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While the literature commonly analyses child poverty and social exclusion data covering a single year, less is known about children who fall in and out of poverty over a longer period. The present research intends to address this gap by investigating the dynamics of child poverty and social exclusion in Portugal. The purpose of this paper is twofold: first, to investigate child poverty and social exclusion trajectories; and second to examine their association with specific socio-demographic and economic factors.

Applying the definition of “at risk of poverty or social exclusion” given by Statistical Office of the European Communities (EUROSTAT), the analysis extends beyond the concept of income poverty. The authors apply Portuguese data sourced from the European Statistics on Income and Living Conditions for the period 2008–2011 to suggest a longitudinal poverty and social exclusion typology to analyse child poverty and social exclusion dynamics.

The findings report that children constitute the age group experiencing the worst poverty and social exclusion trajectories. Furthermore, the presence of children in the household seems to be an increasing factor of poverty and social exclusion. This information is relevant to improving the design of children and family-focussed social policies as well as contributing to the setting of targets in order to achieve EU 2020 goals including the alleviation of poverty in general and of child poverty in particular.

The main contribution to child poverty studies derives from our analysis of the dynamics driving child poverty and social exclusion. The authors apply a methodological framework that is applicable to other EU member states and can thus enable an international comparison of poverty and social exclusion trajectories.

This research aims to identify and organise the conditions of organisational readiness for digital transformation.

This qualitative study comprises three case studies within manufacturing companies from different sizes and industries located in Sweden. Plant visits and in-depth interviews bring to light companies' experiences with initial steps towards digital transformation. A set of conditions for digital organisational readiness was translated into a questionnaire and tested with one of the studied companies.

This paper organises and tests digital organisational readiness conditions to support companies' initial steps on digital transformation. The results are put in perspective of established change management theory and previous studies about digital transformation. The findings will conclude in a questionnaire to support dialogue and digital organisational readiness assessments.

Additional conditions for the initial phase of digital transformation could possibly be found if more cases had been included in the study.

The article identifies a set of conditions translated into a questionnaire that should be used as a dialogue tool to create strategic alignment and support companies in their initial discussions. If this process can be faster and more efficient, the company can achieve a competitive advantage against competitors.

This research's relevance relies on the fact that companies are advancing in adopting digital technologies without being ready from an organisational perspective. This gap creates barriers for companies' digital maturing processes, stopping them from having full access to digital technologies' benefits.

Child poverty has become an important issue in social and political agendas. In Portugal, almost 25 per cent of children are at risk of poverty and the most vulnerable age group. This paper seeks to investigate child poverty in Portugal, using European Union Statistics on Income and Living Conditions microdata for 2004‐2008. It explores the sociodemographic and economic attributes associated with child poverty in order to design the profile of income‐poor children. Furthermore, it analyses social policies efficiency in reducing child poverty.

The paper uses descriptive methods as well as econometric tools to select the characteristics of income‐poor children and to evaluate social policy efficiency. The estimation of a discrete choice model allows inference purposes.

Results obtained show that children included in large households and in lone‐parent households, are particularly at risk. Social policies undertaken had roughly impact on child poverty.

This paper aims to contribute to the literature by establishing the profile of income‐poor children, which may contribute to design more efficient policies to tackle the problem of child poverty. The policy evaluation sheds light on the results obtained by the current policies suggesting the discussion of new programs.

The aim of this paper is to evaluate poverty among children using a multidimensional concept of child poverty.

In light of this conceptualization, the paper applies methodological framework that uses fuzzy set theory to evaluate deprivation intensity among children. It also investigates the socio‐demographic and economic characteristics that most affect deprivation risk by estimating a discrete choice model.

Results show that children included in large families or with uncommon compositions are particularly at risk. Unemployment of parents also plays an important role in determining deprivation. Children's deprivation is particularly affected by issues related to education, health, housing and social integration.

This paper proposes a concept that is child‐cantered. It also constructs measures of child poverty/deprivation based on specific child indicators of well‐being. Finally uses the paper econometric techniques to evaluate socio‐demographic and economic attributes in determining deprivation odds.

The purpose of this study is to evaluate and enhance sustainable manufacturing practices across various industries, focusing on environmental, economic and social dimensions, to promote a comprehensive understanding and implementation of sustainability, thereby improving overall industry performance and fostering long-term ecological and economic health.

The study uses multi-criteria decision-making-multivariate analysis technique to examine sustainable manufacturing practices (SMPs) in the Indian manufacturing sector. It identifies 11 SMP criteria through literature review and expert recommendations. Data are collected through questionnaires, expert committees and interviews. The study focuses on four key industries: automobile, steel, textile and plastic. Techniques like principal component analysis (PCA), technique for order preference by similarity to ideal solution (TOPSIS) and complex proportional assessment (COPRAS) are used to rank and assess performance.

The Indian automobile industry has shown the most effective SMPs compared to steel, textile and plastic sectors. The automobile sector is the benchmark for sustainable measures, emphasizing the importance of green practices for environmental, social and economic performance. Recommendations extend beyond the automobile sector to cement, electronics and construction.

The research emphasizes the importance of SMPs across various industries, focusing on economic, environmental and social considerations. It advocates for a holistic approach that enhances resource efficiency and minimizes ecological footprint.

The study uses ranking methods like PCA-integrated TOPSIS and COPRAS to evaluate performance in different industries, focusing on the benchmarked automobile sector. The research offers valuable insights and advocates for the widespread adoption of sustainable policies beyond the studied sectors.

The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (1) analysis of AI and AI concepts/technologies; (2) in-depth exploration of case studies from a great number of industrial sectors; (3) data collection from the databases (websites) of AI-based solution providers; and (4) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations.

This study has called on the theory of IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically, of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase.

AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically, its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can, therefore, enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes.

AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared toward a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations, and at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships and scalable infrastructure.

This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 case studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.

Social network sites are key marketing tools that allow brands to connect and engage with consumers. However, there is still a lack of evidence of their value for football brands. This research aims to understand the motivations for fans to engage with their favourite football brands on Facebook and Instagram.

An online survey was performed, resulting in 214 valid responses. As the social media strategy followed by the football brand analysed was built around games, the authors divided fans into two groups based on the main method in which the club's games are watched: in stadium versus mediated. Multiple linear regression analysis was used to explore the relationship between motivations and fans' engagement, through content consumption and contribution, on Facebook and Instagram. Analysis was performed first with the whole sample and then by group (stadium attendance vs mediated attendance fans).

The findings show that social influence, entertainment, searching for information and rewards are the most relevant motivations for consumers to engage with brand-related content on Facebook. Entertainment, rewards and social influence are the main motivations influencing consumer interactions on Instagram. Group moderation was only confirmed in the impact of social influence on Facebook page content consumption.

The results provide valuable insights into the social media marketing activities of sports brands, which will assist brand managers to develop strategies for effectively stimulating engagement with the different groups of fans.

We present a critical literature review debating Brazilian research on social and environmental accounting (SEA). The aim of this study is to understand the role of politics in the construction of hegemonies in SEA research in Brazil. In particular, we examine the role of hegemony in relation to the co-option of SEA literature and sustainability in the Brazilian context by the logic of development for economic growth in emerging economies. The methodological approach adopts a post-structural perspective that reflects Laclau and Mouffe’s discourse theory. The study employs a hermeneutical, rhetorical approach to understand and classify 352 Brazilian research articles on SEA. We employ Brown and Fraser’s (2006) categorizations of SEA literature to help in our analysis: the business case, the stakeholder–accountability approach, and the critical case. We argue that the business case is prominent in Brazilian studies. Second-stage analysis suggests that the major themes under discussion include measurement, consulting, and descriptive approach. We argue that these themes illustrate the degree of influence of the hegemonic politics relevant to emerging economics, as these themes predominantly concern economic growth and a capitalist context. This paper discusses trends and practices in the Brazilian literature on SEA and argues that the focus means that SEA avoids critical debates of the role of capitalist logics in an emerging economy concerning sustainability. We urge the Brazilian academy to understand the implications of its reifying agenda and engage, counter-hegemonically, in a social and political agenda beyond the hegemonic support of a particular set of capitalist interests.

Different technologies may currently be used to produce dental prostheses, such as additive manufacturing and traditional milling. This study aims to evaluate and improve the fabrication process for hot-pressed porcelain dental prostheses and compare the use of masked stereolithography apparatus (MSLA) casting to computer-aided design/computer-aided manufacturing (CAD/CAM) casting. The cost-benefit analysis of producing dental prostheses through various technologies, including additive manufacturing and traditional milling, has not been fully explored. The cost of materials and processes used to produce prostheses varies based on complexity of design and materials used, and long-term effects, such as durability and wear and tear, must be taken into account.

Using key elements of part costs and estimation cost models, a multivariable approach was used to evaluate the practicality of the recommended strategy and process improvement.

The research found that MSLA casting provides a higher return on investment than CAD/CAM casting, and the optimized production process could be more suitable for the size and annual demand for prostheses.

Overall, this study highlights the need for a more comprehensive understanding of the cost-benefit analysis of different dental prosthesis production methods and emphasises the importance of evaluating long-term effects on the cost-benefit analysis.

The Industry 4.0 phenomenon is bringing unprecedented disruptions for all traditional business models and hastening the need for a redesign and digitisation of activities. In this context, the literature concerning the digital supply chain (DSC) and its capabilities are in the early stages. To bridge this gap, the purpose of this paper is to propose a framework for digital supply chain capabilities (DSCCs).

This paper uses a narrative literature approach, based on the main Industry 4.0 elements, supply chain and the emerging literature concerning DSC disruptions, to build an integrative framework to shed light on DSCCs.

The study identifies seven basic capabilities that shape the DSCC framework and six main enabler technologies, derived from 13 propositions.

The proposed framework can bring valuable insights for future research development, although it has not been tested yet.

Managers, practitioners and all involved in the digitalisation phenomenon can utilise the framework as a starting point for other business digitalisation projects.

This study contributes to advancing the DSC literature, providing a well-articulated discussion and a framework regarding the capabilities, as well as 13 propositions that can generate valuable insights for other studies.