Search results

This study combines different perspectives on herding, viewing it as a social network heuristic in comparison to other heuristics. The purpose is to use the heuristic view of herding as found in early literature and test it on grounds of efficiency and payoff, in essence, combining the heuristic and rational agent view of herding. The simulated double auction setting includes agents embedded in a social network, allowing for an examination of herding alongside rational behaviour and imperfect signals.

In each round of the simulation, levels of homophily, density and fractions of types of agents is set and agents are allowed to follow their respective heuristics under those conditions. Characteristics of the social network, such as the size, levels of different homophilies, density and fractions of different types of agents are varied randomly to gauge their effect on the performance of herders vis-à-vis others and the overall market efficiency through simulation based approach. The data used for the study has been developed in Python and linear models are estimated using R.

Herding decreases total surplus in private value double auctions, but herders are not worse off than other agents and perform equally in common value auctions. Further, herders and random offerers reduce payoffs of other agents as well, and herding effects the surplus per transaction and not the quantum.

This study explores herding as a strategic behaviour coexisting with rationality and other strategies in specific circumstances. It presents intriguing findings on the impact of herding on individual outcomes and market efficiency, raising new avenues for future research. Implication to research includes a dent on the “sieve” argument of markets rooting out irrationality and from it, a policy implication that follows is the need for corrective measures as markets cannot self-correct this, given herders do not perform worse than others.

The study links the phenomenon of herding to the dynamics of social networks and heuristic-based learning mechanisms that sets apart this research from the majority of existing literature, which predominantly conceptualizes herding as an outcome derived from a perfect Bayesian Equilibrium and a rational learning process.

This study aims to investigate how renewable energy consumption impacts economic risk in volatile economies, with a specific focus on the moderating effects of GDP per capita and trade openness. It further explores the nonlinear relationships between renewable energy consumption and economic risk, highlighting the role of gross domestic product per capita (GDP per capita) and trade openness in moderating these effects.

The dynamic panel threshold model (DPTM) is used to analyze data from the World Development Indicators (WDI) 2023 data set. Key variables include renewable energy share, gross domestic product per capita (GDP per capita), economic rent, trade openness and a composite economic volatility index. Quantile regression (QR) ensures robustness across different economic risk levels.

Trade openness significantly moderates the relationship between renewable energy consumption and economic risk. In high trade openness economies, renewable energy consumption markedly reduces economic risk. Higher GDP per capita also enhances the stabilizing effects of renewable energy. These findings highlight the importance of integrated economic policies that foster both renewable energy adoption and economic openness for sustainable development.

The findings offer policymakers insights into promoting economic stability through renewable energy investments. Policies enhancing trade openness and economic growth can significantly amplify the benefits of renewable energy, contributing to sustainable development goals.

The study underscores renewable energy’s potential to foster long-term economic stability and environmental sustainability, ensuring current development does not compromise future needs.

This study advances the understanding of nonlinear relationships between renewable energy and economic risk, filling a gap in existing literature. It incorporates the moderating roles of GDP per capita and trade openness within a nonlinear framework.

Over the last decades, the need for sustainable energy production and consumption has been heavily discussed. However, there has been no consensus in the extant literature. Thus, to this end, this study aims to explore the long-run and causality connection among disaggregated energy consumption, environmental tax and economic growth in a carbon-function framework for Turkey.

This study uses annual frequency data for econometrics analysis. To this end, our analysis utilizes the autoregressive distributive lag (ARDL) technique for cointegration and long-run analysis, while the Granger causality was used for causality direction.

Economic growth drives Turkey’s Load Capacity Factor (LCF), indicating energy efficiency is linked to economic performance. Renewable energy boosts LCF, while nonrenewable energy hinders it. Population growth positively affects energy efficiency, but environmental taxes have minimal impact, suggesting policy reform is needed. These outcomes have far-reaching implications for macroeconomic policies and environmental sustainability in Turkish economy energy mix amidst its growth path.

The findings suggest the need for policy reforms prioritizing renewable energy investments to enhance Turkey’s energy efficiency and sustainability. Additionally, the current environmental tax structure requires reevaluation to support sustainable energy practices better. These policy changes are crucial for balancing Turkey’s economic growth with environmental goals, ensuring a more sustainable energy future.

This study explores the role of government policy in form of environmental tax in environmental performance in Turkey.

Multiple input multiple-output (MIMO) has emerged as one among the many noteworthy technologies in recent wireless applications because of its powerful ability to improve bandwidth efficiency and performance, i.e. through developing its unique spatial multiplexing capability and spatial diversity gain. For carrying out an enhanced communication in next-generation networks, the MIMO and orthogonal frequency division multiple systems were combined that facilitate the spatial multiplexing on resource blocks (RBs) based on time-frequency. This paper aims to propose a novel approach for maximizing the throughput of cell-edge users and cell-center users.

In this work, the specified multi-objective function is defined as the single objective function, which is solved by the introduction of a new improved algorithm as well. This optimization problem can be resolved by the fine-tuning of certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation. The fine-tuning of parameters is attained by a new improved Lion algorithm (LA), termed as Lion with new cub generation (LA-NCG) model. Finally, the betterment of the presented approach is validated over the existing models in terms of signal to interference plus noise ratio, throughput and so on.

On examining the outputs, the adopted LA-NCG model for 4BS was 66.67%, 66.67% and 20% superior to existing joint processing coordinated multiple point-based dual decomposition method (JC-DDM), fractional programming (FP) and LA models. In addition, the throughput of conventional JC-DDM, FP and LA models lie at a range of 10, 45 and 35, respectively, at the 100th iteration. However, the presented LA-NCG scheme accomplishes a higher throughput of 58. Similarly, the throughput of the adopted scheme observed for 8BS was 59.68%, 44.19% and 9.68% superior to existing JC-DDM, FP and LA models. Thus, the enhancement of the adopted LA-NCG model has been validated effectively from the attained outcomes.

This paper adopts the latest optimization algorithm called LA-NCG to establish a novel approach for maximizing the throughput of cell-edge users and cell-center users. This is the first that work uses LA-NCG-based optimization that assists in fine-tuning certain parameters such as assigned power for RB, cell-center user, cell-edge user and RB allocation.

The study aims to present demand chain management (DCM) modeling of Indian apparel retailers. This will result in a structured model presenting contextual interrelationship among DCM variables so that retailers can proactively manage their demand chain.

The research follows an exploratory research design. It initially involves identification and analysis of influential factors of the implementation of DCM practices through the review of literature. Then, these variables were analyzed using total interpretive structural modeling or TISM followed by a statistical verification and case-based validation of the model.

The major findings of the paper are: top-management commitment and support, information management and supply chain agility in supply chain are the most significant enablers with the highest driving power. The other apparel retail specific significant variables are assortment planning, category management and marketing orientation. The model also indicates that the firms that implement customer-centric DCM practices do well in terms of organizational performance and thereby achieve differential advantage over their competitors.

Because the literature on DCM is still in nascent stage, the study bases itself on interpretive method; that is, TISM of analysis with a limited number of experts. Future studies may consider larger sample with more advanced statistical tools such as structural equation modeling for further validation of the findings.

The novelty of the paper lies in the study of an emerging supply chain philosophy; that is, DCM and its key practices per se. It has rarely been studied from the theory building perspective hitherto. Moreover, TISM-based approach is applied for the first time to study the DCM practices and its drivers vis-à-vis dependents.

Pearl millet (Pennisetum glaucum) is a rich source of nutrients as compared to the major cultivated cereal crops. However, major factors which limit its utilization are the presence of anti-nutritional factors (phytate, tannins and polyphenols) which lower availability of minerals and poor keeping quality because of higher lipase activity. Therefore, this paper aims to focus on the impact of different processing methods on the nutrient composition and anti-nutritional components of pearl millet.

This is a literature review study from 1983 to 2017, focusing on studies related to pearl millet processing and their effectiveness in the enrichment of nutritional value through reduction of anti-nutritional compounds.

From the literature reviewed, pearl millet processing through various methods including milling, malting, fermentation, blanching and acid as well as heat treatments were found to be effective in achieving the higher mineral digestibility, retardation of off flavor, bitterness as well as rancidity problems found during storage of flour.

Through this review paper, possible processing methods and their impact on the nutrient and anti-nutrient profile of pearl millet are discussed after detailed studied of literature from journal articles and thesis.

Sustainability performances (SPs) are the most crucial performances for an organisation in today's world, and they can be measured by economic, social, and environmental metrics. Previous research has not been clear on the role of total productive maintenance (TPM) in the context of sustainability, which motivates the authors to investigate the relationship between TPM and various SPs of the manufacturing firm. Therefore, current research investigates the relationship between TPM and the overall sustainability of the manufacturing firm from the perspective of resource-based view (RBV) theory.

The current study proposed, tested and validated a conceptual framework using partial least squares structural equation modelling (PLS-SEM). A total of 326 responses were received to validate the conceptual framework in smartsPLS 3.0 software.

The research outcomes indicate that TPM considerably impacts a manufacturing firm's economic, environmental and social performance.

This research demonstrates that outstanding shop floor behaviour, such as TPM, can become an important asset to offer competitive advantages in a manufacturing firm. Similarly, TPM might serve as a roadmap for leveraging overall sustainability for manufacturing companies. The study indicates the establishment of a sustainability-oriented training protocol while practising TPM.

No past investigation indicates that a shop floor activity like TPM could be used as an input to offer sustainability in a single index for a manufacturing firm from the perception of RBV theory.

Over the next decade, humanity is going to face big environmental problems, and considering these serious issues, businesses are adopting environmentally responsible practices. To put forward specific measures to achieve a more prosperous environmental future, this study aims to develop an environment-based perspective framework by integrating the Internet of Things (IoT) technology into a sustainable automotive supply chain (SASC).

The study presents a conceptual environmental framework – based on 29 factors constituting four stakeholders' rectifications – that holistically assess the SASC operations as part of the ReSOLVE model utilizing IoT. Then, experts from the SASC, IoT and sustainability areas participated in two rigorous rounds of a Delphi study to validate the framework.

The results indicate that the conceptual environmental framework proposed would help companies enhance the connectivity between major IoT tools in SASC, which would help develop congruent strategies for inducing sustainable growth.

This study adds value to existing knowledge on SASC sustainability and digitalization in the context where the SASC is under enormous pressure, competitiveness and increased variability.

Blockchain is widely applied in e-voting, shared economy areas and other government functioning. Fragmented findings and distributed literature need consolidation for a holistic view of the research domain. The purpose of this study is to comprehensively reviews the blockchain applications for government organizations and presents the past, present and future trends of blockchain applications for government organizations.

Systematic review protocol instrumentalized the systematic review of research articles published from 2013 to 2021. Science mapping discerns scientific actors’ trends and performance analysis like most influential authors, documents and sources. Content analysis of selected data set unfolds the past, present and future of blockchain applications for government organizations.

Blockchain technology offers enormous potential for the transformation of government organizations and public services. The primary areas are cryptocurrency, e-voting, shared economy, smart contracts, financial and health services, tourism, logistics and water sustainability.

This study reviewed only published research in journals and conference proceedings and excluded book reviews, book chapters and editorials from the review set. This study persuades governments and policymakers to invest in blockchain technology for transforming government organizations and public services.

This study highlights the importance of blockchain in government-controlled public departments, enhancing transparency and efficiency in public life.

Blockchain technology enhances transparency, traceability and accountability of public records.

This study pioneers in chronologically highlighting the importance of blockchain in government-controlled public departments.

The paper aims to enhance the understanding of robust food supply chains (FSC) by exploring the capabilities of various digital technologies and examining their interactions.

This study finding shows that digital technology enhances the resilience of the FSC by improving visibility, traceability and adaptability. This resilience provides a competitive advantage, ultimately enhancing the overall business performance.

In developing countries, inadequate infrastructure, poor Internet connectivity and diverse stakeholder systems pose challenges to implementing advanced digital solutions in the FSC.

This paper is among the first to investigate the impact of digital technology on FSC resilience, exploring visibility, flexibility and collaboration.