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The primary objective of this research is to explore the potential of utilizing Global Consciousness Project (GCP) data as a tool for understanding and predicting market sentiment. Specifically, the study aims to assess whether incorporating GCP data into econometric models can enhance the comprehension of daily market movements, providing valuable insights for traders.

This study employs econometric models to investigate the correlation between the Standard & Poor's 500 Volatility Index (VIX), a common measure of market sentiment and data from the GCP. The focus is particularly on the largest daily composite GCP data value (Max[Z]) and its significant covariation with changes in VIX. The research employs interaction terms with VIX and daily returns from global markets, including Europe and Asia, to explore the relationship further.

The results reveal a significant relationship with the GCP data, particularly Max[Z] and VIX. Interaction terms with both VIX and daily returns from global markets are highly significant, explaining about one percent of the variance in the econometric model. This finding suggests that variations in GCP data can contribute to a better understanding of market dynamics and improve forecasting accuracy.

One limitation of this study is the potential for overfitting and P-hacking. To address this concern, the models undergo rigorous testing in an out-of-sample simulation study lasting for a predefined one-year period. This limitation underscores the need for cautious interpretation and application of the findings, recognizing the complexities and uncertainties inherent in market dynamics.

The study explores the practical implications of incorporating GCP data into trading strategies. Econometric models, both with and without GCP data, are subjected to an out-of-sample simulation where an artificial trader employs S&P 500 tracking instruments based on the model's one-day-ahead forecasts. The results suggest that GCP data can enhance daily forecasts, offering practical value for traders seeking improved decision-making tools.

Utilizing data from the GCP is found to be advantageous for traders as noteworthy correlations with market sentiment are found. This unanticipated finding challenges established paradigms in both economics and consciousness research, seamlessly integrating these domains of research. Traders can leverage this innovative tool, as it can be used to refine forecasting precision.

This study investigates the relation between ESG-driven investment strategies and the performance of infrastructure funds. More specifically, this study examines the impact of the different dimensions – environmental (E), social (S) and governance (G) – of the ESG profile of infrastructure funds on their performance.

To study the risk-return properties of infrastructure funds and the relationship with their ESG profiles, an econometric analysis is conducted, based on a sample of 180 listed, ESG-oriented infrastructure funds identified through Refinitiv Eikon.

The results show that infrastructure funds with more solid environmental investment policies experience a lower performance, while those with a stronger social orientation yield a superior performance. Governance-related investment policies seem trivial in determining the performance of these funds. Further analysis shows that ESG controversies have a negative impact on infrastructure funds' performance, whereas Emissions and Resource Use scores, both proxying for different elements under the environmental pillar, have opposite signs. Finally, the Community score has a positive impact on funds' performance consistent with the positive impact of the social pillar score. The study also provides a number of sub-sample analyses to shed light on the conditions under which each pillar has significant impact on funds’ performance.

First, infrastructure funds should choose the composition of their portfolio holdings in a way that the total return is not penalized by the prevalence of the tricky E aspects (compliance with environmental regulations) over the main benefits of the S dimension. Second, fund managers need to bet on infrastructures with an expected impact on the social pillar dimension such as those aimed at promoting the wealth of the local communities (e.g. hospitals, schools). Third, to strengthen the fund's social dimension, fund managers must increase the dollar amount of the assets under management to count on a higher firepower.

This study makes three contributions to literature. First, the ESG profiles of the infrastructure funds operating both at local and global level and their relationship with annual performance are studied. Second, the different dimensions of the ESG profile of infrastructure funds are investigated by measuring their impact on performance. Third, the study sheds light on some detailed but relevant aspects of this phenomenon by analyzing the breakdown of the ESG profile of infrastructure funds into four sustainability sub-scores capturing their efforts to reduce CO2 emissions, the use of polluting materials and to influence local communities as well their exposure to the risk of litigation due to the occurrence of ESG controversies. This study addresses the extent to which the adoption of ESG investment policies by the infrastructure funds have an impact on their performances.

This study aims to examine the solvency and performance persistence of defined benefit private and public pension plans (DBPPs) in the Republic of Congo.

The authors use the 2 × 2 contingency table approach and the time product ratio (TPR)-based cross-product ratio (CPR) on data covering ten years from 2011 to 2020, with variable funded ratios and excess returns, to determine the solvency and performance persistence of defined benefit pension plans.

The authors document a lack of solvency and performance persistence in DBPP funds. They conclude that the solvency and performance of DBPP funds are not repetitive. The previous year's private and public defined benefit pension funds’ results do not repeat in the current year. Hence, the current solvency and performance of defined benefit pension funds are not good predictors of future funds' solvency and performance.

To the best of the authors’ knowledge, this study is the first to combine solvency and performance to examine the persistence of defined benefit pension plans in sub-Saharan Africa.

Promoting electric vehicles (EVs) is an effective way to achieve carbon neutrality. If EVs are widely adopted, this will undoubtedly be good for the environment. The purpose of this study is to analyze the impact of network externalities and subsidy on the strategies of manufacturer under a carbon neutrality constraint.

In this paper, the authors propose a game-theoretic framework in an EVs supply chain consisting of a government, a manufacturer and a group of consumers. The authors examine two subsidy options and explain the choice of optimal strategies for government and manufacturer.

First, the authors find that the both network externalities of charging stations and government subsidy can promote the EV market. Second, under a relaxed carbon neutrality constraint, even if the government’s purchase subsidy investment is larger than the carbon emission reduction technology subsidy investment, the purchase subsidy policy is still optimal. Third, under a strict carbon neutrality constraint, when the cost coefficient of carbon emission reduction and the effectiveness of carbon emission reduction technology are larger, social welfare will instead decrease with the increase of the effectiveness of emission reduction technology and then, the manufacturer’s investment in carbon emission reduction technology is lower. In the extended model, the authors find the effectiveness of carbon emission reduction technology can also promote the EV market and social welfare (or consumer surplus) is the same whatever the subsidy strategy.

The network externalities of charging stations and the subsidy effect of the government have a superimposition effect on the promotion of EVs. When the network effect of charging stations is relatively strong, government can withdraw from the subsidized market. When the network effect of charging stations is relatively weak, government can intervene appropriately.

Comparing previous studies, this study reveals the impact of government intervention, network effects and carbon neutrality constraints on the EV supply chain. From a sustainability perspective, these insights are compelling for both EV manufacturers and policymakers.

The purpose of this study is to develop an integrated model for the stochastic multiproject scheduling and material ordering problems, where some of the prominent features of offshore projects and their environmental-degrading effects have been embraced as well. The durations of activities are uncertain in this model. The developed formulation is tri-objective that seeks to minimize the expected time, total cost and CO₂ emission of all projects.

A new version of the multiobjective multiagent optimization (MOMAO) algorithm has been proposed to solve the amalgamated model. To empower the MOMAO, various procedures of this algorithm have been modified based on the multiattribute utility theory (MAUT) technique. Along with the MOMAO, this study has employed four other meta-heuristic methodologies to solve the model as well.

The outputs of the MOMAO have been put to test against four other optimizers in terms of convergence, diversity, uniformity and computation times. The results of the Mean Ideal Distance (MID) metric have revealed that the MOMAO has strongly prevailed its rival optimizers. In terms of diversity of the acquired solutions, the MOMAO has ranked the first among all employed optimizers since this algorithm has offered the best solutions in 56.66 and 63.33% of the test problems regarding the diversification metric and hyper-volume metrics. Regarding the uniformity of results, which is measured through the spacing metric (SP), the MOMAO has presented the best SP values in more than 96% of the test problems. The MOMAO has needed more computation times in comparison to its rivals.

A real case study comprising two concurrent offshore projects has been offered. The proposed formulation and the MOMAO have been implemented for this case study, and their effectiveness has been appraised.

Very few studies have focused on presenting an integrated formulation for the stochastic multiproject scheduling and material ordering problems. The model embraces some of the characteristics of the offshore projects which have not been adequately studied in the literature. Limited capacities of the offshore platforms and cargo vessels have been embedded in the proposed model. The offshore platforms have spatial limitations in storing the required materials. The vessels are also capacitated and they also have limited shipment capacities. Some of the required materials need to be transported from the base to the offshore platform via a fleet of cargo vessels. The workforces and equipment can become idle on the offshore platform due to material shortage. Various offshore-related costs have been integrated as a minimization objective function in the model. The cargo vessels release CO₂ detrimental emissions to the environment which are sought to be minimized in the developed formulation. To the best of the authors' knowledge, the MOMAO has not been sufficiently employed as a solution methodology for the stochastic multiproject scheduling and material ordering problems.

This article undertakes a literature review on showrooming, offering an exhaustive overview of research publications and future research objectives that will contribute to extending the understanding of the phenomenon.

The showrooming literature has been collected from journals indexed by SCOPUS and ranked by ABDC. This was later analysed with the SPAR-4-SLR framework and the TCCM methodology (theories, contexts, characteristics, and methodologies) proposed by Paul et al. (2021) and Paul and Rosado-Serrano (2019).

The insights of this review include bibliometrics of showrooming research and the number of explored showrooming theories, methodologies, and contexts from which the phenomenon has been studied. It also highlights the various aspects that might be considered while building an optimal approach.

Articles published in SCOPUS-indexed and ABDC-ranked journals between 2012 and August 2022 were considered. Some articles published in conference proceedings and journals, not fulfilling the aforementioned criteria, might have been missed.

SPAR-4-SLR and TCCM methodologies would aid the researchers in further exploration of this phenomenon and suggest options for enhancing customer experience (CX) eventually leading to customer retention. Retail channel managers will find this knowledge handy in “encouraging loyal showrooming” and ensuring business sustainability.

This study uses the novel SPAR-4-SLR framework to structure the review, while TCCM methodology sheds light on the showrooming from the perspective of various theories, contexts, characteristics, and methodologies. The scope for further research identified through the above-mentioned framework and methodology would be of high value to the researchers and practitioners alike.