bluuebunny/crossref_metadata_2025_split huggingface dataset:
Secondary school teachers' beliefs and needs about hikikomori and social withdrawn students
Authors: Concina Eleonora , Frate Sara , Biasutti Michele 
Published: January 2024

PurposeHikikomori is a multidimensional condition, characterized by voluntarily social withdrawal, impacting the relational dimension of life. The current study aims to examine secondary school teachers' beliefs, knowledge and needs on hikikomori and students' social withdrawal.Design/methodology/approachA qualitative method consisting of a semi-structured interview is adopted with 22 Italian secondary school teachers. The interview questions are focused on the beliefs, the profile, the conditions and the role of the school for adolescents socially retired.FindingsData are analysed using content analysis based on the grounded theory framework. Forty-nine codes emerged from the inductive analysis, which were sorted into the following categories: (1) Characteristics of hikikomori and social withdrawal; (2) Origin, causes and consequences; (3) Sources of information; (4) Socio-relational modalities; (5) Teachers' needs and role of the school. Teachers are aware of the educational issues and risks related to hikikomori and claim for more institutional support. Teachers reconsider the way of working in class for preventing the risk of self-isolation, supporting the development of social and emotional skills, and encouraging collaboration and positive exchanges among students. Participants mention a personalized student-centred method where families and external agencies support the school system.Originality/valueAlthough several clinical and psychological interventions have been developed for treating the hikikomori’s self-isolation and concomitant mental disorders, few plans have been implemented for reducing the risk of adolescents' social withdrawal. Preventing hikikomori is crucial as well as to investigate the role and the needs of school teachers, and the current study has tried to explore these.
Street-level bureaucrats in the professional context of primary care social services in Spain
Authors: Sánchez-Castiñeira Sergio 
Published: January 2024

PurposeThis study aims to identify the street-level approaches of professional workers in complex public social service organisations when attending to social assistance claimants.Design/methodology/approachThe study employed a multifaceted approach comprising documentary analysis, semi-structured individual interviews (17) and focus group discussions (8) with qualified frontline social workers from primary care social services in Tarragona.FindingsSocial workers embodied three specific sets of cognitive, normative and emotional dispositions when attending to people with low incomes. First, the compassionate approach conceives clients as defensive regarding social services and emotionally vulnerable because of deprivation. Second, the instructional approach frames clients as being baffled by a new, precarious, institutional and economic context. They also lack information, abilities and the proper mindset to conceive of and attain available welfare and occupational resources. Third, the enforcement approach tends to define clients as suspicious, trying to obtain an excessive and unfair advantage of the welfare system that would eventually hamper their social opportunities.Originality/valueResearch thus far has tended to define public social assistance programmes in Southern welfare state contexts as mostly inefficient and hardly relevant residual social policies. The street-level approach shows that social workers try to resist the mere administrative processing of benefits, which is a professionally troubling and organisationally unsustainable way to proceed. They attempt to help clients by providing inclusive content in order to implement their benefits.
Theorizing Islamic entrepreneurship from an Islamic governance perspective
Authors: Tarip Iznan 
Published: January 2024

Purpose
Research on Islamic entrepreneurship (IE) is growing but with difficulty. After reviewing six IE models, several problematic inconsistencies were identified, which can be categorized into three: sporadic objectives of IE, diverging core components and inconsistent levels of analysis. This study aims to articulate a synthesized IE model.

Design/methodology/approach
An Islamic critical realist synthesis is used by combining six IE models and situating them within an Islamic system of governance. Specifically, the Islamic governance conceptual matrix is used to offer an objective template.

Findings
A synthesized model of IE within an Islamic system of governance comprises the following: the objectives of IE are not to be confined to only financial sustainability, but to achieve all five Maqās.id of the Sharī’ah (i.e. preservation and promotion of faith, life, intellect, posterity and wealth); models of IE should cover the individual, group, organizational, societal, state and international levels for comprehensiveness; tawhīd is crucial to distinguish between IE and conventional entrepreneurship; halal IE activities can be further categorized into obligatory, recommended, permissible and reprehensible; Islamic values are to be differentiated from cultural values; and both Islamic and cultural values shape IE and vice versa.

Research limitations/implications
Limited number of models of IE were included in this synthesis. Although the resulting synthesized model is extensive, additional models of IE can be built to extend or even challenge it.

Originality/value
A novel model of IE within an Islamic system of governance is presented.
Joint torque prediction of industrial robots based on PSO-LSTM deep learning
Authors: Xiao Wei , Fu Zhongtao , Wang Shixian , Chen Xubing 
Published: January 2024

Purpose
Because of the key role of joint torque in industrial robots (IRs) motion performance control and energy consumption calculation and efficiency optimization, the purpose of this paper is to propose a deep learning torque prediction method based on long short-term memory (LSTM) recurrent neural networks optimized by particle swarm optimization (PSO), which can accurately predict the the joint torque.

Design/methodology/approach
The proposed model optimized the LSTM with PSO algorithm to accurately predict the IRs joint torque. The authors design an excitation trajectory for ABB 1600–10/145 experimental robot and collect its relative dynamic data. The LSTM model was trained with the experimental data, and PSO was used to find optimal number of LSTM nodes and learning rate, then a torque prediction model is established based on PSO-LSTM deep learning method. The novel model is used to predict the robot’s six joint torque and the root mean error squares of the predicted data together with least squares (LS) method were comparably studied.

Findings
The predicted joint torque value by PSO-LSTM deep learning approach is highly overlapped with those from real experiment robot, and the error is quite small. The average square error between the predicted joint torque data and experiment data is 2.31 N.m smaller than that with the LS method. The accuracy of the novel PSO-LSTM learning method for joint torque prediction of IR is proved.

Originality/value
PSO and LSTM model are deeply integrated for the first time to predict the joint torque of IR and the prediction accuracy is verified.
Unlocking the full potential of reality capture for construction project cost monitoring: a mixed review
Authors: Fobiri Godfred , Musonda Innocent , Muleya Franco 
Published: January 2024

Purpose
Digital data acquisition is crucial for operations in the digital transformation era. Reality capture (RC) has made an immeasurable contribution to various fields, especially in the built environment. This paper aims to review RC applications, potentials, limitations and the extent to which RC can be adopted for cost monitoring of construction projects.

Design/methodology/approach
A mixed-method approach, using Bibliometric analysis and the PRISMA framework, was used to review and analyse 112 peer-reviewed journal articles from the Scopus and Web of Science databases.

Findings
The study reveals RC has been applied in various areas in the built environment, but health and safety, cost and labour productivity monitoring have received little or no attention. It is proposed that RC can significantly support cost monitoring owing to its ability to acquire accurate and quick digital as-built 3D point cloud data, which contains rich measurement points for the valuation of work done.

Research limitations/implications
The study’s conclusions are based only on the Scopus and Web of Science data sets. Only English language documents were approved, whereas others may be in other languages. The research is a non-validation of findings using empirical data to confirm the data obtained from RC literature.

Practical implications
This paper highlights the importance of RC for cost monitoring in construction projects, filling knowledge gaps and enhancing project outcomes.

Social implications
The implementation of RC in the era of the digital revolution has the potential to improve project delivery around the world today. Every project’s success is largely determined by the availability of precise and detailed digital data. RC applications have pushed for more sustainable design, construction and operations in the built environment.

Originality/value
The study has given research trends on the extent of RC applications, potentials, limitations and future directions.
Building a knowledge sharing climate amid shadows of sabotage: a microfoundational perspective into job satisfaction and knowledge sabotage
Authors: Perotti Francesco Antonio , Rozsa Zoltan , Kuděj Michal , Ferraris Alberto 
Published: January 2024

Purpose
Drawing on the microfoundations theory and rational choice sociology, this study aims to investigate knowledge-sharing microfoundations through knowledge sabotage behaviours in the workplace. As such, it aims to shed light on the adverse impact of knowledge sabotage on a knowledge-sharing climate.

Design/methodology/approach
As a quantitative deductive study, it is based on information collected from 329 employees of European companies by self-administered online surveys. Data validity and reliability has been assessed through a confirmatory factor analysis, and data analysis was carried out by using a covariance-based structural equation modelling technique.

Findings
The findings from the empirical investigation supported the baseline hypotheses of the multilevel conceptual model, which is the positive relationship between organizational trust and environmental knowledge sharing. Then, recurring to a microfoundational exploration, this study supports the mediating indirect effect of job satisfaction and knowledge sabotage in affecting knowledge sharing as a social outcome.

Research limitations/implications
This study concurs to broaden knowledge-sharing awareness among scholars and practitioners, by focusing on knowledge sabotage as its most pernicious counterproductive behaviour. Furthermore, this research provides valuable guidance for the future development of research based on multilevel investigations.

Originality/value
This study builds on the need to explore the numerous factors that affect knowledge sharing in economic organizations, specifically focusing on knowledge sabotage. Adapting Coleman’s bathtub, the authors advance the first multilevel conceptual model used to unveil the knowledge-sharing microfoundations from the perspective of a counterproductive knowledge behaviour.
Optimizing routes of municipal waste collection: an application algorithm
Authors: Ferrão Caroline Cipolatto , Moraes Jorge André Ribas , Fava Leandro Pinto , Furtado João Carlos , Machado Enio , Rodrigues Adriane , Sellitto Miguel Afonso 
Published: January 2024

PurposeThe purpose of this study is to formulate an algorithm designed to discern the optimal routes for efficient municipal solid waste (MSW) collection.Design/methodology/approachThe research method is simulation. The proposed algorithm combines heuristics derived from the constructive genetic algorithm (CGA) and tabu search (TS). The algorithm is applied in a municipality located at Southern Brazil, with 40,000 inhabitants, circa.FindingsThe implementation achieved a remarkable 25.44% reduction in daily mileage of the vehicles, resulting in savings of 150.80 km/month and 1,809.60 km/year. Additionally, it reduced greenhouse gas emissions (including fossil CO2, CH4, N2O, total CO2e and biogenic CO2) by an average of 26.15%. Moreover, it saved 39 min of daily working time.Research limitations/implicationsFurther research should thoroughly analyze the feasibility of decision-making regarding planning, scheduling and scaling municipal services using digital technology.Practical implicationsThe municipality now has a tool to improve public management, mainly related with municipal solid waste. The municipality reduced the cost of public management of municipal solid waste, redirecting funds to other priorities, such as public health and education.Originality/valueThe study integrates MSW collection service with an online platform based on Google MapsTM. The advantages of employing geographical information systems are agility, low cost, adaptation to changes and accuracy.
Experimental investigation of mechanical and comfort properties of banana/cotton blended woven fabrics
Authors: Kharbanda Amanpreet Kaur , Dasarathan Kamal Raj , Sinha S.K. , Senthil Kumar T. , Senthil Kumar B. 
Published: January 2024

Purpose
Through this study, four different types of woven fabric structures were created by using cotton/banana blends with a 70:30 ratio by varying the weaving specifications. This study aims to investigate the comfort and mechanical properties of these woven materials.

Design/methodology/approach
Taguchi L16 experimental design (5 factors and 4 levels) with response surface methodology tool was used to optimize mechanical and comfort characteristics. The yarn samples used in this study are cotton/banana with a blend ratio of 70:30. Fabric type (A), grams per square metre (GSM; B), yarn count (C), fabric thickness (D) and cloth cover factor (E) are the chosen process characteristics.

Findings
The highest tensile strength and tearing strength of the cotton/banana blended fabric samples were obtained as 326.3 N and 90.3 k.gf/cm, respectively. Similarly, the highest thermal conductivity and overall moisture management capacity values were found to be 0.6628 and 3.06 W/mK X10−4, respectively. The optimized process parameters for obtaining maximum mechanical properties were using canvas fabric structure, 182 GSM, 36s Ne yarn count, 0.48 mm fabric thickness and 23.5 cloth cover factor. Similarly, the optimized process parameters for obtaining maximum comfort properties were achieved using a twill fabric structure, 182 GSM, 32s Ne yarn count, 0.4 mm fabric thickness and 23 cloth cover factor.

Originality/value
In contrast to synthetic fabrics, banana fibre and its blended materials are significant ecological solutions for apparel and functional clothing. Products made from banana fibre are a sustainable and green alternative to conventional fabrics. Banana fibre obtained from the pseudostem of the plant has an appearance similar to ramie and bamboo fibres. Numerous studies showed that banana fibre could absorb significant moisture and be spun into yarn through ring and rotor spinning technology. On the other hand, this fibre can be easily combined with cotton, jute, wool and synthetic fibre. The present utilization of pseudostem of banana plant fibre is very minimal. This type of research improves the usability of bananas their blended fabrics as apparel and functional wear.
Multi-layers deep learning model with feature selection for automated detection and classification of highway pavement cracks
Authors: Elghaish Faris , Matarneh Sandra , Abdellatef Essam , Rahimian Farzad , Hosseini M. Reza , Farouk Kineber Ahmed 
Published: January 2024

PurposeCracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.Design/methodology/approachTo enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.FindingsThe study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.Practical implicationsWith an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.Originality/valueThe proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.
Effectiveness of LEP activities in enhancing EFL students' oral communication skills at King Khalid University
Authors: Mahdi Dawood Ahmed 
Published: January 2024

PurposeThis paper aims to investigate the effectiveness of Language Enhancement Program (LEP) activities in enhancing the oral communication skills of English as a Foreign Language (EFL) students at King Khalid University.Design/methodology/approachA mixed-method study design was used. Simple random sampling techniques were adopted to recruit the study participants. For the survey, a total of 58 male students were recruited and for interviews, 20 male students were selected. The 58 participants were randomly divided into experimental and control groups. The traditional method was adopted to teach the control group, while LEP activities were conducted to teach the experimental group.FindingsA post-test was conducted to examine the oral communication skills of the participants of the experimental group. The scores of both groups were analyzed using a t-test value at a significant level of 0.05. The content analysis method was adopted to assess the enhancement of the oral communication of the students enrolled in the LEP. The questionnaires and interview results showed that the LEP program has a central focus on improving students' oral communication skills.Research limitations/implicationsIt is limited to the EFL students at King Khalid University.Practical implications LEP is a good program and can be implemented in Saudi Universities.Social implicationsStudents can interact with one another through the LEP activities that promote their English proficiency as well as their personality characteristics.Originality/valueThe paper spells out the role of LEP activities in improving students' oral communication in English and students' opinions about LEP activities in enhancing their English language in different types of communicative contexts. Further, it suggests some pedagogical implications for overcoming the difficulties faced by EFL students in various communicative contexts.
Talent management and employee ambidexterity: the moderating role of learning organization
Authors: Akbar Hinadi , Anas Mohammad 
Published: January 2024

Purpose
This study aims to examine the influence of the talent management (TM) process on employee ambidexterity (EA) and the moderating role of learning organizations in Indian IT and ITes organizations.

Design/methodology/approach
The study is descriptive and based on empirical data from 390 IT and ITES employees from India. Data were collected using three valid and reliable questionnaires. Data were analysed using partial least squares structural equation modelling.

Findings
The findings show that the TM process significantly impacted EA. The moderating effects of the four dimensions of learning organization (LO) on the relationship between the TM process and EA were also noteworthy, even though no direct association was found to be significant. Regarding demographic variables, male and female employees do not vary considerably in their perception of TM process and EA in LO.

Originality/value
The study’s novelty lies in creating and discussing a synthesis of exploration and exploitation stemming from EA in learning organization.
Learning from coproducing digital courses in sexual health in higher education in Norway
Authors: Areskoug Josefsson Kristina , Lunde Gerd Hilde 
Published: January 2024

Purpose
Sexual health is insufficiently addressed in health care and higher education, which can lead to lower quality of life and negative health outcomes. To improve the situation, it is necessary to address both the needs of patients and professionals and collaboratively engage in finding sustainable solutions. The purpose of this paper is to explore the feasibility and value of large-scale digital coproduction in higher education.

Design/methodology/approach
A study of a project that developed seven interprofessional, digital master-level courses covering different topics related to sexual health. The project was performed through digital coproduction in higher education, with over 100 persons with various backgrounds working together online in designing content and novel digital learning activities.

Findings
Large-scale digital coproduction in higher education is feasible and valuable, but the process demands sensitive leadership, understanding of coproduction processes and willingness to learn from each other. To meet the demands from practice it is important to understand the complexity, ever-changing and unpredictable working life changes which, in turn, demands engagement in continuous learning, training activities and the need for formal education.

Originality/value
The study provides learning of the feasibility of the value of large-scale digital coproduction in higher education, which is a novel way of working in higher education.