Quality & Quantity

Relying on a Quasi-natural experiment of ecological resettlement in the western pastoral areas in 2004 and retrospective data across an extended period, this paper explores the long-term impacts of ecological resettlement on the incomes of herder households in western pastoral areas of China. We find that ecological resettlement increased herder household incomes from the long-term. The positive impact of ecological resettlement on herder households’ incomes has been greater for households with male heads and for larger households and those with larger pastureland areas. Ecological resettlement mainly realizes the increase of household income by increasing cordyceps income and subsidy income. Ecological resettlement increases the living and health care expenses of herder households, which in turn leads to a significant increase in the probability of loans being taken and, in the amount, borrowed. Cordyceps income and subsidy income are the main sources of income from which resettled herder households repay loans.

The economic and fitness complexity (EFC), a novel approach in economic geography and innovation studies, is a data-driven empirical method that applies complex systems and network theory techniques. It is adept at analyzing high-dimensional data using bipartite graphs and dimensionality reduction methods, providing detailed insights into economic activities. These techniques help uncover latent patterns and relationships in economic activities. The EFC method plays a crucial role in assessing the complexity of patents and the economic diversification ability of municipalities in investing in advanced technologies. Hence, EFC’s framework offers new insights into economic complexity and innovation systems. The paper presents an analysis of Italian patents in 2021–2023 at the NUTS3 level, combining with patent information and economic sectors, following the International Patent Classification and the Italian one on ATECO codes. Patent data are a key indicator of technological innovation analysis. The analysis of the fitness distribution of Italian municipalities points out a significant disparity between the northern and southern regions. In northern Italy, many municipalities, including smaller ones, exhibit good fitness index values, indicating a more widespread distribution of economic capabilities in the northern regions compared to the south. The further objective of the work was to describe the level of innovation of municipalities through patents and to understand whether this process identifies municipalities and areas that already fall within industrial districts

Communicating with social media has become a widely used mode among individuals and stakeholders. In this research, we investigate the relationship between the use of social media in the Italian community of patients with multiple sclerosis. To this end, we collected two kinds of data: (1) information through a structured questionnaire administered to multiple sclerosis patients and (2) tweets about multiple sclerosis from 2018 to 2023. We analysed data from the questionnaire using clustering methods to identify the social profiles of multiple sclerosis patients. The tweets, however, were examined using a double strategy: text analysis techniques and models to identify sentiment and topics and the use of social network analysis algorithms to reconstruct the community of the most active users and topics. The results highlighted a significant presence on social media platforms, particularly Facebook, which protects participants and promotes communication through closed groups. Furthermore, tweeting is the preferred tool for communication among trade associations, hospitals, politicians, and traditional media, which promote awareness of new therapies or opportunities for patients.

With the growing availability of panoramic street-level imagery, on platforms such as Google Street View, researchers can investigate urban landscapes in innovative ways. Virtual neighbourhood audits enable the use of this information to describe urban landscapes and their implications for people. However, most publications exploring these possibilities rely on highly specialized programming skills not yet generalized among social researchers. In this study, we make use of panoramic street-level imagery to assess five municipalities and the territorial and spatial inequalities that shape them. It adds to the literature by proposing a mixed method approach that accounts for urban landscape multi-thematic dimensionality, combining a non-computational data extraction procedure with a multivariate analysis that researchers with low programming expertise can replicate. Observational data not only captured the differences between territories as previously known but also provided new insights into territorial inequalities, offering considerations for potential urban management priorities. Illustrating alternative ways to use open visual data enhances the possibility of insight and understanding of urban landscapes, identifying promising areas for multidisciplinary partnerships.

This study presents a multidimensional analysis of socioeconomic disparities across European regions between 2019 and 2023, focusing on key Sustainable Development Goal (SDG) indicators related to poverty (SDG 1), quality education (SDG 4), gender equality in employment (SDG 5), and decent work and economic growth (SDG 8). Using a dataset at the NUTS-2 regional level, the research adopts a three-dimensional analytical framework to examine how these regions vary across multiple variables over different periods. The study’s core methodology is the Tucker 3 Clustering model, specifically designed to manage complex multidimensional datasets. An in-depth analysis of the T3Clus clusters highlights shared features and regional differences, emphasizing the key drivers of socioeconomic inequalities. The study contributes to policy discussions by shedding light on the interconnectedness of poverty, education, and employment conditions across Europe. It provides valuable insights into how socioeconomic conditions have evolved, identifying 2020 as a turning point year, and pinpoints areas in need of intervention to support more equitable development across Europe. In fact, the analysis reveals significant regional disparities in socioeconomic conditions across European NUTS-2 regions, with Southern Italy, Greece, and parts of Eastern Europe exhibiting the highest levels of poverty and employment challenges, while regions in the Netherlands, Switzerland, and Scandinavia demonstrate stronger socioeconomic conditions. In particular, policies promoting labor market integration, gender equality, and educational access are essential to support vulnerable NUTS-2 regions, while high-performing NUTS-2 regions can serve as benchmarks for best practices.

Rising popularity of green marketing has resulted in an enhanced incidence of greenwashing practices, leading to consumer skepticism. We examine how greenwashing behavior impacts consumers’ purchase intentions, by studying the relationship between greenwashing behavior of firms and consumers’ purchase intentions, mediated by green skepticism, brand embarrassment, and brand hate. We collected responses from 430 respondents using the mall intercept method. The research model and hypotheses have been verified through structural equation modeling (SEM), and the impact of mediation and moderated mediation was examined through conditional PROCESS modeling. We found that customers with high environmental beliefs are more skeptical of greenwashed brands, resulting in brand embarrassment, and further translating into hatred for such brands, adversely impacting the purchase intention. We conclude by discussing theoretical and managerial implications.

Anxiety disorders are among the most prevalent mental health conditions globally, and studying their trends helps societies provide better care and prevention plans. The present study aimed to explore the global trend of anxiety disorders and antianxiety medication-related terms using Google Trend analysis and forecast future trends. A Google Trend analysis was performed on specific search terms related to anxiety disorders from June 2005 to August 2023. Curve estimation regression analysis was conducted for the five main categories, and the Expert Modeler in SPSS software was used to model the data. Auto-Regressive Integrated Moving Average was applied for forecasting. Our model for anxiety-related search terms over the internet accurately predicted trends reported by the World Health Organization. The seasonally adjusted model for anxiety-related terms, BNZs, SSRIs, gabapentin, trazodone, and the sum of all drugs estimated changes of 16.8%, − 44.9%, 18.2%, 67.3%, 36.6%, and 16.8% respectively, from August 2023 to January 2030. Searches for anxiety-related terms and medications have shown an increasing trend since 2005. The forecasting model indicates a 16% rise in general anxiety searches from 2023 to 2030, with medication-related terms predicted to increase more rapidly.

In the wake of global challenges to food security, including wars, famines and climate change, ending world hunger by 2030 seems unachievable unless proactive socioeconomic and agricultural policies enable adequate and efficient food production, access, and stability at the national level. Despite having a diverse agricultural base, Pakistan faces a growing food insecurity challenge. Our study adopts a postmodern policy perspective to examine predictors of food insecurity in Pakistan, given the research gap on experiential food security and post-COVID-19 food security recovery. We conduct empirical analysis using Pakistan Social and Living Standards Measurement (PSLM) 2018–19 Survey and the Special Survey on Evaluating the Impact of COVID-19 to assess pre- and during COVID-19 food insecurity levels in Pakistan. Our contribution is to expand the current postmodern theoretical debate on the prevalence of food insecurity by using household-level food insecurity experiential data to identify moderately and severely food insecure households across Pakistan.

Tourism is nowadays fully acknowledged as a leading industry contributing to boost the economic development of a country. This growing recognition has led researchers and policy makers to increasingly focus their attention on all those concerns related to optimally detecting, promoting and supporting territorial areas with a high tourist vocation, i.e., Local Tourism Systems. In this work, we propose to apply the biclustering data mining technique to detect Local Tourism Systems. By means of a two-dimensional clustering approach, we pursue the objective of obtaining more in-depth and granular information than conventional clustering algorithms. To this end, we formulate the objective as an optimization problem, and we solve it by means of Tabu-search. The obtained results are very promising and outperform those provided by classic clustering approaches.

This study explores the digital access and digital engagement among senior small business owners (SBO), addressing a gap in the literature which has predominantly focused on general population rather than SBOs in a particular age group (60 years old or older). Utilizing the Resources and Appropriation theory, we examine survey data collected from senior SBOs in San Antonio, the seventh largest city in U.S. This study advances existing research by providing an in-depth analysis of how socioeconomic and demographic factors uniquely shape digital engagement and business revenue outcomes among senior SBOs, a largely understudied group. By integrating the Resources and Appropriation (RA) theory, our findings reveal that beyond business characteristics, key structural inequalities such as race, ethnicity, education, and household income, profoundly influence revenue expectations as well as digital engagement levels via digital access and appropriation. Leveraging Partial Least Squares-Path Modeling (PLS-PM), we demonstrate that not only access but also the motivations for internet use are critical determinants of digital engagement among senior SBOs. These insights challenge the assumption that digital disparities are merely generational and instead underscore the systemic barriers that hinder full participation in the digital economy. Our findings make a compelling case for targeted interventions, including expanded broadband access and tailored digital literacy training, to foster economic resilience and equitable growth for senior SBOs in an increasingly digital business landscape.

Agricultural activity, closely linked to nature, faces significant sustainability challenges, requiring appropriate technology. In this paper, we explore the willingness of agricultural producers to adopt technological innovations, analysing both the drivers that motivate change and the barriers that hinder it. By understanding these factors, we seek to provide a more complete perspective on how to facilitate the transition towards sustainable and resilient agricultural practices. There is prolific literature about innovation, addressing the subject from different perspectives. However, little is known about the intricacies of the adoption process, right where initiatives prosper or fail. Indeed, the Innovation Diffusion Theory predicts the rate of adoption, the Technology Acceptance Model postulates the key adoption factors, the Concern Based Adoption Model focuses on the service delivery and barriers; nevertheless, all of them simplify the adoption process into a binary phenomenon, where the aware decision maker has either fully adopted the new technology or rejected it completely. No intermediate state is allowed. We propose in this article a technology adoption model focused on the attitudinal evolution of the decision maker along a multi-stage process, ranging from technology awareness through contemplation and decision to change, to implementation and maintenance. Our empirical approach involves an econometric model applied to behavioural and attitudinal data from agricultural producers in Argentina, to identify the drivers and barriers, either related to the technology characteristics, the company situation, or the psycho-demographic characteristics of the farmers. This information is useful for policy development towards sustainability and provides indicators to measure progress.

The impact of uncertainty on the efficiency of supply chain management is a significant factor that must be considered. This requires an understanding of the underlying causes of uncertainty and the implementation of appropriate strategies to address them. Furthermore, this can lead to an improvement in material and information flow as well as an enhancement in liquidity. The present study is being conducted in two phases: a qualitative and a quantitative phase. The objective of the qualitative phase is to identify the underlying causes of uncertainty through library research. In the quantitative phase, our research contributes to the existing literature by combining the Analytic Hierarchy Process and Cognitive Maps approaches to identify the relationships between the sources of uncertainty at each level of the Fast-moving Consumer Goods supply chain. The results revealed nine effective sources of uncertainty in the supply chain. Although the Analytic Hierarchy Process questionnaire is not without its shortcomings, including the potential for biased expert opinion, we have implemented measures to mitigate these issues and improve the reliability of the questionnaire. Moreover, experts have identified potential solutions to mitigate uncertainty in a supply chain. These include conducting a comprehensive assessment, developing contingency plans and protocols, implementing technology solutions, investing in training, and focusing on customer preferences.

This study focuses on the nexus between financial inclusion and energy intensity of G7 nations from 1993 to 2023 with the expectations of presenting energy efficiency as a solution to global energy issues. During the estimation process, this study employs advanced quantile methodologies such as the Quantile unit root tests, the Quantile cointegration analysis, the Quantile-on-Quantile regression, and quantile Granger causality to determine the nonlinear and heteroscedastic relationship between financial access and energy consumption. The study shows that different countries’ cointegration relationship is not symmetric; this means, that financial inclusion reduces energy-intensive countries. This strongly implies that enhanced financial service increases access to technologies and practices such as energy-efficient technology and sustainable energy solutions thereby decreasing the consumption of energy. Further, the study establishes that there is a two-way causality between on one hand, financial inclusion and on the other hand energy intensity, which connects both financial and energy industries. The outcomes also point out that financial inclusion affects each quantile in different manners so policies aimed at the level of financial development and energy consumption should be developed. These observations reinforce the importance of aligning policies aimed at enhancing financial liberalization with energy liberalization in a bid to offer energy to societies. The findings of the study are important for policy makers, especially in underpinning the significance of financial access in controlling energy consumption and achieving the objective of sustainability in the G7 countries.

This study investigates the effects of internship education programs on graduates’ employability in Saudi Arabian higher education institutions, focusing on the mediating role of students’ IT skills. A quantitative approach using partial least squares structural equation modeling (PLS-SEM) was employed to analyze data from 325 graduate students across four Saudi Arabian universities. The findings revealed that students’ IT skills significantly affected their employability. Organizational resources, program design and structure, and program relevance positively affect students’ IT skills, which fully mediates the relationship between these internship program aspects and graduates’ employability. Surprisingly, internship duration, intensity, mentorship, and supervision did not directly influence IT skills or employability. These results emphasize the importance of well-designed, industry-relevant internship programs that prioritize IT skill development to enhance graduates’ employability. This study contributes to the literature on internship education and employability in Saudi Arabian higher education by providing empirical support for experiential learning theory in the context of IT skill acquisition through internships. It also offers insights into how internships can support the Saudi Vision 2030 objectives. This study highlights the need for collaboration among higher education institutions, policymakers, and industry stakeholders to optimize internship programs. Limitations include the cross-sectional nature of the data and the focus on specific internship aspects and IT skills. Future research could employ longitudinal designs and explore a broader range of skills and program components.

This aims to compare the intrinsic motivations of the hosts of the online travel communities Airbnb and CouchSurfing in the district of Miraflores–Lima, Peru, from the perspective of millennial guests. Through a qualitative approach, it was using Atlas. ti software analyzed 516 comments. They were identified to identify three main intrinsic motivations of the host: Social Interaction, Altruism (being the most relevant in both Airbnb and CouchSurfing), and Cultural Capital (with greater importance for CouchSurfing). Comparing intrinsic host motivations is key to understanding how to attract new hosts and potential and encourage current guests to change roles and become hosts. In this way, the sustainability of the economic well-being and growth of shared accommodations promoted through the Internet is enhanced, as the balance between hosts and guests can be maintained, which is critical for the community’s survival.

The comic book market is a niche that has significantly benefited from the more time spent at home by people during the outbreak of Covid-19. This is particularly true in Italy, where sales of comics have met an unprecedented expansion during the two decades before the pandemic. It is therefore important for policy makers to understand the impact of Covid-19 on the economic performance of the Italian comic book industry. To this purpose, yearly data on 10 financial ratios are collected for a panel of 13 Italian comic publishers in the period 2011-2022, and an interrupted time series (ITS) design based on panel vector autoregressive (PVAR) models is applied. In order to maximize the statistical power of the design given the small number of both time points ( T=9 T = 9 ) and cross-sectional units ( n=13 n = 13 ), we correct least squares estimates based on the half panel jackknife and perform model reduction through system sequential elimination. The results highlight a worsened performance for small publishers used to base most of their business on comic fairs (Round Robin, Magic Press Edizioni), while companies able to renew their business by launching new products and re-editions (the historic Astorina and Sergio Bonelli Editore, but also the young Tunué) experienced a significant growth compared to the pre-pandemic period. Publishers that dedicated most of their activity to the publication of manga (Bao Publishing, Coconino, Edizioni BD, Edizioni Star Comics, and also Panini with the label Planet Manga) received even more benefits from the pandemic.

Bibliometric studies are recognized as a rigorous and effective method for the assessment of large quantities of scientific information. This paper's objective is to outline the intellectual structure of risk management research in Microfinance and Financial Inclusion (MFI) and agricultural risk across the SAARC countries. The research investigates the changing patterns of teamwork among scholars from various areas of study. It identifies different groups of countries and organizations within the SAARC region. A bibliometric framework was developed to look at 1,737 articles published from 2004 to 2023. The study was employed using the bibliometrix R tool, which helped in collecting and the merging data from both databases (Web of Science and Scopus). The results explore the significant impact of MFI and its importance in lowering agricultural risks in South Asia. This research offers insights into developing trends and proposes future research directions to enhance risk management strategies across various sectors. This analysis is among the first to scrutinize the scientific pathways of risk management issues via computational methods. It enhances the understanding of the existing research on MFI and agricultural risk within the SAARC region, thereby aiding practitioners, scholars, and policymakers in developing more robust strategies for sustainable development.

Identifying digital platform companies has proven to be a formidable task due to the intricate nature of these organizations. This complexity often results in incomplete depictions, exacerbated by the inherent tendency of digital platforms to blur traditional sectoral boundaries. To bridge this knowledge gap, we propose an innovative methodology that harnesses the power of Natural Language Processing (NLP) techniques for the systematic identification of digital platform companies on a global scale. Moreover, we present an applied exercise aimed at creating a comprehensive world map that precisely locates these platform companies. Our approach and exercise offer four distinct contributions: (i) our methodology validates an artificial intelligence algorithm-based approach for identifying companies based on the products and services they offer. This not only enhances the accuracy of our identification process but also sets a precedent for the application of AI in this context; (ii) by facilitating the identification of digital platform firms, our methodology empowers researchers in the fields of business and economics. This empowerment enables a more precise and comprehensive understanding of the intricacies of the platform economy, thereby facilitating in-depth research and analysis; (iii) our findings provide invaluable insights for policymakers who grapple with the complexities of the platform economy. These insights serve as a crucial tool for crafting effective regulations and fostering healthy competition within the digital marketplace, ultimately benefiting consumers and businesses alike; (iv) through the visual representation of platform company distribution on our map, we offer a tangible means to test and refine existing theories regarding how these companies operate and thrive in various regions. This empirical validation contributes to the advancement of platform geography theories, particularly those related to value creation and appropriation.

Missing data is a common issue in structural equation modeling, including partial least squares structural equation modeling, which can lead to biased results and weaker statistical conclusions. Despite the popularity of partial least squares structural equation modeling in multivariate analysis, there is a lack of rigorous handling and reporting of missing data in the literature. Most studies rely on complete case analysis and mean imputation, overlooking modern techniques. Recent works have applied and evaluated modern approaches but lack insights into parameters that influence performance. This paper addresses this gap by evaluating the performance of various imputation methods, including expectation maximization and multiple imputation, through the analysis of simulated datasets with varying sizes and levels of missingness. In contrast to previous works, this study considers the effects of factors such as multivariate distribution and model complexity. The findings provide evidence for the superior performance of donor-based multiple imputation techniques and the impact of properties of data and model. In addition, we present recommendations for researchers on how to effectively manage missing data in partial least squares structural equation modeling, enhancing the reliability and validity of their results.

This study explores the use of large language models (LLMs) to predict emotion intensity in Polish political texts, a low-resource language context. The research compares the performance of state-of-the-art LLMs against a supervised model trained on an annotated corpus of 10,000 social media texts, evaluated for the intensity of emotions by expert judges. The findings indicate that while the supervised model generally outperforms LLMs, offering higher accuracy and lower variance, LLMs present a viable alternative, especially given the high costs associated with data annotation. The study highlights the potential of LLMs in low-resource language settings and underscores the need for further research on emotion intensity prediction and its application across different languages and continuous features. The implications suggest a nuanced decision-making process to choose the right approach to emotion prediction for researchers and practitioners based on resource availability and the specific requirements of their tasks.

This study explores how accounting education influences students’ readiness for the digitalization of the accounting profession, with a particular focus on Tunisian accounting students. A qualitative approach is employed, utilizing cognitive mapping techniques with a sample of 74 students. Through cognitive mapping, key cognitive structures related to factors such as faculty expertise in digital accounting, resource availability, and continuous professional development initiatives are identified. Faculty expertise, in this context, refers to instructors’ proficiency with digital tools and their ability to incorporate these tools into their teaching. Resource availability pertains to the access students have to digital learning tools and development opportunities that enable engagement with modern accounting technologies. The findings indicate that faculty expertise and access to digital resources significantly contribute to students’ readiness for digital accounting tasks, fostering skills that go beyond traditional accounting practices. A practical recommendation is to implement targeted faculty development programs and increase funding for digital tools, ensuring alignment between accounting curricula and industry standards. This study underscores the importance of embedding digital literacy into accounting education, offering insights for educational institutions and policymakers on preparing students for the evolving technological demands of the accounting profession.

In this study we describe several diagnostic methods for Generalized Estimating Equations approach. The principal idea behind generalized estimating equations is to generalize and extend the usual likelihood score equation for a generalized linear model by including the covariance matrix of the clustered responses. The advantage of generalized estimating equations is that we do not need to specify the whole response distribution, only the mean structure and, with the aim to increase efficiency, the covariance structure consisting of a working correlation matrix along with the variance function defining the mean–variance relationship. The paper investigates, from a methodological point, to the identification of the best subset of variables, considering the link between the coefficient of determination and Wald Statistics. Some diagnostic measures and a simulated envelope for checking the adequacy of GEE method will be presented. In particular, diagnostic measures are considered and applied to a dataset to assess the impact that some economic-financial variables have on the points made by football teams participating in the Series A League.

This research investigates how distinct forms of motivation, altruistic and egoistic, influence customers’ purchasing intentions for green products. Three hundred respondents were surveyed using a structured questionnaire to obtain the data. This quantitative study employs the structured equation model (SEM) approach. Data were analyzed by Smart PLS version 4.1.0.8 using the PLS algorithm and bootstrapping. The study results indicated that health, economic, and social concerns positively correlated with altruistic and egoistic motivation. In contrast, political concerns had no impact on altruistic and egoistic motivation. The study also showed altruistic and egoistic motivation significantly and positively influenced green product purchase intention. The results show that altruistic motivations often increase purchase intention for green items by appealing to people’s desire to contribute positively to environmental sustainability. This study has elucidated sustainable consumption behaviors, including the consumption of green products, and identified the most influential motivational factors. Additionally, it conveys a clear message to policymakers, enabling them to promote consumer adoption of green products.

The underrepresentation of women in science, technology, engineering and mathematics (STEM) fields has been a subject of extensive research and policy debate. However, there is limited clarity regarding the specific mechanisms that generate these disparities, and which interventions are most effective in reducing the gap. In this study, we use causal decomposition analysis to estimate how the gender gap in STEM participation would change if we were to intervene on women’s self-efficacy beliefs in mathematics. Women tend to underestimate their abilities in math-related fields, which can affect their educational and career choices. The question we ask is to what extent the gender gap in individuals’ enrollment in STEM majors and identification with mathematics would be reduced if self-efficacy in mathematics were set to be equal across gender categories. The results suggest that equalizing this target factor will reduce the observed disparities in math identity by 53%, and in the enrollment of STEM majors by 2.5%. The modest influence of self-efficacy on enrollment disparities suggests that it is not the predominant factor. We discuss the implications of our empirical findings, as well as how causal decomposition analysis can benefit social and behavioral disparities research.

Primary data collection has become increasingly challenging. There are many reasons for this: Not only has the cost of conducting face-to-face interviews increased and the number of available interviewers decreased, but the ongoing shift to web-based interviewing has resulted in shorter questionnaires, making it difficult to accurately measure latent constructs and cover a wide range of topics. Therefore, despite the advantage of conducting primary data collection to match one's research questions, secondary data analysis is often more feasible. For this purpose, data archives such as the Consortium of European Social Science Data Archives (CESSDA) provide a large amount of high-quality data. However, a common problem when working with secondary data is that important variables are missing in one dataset and are only available in another. We propose a possible solution to overcome this problem by using "data fusion", which allows to augment one dataset by including the missing variables that are initially only available in another dataset. From a statistical point of view, this corresponds to a missing value problem, which is why multiple imputation is often used to fuse datasets. Despite this promising idea, data fusion is only sporadically applied in the social sciences. This paper discusses the potential of this statistical technique in the context of social science research and derives a guide for practitioners interested in applying the method to their own research. The method and potential are discussed via an example using data from the European Social Survey (ESS) and the Austrian Social Survey (ASS).