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Previous research has focused on the outcomes of telework, investigating the advantages and disadvantages of teleworking for employees. However, these investigations do not examine whether there are differences between teleworkers when evaluating the advantages and disadvantages of teleworking. The aim of this study is to identify of distinct classes of teleworkers based on the advantages and disadvantages that teleworking has for them.

This study used secondary survey data collected by the Spanish National Statistics Institute (INE). A sample of 842 people was used for this study. To identify the distinct classes of teleworkers, their perceived advantages and disadvantages of teleworking were analyzed using latent class analysis.

Three different classes of teleworkers were distinguished. Furthermore, sociodemographic covariates were incorporated into the latent class model, revealing that the composition of the classes varied in terms of education level, household income, and the amount of time spent on teleworking per week. This study also examined the influence of these emergent classes on employees’ experience of teleworking.

This study contributes to previous research investigating if telework is advantageous or disadvantageous for teleworkers, acknowledging that teleworkers are not identical and may respond differently to teleworking.

This paper focuses on patterns of film consumption within cultural consumption more broadly to assess trends in consumerism such as eclectic consumption, individualised consumption and omnivorous/univorous consumption and whether economic background and status feature in shaping cultural consumption. We focus on film because it is widely consumed, online and offline, and has many genres that vary in terms of perceived artistic and entertainment value. In broad terms, film is differentiated between mainstream commercially driven film such as Hollywood blockbusters, middlebrow “feel good” movies and independent arthouse and foreign language film. Our empirical statistical analysis shows that film consumers watch a wide range of genres. However, films deemed to hold artistic value such as arthouse and foreign language feature as part of broad and wide-ranging pattern of consumption of film that attracts its own dedicated consumers. Though we found that social and economic factors remain predictors of cultural consumption the overall picture is more complex than a simple direct correspondence and perceptions of other cultural forms also play a role. Those likely to consume arthouse and foreign language film consume other film genres and other cultural forms genres and those who “prefer” arthouse and foreign language film have slightly more constrained socio-economic characteristics. Overall, we find that economic and cultural factors such income, education, and wider consumption of culture are significant in patterns of film consumption.

At the beginning of the 21st century, a new class of information workers, the “information have-less” has risen. This class of workers alleviates the influence of information and communication technologies (ICTs) revolution on poverty and unemployment. The purpose of this study is to investigate the presence of this class of workers in Egypt and assess the size and potential growth of this category of workers.

The study clarifies the conceptual framework of the new division of labor, in the information age. The Central Agency for Public Mobilization and Statistics, American Chamber of Commerce in Egypt, Ministry of Communications and Information Technology and Information and Decision Support Center websites provided secondary data for this study. These data are used to assess the size of “the information have less” in Egypt.

The division of work and class, in the 21st century, depends on the level of skills possessed to work with ICTs. So, class and labor nowadays could be divided into self-programmable labor (Innovators). Information have-less labor class, adding value to the economy by learning skills and presenting repetitive work. Generic labor class, who cannot work with ICTs, and work in jobs, that do not need computers or other ICTs. The study has shown that the “information have-less” labor class is present in Egypt since the beginning of the 21st century, in all its categories; entrepreneurism, the service sector and the manufacturing sector. There are approximately 50% of this labor class in the service sector and only 13% of the information have-less works in manufacturing sector despite the great opportunities that Egypt has to expand manufacturing to absorb more employment. The inclusion of information technology (IT), in all domains, has not decreased employment in Western countries but has reallocated information have-less employment toward the service sector, and there would probably be the same effect in Egypt.

The study highlights the need for Egyptian policymakers to encourage the manufacturing and service sectors to provide huge working opportunities. The Egyptian government has to change the educational policies, at all stages, to include digital learning skills so IT can be incorporated in a wide range of economic activities. Further research includes: conducting a survey to measure the contribution of the entrepreneurial part of the information have-less employment in Egypt. In addition, a model may be developed, by the researcher to examine the reallocation of employees in Egypt.

Studying employment, in Egypt, using the conceptual framework of the information age is rarely being done.

This study aims to investigate the associations among decent work (DW), capabilities and the flourishing of employees in a South African context.

A cross-sectional survey was conducted with a convenience sample (N = 436) of early childhood development practitioners from two South African provinces. A demographic questionnaire, the Decent Work Scale, the Capability Set for Work Questionnaire and the Flourishing-at-Work Scale were administered.

Latent class analysis showed four capability sets: robust, relational, knowledge/skills and weak capability sets. Employees with a robust capability set were more inclined to report DW than those with knowledge/skills and weak capability sets. Employees with a weak capability set were significantly less inclined to report organisational values that complement family and social values than the other three capability sets. Employees with a robust capability set reported significantly higher emotional well-being (EWB), psychological well-being (PWB) and social well-being (SWB) levels than those with relational, knowledge/skills and weak capability sets. DW was significantly related to EWB, PWB and SWB.

This study contributes to the literature regarding DW, capabilities and flourishing of employees in a non-western, educated, industrialized, rich and democratic and non-POSH context. The study highlights the need for well-being policies that focus on DW and the capabilities of people in disadvantaged positions. These together would strengthen their agency for converting capabilities into well-being.

This paper aims to discuss the development of a defect classification system that can be used to detect and classify powder bed surface defects from captured layer images without the need for specialised computational hardware. The idea is to develop this system by making use of more traditional machine learning (ML) models instead of using computationally intensive deep learning (DL) models.

The approach that is used by this study is to use traditional image processing and classification techniques that can be applied to captured layer images to detect and classify defects without the need for DL algorithms.

The study proved that a defect classification algorithm could be developed by making use of traditional ML models with a high degree of accuracy and the images could be processed at higher speeds than typically reported in literature when making use of DL models.

This paper addresses a need that has been identified for a high-speed defect classification algorithm that can detect and classify defects without the need for specialised hardware that is typically used when making use of DL technologies. This is because when developing closed-loop feedback systems for these additive manufacturing machines, it is important to detect and classify defects without inducing additional delays to the control system.

This paper aims to develop predictive methods, based on recurrent neural networks, useful to support facility managers in building maintenance tasks, by collecting information coming from a computerized maintenance management system (CMMS).

This study applies data-driven and text-mining approaches to a CMMS data set comprising more than 14,500 end-users’ requests for corrective maintenance actions, collected over 14 months. Unidirectional long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) recurrent neural networks are trained to predict the priority of each maintenance request and the related technical staff assignment. The data set is also used to depict an overview of corrective maintenance needs and related performances and to verify the most relevant elements in the building and how the current facility management (FM) relates to the requests.

The study shows that LSTM and Bi-LSTM recurrent neural networks can properly recognize the words contained in the requests, thus correctly and automatically assigning the priority and predicting the technical staff to assign for each end-user’s maintenance request. The obtained global accuracy is very high, reaching 93.3% for priority identification and 96.7% for technical staff assignment. Results also show the main critical building elements for maintenance requests and the related intervention timings.

This work shows that LSTM and Bi-LSTM recurrent neural networks can automate the assignment process of end-users’ maintenance requests if trained with historical CMMS data. Results are promising; however, the trained LSTM and Bi-LSTM RNN can be applied only to different hospitals adopting similar categorization.

The data-driven and text-mining approaches can be integrated into the CMMS to support corrective maintenance management by facilities management contractors, i.e. to properly and timely identify the actions to be carried out and the technical staff to assign.

The improvement of the maintenance of the health-care system is a key component of improving health service delivery. This work shows how to reduce health-care service interruptions due to maintenance needs through machine learning methods.

This study develops original methods and tools easily integrable into IT workflow systems (i.e. CMMS) in the FM field.

This study aims to gain a first explorative view on what intrinsic/extrinsic attributes a generic cup for hot beverage should possess to be perceived as eco-friendly, and how some attributes of a hot beverage could influence consumers’ purchase decision and willingness to pay (WTP) for that beverage dispensed by vending machines (VMs).

A questionnaire was developed in 2021 and sent to all students of an Italian university campus. For the first goal, students were asked to assign a score to some eco-friendly intrinsic/extrinsic attributes using a five-point Likert scale. For the second aim, a choice experiment with six scenarios was developed.

Both intrinsic and extrinsic attributes play a key role in shaping students’ opinions and preferences. Results indicate that students are attracted by the idea of a cup that communicates its environmental properties through corresponding labels and information, and it is made by materials that guarantee biodegradability, recyclability or reusability.

The research represents the first academic attempt to provide a first consumers’ viewpoint on the importance of eco-friendly attributes of cups for hot beverages able to influence consumers’ perceptions and consumption choices of hot beverages dispensed by VMs.

This study focused on identifying measurable constructs of a restorative classroom and appropriate metrics to measure those constructs through content validity analysis of a direct observation tool. The tool was designed to assess restorative practices implementation in the classroom in the context of professional development supporting teachers in a fundamental reorientation towards non-punitive discipline.

The authors administered a 30-item survey to a panel of 14 experts in restorative practices implementation in schools asking them to provide quantitative and qualitative feedback on the tool's content, metrics, and utility for building teachers' skill and confidence in promoting a restorative classroom. The authors calculated item-level content validity indices and scale-level content validity indices. To interpret findings, the authors applied acceptability criteria recommended in the literature. The authors used qualitative coding to analyze qualitative responses and contextualize quantitative findings.

Quantitative results indicated that the tool's structure and measures of teacher behavior were acceptable. The student behavior scale did not meet the acceptability criterion. Qualitative feedback indicated that observation and later co-reflection on teachers' use of specific restorative skills was deemed helpful to teacher implementation of restorative practices. Observations of student behaviors, however, needed to be broadened to emphasize student voice and agency and the quality of student interactions.

Novel approaches to measurement are needed to facilitate teacher implementation of restorative practices as schools adopt those practices to promote equitable student agency, engagement and belonging in a pivotal shift from existing punitive discipline systems.

This paper investigates the determinants of compulsive buying behaviour (CBB) and ascertains the effect of CBB on the propensity of an individual to be dependent on loans and fall into financial trouble. The study additionally examines the moderating effect of financial management on the hypothesized relationships.

The survey method of research was adopted using questionnaires as the principal means of data collection. The predicted relationships of the study were tested using the partial least square structural equation modelling technique.

The authors’ results suggest materialism, socioeconomic status and financial management skills of an individual are significant predictors of CBB. The authors also find CBB to be positively associated with loan dependence and the authors’ analysis suggests financial management skills moderate the hypothesized relationships.

Findings of this study suggest buying compulsively increases the risks of over-dependence on loans and can be indirectly associated with the risk of individuals falling into financial trouble.

The findings highlight the adverse effects of CBB on loan dependence and financial trouble and the moderating effect of financial management on the dominant factors that influence CBB.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.