Search results

Entrepreneurship institutions exhibit substantial gender discrimination despite worldwide efforts to decrease the phenomenon. The MENA area has a low percentage of women entrepreneurs since little is known about women’s desire to start their businesses. The authors use the theory of planned behavior (TPB) to explain what influences women's propensity toward entrepreneurship and what factors discourage them.

TPB is a psychological theory explaining how individuals act in certain situations. The authors created their database by using a systematic questionnaire. Overall, 350 women entrepreneurs contributed to their dataset. Finally, the authors used structural equation modeling to verify their hypotheses.

This study helps them to shed light to better understand the dynamics of Entrepreneurial Intention, in women from Lebanon. The authors do not find any relationship between lack of knowledge, funding, networking and entrepreneurial startup intention for Lebanese women. The role of dynamic capabilities in the entrepreneurial landscape of Lebanon, particularly for women, is substantially highlighted by the full mediation observed in the relationship between lack of knowledge and entrepreneurial start-up intentions. The findings discovered that these capabilities could fully mediate the negative impact of lack of networking on the intention to commence entrepreneurial ventures.

This research illustrates and explains how dynamic capabilities mediate the relationship between women entrepreneurs' challenges and their intention to start a business in the Lebanese context.

Environmental, social and governance (ESG) issues have become the cornerstone of investment decisions in firms today. With that, publicly traded ESG indices (like the BSE ESG 100 index in India) have come into existence. The existing literature signifies that ESG generates financial implications and induces stability. The current study aims to test whether the firms listed on the ESG index (ESG-sensitive firms) face less financial distress than those not listed on such an index.

The study applies panel data difference-in-differences (DID) regression by considering ESG as an unstaggered treatment to 74 non-financial firms listed on India's Bombay Stock Exchanges (BSE) 100 index. In total, 42 firms are ESG treated as they got listed on the BSE ESG 100 index, formed in 2017. The remaining 32 firms form the control group. The confidence intervals and standard errors are estimated using clustered robust errors and the Donald and Lang method.

Listing on the ESG index matters for financial stability; differences in financial distress are significant on financial distress. ESG-sensitive firms face less financial distress than non-ESG firms (or firms not perceived as ESG-sensitive). The results are consistent across two financial distress measures, Altman z-scores for emerged and emerging markets. Thus, the DID in distress status between ESG-sensitive and non-ESG firms matter.

The study creates vibrant implications for practitioners using ESG to reduce financial distress.

The study is one of its kind to test the treatment effects of ESG on firm value and quantify treatment effects on financial distress.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

Irritable bowel syndrome (IBS) is a prevalent functional gastrointestinal disorder that significantly impacts the quality of life of affected individuals. Diet has been identified as a potential modifiable risk factor for IBS, yet its association with IBS risk in the Kurdish adult male population remains understudied. This case-control study aimed to investigate the association between the lifeline diet score (LLDS), a validated tool assessing overall diet quality and the risk of IBS in Kurdish adult men.

A total of 200 Kurdish adult men were recruited, comprising 100 IBS patients (cases) and 100 healthy controls without IBS. Medical records and interview questionnaires were used to confirm IBS diagnoses, while detailed dietary questionnaires were administered to assess participants’ dietary habits and calculate their respective LLDS scores. Logistic regression analysis was used to examine the association between LLDS and IBS risk, adjusting for potential confounding factors.

Participants with higher LLDS scores exhibited a significantly reduced risk of IBS compared to those with lower LLDS scores (OR= 0.38, 95% confidence intervals = 0.18–0.77; p <0.001). The association remained statistically significant even after controlling for potential confounders such as socioeconomic status, age, eating rate, protein, fat, physical activity and body mass index.

This case-control study demonstrates a significant inverse association between the LLDS and the risk of IBS in Kurdish adult men. Higher LLDS scores, indicative of a healthier and more balanced diet, were associated with a decreased risk of IBS. These findings highlight the potential role of dietary patterns in IBS prevention and management within the Kurdish adult male population.

Research has established that 38%, 56% and 66% of training is not transferred to work immediately, six months and 12 months after training, respectively. This has led scholars to advocate the continuous examination of factors that enhance training transfer to have a comprehensive understanding of the factors that enhance it. As a result, this study aims to examine transfer opportunity as a pretraining factor and its influence on assimilated training content (in-training factor); the influence of assimilated training content on motivation to transfer (post-training factor) and training transfer; the influence of motivation to transfer on training transfer; and the mediating role of motivation to transfer in the relationship between assimilated training content and training transfer.

A structural equation model is developed to test the five hypotheses formulated in this study using survey data obtained from 195 respondents who attended various training programs across different organizations. Following the assessment of the measurement model, the determination of the significance of the hypothesized paths is assessed based on the bias-corrected and accelerated confidence intervals obtained from the bootstrapping of 10,000 subsamples.

The findings of this study are that: transfer opportunity positively influences assimilated training content; assimilated training content positively influences motivation to transfer and training transfer; motivation to transfer positively influences training transfer; and motivation to transfer plays a complementary mediation role between assimilated training content and training transfer.

The nature of the work environment regarding the opportunity to transfer training influences trainees’ assimilation of the training content when they undergo training. Hence, organizations need to ensure that employees are always afforded the opportunity to transfer training content assimilated from previously attended training programs to assimilate the content of subsequent training programs. Furthermore, for training to culminate in training transfer, organizations and, more specifically, learning and development practitioners ought to pay attention to trainees’ assimilation of the content of training programs.

To the best of the authors’ knowledge, this is the first study to empirically consider transfer opportunity as a direct antecedent of assimilated training content. More so, it is one of few studies to empirically examine the influence of assimilated training content on training transfer.

The gift-giving literature has documented several cases in which givers and recipients do not see eye-to-eye in gift-giving decisions. To help integrate this considerable segment of the gifting literature, this paper aims to develop a social norms-based framework for understanding and predicting giver-recipient asymmetries in gift selection.

Five experimental studies test the hypotheses. Participants in these studies evaluate gifts used in previous research, choose between gifts as either gift-givers or gift-recipients, and/or indicate their level of discomfort with choosing different kinds of gifts. The gifts vary in ways that allow the authors to test the social norms-based framework.

Gift-giving asymmetries tend to occur when one of the gifts under consideration is less descriptively, but not less injunctively, normative than the other. This theme holds for both asymmetries recorded in the gift-giving literature and novel ones. Indeed, the authors document new asymmetries in cases where the framework would expect asymmetries to occur and, providing critical support for the framework, the absence of asymmetries in cases where the framework would not expect asymmetries to emerge. Moreover, the authors explain these asymmetries, and lack thereof, using a mechanism that is novel to the literature on gift-giving mismatches: feelings of discomfort.

This research has multiple theoretical implications for the literatures studying gift-giving and social norms. A limitation of this work is that it left some (secondary) predictions of its model untested. Future research could test some of these predictions.

Billions of dollars are spent on gifts each year, making gift-giving a research topic of great practical importance. In addition, the research offers suggestions to consumers giving gifts, consumers receiving gifts, as well as marketers.

The research is original in that it creates a novel framework that predicts both the presence and absence of gift-giving asymmetries, introduces a psychological mechanism to the literature on giver-recipient gift choice asymmetries, and unifies many of the mismatches previously documented in this literature.

This study aims to investigate specific green-brand affect in terms of commitment and connection through the morality–mortality determinants of consumer social responsibility and the assumptions of terror management theory in the proposed three-layered framework. Religiosity serves as a moderator within the framework.

Data are collected in Taipei, Taiwan, while quota sampling is applied, and 420 valid questionnaires are collected. The partial least squares technique is applied for data analysis.

With the contingent role of religiosity, consumer social responsibility influences socially conscious consumption, which in turn drives the commitment and connection of green-brand affect. The death anxiety and self-esteem outlined in terror management theory influence materialism, which then drives green-brand commitment; however, contrary to expectations, they do not drive green-brand connection.

By considering green brands beyond their cognitive aspects and into their affective counterparts, morality–mortality drivers of green-brand commitment and green-grand connection are explored to provide unique contributions so as to better understand socially responsible consumption.

The purpose of this paper is to analyse the influence of economic growth, digitalisation, eco-innovation, energy consumption and patents on environmental technologies on the volume of greenhouse gas emissions (GHG) recorded in European countries for a period of nine years (2010–2018).

Two empirical methods were integrated into the theoretical approach developed based on the analysis of the current scientific framework. Multiple linear regression, an extended version of the OLS model, and a non-causal analysis as a robustness method, Dumitrescu–Hurlin, were used to achieve the proposed research objective.

Digitalisation described by the number of individual Internet users and patents on environmental technologies determines the amount of GHG in Europe, and economic growth continues to have a significant effect on the amount of emissions, as well as the consumption of renewable energy. European countries are not framed in well-established patterns, but the economic growth, digitalisation, eco-innovation and renewable energy have an impact on the amount of GHG in one way or another. In many European countries, the amount of GHGs is decreasing as a result of economic growth, changes in the energy field and digitalisation. The positive influence of economic growth on climate neutrality depends on its degree of sustainability, while patents have the same conditional effect of their translation into environmentally efficient technologies.

This study has a number of limitations which derive, first of all, from the lack of digitalisation indicators. The missing data restricted the inclusion in the analysis of variables relevant to the description of the European digitalisation process, also obtaining conclusive results on the effects of digitalisation on GHG emissions.

A similar analysis of the relationship among the amount of greenhouse gas emissions and economic growth, digitalisation, eco-innovation and renewable energy is less common in the literature. Also, the results can be inspirational in the sphere of macroeconomic policy.

The research objective of this paper is to investigate the direct and indirect impacts of green finance on agricultural carbon total factor productivity (ACTFP) within the framework of the carbon peaking and carbon neutrality (dual carbon) goals, while also identifying the driving factors through an exponential decomposition of ACTFP, aiming to provide policy recommendations to enhance financial support for low-carbon agricultural development.

In this paper, the Global Malmquist Luenberger (GML) Index method was employed to analyze and decompose the ACTFP, while the direct and spillover effects of China’s green finance pilot policy (GFPP) on ACTFP were assessed using the difference-in-differences (DID) method and the spatial differences-in-differences (SDID) method, respectively.

After the implementation of the GFPP, the ACTFP in the pilot area has experienced significant improvement, with the enhancement of technical efficiency serving as the main driving force. In addition, the GFPP exhibits a positive low-carbon spatial spillover effect, indicating it benefits ACTFP in both the pilot and adjacent areas.

Within the framework of the dual carbon goals, the paper highlights agriculture as a significant carbon emitter. ACTFP is assessed by considering the agricultural carbon emission factor as the sole non-desired output, and the impact of the GFPP on ACTFP is investigated through the DID method, thereby providing substantial validation of the hypotheses inferred from the mathematical model. Subsequently, the spillover effects of GFPP on ACTFP are analyzed in conjunction with the spatial econometric model.