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The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

The reality with many developing countries is that the countries have failed to create enough jobs for the poor and vulnerable. Under such circumstances, vulnerable employment plays a critical role in providing earning opportunities to people who are unemployed and determining the economic and social progress of such economies. The study aims to examine the possible non-linear relationship between vulnerable employment and growth in light of this background.

The study employed five-yearly averaged data of 73 developing countries for the period 2000–2019. The empirical analysis is performed using the dynamic panel data analysis and the two-step system generalised method of moments (GMM) approach. The estimations are run separately for male, female and total vulnerable employment. The threshold levels are obtained using Sasabuchi (1980) and Lind and Mehlum (2010) (SLM) test. Several sensitivity checks are performed to validate the results.

The findings of the study suggest a non-linear U-shaped relationship between vulnerable employment and growth. Thus, a positive association between vulnerable employment and growth is witnessed at higher levels of vulnerable employment. At lower levels, the relationship is negative. Threshold levels for male, female and total vulnerable employment are 46.80%, 49.29 and 50.94%, respectively. Therefore, vulnerable employment beyond the threshold levels is found to be positively associated with growth.

Countries below the threshold level of vulnerable employment should understand why these workers are not able to contribute to the growth despite working so hard. If any socio-economic barriers hinder their contribution towards growth, such barriers require greater policy attention. Countries with vulnerable employment levels above the threshold level should recognise the contributions of these workers towards the growth and actively support them in increasing their economic contribution. In either case, given the precarious circumstances under which these workers work and the pittance earnings, policy interventions aimed at ensuring decent working conditions and better earnings for these workers are encouraged.

The current study is the first one to examine the relationship between vulnerable employment and growth to the best of the author's knowledge. As such, it makes novel contributions to the literature on development policy.

Entrepreneurs are generally considered to be committed in order to strive for highly desirable goals, such as growth or commercial success. However, commitment is a multidimensional concept and may have asymmetric relationships with positive or negative entrepreneurial outcomes. This paper aims to provide a nuanced perspective to show under what conditions commitment may be detrimental for entrepreneurs and lead to overinvestment.

Using a sample of entrepreneurs from incubators in France (N = 437), this study employs a configurational perspective, fuzzy-set qualitative comparative analysis (fsQCA), to identify which commitment profiles lead entrepreneurs to overinvest different resources in their entrepreneurial projects.

The paper exposes combinations of conditions that lead to overinvestment and identifies five different commitment profiles: an “Affective profile”, a “Project committed profile”, a “Profession committed profile”, an “Instrumental profile”, and an “Affective project profile”.

The results show that affective commitment is a necessary condition for entrepreneurs to conduct overinvesting behaviors. This complements previous linear research on the interdependence between affect and commitment in fostering detrimental outcomes for nascent entrepreneurs.

Simulations exist for the prediction of the behaviour of building structural systems under fire, including two-way coupled fire-structure interaction. However, these simulations do not include detailed models of the connections, whereas these connections may impact the overall behaviour of the structure. Therefore, this paper proposes a two-scale method to include screw connections.

The two-scale method consists of (a) a global-scale model that models the overall structural system and (b) a small-scale model to describe a screw connection. Components in the global-scale model are connected by a spring element instead of a modelled screw, and the stiffness of this spring element is predicted by the small-scale model, updated at each load step. For computational efficiency, the small-scale model uses a proprietary technique to model the behaviour of the threads, verified by simulations that model the complete thread geometry, and validated by existing pull-out experiments. For four screw failure modes, load-deformation behaviour and failure predictions of the two-scale method are verified by a detailed system model. Additionally, the two-scale method is validated for a combined load case by existing experiments, and demonstrated for different temperatures. Finally, the two-scale method is illustrated as part of a two-way coupled fire-structure simulation.

It was shown that proprietary ”threaded connection interaction” can predict thread relevant failure modes, i.e. thread failure, shank tension failure, and pull-out. For bearing, shear, tension, and pull-out failure, load-deformation behaviour and failure predictions of the two-scale method correspond with the detailed system model and Eurocode predictions. Related to combined load cases, for a variety of experiments a good correlation has been found between experimental and simulation results, however, pull-out simulations were shown to be inconsistent.

More research is needed before the two-scale method can be used under all conditions. This relates to the failure criteria for pull-out, combined load cases, and temperature loads.

The two-scale method bridges the existing very detailed small-scale screw models with present global-scale structural models, that in the best case only use springs. It shows to be insightful, for it contains a functional separation of scales, revealing their relationships, and it is computationally efficient as it allows for distributed computing. Furthermore, local small-scale non-convergence (e.g. a screw failing) can be handled without convergence problems in the global-scale structural model.

Near-fault pulse-like ground motions have distinct and very severe effects on reinforced concrete (RC) structures. However, there is a paucity of recorded data from Near-Fault Ground Motions (NFGMs), and thus forecasting the dynamic seismic response of structures, using conventional techniques, under such intense ground motions has remained a challenge.

The present study utilizes a 2D finite element model of an RC structure subjected to near-fault pulse-like ground motions with a focus on the storey drift ratio (SDR) as the key demand parameter. Five machine learning classifiers (MLCs), namely decision tree, k-nearest neighbor, random forest, support vector machine and Naïve Bayes classifier , were evaluated to classify the damage states of the RC structure.

The results such as confusion matrix, accuracy and mean square error indicate that the Naïve Bayes classifier model outperforms other MLCs with 80.0% accuracy. Furthermore, three MLC models with accuracy greater than 75% were trained using a voting classifier to enhance the performance score of the models. Finally, a sensitivity analysis was performed to evaluate the model's resilience and dependability.

The objective of the current study is to predict the nonlinear storey drift demand for low-rise RC structures using machine learning techniques, instead of labor-intensive nonlinear dynamic analysis.

This study aims to investigate the design and optimization of landing gear buffers to improve the landing-phase comfort of civil aircraft.

The vibration comfort during the landing and taxiing phases is calculated and evaluated based on the flight-testing data for a type of civil aircraft. The calculation and evaluation are under the guidance of the vibration comfort standard of GB/T13441.1-2007 and related files. The authors establish here a rigid-flexible coupled multibody dynamics finite element model of one full-size aircraft. Furthermore, the authors also implement a dynamic simulation for the landing and taxiing processes. Also, an analysis of how the main parameters of the buffers affect the vibration comfort is presented. Finally, the optimization of the single-chamber and double-chamber buffers in the landing gear is performed considering vibration comfort.

The double-chamber buffer with optimized parameters in landing gear can improve the vibration comfort of the aircraft during the landing and taxiing phases. Moreover, the comfort index can be increased by 25.6% more than that of a single-chamber type.

To the best of the authors’ knowledge, this study first investigates the evaluation methods and evaluation indexes on the aircraft vibration comfort, then further conducts the optimization of the parameters of landing gear buffer with different structures, so as to improve the comfort of aircraft passengers during landing process. Most of the current studies on aircraft landing gear have focused on the strength and safety of the landing gear, with very limited research on cabin vibration comfort during landing and subsequent taxiing because of the coupling of runway surface unevenness and airframe vibration.

This study examines the non-linear impact of financial development on income inequality and analyses the mediators through which financial development affects income inequality.

The study uses a dynamic panel threshold method with an endogeneous threshold variable on a comprehensive sample of 85 countries over the period of 1996-2015.

The author finds that financial development activities increase income inequality in developed countries. However, financial development promotes income equality in developing countries. Further, the study finds that education and institutional quality are the channels through which financial development has non-linear impacts on income inequality.

The study explores relatively new method to examine the nonlinear impact of financial development and also considers new dataset for the main explanatory variable.

This study adopts a new macro-perspective to explore the complex and dynamic links between financial instability and the Euro-American green equity market. Its primary focus and novelty is to shed light on the non-linear and asymmetric characteristics of dependence, causality, and contagion within various time and frequency domains. Specifically, the authors scrutinize how financial instability in the U.S. and EU interacts with their respective green stock markets, while also examining the cross-impact on each other's green equity markets. The analysis is carried out over short-, medium- and long-term horizons and under different market conditions, ranging from bearish and normal to bullish.

This study breaks new ground by employing a model-free and non-parametric approach to examine the relationship between the instability of the global financial system and the green equity market performance in the U.S. and EU. This study's methodology offers new insights into the time- and frequency-varying relationship, using wavelet coherence supplemented with quantile causality and quantile-on-quantile regression analyses. This advanced approach unveils non-linear and asymmetric causal links and characterizes their signs, effectively distinguishing between bearish, normal, and bullish market conditions, as well as short-, medium- and long-term horizons.

This study's findings reveal that financial instability has a strong negative impact on the green stock market over the medium to long term, in bullish market conditions and in times of economic and extra-economic turbulence. This implies that green stocks cannot be an effective hedge against systemic financial risk during periods of turbulence and euphoria. Moreover, the authors demonstrate that U.S. financial instability not only affects the U.S. green equity market, but also has significant spillover effects on the EU market and vice versa, indicating the existence of a Euro-American contagion mechanism. Interestingly, this study's results also reveal a positive correlation between financial instability and green equity market performance under normal market conditions, suggesting a possible feedback loop effect.

This study represents pioneering work in exploring the non-linear and asymmetric connections between financial instability and the Euro-American stock markets. Notably, it discerns how these interactions vary over the short, medium, and long term and under different market conditions, including bearish, normal, and bullish states. Understanding these characteristics is instrumental in shaping effective policies to achieve the Sustainable Development Goals (SDGs), including access to clean, affordable energy (SDG 7), and to preserve the stability of the international financial system.

This analysis examines the relationship between corruption and firm productivity in Vietnam.

The authors apply the system generalized method of moments estimation approach on a panel dataset constructed from comprehensive enterprise surveys covering all the sectors over the 2011–2020 period.

The results confirm a non-linear relationship between corruption and firm productivity. Where corruption is severe, leaving corruption alone tends to benefit firm productivity because efforts to control corruption are likely to cause greater delays. In less corrupt provinces, corruption appears to harm firm productivity while efforts to control corruption provide significant productivity gains. This U-shaped relationship is confirmed for small firms and those in the private sector sub-samples. Intriguingly, this study reveals that the U-shaped relationship does not apply to micro, medium, large firms, state-owned firms and foreign-invested firms because corruption is found to have no significant impact on productivity among these sub-samples. Changes in regulations after 2014 toward promoting a transparent business environment are shown to foster the positive impact of lowering corruption on firm productivity.

This study suggests that lowering corruption is beneficial for firm productivity at the micro level. However, where corruption is severe, monitoring corruption alone is likely to cause adverse effects on productivity due to increased bureaucratic delays. Institutional reforms might play an important role in leveraging the effects of lowering corruption on productivity in highly corrupt areas.

This paper sheds new light on the relationship between corruption and firm productivity in the broad existing literature and especially in the limited number of studies for Vietnam.

This study aims to examine the connection between scholars' research performance and the multidisciplinary nature of their collaborative research. Furthermore, in response to mixed results regarding the effects of multidisciplinarity on research performance, this study explores how human resource management (HRM) practices may moderate this link.

The authors built a model based on the theoretical arguments and empirical evidence found in the review of diversity and HRM literature. The authors also performed a quantitative study based on a sample of scholars in the field of management. Different econometric estimations were used to test the proposed model.

The results of this empirical analysis suggest that multidisciplinary research has a non-linear effect on research performance. Certain HRM practices, such as development and collaboration, moderated the curvilinear relationship between multidisciplinarity and performance, displacing the optimum to allow higher performance at higher levels of multidisciplinary research.

The paper provides advances on previous works studying the curvilinear relationship between multidisciplinarity and the researchers' performance, confirming that multidisciplinarity is beneficial up to a threshold beyond which these benefits are attenuated. In addition, the findings shed light on important issues related to team-oriented HRM practices associated with the outcomes of multidisciplinary research.