Open Innovation in the Financial Sector: A Mixed-Methods Approach to Assess Bankers’ Willingness to Embrace Open-AI ChatGPT

As open innovation and Artificial Intelligence (AI) become more prevalent in financial institutions, early adoption of Chatbots will have a competitive advantage. However, ChatGPT is still less common in the financial sector than in other industries. This study attempts to understand bankers’ perceptions towards using ChatGPT. Towards this, the study employed an exploratory sequential mixed-methods approach. Eventually, 10 bank professionals participated in the preliminary semi-structured interviews to gain insight into their perceptions. Sequentially, the study cross-sectionally examined the identified factors among 368 bankers to triangulate the framework with empirical evidence. The Thematic Content Analysis (TCA) analysis identified seven new factors related to bankers’ use of ChatGPT, which were primarily validated in PLS-SEM assessments. The results showed the positive effect of performance expectancy, social influence, facilitating conditions, awareness, innovativeness, and system quality on ChatGPT usage and the negative effect of technology self-efficacy and IT features. Intriguingly, the moderating effects of central bank support were positively confirmed for innovativeness and social influence, but negative for the relationship between technology self-efficacy, awareness, and bankers’ intention. This study offers a highly predictive model contemplating the applicability of an extended UTAUT model to explain the use of ChatGPT in the banking sector. Accordingly, we suggest that decision-makers should emphasize improving the individual attributes of their human capital towards technology and improving AI system quality, as well as working closely with government-powered authorities that would facilitate the diffusion process of AI Chatbots in the banking sector

Fundraising Appeals for the COVID-19 Epidemic Fight: A Cross-Country Study of Donor Responses

This research explores the intrinsic and extrinsic motivations driving donors to engage in fundraising appeals launched through social networking sites (SNSs) to mitigate COVID-19’s impact on vulnerable communities from a cross-national perspective. The research adopted a quantitative approach through a web-based survey; a total of 801 donors were obtained from Kuwait and Bahrain and were useable for the analysis. Smart PLS structural equation modelling was used to validate the research model and derive significant insights. In the Kuwaiti sample, we found that humanitarian projects, internet technology, SNSs and religiosity significantly drive donor attitudes towards online donation. All these relationships are indirectly related to the intention to give via SNS through the mediating role of attitudes. As for the Bahraini sample, humanitarian projects, non-profit organizations (NPOs), SNSs, and religiosity significantly influence donors’ attitudes. Attitudes, on the other hand, have a visible mediating role in the relationships between these predictors and behavioral intentions. The findings could be useful for the development of appropriate policies that boost online monetary donations to support emergency aid for communities crushed by the pandemic. This research differs from the existing literature in that its multi-national study scrutinizes the incorporation of both internal and external predictors of fundraising activities into a distinctive related context such as SNSs, particularly in a time of epidemiological crises such as COVID-19

Understanding the diffusion of AI-generative (ChatGPT) in higher education: Does students' integrity matter?

ChatGPT, an AI-powered language model, is revolutionising the academic world. Scholars, researchers, and students use its advanced capabilities to achieve their educational objectives, including generating innovative ideas, delivering assignments, and conducting extensive research projects. Nevertheless, the use of ChatGPT among students is contentious, giving rise to significant apprehensions regarding integrity and AI-facilitated deceit. At the same time, scholarly communities currently need more well-defined standards for adopting such academia-oriented technology. This study aims to determine students' use of ChatGPT using the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT), notably the role of students' integrity in determining adoption behaviour. The analysis of 921 responses demonstrated that the utilisation of ChatGPT is influenced positively by performance expectancy, social influence, educational self-efficacy, technology self-efficacy, and personal anxiety. Conversely, student integrity was found to negatively impact usage. Remarkably, student integrity has a positive moderating effect between effort expectancy and ChatGPT usage. At the same time, it has a negative moderating effect on the link between performance expectancy and technology self-efficacy with ChatGPT usage. Hence, we propose that the academic community, AI language model developers, publishers, and relevant stakeholders collaborate to establish explicit rules for the utilisation of AI chatbots in an ethical manner for educational purposes