This research aims to find how effective word of mouth in a B2B environment is based on loyalty and customer satisfaction. Word of mouth (WOM) influence among the knitting industry is very important, since it provides sustainability for the business itself. Using PLS (Partial Least Square) modeling, this study provides additional evidence indicating customer satisfaction has both direct and indirect effects on B2B partners’ positive WOM activities. The B2B samples were collected from knitting industries in Indonesia. The research finding indicates that a high level of customer satisfaction directly increases positive WOM activities; the mediation effect of customer loyalty between customer satisfaction and WOM activities also has a significant effect.
This study investigates the relationship between online marketing and social media apps (Facebook, Instagram, Snapchat, and LinkedIn) or content apps (YouTube and Netflix), focusing on Albanian markets. Based on the analysis of 525 Albanian social media users from different age groups, the findings illustrate that 90% of the respondents prefer to use a smartphone rather than a personal computer in their daily life. Except for LinkedIn and Facebook, all social media apps had higher usage rates among the younger demographic, according to data analysis. The most popular app is Facebook. Based on age-based differentiation strategies, our data support the findings. Every app can help businesses reach the Youth category (aged 14 to 24), but social networking apps and YouTube should be the main priorities. Adults in the target audience in the 25–45 age range can be found primarily on Facebook, Instagram, and YouTube, while people over 45 can be found primarily on Facebook. Specifically, companies investing in online marketing can use Instagram and YouTube, which are more popular among younger social media users, while older users prefer Facebook and LinkedIn. Meanwhile, it would be better not to use in-app purchase strategies for companies that intend to invest through their apps.
This article aims to apply blockchain data theories to trust management in a supply chain system to solve the trust crisis brought on by opportunistic behavior in supply chain management. Incremental implications are offered to increase supply chain managers' understanding of the underlying process of building trust using blockchain data technologies. A comprehensive literature review was undertaken to build a theoretical framework of how the trust management mechanism interacts with blockchain data and supply chain management. Design and methodology integrated multiple theories with the trust management application in supply malmanagement using blockchain data technology. This work identifies the coupling relationship between blockchain data and supply chain trust management and reveals the cultural, social, and economic attributes of trust manifested in interactions of the consensus-trust mechanism and interest motivations.
With the complexity and increasing risks of financial markets, financial regulation has become more urgent and important. This article designs and analyzes a financial regulatory detection and collaborative decision-making system based on artificial intelligence to address this issue. The system aims to effectively measure systemic financial risks and provide early warning to protect investors' interests, guide enterprise decision-making, and strengthen financial supervision. Through the nonlinear Glenmorangie distillery causality test and correlation analysis, the indicators that have a significant impact on the systemic financial risk are screened out, which further verifies the characteristics of the financial Systemic risk that has a wide range of sources and is complex and diverse. In the construction of a systematic financial risk warning model, the Twin SVM model was adopted, and the optimal parameter settings of the model were determined through experimental comparison of different kernel functions and hysteresis periods. The Twin SVM model exhibits excellent predictive power, stability, and generalization performance, which can accurately predict pressure information in the financial market. The artificial intelligence based financial regulatory detection and collaborative decision-making system designed in this article can effectively measure systemic financial risks, provide early warning, and provide important reference and decision-making basis for investors, enterprise managers, and financial regulators.
This study explores the relationship between Croatia and the U.S., Russia, and China, providing a comparative qualitative analysis of trade and investment flows between these countries. Using a Mixed Methods approach combined with a literature review, evaluation of a range of data sources, analysis of bilateral trade data and foreign direct investment flows, and other economic indicators, the study analyzes the drivers of and implications for economic ties between Croatia and these countries. The study also examines the impact of economic sanctions on Croatia's relationship with Russia. The research finds that the U.S. is Croatia's largest trading partner, while Russia and China are important sources of foreign direct investment. Finally, the study provides recommendations for future research, highlighting the need for a greater focus on trade in services, investment flows, and comparative analysis with other countries in the Balkans region. Overall, the study contributes to understanding the complex economic relationship between Croatia and the U.S., Russia, and China and offers recommendations for greater diversification of Croatia's trade and investment partners and the importance of monitoring the political and economic risks in economic cooperation.
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