Socio-economic Planning Sciences最新文献

Digitalization has profoundly reshaped work modes and lifestyles and impacted individuals' life satisfaction. However, there has been limited research exploring this issue while comparing the effects before and during the COVID-19 pandemic. Furthermore, investigating heterogeneity across different socio-economic groups is crucial. This study uses data from the latest three waves of the European Social Survey in 2016, 2018, and 2020 to examine the influence of Internet usage on life satisfaction, unravel its underlying mechanisms, and conduct heterogeneity analysis with the fixed-effects ordered logit model and propensity score matching method. The empirical findings reveal the following: (a) Internet usage has significant and positive effects on life satisfaction, although the marginal effects of Internet usage decreases as respondents’ life satisfaction increases; (b) respondents with a “right” political tendency, higher levels of social interaction and trust, females, older individuals, higher income earners, those with lower education levels, better health conditions, and stronger religious beliefs tend to report higher life satisfaction; (c) work flexibility, work–life balance, and team engagement are identified as essential mediating factors in the relationship between Internet usage and life satisfaction; (d) Internet usage has had a significant and positive effect on life satisfaction since the outbreak of the COVID-19 pandemic, whereas this was not the case before the pandemic; and (e) the influence of Internet usage on life satisfaction is more pronounced among young, affluent communities, well-educated individuals, Eastern and Central Europeans, non-managers, and employees of central/local governments and private firms. This study underscores the rapid socio-economic transformation induced by digitalization in Europe and provides valuable insights on leveraging the Internet to improve individual life satisfaction in the post-pandemic era.

Benford’s law is a mathematical model, very recurrent in practice for a wide variety of datasets, used to represent the frequencies of digits. A well-established usage of Benfordness statistical testing lies within investigations aimed to ascertain if balance sheet and income statement data are genuine. A typical, frustrating problem of Benfordness statistical tests on big, practical datasets is that they often provide p-valuessmaller than expected when the Benfordness null hypothesis is very realistic. A possible reason is that data are contaminated by some kind of noise. In this paper we propose the deconvolution approach to alleviate this issue, using both simulated and real data.

Besides international efforts to address poverty and hunger, there are still many disparities among households, therefore innovative approaches and techniques are required to address the multiple facets of poverty, especially food poverty.

Given the complexity in defining this phenomenon, the fuzzy approach based on an expenditure-related metric is adopted as it provides valuable insights into the Italian food poverty situation thus improving the results generally provided using by the classical binary (or crisp) set theory.

By considering the equivalised household food expenditure obtained from the 2021 Italian Household Budget Survey data, the fuzzy set approach is used to measure food poverty paying particular attention to vulnerable households at a detailed territorial level. The results show that food poverty is lower in the Northern-Central regions than in Southern Italy and highlight the vulnerability of families with children and single-parent households.

Pre-positioning relief inventory ensures timely delivery of in-kind aid after a catastrophe. Tragic disasters like major earthquakes are rare and unpredictable; therefore, stockpiled items may not be used. To avoid over-stocking and reduce shortage risk, the cooperation of two humanitarian agencies in supporting each other in case of shortages is suggested in the literature. In this study, we utilize newsvendor-based quantitative models to optimize the pre-disaster stocking decisions of agencies under centralized and decentralized cooperation mechanisms. In the former, both agencies jointly determine their inventory levels to maximize their combined benefits of relief operations, whereas, in the latter, each agency establishes its stocking level in isolation via a game theoretic approach. In both systems, the two agencies agree to transship their excessive items to the other party if needed. In this regard, we investigate the situation where only a portion of the transshipped items, denoted as the reliability factor, can be received and effectively utilized at the destination due to the chaotic nature of the disaster. Considering a deterministic reliability factor, we obtain the singular optimal inventory levels in the centralized system and identify the unique Nash Equilibrium in the decentralized system. Subsequently, we formulate a two-stage stochastic program, considering a random reliability factor for both cooperation systems. The study concludes by offering a range of managerial insights. Our analyses quantify the sub-optimality resulting from decentralized decision-making across diverse parameter settings using the concept of the price of anarchy. The findings highlight that centralized cooperation becomes particularly advisable when the average demand within either agency is high, the transshipment process is secure (i.e., the reliability factor is high), and transshipment costs remain low.

Scholars have only recently been focusing on the study of knowledge in the formation of loyalty in cultural festivals and much remains to be understood regarding this. The present study adopts a managerial standpoint and develops a knowledge-based model of attendee loyalty in cultural festivals in which the role of authenticity is also evaluated. It considers two knowledge-based constructs, prior knowledge and acquired knowledge, referring to the cultures and the traditions evoked during the festival, and intercepts their role with respect to loyalty also in conjunction with authenticity. Based on data collected from 306 attendees at a cultural festival organized annually in South Italy, a co-variance-based SEM was performed. Results highlight that prior and acquired knowledge positively influence loyalty. Perceived authenticity acts as a mediator in these relationships. These findings provide theoretical and practical implications for researchers and festival organizers.

Green transition driven by clean technology diffusion attaches importance to sustainable development. Therefore, choosing the optimal strategy in the inter-regional diffusion of clean technology has attracted the attention of local governments. In this regard, developed regions leading the R&D activities of clean technology can choose to fund or not fund diffusion, and developing regions can adopt absorption strategy or substitution strategy as well. An evolutionary game model was developed in this paper to analyze the stable strategies of local governments in the inter-regional diffusion of clean technology. In general, there are four possible stable equilibrium points. The result indicates that a collaboration relationship aiming at technology transfer among regions is likely to be established when developed regions and developing regions gain more from funding diffusion and subsidizing absorption, respectively. Especially, an increase of incentives from the central government, as well as an increase of cost performing independent clean R&D activities, improves the inter-regional diffusion network. This paper, therefore, provides a mathematical approach to examine the interaction of clean technology diffusion, and sheds light on actor behavior in games of inter-regional coordination.

The analysis of social inequalities is a topic of current interest and is studied as a factor in the evolution and measurement of the level of well-being. A fundamental prerequisite for a correct statistical analysis of this phenomenon is the need to share a univocal definition of the concept of social inequalities. This work starts from the need to identify territorial areas and/or population subgroups characterized by situations of hardship or strong social exclusion through the construction of indicators that can estimate situations of social inequalities in small areas. Scientific research options have been oriented towards the establishment of a multidimensional approach, sometimes renouncing dichotomous logic to go as far as fuzzy classifications in which each unit simultaneously belongs and does not belong to the selected category. Multidimensional statistical analysis methodologies (TFR method) and Density Based Spatial Clustering methods (DBSCAN) will therefore be used to aggregate adjacent spatial units with high intensity of social inequalities.

I study individual consumers’ choices of getting vaccinated. A vaccine reduces the severity of an infectious illness, but may produce side-effects and other disutilities at the time of administration. Such private benefits and disutilities vary across consumers in the population. The infection probability depends negatively on the total mass of vaccinated consumers. This is an externality. One consumer’s vaccination choice has negligible contribution to the total mass of vaccinated consumers. Consumers do not internalize the externality. I characterize a unique Vaccination Equilibrium, the sustainable vaccination mass resulting from individual decisions. I show how vaccine improvements in benefits, side-effects, and infection likelihood change the Vaccination Equilibrium. The first-best or efficient vaccination mass takes into account the externality and consumers’ benefits and costs. The unique Vaccination Equilibrium is never first best. Taxes, subsidies, and mandates may change the Vaccination Equilibrium.

In this study, the classical CCR-IO DEA model (an input-oriented form of the DEA model of Charnes, Cooper, and Rhodes (1978)) is extended to an intuitionistic fuzzy environment. For this aim, the trapezoidal intuitionistic fuzzy numbers are used to show the values of inputs and outputs. An intuitionistic fuzzy number is characterized by membership, non-membership, and hesitancy degrees. Therefore, such type of fuzzy numbers is more complete and contains more information to reflect a fuzzy type of uncertainty comparing to the conventional fuzzy values. In order to solve the proposed intuitionistic fuzzy CCR-IO model, for the first time a possibility-based approach is developed for the intuitionistic fuzzy DEA models. In this approach for the first time the $$ measure of fuzzy events is extended to intuitionistic fuzzy DEA models, therefore, some new definitions and propositions are presented and clarified. This measure is more flexible and effective comparing to the other measures such as credibility, necessity, and possibility measures. A case study from the health care sector of Iran is considered to evaluate the proposed solution approach numerically. The case study is solved under different scenarios, a full sensitivity analysis is performed, and finally some useful managerial insights are presented. In addition, the results of the proposed intuitionistic CCR-IO model are compared to the classical models of the literature.

The aim of this study is to propose a novel analytical framework to evaluate the national innovation system (NIS) based on dynamic-network data envelopment analysis. This framework is then applied to a sample of 23 countries involved in oil production, enabling a comparative analysis of their performance in relation to the top 10 innovative countries as determined by the Global Innovation Index (GII).

Unlike the GII, the proposed framework measures the efficiency score of each NIS, generates a new index based on these scores, suggests a set of target policies for countries aiming to improve their ranks, and conducts an in-depth longitudinal analysis to identify the underlying factors influencing efficiency dynamic over time.

The empirical results show that the efficiency of the selected countries remains relatively steady over the designated study period. Upon closer examination at the country level, it becomes evident that Korea and Sweden emerge as the frontrunners in terms of performance, thereby establishing themselves as leaders in the field of innovation. Germany and Ukraine are identified as leaders in knowledge development, whereas Romania and Singapore are recognized as leaders in knowledge commercialization.

To enhance the performance of the inefficient NIS, this study proposes targeted improvement policies, prioritizing underperforming countries to elevate their overall innovation system. These policy interventions aim to bridge the gap between the least efficient nations and global best practices, fostering a more competitive innovation landscape. Ultimately, the proposed analytical framework holds the potential to foster the performance of each country's national innovation system.