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In this paper, the authors investigate the impact of the confirmation bias on returns, expectations and hedging of optimistic and pessimistic traders in the cryptocurrencies, commodities and stock markets before and during COVID-19 periods.

The authors investigate the impact of the confirmation bias on the estimated returns and the expectations of optimistic and pessimistic traders by employing the financial stochastic model with confirmation bias. Indeed, the authors compute the optimal portfolio weights, the optimal hedge ratios and the hedging effectiveness.

The authors find that without confirmation bias, during the two sub periods, the expectations of optimistic and pessimistic trader’s seem to convergence toward zero. However, when confirmation bias is particularly strong, the average distance between these two expectations are farer. The authors further show that, with and without confirmation bias, the optimal weights (the optimal hedge ratios) are found to be lower (higher) for all pairs of financial market during the COVID-19 period as compared to the pre-COVID-19 period. The authors also document that the stronger the confirmation bias is, the lower the optimal weight and the higher the optimal hedge ratio. Moreover, results reveal that the values of the optimal hedge ratio for optimistic and pessimistic traders affected or not by the confirmation bias are higher during the COVID-19 period compared to the estimates for the pre-COVID period and inversely for the optimal hedge ratios and the hedging effectiveness index. Indeed, either for optimists or pessimists, the presence of confirmation bias leads to higher optimal hedge ratio, higher optimal weights and higher hedging effectiveness index.

The findings of the study provided additional evidence for investors, portfolio managers and financial analysts to exploit confirmation bias to make an optimal portfolio allocation especially during COVID-19 and non-COVID-19 periods. Moreover, the findings of this study might be useful for investors as they help them to make successful investment decision in potential hedging strategies.

First, this is the first scientific work that conducts a stochastic analysis about the impact of emotional biases on the estimated returns and the expectations of optimists and pessimists in cryptocurrency and commodity markets. Second, the originality of this study stems from the fact that the authors make a comparative analysis of hedging behavior across different markets and different periods with and without the impact of confirmation bias. Third, this paper pays attention to the impact of confirmation bias on the expectations and hedging behavior in cryptocurrencies and commodities markets in extremely stressful periods such as the recent COVID-19 pandemic.

This paper aims to investigate the role of the service provider in determining customer satisfaction in sharing economy services. The authors sought to examine how the intrinsic and extrinsic cues along with their interactions influence customer satisfaction.

This research uses a mixed-methods design to test the hypotheses. Study 1 uses secondary data from Inside Airbnb. Study 2 uses a 2 × 2 × 2 between-subject experimental design.

Both studies support the confirmation bias perspective over the expectancy-confirmation perspective in explaining the interplay among different cues in determining customer satisfaction. In the context of Airbnb, in the absence of a Superhost badge, if hosts adopt a reactive communication style, physical presence has a greater impact on customer satisfaction compared to virtual presence.

This study extends the services marketing literature and cue utilization theory by investigating the dynamic interactions among multiple intrinsic and extrinsic service cues. It shed new light on how a combination of these cues may become additive or redundant in determining customer satisfaction. This study contributes to the services marketing literature by addressing the interactive nature of sharing economy services and the neglected role of service providers.

The criteria importance through intercriteria correlation (CRITIC) and range of value (ROV) combined methods were used to determine a single index for all multiple responses.

This paper used cold metal transfer (CMT) and pulse metal-inert gas (MIG) welding processes to study the weld-on-bead geometry of AA2099-T86 alloy. This study used Taguchi's approach to find the optimal setting of the input welding parameters. The welding current, welding speed and contact-tip-to workpiece distance were the input welding parameters for finding the output responses, i.e. weld penetration, dilution and heat input. The L9 orthogonal array of Taguchi's approach was used to find out the optimal setting of the input parameters.

The optimal input welding parameters were determined with combined output responses. The predicted optimum welding input parameters were validated through confirmation tests. Analysis of variance showed that welding speed is the most influential factor in determining the weld bead geometry of the CMT and pulse MIG welding techniques.

The heat input and weld bead geometry are compared in both welding processes. The CMT welding samples show superior defect-free weld beads than pulse MIG welding due to lesser heat input and lesser dilution.

This paper aims to present a novel lightweight distribution grid operating robot system with focus on lightweight and multi-functionality, aiming for autonomous and live-line maintenance operations.

A ground-up redesign of the dual-arm robotic system with 12-DoF is applied for substantial weight reduction; a dual-mode operating control framework is proposed, with vision-guided autonomous operation embedded with real-time manual teleoperation controlling both manipulators simultaneously; a quick-swap tooling system is developed to conduct multi-functional operation tasks. A prototype robotic system is constructed and validated in a series of operational experiments in an emulated environment both indoors and outdoors.

The overall weight of the system is successfully brought down to under 150 kg, making it suitable for the majority of vehicle-mounted aerial work platforms, and it can be flexibly and quickly deployed in population dense areas with narrow streets. The system equips with two dexterous robotic manipulators and up to six interchangeable tools, and a vision system for AI-based autonomous operations. A quick-change tooling system ensures the robot to change tools on-the-go without human intervention.

The resulting dual-arm robotic live-line operation system robotic system could be compact and lightweight enough to be deployed on a wide range of available aerial working platforms with high mobility and efficiency. The robot could both conduct routine operation tasks fully autonomously without human direct operation and be manually operated when required. The quick-swap tooling system enables lightweight and durable interchangeability of multiple end-effector tools, enabling future expansion of operating capabilities across different tasks and operating scenarios.

Food loss and wastage is an issue of global concern and the household sector is one of the biggest contributors to this. Solar drying has been explored by many eminent researchers as a solution to this problem but there have been concerns about the lack in designs, higher cost, lower performance, and consumer acceptability. The present research aims to design a small-scale domestic solar dryer by using computer software.

Response surface methodology (RSM) and computational fluid dynamics (CFD) are used to design the domestic solar dryer. Initially, design variables (inlet and outlet vent height) are identified and a design of experiments has been created using RSM for set of experimental runs. The experimental runs suggested by RSM were carried out using CFD simulation in COMSOL Multiphysics software and the results were used for optimization of response variables (outlet velocity and drying chamber temperature) in RSM.

Outlet vent height was found to be most significantly affecting parameter to both the responses. The optimum values of inlet and outlet vent heights were 0.5 and 2.5 cm, respectively with the overall desirability of 0.728. The model accuracy was tested by conducting a confirmation test as post processing in design expert software.

Designing a solar dryer is a complex, costly and time consuming process, this study presents an easy, economic and fast method to design a new solar dryer. It would help researchers to design and develop new domestic as well as large size industrial solar dryer.

The authors' research examines how negative electronic word-of-mouth (e-WOM) alters focal customers' post-recovery justice perceptions and attitudes to determine their future behavior with the service provider. Specifically, this paper develops and tests a conceptual model to investigate how negative e-WOM alters focal customers' perceptual and attitudinal outcomes after the service recovery experience. It also examines the post-recovery effect of negative e-WOM on focal customers’ willingness to patronize the service after their recovery experience.

To test the hypotheses, two pretests and two experimental studies with created scenarios in the retail context were conducted.

The authors' findings reveal that services are judged during and well beyond failure and recovery occurrences. To maintain a loyal customer base, service managers should develop processes that address service complaints both within and beyond the service consumption stage. The authors also find that despite a favorable recovery, focal customers gravitate toward the failure experience and develop unfavorable attitudes toward the service provider, leading to likely defections.

The authors' research demonstrates the persuasive power of negative e-WOM at the post-service recovery stage, making a unique contribution to the service recovery literature. This research also contributes to the persuasive effect of negative e-WOM, demonstrating message context as a boundary condition of negative e-WOM effects. In general, the authors' work highlights the importance of understanding the psychological processes involved in eliciting the persuasive influence of negative e-WOM in the post-service recovery stage that may lead to the defection of “so-called” successfully recovered customers.

This paper presents a developed BIMxAR application, an integration of building information modeling (BIM) with augmented reality (AR) linked with real-time online database to support the building facility management work. The primary aim of this research was to develop and empirically examine the applicability of a BIM-based AR (BIMxAR) application in building facility management.

The BIMxAR application was developed and experimented with maintenance work of a university laboratory building. The experiment consisted of a comparison of supportive maintenance tasks performed using the traditional approach and the BIMxAR approach by 38 university students. The time taken to complete each task was recorded and analyzed using statistical analyses to compare the performance between the tasks completed using each approach.

The results indicated that the group using the BIMxAR application approach completed the tasks correctly in a significantly shorter time compared to that using the traditional approach. The findings supported the applicability of the developed BIMxAR application and the improvement of the building facility management tasks when using the proposed approach.

This paper presents a methodological approach in developing a mobile application that integrates BIM with AR for facility management work, leveraging real-time information exchange through a cloud-based platform. The paper also provides empirical evidence that demonstrates how the integration between BIM and AR could be achieved and implemented to help facilitate building maintenance tasks.

This study aims to systematically review the literature on digital consumers’ decision-making in the banking, financial services and insurance (BFSI) sector and proposes an integrative framework.

By combining databases such as Web of Science and Elton B. Stephens Company (EBSCO), we identified, analyzed and synthesized 53 peer-reviewed empirical articles that explore the connection between digital solutions in the BFSI sector and various phases and constructs of the consumer decision-making process. We examined the dependent variables (DVs) used to operationalize consumer decision-making, performed a thematic analysis of the papers and proposed an integrative framework.

The reviewed articles have garnered more attention from marketing researchers than from BFSI or artificial intelligence scholars, often employing traditional behavioral and experimental methodologies that have several limitations. We identified 38 DVs used to operationalize consumer decision-making, with the most frequently recurring constructs being “Intention to use,” “Utilization,” “Satisfaction,” “Perceived usefulness” and “Trust.” We propose an integrative framework that groups these DVs into three main clusters: subjects’ perceptions, user experience and adoption/usage choice. This systematic literature review highlights the increasing importance of emotion in recent decades and underscores the difficulty of establishing a framework where relationships between variables are direct and unidirectional, as traditional economic theories assume.

To the best of the authors’ knowledge, this is the first study to provide a comprehensive and systematic understanding of the DVs and the research methods used to study the impact of recent digital solutions on consumer decision-making in the BFSI sector. Further, a framework is proposed that can offer a new perspective for consumer research.

This study aims to investigate the impact of titanium oxide (TiO₂) filler on the coefficient of friction (COF) and specific wear rate (SWR) in flax fiber reinforced epoxy composites (FFRCs) under abrasive wear conditions utilizing the Taguchi approach. The primary objective is to enhance wear resistance and promote the development of sustainable materials for various applications.

Epoxy/flax composites with varying TiO₂ filler content (0–8 wt%) are fabricated through the hand layup method. Subsequently, wear testing is conducted following ASTM G99-05 standards. The Taguchi design of experiments (DOE) and analysis of variance (ANOVA) are utilized for statistical analysis.

Results indicate a significant improvement in abrasive wear properties with the incorporation of TiO₂ filler. The COF is found to be most influenced by the normal load (55.19%), followed by grit size, wt% TiO₂ filler and sliding distance. SWR is found to be most influenced by the grit size (42.92%), followed by wt% TiO₂, normal load and sliding distance. Notably, the Taguchi model aligns well with experimental results, demonstrating its efficacy in predicting the abrasive wear behavior of FFRCs.

This research introduces a novel hybrid composite that combines TiO₂ filler and flax fibers, showcasing their potential to enhance the tribological properties of epoxy composites. The study offers valuable insights into optimizing abrasive wear test variables in natural fiber-reinforced composites using Taguchi DOE and ANOVA, crucial for improving the performance of sustainable materials in engineering applications.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.