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Sharing and obtaining information over social media has enabled people to express their opinions regarding any event. Since the tweets regarding the Russia-Ukraine war were extensively publicized on social media, this study aims to analyse the temporal sentiments people express through tweets related to the war.

Relevant hashtag related to the Russia-Ukraine war was identified, and tweets were downloaded using Twitter API, which were later migrated to Orange Data mining software. Pre-processing techniques like transformation, tokenization, and filtering were applied to the extracted tweets. VADER (Valence Aware Dictionary for Sentiment Reasoning) sentiment analysis module of Orange software was used to categorize tweets into positive, negative and neutral ones based on the tweet polarity. For ascertaining the key and co-occurring terms and phrases in tweets and also to visualize the keyword clusters, VOSviewer, a data visualization software, was made use of.

An increase in the number of tweets is witnessed in the initial days, while a decline is observed over time. Most tweets are negative in nature, followed by positive and neutral ones. It is also ascertained that tweets from verified accounts are more impactful than unverified ones. russiaukrainewar, ukraine, russia, false, war, nato, zelensky and stoprussia are the dominant co-occurring keywords. Ukraine, Russia and Putin are the top hashtags for sentiment representation. India, the USA and the UK contribute the highest tweets.

The study tries to explore the public sentiments expressed over Twitter related to Russia-Ukraine war.

The study aims to shed light on the current state of “Dataset repositories” indexed in Directory of Open Access Repositories (OpenDOAR).

From each repository/record information, the Open-Access Policies, Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), year of creation and the number of data sets archived in the repositories were manually searched, documented and analyzed.

Developed countries like the United Kingdom and the USA are primarily involved in the development of institutional open-access repositories comprising significant components of OpenDOAR. The most extensively used software is DSpace. Most data set archives are OAI-PMH compliant but do not follow open-access rules. The study also highlights the sites’ embrace of Web 2.0 capabilities and discovers really simple syndication feeds and Atom integration. The use of social media has made its presence known. Furthermore, the study concludes that the number of data sets kept in repositories is insufficient, although the expansion of such repositories has been consistent over the years.

The work has the potential to benefit both researchers in general and policymakers in particular. Scholars interested in research data, data sharing and data reuse can learn about the present state of repositories that preserve data sets in OpenDOAR. At the same time, policymakers can develop recommendations and policies to assist in the construction and maintenance of repositories for data sets.

According to the literature, there have been numerous studies on open-access repositories and OpenDOAR internationally, but no research has focused on repositories preserving content-type data sets. As a result, the study attempts to uncover various characteristics of OpenDOAR Data set repositories.

The study aimed to develop a disruptive production process innovation model from an emerging economy's agro-based dairy farming perspective based on the case of a Bangladeshi model dairy farm named Central Cattle Breeding and Dairy Farm (CBBDF), Savar, Dhaka.

The study used qualitative in-depth interviews, participants and document observation. Around 20 key informants were purposefully selected from the case organization.

The findings propose two different models for disruptive production process innovation toward sustainable agribusiness dairy farming from an emerging economy perspective and the modified process model from traditional dairy farms to modern dairy based on the stakeholders' (such as consumers, owners, managers and government) demand.

This study is the first attempt to develop a disruptive production process innovation and technology model for the dairy industry and its stakeholders' responsibilities from the experience of an emerging economy. The value of the research is in identifying factors under traditional dairy farming that need to be reduced and eliminated, and the factors under society, health concerns and value-added to existing farms need to be raised as per the global industry standard.

This study investigates the effectiveness of trade unions in preserving and promoting the rights of the worker, and being their voice in ensuring safe working conditions as part of the firms’ CSR activities.

Data were collected from employees, managers, and owners of ready-made garments firms in Bangladesh. An open-ended survey instrument was used and distributed widely. Analysis of the 200 responses was undertaken using the qualitative narrative technique.

The findings show that, unlike traditional labor-management relations, in Bangladesh managers tend to have a more favorable attitude towards unions than employees do. The strong political links between the factory owners and the unions’ leadership raise questions about whose interest the unions represent.

The authors highlight how adopting a CSR agenda can help unions make better representations on behalf of their members. This, in turn, can enhance the workforce’s efficiency and help strengthen the sector to develop processes to help face disruptions like those experienced during the COVID-19 pandemic.

The study is unique in addressing the role of unions in promoting CSR activities in emerging economies, where the formal institutional application remains limited. The study’s findings can help explain some of the sector’s challenges.

The study examined the influence of servant leadership on organisational citizenship behaviour (OCB) of employees in Ghana through the intervening roles of public service motivation (PSM) and the length of time spent with the leader.

A structured questionnaire was used to gather data from 328 randomly sampled respondents within the six metropoles in Ghana for data analysis. The partial least squares structural equation modelling (PLS-SEM) was used to address the hypotheses of the study.

The study found that servant leadership has a significant positive influence on OCB and PSM. The study also found that PSM significantly and positively influences OCB. The study finally established that whiles PSM significantly mediated the relationship between servant leadership and OCB, the length of time spent with leaders did not moderate the servant leadership and OCB nexus.

The study implies that stakeholders in the local government sector, such as the Local Government Service Board, should emphasise employing or promoting supervisors who are servant leadership-driven to occupy key positions within the local government structure. Efforts could also be made to train the supervisors to acquire servant leadership traits.

The results from the study provide significant insights on how the public sector in Ghana and beyond can identify, develop and maintain servant leaders to promote employees OCB. Also, being a pioneering study in Ghana, the results and recommendations from the study have set the pace for future scholars to discuss issues that engulf the public sector.