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The study utilized a quantitative approach to investigate student satisfaction, focusing on the lecturers’ knowledge, quality of delivery, student support and evaluation. The population included second- to fourth-year undergraduates, data from a sample of 600 were collected through a structured questionnaire using stratified random sampling and analyzed using structural equation modeling (SEM).

The aim of this study is to examine the various dimensions of academic staff quality that affect student satisfaction within a selected private higher educational institute in Sri Lanka, providing a clear understanding of the dimensions of academic staff quality and recognizing the important role of the said dimensions in shaping the educational experience of the students.

Results revealed that effective support for students, lectures’ broader knowledge, quality of delivery and quality of evaluation were significant predictors of student satisfaction and that they are crucial indicators of how strong the impact of the academic staff is in contributing to overall undergraduate student satisfaction.

This study filled a gap in higher education research in Sri Lanka by offering empirical evidence on the impact of academic staff quality on satisfaction among students in private universities. It serves as a valuable reference for those exploring higher education concepts, providing a novel understanding of the influence of the key component of academic staff quality.

The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation.

The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach.

The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration.

Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy.

Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society.

This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.

The COVID-19 pandemic has resulted in countries reacting differently to an ongoing crisis. Latent to this reaction mechanism is the inherent cultural characteristics of each society resulting in differential responses to the epidemic spread. In this study, the moderated moderation role of culture, on information dissemination by media during epidemic recovery-phase has been investigated.

Hofstede’s cultural factors are hypothesized to moderate the moderating effect of free-liberal media on the relationship of COVID-19 recovery rate and human mobility. Panel regression model, using mobility data and recovery rate across 95 countries for a period of 170 days has been preferred to test the hypotheses. The results are further substantiated using factor wise interaction plots and slope difference analysis.

The findings suggest that societies with high power distance and masculinity scores strengthen the impact of media on the relationship between COVID-19 recovery rate and mobility whereas, high individualistic and long-term orientation societies weaken the same effect. However, similar conclusions were not confirmed for uncertainty avoidance. Cross-cultural impact, as elucidated by this study, forms a crucial element in policy formulation on epidemic control by indigenous Governing bodies.

While most of the studies emphasizing on cultural characteristics of a society in an epidemic situation covers the growth phase of infection, This research talks about the recovery-phase of the epidemic and the effect of culture.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2023-0314

Data quality assurance (DQA) is essential for enabling the sharing and reuse of research data, especially given the increasing focus on data transparency, reproducibility, credibility and validity in research. Although the literature on research data curation is vast, there remains a lack of theory-guided exploration of DQA modeling in research data repositories (RDRs).

This study addresses this gap by examining 12 distinct cases of DQA-related knowledge organization tools, including four metadata vocabularies, three metadata schemas, one ontology and four standards used to guide DQA work in RDRs.

The study analyzed the cases utilizing a theoretical framework based on activity theory and data quality literature and synthesized a model and a knowledge artifact, a DQA ontology (DQAO, Lee et al., 2024), that encodes a DQA theory for RDRs. The ontology includes 127 classes, 44 object properties, 7 data properties and 18 instances. The article also uses problem scenarios to illustrate how the DQAO can be integrated into the FAIR ecosystem.

The study provides valuable insights into DQA theory and practice in RDRs and offers a DQA ontology for designing, evaluating and integrating DQA workflows within RDRs.

This study aims to investigate the impact of news quality on users’ risk perceptions toward online news and its subsequent influence on perceived believability and user engagement in sharing news. Additionally, we explore the moderating effects of fake news awareness and social tie variety.

Drawing upon the social amplification of risk framework, this study investigates the relationship between news quality and users’ news-sharing behaviors, along with its underlying mechanism. An online questionnaire involving 399 eligible participants was employed for hypotheses testing, and the structural equation model served as the main analytical method.

The influence of news quality on users’ news-sharing behavior is sequentially mediated by risk perception and perceived believability. Individuals with a heightened awareness of fake news or a diverse social tie are more inclined to perceive greater risks associated with news-sharing behavior and question news authenticity.

This study contributes to the existing literature on users’ news-sharing behaviors by examining the influence of risk perception on the relationship between news quality, perceived believability and users’ news-sharing behavior. Additionally, it explores the moderating effects of fake news awareness and social tie variety. Our findings offer valuable insights into comprehending user inclinations towards news sharing and mitigating the dissemination of fake news.