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This study aims to explore how artificial intelligence (AI) can enhance mental health care in Tanzania, focusing on its potential to enhance mental health services and address challenges in a low-resource setting.

A qualitative case study approach was used, with data collected through semi-structured interviews and focus group discussions involving key stakeholders in mental health and AI, including policymakers, technical experts, health-care providers and patient advocacy groups. Thematic analysis was used to identify key themes related to the opportunities and barriers to AI integration in mental health care.

This study identified several benefits of AI in mental health care, including improved diagnostic accuracy, personalised treatment and the potential for real-time monitoring of patients. However, significant barriers to AI adoption remain, such as infrastructure limitations, data privacy concerns and the need for training and resources to effectively integrate AI into mental health services.

This study contributes to the growing literature on AI in health care by focusing on its application in mental health care in Tanzania, a low-resource setting. The research provides valuable insights into how AI can bridge gaps in mental health service delivery, particularly in underserved regions, while highlighting the challenges that must be addressed for successful implementation.

The emergency of the COVID-19 pandemic has affected organizations differently, leading to adopting alternative business operation models, including digital technologies. The rapid adoption of digital technologies during the COVID-19 pandemic demonstrates how adaptive technologies intersect with human and social institutions in potentially risky or inequitable ways. However, the sustenance of the digital transformations (DTs) induced by the COVID-19 crisis is still unclear. Thus, this study aims to evaluate the sustenance of DTs accelerated by the emergence of the COVID-19 pandemic response in the Tanzanian context amid the crisis.

The study used a qualitative approach that included a literature review and semi-structured interviews with Information and Communication Technology (ICT) personnel from selected public institutions in Tanzania. The interviews elicited interviewees’ perspectives on the following: the digital revolution that occurred during the COVID-19 outbreak; how it affects their work; and the country’s future digital landscape. Additionally, a literature analysis established and generalized the practices used by the governments during the COVID-19 crisis.

The results show that there is enormous, colossal press toward adopting digital solutions. The use of digital technologies, such as digital payments, e-commerce, telemedicine and online education, has attracted the attention of businesses and individuals in Tanzania. Consequently, the move has enabled the country to cope with the cascading impacts of the COVID-19 pandemic and progressive economic recovery to achieve its development goals. Furthermore, the findings offer policy strategies for taking the induced DTs to the next level and sustaining it.

This study reports the lessons from the DTs induced by COVID-19 and their sustenance. It also contributes toward forward-thinking reforms and policy measures that could help Tanzania and the likes harness the DTs'’ potential in the wake of the pandemic.

This study aims to explore AI’s potential impact on the broader landscape of higher education in Tanzania. This study contributes to the ongoing discussion of AI’s potential to transform higher education and highlights the ethical considerations and challenges that must be addressed to ensure its successful implementation. This study informs future research and policy decisions in education and technology by providing a detailed understanding of AI’s perceived benefits and challenges in higher education.

A mixed-methods approach was used, which involves collecting and analyzing quantitative and qualitative data to understand the research problem comprehensively. This approach allowed data triangulation and led to a more robust and detailed understanding of this study.

In this study, it was discovered that enhanced assessment, time-saving, personalized learning, improved accessibility and detecting cheating are the perceived benefits of AI as a tool for enhancing learning in higher education, while cost and infrastructure, academic misconduct, data privacy and security, bias and ethical concerns and lack of human interaction are the perceived challenges of AI as a tool for enhancing learning in higher education. Further, it was revealed that students are more accepting of using AI tools in the classroom because they think they are more effective and engaging. On the other hand, faculty were more cautious and skeptical about employing AI tools in the classroom because they worried about how it would affect their teaching methods and job security.

The data collection was not conducted face-to-face. To fully capture respondents’ emotional responses, feelings, facial expressions, reactions, or body language was challenging. However, a sufficient number of individuals who participated were very cooperative, and their knowledge was very beneficial in understanding the topic.

A unique view of this study is a clear understanding of the perceived benefits and challenges of using AI as a tool for enhancing learning in higher education, as well as the variations in these perceptions among students and faculty. By examining the perspectives of both groups, this study provides a comprehensive understanding of the complex role of AI in higher education. Understanding the broader implications of AI in higher education can inform policy decisions and ensure that AI is used responsibly and ethically.

This study aims to investigate the adoption and use of electronic health record systems (EHRS) in Tanzanian public primary healthcare institutions. The study’s objectives include understanding the factors that affect EHRS adoption, identifying implementation challenges and evaluating the effect of EHRS usage on healthcare delivery. By addressing these research goals, the study aims to contribute insightful information on the current level of EHRS adoption in Tanzanian primary healthcare facilities and contribute to developing strategies to improve EHRS deployment and healthcare in the nation.

This study combined quantitative and qualitative data using a mixed-methods methodology. Both data types were collected and analysed concurrently using a concurrent triangulation approach. The study aimed to comprehend the variables that affect the adoption and use of EHRS in Tanzanian public primary healthcare institutions. Eleven regions spanning various geographic locations and urban–rural dynamics were chosen as research sites. A survey of 122 healthcare employees was conducted with a sample of 31 healthcare facilities. The questionnaire had closed-ended and open-ended questions to gather quantitative and qualitative data. Descriptive statistics and thematic analysis were used in data analysis. Throughout the investigation, ethical standards and confidentiality precautions were observed.

Several factors affect the adoption and use of EHRS. Perceived usefulness and use, support and training, interoperability, data security and privacy, business culture and leadership are all factors. Inadequate infrastructure, power interruptions, duplication of effort and a lack of data analytic expertise were among the difficulties. Among the effects were improvements in data management, service delivery and coordination, productivity and efficiency, medical supply inventory control, billing and revenue collection.

This study, which complements earlier research that has concentrated chiefly on specialised healthcare settings, gives new insights by investigating the adoption and utilisation of EHRS, especially in primary healthcare institutions. The findings give policymakers and healthcare professionals in Tanzania and other nations vital information to help them decide whether to embrace and use EHRS in primary healthcare.

The study aims to explore the utilisation of Clinical Decision Support (CDS) tools in Tanzanian healthcare facilities by identifying the tools used, the challenges encountered and the adaptive strategies employed by healthcare practitioners. It utilises an Activity Theory (AT) approach to understand the dynamic interactions between healthcare providers, CDS tools and the broader healthcare system.

The research adopts a qualitative approach in two prominent regions of Tanzania, Dar es Salaam and Dodoma. It involves semi-structured interviews with 26 healthcare professionals and key stakeholders across ten healthcare facilities, supplemented by document reviews. The study employs AT to analyse the interactions between healthcare professionals, CDS tools and the broader healthcare system, identifying best practices and providing recommendations for optimising the use of CDS tools.

The study reveals that Tanzanian healthcare practitioners predominantly rely on non-computerised CDS tools, such as clinical guidelines prepared by the Ministry of Health. Despite the availability of Health Information Systems (HIS), these systems often lack comprehensive decision-support functionalities, leading practitioners to depend on traditional methods and their professional judgement. Significant challenges include limited accessibility to updated clinical guidelines, unreliable infrastructure and inadequate training. Adaptive strategies identified include using non-standardised tools like Medscape, professional judgement and reliance on past experiences and colleagues’ opinions.

The investigation was constrained by access limitations because it was challenging to get some respondents to share information. However, a sufficient number of individuals participated in the interviews, and their knowledge was very beneficial in understanding the procedures and tools for clinical decision support.

This study contributes to AT by extending its application to a low-resource healthcare setting, uncovering new dimensions of the theory related to socio-cultural and technological constraints in healthcare facilities in Tanzania. It provides valuable insights into the practical barriers and facilitators of HIS and CDS tool implementation in developing countries, emphasising the need for context-specific adaptations, robust training programs and user-centred designs. The findings highlight the resilience and imagination of healthcare practitioners in adapting to systemic limitations, offering recommendations to enhance clinical decision-making and improve patient care outcomes in Tanzania.

The objective of this research is to examine the accelerated adoption mechanisms of emerging technologies in information systems. Its goal is to comprehend the drivers behind the prompt assimilation of technology trends such as TikTok, ChatGPT, mobile payment schemes, cryptocurrency and VR.

The study follows the systematic literature review methodology (using the PRISMA protocol to guide the selection of scholarly materials from Google Scholar, Scopus and Springer). Specifically, the research draws on identified literature on the adoption trajectories of technologies (ChatGPT, TikTok, cryptocurrency, mobile payment systems, and virtual reality) to systematically assess pertinent insights, and draws on theoretical lenses of Disruptive Innovation Theory to reach interpretations.

The study indicates that the prompt assimilation of technology is shaped by several variables such as user-centered design, network effects, content powered through algorithms, viral trends, ease-of-use and accessibility features, engagement levels and retention rates.

The selection of specific platforms may limit the generalizability of findings.

The emergence of new technologies is causing a shift in societal behaviors and norms, which has significant social implications. While platforms such as TikTok offer opportunities for community-building, there are concerns regarding digital divide and privacy issues that need to be addressed. So understanding the impact of these changes becomes vital for achieving fairness in access and making technology's potential transformation practicalized effectively.

This research enhances the current body of literature by presenting a thorough examination of the non-linear patterns involved in adopting advanced technologies. By combining knowledge from numerous fields, this study delivers an integrated comprehension regarding what factors prompt swift adoption.