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The study attempts to investigate the relationship between emotional biases (loss aversion bias, overconfidence bias, and regret aversion bias) and investment decisions through a meta-analysis approach.

A meta-correlation analysis was done using sample size and correlation (r) data from several relevant studies that look at how emotional biases (loss aversion bias, regret aversion bias, and overconfidence bias) affect investment decisions. Additionally, beta coefficients (ß) were also converted to correlation coefficients (r) from six studies.

This study analysed 31 empirical studies and found a significant positive correlation between emotional biases and investment decisions [loss aversion bias (r = 0.492), regret aversion bias (r = 0.401), and overconfidence bias (r = 0.346)]. We set the statistical significance threshold at 0.05.

The review covered 31 online research publications that showed significant heterogeneity, possibly influenced by various methodological, population, or other factors. Furthermore, the use of correlational data restricts the ability to establish causation.

This is a novel attempt to integrate the results of various studies through meta-analysis on the relation between these emotional biases (loss aversion, overconfidence, and regret aversion) and investment decisions.

The widespread usage of artificial intelligence (AI) is prompting a number of ethical issues, including those involving concerns for fairness, surveillance, transparency, neutrality and human rights. The purpose of this manuscript is to explore possibility of developing cognitive morality in AI systems.

This is explorative research. The manuscript investigates the likelihood of cognitive moral development in AI systems as well as potential pathways for such development. Concurrently, it proposes a novel idea for the characterization and development of ethically conscious and artificially intelligent robotic machines.

This manuscript explores the possibility of categorizing AI machines according to the level of cognitive morality they embody, and while doing so, it makes use of Lawrence Kohlberg's study related to cognitive moral development in humans. The manuscript further suggests that by providing appropriate inputs to AI machines in accordance with the proposed concept, humans may assist in the development of an ideal AI creature that would be morally more responsible and act as moral agents, capable of meeting the demands of morality.

This manuscript has some restrictions because it focuses exclusively on Kohlberg's perspective. This theory is not flawless. Carol Gilligan, one of Kohlberg's former doctoral students, said that Kohlberg's proposal was unfair and sexist because it didn't take into account the views and experiences of women. Even if one follows the law, they may still be engaging in immoral behaviour, as Kohlberg argues, because laws and social norms are not perfect. This study makes it easier for future research in the field to look at how the ideas of people like Joao Freire and Carl Rogers can be used in AI systems.

It is an original research that derives inspiration from the cognitive moral development theory of American Professor named Lawrence Kohlberg. The authors present a fresh way of thinking about how to classify AI systems, which should make it easier to give robots cognitive morality.

Evaluation of the extent of transformability of an existing system into an industry 4.0 (I4.0) compatible system is indispensable for both the technical and economic planning for implementing I4.0. This paper aims to propose a procedure to evaluate the transformability of an existing manufacturing system into an I4.0 system.

Six significant components of a manufacturing system and their five levels of modifications essential for the decision of transformation are identified. Based on expert opinion on facilitation and the impact of the transformation of one component on the transformation of others, a graph theory-based procedure for estimation of transformability index (TI) along with its relative and threshold values is proposed.

The paper introduced the concept of transformability into manufacturing systems. It proposed a simple procedure for calculating the ideal, relative and threshold value for TI to assess the suitability of the up-gradation of any manufacturing system into the I4.0 system.

Though the proposed procedure is based on six system components and their five levels of facilitation, it is quite versatile and able to integrate new components and different facilitation levels according to system requirements for their impact analysis in the transformation process. It can be extended to other domains like services and health care. Further, it can be used to estimate and establish the transformability criteria of a factory/service unit/industry from its current state to any regime.

The proposed method for deducing the TI, relative transformability index (RTI) and their threshold values would be a handy tool for decision-makers to assess the upgrading suitability of the entire manufacturing system and its component for use in the new regime or scrapping. It would provide mathematical and scientific support to the transformability decisions by assessing the influence of transforming one component to others and the system. This study would pave the way for further explorations in the domain of transformability.

In the light of available literature and best of the author’s knowledge, this study is the first of its kind that has applied the concept of transformability of existing manufacturing systems toward I4.0 compatible systems and proposed a procedure to estimate TI, RTI and their threshold values.

The study aims to analyze the barriers in the adoption of Industry 4.0 (I4.0) practices in terms of prioritization, cluster formation and clustering of empirical responses, and then narrowing them with identification of the most influential barriers for further managerial implications in the adoption of I4.0 practices by developing an enhanced understanding of I4.0.

For the survey-based empirical research, barriers to I.40 are synthesized from the review of relevant literature and further discussions with academician and industry persons. Three widely acclaimed statistical techniques, viz. principal component analysis (PCA), fuzzy analytical hierarchical process (fuzzy AHP) and K-means clustering are applied.

The novel integrated approach shows that lack of transparent cost-benefit analysis with clear comprehension about benefits is the major barrier for the adoption of I4.0, followed by “IT infrastructure,” “Missing standards,” “Lack of properly skilled manpower,” “Fitness of present machines/equipment in the new regime” and “Concern to data security” which are other prominent barriers in adoption of I4.0 practices. The availability of funds, transparent cost-benefit analysis and clear comprehension about benefits will motivate the business owners to adopt it, overcoming the other barriers.

The present study brings out the new fundamental insights from the barriers to I4.0. The new insights developed here will be helpful for managers and policymakers to understand the concept and barriers hindering its smooth implementation. The factors identified are the major thrust areas for a manager to focus on for the smooth implementation of I4.0 practices. The removal of these barriers will act as a booster in the way of implementing I4.0. Real-world testing of findings is not available yet, and this will be the new direction for further research.

The new production paradigm is highly complex and evolving. The study will act as a handy tool for the implementing manager for what to push first and what to push later while implementing the I4.0 practices. It will also empower a manager to assess the implementation capabilities of the industry in advance.

PCA, fuzzy AHP and K means are deployed for identifying the significant barriers to I4.0 first time. The paper is the result of the original conceptual work of integrating the three techniques in the domain of prioritizing and narrowing the barriers from 16 to 6.

The purpose of this paper is to bridge the gap between physical and metaphysical entities in the context of product/system design. The paper talks about the grey area of human psychology and presents a theoretical framework for the identification and selection of psychological attributes for designers.

On the basis of a literature review, this paper identifies various psychological attributes affecting the performance of designers in a team environment and then analyses the same.

The paper talks about a new shift in engineering design and designs for maintainability of mechanical systems.

A procedure based on the analytic hierarchy process method is applied. The developed procedure is useful in the assessment and selection of coveted psychological attributes for personnel in general and for designers in particular.

The paper highlights the immense role of psychology in engineering design, especially in the design for maintainability of mechanical systems. The paper will be useful to researchers, designers, maintenance personnel and professionals from the domain of engineering design, irrespective of their field of application. This paper is equally useful for human resource and management professionals/researchers.

This paper aims to put forward a labelling system capable of reflecting the level of different Industry 4.0 (I4.0)features present in a manufacturing system and further propose a comparative index to collectively estimate and compare the system automation level.

Data for the empirical study were collected from interactions with the practising managers and experts. A relationship among the six I4.0 features is developed with fuzzy cognitive maps.

The paper proposed a simple and easy-to-understand labelling system for I4.0 systems, which indicates the automation level in each of six dimensions of any manufacturing system. The system is further strengthened by a proposed automation comparative index (ACI), which collectively reflects the automation level on a scale of “0” to “1”. Thus, the labelling system and parameter could help in comparing the level of automation in the manufacturing system and further decision-making.

Only seven industrial sectors are illustrated in the paper, but the proposed concept of the classification scheme and ACI find their applicability on a large spectrum of industries; thus, the concept can be extended to other industrial sectors. Furthermore, a threshold value of ACI is a differentiator between a I4.0 and other automated systems. Both aspects have the scope of further work.

The way and pace by which the industrial world takes forward the concept of I4.0, soon it will need a labelling system and a parameter to assess the automation level of any automated system. The scheme assesses the automation level present in a manufacturing system. It will also estimate the level of the presence of each of all six attributes of an I4.0 system. Both labelling system and ACI will be the practical tools in the hands of the practising managers to help compare, identify the thrust areas and make decisions accordingly.

To the best of the authors’ knowledge, this is the first study of its kind that proposed the labelling system and automation comparison index for I4.0 systems.

This paper aims to focus on studying the influence of gradient parameters, namely, thickness coefficient, length coefficient and height ratio of auxetic structure on responses such as strength, stiffness and specific energy absorption (SEA) under compressive loading. Optimization of significant parameters is also performed to maximize responses. Further, efforts have also been made to develop regression models for strength, stiffness and SEA of auxetic structure.

Central composite design of response surface methodology is used for planning experiments. Auxetic structures of acrylonitrile butadiene styrene (ABS) and poly-lactic acid (PLA) materials are fabricated by the material extrusion (ME) technique of additive manufacturing. Fabricated structures are tested under in-plane uniaxial compressive loading. Grey relational analysis is used for the optimization of gradient parameters of the unit cell of auxetic structure to maximize responses and minimize weight and time of fabrication.

From the analysis of variance of experimental data, it is found that the compressive strength of auxetic structures increases with a decrease in length coefficient and height ratio. In the case of ABS structures, stiffness increases with a decrease in thickness coefficient and length coefficient, while in the case of PLA structures, stiffness increases with a decrease in length coefficient and height ratio. SEA is influenced by length coefficient and thickness coefficient in ABS and PLA structures, respectively. Based on the analysis, statistical non-linear quadratic models are developed to predict strength, stiffness and SEA. Optimal configuration of auxetic structure is determined to maximize strength, stiffness, SEA and minimize weight and time of fabrication.

The present study is limited to re-entrant type of auxetic structures made of ABS and PLA materials only under compressive loading. Also, results from the current study are valid within a selected range of gradient parameters. The findings of the present study are useful in the optimal selection of gradient parameters for the fabrication of auxetic structures of maximum strength, stiffness and SEA with minimum weight and time of fabrication. These outcomes have wide applications in domains such as automotive, aerospace, sports and marine sectors.

Limited literature is available on studying the influence of gradient parameters of ME manufactured auxetic structure of ABS and PLA materials on responses, namely, strength, stiffness and SEA under compressive loading. Also, no work has been reported on studying the influence of gradient parameters on mechanical properties, weight and time of fabrication of auxetic structures. The present study is an attempt to fulfil the above research gaps.

Fused filament fabrication (FFF) is a widely used 3D printing technique for the fabrication of mechanical metamaterials with intricate geometries. Gradient strategy is applied to geometric parameters of gradient star re-entrant hybrid auxetic (GSRA) structure. Deformation behaviour is studied under compressive loading. The purpose of this study is to investigate the influence of gradient geometric parameters on mechanical properties, namely, specific strength (SS), specific modulus (SM) and specific energy absorption (SEA).

Response surface methodology (RSM) is implemented for the design of experiments of gradient geometric parameters to minimize the number of experimental tests. Acrylonitrile butadiene styrene material is used for the fabrication of GSRA structures by FFF technique. The best set of gradient parameters has been optimized maximizing all three responses using RSM and artificial neural network optimization technique.

During compressive testing, row-wise deformation is observed with two-stage plateau regions, which results in increase in SEA of the structure. Furthermore, based on analysis of variance and 3D response plots, it is found that height gradient is the most influencing gradient geometric parameter on SS and SM, whereas the wall thickness gradient has maximum influence on SEA. Meanwhile, the interaction effect of wall thickness gradient and height gradient has maximum influence on SS, SM and SEA.

This study of applying gradient strategy to geometric parameters is limited to GSRA structure under compressive loading. In addition, findings are valid within the selected range of gradient geometric parameters. These findings are useful for the selection of gradient geometric parameters to maximize SS, SM and SEA of GSRA structure simultaneously. These outcomes pave the way for designing light-weight gradient hybrid auxetic structures in the field of construction, aerospace, automobile and biomedical engineering.

Limited experimental study is available on investigating the influence of gradient geometric parameters on mechanical properties, namely, SS, SM and SEA, and deformation behaviours of hybrid auxetic structures. This study directly addresses the above research gaps.

Blockchain technology (BCT) is one of the latest disruptive innovations, brought along with-it immense scope of diverse applications contributing towards sustainable development (SD). The consistent increase in the publications reveal that the application of BCT for SD has become popular among researchers and practitioners in past few years, in turn, urged for a systematic literature review (SLR) to get an insight into the research journey travelled so far and setting directions for future research in this area. Therefore, this study aims to identify, map and synthesize the available literature on application of BCT for SD.

The automatic and manual search resulted into 1,277 studies from Scopus and Web of Science database. Further applying inclusion and exclusion criterion resulted in bringing out total of 157 studies, which were termed as primary studies. Based on the results of descriptive analysis, conducted through Bibliometric and VOSviewer software, the characteristics of BCT and its key capabilities, contributing towards shaping the recent SD literature, were critically examined. Identified research themes for clustering primary studies were aligned with United Nations Sustainability Development Goals (UNSDG). A mind-map was also prepared on the basis of thematic classification of primary studies.

The research themes “business practice and economic sustainability”, “agriculture and food security” and “business practice and environment sustainability” were found to be the focal points of scholarly attention. Synthesis and analysis of primary studies resulted into classification of research gaps under four categories – theoretical foundation, methodological limitation, research themes and technology implementation challenges. The study was concluded by sensitizing and sanitizing the concrete research questions for future research.

The research findings shall be a roadmap for research scholars, academicians and practitioners to comprehend the present state of knowledge in the domain of “BCT application for SD” and decide upon adopting the future course of action to attain the UNSDGs by the year 2030.

To the best of the authors’ knowledge, the current study is the first attempt in its own sense to analyse and synthesize the available literature on “attaining SD through BCT” using SLR approach.

This paper aims to focus on an experimental study of surface roughness, dimensional accuracy and time of fabrication of parts produced by fused deposition modelling (FDM) technique of additive manufacturing. The fabricated parts of acrylonitrile butadiene styrene (ABS) material have pyramidal and conical features. Influence of five process parameters of FDM, namely, layer thickness, wall print speed, build orientation, wall thickness and extrusion temperature is studied on response characteristics. Furthermore, regression models for responses are developed and significant process parameters are optimized.

Comprehensive experimental study is performed using response surface methodology. Analysis of variance is used to investigate the influence of process parameters on surface roughness, dimensional accuracy and time of fabrication in both outer pyramidal and inner conical regions of part. Furthermore, a multi-response optimization using desirability function is performed to minimize surface roughness, improve dimensional accuracy and minimize time of fabrication of parts.

It is found that layer thickness and build orientation are significant process parameters for surface roughness of parts. Surface roughness increases with increase in layer thickness, while it decreases initially and then increases with increase in build orientation. Layer thickness, wall print speed and build orientation are significant process parameters for dimensional accuracy of FDM parts. For the time of fabrication, layer thickness and build orientation are found as significant process parameters. Based on the analysis, statistical non-linear quadratic models are developed to predict surface roughness, dimensional accuracy and time of fabrication. Optimization of process parameters is also performed using desirability function.

The present study is restricted to the parts of ABS material with pyramidal and conical features only fabricated on FDM machine with delta configuration.

From the critical review of literature it is found that some researchers have made to study the influence of few process parameters on surface roughness, dimensional accuracy and time of fabrication of simple geometrical parts. Also, regression models and optimization of process parameters has been performed for simple parts. The present work is focussed on studying all these aspects in complicated geometrical parts with pyramidal and conical features.