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Purpose: The paper's main goal is to examine the relationship between the video marketing of financial technologies (Fintechs) and their vulnerable website customers' brand engagement in the ongoing coronavirus disease 2019 (COVID-19) crisis. Design/methodology/approach: To extract the required outcomes, the authors gathered data from the five biggest Fintech websites and YouTube channels, performed multiple linear regression models and developed a hybrid (agent-based and dynamic) model to assess the performance connection between their video marketing analytics and vulnerable website customers' brand engagement. Findings: It has been found that video marketing analytics of Fintechs' YouTube channels are a decisive factor in impacting their vulnerable website customers' brand engagement and awareness. Research limitations/implications: By enhancing video marketing analytics of their YouTube channels, Fintechs can achieve greater levels of vulnerable website customers' engagement and awareness. Higher levels of vulnerable customers' brand engagement and awareness tend to decrease their vulnerability by enhancing their financial knowledge and confidence. Practical implications: Fintechs should aim to increase the number of total videos on their YouTube channels and provide videos that promote their customers' knowledge of their services to increase their brand engagement and awareness, thus reducing their vulnerability. Moreover, Fintechs should be aware not to over-post videos because they will be in an unfavorable position against their competitors. Originality/value: This research offers valuable insights regarding the importance of video marketing strategies for Fintechs in promoting their vulnerable website customers' brand awareness during crisis periods. [ABSTRACT FROM AUTHOR]

Agriculture is essential to any country's economy. Agriculture is crucial not only for feeding a country's population but also for its impact on other businesses. The paradox of agri-food companies generating substantial profits despite seemingly high product prices is explored in this article, focusing on the role of digital marketing within the agri-food industry. Enhanced digital marketing performance leads to efficient advertising campaigns, through reduced advertising costs and increased resource efficiency. To do so, the authors collected web analytical data from five established agri-food firms with the highest market capitalization. Then, linear regression and correlation analyses were used, followed by the utilization of fuzzy cognitive mapping (FCM) modeling. The analysis revealed that increased traffic through search sources is associated with reduced advertising costs. Additionally, enhanced website engagement contributes to lower advertising expenses, emphasizing the optimization of the user experience. However, it has been discovered that allocating funds for social media advertising eventually results in higher expenses with higher website-abandoning rate. Ultimately, successful management of the balance between product costs and profitability in the agri-food sector lies on the increased use of search sources and greatly reducing the use of social media sources. [ABSTRACT FROM AUTHOR]

In the agri-food industry, strategic digital branding and digital marketing are essential for maintaining competitiveness. This study examines the economic dynamics and impact of desktop and mobile customer analytics on digital branding strategies within the sector. Through a comprehensive literature review, this research utilizes empirical evidence to validate hypotheses regarding the influence of desktop and mobile analytics metrics on key digital branding metrics and value creation. This study explores various branding indicators by utilizing descriptive statistics, correlation analyses, regression models, and fuzzy cognitive mapping (FCM). The findings reveal significant correlations between desktop and mobile analytics and digital branding outcomes, underscoring the critical role of digital analytics and Decision Support Systems (DSSs) in shaping modern branding strategies in the agri-food industry. This study highlights the economic implications of desktop and mobile customer analytics on digital branding, providing insights to enhance market performance and foster sustainable growth in the agri-food sector. [ABSTRACT FROM AUTHOR]

The recent breakthrough of a net energy gain from a fusion reaction renders tangible several avenues for fusion energy. This article argues that fusion has the potential to be the next General-Purpose Technology (GPT) due to its several non-electricity applications. Following the evolutionary theory of technological change, this paper analyzes the evolution of fusion technologies at two different levels. At the micro level, this paper analyzes a possible evolution path for the fusion industry and for other industries that will be impacted by the developments of fusion, such as transportation and aerospace. At the macro level, this paper analyzes a possible evolution path for the diffusion of fusion within each national innovation system and across countries, a global commission. Both levels of analysis share the need for managed co-opetition via open science, where competitors cooperate under the supervision of national or international agencies. This article concludes with several perspectives that provide insightful contributions for policymakers, practitioners, and politicians interested in understanding how the evolution of fusion technologies will likely affect the funding of research and development and the direction and progress of technological change, and it illustrates the dynamic relation between a GPT and economic growth. [ABSTRACT FROM AUTHOR]

This empirical study explores the factors influencing farmers' decisions to withdraw from homesteads with compensation in the Chengdu–Chongqing area. Based on survey data from rural households, a structural equation model was constructed to investigate the relationships between individual characteristics of farmers, household characteristics, and the characteristics of farmers' homesteads and their impact on farmers' employment, life, and income. The results indicate that household characteristics influence farmers' willingness to quit homestead with compensation, significantly impacting the proportion of non-agricultural employment, stability of non-agricultural employment, agricultural income, and per capita annual income. Furthermore, higher education levels, younger age, and a fondness for urban life also influence farmers' willingness to withdraw from homesteads. The study highlights the importance of reasonable compensation calculation methods and modes in promoting homestead withdrawal. The findings offer valuable insights for policymakers in designing more effective compensation programs and other policies to promote sustainable rural development in the Chengdu–Chongqing area. This study underscores the importance of technology transfer in rural development and highlights the potential of homestead withdrawal policies as a form of technology transfer. The findings have significant theoretical and managerial implications for policymakers to design more effective compensation programs and other policies promoting sustainable rural development in the Chengdu–Chongqing area. [ABSTRACT FROM AUTHOR]

Agriculture firms face an array of struggles, most of which are financial; thus, the role of decision making is discerned as highly important. The agroeconomic indexes (AEIs) of Agriculture Employment Rate (AER), Chemical Product Price Index (CPPI), Farm Product Price Index (FPPI), and Machinery Equipment Price Index (MEPI) were selected as the basis of this study. This research aims to examine the connection between digital marketing analytics and the selected agroeconomic indexes while providing valuable insights into their decision-making process, with the utilization of AI (artificial intelligence) models. Thus, a dataset of website analytics was collected from five well-established agriculture firms, apart from the values of the referred indexes. By performing regression and correlation analyses, the index relationships with the agriculture firms' digital marketing analytics were extracted and used for the deployment of the fuzzy cognitive mapping (FCM) and hybrid modeling (HM) processes, assisted by using artificial neural network (ANN) models. Through the above process, there is a strong connection between the agroeconomic indexes of AER, CPPI, FPPR, and MEPI and the metrics of branded traffic, social and search traffic sources, and paid and organic costs of agriculture firms. It is highlighted that agriculture firms, to better understand their sector's employment rate and the volatility of farming, chemicals, and machine equipment prices for future investment strategies and better decision-making processes, should try to increase their investment in the preferred digital marketing analytics and AI applications. [ABSTRACT FROM AUTHOR]

Background: Due to increased globalization and its subsequent rise in competitiveness, the role of supply chain services (3PL) in managing logistics, reducing operational and non-operational costs, and managing customer and supplier relationships, have become of utmost importance. Customer-centric production has led to the development of a close relationship between production processes. Amidst all this, the demand for logistic services has dramatically increased, thus putting more pressure on firms for enhanced operational results, and leading to the outsourcing of their internal and external logistic activities. On the other hand, supply chain firms that provide 3PL services seek to enhance their sustainability and predict their customers' demand. Methods: The authors collected quantitative data from 81 firms that operate in various industrial sectors in Greece. A questionnaire was sent for completion, in which firms could rate and evaluate various aspects that were discerned as important for deciding to cooperate with a 3PL service provider and maintain this cooperation in the long run. To extract the required outcomes, statistical analyses like categorical regression (CATREG) and MANOVA were utilized. Results: The demand for 3PL services was affected by 3PL service providers' operational performance based on accuracy, reputation, and IT capabilities, while the customer firms' intention for maintaining cooperation with 3PL service providers was affected by their reliability level, improved service efficiency, and trustworthiness. Conclusions: 3PL service providers should seek to improve the reputation, IT infrastructure, and accuracy of their firm's operations to have a continuous demand for their services. Apart from that, 3PL service providers to maintain the cooperation with their customers, need to enhance the levels of their services reliability and efficiency, while also creating a bond of trust with their existing customers. [ABSTRACT FROM AUTHOR]

Globalization has gotten increasingly intense in recent years, necessitating accurate forecasting. Traditional supply chains have evolved into transnational networks that grow with time, becoming more vulnerable. These dangers have the potential to disrupt the flow of goods or several planned actions. For this reason, increased resilience against various types of risks that threaten the viability of an organization is of major importance. One of the ways to determine the magnitude of the risk an organization runs is to measure how popular it is with the buying public. Although risk is impossible to eliminate, effective forecasting and supply chain risk management can help businesses identify, assess, and reduce it. As a result, good supply chain risk management, including forecasting, is critical for every company. To measure the popularity of an organization, there are some discrete values (bounce rate, global ranking, organic traffic, non-branded traffic, branded traffic), known as KPIs. Below are some hypotheses that affect these values and a model for the way in which these values interact with each other. As a result of the research, it is clear how important it is for an organization to increase its popularity, to increase promotion in the shareholder community, and to be in a position to be able to predict its future requirements. [ABSTRACT FROM AUTHOR]

Emerging technologies in the digital context can favor industrial sector firms in their aim to improve their performance. Digitalization is mainly expressed through the utilization of big data that originate from various sources. Blockchain technology has led to the extended adoption of capitalization of Decentralized Finance (DeFi) services, such as cryptocurrency trade platforms. Supply chain firms, in their quest to exploit any means and collaborations available to promote their services, could place advertisements on DeFi's social media profiles to boost their financial performance. Social media analytics, as a part of the big data family, are an emerging tool for promoting a firm's digital transformation, based on the plethora of customer behavioral data they provide. This study aims to examine whether the social media analytics of DeFi platforms are capable of affecting their website visibility, as well as the financial performance of supply chain firms. To do so, the authors collected data from the social media profiles of the most-known DeFi platforms and web analytics from the most significant supply chain firms' websites. For this purpose, proper statistical analysis, Fuzzy Cognitive Mapping, Hybrid Modeling, and Cognitive Neuromarketing models were adopted. Throughout the present research, it has been discerned that from an increase in the social media analytics of DeFi platforms, their website visibility increases, while the organic and paid traffic costs of supply chain firms decrease. Supply chain firms' website customers tend to increase at the same time. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of today's digitized markets, organizations harness the power of vast and swiftly accessible data to glean invaluable insights. A significant portion of this data emanates from user behavior on business websites. Unraveling the intricacies of this user behavior has become paramount for businesses, serving as the compass guiding the adaptation and evolution of their digital marketing strategies. Embarking on an exploration of this digital frontier, our study delves into the virtual domains of enterprises entrenched in the supply chain sector of the Greek economy. The spotlight falls upon four dominant transportation firms of the Greek supply chain sector, to unravel the relationship between their website activities and the prediction of their stock market prices. Our analytical tools, adorned with sophisticated statistical methodologies, embracing normality tests, correlations, ANOVA, linear regressions and the utilization of regression residual tests were deployed with precision. As the analytical methodology was deployed, a revelation emerged: The digital footprints left by customers on the virtual domains of supply chain firms provided the ability to predict and influence stock prices. Metrics such as bounce rates, the influx of new visitors and the average time on websites emerged as important factors, that could predict the fluctuations in the stock prices of these Greek supply chain firms. Web analytics have been discerned as a determining factor for predicting the course of transportation firms' stock prices. It serves as a clarion call for global scrutiny, inviting scholars and practitioners alike to scrutinize analogous firms on a global canvas. In this convergence of virtual footprints and financial trajectories lies not just a revelation for today but a harbinger of insights that resonate far beyond the digital borders of the Hellenic transportation sector. [ABSTRACT FROM AUTHOR]

Due to the volatility of the markets and the ongoing crises (COVID-19, the Ukrainian war, etc.), investors are keen to exploit any potential chances to make profits. For this reason, the idea of harvesting data from cryptocurrency market users takes an innovative step. Potential investors in supply chain firms in the fertilizer industry need to know whether the observation of data originating from the cryptocurrency market is capable of explaining their stock price variation. The authors identify the innovative utilization of cryptocurrency markets' user analytical data to model and predict the stock price of supply chain firms in the fertilizer industry stock price. The main aim of this research is to evaluate the contribution of cryptocurrency market big data as a predicting factor for the stock price of fertilizer market firms. Such a finding improves the knowledge and decision-making of potential investors in the fertilizer market. Moreover, this study seeks to highlight the benefits of utilizing cryptocurrency market big data for other financial purposes, apart from stock price prediction. The analytical data was derived from cryptocurrency websites and applications and was then processed through statistical analysis (correlation and linear regressions), Fuzzy Cognitive Maps (FCM), and Hybrid Modeling (HM) modeling. The hybrid model's simulation showed that analytical data from the cryptocurrency markets tend to explain and predict the stock price of supply chain firms in the fertilizer industry. Such data refer to Bitcoin's website organic keywords and traffic costs, as well as paid traffic costs from cryptocurrency trade websites/apps. A rise in Bitcoin and cryptocurrency trade websites' organic and paid traffic costs tend to increase supply chain firms in the fertilizer industry's stock prices, while Bitcoin's website organic keywords variation decreases accordingly. [ABSTRACT FROM AUTHOR]

Deep learning has experienced an increased demand for its capabilities to categorize and optimize operations and provide higher-accuracy information. For this purpose, the implication of deep learning procedures has been described as a vital tool for the optimization of supply chain firms' transportation operations, among others. Concerning the indexes of transportation operations of supply chain firms, it has been found that the contribution of big data analytics could be crucial to their optimization. Due to big data analytics' variety and availability, supply chain firms should investigate their impact on their key transportation indexes in their effort to comprehend the variation of the referred indexes. The authors proceeded with the gathering of the required big data analytics from the most established supply chain firms' websites, based on their (ROPA), revenue growth, and inventory turn values, and performed correlation and linear regression analyses to extract valuable insights for the next stages of the research. Then, these insights, in the form of statistical coefficients, were inserted into the development of a Hybrid Model (Agent-Based and System Dynamics modeling), with the application of the feedforward neural network (FNN) method for the estimation of specific agents' behavioral analytical metrics, to produce accurate simulations of the selected key performance transportation indexes of supply chain firms. An increase in the number of website visitors to supply chain firms leads to a 60% enhancement of their key transportation performance indexes, mostly related to transportation expenditure. Moreover, it has been found that increased supply chain firms' website visibility tends to decrease all of the selected transportation performance indexes (TPIs) by an average amount of 87.7%. The implications of the research outcomes highlight the role of increased website visibility and search engine ranking as a cost-efficient means for reducing specific transportation costs (Freight Expenditure, Inferred Rates, and Truckload Line Haul), thus achieving enhanced operational efficiency and transportation capacity. [ABSTRACT FROM AUTHOR]

Recent developments in blockchain technology have enabled the development of wallet applications for storing peoples' cryptocurrency reserves. Cryptocurrency wallet applications could deploy affiliate marketing processes to increase the visibility of their products. From these affiliate marketing processes, supply chain firms in the air forwarding sector that seek to advertise their services to a larger audience could be benefited. This research examines whether affiliate marketing initials of cryptocurrency wallet applications affect their digital marketing efficiency, as well as whether air forwarding firms' website visibility could be benefited from them. After collecting the required Web Analytic data, the authors performed statistical analysis (correlations and linear regressions), followed by Fuzzy Cognitive Mapping (FCM) macroanalysis and Hybrid Modeling (HM) microanalysis to assess the outcomes of cryptocurrency wallet applications' affiliate marketing programs. Hence, from the deployed methodology, valuable insights arose. The first part of the produced outcomes concerns the effect of cryptocurrency wallet application affiliate marketing metrics on their digital marketing results. The increased implications of affiliate marketing metrics (referring domains, backlinks, etc.) decrease the number of pages their visitors see. Regarding the air forwarder firms' website visibility, specific metrics (branded and referral traffic) were increased and others (social traffic) were decreased from the increment of cryptocurrency wallet application affiliate marketing metrics (backlinks, internal links, etc.). Supply chain firms, in the air forwarding sector, could have increased website visibility by deploying advertisements and affiliate marketing initiatives with cryptocurrency wallet organizations. Summing up, specific affiliate marketing metrics of cryptocurrency wallet applications are capable of significantly impacting their digital marketing performance and also constitute determinant factors of supply chain firms' website visibility. [ABSTRACT FROM AUTHOR]

From the outset of the war in Ukraine, extensive crises in many sectors of the world economy have occurred, with firms offering services and products both online and through physical stores facing serious problems. These problems are mainly related to higher operational costs and the lack of website visibility. For this research study, centralized payment network organizations (CPNs), firms providing online payment services through their networks, were selected and analytical data from their websites were collected for a period of 6 months. The main focus of this research study is to evaluate benefits and the role of social media strategies for CPNs' digital marketing performance during crisis events and to also assess their utility as a risk-management tool. Following data collection, the authors performed statistical processes (regression and correlation analysis) and stationary modeling with Fuzzy Cognitive Mapping (FCM) tools; finally, dynamic simulations were performed by utilizing Agent-Based Models (ABM). The authors suggest that various variables of CPNs' social media platforms can aid in improving their digital marketing performance and, using proper analysis, can lead to higher user social engagement, thus rendering social media strategy a useful risk-management tool. [ABSTRACT FROM AUTHOR]

Today, more than ever, the popularity of decentralized payment systems has risen, creating an outbreak of new cryptocurrencies hitting the market. Unique websites have been staged for each cryptocurrency, where information and means for mining cryptocurrencies are available daily. People visit those cryptocurrency websites either from desktop or mobile devices. Thus, the impulsion for appropriate promotion of cryptocurrency websites and customer factors affecting it rises. The above process increases cryptocurrency organizations' website visibility, raising the need for customer relationships and satisfaction optimization concerning organizations' supply chain strategy. Research data were collected from 10 well-known cryptocurrency websites, regarding mobile and desktop devices, in 180 days, regarding on-site web analytics. Therefore, a model consisting of three stages was applied. Starting phase of the model pertains to statistical and regression analysis of cryptocurrency web analytics, followed by Fuzzy Cognitive Mapping and Agent-Based Model deployment. Throughout this study, methods for promoting cryptocurrency websites can be deduced from assessing specific website metrics and device preferences. Research results indicate that web analytics give a clearer image of customer behavior in cryptocurrency websites and, therefore, provide opportunities for further website optimization through increased web traffic and digital reputation. [ABSTRACT FROM AUTHOR]

Crises are always challenging for banking systems. In the case of COVID-19, centralized payment networks and FinTech companies' websites have been affected by user behavior globally. As a result, there is ample opportunity for marketing managers and professionals to focus on big data from FinTech websites. This can contribute to a better understanding of the variables impacting their brand name and how to manage risk during crisis periods. This research is divided into three stages. The first stage presents the web analytics and the data retrieved from the FinTech platforms. The second stage illustrates the statistical analysis and the fuzzy cognitive mapping (FCM) performed. In the final stage, an agent-based model is outlined in order to simulate and forecast a company's brand name visibility and user behavior. The results of this study suggest that, during crises, centralized payment networks (CPNs) and FinTech companies with high organic traffic tend to convert new visitors to actual "customers". [ABSTRACT FROM AUTHOR]