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Research has established the prevalence and relevance of moral injury in healthcare workers, though less attention has been paid to the different classes of potentially morally injurious events (PMIEs) experienced by this population and their impact. This exploratory study sought to examine the frequency of self- and other-generated PMIE classes and their associations with demographic characteristics and well-being outcomes among mental healthcare staff.

Secondary analysis of data drawn from two cross-sectional surveys of 267 frontline and leadership staff from mental healthcare settings in the UK was conducted. Responses on the Moral Injury Events Scale and the Short Professional Quality of Life Scale were extracted for analysis.

Betrayal by others was most frequently endorsed (61.8%), whilst self-transgressions were least frequently reported (25.5%). After controlling for the number of PMIE classes experienced, betrayal significantly predicted secondary traumatic stress (p = 0.01) and burnout (p = 0.04). Additionally, other transgressions significantly predicted secondary traumatic stress (p = 0.008). The predictive effects of self-transgressions on burnout, secondary traumatic stress and compassion satisfaction were all nonsignificant after controlling for the number of PMIE classes experienced.

Findings highlight differences in the frequency and impact of self and other PMIEs experienced by healthcare professionals. Reducing cumulative exposure to differential PMIE classes appears to be of critical importance to improving occupational well-being in this group.

To our knowledge, this study is the first to explore the associations between PMIE classes and occupational well-being in a mental healthcare population, inclusive of frontline and leadership staff.

Explore how the degree of humanization affects user misconduct, and provide effective misconduct prevention measures for the wide application of artificial intelligence in the future.

Based on the “Uncanny Valley theory”, three experiments were conducted to explore the relationship between the degree of humanization of service machines and user misbehavior, and to analyze the mediating role of cognitive resistance and the moderating role of social class.

There is a U-shaped relationship between the degree of humanization of service machines and user misbehavior; Social class not only regulates the main effect of anthropomorphism on misbehavior, but also regulates the intermediary effect of anthropomorphism on cognitive resistance, thus affecting misbehavior.

The design of the service robot can be from the user’s point of view, combined with the user’s social class, match different user types, and provide the same preferences as the user’s humanoid service robot.

This study is an important reference value for enterprises and governments to provide intelligent services in public places. It can prevent the robot from being vandalized and also provide users with a comfortable human-computer interaction experience, expanding the positive effects of providing smart services by government and enterprises.

This study avoids and reduces users' misbehavior towards intelligent service robots, improves users' satisfaction in using service robots, and avoids service robots being damaged, resulting in waste of government, enterprise and social resources.

From the perspective of product factors to identify the inducing factors of improper behavior, from the perspective of social class of users to analyze the moderating effect of humanization degree and user improper behavior.

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.

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.

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 chapter challenges middle-class bias in work-life literature by examining work-life balance dynamics through a social class perspective. It reveals class-based disparities in physical, temporal, and psychological outcomes, including the role of economic capital in work-life balance and the challenges encountered by the socially mobile in achieving psychological balance. It emphasizes the need to acknowledge social class implications for work-life balance and urges organizations to address class-based inconsistencies and inequalities in their practices.

The purpose of this study is to provide the location of natural disasters that are poured into maps by extracting Twitter data. The Twitter text is extracted by using named entity recognition (NER) with six classes hierarchy location in Indonesia. Moreover, the tweet then is classified into eight classes of natural disasters using the support vector machine (SVM). Overall, the system is able to classify tweet and mapping the position of the content tweet.

This research builds a model to map the geolocation of tweet data using NER. This research uses six classes of NER which is based on region Indonesia. This data is then classified into eight classes of natural disasters using the SVM.

Experiment results demonstrate that the proposed NER with six special classes based on the regional level in Indonesia is able to map the location of the disaster based on data Twitter. The results also show good performance in geocoding such as match rate, match score and match type. Moreover, with SVM, this study can also classify tweet into eight classes of types of natural disasters specifically for the Indonesian region, which originate from the tweets collected.

This study implements in Indonesia region.

(a)NER with six classes is used to create a location classification model with StanfordNER and ArcGIS tools. The use of six location classes is based on the Indonesia regional which has the large area. Hence, it has many levels in its regional location, such as province, district/city, sub-district, village, road and place names. (b) SVM is used to classify natural disasters. Classification of types of natural disasters is divided into eight: floods, earthquakes, landslides, tsunamis, hurricanes, forest fires, droughts and volcanic eruptions.

This study examined the factors influencing willingness-to-repurchase for each class of airline service, and integrate the constructs of service quality, satisfaction and willingness-to-repurchase which were rooted on Engel-Kollat-Blackwell (EKB) model. The study focuses on the domestic and international arrival of passengers at Murtala Muhammed International Airport in Lagos and Nnamdi Azikwe International Airport in Abuja. Information was gathered from domestic and foreign passengers who had post-purchase experience and had used the airline's services more than once. The survey data were obtained concurrently from arrival passengers at two major international airports using an electronic questionnaire through random and purposive sampling techniques. The data was analysed using the ordinal logit model and structural equation model. From the 606 respondents, 524 responses were received but 489 responses were valid for data analysis and reporting and were obtained mostly from economy and business class passengers. The study found that the quality of seat pitch, allowance of 30 kg luggage permission, availability of online check-in 24 hours before the departing flight, quality of space for legroom between seats, and the quality of seats that can be converted into a fully flatbed are the major service factors influencing willingness-to-repurchase economy and business class tickets. Also, it was found that passengers' willingness to repurchase is influenced majorly by service quality, but not necessarily influenced by satisfaction. These results reflect the passengers' consciousness of COVID-19 because the study was conducted during the heat of COVID-19 pandemic. Recommendations were suggested for airline management based on each class.

This chapter explores the writings of Pakistani sociologist Hamza Alavi, especially on the post-colonial state, ethnicity, peasantry and kinship relations. In contradistinction to most (partial) uptakes of Alavi, I evaluate his work as a whole in order to shed light on its continuities and discontinuities. I demonstrate both the strengths and pitfalls of Alavi's theorisation of the post-colonial state, mode of production and ethnicity by placing him in context of wider Marxist debates at the time. I then suggest that Alavi's other work (e.g. on the peasantry and kinship relations) may serve to complement the weaknesses of the former. Thus, by reading Alavi contra Alavi, I advocate for an ‘integral’ perspective on the relations between civil and political society, arguing for a conjunctural awareness of mediations between the same, and their imbrications with differentiated relations of class, ethnicity and kinship.

The purpose of this paper is threefold: first, to examine the influence of the quality of online classes on student satisfaction and student loyalty; second, to explore the mediating role of student participation between online classes and student satisfaction; third, to examine the mediating role of student satisfaction between online classes and student loyalty.

The primary data is collected from the postgraduate students in Jammu and Kashmir, India. A descriptive research design was applied to collect the primary data via Google questionnaire with the COVID-19 pandemic in mind to maintain social distance.

The result reveals that online classes and student participation positively affect student satisfaction. Student participation is a partial mediator between online classes and student satisfaction, with 0.393 Variance Accounted For (VAF). In comparison, it is a full mediator between online classes and student loyalty with 0.96 VAF.

The current study was restricted to postgraduate students, while future research could include research scholars, graduate students, and others. Only government institutions in Jammu and Kashmir were employed for the present study. The study may eventually be expanded to include private universities and more states.

The suggested approach can help teachers, students, policymakers and administrators. The study emphasizes the crucial elements of student engagement and happiness, which must be prioritized to improve online teaching and learning effectiveness and win over students' loyalty.

The study tested the conceptual framework of an integrated approach connecting online classes, student participation, student satisfaction and student loyalty. The study was conducted during the COVID-19 pandemic lockdown period, as it was the need of the hour to come out of such a crisis in the future. The present study can also provide a roadmap for transformation from physical to virtual classrooms.