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The paper aims to analyse the meaning and extension of discretionary power of social service professionals within network-based interventions.

Empirically, the paper is based on a case study of a network-based policy involving private and public organisations in the Northeast of Italy (Province of Trento).

The paper identifies netocracy as a social policy logic distinct from bureaucracy and professionalism. What legitimises netocracy is neither authority nor expertise but cooperation, the activation of connections and involvement, considered “good” per se. In this framework, professionalism and discretion acquire new and problematic meanings compared to street-level bureaucracy processes.

Based on a case study, the research results cannot be generalised but pave the way to further comparative investigations.

The paper reveals that the position of professionals in netocracy is to some extent trickier than that in a bureaucracy because netocracy seems to have the power to encapsulate them and make it less likely for them to deviate from expected courses of action.

Combining different literature streams – street level bureaucracy, professionalism, network organisations and welfare governance – and building on an original case study, the paper contribute to understanding professionalism in welfare contexts increasingly characterised by the combination of bureaucratic, professional and network logics.

This chapter explores how eight children from five Bolivian migrant families living in Madrid perceive their participation within their families. Children understand their participation as taking responsibility for domestic chores and taking care of younger siblings. Children's ideas of participation are associated with their school experience and are about simply having a voice in everyday mundane interactions with adults and peers.

Parents' cultural values, power and authority dominate decisions in the family. These children were born in Spain and practices in their family homes are influenced by their parent's strong cultural ties with Bolivia. The data collected show that the lives of these children and their views of participation need to be understood beyond the binary of the Global North and Global South (Twum-Danso Imoh et al., 2019).

The research employed the ‘routes of participation’, a playful and creative research method that aimed to empower children to explore their ‘interdependent agency’ (Abebe, 2019) and the meaning of participation within the context of their family lives. We conclude that any successful intervention with children needs to understand the meaning of what children say in relation to the various situations in which they live. Listening to children's voices and paying attention to the language that they use in their everyday lives should continue to be the basis of child-centred research and child-centred practice. The chapter encourages to reflect on the value of culturally grounded playful activities to understand children's agentic experiences and their contribution that they can make to their own lives.

The purpose of this paper is to perform a meta-bibliometric analysis of the banking service quality and to provide various research streams. Another goal is to provide future research questions to explore these areas in more detail.

The authors used three major academic databases (Scopus, ProQuest and EBSCO) from 1980–2021 and found 226 papers, where they performed the meta-bibliometric analysis by performing co-citation analysis, co-authorship analysis, cartographic analysis and content analysis.

The SERVQUAL model is the most used conceptual framework and model to study banking service quality. Structural equation modeling (SEM), followed by partial least squares SEM, is a majorly used method. There are three research streams in the literature: retail banking service quality, internet banking service quality and mobile banking service quality. Retail banking is the most studied stream, whereas mobile banking is the least studied stream. The study on banking service quality was initially conducted in developed markets, and later in emerging markets. All three streams have scope for further exploration, and hence, 44 future research questions have also been proposed.

This study has implications for bank managers, academicians and future researchers.

This study is unique in that it examines the meta-bibliometric dimensions of banking service quality and provides directions for future research for academicians and researchers.

The purpose of this paper is to comprehensively review a large and heterogeneous body of academic literature on investors' feedback trading, one of the most popular trading patterns observed historically in financial markets. Specifically, the authors aim to synthesize the diverse theoretical approaches to feedback trading in order to provide a detailed discussion of its various determinants, and to systematically review the empirical literature across various asset classes to gauge whether their feedback trading entails discernible patterns and the determinants that motivate them.

Given the high degree of heterogeneity of both theoretical and empirical approaches, the authors adopt a semi-systematic type of approach to review the feedback trading literature, inspired by the RAMESES protocol for meta-narrative reviews. The final sample consists of 243 papers covering diverse asset classes, investor types and geographies.

The authors find feedback trading to be very widely observed over time and across markets internationally. Institutional investors engage in feedback trading in a herd-like manner, and most noticeably in small domestic stocks and emerging markets. Regulatory changes and financial crises affect the intensity of their feedback trades. Retail investors are mostly contrarian and underperform their institutional counterparts, while the latter's trades can be often motivated by market sentiment.

The authors provide a detailed overview of various possible theoretical determinants, both behavioural and non-behavioural, of feedback trading, as well as a comprehensive overview and synthesis of the empirical literature. The authors also propose a series of possible directions for future research.

The present study is intended to develop an effective approach to the real-time modeling of general dynamic nonlinear systems based on the multidimensional Taylor network (MTN).

The authors present a detailed explanation for modeling the general discrete nonlinear dynamic system by the MTN. The weight coefficients of the network can be obtained by sampling data learning. Specifically, the least square (LS) method is adopted herein due to its desirable real-time performance and robustness.

Compared with the existing mainstream nonlinear time series analysis methods, the least square method-based multidimensional Taylor network (LSMTN) features its more desirable prediction accuracy and real-time performance. Model metric results confirm the satisfaction of modeling and identification for the generalized nonlinear system. In addition, the MTN is of simpler structure and lower computational complexity than neural networks.

Once models of general nonlinear dynamical systems are formulated based on MTNs and their weight coefficients are identified using the data from the systems of ecosystems, society, organizations, businesses or human behavior, the forecasting, optimizing and controlling of the systems can be further studied by means of the MTN analytical models.

MTNs can be used as controllers, identifiers, filters, predictors, compensators and equation solvers (solving nonlinear differential equations or approximating nonlinear functions) of the systems of ecosystems, society, organizations, businesses or human behavior.

The operating efficiency and benefits of social systems can be prominently enhanced, and their operating costs can be significantly reduced.

Nonlinear systems are typically impacted by a variety of factors, which makes it a challenge to build correct mathematical models for various tasks. As a result, existing modeling approaches necessitate a large number of limitations as preconditions, severely limiting their applicability. The proposed MTN methodology is believed to contribute much to the data-based modeling and identification of the general nonlinear dynamical system with no need for its prior knowledge.

This study aims to elaborate on the interface between water scarcity and consumption and, specifically to focus on, how consumers’ perceptions and views towards (un)sustainable water consumption can inform the development of sustainable water practices and environmental policies.

A qualitative methodology was adopted, consisting of 34 in-depth interviews with consumers, in four cities in the United Kingdom (UK). The semi-structured interviews sought to examine sustainable water consumption practices in more depth within an unexplored context, and secondary data were also used to explore the phenomenon in more depth.

The findings provide novel theoretical and empirical insights around the invisibility and imperceptibility of (un)sustainable water consumption. The findings suggest how interdisciplinary and collaborative practices can enhance more responsible and sustainable consumption of water resources in the UK.

The study can inform marketing strategies on community engagement with localised water sustainable actions and encourage policymakers to develop concrete marketing communications and increased public awareness regarding water scarcity.

Considering the slow growth of water scarcity, the findings can prompt marketing scholars to explore similar phenomena, within seemingly unaffected countries, urban centres, industries and marketplaces.

Previous studies focused on the measurement of household water consumption in the water-scarce context of the Southern Hemisphere. Challenging geographical perceptions of water shortage, this study focuses on the UK and highlights how the multidimensional, finite and invisible nature of water consumption requires collaborative efforts in tackling water scarcity.

This paper focuses on image-to-text manuscript processing through Handwritten Text Recognition (HTR), a Machine Learning (ML) approach enabled by Artificial Intelligence (AI). With HTR now achieving high levels of accuracy, we consider its potential impact on our near-future information environment and knowledge of the past.

In undertaking a more constructivist analysis, we identified gaps in the current literature through a Grounded Theory Method (GTM). This guided an iterative process of concept mapping through writing sprints in workshop settings. We identified, explored and confirmed themes through group discussion and a further interrogation of relevant literature, until reaching saturation.

Catalogued as part of our GTM, 120 published texts underpin this paper. We found that HTR facilitates accurate transcription and dataset cleaning, while facilitating access to a variety of historical material. HTR contributes to a virtuous cycle of dataset production and can inform the development of online cataloguing. However, current limitations include dependency on digitisation pipelines, potential archival history omission and entrenchment of bias. We also cite near-future HTR considerations. These include encouraging open access, integrating advanced AI processes and metadata extraction; legal and moral issues surrounding copyright and data ethics; crediting individuals’ transcription contributions and HTR’s environmental costs.

Our research produces a set of best practice recommendations for researchers, data providers and memory institutions, surrounding HTR use. This forms an initial, though not comprehensive, blueprint for directing future HTR research. In pursuing this, the narrative that HTR’s speed and efficiency will simply transform scholarship in archives is deconstructed.