Multidimensional Analytical Framework Research Articles

Analysis and study of digital economy level measurement index

PurposeThis study aims to assess the level of development of the digital economy by constructing a comprehensive measurement system. It explores regional differences within China’s digital economy, highlighting the varying degrees of digital infrastructure, industrialization, governance and innovation capabilities across provinces.Design/methodology/approachA multidimensional analytical framework including digital infrastructure, industrialization, digitization, governance and innovation was developed. Entropy methods were used to calculate the weights of each dimension. The coupled coordination degree model and the Tobit model with random effects panel are applied to analyze the current situation, discrepancies and influencing factors.FindingsThis study reveals significant regional differences in the development of China’s digital economy, characterized by a pattern of “strong in the east, weak in the west; high in the south, low in the north.” This geographical imbalance exacerbates the “polarization effect” and the “siphon effect,” where resources and growth tend to concentrate in already developed areas, further intensifying regional inequalities. The development of the digital economy is driven by principles of innovation, coordination and sharing, which facilitate the creation and dissemination of new technologies and collaboration across different sectors. However, this progress is also constrained by considerations of environmental sustainability (green) and economic openness.Originality/valueThis paper contributes to the body of knowledge by providing a novel multidimensional measurement system for the level of digital economy development. The unique application of the coupled coordination degree model and Tobit model to analyze regional differences and influencing factors provides insights into the dynamics of China’s digital economy.

Construction of Overseas Social Responsibility Image of Chinese State-Owned Enterprises Based on Corpus: Overseas Social Responsibility Image of Chinese State-Owned Enterprises

As the "cornerstone" of the socialist economy with Chinese characteristics and the "pioneer" in expanding international markets, state-owned enterprises' overseas social responsibility image directly affects the nation's image and reputation. Drawing on the latest achievements in discourse analysis, this study constructs a multidimensional analytical framework for the overseas social responsibility image of Chinese state-owned enterprises based on a corpus. Using state-owned enterprise social responsibility reports as the corpus, the study examines the linguistic features of the texts and the constructed social responsibility image from the perspectives of thematic words and phrases, intertextuality and interdiscursivity, modal verbs, and explores the underlying socio-cultural context. The findings of the research are as follows: 1) State-owned enterprise social responsibility reports construct multiple social responsibility images, including those of loyal party patriots, guardians of social stability and sustainability, practitioners of shared prosperity, industry leaders, and promoters of firm theoretical beliefs, thereby promoting traditional culture. 2) Furthermore, the reports exhibit positive traits such as openness, diversity, innovation, inclusivity, moderation, and friendliness. The research also indicates that the multifaceted and three-dimensional corporate social responsibility image of state-owned enterprises is closely related to their unique mission and status, as well as China's distinctive cultural context. This study also aims to provide insights into the construction of social responsibility images for non-state-owned enterprises.

Application of Machine Learning Models in Asset Allocation

This study embarks on an exploration of machine learning (ML) models in forecasting the trends of stock indices, with a specific focus on different industries within the Chinese market. Moving beyond the confines of traditional linear regression and standard multi-factor approaches, our research adopts a multi-dimensional analytical framework to decode the complex relationships between various factors and the future returns of industry-specific Exchange-Traded Funds (ETFs) in China. The paper innovatively applies both linear and nonlinear ML models to predict directional shifts in ETF returns, a domain not extensively studied previously. We conduct a thorough comparative analysis of these models, assessing their predictive prowess and dissecting the influence of diverse factors on different industry sectors. This investigation reveals distinct patterns and factor sensitivities unique to each sector, offering new insights into their dynamics. The results are pivotal for asset allocation and investment strategies, as they highlight the nuanced role of ML in financial forecasting. By bridging the gap between traditional financial models and advanced ML techniques, our study presents a novel perspective that enriches the strategic planning in financial markets, especially in the context of the rapidly evolving Chinese economy.

Nexus between carbon dioxide emissions, population, migration, foreign direct investment, and gross domestic product: New evidence in the context of Vietnam

This research investigates the complex interplay between carbon dioxide emissions, population dynamics, migration patterns, foreign direct investment, and gross domestic product in Vietnam. The paper uses the unrestricted fixed and random panel data method. The data is collected from the World Bank and Vietnam General Office of Statistics. This study employs a multidimensional analytical framework in the country's rapid economic growth context, combining quantitative and qualitative methods to unravel the nuanced relationships within this nexus. The main finding of this study reveals significant correlations between carbon dioxide emissions, population, migration, FDI- foreign direct investment inflows, and GDP- gross domestic product growth, highlighting the pivotal role of these factors in shaping Vietnam's economic landscape. The study draws on comprehensive datasets spanning multiple years. The research reveals significant correlations, notably the positive link between foreign direct investment inflows and gross domestic product growth. Simultaneously, the study sheds light on the environmental challenges associated with urbanization and the socioeconomic impacts of internal migration. The findings of this paper contribute to the theoretical understanding of these interconnected factors, emphasizing the need for an integrated policy approach. Policy recommendations include sustainable urban development strategies, sector-specific foreign direct investment initiatives, and inclusive migration policies. Additionally, fostering green technologies, community engagement, and international collaborations are proposed to advance Vietnam's sustainable development goals. This research is a foundation for policymakers, scholars, and stakeholders, offering actionable insights to guide Vietnam toward a balanced and resilient future. The study's methodology, findings, and policy implications contribute to the academic discourse on sustainable development and provide remarkable guidance for navigating the complexities of economic growth and environmental stewardship in emerging economies.

Digital Traceability in Agri-Food Supply Chains: A Comparative Analysis of OECD Member Countries.

In an era marked by globalization and rapid technological advancements, the agri-food sector confronts both unprecedented challenges and opportunities. Among these, digital traceability systems have emerged as pivotal in enhancing operational efficiencies, ensuring food safety, and promoting transparency throughout the supply chain. This study presents a comparative analysis of digital traceability adoption and its impact across member countries of the Organization for Economic Co-operation and Development (OECD). By utilizing a multidimensional analytical framework, this study investigates national regulations, legal frameworks, and key food commodities affected by digital traceability implementations. It systematically assesses the efficacy of these systems in meeting consumer transparency expectations, regulatory compliance, and the overarching goal of sustainable agri-food supply chains. Through case studies and empirical evidence, the paper elucidates the complex interplay between technological innovation and regulatory environments, offering insights into best practices and potential integration barriers. Ultimately, this comprehensive investigation contributes to the scholarly discourse on digital traceability, providing actionable recommendations for policymakers, industry stakeholders, and academia to navigate the complexities of modern agri-food systems.

Decoding algorithmic literacy among journalists

Recent developments in generative Artificial Intelligence (AI) have revitalized academic discourse on algorithmic systems, particularly on their potential, ethical considerations, risks, and regulatory challenges. Extensive research has examined how algorithms affect communication processes, focusing on their influence on news organizations, journalistic practices, public-media dynamics, media literacy, and combating disinformation and filter bubbles. An emergent strand of research defines and measures the multidimensional concept of algorithmic literacy. However limited research exists on the intersection of algorithmic literacy and journalism. This gap is particularly concerning given the pivotal role of journalism in shaping public discourse, informing citizens, upholding democratic values and contrasting disinformation. Understanding how journalists perceive and engage with algorithms is essential, as these technologies significantly influence their professional tasks, including content production and distribution. In Portugal, where newcomer journalists work in precarious conditions and digital media transformation is rapidly evolving, understanding how journalists interact with and perceive algorithms is vital. Our study, through a multi-phased approach, aims to fill this gap questioning how can algorithmic literacy, encompassing cognitive, attitudinal and behavioural dimensions, be effectively assessed among professional journalists? The exploratory results present a validated methodological tool, instrument based on a multi-dimensional analytical framework and specifically designed to measure algorithmic literacy levels and to assess journalists’ experiences. Critical discussion addresses the methodological procedures and preliminary findings from the pre-test, offering insights into Portuguese journalists' understanding, perceptions, and competencies regarding algorithmic systems. By shedding light on the cognitive, affective, and behavioural aspects of journalists’ engagement with algorithms, this study contributes to a deeper understanding of the algorithmic literacy among journalists, which is essential to sustain the quality of their work and for an effective counteraction against disinformation. It also opens avenues for similar studies in other geographical or professional contexts.

Mining public behavior patterns from social media data during emergencies: A multidimensional analytical framework considering spatial–temporal–semantic features

AbstractStudying human behavioral patterns from social media data is an important part of emergency management. However, the multidimensional characteristics of social media data have rarely been fully utilized. This study proposes a multidimensional analytical framework for social media user behavior that integrates time–geographic–semantic features. The framework defines the spatiotemporal semantic multidimensional relationship of social media user behavior and maps it into a time–geographic–semantic (TGS) cube, based on which a TGS‐weighted similarity measure was created. We then applied a spectral clustering algorithm to cluster the trajectories of the user behavior. Subsequently, a prefix‐projected pattern growth algorithm was used to mine frequent semantic patterns from the clustering results and analyze their spatiotemporal distribution characteristics. Taking the COVID‐19 pandemic as a case study, we analyzed Weibo user behavior in China from January 9 to March 10, 2020. The results showed that the clustering of TGS similarity was better than that of the commonly used edit distance on real and longest common subsequences. Five semantic patterns of public responses were identified during the COVID‐19 pandemic. The semantic patterns of categories 1, 2, 4, and 5 were “spindle‐shaped,” meaning that their core semantics were stable and concentrated on one or several topics despite the frequent semantic changes in the middle stage. Category 3 was “wave‐shaped,” indicating that their semantics fluctuated between serval topics during the pandemic. This discovery shows that the framework is suitable for analyzing and comprehensively understanding public behavior during pandemic emergencies. This framework has good universality and great potential for extension to other emergencies.

Equity in learning paths and contrast to early school leaving: the complexity of the factors involved in the school experiences of foreign students

The European guidelines and orientation in recent years have been focus on the promotion of equity in the learning paths and in the counteract early school leaving. Although many actions to reduce early school leaving have been implemented, today there remain many students who fail to achieve satisfactory school outcomes and who leave the school system early, especially among students with a migrant background. To understand the dynamics that can lead students in conditions of fragility toward negative educational paths, it is necessary to employ a complex and multidimensional analytical framework that considers the multiple variables involved. The ecological and cultural theoretical model of development highlights the complexity of the factors that can influence students’ learning trajectories, assuming a systemic and multifactorial interpretative approach. Starting from these premises, this study aims to explore the impact of individual and contextual factors in the learning pathways of foreign-born students, taking the students’ perspective. Two hundred and forty-four students attending the second level of secondary school answered to a survey, which explores their perception about: the meaning attributed to the school; the role of teachers and the family in supporting their learning path; their involvement in extra scholastic activities. The results highlight the inclusive role play by the school, which represent a place for integration into the community for foreign students; the central role of relationship with teachers and the emotional support received by the family, in counteracting early school living; the participation of students with a migratory background in their extra scholastic activities as element of protection. The results suggest that to respond to foreign students’ complex educational needs, school cannot act as an isolated entity, but rather should build networks that involve families and external contexts.

Industry 4.0 maturity and readiness assessment: an empirical validation using Confirmatory Composite Analysis

Manufacturing enterprises’ business operations are expected to be significantly transformed by Industry 4.0 technologies. However, due to a lack of solid assessment framework and procedures to evaluate the current status and readiness to change in the academic literature, many manufacturing firms frequently fail to assess, assimilate, or struggle to implement Industry 4.0 technologies and processes. To fill this need, this research proposes a novel multi-dimensional analytical framework and indicators for assessing Industry 4.0 maturity and preparedness. This framework include-six dimensions and 32 indicators that have been tested with reference to a large multinational steel manufacturing company. The study employed CCA and IPMA analysis techniques to validate the survey data from the case company. The results support and verify the status wherein the supply chain dimension has the highest performance, and the business model dimension has the lowest in terms of Industry 4.0 maturity and readiness status. The framework and index thus developed have practical relevance as it is easy to understand, interpret and validate in any other manufacturing organization. Furthermore, this study is the first to apply the structural equation modelling technique, i.e. CCA and IPMA, to evaluate Industry 4.0 maturity and readiness from a developing country perspective in the context of steel manufacturing.

Reframing the High-Technology Landscape in Greece: Empirical Evidence and Policy Aspects

Purpose: The major purpose of the paper is to explore, identify and highlight the general and specialized (sector-specific) long-term technology trends following the evolution of technology-intensive sectors in Greece during the last years. In a similar vein, sectoral differentiations have been identified and highlighted (e.g. evolution, growth, size distribution, industrial dynamics) as an analytical dimension of innovative activity across the high-technology sectoral domain. Design/methodology/approach: The paper methodology consists of a systematic review of primary data collected through the Eurostat/SBS database and the SME Performance Review 2022, as one of the major tools the European Commission uses to monitor and assess countries' progress in implementingthe SmallBusinessAct (SBA). The methodological approach of the paper involves the mapping of sector-specific trends in terms of technology-intensive categories, size classes and underlying sectoral characteristics and dynamics trends. Findings: The exploration of detailed and long-term data within the landscape of technology-intensive sectors in Greece, provides a clearer picture for the upward and downward trends, the sector-specific differentiations and the upcoming challenges. High-tech enterprises constitute a considerably important part of the country’s productive base with gradually increasing trends in all relevant categories. This is further motivated by the development of start-ups and spin-offs in several fields of higher technological specialization (‘deep tech’). Research limitations/implications: It is widely accepted that aggregated technology growth is a long-term and multi-level process within an economy. Further research regarding the actual and potential spillover effects of technology sectors across the wider economy constitutes an important area of further research. The paper provides a multi-dimensional analytical framework to identify sector-based technological and industrial underlying dynamics and understand long-term sectoral characteristics and trends within the high-tech industry evolution in Greece. Originality/value: The paper provides an analytical approach to explore the underlying industrial dynamic trends within the technology-intensive sectors in Greece. The exploration of detailed and long-term data within the landscape of technology-intensive sectors in Greece provides an overall view of the underlying technology-intensive sectors’ dynamics, the sector-specific differentiations, the upcoming challenges and the innovation policy implications.

Investigating the local socio-spatial integration of the Belval knowledge district into Esch/Alzette: A dissimilarity-based approach

The social and spatial integration of knowledge-related urban development projects into their urban environment is seen by policymakers as a necessity to unlock local and regional growth dynamics and regeneration. However, the understanding of socio-spatial integration, and how it can be scientifically analysed and interpreted at a local scale remains somewhat vague when the concept is not approached in its totality and measured by unidimensional indicators. The current article first proposes a holistic and multidimensional analytical framework to measure and analyse the socio-spatial integration of knowledge districts. Then, it focuses on one specific dimension – the structural dissimilarities between territories – and suggests an indicator-based multivariate analysis that is applied to the case of Belval, in Esch/Alzette (Luxembourg). Our findings for the Belval case study show that the structural dissimilarities between knowledge districts and adjacent neighbourhoods are mainly due to the young, international and professional profile of the population this place attracts, while differentials in terms of socio-economic status are much less significant than expected. In other words, the specificity of this knowledge district lies in the educational and migratory backgrounds of its inhabitants, rather than in their economic wealth. Accordingly, we call for a more nuanced debate concerning the urban integration of knowledge districts.

Proficiency scales for early writing development

Despite the growing prominence of writing in both educational contexts and society at large, research-based knowledge about the development of writing remains lacking, particularly regarding the developmental trajectories that students pass through as they encounter and grapple with the complex system of more formal writing during their primary years of school. This article presents a study that examined writing development in Danish primary grade students (ages 6 to 8) from a linguistic perspective to identify developmental patterns in students’ writing skills during these years. In the study, we applied a multidimensional analytical framework based on a text-oriented model of writing, for the coding of a large number of digitalised student texts (N=803). Subsequently, we analysed the coding results using statistical Rasch theory. Through this procedure, we were able to identify developmental patterns in the students’ writing in the form of different proficiency groups along four textual dimensions and to describe a number of linguistic levels for each dimension. Furthermore, the article discusses didactic potentials and limits from using proficiency groups when teaching writing in primary grades.

Understanding the COVID-19 Impact on Young People and Precarity Drawing on a New Multidimensional Analytical Framework

Understanding the COVID-19 Impact on Young People and Precarity Drawing on a New Multidimensional Analytical Framework

Public administration, legal culture, and empirical research: Residential policies for the Sinti in Venice

This article proposes a model for analyzing contested local policies for Roma and Sinti people, starting with a case study on a Sinti “village” established by Venice’s local authority. A multidimensional analytical framework is adopted, investigating how the local setting and the political and discursive opportunity structures existing at higher territorial levels intersect, and how local residents’ collective actions influence the public authorities’ behavior. Concerning the first aspect, we identify a mutually reinforcing effect of legal culture, policies, and common-sense representations that contributes to consolidating the representation of Roma and Sinti people as “foreigners” and “nomads.” As for the second aspect, our analysis shows how the different perceptions of citizenship (and of the relationship between the Sinti and citizenship, in particular) of the various protagonists in the conflict affect the practices that are implemented.

One and the Same? A Systematic Literature Review of Residential Property Investor Types

This article presents a systematic literature review on residential property investor types in selected social science disciplines and critically evaluates the status quo of academic engagement within this diverse group of property market actors. A recurring critique in recent years has been the minimal acknowledgment of investor heterogeneity particularly in relation to urban development and the financialization of housing. Yet, to date, there is no systematic evidence supporting these contentions. Therefore, we conducted an exhaustive literature review of residential investment landscapes through the Web of Science citation database in the following fields: Urban and regional planning, geography, sociology, urban studies, public administration, and economics. Subsequently, we methodically searched for the types of investors addressed, and investor categories employed, in journal articles published between 2000 and 2019. Following a meta-categorization of the results, we demonstrate how existing literature differentiates investors in terms of their spatial scale of operation, size and social composition, investment object and finance, or investment and social behavior. Additionally, we highlight the key topics and issues addressed in the reviewed literature within each meta-category. We propose to turn the four meta-categories into a multidimensional analytical framework as a point of departure for a more nuanced and in-depth understanding of investor differentiations, a tool that is urgently needed in Planning Studies and related disciplines. Furthermore, we argue that mixed method approaches combining hard and quantifiable with soft behavioral investor characteristics, as well as institutional analyses combining structural considerations with actors’ agency, are indispensable to disentangle contemporary residential property market dynamics.

Industrial water-use efficiency in China: Regional heterogeneity and incentives identification

Progress has been made in improving water-use efficiency (WUE) in China, whereas problems such as unbalanced regional and industrial WUE development still exist. In this study, the WUE of 30 provinces in mainland China at the overall level as well as three industrial (i.e., primary, secondary, and tertiary industrial) levels are investigated. The study covers the time period 2005 to 2015 and is based on the Slacks-based measure approach combined with the Super-efficiency model dealing with undesirable outputs; in addition, a multidimensional analytical framework was developed in this study. Afterwards, the geographical detector model is applied to identify the driving forces of WUE, serving as a reference for policies and strategies needed to improve WUE. Results indicate that China’s overall WUE has been improving since 2005, though further progress is necessary. At the regional level, five regions (i.e., water abundance, vulnerability, stress, scarcity, and absolute scarcity) grouped by the availability of water resources per capita are all the most efficient in terms of water use in the primary industry. The absolute scarcity region performs best in all four dimensions despite the scarcity of water resources per capita, yet the water stress region has the lowest WUE. At the provincial level, provinces in the eastern coastal region, especially the south-eastern coastal region, have the highest WUE, whereas those in the interior south-eastern and south-western region have the lowest. Industrial structure, research and development intensity, and higher education are the main driving forces of WUE. Their mutual interactions and their interactions with other indicators are highly influential.

Overcoming socio-ecological vulnerability through community-based social learning: the case of Lomba do Pinheiro in Porto Alegre, Brazil

ABSTRACTCommunity-based initiatives and collective learning practices are key themes in sustainability transition processes. This paper presents the results of a participatory study that investigated a local initiative in the community of Lomba do Pinheiro in south Brazil to examine social learning processes in the context of socio-ecological vulnerability. In this community, a group composed of local residents and members representing the public sector and local educational institutions has promoted several learning-oriented actions aimed at restoring a degraded local watershed and improving residents’ livelihoods. This study used social learning as a lens through which the initiative enacted by this group may be understood, and analysed how local conditions, determined by a context of vulnerability, have influenced local processes. We applied a multi-dimensional analytical framework that included individual, collective, and territorial dimensions. The analysis focused on the leading group, the individuals who comprise it, and their actions in the territory, while considering local constraints. Our findings highlight the importance of (1) shared values, mutual trust, and affective bonds for group cohesion as well as concerted action, equalisation of diverse languages within the group, knowledge integration, and initiative persistence; (2) a practical-reflexive approach based on a sequence of actions that catalyses group learning and facilitates advancement within the wider community; and (3) the role of inter-sectoral articulations and the establishment of partnerships to support actions. This paper raises questions about the limits of an exclusively bottom-up approach to solve complex problems in the context of extremely precarious conditions.

A study on carbon transfer and carbon emission critical paths in China: I-O analysis with multidimensional analytical framework.

As environmental issues aggravated heavily, China faces increasing pressure and challenges on carbon emission reduction and distribution. we used non-competitive input-output table (I-O table) combined with the methods of Structural Path Analysis (SPA) and Multidimensional Analytical Framework (MAF), based on the data of China in 2012, to analyze the current situation of inter-sector carbon emission transfer and identify the key sectors and the critical paths from multiple perspectives. Our results show that total fixed capital formation is the main final demand. The electricity, petroleum, and metal smelting are the largest carbon outflow sectors, which emit carbon at the upstream of the path. Construction and other services are the most obvious carbon inflow sectors, which belong to the middle and downstream of the path and lead to indirect carbon emissions through their demands for other sectors. "Metal smelting → Construction → Total fixed capital formation," "Nonmetallic products → Construction → Total fixed capital formation," and "Petroleum → Urban consumption," "Electricity → Urban consumption" are the top four paths with large carbon emission, which deserve attention. Finally, this paper puts forward some policy implications on emission reduction based on the results.

The Impact of Reforms of National Health Insurance on Solidarity in the Netherlands: Comparing Health Care Insurance and Long-Term Care Insurance

Context: Throughout Europe, the financial risks of health and long-term care are covered to varying degrees through models of national (health) insurance. Such insurance draws upon the principle of solidarity. Much is unknown on the solidarity-effects of reforms in national insurance schemes. Objective: To present an empirical analysis of the effects of recent reforms in national health insurance on solidarity in one country. Methods: We conducted a comparative analysis of the 2006 health care insurance reform and the 2015 long-term care insurance reform in the Netherlands. A multidimensional analytical framework of solidarity was developed to study the solidarity-effects of both reforms. Findings: Reforms of health care and long-term care insurance in the Netherlands had some solidarity effects, but they should not be overstated. We found evidence for increased and decreased solidarity. Health care insurance seems more ‘immune’ to reductions in solidarity than long-term care insurance. Limitations: The present case study involves reforms in the Netherlands. The solidarity framework is specifically designed for the study of solidarity-effects of reforms on national health and long-term care insurance. Effects on informal arrangements for care are beyond the scope of this study. More detailed and quantitative research is required to investigate how the reforms played out for specific groups, for instance the frail elderly, people with a disability and people with rare conditions. Similarly, long-term effects require further investigation. Implications: Given the limited scope of our analysis, more comparative research (including on an international scale) is required to develop systematic insight into the solidarity-effects of reforms in national health and long-term care insurance.

Men in upheaval: integrating Hong Kong politics and critical studies of men and masculinities

In this short review, we explain the need for a multidimensional analytical framework for exploring how the construction of men and masculinities has shaped Hong Kong’s social and political development, and how politics both enable and constrain men in their lived experiences and practices in various life spheres at the city’s current political juncture. We observe that the majority of the extant research on men and masculinities in Hong Kong is depoliticized, whereas studies on current Hong Kong politics are largely gender-blind. However, the ever-tightening grip of the Chinese Communist Party and the instances of political resistance in response, namely, the Umbrella Movement of 2014 and Fishball Revolution of 2016, suggest that the influence of the wider political situation has penetrated every dimension of life, which has considerable implications for masculine identities and practices. Rather than continuing to conduct the depoliticized masculinity studies and genderless political studies we have seen to date, it is time for a thorough investigation of the dynamic intersection of men’s practices and the political context of Hong Kong.