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Crisis services personnel are frequently deployed around the globe under highly demanding conditions. This raises the need to better understand the deployment process and more especially, sustainable reintegration after deployment. Despite recent research efforts, the study of the post-deployment stage, more specifically the reintegration process, remains fragmented and limited. To address these limitations, this review aims at (1) describing how reintegration is conceptualised and measured in the existing literature, (2) identifying what dimensions are associated with the reintegration process and (3) identifying what we know about the process of reintegration in terms of timing and phases.

Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, the authors identified 5,859 documents across several scientific databases published between 1995 and 2021. Based on predefined eligibility criteria, 104 documents were yielded.

Research has primarily focused on descriptive studies of negative individual and interpersonal outcomes after deployment. However, this review indicates that reintegration is dynamic, multi-sector, multidimensional and dual. Each of its phases and dimensions is associated with distinct challenges.

To the authors’ knowledge, this is the first research that investigates reintegration among different crisis services and provides an integrative social-ecological framework that identifies the different dimensions and challenges of this process.

The purpose of this paper is to show that lesson study by including elements from music and music education can sustainably expand and improve the dialogical space for teaching and learning in higher education, especially for primary and secondary teacher education students.

For the first time under the topic “Lesson Study: Music in Dialogue,” corresponding study programs were prepared at the University College of Teacher Education, Lower Austria. The data material from which answers to research questions can be generated are the “Didactic Design Pattern” and classical research lesson planning, observation and discussion instruments. Moreover, discussion protocols of the reflection meetings offered insights the participants gained through sharing their experience of a series of lesson study cycles including focussed collaboration between mentors, teachers, teacher education students and primary school pupils.

Within the lesson study groups, the space for cooperation and dialogue widened considerably and the interest in the work and expertise of each other increased. Based on the principles of a “community of practice,” this study shows the positive effects of professional collaboration on primary and secondary teacher education students and a lasting impact on their pupils’ learning. Thereby, the dialogical principle was found to play a central and important role. In connection with music- and art-related processes, previous limitations in teaching and learning with music can be exceeded for pupils, teacher education students and teachers.

This study, therefore, provides new insights into questions of organization and implementation, as well as scientific and didactic support in professional learning communities.

So far, there has been little practitioner research through lesson study in the field of music education. In particular, lesson study enhancing the cooperation between music education and other subject areas through dialogical-integrative work has brought about knowledge and insights of great importance for the further development of an appropriate didactic approach in dialogic music education.

The International Network of Research Management Societies (INORMS) celebrated its 20^th anniversary in 2021. It was established to increase communication among research management societies. The need for a formal international research management community developed because there was (1) increased international funding of research, (2) the number of international research collaborations was growing, and (3) there was a need to understand research regulations in other countries. INORMS sought to address these issues through international congresses and by providing a forum for member societies to work more closely together on common issues. Membership in INORMS steadily increased over the years. The 20^th anniversary meeting was highlighted with the signing of the Hiroshima Statement that described a research manager’s principles and responsibilities, which include collegiality, inclusiveness, professionalisation, innovation, and accountability. This chapter summarises the factors that led to the formation of INORMS and its history.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

This study aimed to test some dynamics related to YouTube advertising clutter and viewers' attitudes towards YouTube channels operated by traditional television (TV) companies (These YouTube channels tend to have a greater ad clutter since their videos have embedded ads of programme sponsors in addition to regular YouTube advertisements).

Employing a positivistic, quantitative methodology, the study collected data through an online survey from a sample of 295 YouTube viewers in Sri Lanka. Data were analysed using the regression-based PROCESS macro, utilising the SPSS software.

Ad clutter has a negative impact on viewers' attitudes towards the YouTube channel, but only indirectly, through irritation. Skippability of advertisements does not moderate the relationship between ad clutter and ad irritation, but non-skippability increases ad irritation (direct relationship).

YouTube channel owners should pay careful attention to having an optimum level of advertising because clutter resulting in high ad irritation leading to a negative attitude towards the channel could reduce subscriptions. Offering an ad skippability option is unlikely to reduce irritation if there is high clutter.

Although ad clutter is well-researched, its impact on viewer responses to online media channels is uncommon. With YouTube poised to become a top ad revenue-generating medium, this study contributes by examining some dynamics related to ad clutter in a possibly high-clutter context.

The aim of this study was to investigate the effect of a discoursal approach on Iranian intermediate EFL learners’ reading comprehension ability. A Quick Placement Test (QPT) was used to select 60 intermediate EFL learners as the participants of this study. Then, they were divided into experimental and control groups. Each group consisted of 30 learners. Prior to the treatment, the participants of both groups were given a pre-test to determine their reading comprehension levels. The experimental group was exposed to the Hymes’ model. In the control group the researcher used a traditional approach for teaching the reading skill. Then a post-test was administered to both groups. An independent samples t-test between post-tests of the study and a paired-samples t-test between the pre-test and post-test of the groups of the study were run. The results of the study indicated that applying Hymes’ model improved the learners’ reading comprehension while the traditional approach did not.

ﻛﺎ ن اﻟ ﮭد ف ﻣ ن ھذه اﻟ د را ﺳﺔ ھو اﻟﺗﺣ ﻘﯾ ق ﻓ ﻲ ﺗﺄﺛﯾ ر اﻟﻧﮭ ﺞ اﻟﺗد ر ﯾﺑ ﻲ ﻋ ﻠ ﻰ ﻗد ر ة اﻟ ط ﻼ ب ا ﻹ ﯾ ر اﻧ ﯾﯾ ن ﻋ ﻠ ﻰ ﺗ ﻌﻠ م اﻟﻘ ر ا ءة واﻟ ﻛﺗﺎﺑ ﺔ ﻓﻲ اﻟﻠ ﻐﺔ ا ﻹﻧ ﺟﻠﯾ زﯾﺔ ﻛﻠ ﻐﺔ أ ﺟﻧﺑﯾﺔ. ﺗم ا ﺳﺗ ﺧدام ا ﺧﺗﺑﺎ ر ﺗ ﺣدﯾد اﻟ ﻣ ﺳﺗ و ى اﻟ ﺳرﯾ ﻊ (QPT) ( ﻟﻠﺗﺄ ﻛد ﻣ ن ﺗ ﺟﺎﻧ س ﻣﺗ ﻌﻠ ﻣ ﻲ اﻟﻠ ﻐﺔ اﻹ ﻧ ﺟﻠﯾ زﯾ ﺔ ﻛﻠ ﻐﺔ أ ﺟﻧﺑﯾ ﺔ اﻟذﯾ ن ﻛﺎﻧ وا ﻓ ﻲ ﻓ ﺻ ﻠﯾ ن د را ﺳﯾﯾ ن. ﺛم، ﺗم ﺗﻘ ﺳﯾ ﻣﮭم ﺑ ﺷﻛل ﻋﺷواﺋ ﻲ إﻟ ﻰ ﻣ ﺟﻣوﻋﺎ ت ﺗ ﺟرﯾﺑﯾ ﺔ وﻣراﻗﺑ ﺔ. ﻛل ﻣ ﺟﻣوﻋﺔ ﺗﺗﺄﻟ ف ﻣ 30 ﻣﺗ ﻌﻠ ﻣﺎ. ﻗﺑ ل اﻟ ﻌ ﻼج، ﺗم إ ﻋطﺎ ء اﻟ ﻣ ﺷﺎ رﻛﯾ ن ﻣ ن ﻛ ﻼ اﻟ ﻣ ﺟﻣوﻋﺗﯾ ن ﻗﺑ ل ا ﻻﺧﺗﺑﺎ ر ﻟﺗ ﺣدﯾد ﻣ ﺳﺗ وﯾﺎ ت اﻟ ﻔ ﮭم اﻟ ﻘ ر ا ءة ﻟ دﯾ ﮭم. ﺗ ﻌر ﺿ ت اﻟﻣ ﺟ ﻣو ﻋ ﺔ اﻟ ﺗ ﺟ ر ﯾﺑﯾ ﺔ ﻟﻧ ﻣو ذ ج اﻟ ﺗ ر اﺗﯾ ل . ﻓ ﻲ اﻟﻣ ﺟ ﻣو ﻋ ﺔ اﻟ ﺿ ﺎﺑ ط ﺔ، ا ﺳ ﺗ ﺧ دم اﻟ ﺑﺎ ﺣ ث اﻟﻣﻧ ﮭ ﺞ اﻟ ﺗﻘﻠﯾد ي ﻟﺗد ر ﯾ س ﻣﮭﺎ ر ة اﻟ ﻘ ر ا ءة. ﺛم ﺗدا ر ﺑ ﻌد ا ﻻ ﺧ ﺗﺑﺎ ر ﻟ ﻛ ﻼ اﻟ ﻔ ر ﯾﻘﯾ ن . ﺗم إ ﺟ ر ا ء ا ﺧ ﺗﺑﺎ ر ﻣ ﺳ ﺗﻘ ل ﻟ ﻌﯾﻧﺎ ت t ﺑﯾن ا ﻻ ﺧ ﺗﺑ ﺎ ر ﯾ ن اﻟﻼ ﺣ ﻘﯾ ن ﻟﻠد ر ا ﺳ ﺔ و ا ﺧ ﺗﺑﺎ ر t اﻟﻣﻘﺗ ر ن ﺑﺎﻟ ﻌﯾﻧﺎ ت ﺑﯾ ن ا ﻻ ﺧ ﺗﺑﺎ ر اﻟﻣ ﺳ ﺑ ق و ا ﻻ ﺧ ﺗﺑﺎ ر اﻟﻼ ﺣ ق ﻟ ﻣ ﺟ ﻣو ﻋ ﺎ ت اﻟد ر ا ﺳ ﺔ. أ ﺷ ﺎ ر ت ﻧﺗﺎﺋ ﺞ اﻟد ر ا ﺳ ﺔ إﻟﻰ أ ن ﺗ ط ﺑﯾ ق ﻧ ﻣو ذ ج ھ ﺎﯾ ﻣز ﻗد ﺣ ﺳ ن ﻣ ن ﻓ ﮭم اﻟ ﻘ ر ا ءة ﻟﻠﻣﺗ ﻌﻠ ﻣﯾ ن ﻓ ﻲ ﺣ ﯾ ن أ ن اﻟ ﻧ ﮭ ﺞ اﻟ ﺗﻘﻠﯾد ي ﻟ م ﯾﻔ ﻌل ذﻟ ك

This study aims to contribute to the body of scientific knowledge about teaching and promoting social entrepreneurship in higher education institutions (HEIs) based on a measurement before and after concluding an educational experience.

It tests hypotheses to draw conclusions from analyzing the pre- and post-test results of three study cases with different training experiences, to know the characteristics of the 304 participants.

The study indicated that incorporating transversal social entrepreneurship projects in various courses resulted in students feeling more capable regarding their social entrepreneurship potential.

The study presents the analysis of social entrepreneur training in three different curricular study cases. The information obtained adds value to social entrepreneurship education research that takes social entrepreneurship beyond business schools.

This paper aims to study the extent of use of artificial intelligence (AI) in the modern organization; to comprehend the changing nature of future jobs in the context of application of AI; and to study the impact of AI on the economy of the country with special reference to the job market. Given the critical scenario of labor intensive Indian economy, the paper intends to show how AI shall affect rather coexist with human intelligence or labor.

The research on implementation of AI in different industries and its effect on job market are at a nascent stage. There is a dearth of literature. Hence, this study followed a qualitative approach to have a better understanding of the research questions as Bhattacherjee (2012) confirms that employing an interpretive paradigm (qualitative analysis as the analysis of data, e.g. data from interview transcripts) is the more productive way to study social order and that it is achieved through “subjective interpretation of participants involved, such as by interviewing different participants and reconciling differences among their responses using their own subjective perspectives”. Sample selection: The selection technique utilized is purposive sampling. The respondents in this research are the general managers and HRs from different companies. A total of 14 senior professionals from various sectors were approached for the interview out of which seven people gave their consent to take interview. Seven senior HR professionals, mainly general managers and HRs from various sectors viz. oil and gas sector, manufacturing, healthcare, construction, media, power and energy and retail were interviewed to understand how they are using AI in their respective fields. Inclusion Criteria: (1) Generally, the people covered under the research are from the decision-making level of their companies so they are in a position to give strategic perspective as well as day to day implication of implementation of AI. (2) Respondents have adequate knowledge of the respective industry to which they belong. (3) Respondents have reasonable industry of dealing with Human Resource Management and national economy as a whole assessment tool and its administration procedures. A narrative approach was adopted to have a better understanding of the research questions and comprehend their views regarding implementation of AI in their respective companies. A semi structured open ended interview was administered to steer the discussion around the research questions. The respondents were interviewed over the phone and each respondent shared their stories. Analysis of data: The narrations were then transcribed by online transcriber website otter.ai.com. The common keywords as prescribed by the website are as: AI, strategy, learning and implementation. The extracts of the discussions are noted in the next segment of the paper. As and when required this research also used secondary data from the journals, literature available in the websites to understand the implementation of AI globally.

A country where the government itself admits 90% of its workforce belongs to informal sector and conspicuously exits a multi-faceted stark digital divide (Huberman, 2001; DiMaggio et al., 2001; Guillen, 2006; Servon, 2002) wherein gap of digital divide is significant between the rural and urban India (Dasgupta et al., 2002; Nath, 2001; Singh, 2007; Mahajan, 2003; Dutta, 2003) talking of educating, applying and implementing AI seems to be “ a distant dream” but an “ambiguous ambition ”

Prior to implementation of AI that India has to ensure, the basic hygiene factors of informal sector labor force like social security, 2008, low wages and lack of legal protection, unpaid overtime and occupational health problems, poor bargaining power, working without leave under coercion, child care issues and health ailments(for which mere legislation or statutarization is just a formality executed than taking real action) to take the majority of Indian workforce to attain the motivational factor to acquire the knowledge and skill of AI and to implement it.

The AI and its adoption are still at their embryonic stage in Indian companies. With the adoption of such sophisticated technology, in one side, the organizations are dreaming of efficiency, higher productivity and better organizational performance whereas on the other side requirement of changing skill sets and decreasing manpower, creating fear among the mass, which results in hard resistance against the implementation process of AI. On the other hand, lack of expertise and high cost of adoption is also hindering AI to implement in the organizations. The adoption and implementation stage of AI vary from organization to organizations, as well as functions to functions. While the marketing departments of several organizations are using advanced level of AI, there, the HR departments are using AI at the very initial stage. But it is evident from the above discussions that adoption of AI in business functions is inevitable and only it is a matter of time. With the COVID-19 pandemic this has become the utmost necessity for many organizations, particularly who works across the globe. HR partners of the businesses are also adopting AI at a fast pace to do away with the mundane works and deliver efficient services to the stakeholders. It is understood from the discourse that the prerequisite for a successful implementation of AI across the industries throughout the country, needs a concerted effort from industries, academia and government.

The answer lies in Keynesian economics. The central tenet of which is government intervention rather investment to stabilize and progress the economy by way of spreading Internet connectivity, basic literacy and computer literacy, then only truly AI can be effective in a greater scale.

A study on application of artificial intelligence in the pandemic era from a wider perspective, this work is an empirical investigation into the benefits and limitations of artificial intelligence for human potential and labour -intensive pandemic ridden Indian economy.