bluuebunny/crossref_metadata_2025_split huggingface dataset:
Low-resource multi-granularity academic function recognition based on multiple prompt knowledge
Authors: Liu Jiawei , Xiong Zi , Jiang Yi , Ma Yongqiang , Lu Wei , Huang Yong , Cheng Qikai 
Published: August 2024

Purpose
Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Design/methodology/approach
Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Findings
Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

Originality/value
In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.
The determinants of digital competencies in accounting: an empirical study in Vietnam
Authors: Pham Tra Lam , Tran Thi Phuong Thanh , Dau Thi Kim Thoa , Tran Anh Hoa 
Published: August 2024

PurposeThis study investigates the determinants of digital competencies (DC) of students and professionals in accounting.Design/methodology/approachA total of 278 valid questionnaires were collected from 145 accounting students and 133 professionals to test the relationship between some characteristics of learners through the t-test and two-way ANOVA test.FindingsThe results showed a difference in DC between students and professionals. Whereas the characteristics of students do not influence their DC, gender and the number of short courses significantly affect their DC. Information and communication technologies (ICTs) should be provided in accounting programs as core competencies.Research limitations/implicationsA sample size that is not too large and does not include learners and professionals in the central and northern areas is problematic.Practical implicationsThe key findings support the development of policies for digital transformation in accounting.Originality/valueAccounting is one of the professions that must keep up with the changes in the world, which is in the process of rapid digitalization. Hence, this study is valuable for accounting practitioners, higher education institutions and managers to enhance DC continuously.
The conceptualisation of employability among higher education administrators in Sri Lanka’s turbulent labour market
Authors: Samaradivakara Chandrani , Bell Robin , Raghubansie Antonius 
Published: August 2024

PurposeEmployability is an established research theme in Western literature; however, in developing economies, the concept remains unclear and underresearched. This study addresses this lacuna by exploring how Sri Lankan higher education (HE) administrators conceptualise employability and which capitals they perceive as needing development to achieve employability.Design/methodology/approachThe research utilised semi-structured interviews with an expert panel responsible for leading the development of the employment agenda within Sri Lankan HE. The purposive sample included four Sri Lankan vice chancellors (VCs), representing leadership at 27% of the country’s publicly funded universities and the HE funding commission. The qualitative data collected were thematically analysed to identify how employability was conceptualised and the perceived employability skills and capitals required.FindingsThe findings demonstrated that employability was conceptualised as requiring transferable skills and job-specific occupational skills. This view of employability represents a narrow definition with an emphasis on skill development rather than longer-term capability building. The results show the applicability of Bourdieu’s (1986) capitals in the Sri Lankan HE context, with cultural and social capital and proficiency in the English language critical to meeting employability objectives.Originality/valueThis research addresses the shortage of research about the concept and requirements of employability in developing countries. Most employability studies have drawn on samples from students, lecturers and employers, whilst this study considers the phenomenon from the perspectives of strategic administrators in HE who guide the employability agenda in practice. These insights are important in informing policymakers to calibrate a more balanced approach by incorporating employability into the Sri Lankan HE curriculum and sector strategy.
Impact of digital logistics platforms to facilitate more regional food in the hospitality sector and communal catering
Authors: Mendoza Pardo Christine , Fikar Christian 
Published: August 2024

PurposeThis paper studies digitalization projects aiming to increase the share of regional food in the hospitality sector and communal catering. The focus is set on influencing factors and underlying feedback structures that arise through the digitalization of regional food supplies. The results can guide stakeholders to get a better understanding of key influencing factors and complexities from a holistic perspective.Design/methodology/approachA systems thinking approach is employed to model regional food networks. The influencing factors were found in the literature and in two model regions. Feedback loops and underlying structures were explained and validated through semi-structured interviews. Findings are visualized in causal loop diagrams (CLDs) and are used for theory development.FindingsThe presentation in CLDs helps understanding the influence of digital logistics platforms on the entire system of regional food supply and not just on isolated parts. Among others, cooperation, trust and digital solutions were identified as key success factors when aiming to increase the share of regional food in the hospitality sector and communal catering.Research limitations/implicationsThe work focused on the underlying feedback structures occurring in regional food supply in two, rural Bavarian regions in Germany with their unique geographical settings. Findings may, consequently, not be entirely transferable to other regions with varying characteristics. Further research needs to be done to see how much these regional parameters influence digital logistics platforms.Originality/valueThe paper contributes to the existing scientific literature by showing the impact coming from digital logistics platforms on regional food supply systems. Developing CLDs provides a basis for future work and facilitates discussion for researchers and practitioners to support future real-world implementations.
Medicine distribution pattern detection in pharmaceutical supply chains: a new Kth-proximity density-distance-based method
Authors: Delgoshaei Aidin , Ariffin Mohd Khairol Anuar Mohd 
Published: August 2024

Purpose
Medicine distribution logistics pattern in pharmaceutical supply chains is a hot topic, which aims to predict applicable and efficient medicine distribution patterns so that the medicine can be distributed effectively. This research aims to propose a new method, named density-distance method, that works based on Kth proximity using patient features (including age, gender, education, inherent diseases, systemic diseases and disorders); geographical features (city, state, population, density, land area) and supply chain features (destination and transportation system).

Design/methodology/approach
The proposed method of this research consists of two main phases where in the first phase, quantitative data analytics will be carried out to find out the significant factors and their impacts on medicine distribution. Then, in the next phase, a new Kth-proximity density-distance-based method is proposed to determine the best locations for the wholesalers while designing a supply chain.

Findings
The findings show that the proposed method can effectively design a supply chain network using realistic features. In addition, it is found that while the distance-density aggregate index is applied, the wholesalers' locations will be different compared to classic supply chain designs. The results show that age, public hygiene level and density are the most influential during designing new supply chains.

Practical implications
The outcomes of this research can be used in the medicine supply chains to predict appropriate medicine distribution logistics patterns.

Originality/value
In this research, the machine learning method based on the nearest neighbor has been used for the first time in the design of the supply chain network.
Structural integrity assessment of an offshore platform using RB-FEA
Authors: Di Nicola Felice , Lonardi Graziano , Fantuzzi Nicholas , Luciano Raimondo 
Published: August 2024

PurposeThe paper aims to analyze the structural integrity of an existing offshore platform located in the Northern Adriatic Sea, followed by the topside decommissioning and the re-utilization of the jacket as a wind turbine support. The structural integrity assessment against the in-place and the long-term actions is accomplished by using a reduced basis finite element method (RB-FEA) software program assessing the capability of the jacket to be used as a support for wind turbines at the end of its life cycle as oil and gas (O&G) platform.Design/methodology/approachThe project starts by modeling the jacket, and subsequently, the structural analyses for the in-place loads in operative and extreme conditions are performed. Then, the fatigue analysis is carried out in order to define the cumulative damage necessary to evaluate the possibility to use the jacket as a wind turbine support.FindingsThe results show that the jacket, at the end of the service life as O&G platform, is able to withstand the loads produced by the installation of the wind turbine since the analyses are satisfied even with the conservative approach used which overestimates the thickness loss assuming a linear increasing value during the service life.Research limitations/implicationsBecause of the chosen approach, the study presents some limitations, especially concerning the real state of the platform which has been defined considering the thickness loss only. Additionally, a 1D model was used to perform the analyses, and hence, a 3D model could help in evaluating the critical points with higher precision.Practical implicationsThe assessment of the structure could be improved by modeling a digital twin of the asset allowing a real-time monitoring which, however, involves a huge amount of data to be processed, so a suitable simulation technology must be used.Originality/valueThe RB-FEA proposed by Akselos is suitable to perform the analyses speeding up the processing of the data even in real time.
“You are you, and I am me”: investigating inter-organisational collaboration between multiple emergency services
Authors: Zaghloul Fatema 
Published: August 2024

PurposeIn the past decade, financial austerity has brought significant pressure upon emergency services in the UK. For the British Government, one approach to alleviate this pressure was to increase collaborative efforts in core functions (e.g. information systems and technology) between multiple organisations. Despite the consensus that collaborations are key in addressing complex problems, the majority fail or become discontinued. This research explores the development of collaborative information infrastructures between one Police Force and two Fire and Rescue Services in the UK, with a specific focus on how the difference in culture, identities and rules and norms, can work in a collaborative emergency service environment.Design/methodology/approachThis study investigated an example of successful development of a collaborative information infrastructure within the context of public safety and, specifically, the technology-based structures that underpin information management. A case study approach was taken, combining semi-structured interviews, document analysis and site visits. The study used activity theory as a theoretical and analytical framework.FindingsThe research revealed that creating a shared identity is not essential in emergency services collaboration, and organisations may maintain their separate identity, given they address other elements of the activity system (e.g. leadership, motivated subjects). However, pursuing this strategy will create multiple tensions throughout the collaboration process.Originality/valueThe study contributes to the information systems literature concerning inter-organisational collaborations in the public sector providing a novel view to the implications of maintaining separate identities, and the significance of misalignment between interacting activity systems.
Why do Indian exporting firms borrow in foreign currency?
Authors: Bhoi Lilu 
Published: August 2024

Purpose
Since the liberalization policy of 1991, India has focused on export-led growth. However, the performance of international trade remains poor. This study aims to examine the role of credit constraints on the choice of Indian manufacturing firms to borrow in foreign currency. First, it explores the role of export activities in foreign currency borrowing (FCB). Second, it investigates how credit constraints forced these firms for foreign currency loans.

Design/methodology/approach
The study analysed data from 1,412 firms listed on the Bombay Stock Exchange in the manufacturing sector, covering the period from 1991 to 2022. A random effects probit model was used to examine the role of credit constraints on FCB, incorporating the influence of micro, small and medium enterprises (MSMEs) status and export activities. Additionally, a two-step system-generalized method of moment was used for robustness checks.

Findings
Export activities significantly influence FCB, with exporting firms showing a higher propensity to borrow foreign currency compared to domestically operating firms because of the increased funding needs of export activities. Larger firms are more likely to secure FCB than MSMEs, benefiting from collateral advantages. MSME exporting firms exhibit a higher tendency to borrow in foreign currency compared to large exporting firms.

Research limitations/implications
This study focuses on firm-level data and considers only demand-side credit constraints. It does not examine supply-side credit constraints affecting FCB.

Social implications
This study underscores the credit constraints faced by MSME exporters in the domestic market, leading them to rely on FCB. These insights are valuable for policymakers aiming to reduce MSMEs' dependency on FCB and enhance their export performance.

Originality/value
The findings highlight that MSME exporting firms are more inclined to borrow in foreign currency than their larger counterparts. This tendency is driven by the credit constraints MSMEs face because of asymmetric information and underdeveloped financial markets, which compel them to seek FCB.
Forecasting international tourist arrivals in South Korea: a deep learning approach
Authors: Zhang Siyu , Lin Ze , Yhang Wii-Joo 
Published: August 2024

Purpose
This study aims to develop a robust long short-term memory (LSTM)-based forecasting model for daily international tourist arrivals at Incheon International Airport (ICN), incorporating multiple predictors including exchange rates, West Texas Intermediate (WTI) oil prices, Korea composite stock price index data and new COVID-19 cases. By leveraging deep learning techniques and diverse data sets, the research seeks to enhance the accuracy and reliability of tourism demand predictions, contributing significantly to both theoretical implications and practical applications in the field of hospitality and tourism.

Design/methodology/approach
This study introduces an innovative approach to forecasting international tourist arrivals by leveraging LSTM networks. This advanced methodology addresses complex managerial issues in tourism management by providing more accurate forecasts. The methodology comprises four key steps: collecting data sets; preprocessing the data; training the LSTM network; and forecasting future international tourist arrivals. The rest of this study is structured as follows: the subsequent sections detail the proposed LSTM model, present the empirical results and discuss the findings, conclusions and the theoretical and practical implications of the study in the field of hospitality and tourism.

Findings
This research pioneers the simultaneous use of big data encompassing five factors – international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases – for daily forecasting. The study reveals that integrating exchange rates, oil prices, stock market data and COVID-19 cases significantly enhances LSTM network forecasting precision. It addresses the narrow scope of existing research on predicting international tourist arrivals at ICN with these factors. Moreover, the study demonstrates LSTM networks’ capability to effectively handle multivariable time series prediction problems, providing a robust basis for their application in hospitality and tourism management.

Originality/value
This research pioneers the integration of international tourist arrivals, exchange rates, WTI oil prices, KOSPI data and new COVID-19 cases for forecasting daily international tourist arrivals. It bridges the gap in existing literature by proposing a comprehensive approach that considers multiple predictors simultaneously. Furthermore, it demonstrates the effectiveness of LSTM networks in handling multivariable time series forecasting problems, offering practical insights for enhancing tourism demand predictions. By addressing these critical factors and leveraging advanced deep learning techniques, this study contributes significantly to the advancement of forecasting methodologies in the tourism industry, aiding decision-makers in effective planning and resource allocation.
Assessment of academic burnout among undergraduate medical and health sciences students: a survey study from the United Arab Emirates
Authors: Salama Rasha Aziz Attia , Iqbal Saima Javaid , Mohsin Mariam Mehwish , Mohsin Aisha Iram , Wadid Nihal Amir 
Published: August 2024

Purpose
This study aims to assess the prevalence of burnout among undergraduate medical and health science students and to identify the factors associated with its development.

Design/methodology/approach
A cross-sectional study was conducted involving students from Ras Al Khaimah Medical and Health Science University. The Maslach Burnout Inventory-Student Survey was used to evaluate three subscales of burnout. Ethical considerations were met, and informed written consent was obtained from participants. Bivariate and multiple logistic regression analyses were conducted to examine the relationship between demographic, personal and school environment factors and burnout.

Findings
Out of 453 students surveyed, the majority were females (74.8%) aged 18–20 years (60%). Significant levels of burnout were reported, with emotional exhaustion, depersonalization and reduced personal accomplishment affecting over half of the participants (69%). Factors such as being in the clinical years, long-distance commuting challenges and involuntary college selection were identified as predictors of burnout.

Research limitations/implications
While the study provides valuable insights, limitations may include the specific context of the university and potential biases in self-reported data.

Practical implications
The findings underscore the importance of raising awareness, maintaining work-life balance and implementing coping strategies to support student well-being and foster a positive learning environment. Suggestions include time management and seeking guidance from mentors or mental health professionals.

Originality/value
This study contributes to the understanding of burnout among medical and health science students, especially within the unique context of the United Arab Emirates, given the convergence of students from diverse nationalities and cultural backgrounds.
Exploring mixed-reality approaches for inclusive blended learning in higher education
Authors: Burnett Gary 
Published: August 2024

Purpose
This paper aims to investigate how mixed-reality (MR) can be used to enhance inclusivity in synchronous working activities where groups of people may be present either face-to-face or online – focusing on blended learning in the higher education (HE) context.

Design/methodology/approach
In a research methods module, 140 university master’s-level students were given the option to attend and engage in seminars/practical sessions either in a real lecture room or online through a virtual version of an equivalent environment, accessible on everyday devices. The MR platform provided two-way video “windows” enabling all students on the module to freely interact with staff and their peers. Attendance was recorded throughout the semester and survey data was captured after all sessions (33% response rate). In addition, student activity was video recorded within both the real and virtual spaces.

Findings
Attendance was high throughout the semester and students were very positive about this novel approach to their education. Students chose to either engage purely in-person, online or as a mixture of the two; performance on the module was not impacted by this decision. The survey highlighted several factors that impacted on students’ choice of learning space, including those related to intrinsic characteristics of individual students (e.g. desire for traditional/novel social interactions) and those related to extrinsic influences (e.g. weather and travel demands). Video analysis revealed numerous differences in the behaviours exhibited across the virtual and real spaces, related to individual versus team mentalities.

Practical implications
Conclusions are drawn regarding how MR can be used in HE and potentially other work-related contexts to enhance engagement, a sense of community and ultimately learning/productivity outcomes.

Originality/value
This study investigated how a large (>100) cohort of students responded to an MR blended learning experience across a whole semester. In this respect, to the best of the author’s knowledge, this is the first study to consider such issues at this scale in a university education setting.