Empirical Results Research Articles

Party system transformation from below: protests by Jobbik and LMP

AbstractRecent changes in European politics—such as the rise of so-called movement parties on both the left and the right—call for a rethinking of the links between protest dynamics and party system transformation. In this paper, we contribute to the theoretical and empirical understanding of the mechanisms that unfold in periods of intensified interactions between protest and electoral politics through a case study of the transformation of the Hungarian party system. We ask what mechanisms drive parties' involvement in protests before and after entering parliament. We focus on two competitors: the radical right Movement for a Better Hungary (Jobbik) and the green party Politics Can Be Different (LMP). Using a dataset of protest events compiled from Hungarian news agency reports and official police records, we map the protest network mobilized by the two parties between 2002 and 2022. We analytically distinguish and empirically identify three different mobilization mechanisms: strengthening the party's profile by protesting its own issue, using protest mobilization to build alliances with other parties and actors, or relying on protest to establish a presence at the local level. The empirical results show that Jobbik relies on protest through a combination of different mechanisms, while LMP uses protest to broaden its issue profile and build alliances with other political parties. Over time, both parties increasingly rely on protest to establish a local presence outside of Budapest.

Can Consumers’ Altruistic Inferences Solve the CSR Initiative Puzzle? A Meta-analytic Investigation

AbstractResearch into consumer responses to corporate social responsibility (CSR) initiatives has expanded in the past four decades, yet the evidence thus far provided does not paint a cohesive picture. Results suggest both positive and negative consumer reactions to CSR, and unless such mixed findings can be reconciled, the outcome might be an amalgamation of disparate empirical results rather than a coherent body of knowledge. The current meta-analysis therefore tests whether the mixed findings might reflect consumers’ distinct, altruistic inferences across various contingency factors. On the basis of 337 effect sizes, involving 584,990 unique respondents, in 162 studies published between 1996 and 2021, this study reveals that altruistic inferences are central to the current CSR paradigm, such that they mediate the effects of CSR initiatives on consumer responses across multiple contingencies. The mediation by altruistic inferences is stronger (weaker) in conditions favorable to dispositional (situational) motive attributions. Furthermore, consumers respond more favorably to cause marketing or philanthropy rather than business-related CSR initiatives, when the initiative is environmental (vs. social), the firm’s offering is utilitarian (vs. hedonic), the CSR initiative takes place in self-expressive (vs. survival) cultures and in earlier (vs. later) periods. These findings offer several ethical implications, and they inform both practical recommendations and an agenda for further research directions.

A grasp of police research: characteristics of empirical research on police careers based on multiple scoping reviews

PurposeThis article provides an overview of the literature characteristics of empirical research on topics related to police careers from 2000 to 2021. Methodology, distribution in time and space and types of publication are presented. Recommendations for new research avenues are given.Design/methodology/approachEight scoping reviews on specific topics were carried out by criminology students under the close guidance and supervision of the first author, an academic researcher. The reviews followed the same procedure, enabling an overarching reflection.FindingsThe scoping reviews resulted in 179 unique publications for analysis. It appears that the topic of police professional competencies is studied more often in the field of police careers (n = 55), in contrast to informal learning in police training (n = 4) which was studied the least frequently. Since 2012, there has been a noticeable increase in the number of publications. Publications in scientific journals are by far the most common (n = 153), as is a quantitative research design (n = 123). All topics have been studied in Europe and North America.Research limitations/implicationsMore qualitative research and international dissemination of empirical results are recommended to gain a deeper understanding of police careers. As for the limitations, specific topics were selected, which limits the scope of the findings. Working with students for data collection has its benefits in terms of workload, but comes with potential limitations in terms of quality. It is recommended to conduct a screening using the four-eyed principle, as was done here by the first and second authors. Additionally, the review protocol (e.g. keywords and databases) has an influence on the outcome. Different choices may lead to different results.Originality/valueA comprehensive analysis of police career literature is made based on eight scoping reviews that followed the same procedure. It allows to study the literature in a broad sense rather than studying one topic in depth.

Error bounds for Gauss–Jacobi quadrature of analytic functions on an ellipse

For the Gauss–Jacobi quadrature on [ − 1 , 1 ] [-1,1] , the location is estimated where the kernel of the error functional for functions analytic on an ellipse and its interior in the complex plane attains its maximum modulus. For the Jacobi weight ( 1 − t ) α ( 1 + t ) β (1-t)^\alpha (1+t)^\beta ( α > − 1 \alpha >-1 , β > − 1 \beta >-1 ) except for the Gegenbauer weight ( α = β \alpha =\beta ), the location is the intersection point of the ellipse with the real axis in the complex plane. For the Gegenbauer weight, it is the intersection point(s) with either the real or the imaginary axis or other axes with angle 1 4 π \tfrac {1}{4}\pi and 3 4 π \tfrac {3}{4}\pi . Our results support the empirical results provided by Gautschi and Varga [SIAM J. Numer. Anal, 20 (1983), pp. 1170–1186] for the Jacobi weight, the Gegenbauer weight and the Legendre weight. The results obtained are also illustrated numerically.

Health effect of public sports services and public health services: empirical evidence from China.

There is no clear explanation for the extraordinary rebound in China's population mortality over the past decade. This paper utilizes panel data from 31 Chinese provinces from 2010 to 2020 to determine the distinct impacts of public sports services (PSS), public health services (PMS), and their interaction on population mortality. Empirical results show that public sports services significantly reduce mortality. Every unit increase in public sports services reduces mortality by about 2.3%. It is characterized by delayed realization. Public health services were surprisingly associated with a rebound in mortality. Further studies found strong health effect from interaction of public sports and health services. The effect was significantly strengthened in areas with fewer extreme temperatures or developed economy. The findings have important policy implications for the high-quality development of public sports and health services. It also emphasizes integration of sports and medicine and mitigates health risks associated with extreme temperatures.

Does the source of oil price shock matter for Indian sectoral stock returns? A time-frequency approach to analyse dynamic connectedness and spillovers

ABSTRACT This paper examines the connectedness and spillovers between decomposed oil shocks and Indian sectoral equities in a time-frequency domain using the most recent Barunik and Krehlik (2018) approach. Our empirical results show that the oil demand shock is the major spillover transmitter across all time horizons followed by the risk shocks; while oil supply shock appears to be a net receiver in all the frequency bands indicating the differential impact of oil shocks. Among the sectors, Basic Materials and Finance receive the highest spillovers from oil shocks in the short-term; while Consumer Discretionary Goods & Services and Industrials join the list in the medium- and long-term. FMCG, Health, Telecom and IT sectors receive the least spillovers in all the bands, making them apt for investments during periods of high volatility. Our empirical results are robust to the application of the alternative TVP-VAR time-frequency parameter framework. The portfolio analysis shows that inclusion of stocks in Oil and sectors like Metal and Telecom significantly reduces the portfolio risk. Dynamic connectedness analysis reveals that the spillovers dramatically increase during times of extreme turmoil, especially during the Global Financial Crisis (2008–2009) and the COVID-19 pandemic. Policy implications of our empirical results are also discussed.

Optimizing Business-to-Business Customer Satisfaction Analysis through Advanced Two-Stage Clustering: Insights from Industrial Parks

Optimizing Business-to-Business Customer Satisfaction Analysis through Advanced Two-Stage Clustering: Insights from Industrial Parks

An online transfer learning based multifactorial evolutionary algorithm for solving the clustered Steiner tree problem

The rapid increase in the number and computing power of devices interacting through the network over the past few years has led to a growing trend of decentralized clustering network architecture. The Clustered Steiner Tree Problem (CluSteiner) is a recently introduced NP-Hard problem and is assessed to be the core of efficient multicast routing in such networks. However, research on this problem in terms of theory and algorithmic design is still in its infancy as current solving approaches are limited in the literature. Lately, the Multifactorial Evolutionary Algorithm (MFEA) has been applied to solve several clustering-structure network design problems for the reason that these problems rarely exist independently and are often deployed concurrently in practice. To effectively transfer knowledge between problems while solving them simultaneously, this paper proposes an approach based on the novel data-driven MFEA-II to deal with the CluSteiner problem. In the proposal, an efficient encoding and a two-level decoding mechanism are introduced, combined with a module capable of online learning the amount of knowledge transferred between tasks that help the algorithm to explore potential regions of the search space. Experiments are undertaken on various test instances to evaluate the efficacy of the proposed algorithm. The empirical results indicate that our proposal can perform well on both metric and non-metric graphs with the statistical tests used to verify its efficiency over other baseline algorithms regarding the solution quality and convergence trend.

Digital finance, financing constraints, and green technological innovation: A spatial analysis

This study investigates the role of financing constraints in mediating the influence of digital finance on green technological innovations by using the spatial Durbin model. Based on the panel data of 273 Chinese cities from 2011 to 2020, the empirical results demonstrate that local and neighboring financing constraints mediate more than 50% of the direct impact of local digital finance on the quantity and quality of green innovation. Moreover, these financing constraints completely mediate the indirect impact of neighbors' digitalization levels, one of the three indicators of digital finance. This underscores the importance of addressing financing constraints and fostering regional cooperation to enhance the efficacy of digital finance in advancing green technology.

New maize inbred development method I: Inter‐heterotic group composite and tester choice

AbstractCurrent maize (Zea mays L.) breeding methods and the limited germplasm infusion and mixing may slow the rate of genetic gain in the future. Using inter‐heterotic group composites for maize inbred development has been questioned because of the issue of choosing appropriate testers. However, three observations can be revisited to facilitate the exploration of alternative methods. First, since the adoption of single‐cross hybrids and the establishment of heterotic groups, the amount of heterosis has only been increasing slightly. Second, genomic analysis indicated the frequencies of the favorable alleles had significantly increased after a century's breeding. Third, the divergence of maize heterotic groups was preceded by the merging of landraces and diverse inbreds, after which three major heterotic groups were established. The objective of this study was to design different scenarios to generate inter‐heterotic group composites and evaluate their usefulness in the commercial breeding program. We investigated two methods: the inter‐heterotic group composite method (termed as super population [SupPop]) and the modified SupPop method (ModiSP, that is, backcrossing SupPop lines with elite inbreds). The results showed that the best scenario for the SupPop method was to use the SupPop inbreds to cross with a tester from the B14 group. When all four heterotic groups were mixed, the favored tester was from stiff stalk. However, ModiSP showed a higher chance of producing the best yield performance. These empirical results suggest that solutions can be proposed to identify the testers so that using inter‐heterotic group composites for inbred development can be considered.

Constrained Tiny Machine Learning for Predicting Gas Concentration with I4.0 Low-cost Sensors

Low-cost gas sensors (LCS) often produce inaccurate measurements due to varying environmental conditions that are not consistent with laboratory settings, leading to inadequate productivity levels compared to high-quality sensors. To address this issue, we propose the use of Machine Learning (ML) to predict accurate concentrations of pollutant gases acquired by LCS integrated into an embedded Internet of Things platform. However, a key challenge is to optimize an accurate ML design under low memory and computation power constraints of microcontrollers (MCUs) while maintaining accurate ML scores. After data analysis and pre-processing, we assess and analyze the performance of five ML algorithms to predict the concentration of pollutants gases from multiple specifications (weather, presence of other gases, etc.). To support the experiments, datasets from three sources are used: (1) VOCSens, (2) Belgian Interregional Environment Agency cell, and (3) Visual-Crossing. Once the best model was optimized and validated, multiple hard constraints were added to the selected ML structure to satisfy material and expert requirements. Trained models were ported to be implemented locally in a MCU after comparing several porting libraries. The assembled code obtained is evaluated based on two metrics: storage memory consumption and inference time, relative to the highest attainable capacities. The improved random forest is the best ML model for the used dataset with an R2 score meeting of 0.72 and Root Means Square Error of 0.0028 ppm. The best generated Tiny-ML model needs 3% of RAM and 98% of Flash storage. The empirical results prove that the developed ML algorithm applied to LCS provides high accuracy to predict pollutant gases. This algorithm can also be used to adjust the LCS systems to provide calibrated data in real time, even if the platform being used is not particularly advanced or powerful.

Examining Property Value Increment along Greenways: A Hedonic Pricing Analysis in Chengdu, China

Examining Property Value Increment along Greenways: A Hedonic Pricing Analysis in Chengdu, China

Unpacking the impact of entrepreneurial learning on business model innovation in internet startups: Mediating roles of digital capabilities

While researchers recognize the central role of business model innovation (BMI) in the success of digital startups, empirical research systematically exploring the pathways of BMI in the digital era for such startups remains scarce. This study employs the dynamic capability theory to construct a theoretical framework termed the “learning capability model” to examine the influence of entrepreneurial learning on BMI. We collected questionnaires from 386 Internet startups and utilized hierarchical regression analysis to explore the impact of digital capabilities on these startups. We first identified two different types of entrepreneurial learning: exploratory learning and exploitative learning. Subsequently, we tested for their effects on BMI. The empirical results indicate that both exploratory and exploitative learning significantly impact BMI. However, exploitative learning exerts a greater influence than exploratory learning. These tests revealed that digital sensing capabilities, digital operational capabilities, and digital resource collaboration capabilities have a partial mediating effect on the relationship between entrepreneurial learning and BMI. Among these, digital operational capabilities exert the most significant mediating effect, while digital sensing capabilities exhibit weaker mediating effects. This study contributes to our understanding of digital capabilities and provides practical insights for Internet startups.

The effect of off-balance sheet activities on credit risk of African banks

Purpose The purpose of this study is to analyze the effect of off-balance sheet activities on the credit risk of African banks. Design/methodology/approach The theory about the relationship between off-balance sheet activities and bank risk was used to construct a conceptual model of the effect of off-balance sheet on credit risk in an African context. The accounting approach is chosen by collecting accounting data extracted from the annual reports of 24 private and conventional African banks during the period 2010–2019. Both statistical and empirical studies are conducted. The statistical study aims to give a description of sample banks in terms of off-balance sheet activities and key financial indicators. The empirical study has the goal of exploring the correlations between, on the one hand, credit risk and, on the other hand, off-balance sheet ratio and control variables (bank- and country-specific variables). This study is based on dynamic panels using the two-step generalized method of moments technique to estimate regressions between credit risk and the explanatory variables. Findings The statistical study reveals that sample banks use moderately off-balance sheet activities; most of them use essentially guarantees and letters of credit, have satisfactory financial indicators and are slightly exposed to credit risk. The empirical results from the two-step generalized method of moments technique disclose that off-balance sheet activities have an intensifying effect on the credit risk of African banks. However, the increasing effect can be minimized when African banks use moderately off-balance sheet activities. Practical implications Using judiciously off-balance sheet activities does not exacerbate the exposure of African banks to credit risk. Therefore, managers of African banks are recommended to maintain a moderate level of off-balance sheet activities, especially guarantees and letters of credit. Originality/value The findings of this study eliminate the opacity about the effect of off-balance sheet activities on credit risk. Moreover, this study fulfills the huge gap in the related literature by completing the scarcity of recent studies, considering all items of the off-balance sheet, focusing on the African context, describing off-balance sheet activities and financial indicators of sample banks due to a statistical study and estimating regressions of dynamic panels between credit risk and both bank-specific and country-specific variables due to a two-step generalized method of moments technique.

Leveraging cascading information for community detection in social networks

Information exchange among individuals is one of the key factors leading to formation of modular structures in social networks, often referred as communities. In this paper, we devise a novel information diffusion based approach to leverage the latent information exchange among the individuals for identifying communities. Designed an algorithm called PraSar, which has mainly two phases namely seeding and unification. In seeding phase, closely connected groups of nodes called seed communities are identified by analyzing the cascades obtained during information diffusion. In unification phase, the external connections of seed communities are reduced by unifying two or more seed communities. Involvement of cascade that are formed during the diffusion process enables PraSar to operate locally and giving an overall complexity of O(n2). The theoretical foundation of the proposed approach is established by various theorems. Empirical results on diverse real world datasets are evident for the effectiveness and competitiveness of the PraSar algorithm over state-of-the-art community detection algorithms.

Double transition of young migrants in Luxembourg: vulnerable, resilient and empowering integration trajectories in the period of youth.

Migrant integration trajectories have become more complex, open, uncertain, and continuously changing, over time. For young migrants, their integration endeavour intersects with their process of transition to adulthood, a double transition that poses additional challenges. Recent theoretical perspectives such as "liquid integration" aim at focusing on the dynamic, processual, and temporal nature of migrant integration. The present article focuses on the dynamic interplay of obstacles and enablers that, over time, interact to construct complex, often non-linear, and open-ended integration and coming of age trajectories of young migrants (aged from 18 to 30 years) coming from outside the European Union (EU) to EU countries. Empirical results from the H2020 MIMY (Empowerment through liquid Integration of Migrant Youth in vulnerable conditions) research project in Luxembourg will be presented. In order to address the goal of the research, qualitative data were gathered by means of N = 38 interviews with young migrants with different migratory paths, characteristics and experiences, and specifically included: young migrants from non-EU Portuguese-speaking countries (N = 16), refugees living in reception centres (N = 15), migrants who since arriving in Luxembourg have become publicly visible (N = 7). Content analysis of the interviews allowed a twofold purpose: (1) capturing the unfolding of intersectional integration obstacles that over time play a decisive role in the building of conditions of vulnerability of the double transition under analysis; (2) capturing the multidimensional resources that interactively build up to give rise to resilient and empowering integration and coming of age experiences. The identification of decisive multidimensional obstacles and resources present in the integration endeavour during the process of coming of age allowed us to capture differentiated routes of vulnerability, on the one hand, and resilience/ empowerment on the other. Key ingredients of both vulnerable and more resilient and empowering integration and coming of age trajectories are identified as well as their relational dynamics, enabling to address key challenges for the resilience and empowerment of young migrants in the process of negotiating their transition to adulthood amidst their integration challenges in the Luxembourgish society.

Social networks, inclusive finance, and residents' well-being: An empirical study based on CFPS

Using data from the China Family Panel Studies (CFPS), this paper investigates how social networks and inclusive finance affect residents' well-being. The empirical results indicate that social networks can significantly enhance residents' well-being, and inclusive finance can play a significant positive moderating role in the influence of social networks on residents' well-being, suggesting a complementary relationship between the impacts of social networks and inclusive finance on residents' well-being. Further analysis shows that the positive moderating effect of inclusive finance in the relationship between social networks and residents' well-being is more pronounced in regions with higher levels of marketization.

Digital finance development and equalization of educational opportunities: An empirical analysis based on CFPS panel data

This paper focuses on micro-level Chinese households and empirically examines the impact of digital finance development on the equalization of educational opportunities, utilizing data from the China Family Panel Studies (CFPS). Empirical results indicate that digital finance development significantly enhances the level of equalization of educational opportunities. After a series of robustness checks, these conclusions remain valid. Further analysis reveals that the level of marketization and the individual liquidity constraint level of respondents play a significant moderating role in the positive impact of digital finance development on the equalization of educational opportunities.

Empirical examination of the Black–Scholes model: evidence from the United States stock market

Option pricing is crucial in enabling investors to hedge against risks. The Black–Scholes option pricing model is widely used for this purpose. This paper investigates whether the Black–Scholes model is a good indicator of option pricing in the United States stock market. We examine the relevance of the Black–Scholes model to certain stocks using paired sample t-test and Corrado and Miller’s approximation for the implied volatility. Empirical tests are applied to determine the significance of the relationship between the actual market values and the Black–Scholes model values. Paired sample t-tests are applied to 582 call options and 579 put options. The empirical test results show that there is no significant difference between the actual market premium value and the Black–Scholes model premium value for seven out of nine stocks considered for call options, and four out of nine stocks considered for put options. Thus, we conclude that the Black–Scholes option pricing model can be used to price call options but is not suitable for pricing put options in the United States stock market.

Anti-corruption, credit supply, and agricultural economic development

This study analyzes regional panel data from China (2013–2019) to explore how the government's anti-corruption efforts and credit supply affect agricultural economic development. The empirical results show that anti-corruption and credit supply can effectively enhance agricultural economic development, and there is a complementary relationship between anti-corruption and credit supply, meaning that anti-corruption measures and credit supply can jointly promote the development of the agricultural economy. Further analysis indicates that the complementary and synergistic effect of the two on agricultural economic development is more pronounced in sample groups with a lower degree of marketization.