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This research aims to explore how social media influences the academic performance of university students in Bangladesh and examines the benefits and drawbacks of its usage.

We distributed a well-structured questionnaire among students enrolled in various programs at different universities in Bangladesh to collect data. We analyzed the data using factor analysis and regression models to uncover the impact of social media on academic performance.

Our research findings demonstrate that social media plays a crucial role in facilitating communication, information sharing and content development among university students in Bangladesh. Excessive reliance on social media can lead to dependence and hinder innovation, as students tend to excessively rely on readily available resources.

We relied on self-reported data from a limited sample size, which may affect the generalizability of our findings.

This study highlights the need to promote responsible use of social media among university students in Bangladesh to enhance their academic performance. We recommend implementing effective policy measures to control and manage undesired usage patterns, foster an intellectually equipped student body and contribute to the development of a knowledgeable and successful Bangladesh.

This research makes a significant contribution by examining the influence of social media on academic performance among university students in Bangladesh. It proposes practical policy measures to address the drawbacks associated with excessive reliance on social media, thereby contributing to decision-making and intervention strategies for promoting responsible usage.

From these data, the authors construct an uncertainty index through the use of a vector autoregressive (VAR) model to measure the impact of uncertainty on GDP, controlling for inflation, which may affect macroeconomic performance. Results indicate that uncertainty is negatively correlated with the economic cycle and the inter-annual variation of the biannual average product.

This study empirically explores the dynamics of expectations of the Uruguayan manufacturing firms about industrial economic growth. This study explores the dynamics of the industrial economic growth expectations of Uruguayan manufacturing firms. The empirical research is based on firms' expectations data collected through a monthly survey carried out by the Chamber of Industries of Uruguay (CIU) in 2003–2018.

Granger causality tests show that uncertainty Granger-causes industrial production growth and a one standard deviation shock on uncertainty generates a contraction in the industrial production growth rate. Finally, the authors use statistical and network tools to identify groups of firms with similar performance on expectations. Results show that higher uncertainty is associated with smaller, more interconnected groups of firms, and that the number of homogeneous groups and the distance between groups increases with uncertainty. These findings suggest that policies focused on the coordination of expectations can lead to the development of stable opinion groups.

The paper introduces new data and new methodologies to analyze the dynamics of expectations of manufacturing firms about industrial economic growth.

1. An empirical approach to compare expectations of firms is introduced.

2. The occurrence of groups of opinion is tested.

3. Central companies in the network of expectations are detected.

4. More uncertainty implies a higher degree of discrepancy between the overall firm’s opinions and more compact opinion groups.

An empirical approach to compare expectations of firms is introduced.

The occurrence of groups of opinion is tested.

Central companies in the network of expectations are detected.

More uncertainty implies a higher degree of discrepancy between the overall firm’s opinions and more compact opinion groups.

This study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities.

The study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework.

The study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives.

The study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way.

To the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.

This paper aims to investigate the short- and long-run influence of core banking solutions (CBSs) on productive efficiency and identify the presence of potential network externalities arising from CBS adoption. This paper further examines the differential behaviour of long-term effects across the banking structure.

This study uses a panel data set of Indian commercial banks from 2005 to 2021. Economic efficiency is quantified using VRS-based DEA programming algorithms. Productivity changes are measured through an input-oriented, DEA-based Malmquist productivity index. Short- and long-run effects are examined through a finite autoregressive distributed lag model, estimated through a pooled mean-group estimator.

Findings suggest that CBS adoption negatively correlates with cost structure until the first year of adoption. Nevertheless, significant benefits are visible from the third year. Furthermore, such associations are highly susceptible to the industry structure. CBS results in higher incremental benefits for private banks vis-à-vis state-owned banks. Large banks receive significant and quicker productivity improvements from CBS vis-à-vis small banks. Bank age guides CBS–performance associations, highlighting that mature banks may face the issue of legacy infrastructure in CBS adoption. The resultant networking externalities are significant as they enhance the attractiveness of the network, which subsequently augments inter-branch and inter-bank communications.

To the best of the authors’ knowledge, this study is the first to recognise the stickiness of one of the most homogeneously adopted technological innovations in the Indian banking sector. The presence of a conjoint technological network has the potential to enhance the service delivery process and ensure superior returns for Indian banks.

This study analyzes the role of cooperative behavior in facing the risk of encouraging innovative agricultural production projects by small-scale farmers in the context of farmers in developing countries.

A within-subjects field experiment was conducted with small-scale Colombian panela cane farmers. The authors used the collected data to run the regression analyses.

The results suggest that when small farmers can follow cooperative behavior by joining a group and pooling resources to face risk, they are more willing to invest in a novel and profitable alternative, albeit riskier. However, the possibility of cooperating with a group to invest in a novel production project depends on its expected risk level.

These results will help develop agricultural policies for sustainable development. Establishing informal networks for small-scale farmers to deal with unpredictable risks may aid in developing innovative systems.

Agriculture is highly vulnerable to climatic impacts, which, combined with the inherent risk of innovation, may reduce small farmers' willingness to adopt innovation. Cooperation appears to be a mechanism for pooling resources and facing risk.

Research has focused on experimentally testing the effect of cooperative behavior when facing risk. The authors contribute to the literature by demonstrating the impact of the ability of small-scale farmers in rural areas to collectively manage risk on investment in innovative projects.

The main purpose of this paper is to explore whether the nature of innovation is accumulative or radical and to what extent past year accumulation of technology stock can predict future innovation. More importantly, the authors are concerned with whether a change of policy regime or a variance in the quality of technology will moderate the nature of innovation.

The authors examined a dataset of 3.6 million Chinese patents during 1985–2015 and constructed more than 5 million citation pairs across 8 sections and 128 classes to track knowledge spillover across technology fields. The authors used this citation dataset to calculate the technology innovation network. The authors constructed a measure of upstream invention, interacting the pre-existing technology innovation network with historical patent growth in each technology field, and estimated measure's impact on future innovation since 2005. The authors also constructed three sets of metrics – technology dependence, centrality and scientific value – to identify innovation quality and a policy dummy to consider the impact of policy on innovation.

Innovation growth is built upon past year accumulation and technology spillover. Innovation grows faster for technologies that are more central and grows more slowly for more valuable technologies. A pro-innovation and pro-intellectual property right (IPR) policy plays a positive and significant role in driving technical progress. The authors also found that for technologies that have faster access to new information or larger power to control knowledge flow, the upstream and downstream innovation linkage is stronger. However, this linkage is weaker for technologies that are more novel or general. On most occasions, the nature of innovation was less responsive to policy shock.

This paper contributes to the debate on the nature of innovation by determining whether upstream innovation has strong predictive power on future innovation. The authors develop the assumption used in the technology spillover literature by considering a time-variant, directional and asymmetric matrix to model technology diffusion. For the first time, the authors answer how the nature of innovation will vary depending on the technology network configurations and policy environment. In addition to contributing to the academic debate, the authors' study has important implications for economic growth and industrial or innovation management policies.