-stability safeguards, including smoother procurement scheduling and a greater emphasis on supply-expanding projects during inflationary episodes.

Moddassir Khan Nayeem, Omar Abbaas, Suzan Alaswad et al.

Determining the optimal selling price is a challenge in revenue management, especially in markets characterized by nonlinear and price-sensitive demand. While traditional models, such as linear, power, and exponential demand functions, offer analytical convenience, they often fail to capture realistic purchase dynamics, leading to suboptimal pricing. The logit demand function addresses these limitations through its bounded, S-shaped curve, offering a more realistic representation of consumer behavior. Despite its advantages, most existing literature relies on heuristic approaches, such as pricing at the inflection point, which prioritizes maximum price sensitivity but does not guarantee maximum revenue. This study proposes a novel, exact pricing algorithm that analytically derives the revenue-maximizing price under the logit demand function using the Lambert W function. By providing a closed-form solution, the approach eliminates reliance on heuristic iterative methods and corrects the common practice of considering the inflection point price as market price. In fact, we demonstrate that the optimal price is consistently lower than the inflection-point price under reasonable assumptions, leading to lower prices for consumers and higher revenue for sellers. Numerical experiments illustrate the proposed algorithm and examine the changes in the optimality gap as demand function parameters vary. Results indicate that the optimal price is consistently lower than the inflection-point price, with an average 20% price reduction accompanied by a 15% increase in revenue.

Ekaterina Solodneva, Alexandra Khirianova, Aleksandr Katrutsa et al.

Modern recommender systems excel at optimizing search result relevance for e-commerce platforms. While maintaining this relevance, platforms seek opportunities to maximize revenue through search result adjustments. To address the trade-off between relevance and revenue, we propose the $\mathsf{RARe}$ ($\textbf{R}$aising $\textbf{A}$dvertisement $\textbf{Re}$venue) framework. $\mathsf{RARe}$ stacks a click model and a reranking model. We train the $\mathsf{RARe}$ framework with a loss function to find revenue and relevance trade-offs. According to our experience, the click model is crucial in the $\mathsf{RARe}$ framework. We propose and compare two different click models that take into account the context of items in a search result. The first click model is a Gradient-Boosting Decision Tree with Concatenation (GBDT-C), which includes a context in the traditional GBDT model for click prediction. The second model, SAINT-Q, adapts the Sequential Attention model to capture influences between search results. Our experiments indicate that the proposed click models outperform baselines and improve the overall quality of our framework. Experiments on the industrial dataset, which will be released publicly, show $\mathsf{RARe}$'s significant revenue improvements while preserving a high relevance.

Farzad Pourbabaee, Sophie Yanying Sheng, Peter McCrory et al.

We study the problem of position allocation in job marketplaces, where the platform determines the ranking of the jobs for each seeker. The design of ranking mechanisms is critical to marketplace efficiency, as it influences both short-term revenue from promoted job placements and long-term health through sustained seeker engagement. Our analysis focuses on the tradeoff between revenue and relevance, as well as the innovations in job auction design. We demonstrated two ways to improve relevance with minimal impact on revenue: incorporating the seekers preferences and applying position-aware auctions.

Daniel Adelman, Christiane Barz, Alba V. Olivares-Nadal

This paper introduces an algorithm that dynamically generates basis functions to approximate the value function in Network Revenue Management. Unlike existing algorithms sampling the parameters of new basis functions, this Nonlinear Incremental Algorithm (NLIAlg) iteratively refines the value function approximation by optimizing these parameters. For larger instances, the Two-Phase Incremental Algorithm (2PIAlg) modifies NLIAlg to leverage the efficiency of LP solvers. It reduces the size of a large-dimensional nonlinear problem and transforms it into an LP by fixing the basis function parameters, which are then optimized in a second phase using the flow imbalance ideas from Adelman and Klabjan (2012). This marks the first application of these techniques in a stochastic setting. The algorithms can operate in two modes: (1) Standalone mode, to construct a value function approximation from scratch, and (2) Add-on mode, to refine an existing approximation. Our numerical experiments indicate that while NLIAlg and 2PIAlg in standalone mode are only feasible for small-scale problems, the heuristic version of 2PIAlg (H-2PIAlg) in add-on mode, using the Affine Approximation and exponential ridge basis functions, can handle extremely large instances that may cause benchmark network revenue management methods to run out of memory. In these scenarios, H-2PIAlg delivers substantially better policies and upper bounds than the Affine Approximation. Furthermore, H-2PIAlg achieves higher average revenues in policy simulations compared to network revenue management benchmarks in instances with limited capacity.

Wu DingYi, Yasuyuki Yamaoka, Sitsada Sartamorn et al.

This study aims to make theoretical and practical contributions by addressing stagnation in the context of digital transformation (DX) and proposing specific measures. Focusing on the banking industry’s skilled response to rapid changes to maintain and improve competitiveness, this study employs quantitative methods to investigate the expectations and assessments of Chinese financial service users regarding Internet banking. With a clear objective, this study seeks to contribute theoretically and practically by addressing stagnation in DX. Specifically, it focuses on the banking industry’s response to rapid changes, employing quantitative methods to assess and understand Chinese financial service users’ expectations of Internet banking. The results reveal that the prevalent use of payment services through mobile applications has significantly expanded the scope of financial services among citizens. Key factors driving innovation in the financial industry through fintech include close communication with consumers, service enhancement and sophistication and ensuring reliability. Privacy and the ethical use of personal information have been found to function as an indirect pathway that plays a vital role in socio-economic activities, acting as a critical element for the future development of the financial industry. These findings provide actionable insights for fostering innovation and development in the financial sector. The uniqueness of this study lies in its primary quantitative data analysis, which compares the prospects of financial services in China’s advanced DX market. It shows the path the banking industry should take, emphasising the simplicity of mobile applications and the high frequency with which vital components are used. Going beyond theoretical insights, this research is a practical guide for implementing specific actions in a real business environment. It provides valuable insights into the Chinese market and offers guidelines for the broader financial industry currently navigating the intense waves of DX, ultimately aiming for sustainable and effective DX.

Aprih Santoso, Susanto, R. Dwi Widi Pratito et al.

Background: MSMEs dominate the Indonesian economy. The growth of MSMEs shows that the number of MSMEs is quite large and continues to increase every year, thus indicating that MSMEs may be able to generate tax revenue. Semarang City is no exception. However, the problem is that MSMEs still view taxes as a burden that must be minimized and there is a lack of supervision from the tax authorities. Purpose: The aim of the research is to determine the influence of personal norms and tax compliance intentions on tax compliance behavior in MSMEs for processed agricultural products in Semarang City. Design/methodology/approach: The researcher used TPB. The idea that social and moral norms originate from subjective norms is examined in this study. Social norms focus more on providing social expectations and often aim to maintain harmony in a group or society, originate from society or social groups, and are often taught or enforced through socialization, social supervision, and social sanctions. Moral norms focus on behavior that is considered right or wrong from a moral or ethical perspective, are often related to a sense of personal responsibility or justice, originate from internal values, ethics, religion, or philosophy believed by the individual, and can vary from one individual to another, although they are often also influenced by certain religious or philosophical teachings. Three variables personal norms, intentions, and behavior, were used in this study. The questionnaires were given to UMKM taxpayers for processed agricultural products in Semarang City, totaling 120 respondents. PLS-SEM was used for the analysis. Findings/Result: The first hypothesis is that personal norms have an impact on intentions. The second hypothesis is accepted which shows that tax compliance intentions have a positive and significant influence on tax compliance behavior. The Influence of Personal Norms on Intention where the statistical value for the personal norm and intention variables are respectively, the P value is 0.000 < 0.050 and the T statistic value is 7.706 > 1.96 so that the hypothesis is accepted. The Influence of Intention on Behavior, where the P value is 0.002 < 0.050 and the T statistic value is 3.117 > 1.96 so that the hypothesis is accepted. The Influence of Personal Norms on Behavior, where the P value is 0.000 < 0.050 and the T statistic value is 4.010 > 1.96 so that the third hypothesis - namely, that personal norms have a major impact on behavior is accepted. The Influence of Intention on Behavior where the P value is 0.002 < 0.050 and the T statistic value is 3.117 > 1.96 so that the hypothesis of intention has a major impact on behavior is accepted by the test. Conclusion: The behavior is very positively influenced by personal norms and the intention to comply with taxes in MSMEs for processed agricultural products in the city of Semarang. The implications are: The Importance of Tax Education and Counseling, The Role of Moral and Ethical Values in Tax Compliance, The Influence of the Social Environment, The Need to Improve the Perception of Ease in Paying Taxes. Originality/value (State of the art): Originality/value of this research is that this research highlights the importance of education and counseling about taxes for MSMEs, as well as the need for policies that support and facilitate the taxation process for this sector. Keywords: tax compliance, agricultural products, MSMEs, personnel norms, social norms

Shaoyuan Huang, Zheng Wang, Zhongtian Zhang et al.

As live streaming services skyrocket, Crowdsourced Cloud-edge service Platforms (CCPs) have surfaced as pivotal intermediaries catering to the mounting demand. Despite the role of stream scheduling to CCPs' Quality of Service (QoS) and throughput, conventional optimization strategies struggle to enhancing CCPs' revenue, primarily due to the intricate relationship between resource utilization and revenue. Additionally, the substantial scale of CCPs magnifies the difficulties of time-intensive scheduling. To tackle these challenges, we propose Seer, a proactive revenue-aware scheduling system for live streaming services in CCPs. The design of Seer is motivated by meticulous measurements of real-world CCPs environments, which allows us to achieve accurate revenue modeling and overcome three key obstacles that hinder the integration of prediction and optimal scheduling. Utilizing an innovative Pre-schedule-Execute-Re-schedule paradigm and flexible scheduling modes, Seer achieves efficient revenue-optimized scheduling in CCPs. Extensive evaluations demonstrate Seer's superiority over competitors in terms of revenue, utilization, and anomaly penalty mitigation, boosting CCPs revenue by 147% and expediting scheduling $3.4 \times$ faster.

Shivam Shukla, Sudhir Kumar Shukla

The present study explores the presence and impact of automation bias and status quo bias on the capital market investment decisions of Indian retail investors. In behavioral economics the term automation bias is known as the excessive dependency of investors on automated or computer generated information for stock selection decisions. On the other hand, status quo bias is the inherent tendency of an investor to keep his portfolio unaltered irrespective of the changing dynamics of capital market for a variety of reasons. In this study an attempt has been made to figure out the extent of presence and degree of impact of both the biases in the investment decisions of investors. The study is based on data collected through a five point Likert scale questionnaire framed to figure out answers to the research questions. The questionnaire was distributed among 496 retail investors of National Stock Exchange (NSE) and Bombay Stock Exchange (BSE). The outcome of this study clearly point out that there is a definite presence of automation bias and status quo bias in investment decisions of the capital market investors of India and there is a considerable and statistically significant (p

Kofi Nyantakyi Asare, Samusevych Yaryna Valentynivna

This study presents a comprehensive bibliometric analysis of research on financial fraud, tax tools, and economic security. Using a dataset of articles published between 2016 and 2022, we analyzed keyword co-occurrence, journal impact, citations, and geographical and Institutional patterns. Our results identify the most productive authors and institutions, influential sources, major research themes, and potential future research directions. We find that the research on financial fraud, tax tools, and economic security is a multidisciplinary and international field, with a focus on fraud detection, and national security among others. It was also discovered that the number of publications has significantly increased year after year. Our study provides valuable insights into the current state of research on this topic and identifies opportunities for future research and innovation. The findings of this study have important implications for researchers, practitioners, and policymakers working in the field of financial fraud, tax tools, and economic security.

Abderrahmane Bensaad, Samia Azzazi

This study aims to evaluate the performance of monetary policy tools in terms of their impact on macroeconomic indicators to achieve monetary stability in Algeria, by studying their contribution to achieving domestic and external stability. The study found that monetary stability has experienced fluctuations from time to time due to the Algerian economy's connection to external shocks on oil prices. Therefore, the study used the Autoregressive Distributed Lag (ARDL) bound test model to determine the impact of monetary policy tools, namely the money supply, mandatory reserves, discount rate, interest rate, and real GDP on monetary stability in Algeria for the period 1990-2021, through several standard tests that concluded that the model is free from standard problems and valid for estimation. The results of the study indicate the existence of a long-term equilibrium relationship between monetary policy tools and monetary stability in Algeria, where the growth of the money supply has a positive effect on monetary stability, while the discount rate and real GDP have a negative effect on monetary stability in both the long and short term. In the short term, mandatory reserves have a positive effect on monetary stability, while interest rates have a negative effect on monetary stability.

Fella Bekhouche Ouahdi, Fatima Zahra Boukhedimi

Corporate governance aims to control management decisions and determine their general lines. Thus, the application of the governance system must take into account the nature of the company itself, the conditions in which it operates, its organizational structure and the administrative culture of its actors. While good governance remains the result of good practices. The objective of this article is to demonstrate the contribution of employee financial participation in governance, through the governance regime, the rights to decide and control, the characteristics of employee shareholders in Algeria and thus on performance financial of companies with employee shareholding, based on mechanisms applicable to SME contexts. We will address in a synthetic manner the reality of this contribution of the financial participation of PFS employees through a discussion of the empirical results drawn from a questionnaire survey carried out among employee shareholders working in Algerian SMEs. The results show a low percentage of share capital held by employees with low female participation, an absence of association for employee shareholders, employee shareholding was a financial means to safeguard the activity of public sector companies with critical financial situations. Employee ownership is essential for businesses, promoting employee trust and participation. However, this role is only fully understood by a few companies. It serves as both a collective incentive and a control mechanism. Benefits include better management, financial benefits, business sustainability and job preservation. Despite this, 56.8% of employee shareholders prefer to sell their shares, raising questions about their motivations. A lack of voting rights in the shareholders' council, which requires a review of the regulations to promote employee shareholding in Algeria, in particular for SMEs.

Iryna Kozhushko

Modern global financial development is characterized by the active use of digital technologies in all areas of financial development. This leads to a change in the conditions and form of financial relationships between individual entities, the provision of financial services to the population. The article is devoted to the study of the peculiarities of the transformation of financial services industry under the influence of digitalization of the economy. The object of the study is Ukraine and some EU countries, the study period is 2011-2021. Based on the results of the bibliometric analysis, a generalization of the content and conceptual features of the study of digitalization of the economy was carried out, the most common directions of its analysis and the main directions of the connection of the digitalization of the economy with individual components of the country's development (economic development, cyber security, education and business) were determined. This made it possible to determine the most priority areas of influence of digitalization on the transformation of financial relations. An approach to assessing the level of digitalization of the economy, based on taking into account the values of seven indicators: Index of digitization of the economy and society, Index of implementation of digital technologies, Global index of innovations, Index of network readiness, Index of digitization of the economy, Index of global digital competitiveness, Index of quality of digital life has been developed. A sufficiently high level of digitalization of the economy has been proven in most of the analyzed countries. The average value of the integral indicator of digitalization of the economy ranges from 0.83 to 0.85. Austria (0.99), Lithuania and the Czech Republic (0.91) have the highest values, Ukraine (0.71) has the lowest. With the help of the k-means method, a cluster analysis of countries was carried out according to the integral indicator of digitalization of the economy, and four groups of countries were distinguished. The first cluster includes the countries with the highest average values of indicators of digitalization of the economy, and the fourth - the lowest. In addition, the countries of the first cluster have a significantly higher variation of the components of the integral indicator of digitalization of the economy. The fourth cluster includes countries with more stable values of indicators of digitalization of the economy. Thus, the standard deviation of the values of indicators of digitalization of the economy for the countries of the first cluster varies on average within 3.2-5.5, for the countries of the second cluster - 2.8-4.6, for the third cluster - 2.6-3.9, for the fourth cluster - 2.6-3.9 - 1.4-3.0.

Yusra Nasser AL-Hashimi, Jawaher Sarhan AL-Toobi, Essia Ries Ahmed

The main goal of this research to examine the relationship between Corporate Governance and Firm Performance During COVID-19. Quantitative method, this research used the source from 34 companies’ annual reports, were used secondary data for 2019 and 2020. The secondary data collected was verified utilizing Smart-Partial Least Squares 3.0. The findings found a positive relationship between corporate governance and financial performance in the financial sector for period 2019 and 2020. This indicates that the increase the governance tools will lead to enhancing and improving companies’ overall performance. This current work has added a new discussion to the knowledge body considering the corporate governance tools and their link with performance. Furthermore, conducting such study in the field of accounting provides new insight into the literature among both developed and emerging economies including Oman.

Aishik Mandal, Michaël Perrot, Debarghya Ghoshdastidar

Comparison-based learning addresses the problem of learning when, instead of explicit features or pairwise similarities, one only has access to comparisons of the form: \emph{Object $A$ is more similar to $B$ than to $C$.} Recently, it has been shown that, in Hierarchical Clustering, single and complete linkage can be directly implemented using only such comparisons while several algorithms have been proposed to emulate the behaviour of average linkage. Hence, finding hierarchies (or dendrograms) using only comparisons is a well understood problem. However, evaluating their meaningfulness when no ground-truth nor explicit similarities are available remains an open question. In this paper, we bridge this gap by proposing a new revenue function that allows one to measure the goodness of dendrograms using only comparisons. We show that this function is closely related to Dasgupta's cost for hierarchical clustering that uses pairwise similarities. On the theoretical side, we use the proposed revenue function to resolve the open problem of whether one can approximately recover a latent hierarchy using few triplet comparisons. On the practical side, we present principled algorithms for comparison-based hierarchical clustering based on the maximisation of the revenue and we empirically compare them with existing methods.

Wensheng Gan, Guoting Chen, Hongzhi Yin et al.

Economic-wise, a common goal for companies conducting marketing is to maximize the return revenue/profit by utilizing the various effective marketing strategies. Consumer behavior is crucially important in economy and targeted marketing, in which behavioral economics can provide valuable insights to identify the biases and profit from customers. Finding credible and reliable information on products' profitability is, however, quite difficult since most products tends to peak at certain times w.r.t. seasonal sales cycle in a year. On-Shelf Availability (OSA) plays a key factor for performance evaluation. Besides, staying ahead of hot product trends means we can increase marketing efforts without selling out the inventory. To fulfill this gap, in this paper, we first propose a general profit-oriented framework to address the problem of revenue maximization based on economic behavior, and compute the 0n-shelf Popular and most Profitable Products (OPPPs) for the targeted marketing. To tackle the revenue maximization problem, we model the k-satisfiable product concept and propose an algorithmic framework for searching OPPP and its variants. Extensive experiments are conducted on several real-world datasets to evaluate the effectiveness and efficiency of the proposed algorithm.

Ana Njegovanović

Tourism is attracting increasing attention of various scientific disciplines with the aim of studying phenomena in tourism from a specific disciplinary point of view. On issues in the field of philosophy and tourism, we find a large gap because, unlike many and diverse other scientific studies, a philosophical approach to tourism is practically non-existent. In understanding the complex concept of space and time, we need a basic knowledge of physics and neuroscience. Space and time in neuroscience remain separate coordinates to which we attach our observations. Spatial-temporal sequences of brain activity often correlate with measures of distance and duration, and these correlations may not correspond to neural representations of space or time. MIT neuroscientists have identified a brain circuit in the hippocampus that encodes the time of the event, that is, pyramidal cells (green) have been discovered in the CA2 region of the hippocampus that are responsible for storing critical time information. When we experience a new event, our brain records the memory not only of what happened, but also of the context, including the time and place of the event.

Gerriet Hinrichs, Henning Bundtzen

The occupation of insurance agent involves establishing a relationship of trust with the customer and providing personal and customized advice as a prerequisite for successful sales. This paper summarizes the scientific discussion about the occupation of an insurance salesperson. Coronavirus disease 2019 restrictions have limited face-to-face meetings and complicated large parts of this occupation. The main purpose of the research is to analyze the impact of these restrictions on the sales of 130 insurance branches, comparing the sales of 2019 and 2020 separately by insurance type. This period was chosen because it allows for the usual seasonal volatility to be taken into account. To differentiate according to the type of insurance sold is therefore of interest, because large differences with regard to demand generation and the use of existing customer relationships are to be expected. It shows that consulting-intensive new contracts in the life insurance segment declined noticeably, while the upselling of existing contracts in the non-life insurance segment increased significantly. The research empirically confirms and theoretically proves the importance of personal contact with the customer in the sale of life insurance and pension plans as well as the value of technical tools in upselling non-life policies. The insights from this exceptional coronavirus disease 2019 episode can also be useful in normal times for sales managers in managing insurance premiums to be sold. Further research as well as practitioners should concentrate on shock-resistant consulting approaches and techniques.

George Abuselidze, Mariam Msakhuradze

The work "International Taxation and its impact on Georgian Business Subjects" discusses the essence, types of international taxation and ways to prevent it. Object of international taxation, taxable base and rates, features based on the taxpayer. The approaches of states and its impact on the activities of business entities. The aim of the work was to study the theoretical and methodological bases of international taxation in the tax system of Georgia and to present the existing problems. To get acquainted with the activities of the free industrial zones in our country and to evaluate them. Sharing opinions and expressing one's attitude towards it. The work presents the opinion on the impact of the approaches and recommendations of our country's legislation on international taxation on the business sector of Georgia to correct the current situation.

Xin Shi, Alberto J. Lamadrid L., Luis F. Zuluaga

In a commodity market, revenue adequate prices refer to compensations that ensure that a market participant has a non-negative profit. In this article, we study the problem of deriving revenue adequate prices for an electricity market-clearing model with uncertainties resulting from the use of variable renewable energy sources (VRES). To handle the uncertain nature of the problem, we use a chance-constrained optimization (CCO) approach, which has recently become very popular choice when constructing dispatch electricity models with penetration of VRES (or other sources of uncertainty). Then, we show how prices that satisfy revenue adequacy in expectation for the market administrator, and cost recovery in expectation for all conventional and VRES generators, can be obtained from the optimal dual variables associated with the deterministic equivalent of the CCO market-clearing model. These results constitute a novel contribution to the research of research on revenue adequate, equilibrium, and other types of pricing schemes that have been derived in the literature when the market uncertainties are modeled using stochastic or robust optimization approaches. Unlike in the stochastic approach, the CCO market-clearing model studied here produces uncertainty uniform real-time prices that do not depend on the real-time realization of the VRES generation outcomes. To illustrate our results, we consider a case study electricity market, and contrast the market prices obtained using a revenue adequate stochastic approach and the proposed revenue adequate CCO approach.