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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]

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]