Digital literacy is a critical skill that administrative staff must acquire to continue their business activities at higher education. However, digital literacy studies for administrative staff seem to be neglected in the current literature. This article examines the impact of higher education administrative staff’s digital literacy on their intention to use digital technologies while performing their tasks. For this purpose, a conceptual model consisting of effort expectancy and performance expectancy structures based on the unified theory of acceptance and use of technology and expanded with the digital literacy dimension has been created. Data were collected from 158 participants who were administrative staff of two higher education institutions in Türkiye to evaluate the theoretical model. The data were analysed using the structural equation modelling technique. The findings revealed the relationship between the digital literacy skills of higher education administrative staff and their intention to use digital technology. According to the results, digital literacy has a direct effect on effort expectancy but not on performance expectancy. Also, contrary to our expectations, digital literacy does not directly affect the intention to use. However, digital literacy affects the intention to use digital technology through effort expectancy and performance expectancy. In higher education, personnel with low digital literacy skills should be identified and in-service training should be provided. This is one of the first studies to address the impact of digital literacy on technology acceptance by administrative staff working in higher education.

Various literature studies have been conducted to provide valuable information regarding the current research trend of Unified Technology Acceptance and Use Theory (UTAUT). When the literature was examined, it was seen that the UTAUT research on the adoption of mobile learning (M-learning) was ignored. Therefore, it was deemed necessary to conduct a literature study on the adoption of mobile learning. In this context, 31 research articles on the adoption of M-learning with UTAUT, published from 2003 to 2020, have been discussed for systematic literature research. These 31 specific research publications were discussed under four categories Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions. 63 different factors were identified after systematic literature review, except for UTAUT factors. These factors were grouped under 10 main factors. In addition, the authors in this field were identified by bibliometric analysis and the relationships between each other were determined by citation analysis. In addition to these, prominent terms have been determined according to the keywords and abstracts in the relevant articles. The connections between these terms have been created by the method of co-occurrence. Finally, the links between prominent terms and terms were examined with bibliometric analysis. According to the findings obtained, it has been determined that most UTAUT studies involving M-learning focus on extending UTAUT with external variables. It has been observed that the analyzed studies generally took place in the Asian countries. These studies have been carried out as multidisciplinary. In addition, it has been reported that most of these studies on M-learning take place in higher education settings. It is thought that the findings obtained at the end of the systematic literature review and bibliometric analysis study on the adoption of M-learning with UTAUT will constitute an important reference for academicians in this field.

The widespread use of business intelligence products, services, and applications piques the interest of researchers in this field. The interest of researchers in business intelligence increases the number of studies significantly. Identifying domain-specific research patterns and trends is thus a significant research problem. This study employs a topic modeling approach to analyze domain-specific articles in order to identify research patterns and trends in the business intelligence field over the last 20 years. As a result, 36 topics were discovered that reflect the field’s research landscape and trends. Topics such as “Organizational Capability”, “AI Applications”, “Data Mining”, “Big Data Analytics”, and “Visualization” have recently gained popularity. A systematic taxonomic map was also created, revealing the research background and BI perspectives based on the topics. This study may be useful to researchers and practitioners interested in learning about the most recent developments in the field. Topics generated by topic modeling can also be used to identify gaps in current research or potential future research directions.