The study evaluated factors influencing port users’ intentions to participate in Financial Technology (Fintech) in the ports of Ghana. The study used non-experimental quantitative correlational design and the Extended Unified Theory of the Acceptance and Use of Technology (UTAUT2) as the theoretical foundation to assess whether performance expectancy (PE), behavioral intention (BI), effort expectancy (EE), social influence (SI), facilitating conditions (FC), hedonic motivation (HM), price value (PV), and habit (HT) were predictors of the intention of port users to participate in a Fintech program with age as a moderating factor. The sample comprised 407 individuals who work in the port industry and are between 18 and 64 years old; these were randomly selected through the SurveyMonkey platform. The study used principal component analysis (PCA), confirmatory factor analysis, and structural equation modeling to analyze and report the results. Findings show that PE, EE, and HT were predictors of the behavioral intention of port users to participate in a Fintech in the maritime and ports in Ghana. FC, SI, HM, and PV values could not predict BI for port users to enroll on a Fintech program. Neither did age have a moderating effect on the predictors variable influence on behavioral intention. This study offers a deeper insight into the adoption of Fintech in the port industry and sub-Saharan Africa. The findings can help researchers explain the variations in the UTAUT2 theoretical framework predictions relative to different sectors and disciplines. Researchers who intend to use the UTAUT2 theoretical framework to influence port users BI to enroll in the Fintech program will now consider PE, EE, and HT the most effective adoption factors. From a practical perspective, the study will help managers and stakeholders in ports in Ghana and sub-Saharan Africa focus on the critical constructs as the first steps to implementing a Fintech program. On the other side, port users will also understand their role relative to performance expectancy, effort expectancy, and the habit to cultivate toward Fintech.

Blockchain technology, as a bedrock for distributed ledgers, offers a platform for innovation for a new decentralized and transparent transaction machinery in industries and businesses, and the port is not an exception. The inbuilt features of this technology enrich trust through transparency and traceability within any transaction of data, goods, services, and financial resources. Notwithstanding initial doubts about this technology, lately government and large corporations have offered to adopt and enhance this technology in various fields of applications, from social and legal industries, and finance to design, maritime and port networks. In this paper, the authors review the current status of the Blockchain technology and some of its applications. The potential benefit of such a technology in port and maritime supply chain is then discussed, and a vision for the future Blockchain ready port and maritime supply chain are proposed. The importation of containerized vehicles is used as an example to demonstrate how such technology can be deployed in a global port industry. Finally, the requirements and challenges to adopt this technology in the future in relation to, port and maritime systems are discussed.

Faculty members are crucial to Blended Learning’s success in higher education. Despite substantial research into the elements that drive faculty adoption of BL, few have developed a model to explain how these factors combine and influence faculty intentions to teach in this mode. This study used data collected from 207 professors from 18 universities across Africa, the United States, Europe, and the Middle East to test and validate a Faculty Blended Learning Adoption Model which was derived from a Grounded Theory study. Four model constructs (institutional hygiene readiness, student BL disposition, faculty technology ready, and Pedagogy Technology Fit for BL) mediated by motivation were tested to predict faculty Blended Learning adoption using structural equation modeling. The results demonstrated an excellent model fit, with three of the six hypotheses in this study being supported. Faculty desire to utilize BL was found to be influenced by faculty technology readiness and task technology fit for BL, but not by institutional hygiene readiness or student BL disposition. This research presents a useful model for university administrators to use in their BL implementations. A thorough understanding of this model can assist decision-makers in identifying the factors that influence future faculty acceptance or resistance to blended learning, as well as helping them in enhancing acceptance and usage.

Information about the paper titled "FACULTY PERSPECTIVES ON BARRIERS OF BLENDED-LEARNING ADOPTION: A GHANA TECHNOLOGY UNIVERSITY COLLEGE CASE STUDY" at IATED Digital Library