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The purpose of this study is to define the communicational profile of unattached diplomats and explore the viability of state-centric concepts such as citizen diplomacy when discussing non-state actors emerging from civil society.

The paper uses a comparative, multiple case design focusing on descriptive case studies (Yin, 2018) that explore the diplomatic endeavours and social biographies of “citizens of the world” acting at a global or local level, not explicitly attached to or explicitly against an official, state agenda: Malala Yousafzai, Greta Thunberg and Bill Gates.

The unattached diplomats have organisational mobility but are attached to the cause they promote, a configuration that fundamentally opposes that of the traditional or organisational diplomat. Looking at individuals from a diplomatic perspective, not as instruments or as targets, but rather as agents with their own agenda, issues and diplomatic capital, the unattached diplomats define their lack of attachment through organisational mobility, adversarial positioning or personal financial autonomy with regard to state diplomatic institutions or for-profit/not-for-profit organisations.

A higher number and diversity of case studies can enable the identification of patterns and standards.

This study introduces and operationalises the concept of unattached diplomats. To the best of the author’s knowledge, this study is the first to discuss it in the context of another emerging concept, currently insufficiently researched: civil society diplomacy.

The purpose of this study is to explore the transient characteristics of mixed-flow pumps during startup process.

This study uses a full-flow field transient calculation method of mixed-flow pump based on a closed-loop model.

The findings show the hydraulic losses and internal flow characteristics of the piping system during the start-up process.

Large computational cost.

Improve the accuracy of current numerical simulation results in transient process of mixed-flow pump.

Simplify the setting of boundary conditions in the transient calculation.

Regarding the retail internal supply chain (SC), both retailers and research are currently focused on reactive food waste reduction options in stores (e.g. discounting or donations). These options reduce waste after a surplus has emerged but do not prevent an emerging surplus in the first place. This paper aims to reveal how retailers can proactively prevent waste along the SC and why the options identified are impactful but, at the same time, often complex to implement.

The authors follow an exploratory approach for a nascent topic to obtain insights into measures taken in practice. Interviews with experts from retail build the main data source.

The authors identify and analyze 21 inbound, warehousing, distribution and store-related options applied in grocery retail. Despite the expected high overall impact on waste, prevention measures in inbound logistics and distribution and warehousing have not been intensively applied to date.

The authors provide a structured approach to mitigate waste within retailers' operations and categorize the types of barriers that need to be addressed.

This research provides a better understanding of prevention options in retail operations, which has not yet been empirically explored. Furthermore, this study conceptualizes prevention and reduction options and reveals implementation patterns.

The study discusses the professionalization of academic leadership in Israel by analyzing and comparing two different training programs: the Hebrew University of Jerusalem’s (HUJI) program and the CHE-Rothschild program. The HUJI program began in 2016 to train the professoriate to take charge of leadership positions alongside a separate program for administrative staff, while the CHE-Rothschild program was launched in 2019 to train academic leaders, both professors and administrators from universities and colleges nationwide. The analysis reveals two “ideal types” of collegiality: While Model A (exemplified by the HUJI program) bifurcates between the professoriate and administrative staff, Model B (exemplified by the CHE-Rothschild program) binds administrative and academic staff members through course composition, pedagogy, and content. The study suggests a pattern of redefinition of collegiality in academia: we find that while academic hierarchies are maintained (between academic faculty and administrative staff and between universities and colleges), collegiality in academia is being redefined as extending beyond the boundaries of the professoriate and emphasizing a partnership approach to collegial ties.

Aster Retail (AR) is the retail pharmacy division of the Aster Dr Moopen's Healthcare (ADMH) Group. The group delivers healthcare services across the Middle East, India and the Far East, with a portfolio of hospitals, clinics, diagnostic centres and retail pharmacies. AR, under the leadership of Chief Executive Officer (CEO) Jobilal Vavachan, is well known for its people-centric approach, unique culture and innovative human resource (HR) practices. AR has won multiple awards for HR practices, service quality and business performance. In a recent corporate restructuring (2018), “Aster Primary Care” was carved out by combining the group's Clinics and Retail businesses. This case discusses the evolution of AR's HR journey and the challenges associated with integrating culturally diverse businesses without compromising the values of ADMH and its promise, “We'll Treat You Well.”

Inclusive education is about welcoming diversity, and improving the quality of education for all learners, where no one feels excluded and everyone receives support. Inclusive education is not about meeting one child's needs at the expense of another child; instead, it aims to provide full participation for all learners, both special and regular students (UNESCO, 2005). It has been stated that countries cannot progress in isolation. They must build an overarching educational strategy, which means that educational advancement will be linked to new social and economic policies. A considerable school change is required and, as a result, inclusive schools emphasise diversity as a key function, which helps students learn and caters to individual needs. Objectives and key results (OKRs) are one of the management tools that can be used in schools that focus on inclusive education. They also allow a school's activities to be aligned with its core inclusive education objectives. Furthermore, OKRs can assist a school in setting a challenging goal that will motivate it to grow beyond its current capabilities.

The paper aims to provide an understanding of how the transfer of progressive human resource management (HRM) practices may or may fail to render multinational enterprises (MNEs) institutional entrepreneurs creating change in job quality and decent work to underprivileged workers in the low-pay retail sector in Japan.

The paper draws on survey questionnaire data and interviews with workers and management in a foreign retailer in Japan.

The findings suggest that even where MNEs may provide some measurable material improvements in job quality, in this case equal pay for equal work, the total outcomes are nevertheless shaped by institutional context and constraints. In this case, the improvement in pay was intertwined with flexibility demands that were possible to meet for some workers but not others. In particular, women with care responsibilities and competing demands on their time were not able to experience “decent work” in the same way as others.

The study had a relatively low response rate, due to lack of discretion over time experienced by workers in Japan, as well as limited data on program outcomes, with interviews conducted with a small number of participants.

The study suggests that spaces and opportunities exist for MNEs to diverge from dominant practices in given host country locations and exercise a level of agency as emissaries of decent work but successful outcomes require a very thorough understanding of individual worker experiences within the institutional constraints of given environments.

The study offers insights into the complexities of initiatives by MNEs to contribute to the provision of decent work, particularly for workers in underprivileged positions including women in low-pay sectors such as retail, as firm-level practices lead to variable outcomes when filtered through local institutions.

The study brings together a focus on firm-level practices that inform much of the international HRM and international management scholarship with an emphasis on the experiences of workers, which is pursued in the sociology of work, to investigate whether MNEs can be actors in the realising of the Sustainable Development Goals around decent work.

The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from Transformers (BERT) embeddings. This work proposes a novel BERT-convolutional neural network (CNN)-based model for sentence representation learning and text classification. The proposed model can be used by industries that work in the area of classification of similarity scores between the texts and sentiments and opinion analysis.

The approach developed is based on the use of the BERT model to provide distinct features from its transformer encoder layers to the CNNs to achieve multi-layer feature fusion. To achieve multi-layer feature fusion, the distinct feature vectors of the last three layers of the BERT are passed to three separate CNN layers to generate a rich feature representation that can be used for extracting the keywords in the sentences. For sentence representation learning and text classification, the proposed model is trained and tested on the Stanford Sentiment Treebank-2 (SST-2) data set for sentiment analysis and the Quora Question Pair (QQP) data set for sentence classification. To obtain benchmark results, a selective training approach has been applied with the proposed model.

On the SST-2 data set, the proposed model achieved an accuracy of 92.90%, whereas, on the QQP data set, it achieved an accuracy of 91.51%. For other evaluation metrics such as precision, recall and F1 Score, the results obtained are overwhelming. The results with the proposed model are 1.17%–1.2% better as compared to the original BERT model on the SST-2 and QQP data sets.

The novelty of the proposed model lies in the multi-layer feature fusion between the last three layers of the BERT model with CNN layers and the selective training approach based on gated pruning to achieve benchmark results.

This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.

The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.

The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.

The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.

Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.

Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.

No prior study has used a hybrid framework to examine the performance of a chemical-free farming system.