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A region’s transforming care partnership identified that autistic adults without an intellectual disability (ID) may be falling through gaps in services when presenting with a significant emotional and/or behavioural need in the absence of a mental health diagnosis. The region’s intensive support teams (ISTs) for adults with ID therefore piloted a short-term “behavioural support service” for this population. The purpose of this paper is to evaluate this pilot.

This study represents a mixed-methods service evaluation over a four year pilot period. The quantitative component examined referral rates and demographic data of accepted and declined referrals; and length of referral episodes and Health of The Nation Outcomes Scores (HoNOS) for accepted referrals. The qualitative component used thematic analysis to identify key themes relating to reasons for referral, clinical/therapeutic needs, and the models of support that most informed assessments and interventions at individual and systems levels.

The ISTs accepted 30 referrals and declined 53. Most accepted referrals were male (83%), and under 24 years old (57%). Average HoNOS scores were above the thresholds generally associated with hospital admission. Key qualitative themes were: transitional support; sexual risks/vulnerabilities; physical aggression; domestic violence; and attachment, trauma and personality difficulties. Support mostly followed psychotherapeutic modalities couched in trauma, attachment and second- and third-wave cognitive behavioural therapies. Positive Behaviour Support (PBS) did not emerge as a model of preference for service users or professionals.

This project represents one of the first of this type for autistic adults without an ID in the UK. It provides recommendations for future service development and research, with implications for Transforming Care policy and guidance.

Cryptocurrency arose, and grew in popularity, following the financial crisis of 2008 built upon a promise of decentralizing money and payments. An examination of the history of money and banking in the United States demonstrates that stable money benefits from strict controls and commitments by a centralized government through chartering restrictions and a broad safety net, rather than decentralization. In addition, financial crises happen when the government allows money creation to occur outside of official channels. The US central bank is then forced into a policy of supporting a range of money-like assets in order to maintain a grip on monetary policy and some semblance of financial stability.

In addition, this chapter argues that cryptocurrency as a form of shadow money shares many of the problematic attributes of both the privately issued bank notes that created instability during the “free banking” era and the “shadow banking” activities that contributed to the 2008 crisis. In this sense, rather than being a novel and disruptive idea, cryptocurrency replicates many of the systemically destabilizing aspects of privately issued money and money-like instruments.

This chapter proposes that, rather than allowing a new, digital “free banking” era to emerge, there are better alternatives. Specifically, it argues that the Federal Reserve (Fed) should use its tools to improve public payment systems, enact robust utility-like regulations for private digital currencies and limit the likelihood of bubbles using prudential measures.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

An accurate prediction of the deformation of retaining structures is critical for ensuring the stability and safety of braced deep excavations, while the high nonlinear and complex nature of the deformation makes the prediction challenging. This paper proposes an explainable boosted combining global and local feature multivariate regression (EB-GLFMR) model with high accuracy, robustness and interpretability to predict the deformation of retaining structures during braced deep excavations.

During the model development, the time series of deformation data is decomposed using a locally weighted scatterplot smoothing technique into trend and residual terms. The trend terms are analyzed through multiple adaptive spline regressions. The residual terms are reconstructed in phase space to extract both global and local features, which are then fed into a gradient-boosting model for prediction.

The proposed model outperforms other established approaches in terms of accuracy and robustness, as demonstrated through analyzing two cases of braced deep excavations.

The model is designed for the prediction of the deformation of deep excavations with stepped, chaotic and fluctuating features. Further research needs to be conducted to expand the model applicability to other time series deformation data.

The model provides an efficient, robust and transparent approach to predict deformation during braced deep excavations. It serves as an effective decision support tool for engineers to ensure the stability and safety of deep excavations.

The model captures the global and local features of time series deformation of retaining structures and provides explicit expressions and feature importance for deformation trends and residuals, making it an efficient and transparent approach for deformation prediction.

This paper aims to focus on the issue of high employee turnover in the Indian tech industry. An integrative review is conducted to analyse the past and current state of literature, as well as prepare a research agenda for future studies.

A pool of 72 articles published between 2010 and 2022 is reviewed with a special focus on Indian tech employees. This study elucidates the extent and impact of employee retention strategies through content analysis.

Two broad perspectives have been established in the literature: the reasons for quitting and the explanations for staying. By means of a comprehensive review, this paper combines these two aspects of literature and suggests factors under organization’s control to retain competent tech employees.

The study is designed to integrate the two theoretical viewpoints of employee turnover literature by consolidating the reasons behind quitting behaviour and staying intention. Codes combining the two aspects are presented as a valuable resource to retain tech talent.

The main research questions critically examine online videos that draw attention to a local community of musical practice, noticing how these can potentially be included within the tourism promotion strategies. This paper develops a case study of four videos realised by the Louth County Board of the organisation Comhaltas Ceoltóirí Éireann (CCÉ) in Co. Louth, Ireland, as a part of the FleadhFest 2021 initiative. It highlights the role that virtual spaces have in enhancing a sense of belonging to a music/festival community as well as the possibility that visual and audio supports have in promoting and celebrating a destination and its cultural features.

The analysis involves a netnographic examination of these videos (Janta, 2017), informed by the concept of “tourist gaze” (Urry, 1990; 2002) and influenced by film-induced tourism studies (Beeton, 2005).

Results show how festival and event organisers responded to COVID-19 social restrictions by creating a virtual space for celebrating music heritage and local musicscape, placing an emphasis on local musical scene.

The research aims to inform future developments in how the organisation operates within and engages with virtual space, its members and a wider audience.

This is the first study to consider the virtual activities of CCÉ from an ethnomusicological as well as tourism, perspective.

The new ways to complete financial transactions have been developed by setting up mobile payment (m-payment) platforms and such platforms to access banking in the financial mainstream can transact as never before. But, does m-payment have veiled consequences? To seek an answer, the research was undertaken to explore the dark sides of m-payment for consumers by extending the theory of innovation resistance (IR) and by measuring non-adoption intention (NAI).

Three hundred individuals using popular online m-payment apps such as Paytm, PhonePe, Amazon Pay and Google Pay were surveyed for the primary data. IBM AMOS based structural equation modelling (SEM) was used to analyse the data.

Each m-payment transaction leaves a digital record, making some vulnerable consumers concerned about privacy threats. Lack of global standards prevents consumers from participating in the m-payment system properly until common interfaces are established based on up-to-date standards. Self-compassion (SC) characteristics such as anxiety, efficacy, fatigue, wait-and-see tendencies and the excessive choice of technology effect contribute to the non-adoption of m-payment.

This study proposes a threat model and empirically explores the dark sides of m-payment. In addition, it also unveils the moderator's role of SC in building the structural relationship between IR and NAI.

Islamic banking has undergone significant adaption since its inception. This study aims to investigate why and how Islamic banks adapt their services, using participatory financing as evidence.

A qualitative study is designed, using working capital financing and commodity operations financing in Pakistan as analytical units. The data for each analytical unit is analyzed using a qualitative content analysis, while the findings are synthesized using a cross-case synthesis method.

Findings suggest that participatory financing has undergone extensive adaptation in the Islamic banking industry of Pakistan, in the wake of resolving constraints to participatory financing and increasing its viability. Consequently, participatory finance has emerged as an attractive and viable option in Pakistan. These findings suggest that unlike in the past, where Islamic banks used to buffer themselves from the environment and ignore the market demands, they have learned to respond effectively to the market demands and the challenges posed by the environment.

Findings suggest that the adaptation strategy is more effective than the migration strategy, because it enables the financial service systems to reduce the underlying risks by avoiding emergent threats and eradicating the inherent weaknesses.

The extant literature provides a generalized view on the adaptation process that Islamic banks undergo to comply with their environment. However, it is limited in terms of conceptualizing the adaptations and innovations in their products and the underlying structural variations. The present study fills this gap.

This study investigates the interactions between the US daily public debt and currency power under impacts of the Covid-19 crisis.

The authors employ the multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) modeling to explore the interactions between daily changes in the US Debt to the Penny and the US Dollar Index. The data sets are from April 01, 1993, to May 27, 2022, in which noticeable points include the Covid-19 outbreak (January 01, 2020) and the US vaccination campaign commencement (December 14, 2020).

The authors find that the daily change in public debt positively affects the USD index return, and the past performance of currency power significantly mitigates the Debt to the Penny. Due to the Covid-19 outbreak, the impact of public debt on currency power becomes negative. This effect remains unchanged after the pandemic. These findings indicate that policy-makers could feasibly obtain both the budget stability and currency power objectives in pursuit of either public debt sustainability or power of currency. However, such policies should be considered that public debt could be a negative influencer during crisis periods.

The authors propose a pioneering approach to explore the relationship between leading and lagging indicators of an economy as characterized by their daily data sets. In accordance, empirical findings of this study inspire future research in relation to public debt and its connections with several economic indicators.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-08-2022-0581

This conceptual, multi-voiced paper aims to collectively explore and theorize family entrepreneuring, which is a research stream dedicated to investigating the emergence and becoming of entrepreneurial phenomena in business families and family firms.

Because of the novelty of this research stream, the authors asked 20 scholars in entrepreneurship and family business to reflect on topics, methods and issues that should be addressed to move this field forward.

Authors highlight key challenges and point to new research directions for understanding family entrepreneuring in relation to issues such as agency, processualism and context.

This study offers a compilation of multiple perspectives and leverage recent developments in the fields of entrepreneurship and family business to advance research on family entrepreneuring.