Market Power Research Articles

Cinematic Influence: The Role of Film in Shaping Malaysians' Travel Intentions and Destination Choices

Film tourism has emerged as a significant factor in enhancing tourist numbers by drawing visitors to locations depicted in movies and dramas. This study explores films' influence on Malaysians' perceptions and intentions to travel to these destinations. Utilizing the push and pull theory, the research examines how movies affect Malaysians' travel motivations and destination choices. Data were gathered through an online survey involving 267 Malaysian moviegoers, selected via convenience sampling. The findings indicate that several factors, including the reflection of personal values and interests in movies, significantly influence the intention to travel. Respondents expressed a stronger connection and likelihood to visit destinations featured in films that resonate with their interests. The study suggests that future research could employ different methodologies further to explore the impact of movies on tourist perceptions globally. Additionally, the findings underscore the potential for filmmakers, destination marketing organizations (DMOs), and local authorities to leverage films as powerful marketing tools to enhance tourist demand for real-life film locations.

Profits and markups during the post-COVID-19 inflation shock in the U.S. economy: a firm-level lens

In this paper, I use firm-level data from Compustat to document the evolution of markups among listed U.S. non-financial firms during the height of the COVID-19 pandemic (2020–2022). I show that, continuing its long-term rise, the aggregate (sales-weighted) markup rises markedly in 2020 and moderately in 2021, and returns to pre-pandemic levels in 2022. I then disaggregate this aggregate trend to, first, show that its 2020 growth reflects growing sales concentration. In contrast, the average within-firm markup is steadier than the aggregate. Second, I show that the markup distribution widens during the pandemic, due largely to increases at its 90th percentile. At the same time, however, the cross-sectional distribution obfuscates that firms with low pre-pandemic markups command notable markup growth in both 2021 and 2022. Third, I document key roles for manufacturing, retail, and information services in these dynamics. Together, these patterns are consistent with accounts of profit inflation, wherein U.S. firms’ market power allowed them to maintain – and, for some firms, increase – their margins over direct costs in the face of input cost shocks in 2020–2022.

Evolution of monopoly regulation

The monopolistic organization of markets is as deeply rooted as the competitive one. It requi­res market actors to take this into consideration. It requires national regulators to counteract it or at least to restrict the freedom of monopolists to the extent that they cannot abuse their market power and negatively affect public welfare. Both the ways of monopolization and the ways of counteracting it are evolving, constantly upset­ting the balance between competition and mono­poly, requiring new ways of regulating mono­polies. The authors research the evolution of monopoly regulation: from spot prohibitions of anticompetitive practices in ancient times to the systemic balancing of competitive and state mechanisms of monopoly regulation today. The article is systematized all approaches to mono­po­ly regulation into three groups, such as: antitrust regulation as control and counteraction to abuse of dominant position by a monopolist; direct regulation of permitted monopolies, including licensing, price regulation and regula­tion of access to a key resource; structural reform of monopolistic industries and introduce­tion of competition in the potentially competitive segments of it – outside the bottleneck in the value chain, where a key resource of a permitted monopoly is exploited. The combination of the theoretical foundations of state monopoly regulation with the practice of such regulation within each approach illustrates the synergy of their joint application and opens up prospects for their exploitation not only for traditional forms of monopoly, but also for neo-monopolies that are emerged in the digital unregulated sector

An Evolutionary Game Model of Market Participants and Government in Carbon Trading Markets with Virtual Power Plant Strategies

The utilization of conventional energy sources commonly leads to heightened energy consumption and the generation of specific forms of environmental pollution. As an innovative power management and dispatch system, virtual power plants (VPPs) have the potential to significantly enhance the flexibility and stability of power systems, while supporting carbon reduction targets by integrating distributed energy resources (DERs), energy management systems (EMSs), and energy storage systems (ESSs), which have attracted much attention in the power industry in recent years. Consequently, it can effectively address the variability and management challenges introduced by renewable energy. Furthermore, optimizing power market dispatch and user-side power management plays a pivotal role in promoting the transition of the energy industry towards sustainable development. The current study highlights the unresolved issue of strategic decision-making among market participants, such as energy companies, generation companies, and power distribution companies, despite the potentially significant benefits of VPPs. These entities must carefully evaluate the costs and benefits associated with adopting a VPP. Additionally, governments face the complex task of assessing the feasibility and effectiveness of providing subsidies to incentivize VPP adoption. Previous research has not adequately explored the long-term evolution of these decisions in a dynamic market environment, leading to a lack of adequate understanding of optimal strategies for market participants and regulators. This paper addresses this critical research gap by introducing an innovative bilateral evolutionary game model that integrates VPP and carbon trading markets. By utilizing the model, simulation experiments are carried out to compare different strategic decisions and analyze the stability and long-term evolution of these strategies. Research findings indicate that the adoption of VPP technology by market participants, in conjunction with government policies, results in an average 90% increase in market participants’ earnings, while government revenues see a 35% rise. This approach provides an alternative method for understanding the dynamic interactions between market participants and government policy, offering both theoretical and practical insights. The findings significantly contribute to the literature by proposing a robust framework for integrating VPPs into electricity markets, while offering valuable guidance to policymakers and market participants in developing effective strategies to support the sustainable energy transition. The application of this model has not only enhanced the understanding of market dynamics in theory, but also provided quantitative support for strategic decisions under different market conditions in practice.

The moderating role of corporate governance mechanism on the relationship between product market competition and earnings management: evidence from Bangladesh

ABSTRACT This article evaluates the moderating effects of corporate governance mechanisms on the relationship between earnings management and product market competition. Market concentration, as measured by the HHI has shown significantly negative association with accrual and real earnings management. In contrast, product market power, as measured by the industry size, demonstrates a significant positive association. The result further illustrates that the cost of industry entry significantly impacts accrual earnings management. The corporate governance mechanism (CGI) significantly decreases earnings manipulation. Moreover, the corporate governance mechanism (CGI) considerably moderates the association between market concentration, industry entry cost, and earnings management. Compared to high and low-competitive industries, the impact of market competition on earnings management in moderately competitive industries is more significant. After establishing the Bangladesh Competition Commission, product market competition has shown more impact on earnings management. This research is the initial attempt to examine the moderating effect of corporate governance mechanisms on the relationship between product market competition and earnings management from the perspective of Bangladesh.

A Study of Advertising Influence on Sustainable Consumer Products

Advertising is a powerful marketing tool for mass sales and distribution. One method is viaadvertising marketing. It may be used to market items and services, as well as ideas. Advertisementsmay be used to boost demand for generic or branded products. Advertising adequacy is mostly decidedby how a prospective consumer reacts to and considers an advertisement. The success of advertising isdetermined by how efficiently it handles transactions. As a consequence, advertising is often employedas a single variable to explain deal shifts. The report focuses on the Consumer Sustainable Industry'sChallenges. Media's Impact on Advertising, Advertising's Impact on Consumer Sustainable Buyers,Advertising's Impact on Refrigerator Buyers, Advertising's Impact on TV Buyers, Advertising's Impact onDetergent Powder Buyers.

Influencer-consumer fit and intention to co-create brand value: The serial mediation of influencer authenticity and attitude toward brand

Social media influencer marketing has emerged as an essential marketing strategy in the online interactive environment. This study investigates the impact of influencer-consumer fit (ICF) on behavioral intentions; intention to co-create brand value (ICC) and purchase intention (PI), with the serial mediation of influencer authenticity (IA) and attitude toward brand (ATB). A self-administered questionnaire was distributed to followers of social media influencers in Pakistan. The data were collected from 421 female followers of social media influencers through survey and partial least squares—structural equation modeling was used for data analysis. The findings reveal that ICF impacts IA, while the latter impacts ATB. ATB in turn impacts behavioral intentions. The direct effects suggest that ICF impacts consumers’ PI but not the ICC. However, with the serial mediation of IA and ATB, the relationship becomes significant. The findings of this study may assist managers in building brand strategies to achieve excellence in a highly dynamic and competitive market by leveraging the power of influencer marketing.

Advancing green innovation through the establishment of data regulatory bodies: Insights from the Big Data Bureau in China

Digital technology serves as a significant catalyst for green innovation (GI), intricately linked with policy guidance and strategic planning. Recently, a notable strategic approach fostering the evolution of digital technology is the establishment of Data Regulatory Bodies (DRBs). Leveraging data from 293 cities between 2010 and 2020, this paper employs a difference-in-differences methodology to investigate the impact of DRB establishment on GI. The results reveal a substantial increase in GI levels following DRB establishment. This enhancement is facilitated by improved administrative efficiency, the cultivation of digital finance, and the advancement of enterprise digitization. Furthermore, the effectiveness of these effects is optimized when DRBs are established through the integration of existing departments’ functions into a new department, particularly in cities with stable power and high marketization levels. These findings provide actionable insights for governmental initiatives aimed at fostering GI.

Redesigning automated market power mitigation in electricity markets

Electricity markets are prone to the abuse of market power. Several U.S. markets employ algorithms to monitor and mitigate market power abuse in real-time. The performance of automated mitigation procedures is contingent on precise estimates of firms' marginal production costs. Currently, marginal cost is inferred from the past offers of a plant. We present new estimation approaches and compare them to the currently applied benchmark method. We test the performance of all approaches on auction data from the Iberian power market. The results show that our novel approaches outperform the benchmark approach significantly, reducing the mean (median) absolute estimation error from 11.53 (6.08) €/MWh in the benchmark to 4.03 (2.64) €/MWh for our preferred approach. This approach also performs best in our subsequent simulation of mitigation procedures. Here we find large welfare transfers from supplier to buyer surplus as well as a robust overall welfare gain, stemming from both productive and allocative efficiency gains. Our research contributes to accurate monitoring of market power and improved automated mitigation. Although we focus on power markets, our findings are applicable to monitoring of renewable energy tenders or market power surveillance in rail and air traffic.

Determinasi RAROC Bank menggunakan Faktor Internal dan Faktor Eksternal Bank serta Size sebagai Variabel Moderasi

This study aims to explore the influence of internal and external company factors on bank profitability with size as a moderating variable. The company's financial ratios and macroeconomic variables are used to determine bank profitability, specifically the Risk-Adjusted Return on Capital (RAROC). A panel data model is employed to determine the RAROC in Indonesia for the period 2014 to 2023. This study finds that Non-Performing Loans, Electricity Consumption, Oil Prices, Exchange Rate, and FED Rate do not affect RAROC. However, Net Interest Margin and Total Assets have a significantly negative effect on RAROC, while Market Power has a significantly positive effect on RAROC. Additionally, Total Assets strengthen the negative effect of Non-Performing Loans and Net Interest Margin on RAROC, while Total Assets strengthen the positive effect of Market Power, Electricity Consumption, Oil Prices, Exchange Rate, and FED Rate on RAROC.

Real-time pricing considering carbon constraints for smart grid with multiple energy sources

With the escalation of fossil fuel pollution and the challenges of climate change, the smart grid is forced to address the carbon emissions associated with fossil fuel use. Real-time pricing of smart grid is an effective means to regulate the balance of supply and demand in the grid as well as to achieve energy conservation and environmental protection. This paper takes traditional fossil energy and clean energy power supply as the background, takes social welfare maximization as the goal, combines carbon emission cost with traditional power generation cost, and constructs a real-time pricing model that takes into account carbon emission cost, so that thermal power enterprises participate in the power market and carbon market at the same time. By using duality methods, we transformed the original optimization problem into an equivalent one. Furthermore, a distributed iterative algorithm based on the subgradient projection method is employed to solve the dual problem. The simulation results verify the feasibility and effectiveness of the proposed real-time pricing strategy.

The power of generative marketing: Can generative AI create superhuman visual marketing content?

Generative AI’s capacity to create photorealistic images has the potential to augment human creativity and disrupt the economics of visual marketing content production. This research systematically compares the performance of AI-generated to human-made marketing images across important marketing dimensions. First, we prompt seven state-of-the-art generative text-to-image models (DALL-E 3, Midjourney v6, Firefly 2, Imagen 2, Imagine, Stable Diffusion XL Turbo, and Realistic Vision) to create 10,320 synthetic marketing images, using 2,400 real-world, human-made images as input. 254,400 human evaluations of these images show that AI-generated marketing imagery can surpass human-made images in quality, realism, and aesthetics. Second, we give identical creative briefings to commissioned human freelancers and the AI models, showing that the best synthetic images also excel in ad creativity, ad attitudes, and prompt following. Third, a field study with more than 173,000 impressions demonstrates that AI-generated banner ads can compete with professional human-made stock photography, achieving an up to 50% higher click-through rate than a human-made image. Collectively, our findings suggest that the paradigm shift brought about by generative AI can help advertisers produce marketing content not only faster and orders of magnitude cheaper but also at superhuman effectiveness levels with important implications for firms, consumers, and policymakers. To facilitate future research on AI-generated marketing imagery, we release GenImageNet that contains all of our synthetic images and their human ratings.

Does capital market liberalization enhance overseas market power of investment firms? An empirical study based on Chinese multinational corporations

This study empirically analyzes the 2012–2022 data of Shanghai and Shenzhen A-share investment-oriented listed companies to determine the impact of capital market liberalization on investment firms’ overseas market power. It finds that both capital market liberalization and research and development investment enhance the overseas market power of investment firms. The impact of capital market liberalization varies based on the different ownership properties and life cycles of firms.

Dynamic capacity withholding assessment of virtual power plants in local energy and reserve market

The increasing utilization of distributed generation resources led to the formation of active distribution networks and virtual power plants (VPPs), which have changed the paradigms of electrical energy transactions in local energy markets. The VPPs can form capacity-withholding groups and impose market power to gain more profits, which may increase the costs of energy procurements for consumers. This paper presents an algorithm for the local electricity market operator in distribution networks to assess the dynamic capacity withholding of VPPs in the local energy and reserve markets. The main contribution of this paper is proposing indices to evaluate the dynamic capacity withholding of VPPs in energy and reserve markets. The other contribution of this paper is that it also quantitatively analyzes the impact of withholding processes on the flexibility of the distribution network. An optimization process is used to estimate coordinated offers of VPPs in the energy market in order to prevent the formation of withholding groups. The proposed algorithm was assessed for the 123-bus IEEE test system and the energy and reserve dynamic capacity-withholding indices were determined for different operating conditions.

A real options-based framework for multi-generation liquid air energy storage investment decision under multiple uncertainties and policy incentives

Liquid Air Energy Storage (LAES) is a promising energy storage technology renowned for its advantages such as geographical flexibility and high energy density. Comprehensively assessing LAES investment value and timing remains challenging due to uncertainties in technology costs and market conditions. This study introduces a real options-based framework to evaluate investments in multi-generation LAES systems in China's power market by considering uncertain factors, such as investment cost and electricity price. Additionally, the impact of various incentives, including subsidies and preferential taxation policies, on LAES investment is investigated. The proposed investment decision framework is applied in a case study of a 10 MW-class multi-generation LAES system. Results show that the current economic environment is insufficient to trigger immediate LAES investment. The recommended optimal investment times are 2029 for Guangdong, 2031 for Jiangsu, and 2036 for Beijing and Qinghai without incentives. The levelized cost of storage at the optimal investment time is 0.105-0.174$/kWhe, and the optimal investment value is 882-9269k$. It is also found that preferential taxation policies can increase the LAES investment value. Increasing investment subsidies and technology learning effects have limited impacts on stimulating investments. In contrast, discharge subsidies are more favorable for promoting the deployment of LAES systems.

Data Driven Dominance in Digital Markets: Assessing Indonesian Competition Law in the Digital Age

Digital markets and multi-sided platforms are created by the internet, that are characterized by the use of big data as new market power. Big data enabled dominant and digital-based key players such as Instagram and Facebook/Meta to record and forecast their users’ personal data and spending capacity to increase their economies of scale by tailoring updates to the users’ demand. Therefore, big data becomes an essential market share, and its scarcity determines newer entrants’ ability to enter the market and the existing incumbents’ ability to survive by grappling with fast-paced digital changes. This legal morphology benefits data-driven undertakings to elevate their position in the market and achieve enough independence to influence market behavior. On the other hand, multi-sided platforms refer to where a single undertaking sells different products to different types of consumers on different sides of the platforms that affect each other’s demand due to the cross-group network effect. Undertakings often sell zero-price products to one side while imposing the monetary burden and gaining revenue from the other. This situation triggered legal challenges to the calculation of market share necessary to determine a dominant position that is essentially mathematical based. Furthermore, big data and multi-sided platforms stimulate the rise of novel abusive practices in the digital market. This paper will analyze the Indonesian 1999 competition law’s flexibility to deal with these changes by comparing the existing framework on consumer data protection, big data, and multi-sided platforms management to the European Union competition law.

Probabilistic prediction-based multi-objective optimization approach for multi-energy virtual power plant

Virtual power plants (VPPs) are encountering multiple challenges due to market uncertainties and power network instability. In this paper, a novel probabilistic prediction-based multi-objective optimization framework for VPP is proposed to maximize operating profit while minimizing pollutant emissions and voltage deviations in the distribution network, which considers the uncertainties of wind power and electricity price. In this framework, the VPP that participates in the energy and ancillary service markets firstly aggregates the wind farms, the electric vehicle charging stations (EVCS), and the combined cooling, heating, and power subsystems to improve the utilization efficiency and operational flexibility of multiple energy sources. Then, a new Pareto optimizer, called multi-objective hybrid sand cat swarm optimization and strength firefly algorithm, is proposed to tackle the multi-objective optimization model of VPP. The proposed hybrid algorithm utilizes the advantages of sand cat swarm optimization and strength firefly algorithm mechanisms to facilitate local exploitation and global exploration. Finally, a new deep reinforcement learning probabilistic prediction approach based on quantile regression deep deterministic policy gradient is modeled to evaluate the uncertainties. The proposed models and methods have been thoroughly discussed on a modified distributed network. It is calculated that compared with the VPP without EVCS, the operating profit of the proposed VPP increases by 18.69%, and the emissions and voltage deviation of the proposed VPP are reduced by 3.42% and 10.44%, respectively. Experimental results also prove that the performance of the proposed Pareto optimizer and probabilistic prediction approach is superior to other benchmark techniques.

Joint bidding strategy of multi‐virtual power plant operators

AbstractThe distributed generation of new energy can effectively use the local endemic clean new energy and provide green power. However, due to the small scale and scattered layout of distributed generation resources, it is difficult to participate in the economic dispatch of power systems and even the competition of the power market. Virtual Power Plant (VPP) can act as a carrier of Distributed Energy Resource (DER) to manage its internal energy, to carry out combined bidding in the day‐ahead energy market and the regulation market. When virtual power plants coordinate multiple distributed energy resources to participate in electricity market transactions, the degree of disclosure of their internal privacy information will produce different trading strategies. A Stackelberg game model between multiple virtual power plant aggregators and virtual power plant operators is constructed, and the Kriging model is combined to protect the privacy information in the transactions of virtual power plant operators. Energy management for distributed generation is carried out, and the profit situation of virtual power plant operators under different strategies is analyzed.

Information Gap Decision-Making Theory-Based Medium- and Long-Term Optimal Dispatching of Hydropower-Dominated Power Grids in a Market Environment

In the high-proportion hydropower market, the fairness of the execution of traded electricity and clean energy consumption are two issues that need to be considered in medium- and long-term dispatching. Aiming at the fairness of medium- and long-term optimal dispatching of hydropower-dominated grids and the problem of water abandonment in the power market environment, this paper proposes a medium- and long-term optimal dispatching method for hydropower-dominated grids based on the information gap decision-making theory (IGDT). Firstly, IGDT is used to establish a two-layer model of medium- and long-term optimal dispatching that considers runoff uncertainty, in which the lower layer solves the maximum value of the maximum difference in the contract power completion rate of the power stations, and the upper layer solves the maximum fluctuation range of the interval inflow. Then, a mixed-integer linear programming (MILP)-based single-layer optimization model is obtained through a variety of linearization techniques, and the model is solved via the CPLEX solver (version 12.10.0). The medium- and long-term optimal dispatching of 10 thermal power stations and 22 hydropower stations in Yunnan Power Grid, China, is taken as an example to verify the proposed model. The results show that the maximum difference in the contracted electricity completion rate of each power station is 0.412, and the amount of abandoned hydropower is reduced by 81.33% compared to when the abandoned water penalty function is not considered. It is proved that the proposed model can effectively alleviate the problems of excessive power generation, insufficient power generation and large-scale hydropower abandonment, which are of great significance for realizing the fair dispatching of hydropower-dominated power grids and promoting clean energy consumption in the market environment.

Digital Marketing for the Cultural and Tourism Industry in the Three Eastern Provinces Research on Development Impact

In 2023, the General Office of the State Council issued several measures to unleash the potential of tourism consumption and promote high-quality development of the tourism industry. Local governments actively responded to national policies and promoted the development of cultural and tourism economy. The three eastern provinces are tourist destinations every winter, and their unique scenery and cultural sentiment make the Northeast region stand out during holidays. This winter, Harbin's ice and snow tourism has become popular, even sparking a competition frenzy among other regions in Northeast China and even the National Bureau of Culture and Tourism. This paper uses the SWOT model and PEST model to analyze Northeast China’s natural and human resources, so as to study the help and advantages of digital marketing for its innovation and development, the lessons learned will help to provide a powerful marketing tool and channel for the cultural tourism industry, and promote the creative transformation of the current stage in the development of cultural tourism in Northeast China and throughout the country.