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This study aims to assess the factors influencing the adoption of circular bioeconomy practices by crop farmers.

Data collected from 303 farmers in the region of Western Macedonia (WM) were used to identify the adoption factors of the circular bioeconomy. Principal component analysis (PCA) and exploratory factor analysis (EFA) techniques were applied through a custom-designed and specially structured questionnaire.

The results reveal four main motivations that lead farmers to adopt circular bioeconomy practices in their farming activities: interest in physical-economic resources and factors related to production and consumption; technology and renewable energy and the environment.

This study is based solely on farmers in WM. Furthermore, this study assumes that there will be a strategy to promote a circular bioeconomy under the auspices of the government.

Few studies have focused on the perspectives of crop farmers and what encourages them to adopt circular bioeconomy practices in their crops. Existing barriers are related to economic incentives and constraints. The more circular bioeconomy practices farmers adopt, the greater the positive impact on the environment and rural development, and the factors influencing the adoption of these practices are investigated here.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

As a response to global population growth and increasing demand for food, farmers have been complementing traditional agriculture practices with vertical farming (VF) and indoor hydroponic systems. To facilitate the growth of the VF industry, this paper aims to identify business model elements and their configurations that lead to high firm performance.

The research goals were met by conducting literature reviews coupled with a fuzzy-set qualitative comparative analysis (fsQCA) on five business model elements, “superior” OR “strong” performance as two possible outcomes, and the top-ranked global VF growers listed in the Crunchbase Database.

From the fsQCA results, it was observed that several business model configurations lead to strong firm performance. Vertical farms growing in urban settings and having strong customer engagement platforms, coupled with a presence of business-to-business (B2B) sales channels, are more consistently associated with superior performance. These results imply that the decision configuration of location, along with customer engagement activity and sales activity are differentiating factors between good firm performance and superior firm performance in the case of vertical farms.

This paper contributes to expanding the knowledge of business model theory, business model configurations and VF management, providing specific guidelines for vertical farm owners and investors related to decision-making for higher firm performance, as well as positive environmental, social and economic impact.

The purpose of this study is to evaluate the profit efficiency of decision-making units (DMUs) based on predicted future information to solve the lag problem of improvement benchmarks given by the traditional profit efficiency model.

This paper proposes a two-step profit efficiency evaluation method. The first step predicts the future input and output information of DMUs through the past time-series data, obtaining a likely production possibility set (PPS) and profit frontier for the next period. The second step calculates DMUs' profit efficiency based on the predictions obtained in the first step and provides predictive benchmarking for DMUs.

The empirical results show that the proposed method yields good solutions for the lag problem of benchmarks given in ex-post evaluation, enabling bank managers to use predicted future information to achieve better improvement. Besides, compared with the technical efficiency measure, profit efficiency can better reflect the financial situation of DMUs and give the specific gap between the evaluated and optimal DMU.

For bank managers, the authors' new technique is advantageous for grasping the initiative of development because this technique accounts for the future development of the whole industry and sets forward-looking targets. These advantages can help banks improve in a more favorable direction and improve the asset management ability of banks.

This paper combines the data envelopment analysis (DEA) profit efficiency model with performance prediction and proposes a new two-step profit efficiency model, filling a gap in previous studies.

This chapter aims to estimate the impact of the use of an innovative cultivation method on the social, economic and environmental aspects in the French region Aix-en-Provence, by using the survey data for 200 heterogeneous vegetable producers (organic and conventional). It distinguishes three types of producers in the French region Aix-en-Provence. First, conventional producers (n = 100) who used a high level of mechanization, better access to water, high yield, high labor costs. Second, certified organic producers (n = 70) who used organic technologies such as biotechnology and rotation, low yield, high organic product price compared to conventional products, a family workforce and high transport. Third, noncertified organic producers (n = 30) have used the same technologies as certified organic producers, while they sell their products at the same price as conventional products. Labor is the member of the family. These noncertified farms are marked by high operating and transport costs and low yield compared to conventional producers or certified organic producers. The results show that this cultivation method has a positive effect on the environmental aspect, however a negative one on the social and economic aspect.

Having defined liquidity, the aim is to assess the predictive capacity of its representative variables, so that economic fluctuations may be better understood.

Conceptual variables that are representative of liquidity will be used to formulate the predictions. The results of various machine learning models will be compared, leading to some reflections on the predictive value of the liquidity variables, with a view to defining their selection.

The predictive capacity of the model was also found to vary depending on the source of the liquidity, in so far as the data on liquidity within the private sector contributed more than the data on public sector liquidity to the prediction of economic fluctuations. International liquidity was seen as a more diffuse concept, and the standardization of its definition could be the focus of future studies. A benchmarking process was also performed when applying the state-of-the-art machine learning models.

Better understanding of these variables might help us toward a deeper understanding of the operation of financial markets. Liquidity, one of the key financial market variables, is neither well-defined nor standardized in the existing literature, which calls for further study. Hence, the novelty of an applied study employing modern data science techniques can provide a fresh perspective on financial markets.

因此,就了解經濟週期和經濟發展而言,流動資金是一個極其重要的概念。本研究擬在安全資產的價格預測方面取得進步。安全資產代表了經濟的實際情況,特別是美國的十年期國債。

流動資金的定義上面已說明了; 為進一步了解經濟波動,本研究擬對流動資金代表性變量的預測能力進行評估。

研究使用作為流動資金代表的概念變項去規劃預測。各機器學習模型的結果會作比較,這會帶來對流動資金變量的預測值的深思,而深思的目的是確定其選擇。

只要在私營部門內流動資金的數據比公營部門的流動資金數據、在預測經濟波動方面貢獻更大時,我們發現、模型的預測能力也會依賴流動資金的來源而存在差異。國際流動資金被視為一個晦澀的概念,而它的定義的標準化,或許應是未來學術研究的焦點。當應用最先進的機器學習模型時,標桿分析法的步驟也施行了。

若我們對有關的變量加深認識,我們就可更深入地理解金融市場的運作。流動資金,雖是金融市場中一個極其重要的變量,但在現存的學術文獻裏,不但沒有明確的定義,而且也沒有被標準化; 就此而言,未來的研究或許可在這方面作進一步的探討。因此,本研究為富有新穎思維的應用研究,研究使用了現代數據科學技術,這可為探討金融市場提供一個全新的視角。

The purpose of this paper is to propose a model that will demonstrate how the integration of Salam (exclusive agricultural commodity trade) with Takaful (micro-Takaful – a subdivision of Islamic insurance) and value chain can address major challenges facing the agricultural sector in Kano State, Nigeria.

The study conducted a thorough and critical analysis of relevant literature and existing models of financing agriculture in Nigeria to come up with the proposed model.

The findings indicate that measures undertaken to address the major challenges fail. In view of this, this study proposed Bay-Salam with Takaful and value chain model to solve a number of challenges such as poor access to financing, poor marketing and pricing, delay, collateral requirement and risk issues in order to avail farmers with easy access to finance and provide effective security to financial institutions.

The paper is limited to using secondary data. Therefore, empirical investigation can be carried out to strengthen the validation of the model.

The study outcome seeks to improve the productivity of the farmers through enhancing their access to finance. This will increase their level of production and provide more employment opportunities. In addition, it will boost financial inclusion, income generation, poverty alleviation, standard of living, food security and overall economic growth and development.

The novelty of this study lies in the integration of classical Bay-Salam with Takaful and value chain and create a unique model structure which the researchers do not come across in any research that presented it in Nigeria.

Commercial banks face several risks, including credit, liquidity, operational and disruptive risks. In addition to these risks that are challenging for banks to control and manage, crises and disasters can exert substantially more destructive shocks. These shocks can exacerbate internal risks and cause severe damage to the bank's performance, leading banks to bankruptcy and closure. This study aims to facilitate achieving resilient banking policies through a model-based assessment of business continuity management (BCM) policies.

By applying a system dynamics (SD) methodology, a systemic model that includes a causal structure of the banking business is presented. To build a simulation model, data are collected from a commercial bank in Iran. By presenting the simulation model of the bank's business, the consequences of some given crises on the bank's performance are tested, and the effectiveness of risk and crisis management policies is evaluated. Vensim Personal Learning Edition (PLE) software is used to construct the simulation model.

Results indicate that the current BCM policies do not show appropriate resilience in the face of various crises. Commercial banks cannot create sustainable value for the banks' shareholders despite the possibility of profitability, as the shareholders lack adequate resilience and soundness. These commercial banks do not have the appropriate resilience for the next pandemic after coronavirus disease 2019 (COVID-19). Moreover, the robustness of the current banking business model is very fragile for the banking run crisis.

A forward-looking view of resilient banking can be obtained by combining liquidity coverage, stable funding, capital adequacy and insights from stress tests. Resilient banking requires a balanced combination of robustness, soundness and profitability.

The present study is a combination of bank business management, risk and resilience management and SD simulation. This approach can analyze and simulate the dynamics of bank resilience. Additionally, present of a decision support system (DSS) to analyze and simulate the outcomes of different crisis management policies and solutions is an innovative approach to developing effective and resilient banking policies.

The study seeks to evaluate the effect of capital adequacy and competition on the liquidity creation of Kenyan commercial banks.

Unbalanced panel data from 36 Kenyan commercial banks with licenses from 2001 to 2020 is used in the study. The generalized method of moments (GMM), a two-step system, is employed in the investigation. To increase the robustness and prevent erroneous findings, serial correlation tests and instrumental validity analyses are used. The methodology developed by Berger and Bouwman (2009) is used to estimate the commercial banks' levels of liquidity creation.

The study supports the financial fragility-crowding out hypothesis by finding a significant negative effect of capital adequacy on the liquidity creation of commercial banks. The research also identifies a significant inverse relationship between competition and liquidity creation, depicting competition's value-destroying effect.

A trade-off exists between capital adequacy and liquidity creation, which must be carefully evaluated as changes in capital requirements are considered. The value-destroying effect of competition on liquidity creation presents a case for policy geared toward consolidating banks' operations through possible mergers and acquisitions.

To the best of the authors' knowledge, this is the first study to empirically offer evidence concurrently on the effect of competition and capital adequacy on the liquidity creation of commercial banks in a developing economy such as Kenya. Additionally, the authors employ a novel measure of competition at the firm level.