Search results

The purpose of this study was to address waste management in the food supply chain (FSC) through the integration of inspection processes in production and distribution centers under uncertain conditions, aiming to enhance sustainability across environmental, economic and social dimensions. The study introduces a sustainable forward and reverse FSC network using a closed-loop supply chain network approach to prevent the transfer of spoiled products, ultimately providing competitive advantages to stakeholders.

A robust multi-objective mathematical programming model is proposed, incorporating inspection processes to manage perishable products effectively. The model is solved using the Augmented Epsilon Constraint technique implemented in GAMS software, providing Pareto-optimal solutions tailored to decision-makers’ preferences. Furthermore, the methodology is applied in a real-world case study and solved with the Benders Decomposition algorithm to validate its practicality and effectiveness.

The proposed methodology effectively minimizes waste and enhances sustainability in the FSC by optimizing decision-making processes under uncertainty. The illustrative examples and real case study demonstrate the efficiency of the model and solution approach, highlighting the significant role of inspection in improving all three dimensions of sustainability.

The study offers valuable insights into and tools for food industry managers to make informed strategic and tactical decisions. By addressing waste management through advanced supply chain modeling, the research helps organizations reduce costs, improve sustainability and gain a competitive edge in the market.

This research is novel in its focus on integrating inspection into the FSC network and addressing uncertainty through robust mathematical modeling. It contributes to the existing literature by demonstrating the impact of inspection on sustainability in FSCs and providing practical solutions for real-world implementation.

The purpose of our study is to examine the functioning of internal capital markets (ICMs) within business groups in Iran. We document how the ultimate owner's economic interests in affiliated firms influence their investment and dividend policies.

Using hand-collected data on the ownership structures of Iranian firms, we first identify group-affiliated firms using Almeida et al.’s (2011) method. Having identified business groups, we test a number of hypotheses concerning the dynamics of the ICMs and the implications of the ultimate owner’s incentives for affiliated firms’ behavior.

We first demonstrate that investments of group-affiliated firms are less sensitive to their own cash flow (as compared to stand-alone firms) but are sensitive to the cash flows of other firms affiliated with the same group near or at the bottom of the ownership structure. We next find significant variation in dividend policy within groups, with notably higher dividends for firms close to the ultimate owner. Furthermore, we find that higher investments by firms close to the owner lead to lower dividends by firms positioned far from the owner, but the reverse does not hold.

We are the first to examine the effect of group-affiliated firms’ investments on the dividend policies of other firms based on their position within the group. Our findings illuminate how business groups prioritize funding investments in their closely held firms over paying dividends to outside investors in firms positioned farther from the group owner.

Blockchain technology, renowned for its decentralization, security, reliability, and data integrity, has the potential to revolutionize businesses globally. However, its full potential remains unrealized due to adoption barriers, necessitating further studies to address these challenges. Identifying these barriers is crucial for businesses and practitioners to effectively tackle them. This systematic review analyzed 70 eligible studies out of 1944 gathered from various databases to understand and identify common blockchain adoption barriers. The Technology–Organization–Environment (TOE) framework was the most popular theory used in these studies. Despite differences in variable definitions, financial constraints, lack of stakeholder collaboration and coordination, and social influences like resistance to change and negative perceptions emerged as the top three barriers. The supply chain domain had the highest number of studies on blockchain adoption. Notably, there was a significant increase in studies addressing blockchain adoption in 2023, comprising 34.2% of the total reviewed studies. This review provides a comprehensive overview of identified barriers, serving as a valuable foundation for future research. Understanding these challenges allows researchers to design targeted studies aimed at developing solutions, strategies, and innovations to overcome obstacles hindering blockchain adoption.

Turnover intention is a critical predictor of an employee’s turnover behaviour. A high level of turnover rate significantly affects the productivity and morale of an enterprise. Previous research has indicated that job satisfaction plays a critical role in influencing an employee's turnover intention, but the underlying factors related to job satisfaction remain under-explored, which impedes the development of effective strategies for reducing turnover intention. In addition, little research examined job satisfaction and turnover intention in the context of the COVID-19 pandemic, specifically in the Chinese construction industry. This study aims to investigate the impact of job satisfaction on turnover intention among professionals in the construction industry.

A questionnaire survey was employed to collect viewpoints from 449 professionals in the Chinese construction industry, followed by descriptive analysis, correlation analysis and structural equation modelling analysis to derive results.

The findings indicate that professionals in the industry generally have a slightly high level of job satisfaction while a slightly low level of turnover intention in the special period of the pandemic outbreak. Leadership and management, training and career development and interpersonal relationships are critical underlying factors leading to their turnover intention. Although demographic factors have no moderating effect between job satisfaction and turnover intention, among them, age, marital status and years of working experience have strongly positive relationships with job satisfaction while significantly negative relationships with turnover intention.

The findings provide valuable insights to fully understand the critical factors leading to turnover intention from the perspective of job satisfaction, which is helpful in developing effective measures to address the turnover problems for enterprises in the Chinese construction industry and those industries with similar characteristics in other regions.

Predicting commodity futures trading volumes represents an important matter to policymakers and a wide spectrum of market participants. The purpose of this study is to concentrate on the energy sector and explore the trading volume prediction issue for the thermal coal futures traded in Zhengzhou Commodity Exchange in China with daily data spanning January 2016–December 2020.

The nonlinear autoregressive neural network is adopted for this purpose and prediction performance is examined based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays and ratios for splitting the trading volume series into training, validation and testing phases.

A relatively simple model setting is arrived at that leads to predictions of good accuracy and stabilities and maintains small prediction errors up to the 99.273th quantile of the observed trading volume.

The results could, on one hand, serve as standalone technical trading volume predictions. They could, on the other hand, be combined with different (fundamental) prediction results for forming perspectives of trading trends and carrying out policy analysis.

This study aims to analyze the convergence of environmental degradation clubs in the Association of Southeast Asian Nations (ASEAN). In addition, this study also analyzes the influence of renewable energy and foreign direct investment (FDI) on each club as an intervention to change the convergence pattern in each club.

This study analyzes the club convergence of environmental degradation in an effort to find out the distribution of environmental degradation reduction policies. This study uses club convergence with the Phillips and Sul (PS) convergence methodology because it considers multiple steady-states and is robust. This study uses annual panel data from 1998 to 2020 and ASEAN country units with ecological footprints as proxies for environmental degradation. After obtaining the club results, the analysis continued by analyzing the impact of renewable energy and FDI on each club using panel data regression and the Stochastic Impacts by Regression on Population, Affluence and Technology model specification.

Based on club convergence, ASEAN countries can be grouped into three clubs with two divergent countries. Club 1 has an increasing pattern of environmental degradation, while Club 2 and Club 3 show no increase. Club 1 can primarily apply renewable energy to reduce environmental degradation, while Club 2 requires more FDI. The authors expect policymakers to take into account the clubs established to formulate collaborative policies among countries. The result that FDI reduces environmental degradation in this study is in line with the pollution halo hypothesis. This study also found that population has a significant effect on environmental degradation, so policies to regulate population need to be considered. On the other hand, increasing income has no effect on reducing environmental degradation. Therefore, the use of renewable energy and FDI toward green investment is expected to intensify within ASEAN countries to reduce environmental degradation.

This research is by far the first to apply PS Club convergence to environmental degradation in ASEAN. In addition, this study is also the first to analyze the influence of renewable energy and FDI on each club formed, considering the need for renewable energy use that has not been maximized in ASEAN.

The current study seeks to investigate, why and how faculty members of Indian central universities are using academic social networking sites (ASNs) for research communication and information dissemination, as well as validate and update the results of previous scholarship in this area. To achieve this, the paper uses structural equation model (SEM).

A simple random sampling method was adopted. Online survey was conducted using a well-designed questionnaire circulated via email id among 3384 faculty members of Indian Central Universities. A SEM was designed and tested with International Business Machines (IBM) Amos. Apart from this, Statistical Package for Social Sciences (SPSS) 22 and Microsoft Excel 2010 were also used for data screening and analysis.

The study explores that most of the respondents are in favour of using the ASNs/tools for their professional activities. The study also found that a large chunk of the respondents used ASNs tools during day time. Apart from it, more number of faculty members used ASNs in research work than general purpose. No significant differences were found among the disciplines in use behaviour of ASNs in scholarly communication. Three hypotheses have been accepted while two were rejected in this study.

The study was confined to the twelve central universities, and only 312 valid responses were taken into consideration in this study.

The paper demonstrates the faculty members’ use behaviour of ASNs in their research communication. The study also contributes new knowledge to methodological discussions as it is the first known study to employ SEM to interpret scholarly use of ASNs by faculty members of Indian central universities.

This study examines the effect of temperature-dependent material models for normal-strength (NSC) and high-strength concrete (HSC) on the thermal analysis of reinforced concrete (RC) walls.

The study performs an one-at-a-time (OAT) sensitivity analysis to assess the impact of variables defining the constitutive and parametric fire models on the wall's thermal response. Moreover, it extends the sensitivity analysis to a variance-based analysis to assess the effect of constitutive model type, fire model type and constitutive model uncertainty on the RC wall's thermal response variance. The study determines the wall’s thermal behaviour reliability considering the different constitutive models and their uncertainty.

It is found that the impact of the variability in concrete’s conductivity is determined by its temperature-dependent model, which differs for NSC and HSC. Therefore, more testing and improving material modelling are needed. Furthermore, the heating rate of the fire scenario is the dominant factor in deciding fire-resistance performance because it is a causal factor for spalling in HSC walls. And finally the reliability of wall's performance decreased sharply for HSC walls due to the expected spalling of the concrete and loss of cross-section integrity.

Limited studies in the current open literature quantified the impact of constitutive models on the behaviour of RC walls. No studies have examined the effect of material models' uncertainty on wall’s response reliability under fire. Furthermore, the study's results contribute to the ongoing attempts to shape performance-based structural fire engineering.

This study underscores the critical importance of well-functioning sewer systems in achieving smart and sustainable urban drainage within cities. It specifically targets the pressing issue of sewer overflows (SO), widely recognized for their detrimental impact on the environment and public health. The primary purpose of this research is to bridge significant research gaps by investigating the root causes of SO incidents and comprehending their broader ecological consequences.

To fill research gaps, the study introduces the Multi-Phase Causal Inference Fuzzy-Based Framework (MCIF). MCIF integrates the fuzzy Delphi technique, fuzzy DEMATEL method, fuzzy TOPSIS technique and expert interviews. Drawing on expertise from developed countries, MCIF systematically identifies and prioritizes SO causes, explores causal interrelationships, prioritizes environmental impacts and compiles mitigation strategies.

The study's findings are multifaceted and substantially contribute to addressing SO challenges. Utilizing the MCIF, the research effectively identifies and prioritizes causal factors behind SO incidents, highlighting their relative significance. Additionally, it unravels intricate causal relationships among key factors such as blockages, flow velocity, infiltration and inflow, under-designed pipe diameter and pipe deformation, holes or collapse, providing a profound insight into the intricate web of influences leading to SO.

This study introduces originality by presenting the innovative MCIF tailored for SO mitigation. The combination of fuzzy techniques, expert input and holistic analysis enriches the existing knowledge. These findings pave the way for informed decision-making and proactive measures to achieve sustainable urban drainage systems.

This study aims to investigate the impact of environmental, social and corporate governance (ESG) factors on job satisfaction within the context of small and medium-sized enterprises (SMEs), addressing a notable gap in understanding these relationships.

Data were collected from 97 full-time and part-time employees using a tailored survey instrument. Control variables included demographic factors, such as gender, age, marital status and hierarchical position. The study postulated four moderation hypotheses, conducted rigorous significance tests and demonstrated strong model reliability and validity, along with highly satisfactory fit parameters.

The findings confirm a positive association between the E, S and G factors and job satisfaction perceptions. Marital status was identified as a moderator in the relationship between social dimensions and job satisfaction. In addition, the multigroup analysis revealed variations in the associations between ESG dimensions and job satisfaction across different age groups, marital statuses and hierarchical positions.

The main contribution of this research lies in filling a significant gap in the understanding of how sociodemographic variables influence the relationship between employees and socially responsible behavior in SMEs.

Este estudio empírico tuvo como objetivo investigar el impacto de los factores ESG (ambientales, sociales y de gobierno corporativo) en la satisfacción laboral en el contexto de las pequeñas y medianas empresas (PYME), abordando una brecha notable en la comprensión de estas relaciones.

Se recopilaron datos de 97 empleados a tiempo completo y parcial utilizando un instrumento de encuesta personalizado. Las variables de control incluyeron factores demográficos, como género, edad, estado civil y posición jerárquica. El estudio postuló cuatro hipótesis de moderación, realizó pruebas de significación rigurosas y demostró una sólida confiabilidad y validez del modelo, junto con parámetros de ajuste altamente satisfactorios.

Los hallazgos confirman una asociación positiva entre los factores E, S y G y las percepciones de satisfacción laboral. El estado civil se identificó como un moderador en la relación entre las dimensiones sociales y la satisfacción laboral. Además, el análisis multigrupo reveló variaciones en las asociaciones entre las dimensiones ESG y la satisfacción laboral en diferentes grupos de edad, estados civiles y posiciones jerárquicas.

La principal contribución de esta investigación radica en llenar un vacío importante en nuestra comprensión de cómo las variables sociodemográficas influyen en la relación entre los empleados y el comportamiento socialmente responsable en las PYME.

Este estudo empírico teve como objetivo investigar o impacto dos fatores ESG (ambientais, sociais e de governança corporativa) na satisfação no trabalho no contexto de pequenas e médias empresas (PMEs), abordando uma lacuna notável na compreensão dessas relações.

Os dados foram coletados de 97 funcionários de período integral e parcial usando um instrumento de pesquisa personalizado. As variáveis de controle incluíram fatores demográficos, como gênero, idade, estado civil e posição hierárquica. O estudo postulou quatro hipóteses de moderação, conduziu testes de significância rigorosos e demonstrou forte confiabilidade e validade do modelo, juntamente com parâmetros de ajuste altamente satisfatórios.

Os resultados confirmam uma associação positiva entre os fatores E, S e G e as percepções de satisfação no trabalho. O estado civil foi identificado como moderador na relação entre dimensões sociais e satisfação no trabalho. Além disso, a análise multigrupo revelou variações nas associações entre dimensões ESG e satisfação no trabalho em diferentes faixas etárias, estados civis e posições hierárquicas.

A principal contribuição desta pesquisa está em preencher uma lacuna significativa em nossa compreensão de como as variáveis sociodemográficas influenciam o relacionamento entre funcionários e comportamento socialmente responsável em PMEs.