Transaction Costs Research Articles

Diversified Reward-Risk Parity in Portfolio Construction

Abstract We introduce diversified risk parity embedded with various reward-risk measures and more generic allocation rules for portfolio construction. We empirically test the proposed reward-risk parity strategies and compare their performance with an equally-weighted risk portfolio in various asset universes. The reward-risk parity strategies we tested exhibit consistent outperformance evidenced by higher average returns, Sharpe ratios, and Calmar ratios. The alternative allocations also reflect less downside risks in Value-at-Risk, conditional Value-at-Risk, and maximum drawdown. In addition to the enhanced performance and reward-risk profile, transaction costs can be reduced by lowering turnover rates. The diversified reward-risk parity allocations gain superior performance in the Carhart four-factor analysis.

Social capital in transactions: The role of economic and social trust in the specialty beef production system in Brazil

Abstract This study aims to understand the social capital inherent in agents and the role of social and economic trust in transactions between beef cattle producers and slaughtering cooperatives in the specialty beef production system in Paraná state, Brazil. This qualitative research involved 31 semi-structured interviews with beef cattle producers, cooperatives, and key agents. Results revealed that social capital, comprising networks and informal norms, favors the condition of trust, enabling the construction of a hybrid governance structure under a complex institutional environment. Social and economic trust between agents facilitates transactions, reduces behavioral and market uncertainties, enables ex-post adaptations, and consequently, reduces monitoring costs and transaction costs. Trust based on social aspects, i.e., social trust, was more relevant for the construction of arrangements, while trust based on economic aspects, i.e., economic trust, had a greater impact on the continuity of arrangements. This reveals that looking at only one of them is not enough to understand contractual arrangements. Thus, this study highlights that unfolding the concept of trust and investigating whether it comes from an economic or social basis is important to understand the complexity of such arrangements, which may influence specialty beef production system design and coordination efficiency.

R&D investment and innovation performance under vertical partner concentration

ABSTRACT Vertical exchange partners, both customers and suppliers, are key sources of knowledge about new components, technologies, and markets, exerting a critical influence on firms’ innovation performance. While firms’ reliance on a few suppliers and/or a few customers has become a regularly occurring phenomenon, there is only an insufficient understanding of the effect of vertical partner concentration on the returns to firms’ R&D investments. Building on transaction cost and evolutionary economics theories, we conjecture that vertical partner concentration negatively moderates the relationship between R&D investments and innovation performance. Our analysis is built on a dataset of 768 R&D-active French manufacturing firms derived from two independent and temporally separated surveys, enabling the use of lagged values of the key independent variables. The results reveal that customer concentration, whether accompanied or not by supplier concentration, weakens the positive relationship between R&D investment and innovation performance, but this is not observed in dynamic industry settings.

Peer-to-peer multi-period energy market with flexible scheduling on a scalable and cost-effective blockchain

Peer-to-Peer (P2P) electricity trading is an emerging means for allowing communities to share local energy production and demand response through direct trading. It is projected to play an important role in the future energy market by increasing the incentive to own and operate distributed energy resources (DERs) and participate in demand response (DR). A common approach to implementing P2P trading is with blockchain, however, existing solutions feature limitations. Typically, concerns involve either scalability and high transaction costs or projects implementing a permissioned blockchain that does not provide the trust and decentralization desired. In addition, the majority of market designs are simplified and do not capture the distinct complexity of electricity markets. They do not consider the inter-temporal constraints of appliances and DERs that need to run for multiple periods. Complex market designs that address these issues have not been implemented in blockchain based systems due to the computational limitations of such systems. To improve these two aspects, this paper proposes a market formulation that supports products running multiple periods with flexible scheduling, modeled as a mixed integer linear programming problem that optimizes social welfare. The trading platform is then implemented as a side chain of Polkadot, a network of blockchains designed to be secure, scalable, and economically feasible to foster an ecosystem of application-specific sidechains. This design is demonstrated to improve social welfare and achieve scalability by benchmarking the number of transactions that can be included per second, based upon typical assumptions of the market size. The result is a decentralized and performant system that allows for complex market designs to be implemented in a blockchain environment. This approach simplifies the process for participants, allowing them to bid DER or DR devices, directly and easily, even when this requires constraints across periods.

Sustainable Food Value Chains: Approaches to Transaction Costs in Agro-Alimentary Systems of Developing Countries—A Chile Case Study

This study delves into the dynamics of price linkages and transaction costs in agricultural markets, emphasizing the sustainability of food supply chains. By exploring vertical and horizontal price linkages in agro-farming value chains of a developing country, it addresses the efficiency of market information transmission and the capacity for arbitrage among chain participants. The methodological core of the research involves analyzing price linkages in Chilean horticultural wholesale markets, focusing on key vegetables such as, onions, lettuce, maize, and tomatoes. This analysis is underpinned by a novel approach that models and estimates time-dependent, conditional threshold bands, extending the traditional cointegration models. This method allows a more nuanced understanding of how agricultural market linkages evolve over time, enhancing our comprehension of price transmission behavior and market integration. The results reveal significant non-linear relationships between fuel prices and vegetable prices, particularly in central Chilean regions. This finding challenges the traditional linear perspective, suggesting that factors such as storage capacity and arbitrage behavior can influence price signal transmission. Such insights are crucial for stakeholders in the agribusiness value chain, offering a deeper understanding of market dynamics and aiding in the development of more sustainable and efficient market strategies. This research contributes significantly to the field of agricultural economics by providing a more robust framework for analyzing market behaviors and transaction costs in the context of sustainability and value chains. Its findings have profound implications for both theory and practice, informing policy-making and strategic decision-making in the agribusiness sector.

Knowledge sharing and NPD performance for UK cross – border M&A firms: the moderating role of behavior and outcome controls

Purpose This paper aims to explore how the effect of knowledge sharing through mergers and acquisitions (M&As) on new product development (NPD) performance is contingent upon two different types of control mechanisms: behavior control and outcome control. Design/methodology/approach Leveraging the theory from transaction cost economics, this study provides answers regarding the roles of behavior and outcome controls. The hypotheses were tested empirically across a sample of 143 UK cross-border M&A firms. Findings The results provide the increasing call for an integrative perspective and theory in the M&A literature in that knowledge sharing through M&As is deemed decisive for NPD performance, and while both control mechanisms are effective, behavior control is more effective in enhancing NPD performance than outcome control. Originality/value The relevant M&A studies lack insights into the use of control mechanisms as a way to monitor the target firm’s behavior and performance and reduce the risk of its opportunistic behavior. Appreciating the need for M&A literature that elaborates control strategy and structure, this study incorporates behavior control and outcome control into M&A mechanisms.

The cost of health insurance and entry into entrepreneurship

The cost of health insurance and entry into entrepreneurship

EXPRESS: Spring Forward = Fall Back? the Effect of Daylight Saving Time Change on Consumers’ Unhealthy Behavior

Prior research documents deleterious consequences of the annual clock change to daylight saving time in many contexts, but little is known about the effect the policy has on consumer behavior. While policy debates around ending seasonal clock changes continue, millions of consumers worldwide are potentially adversely affected by the time change. Drawing on the notions of sleepiness and self-control, the authors propose a framework of how the onset of daylight saving time increases unhealthy behavior. The hypotheses are tested via two studies cast in the difference-in-differences modeling framework capturing consumption before and after the time change and across consumers who experience the transition versus who do not. Results of the first study suggest that the onset of daylight saving time increases calorie consumption from packaged snacks that are largely unhealthy, specifically in the evening and on cloudy days. The effect of the end of daylight saving time is not significant, suggesting an overall asymmetric effect of the time change on unhealthy behavior. Study 2 reveals that the onset of daylight saving time decreases fitness center visits, particularly for consumers without healthy consumption habits and with high transaction costs. Analysis of social media data suggests that consumers find the time change disruptive. Overall, the findings imply that public policy makers and businesses should find ways to support consumers around the onset of daylight saving time.

Imperfect legislative agreements and judicial policymaking. A theoretical inquiry

ABSTRACT This article examines how imperfect legislative agreements offer an opportunity for higher courts’ intervention in policymaking. In concrete terms, it explains that lawmakers in certain situations avoid resolving apparent conflicts of interest due to high transaction costs in the legislative process, maintaining statutory criteria considered transparently inefficient or by simply developing regulatory arrangements with a significant degree of indeterminacy. Employing a simplified model of rational choice behaviour, the article asserts that those scenarios constitute an opportunity for strategic involvement of the higher courts in crafting public policy. As an unintended consequence of the imperfect legislative agreements, justices look at them as an indicator that members of the other branches of government have reached weak political compromises or are unable to impose their policy preferences. Therefore, justices can exercise discretion, foreseeing prospects to increase their authority while the risk of an overruling or the implementation of other retaliatory measures remains low.

Patents, Freedom to Operate, and Follow-on Innovation: Evidence from Post-Grant Opposition

We study the blocking effect of patents on follow-on innovation by others. We posit that follow-on innovation requires freedom to operate (FTO), which firms typically obtain through a license from the patentee holding the original innovation. Where licensing fails, follow-on innovation is blocked unless firms gain FTO through patent invalidation. Using large-scale data from post-grant oppositions at the European Patent Office, we find that patent invalidation increases follow-on innovation, measured in citations, by 16% on average. This effect exhibits a U-shape in the value of the original innovation. For patents on low-value original innovations, invalidation predominantly increases low-value follow-on innovation outside the patentee’s product market. Here, transaction costs likely exceed the joint surplus of licensing, causing licensing failure. In contrast, for patents on high-value original innovations, invalidation mainly increases high-value follow-on innovation in the patentee’s product market. We attribute this latter result to rent dissipation, which renders patentees unwilling to license out valuable technologies to (potential) competitors. This paper was accepted by Ashish Arora, entrepreneurship and innovation. Funding: This work was supported by the Deutsche Forschungsgemeinschaft [Collaborative Research Center TRR 190]. F. Gaessler acknowledges financial support from the Spanish Agencia Estatal de Investigación through the Severo Ochoa Programme for Centres of Excellence in R&D [Barcelona School of Economics CEX2019-000915-S]. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2019.02294 .

Price Delay and Market Efficiency of Cryptocurrencies: The Impact of Liquidity and Volatility during the COVID-19 Pandemic

The rise of cryptocurrencies as alternative financial investments, with potential safe-haven and hedging properties, highlights the need to examine their market efficiency. This study is the first to investigate the combined impact of liquidity and volatility features of cryptocurrencies on their price delays. Using a wide spectrum of cryptocurrencies, we investigate whether the COVID-19 outbreak has affected market efficiency by studying price delays to market information. We find that as liquidity increases and volatility decreases, cryptocurrencies demonstrate stronger market efficiency. Additionally, we show that price delay differences during the COVID-19 outbreak increase with higher levels of illiquidity, particularly for highly volatile quintiles. We suggest that perceived risks and high transaction costs in illiquid and highly volatile cryptocurrencies reduce active traders’ willingness to engage in arbitrage trading, leading to increased market inefficiencies. Our findings are relevant to investors, aiding in improving their decision-making processes and enhancing their investment efficiency. Our paper also presents significant implications for policymakers, emphasizing the need for reforms aimed at enhancing the speed at which information is incorporated into cryptocurrency returns. These reforms would help mitigate market distortions and increase the sustainability of cryptocurrency markets.

Blockchain adoption and operational performance: A secondary data analysis on effects and contingencies

PurposeThis study analyzes the performance implications of adopting blockchain to support supply chain business processes. The technology holds as many promises as implementation challenges, so interest in its impact on operational performance has grown steadily over the last few years.Design/methodology/approachDrawing on transaction cost economics and the contingency theory, we built a set of hypotheses. These were tested through a long-term event study and an ordinary least squares regression involving 130 adopters listed in North America.FindingsCompared with the control sample, adopters displayed significant abnormal performance in terms of labor productivity, operating cycle and profitability, whereas sales appeared unaffected. Firms in regulated settings and closer to the end customer showed more positive effects. Neither industry-level competition nor the early involvement of a project partner emerged as relevant contextual factors.Originality/valueThis research presents the first extensive analysis of operational performance based on objective measures. In contrast to previous studies and theoretical predictions, the results indicate that blockchain adoption is not associated with sales improvement. This can be explained considering that secure data storage and sharing do not guarantee the factual credibility of recorded data, which needs to be proved to customers in alternative ways. Conversely, improvements in other operational performance dimensions confirm that blockchain can support inter-organizational transactions more efficiently. The results are relevant in times when, following hype, there are signs of disengagement with the technology.

Assessing the Efficiency and Potential of China’s Apple Exports

This study elucidates the factors impacting China’s apple trade, its efficiency, and opportunities for increased revenue. This study adopts a stochastic frontier gravity model on China’s apple exports data, covering the period from 1997 to 2022 across 38 significant trading partners. The finding demonstrates that the economic growth of China and its trading partners substantially boosts apple export volumes, with a positive correlation between GDP growth and export flows. The research also highlights the deterrent effect of geographical distance on exports and reveals a complex negative relationship between the per capita GDP of importing nations and export efficiency, shedding light on the nuanced nature of trade dynamics. Furthermore, the study finds that the appreciation of China’s currency plays a crucial role in enhancing export efficiency by lowering transaction costs. Meanwhile, increased agricultural land in importing countries presents competitive challenges, impacting export performance negatively. Geographical proximity and infrastructural features, such as shared borders and lack of access to seaport, are identified as significant factors in export efficiency. The analysis unveils considerable untapped export potential in various countries, suggesting a strategic avenue for market expansion. To optimize China’s apple export strategy, policymakers are advised to consider currency management, negotiate trade agreements that mitigate distance and per capita GDP effects, and target markets with high untapped potential, thereby facilitating sustainable growth in China’s apple export sector.

Supply chain concentration and financial performance: the moderating roles of marketing and operational capabilities

PurposeBased on resource dependence theory (RDT) and transaction cost theory (TCT), we aim to investigate the relationship between supply chain concentration and firm performance. Based on the resource-based perspective, we further investigate the moderating effect of marketing and operational capabilities on the relationship between supply chain concentration and firm performance.Design/methodology/approachBased on data from 2,082 firms with 8,371 observations from 2008 to 2020 in China, we use stochastic frontier analysis to calculate marketing capability and operational capability and use multinational regressions to test our research model.FindingsWe find a U-shaped relationship between supplier concentration and firm performance; there is also a U-shaped relationship between customer concentration and firm performance. In addition, the relationship between supplier concentration and firm financial performance is strengthened by the firm’s marketing capability, and the relationship between customer concentration and firm financial performance is weakened by the firm’s operational capability.Originality/valueDrawing from RDT and TCT, this study extends the research on the impact of supply chain concentration on firm performance. The study finds that supply chain concentration and firm performance have a nonlinear relationship, and it is further moderated by marketing capability and operational capability, providing insights for managers.

How Digital Platforms Promote Roles of Nonprofit Human Service Organizations During the COVID-19 in China

ABSTRACT The COVID-19 pandemic compelled nonprofit human service organizations (NPHS) to rapidly adapt to heightened service demands while prioritizing the well-being of their staff and volunteers. Although digital platforms hold promise in facilitating NPHS adaptation, little is known about their impact during the crisis. How and through what pathways do digital platforms help NPHS adapt their service roles in times of crisis? Did digital platforms with different types of sponsorship vary in the way they affect NPHS’s roles? Our study, based on 94 interviews in China, reveals that digital platforms assisted NPHS through three pathways: reducing transaction costs in stakeholder engagement, mitigating information asymmetry in targeting service recipients, and promoting shared responsibility for volunteer participation. Different sponsorship types influenced these pathways: government-sponsored platforms enhanced legitimacy, business-sponsored platforms leveraged private resources, and nonprofit-sponsored platforms bolstered citizen trust. This research informs scholars and practitioners on understanding how NPHS remained agile and innovative while facing extraordinary external challenges.

Determinants of public-private partnership governance structures: evidence from India

ABSTRACT Public-private partnerships (PPPs) represent a continuum of governance structures characterized by wide variability in private sector risk transfer. Despite the widespread adoption of PPPs, few studies examine the reasons for such heterogeneity. We investigate the factors explaining variability across PPP governance structures and their impact on the extent of private sector risk transfer using ordinal logistic regression on a novel dataset of 2330 Indian PPP projects. We find that the risk transfer is mainly driven by economic and fiscal factors, service-specific transaction costs and project-specific factors, while political factors and institutional quality barely influence it.

The Impact of Blockchain Technology on Financial Markets and Its Future Trends: An Economic Perspective Based on Data Analysis

Based on the economic perspective of data analysis, this paper deeply discusses the impact of blockchain technology on the financial market and its future trend. By using econometrics, we systematically analyze how blockchain technology can change the operation mechanism of financial markets, improve transaction efficiency, reduce transaction costs, and enhance the security and transparency of financial markets. The study has found that the decentralized nature of blockchain technology can help to break down the barriers of the traditional financial market and promote the popularization and inclusiveness of financial services. At the same time, the automatic execution function of smart contracts simplifies the process of financial transactions and reduces the risk of human intervention and fraud. In addition, the blockchain technology has also enhanced the trust mechanism of the financial markets by improving the security and traceability of the data. In terms of econometric analysis, we use a large amount of financial data to empirically test the impact of blockchain technology on the performance of the financial market by building regression models, time series analysis and other methods. The results show that there is a significant positive correlation between the application of blockchain technology and financial market efficiency, liquidity and stability. Looking into the future, with the continuous innovation and improvement of blockchain technology, its application in the financial market will be more extensive and deeper. On the one hand, blockchain technology is expected to promote the further digitalization and intelligence of the financial market, and improve the convenience and personalization of financial services. On the other hand, blockchain technology will also promote the cross-border integration and innovation of the financial market, and inject new vitality into the global economic development.

How to promote data sharing among enterprises based on dynamic evolutionary game analysis

ABSTRACT In traditional contexts, enterprises often hesitate to engage in data sharing due to the associated heightened risks and transaction costs. Blockchain technology, leveraging its encryption algorithms and smart contracts, effectively alleviates these challenges, thereby encouraging enterprises to actively participate in data sharing. The adoption of reasonable reward and penalty mechanisms can prevent opportunistic behaviours and stimulate data sharing among businesses. It is noteworthy that blockchain technology and data alliance have a mutually substitutive effect in terms of risk and communication costs, while they complement each other in regulatory and transaction costs. The integration of blockchain technology with data alliance can enhance collaborative revenue and reduce various costs. When distributing collaborative revenue, it is crucial to adhere to the guiding principle of ‘prioritizing efficiency while considering fairness’. Collaboration becomes a reality when all parties collectively achieve a substantial overall revenue threshold. Subsequently, in-depth exploration of fair resource allocation principles is crucial to ensure the equitable distribution of resources.

Current and future development of thermal grids in Switzerland: an organizational perspective

IntroductionThermal grids are key to decarbonizing heating and cooling. However, their development is a complex socio-technical process. This study aims to (1) understand the thermal grid ecosystem’s development under changing political, economic and cultural frame conditions, (2) elicit actors’ perception of options for future development, and (3) identify concrete problem situations for the future development of thermal grids and describe how to address them.MethodsWe draw on 18 semi-structured interviews with decision-makers in utilities, municipal administrations, planning and engineering companies and intermediary organizations.ResultsWith increased strategic importance of thermal grids, the ecosystem has been in a phase of rapid growth and professionalization in the last 10–15 years. This is reflected in a rapid increase in the number of grids, but also in capability and capacity building and greater quality standards. This transformation has been supported by a resource pool (e.g., knowledge base, capacities and capabilities, networks or decision-support tools), built partly through the value network’s activities and partly through purposeful orchestration by associations, authorities, utilities and other actors. While policy developments, energy price fluctuations and changes in customer perceptions spurred demand, thermal grid development is limited by supply-side constraints, especially a skill shortage. Four interrelated problem situations were identified: (1) Municipalities lack the resources to manage the implementation of their energy strategies. (2) The modernization of thermal grids and integration of low-carbon heat sources entails high transaction costs. (3) The implementation of thermal grids is subject to delays and quality risks, with consequences for the timely achievement of decarbonization goals. (4) Customers face uncertainty on available options, as well as on the systemic impacts of their decisions.DiscussionBased on current practices, suggestions by the interviewees, as well as a comparison with the international literature, we identify four development areas to address these problem situations: (1) Empower and support municipalities. (2) Manage the scarcity of qualified workforce. (3) Leverage digital technology for the planning, realization and operation of thermal grids. (4) Pilot technological and organizational innovations together. This complements the set of previously suggested measures to support the development of thermal grids, which focused strongly on technical and regulatory aspects.

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

The growing necessity for sustainable energy solutions underscores the critical need for optimizing solar power systems. Existing methodologies predominantly focus on static strategies with limited adaptability to dynamic environmental and operational conditions, often resulting in suboptimal performance and inefficiency. This research introduces a comprehensive, integrated framework employing Internet of Things (IoT) technologies alongside advanced machine learning and deep learning methodologies to enhance solar power system efficiency and reliability levels. The proposed model integrates four key components: predictive maintenance using Support Vector Machines (SVM) for enhanced anomaly detection, solar power forecasting via Quad Long Short-Term Memory (QLSTM) neural networks, dynamic load balancing through Reinforcement Learning with Deep Q-learning, and the integration with smart grids employing Decentralized Multi-Agent Systems (MAS) with Auction-Based Mechanisms. Each method is selected based on its suitability to address specific challenges within the solar power domain: SVMs for their effectiveness in high-dimensional anomaly detection, QLSTMs for their superior temporal pattern recognition in forecasting, Deep Q-learning for its adaptability in dynamic load management, and MAS for efficient decentralized energy resource coordination operations. The implementation of these methodologies demonstrates significant advancements over traditional approaches. Predictive maintenance facilitated by SVMs leads to a 20% reduction in maintenance costs, while QLSTM-based forecasting achieves a 95% accuracy rate, thereby enhancing grid management and reducing revenue losses. Moreover, reinforcement learning optimizes energy utilization, decreasing wastage and system downtime by 10% and 5% respectively. Lastly, the MAS framework promotes a 20% increase in energy trading efficiency, yielding a 10% reduction in transaction costs and bolstering grid resilience levels. This work represents a significant leap forward in solar power optimization, offering a scalable, efficient, and intelligent framework that paves the way for more sustainable and reliable energy systems. The integration of IoT with machine learning and deep learning presents a paradigm shift in renewable energy management, marking a critical step toward achieving global sustainability objectives..