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This study aims to gain a deeper understanding of the industry practice to guide the formation of support tools with a rigorous theoretical backing. Cross-functional teams are an essential component in new product development (NPD) of complex products to promote comprehensive coverage of product design, marketing, sales, support as well as many other activities of business. Efficient use of teams can allow for greater technical competency coverage, increased creativity, reduced development times and greater consideration of ideas from a variety of stakeholders. While academics continually aspire to propose methods for improved team composition, there exists a gap between research directions and applications found within industry practice.

Through interviewing product development managers working across a variety of industries, this paper investigates the common practices of team utilization in an organizational setting. Following these interviews, this paper proposes a conceptual two-dimensional management support model aggregating the primary drivers of team success and providing direction to systematically address features of team management and composition.

Based on this work, product managers are recommended to continually address the positioning of members throughout the entire NPD process. In the early stages, individuals are to be placed to work on project components with explicit consideration toward the perceived complexity of tasks and individual competency. Throughout the development process, individuals’ positions vary based on new information while continued emphasis is placed on maintaining a shared understanding.

Bridging the gap between theory and application within product development teams is a necessary step toward improved product develop. Industrial settings require practical solutions that can be applied economically and efficiently within their organization. Theoretical reflections postulated by academia support improved team design; however, to achieve true success, they must be applicable when considering product development.

This study aims to identify and theorize the conditions affecting the formation of librarians’ knowledge-hiding behaviour in academic libraries.

This study is a qualitative research based on the grounded theory approach. The data collection method involved in-depth, semi-structured interviews. The data was analysed using the MAXQDA software in three stages: open, axial and selective coding. The study included 22 faculty members and experienced librarians from academic libraries. The participants were selected using a combination of targeted and snowball sampling techniques.

The study yielded 96 open codes, 24 axial codes and 18 selective general codes related to the axial category of knowledge hiding (KH). The librarians’ KH axial coding paradigm pattern was developed in an academic library setting. The study also highlighted some general consequences of KH in academic libraries, such as lobbying and creating knowledge rents, deterioration of organizational relationships and interactions, reducing the competitive advantage of academic libraries and hindering individual and organizational learning.

This study has made a valuable contribution to the identification and explanation of the factors that affect KH. In addition, it has filled a research gap within the library and information science (LIS) field. The findings of the study may offer managers new strategies for addressing the occurrence of KH in academic libraries, and they also add to the existing literature on knowledge management in LIS.

Healthcare providers are under pressure due to increasing and more complex demands for services. Increased pressure on budgets and human resources adds to an ever-growing problem set. Competent leaders are in demand to ensure effective and well-performing healthcare organisations that deliver balanced results and high-quality services. Researchers have made significant efforts to identify and define determining competencies for healthcare leadership. Broad terms such as competence are, however, inherently at risk of becoming too generic to add analytical value. The purpose of this study is to suggest a holistic framework for understanding healthcare leadership competence, that can be crucial for operationalising important healthcare leadership competencies for researchers, decision-makers as well as practitioners.

In the present study, a critical interpretive synthesis (CIS) was conducted to analyse competency descriptions for healthcare leaders. The descriptions were retrieved from peer reviewed empirical studies published between 2010 and 2022 that aimed to identify healthcare services leadership competencies. Grounded theory was utilised to code the data and inductively develop new categories of healthcare leadership competencies. The categorisation was then analysed to suggest a holistic framework for healthcare leadership competence.

Forty-one papers were included in the review. Coding and analysing the competence descriptions resulted in 12 healthcare leadership competence categories: (1) character, (2) interpersonal relations, (3) leadership, (4) professionalism, (5) soft HRM, (6) management, (7) organisational knowledge, (8) technology, (9) knowledge of the healthcare environment, (10) change and innovation, (11) knowledge transformation and (12) boundary spanning. Based on this result, a holistic framework for understanding and analysing healthcare services leadership competencies was suggested. This framework suggests that the 12 categories of healthcare leadership competencies include a range of knowledge, skills and abilities that can be understood across the dimension personal – and technical, and organisational internal and – external competencies.

This literature review was conducted with the results of searching only two electronic databases. Because of this, there is a chance that there exist empirical studies that could have added to the development of the competence categories or could have contradicted some of the descriptions used in this analysis that were assessed as quite harmonised. A CIS also opens for a broader search, including the grey literature, books, policy documents and so on, but this study was limited to peer-reviewed empirical studies. This limitation could also have affected the result, as complex phenomenon such as competence might have been disclosed in greater details in, for example, books.

The holistic framework for healthcare leadership competences offers a common understanding of a “fuzzy” concept such as competence and can be used to identify specific competency needs in healthcare organisations, to develop strategic competency plans and educational programmes for healthcare leaders.

This study reveals a lack of consensus regarding the use and understanding of the concept of competence, and that key competencies addressed in the included papers are described vastly different in terms of what knowledge, skills and abilities they entail. This challenges the operationalisation of healthcare services leadership competencies. The proposed framework for healthcare services leadership competencies offers a common understanding of work-related competencies and a possibility to analyse key leadership competencies based on a holistic framework.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.

While most language departments of the university offer service-learning (SL) subjects based on language teaching, such as “Teaching Chinese as a Second Language in Local Schools” and “Serving the Community through Teaching English,” this paper aims to argue that teaching students to teach language(s) is yet to be the best strategy to serve the service recipients.

SL is widely understood as an experiential learning pedagogy that integrates academic focus, reflection and community service and is shown to be impactful. In Hong Kong, the first university that has made SL a graduation requirement is the Hong Kong Polytechnic University (the University). Considering this, new SL courses have proliferated over the past decade. Adopting a narrative inquiry approach, this paper examines personal narratives from a new SL subject aiming to raise awareness of refugees in Hong Kong. The data includes students’ reflective journals, co-created personal narratives and podcasts and semi-structured interviews.

This paper finds that crafting and recording narratives of shared experiences deepens cultural understanding, cultivates empathy and facilitates language learning in a genuine setting.

Ultimately, this paper advocates a well-designed SL that combines language, content and technology as a powerful, transformational experience for both college students and service recipients.

This paper focuses on a brand new SL course, “Storytelling for Understanding: Refugee Children in Hong Kong,” offered in Semester 1, 2022–2023. The subject was developed by the two authors from a language division affiliated to the University. The deliverables were podcast recordings, co-authored and co-edited by the students and the children.

This purpose of this paper is to report on the development of an evidence-informed framework created to facilitate the formulation of generative artificial intelligence (GenAI) academic integrity policy responses for English medium instruction (EMI) higher education, responding to both the bespoke challenges for the sector and longstanding calls to define and disseminate quality implementation good practice.

A virtual nominal group technique engaged experts (n = 14) in idea generation, refinement and consensus building across asynchronous and synchronous stages. The resulting qualitative and quantitative data were analysed using thematic analysis and descriptive statistics, respectively.

The GenAI Academic Integrity Policy Development Blueprint for EMI Tertiary Education is not a definitive mandate but represents a roadmap of inquiry for reflective deliberation as institutions chart their own courses in this complex terrain.

If repeated with varying expert panellists, findings may vary to a certain extent; thus, further research with a wider range of stakeholders may be necessary for additional validation.

While grounded within the theoretical underpinnings of the field, the tool holds practical utility for stakeholders to develop bespoke policies and critically re-examine existing frameworks.

As texts produced by students using English as an additional language are at risk of being wrongly accused of GenAI-assisted plagiarism, owing to the limited efficacy of text classifiers such as Turnitin, the policy recommendations encapsulated in the blueprint aim to reduce potential bias and unfair treatment of students.

The novel blueprint represents a step towards bridging concerning gaps in policy responses worldwide and aims to spark discussion and further much-needed scholarly exploration to this end.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.