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This study explores practitioners’ perspectives on the perceived gap between university and practice beyond the hard and soft skill paradigm. Utilising Tomlinson’s graduate capital model of employability (2017), we explored human, social, cultural, and psychological capitals to enrich the understanding of this issue and employability. It provided a new perspective, useful for implementing curriculum renewal.

This study utilised a two-stage mixed methods design. Using Tomlinson’s (2017) Graduate capital model as a framework, the first stage involved distributing an online survey to qualified architects in hiring positions practising in Australia. This served as the foundation for generating qualitative and quantitative data. The second stage involved a two-hour practitioner workshop where the survey results were discussed and expanded upon.

Our results found that the practitioner’s perspective on the perceived skills gap is more complex than the hard/soft skill paradigm commonly discussed. Practitioners expressed a need for students/graduates to possess identity and cultural capital to contextualise industry norms and expectations. This knowledge lets students know where and how hard/soft skills are used. Our results also suggest practitioners are concerned with the prevailing individualistic approach to the higher education system and traditional architectural teaching methods, instead suggesting a more industry-aligned collaborative disposition.

By expanding the employability discourse beyond hard/soft skills, the results of this research provide an opportunity for architectural curriculum renewal in line with industry expectations.

Organizational sociology and organization studies have a long history together, while also sharing a proclivity to self-diagnose crises. Instead of taking these assessments at face value, this paper treats them as an object of study, asking what conditions have fueled them. In the case of organizational sociology, there are indications of a connection between rising levels of discontent and community building: self-identified organizational sociologists have progressively withdrawn from general debates in the discipline and turned their attention to organization studies, which, they suspect, has seen dramatic levels of growth at their expense. Organization studies, on the other hand, are still haunted by “a Faustian bargain”: leaning heavily on the authority of the social sciences, business school faculty were able to facilitate the emergence of a scholarly field of practice dedicated to the study of organizations, which they control. However, in doing so, they also set organization studies on a path of continued dependence on knowledge produced elsewhere: notably, by university disciplines such as sociology.

One of the major challenges faced by the world at present is management and treatment of waste. Especially, waste such as polyethylene (plastics) is non-degradable and is causing great damage to our environment. Aquatic environment is one among them that is getting affected by these plastic wastes. Water pollution is a great issue faced in many countries and steps to reduce it are being taken on a wide scale. Unwanted aquatic plants grown in ponds and lakes create problems like totally covering up the surface of the lake that blocks the sunlight for aquatic species and also reducing their total storage. Identifying such unwanted plants and plastics is a very essential part in treating and management of waste. Detection and classification help us to achieve this. With the help of satellites, drone-shot images of many oceans are captured, and the amount of plastic content present is detected using artificial intelligence. In artificial intelligence, we have many algorithms and platforms that help us to achieve object detection. Tensorflow is one such framework that helps us to perform object detection with the help of pre-trained models present in it, and thus, it is used in this study. Object detection uses computer vision to detect objects from images. Convolutional neural networks are a subset of machine learning that is helpful in image processing – in other words, processing of pixel data. In this study, we used the ResNet-50 model involving transfer learning for classifying unwanted plants and plastics. Lakes and ponds are the major places among the other aquatic environments where these kinds of wastes are found, and therefore, this study concentrates on waste present in these aquatic bodies. The lakes and ponds present near residential areas act as a place for storing excess rainwater, which prevents flooding. Many cities, especially residential areas, face a lot of water stagnation problems during the rainy season. Ponds and lakes near these areas contain unwanted plants and plastics present, which makes it a problem to store the rainwater that comes during monsoon. Another problem is that they don’t provide sunlight to enter deep into water, making the aquatic species difficult to survive. Preserving and maintaining such lakes from getting filled with non-degradable plastics and unwanted plant growth becomes very important. Therefore, the lakes and ponds present in such residential areas would be useful to detect the unwanted waste.

In this study, the focus is on detection and classification of the plastics and unwanted plants. The dataset is very important for this study, which is an image dataset. There was not any readily available image data of unwanted plastics available online, and therefore, the images were captured from the lakes and ponds in Kanchipuram district. Images of duckweed, plastics, bulrush and leaves of sky lotus were taken. This dataset consisted a total of 200 images, with 50 images belonging to each category. Having this as the dataset, detection and classification were carried out.

The object detection took place for the plastic, duckweed, bulrush and leaves of sky lotus and the performance metrics such as precision and recall was evaluated to test the accuracy of the detections. Precision is used to calculate the number of correctly identified positive identifications. This is done by dividing the sum of true positives and false positives from the number of true positives. True positives are nothing but the number of correct predictions of positive identifications, and false positives are the number of false predictions of positive identifications. Similarly, recall is used to calculate the number of actual positives identified. We can calculate recall by dividing the sum of true positives and false negatives from the total number of true positives. Here false negatives are the number of false predictions of false identification. This performance metrics was evaluated for the trained model, and we obtained an average precision of 0.81 and an average recall of 0.86. The high precision and recall values of our model show that the model produces accurate results. Therefore, the model is producing good performance in detecting the unwanted plants and plastics from lakes and ponds. The evaluation results were visualized with the help of TensorBoard and are available in fig-4 and fig-5. The loss rate is visualized and is available in fig-6. We can see that the loss rate has reduced over the steps as we pass from 1,000 to 4000th step.

The work was originally carried out in the Kanchipuram district of Tamil Nadu.

The relevance of analytics to the healthcare supply chain is increasing with emerging trends and technologies. This study examines how analytics are used in the healthcare supply chain in the “new normal” environment.

A systematic literature review was conducted by extracting research articles related to analytics in the healthcare supply chain from Scopus. The author used a hybrid review approach that combines bibliometric analysis with a theories, contexts, characteristics, and methodology (TCCM) framework-based review to identify various themes of analytics in the healthcare supply chain.

The hybrid review strategy yielded results that focus on prevalent theories, contexts, characteristics, and methodologies in the field of healthcare supply chain analytics. Future research should explore the resulting antecedents, decision-making processes and outcomes (ADO) framework, which integrates technological, economic, and societal concerns and outcomes. Future research agendas could also seek to apply theoretical perspectives in the field of analytics in the healthcare supply chain.

The result of a review of selected studies adds to the current body of work and contributes to the growth of research in the field of analytics in the healthcare supply chain. It also provides new directions to healthcare supply chain managers and academic scholars.

Contrasting the Madisonian model of democracy with Westminster style arrangements, this chapter examines how American political scientists and policymakers expressed second thoughts about the constitutional settlement of 1787 and looked towards the British political system for possible reforms to their own. In particular, it details the arguments offered by Thomas Finletter, James MacGregor Burns, and others, from the 1940s through to the early 1960s that the United States might adopt certain features of the Westminster model. The chapter analyzes their proposals and the – essentially negative – reception that they received. It concludes by addressing the failure of these proposals and the endurance of fragmented government within the United States.

This study aims to identify the key attributes of esports venues as perceived by spectators. Using the stimulus-organism-response framework, it also examines how these attributes influence customer experience, satisfaction and re-attendance intentions.

This mixed-methods research comprises two main stages. In the qualitative stage (Stage 1), we conducted a literature review, interviews with live esports event attendees and observations at two esports events to identify key esports venue attributes from spectators’ perspectives. In the quantitative stage (Stage 2), we recruited 564 esports game attendees at two live events to validate a measurement scale developed from the qualitative procedures. We also examined the relationships between venue attributes and the three spectator outcomes by using structural equation modeling.

Five key perspectives of esports venue attributes emerged from the qualitative analysis: (1) stage and stage lighting, (2) Internet infrastructure, (3) visibility of display screens, (4) concourse layout and (5) seating comfort. These dimensions were validated through exploratory and confirmatory factor analyses and were found to directly influence spectator experience and satisfaction, but not re-attendance intentions.

This study provides pioneering insights into the specific esports venue attributes that drive spectator outcomes. The findings offer valuable directions for future esports research and practical strategies for esports venue management.

Competitiveness is an important personality trait that has been studied in various disciplines and has been shown to predict critical work outcomes at the individual level. Despite this, the role of competitiveness in groups and teams has received scant attention amongst organizational researchers. Aiming to promote future research on the role of competitiveness as both an adaptive and maladaptive trait – particularly in the context of work – the authors review competitiveness and its effects on individual and team stress and Well-Being, giving special attention to the processes of cohesion and conflict and situational moderators. The authors illustrate a dynamic multilevel model of individual and team difference factors, competitive processes, and individual and team outcomes to highlight competitiveness as a consequential occupational stressor. Furthermore, the authors discuss the feedback loops that inform the different factors, highlight important avenues for future research, and offer practical solutions for managers to reduce unhealthy competition.

School exclusion in England is highly gendered, racialised and classed. For instance, boys are three times more likely than girls to be excluded from school and certain groups, including Black Caribbean boys are subject to disproportionate levels of exclusion. Against this backdrop, I explore the context and consequences of exclusion from English mainstream schooling for young masculinities. The arguments presented also have broader international significance due to a global tendency towards punitive measures in schooling. Through bringing masculinities scholarship into conversation with childhood studies, this chapter aims to present a nuanced theorisation of young masculinities which foregrounds lived experience and is located within the interdisciplinary field of childhood studies. It examines ways in which exclusion and schooling in alternative settings, such as a Pupil Referral school, can shape the identities of boys and their subjectivities. The empirical data demonstrate that excluded boys face severe constraints arising from ways in which they are positioned. Drawing on original qualitative data, it is argued that boys who are categorised in this way demonstrate highly agentic ways of ‘doing boy’. This chapter is underpinned by two questions, firstly, how can we theorise boyhood and school exclusion in ways that recognise excluded boys as agentic and constrained subjects? Secondly, what possibilities for change might our theorisation reveal? This chapter concludes by arguing for intersectional masculinities and strengthened theorisation of childhood studies which explicitly recognises lived experience. Through this discussion, I seek to illuminate the emotional costs of school exclusion and insights into ways to achieve change.

This study aims to examine the mediating role of trainer preparation in the effect employee trainer self-efficacy has on trainer performance.

Using a census, data was collected from internal employee trainers in two universities in Ghana. In testing the hypotheses, a structural equation modelling based on 10,000 bootstrap samples was used, and the BCa confidence intervals were used to establish the significance of the hypotheses.

This study revealed trainer preparation as a complementary partial mediator in the effect trainee engagement self-efficacy and instruction self-efficacy had on trainer performance. In addition, the importance–performance map analyses demonstrated that the factor with the most importance in the model was instruction self-efficacy, yet it was not the highest-performing factor.

This study highlights the mediating role played by preparation in the effect of trainer self-efficacy on trainer performance. In addition, it adds to the dearth of studies that focus on employee trainers while at the same time using data from the trainers themselves.