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The purpose of this article is to present the results of empirical research concerning the identification of the impact of the transfer of companies from the alternative market to the regulated market of the Warsaw Stock Exchange on their operating and net performance.

The study was conducted based on the empirical data of the companies that changed the listing place on the Warsaw Stock Exchange. Data regarding the years before the transfer were collected from the prospectuses of companies prepared mandatorily in connection with the transition to the regulated market. Data regarding the years of the event and subsequent years were obtained from companies' annual reports. As in other studies in the analysis, the operational metrics were used (operating return on sale, operating return on assets, total asset turnover), which was further extended to net profitability ratios (net return on ale, net return on asset, net return on equity). The significance analysis was based on the Student's t-test and Wilcoxon’s test.

The results show that before the transfer from the alternative market to the regulated market, companies improved financial performance. As a result of the change of listing venues, the results already collapsed in the year of the event. The downward trend continued in the following two years, with a noticeable improvement in the third year after the transfer.

The literature lacks such studies based on the Polish market. To the best knowledge of the author, this is one of the first studies in Poland showing the changes in operating and net performance of companies changing the stock listing venues. The research is based on a large group including all companies that have changed listing venues since the beginning of the alternative market in Poland. The article presents an original empirical result that can be used both by managers and investors in their decisions.

The choice of a sales channel for fresh vegetables is an important decision a farmer can make. Typically, the farmers rely on their personal experience in directing the produce to a sales channel. This study examines how sales forecasting of fresh vegetables along multiple channels enables marginal and small-scale farmers to maximize their revenue by proportionately allocating the produce considering their short shelf life.

Machine learning models, namely long short-term memory (LSTM), convolution neural network (CNN) and traditional methods such as autoregressive integrated moving average (ARIMA) and weighted moving average (WMA) are developed and tested for demand forecasting of vegetables through three different channels, namely direct (Jaivasree), regulated (World market) and cooperative (Horticorp).

The results show that machine learning methods (LSTM/CNN) provide better forecasts for regulated (World market) and cooperative (Horticorp) channels, while traditional moving average yields a better result for direct (Jaivasree) channel where the sales volume is less as compared to the remaining two channels.

The price of vegetables is not considered as the government sets the base price for the vegetables.

The existing literature lacks models and approaches to predict the sales of fresh vegetables for marginal and small-scale farmers of developing economies like India. In this research, the authors forecast the sales of commonly used fresh vegetables for small-scale farmers of Kerala in India based on a set of 130 weekly time series data obtained from the Kerala Horticorp.

This paper gives a model of collusion formation and a method of measuring the degree of it among the traders/bidders in the agricultural commodity markets in India. The important assumption is that the bidding is repetitive with a set of common bidders. The theory has been derived based on the behavior of the wholesale market of agricultural commodities in India. The paper is based on full information in the collusion formation. The paper first derives the theoretical structure of the bidders' behavior and thereafter derives a measure of collusion formation with the help of real-life data.

The paper used the standard theory of optimization and the theory of auction and probability statistics.

This is a complete information model of cartel formation. The bidding is repetitive and continues forever in discrete time. Hence bidders behavior is observable. Using the proposed method, if the APMC measures for each market and publishes on a periodic basis, say weekly basis, then it will be easier to break the collusion in the market where relative collision is present. For example, if a farmer has three options to sell in three different markets, then the published data would help them to select the market where the degree of collusion is relatively lower. Moreover, the undesirable loss can be avoided based on the right choice of market. As a result, transaction costs will be optima.

The paper first derives the theoretical structure of the bidders' behavior and thereafter derives a measure of collusion formation with the help of real-life data.

This paper aims to conceptualize and empirically illustrate the challenges that financial market regulation presents to politicians and the organization tasked with specifying regulations and supervising their implementation in the interest of users and consumers of financial instruments. It analyses the problem from the viewpoint of the governor's dilemma and the control/competence conflict, the linked problem of the rent-seeking of agents/intermediators and consumers of financial instruments. Political accountability problems are enhanced by the materiality of the technologies used, i.e. algo trading.

The paper theoretically conceptualizes and empirically illustrates the argument.

The paper finds that regulators of digitalized financial markets are faced with considerable problems and depend on private agents when regulating financial transactions. However, the new technological instruments also offer new possibilities for securing compliance.

Further research should focus more in-depth on the cooperation between public and private actors in the specification and implementation of regulatory details. It should further investigate the conditions which allow regulators to use RegTech in the surveillance of financial firms.

Since financial market transactions are opaque for most users, the creation of more transparency is crucial to hold regulators accountable in their activity of surveillance of financial firms. New algorithm-based technologies may lend important support in doing so.

By linking the different analytical perspectives, i.e. the governor's dilemma vis-à-vis the intermediator or agent and the possible rent-seeking of intermediators, under the condition of a highly developed technology of financial transactions as well as the market structure, the paper offers new insights into the limits as well as new opportunities of regulating financial markets allowing for political accountability of regulators and financial firms.

The Regulatory Technology (RegTech) is said to be the use of information technology for regulatory monitoring, reporting and compliance. It is used to solve regulatory and compliance issues more effectively and efficiently. Regulators with the digitization of regulation and datafication of processes would get empowered to manage volumes of data. RegTech would assist them in understanding innovative products, transactions, risks, reporting and any market manipulation activities in real time. For successful use of RegTech, the regulatory framework of a country should be comprehensive to address issues that may arise in the use of RegTech. Thus, the purpose of this article is to analyze the adequacy of the Saudi Arabian legal framework to address RegTech adoption.

The researcher using logical analysis method analyzed the available laws and interpreted the law to see the applicability and the adequacy of laws to regulate the use of RegTech in Saudi Arabia. The content analysis was also used in this research to analyze the literature. This analysis helped to explain the available literature on the research topic and its relevancy and the gap in literature.

The analysis using the logical and content methodologies shows that Saudi Arabia has general law to address some of the issues that might arise in the adoption of RegTech. Nonetheless, amending some of the existing laws or introducing guidelines could help better uplift of RegTech and similar technologies in Saudi Arabia.

As businesses and regulatory authorities embrace technology for better and efficient delivery of services and products in Saudi Arabia, the research is timely to analyze the adequacy of the laws in Saudi Arabia for adoption of RegTech. In the use of RegTech, issues related to privacy, due diligence, accountability and transparency could arise, however, there is a dearth of literature in these areas relating to technology adoption.

This study aims to propose an approach to assess the security of supply (SS) in a coal-fired electricity generation supply chain subject to public price regulation in Brazil. This study characterizes the Brazilian scenario of coal-fired electricity generation, which represents less than 3.5% of the energy sources.

Data from six mining companies that supply a coal plant were analyzed in a case study. The risks were characterized and objectively estimated through a synthetic multidimensional index. Structural changes in the earnings before interest, taxes, depreciation, amortization and exploration indicator time series of coal companies (CC) were statistically detected.

Empirical evidence demonstrates that the supply chain has a low disruption risk (SS index equal to 0.74). However, when suppliers are individually analyzed, 48.64% of all coal shows moderated disruption risk, and 2.51% is under high risk. In addition, this study finds a drop in the financial results of CC related to public regulation of coal prices. This impacts the security of coal supply.

This study discusses the influence of legal and regulatory policy risks in a coal power generation supply chain and the implications of the SS index as a management tool.

A novel SS index is presented and empirically operationalized, and its dimensions – environmental, occupational, operational, economic-financial and supply capacity – are analyzed.

This study aims to explore how sustainable business models (SBM) can be developed within agri-innovation systems (AIS) and emphasize an integration of the two with a systemic understanding for reducing food loss and value loss in postharvest agri-food supply chain.

This study conducted longitudinal qualitative research in a developing country with food loss challenges in the postharvest supply chain. This study collected data through multiple rounds of fieldwork, interviews and focus groups over four years. Thematic analysis and “sensemaking” were used for inductive data analysis to generate rich contextual knowledge by drawing upon the lived realities of the agri-food supply chain actors.

First, this study finds that the value losses are varied in the supply chain, encompassing production value, intrinsic value, extrinsic value, market value, institutional value and future food value. This happens through two cumulative effects including multiplier losses, where losses in one model cascade into others, amplifying their impact and stacking losses, where the absence of data stacks or infrastructure pools hampers the realisation of food value. Thereafter, this study proposes four strategies for moving from the loss-incurring current business model to a networked SBM for mitigating losses. This emphasises the need to redefine ownership as stewardship, enable formal and informal beneficiary identification, strengthen value addition and build capacities for empowering communities to benefit from networked SBM with AIS initiatives. Finally, this study puts forth ten propositions for future research in aligning AIS with networked SBM.

This study contributes to understanding the interplay between AIS and SBM; emphasising the integration of the two to effectively address food loss challenges in the early stages of agri-food supply chains. The identified strategies and research propositions provide implications for researchers and practitioners seeking to accelerate sustainable practices for reducing food loss and waste in agri-food supply chains.

During the Communist Party of China's endeavors over the past century, China has created “two miracles,” namely, large-scale and rapid economic development and long-term social stability.

The causes for China's achieving the “two miracles” lie in the adherence to the Party's leadership as the political guarantee, the scientific theoretical guidance as the ideological guarantee, the socialist system as well as the national governance system as the institutional guarantee and giving full play of people's creativity under the Party's leadership as the driving force guarantee.

From a political economy point of view, the theoretical logic behind the creation of the “two miracles” is that the combination of the state capacity and the scaling up of markets under the Party's leadership contributes to the rapid economic development and further the long-term social stability based on the financial foundation laid by rapid economic development. The historical experience of the “Two Miracles” can be summed up as the cultivation of state capacity under the leadership of the Party, the synergy and complementarity between the central government and local governments, the combination of development planning and market mechanisms, and the coordination of selective, functional and inclusive industrial policies.

It is necessary to judge future development trends from a medium and long-term development perspective, further promote the co-evolution of the state and the market, reshape the growth regime for high-quality development, fully tap the potential of domestic demand and create a “people-centered” economic development model so as to continue the “two miracles” and achieve a miracle of high-quality development in the second century.

The digital economy is profoundly transforming the manufacturing industry's fundamental concepts and value creation logic, making digital transformation (DT) strategy a crucial decision for manufacturers. And faced with increasingly severe environmental issues, DT may become an important means to achieve sustainable development. This paper mainly discusses the strategic choice of the manufacturer's DT and analyzes the impact of DT on carbon emissions.

Based on the carbon cap-and-trade mechanism, the authors have constructed two decision models to study the DT strategy of the manufacturer, further exploring the impact of the mechanism on the DT strategy and production strategy of the manufacturer. Finally, the authors discussed the effect of manufacturers' DT on their carbon emissions.

The authors found that the manufacturer should initiate DT to enhance their competitiveness, regardless of whether they are in a low digital technology scenario or a high digital technology scenario. Notably, DT can enhance the ability of the manufacturer to respond to external emergencies. In a low digital technology market scenario, both carbon emissions per unit of product and carbon price are positively affecting the digitization level of the manufacturer. In a high digital technology market scenario, the manufacturer will initiate a full degree of DT. Moreover, the impact of DT on total carbon emissions varies in markets with different levels of digital technology.

Innovatively, the authors divided the DT of the manufacturer into market scenarios with low digital technology and high digital technology. Provide the manufacturer with DT decisions according to different scenarios. At the same time, it verifies the uncertainty of DT on carbon emission and enriches the related research.