Search results

Taking the Institute of Philology and Journalism at Ukraine's Taurida National University as a case study, this paper overviews and distils the crisis management measures utilised in transitioning to fully online education during the crises of the Covid-19 pandemic and full-scale Russian invasion and violence.

With the aim of spotlighting the experiences of the people most directly impacted by these two contemporary crises, this case study documents the lived experience of the authors—all of whom are/were teaching staff at the Institute—and Institute students’ responses to online surveys conducted between 2020 and 2022.

The Institute's case study demonstrates that contemporary crisis management via transitioning to fully online learning can be achieved if the following instrumental and methodological components are employed: (1) an initial assessment of the risks and opportunities for the educational community involved; (2) the right choice of online teaching and communications tools; (3) followed by flexibility and gradualism in onward planning (i.e. where technology and pedagogy are understood as interconnected) taking members’ feedback into account. However, the success of these components is contingent upon fulfilling psychological components, with care devoted to: upholding members’ psychological well-being; offering members ongoing technical support; and strengthening trust between members.

This case study offers transferable and adaptable findings for successful crisis management in education, from the Ukrainian context out to the wider world.

The purpose of this study is to identify the impact of online learning on university librarians’ professional development and library services.

A mixed-methods study through an explanatory research design was applied to address the study’s objectives. Quantitative data were gathered from 341 librarians working in 221 universities, while qualitative data were gathered from 27 experts working in 21 different universities of Pakistan.

The findings of the study revealed that online learning has a significant positive impact on the professional development of university librarians. Results revealed that online learning assists in the provision of sustainable, innovative library services in university libraries.

The study has offered a model in light of the study's quantitative and qualitative findings. It contributes to theoretical understanding by expanding the existing knowledge base. It offers managerial insights, enabling the development of policies that foster the professional development of library personnel and the implementation of smart library services.

In the extant research on online knowledge communities (OKCs), little attention has been paid to the influence of membership fluidity on the coevolution of the social and knowledge systems. This article aims to fill this gap.

Based on the attraction-selection-attrition (ASA) framework, this paper constructs a simulation model to study the coevolution of these two systems under different levels of membership fluidity.

By analyzing the evolution of these systems with the vector autoregression (VAR) method, we find that social and knowledge systems become more orderly as the coevolution progresses. Furthermore, in communities with low membership fluidity, the microlevel of the social system (i.e. users) drives the coevolution, whereas in communities with high membership fluidity, the microlevel of the knowledge system (i.e. users' views) drives the coevolution.

This paper extends the application of the ASA framework and enriches the literature on membership fluidity of online communities and the literature on driving factors for coevolution of the social and knowledge systems in OKCs. On a practical level, our work suggests that community administrators should adopt different strategies for different membership fluidity to efficiently promote the coevolution of the social and knowledge systems in OKCs.

In an era where complex technological advances increasingly govern service delivery, it is incumbent on service firms to pioneer innovative strategies to sustain customer engagement and cultivate loyalty. This conceptual paper examines the transformative potential of artificial intelligence (AI) in the realm of online customer communities, with a particular focus on its creation, management and enhancement facets. The authors explore how AI can revolutionize the dynamics of customer interaction, feedback mechanisms and overall engagement within the service industry.

This conceptual paper draws from marketing and management literature focusing on customer communities and AI in service and customer engagement contexts with a robust future research agenda.

A classification of online customer community engagement is provided along with a conceptual framework to guide our understanding of the integration of AI into online customer communities.

This exploration underscores the imperative for service firms to embrace AI-driven approaches to online customer community management, not only as a means to optimize their operations but as a vital strategy to stay competitive in the ever-evolving digital landscape. This paper examines the novel combination of AI with online customer communities and provides the framework in the form of an input-process-output (IPO) model for future research into this integration.

This paper aims to explore the effects of normative and hedonic motivations on continuous knowledge contribution, and how past contribution experience moderates the effects of the motivations on continuous knowledge contribution.

Based on goal-framing theory, the present study proposes a comprehensive theoretical model by integrating normative and hedonic motivations, past contribution experience and continuous knowledge contribution. The data for virtual community members' activities were collected using the Python Scrapy crawler. Logit regression was used to validate the integrative model.

The results show that both normative motivation (reflected by generalized reciprocity and social learning) and hedonic motivation (reflected by peer recognition and online attractiveness) are positively associated with continuous knowledge contribution. Moreover, these effects are found to be significantly influenced by members' past knowledge contribution experience. Specifically, the results suggest that past knowledge contribution experience undermines the influence of generalized reciprocity on continuous knowledge contribution but strengthens the effect of peer recognition and online attractiveness.

Although the emerging literature on continuous knowledge contribution mainly focuses on motivations as antecedents that promote continuous knowledge contribution, most of these studies assume that the relationship between motivating mechanisms and continuous knowledge contribution does not change over time. The study is one of the initial studies to examine whether and how the influence of multiple motivations evolves relative to levels of past contribution experience.

This study aims to explore first-year engineering students’ perceptions of the engineering librarian as an instructor in multimodal environments related to Information Literacy (IL) topics, teaching strategy, content evaluation, organising, planning and support.

A quantitative approach was used through a survey instrument based on an online questionnaire. Questions were adopted and modified from a lecturer evaluation survey. A simple random sampling technique was used to collect data from first-year cohorts of engineering students in 2020 and 2022.

Respondents perception of the engineering librarian as an instructor in multimodal learning environment was good. Findings revealed students’ learning experiences were aligned with IL instruction even though the environment changed from blended to online. However, an emerging theme that continuously appeared was a lack of access to technology.

These findings may help in developing and strengthening the teaching identity of academic librarians as instructors in multimodal learning environments.

To the best of the author’s knowledge, this study is novel in that it evaluates the teaching abilities of an academic librarian in multimodal environments through the lens of students.

The purpose of this study is to understand the scope and nature of information sharing as a form of workplace learning among gig workers.

Data were collected from public social media communities of gig workers in Thailand. In total, 338 posts and 3,022 comments on the posts were analyzed (data corpus N = 3,360). Thailand was selected for the context of this study given its high level of social media penetration, a high percentage of digital service consumption of internet users and the prevalence of app-based gig workers. This study used thematic analysis using inductive and semantic coding to generate themes.

Findings showed two overarching themes of information sharing, which included on-the-job experience and inquiries. One surprising finding was the extent to which gig workers used social media to help others even when it potentially undermined their success.

This study adds evidence to the role of information sharing in workplace learning and illustrates how gig workers who do not have access to traditional training and learning opportunities use social media communities to fill this need.

Given the surge of digitalization and internet infrastructure leading to the rise of gig work worldwide, this study provides a closer look at how gig workers are using social media communities to facilitate workplace learning and support one another amid otherwise difficult and insecure working conditions. It also discusses the role that culture plays in facilitating a cooperative rather than a competitive environment among drivers.

Enterprises build online product community to expect users to contribute: opinion sharing (content contribution) and product consumption (product contribution). Previous literature rarely focused on both. The purpose of this paper is to explain user contribution mechanism by identifying content contribution and product contribution.

This research chose Xiaomi-hosted online product community (bbs.xiaomi.cn) and Huawei-hosted online product community (club.huawei.com) where users can freely share ideas and buy products at the same time. Data were crawled from 109,665 community users to construct dependent variable measurement, and 611 questionnaires were used to verify research hypotheses.

The results indicate that both cognitive needs and personal integration needs have a significant positive impact on browse behavior; social integration needs and hedonic needs have a significant positive impact on content contribution behavior. Browse behavior not only directly affects but also indirectly influences product contribution through content contribution behavior.

Findings of this research provide community managers with useful insights into the relationship between content contribution and product contribution.

This study explains the formation mechanism of user product contribution and reveals the relationship between user content contribution and product contribution in online product community. This paper provides a different way of theorizing user contributions by incorporating uses and gratifications theory into the “Motivation-Behavior-Result” framework.

This study leverages social network analysis (SNA) to visualise the way students interacted with online resources and uses the data obtained from SNA as features for supervised machine learning algorithms to predict whether a student will successfully complete a course.

The exploration and visualisation of the data were first carried out to gain a better understanding of the students, the course(s) each student was enrolled in and each course’s virtual learning resources. Following this, the construction of the social network graphs was performed to depict how each student behaved online before the degree centralities were computed for each of the nodes in a social network graph. Data pre-processing to assign labels based on the final result a student obtained in a course was then performed before we trained and tested models to predict which students did or did not graduate.

The study’s findings demonstrate that the constructed predictive model has good performance, as shown by the accuracy, precision, recall and f-measure metrics. The outcomes also showed that students’ use of online resources is a crucial element that influences how well they perform in their academics.

The similarity index is as low as 9%.

The lack of physical contact and the absence of nonverbal clues could make some learners uncomfortable interacting with others via online learning platforms. Hence, understanding the determinants of students' motivation and engagement in online learning platforms is crucial in harnessing digital technology as an enabler of unrestricted and quality learning experiences.

Drawing on the self-determination theory (SDT), this study investigates the factors associated with student’s motivation to learn (MOL) and their influence on online learning engagement (OLE). Data were collected from 228 university students from the Klang Valley region of Malaysia using the online survey method.

The results of data analysis using the partial least squares structural equation modeling indicate that self-directed learning, computer and Internet self-efficacy and online communication self-efficacy significantly influence MOL. Besides, these factors indirectly influence OLE through MOL.

This study adds to the SDT framework by demonstrating how students' perceptions of autonomy, competence and relatedness through online interaction relate to MOL and OLE.