Does cognitive biased knowledge influence investor decisions? An empirical investigation using machine learning and artificial neural network

Purpose

Current research in the field of behavioural finance has attempted to discover behavioural biases and their characteristics in individual investors’ irrational decision-making. This study aims to find out how biases in information based on knowledge affect decisions about investments.

Design/methodology/approach

In step one, through existing research and consultation with specialists, 13 relevant items covering major aspects of bias were determined. In the second step, multiple linear regression and artificial neural network were used to analyse the data of 337 retail investors.

Findings

The investment choice was heavily impacted by regret aversion, followed by loss aversion, overconfidence and the Barnum effect. It was observed that the Barnum effect has a statistically significant negative link with investing choices. The research also found that investors’ fear of making mistakes and their tendency to be too sure of themselves were the most significant factors in their decisions about where to put their money.

Practical implications

This research contributes to the expansion of the knowledge base in behavioural finance theory by highlighting the significance of cognitive psychological traits in how leading investors end up making irrational decisions. Portfolio managers, financial institutions and investors in developing markets may all significantly benefit from the information offered.

Originality/value

This research is a one-of-a-kind study, as it analyses the emotional biases along with the cognitive biases of investor decision-making. Investor decisions generally consider the shadowy side of knowledge management.

Apple Leave Disease Detection Using Collaborative ML/DL and Artificial Intelligence Methods: Scientometric Analysis

Infection in apple leaves is typically brought on by unanticipated weather conditions such as rain, hailstorms, draughts, and fog. As a direct consequence of this, the farmers suffer a significant loss of productivity. It is essential to be able to identify apple leaf diseases in advance in order to prevent the occurrence of this disease and minimise losses to productivity caused by it. The research offers a bibliometric analysis of the effectiveness of artificial intelligence in diagnosing diseases affecting apple leaves. The study provides a bibliometric evaluation of apple leaf disease detection using artificial intelligence. Through an analysis of broad current developments, publication and citation structures, ownership and cooperation patterns, bibliographic coupling, productivity patterns, and other characteristics, this scientometric study seeks to discover apple diseases. Nevertheless, numerous exploratory, conceptual, and empirical studies have concentrated on the identification of apple illnesses. However, given that disease detection is not confined to a single field of study, there have been very few attempts to create an extensive science map of transdisciplinary studies. In bibliometric assessments, it is important to take into account the growing amount of research on this subject. The study synthesises knowledge structures to determine the trend in the research topic. A scientometric analysis was performed on a sample of 214 documents in the subject of identifying apple leaf disease using a scientific search technique on the Scopus database for the years 2011-2022. In order to conduct the study, the Bibliometrix suite's VOSviewer and the web-based Biblioshiny software were also utilised. Important journals, authors, nations, articles, and subjects were chosen using the automated workflow of the software. Furthermore, citation and co-citation checks were performed along with social network analysis. In addition to the intellectual and social organisation of the meadow, this investigation reveals the conceptual structure of the area. It contributes to the body of literature by giving academics and practitioners a strong conceptual framework on which to base their search for solutions and by making perceptive recommendations for potential future research areas.

Interval-valued intuitionistic fuzzy digraph-matrix approach with PERMAN algorithm for measuring COVID-19 impact on perishable food supply chain

The outbreak of COVID-19 has prompted a substantial shrinkage in various businesses worldwide, the perishable food sector being one of the worst hits. Henceforth, this manuscript intends to analyse the impact of COVID-19 on perishable food supply chains (PFSCs) of developed and developing countries. For this, the study presents the analysis in two steps. In the first step, the study illuminates the particular factors that frame unique sorts of supply chain (SC) disturbances in PFSC. Secondly, the study proposes a unique interval-valued intuitionistic fuzzy set (IVIFS)-based graph theory and matrix approach (GTMA) to analyse the COVID-19 impact index value. In addition to this, the PERMAN algorithm is used to calculate the permanent function. The study has revealed that developing nations should focus more on their technological and infrastructural factors to improve the condition of PFSC during the pandemic. This study's results can be deployed by decision-makers to forestall the operative and long-haul consequences of COVID-19, or any other disruptions to the PFSC, and make plans to overcome the impact. The significance of this manuscript is that the prominent factors degrading the performance of PFSC amidst the pandemic have been highlighted, with their respective impact on developed and developing nations compared. Moreover, a neoteric comprehensive integration of IVIFS-GTMA technique along with the PERMAN algorithm has been utilised in this manuscript. This particular study is inimitable as it supplements existing literature by providing analytical support to the relationship among various factors impacting the PFSC amidst the pandemic.

Uncovering interrelationships between barriers to unmanned aerial vehicles in humanitarian logistics

Recent disasters, such as the ongoing COVID-19 pandemic, have sparked an interest in new applications for unmanned aerial vehicles (UAVs) in humanitarian aid. Nevertheless, there are still many divisive changes that need to be made in order to implement UAVs into a country’s humanitarian sector successfully. Hence, this paper aims to analyze the various barriers hindering the implementation of UAVs in humanitarian logistics for both developed and developing nations. To accomplish this, the study is presented in three steps. First, previous literature and opinions from experts are analyzed to illuminate particular factors that hinder UAV implementation. Next, we propose an interval-valued intuitionistic fuzzy set (IVIFS) based graph theory and matrix approach (GTMA) to calculate a drone implementation hindrance index (DIHI). The GTMA method used in this paper utilizes the PERMAN algorithm to calculate the permanent function. Finally, the DIHI values are plotted and analyzed to compare the readiness of drone implementation between developed and developing economies. A sensitivity analysis is then performed to provide validity to the results obtained. The study has revealed that both types of countries must first improve their inadequate government regulations regarding humanitarian UAVs. Developing countries must also focus on enhancing the technological awareness of their population. The results of this study can be used by policymakers and practitioners to smoothly implement UAVs in their country's humanitarian sector. The general index defined in this paper can also be calculated for specific countries using the steps mentioned in the manuscript.

COVID-19 Ventilator

This study is focussed on the design and modelling of a low-cost ventilator design which can be developed using locally sourced materials in Nigeria. This is meant to aid in the Country’s fight against the current COVID-19 pandemic where there is a shortage of ventilators. The ventilator design in this research was based on a mechanical AMBU bag compression principle using the Volume-Control Ventilation(VCV) mode which will eliminate the need for manual compression which can be tedious and uncontrolled. The design is powered by an electric motor with variable speed and tidal volume control. It also features an alarm which alerts medical personnel of unstable conditions in the system parameters. This prototype shows that the mechanical compression systems is a viable and more economical option which provides the essential features required in the standard existing technologies.

Effect of Preventive Maintenance on Machine Reliability in a Beverage Packaging Plant

This paper investigates the effect of preventive maintenance on the reliabilities of devices in a bottling plant. Six months of real-time maintenance data were analyzed quantitatively. Based on the breakdown events obtained for each machine, mean time between failure (MTBF), mean time to repair (MTTR), and failure rate (λ) values for individual equipment are calculated. The bottle packer, empty bottle inspector (EBI), and palletizer are identified as the plant's critical machines. A breakdown analysis (BDA) is then performed on the bottle packer and from the failure mode of all the reoccurring problems affecting this machine as a result of ineffective PM. An autonomous maintenance (AM) team is set up as part of establishing an effective PM program to improve the reliabilities of the critical machines that were continually falling. A significant reduction in machine breakdowns is observed after two months of rolling out the AM program. As a result, the reliability of bottle packer increased from 55.30% to 70.80%, while EBI and palletizer increased from 89.20% and 87.20% to 92% and 90.50%, respectively.

Implementation barriers of smart technology in Indian sustainable warehouse by using a Delphi-ISM-ANP approach

Purpose

In the digitalization era, supply chain processes and activities have changed entirely, and smart technology impacts each sustainable supply chain movement. The warehouse and distribution of various organizations have started adopting smart technologies globally. However, the adoption of smart technologies in the Indian warehousing industry is minimal. The study aims to identify the implementation barriers of smart technology in the Indian warehouse to achieve sustainability.

Design/methodology/approach

This study employs an integrated Delphi-ISM-ANP research approach. The study uses the Delphi approach to finalize the barriers identified from the detailed literature review and expert opinion. The finalized 17 barriers are modeled using interpretive structural modeling (ISM) to get the contextual relationship. The ISM method's output and analysis using the analytical network process (ANP) illustrate priorities.

Findings

The study's findings showed that the lack of government support, lack of vision and mission and the lack of skilled manpower are the most significant barriers restricting the organization from implementing smart and sustainable supply chain practices in the warehouse.

Practical implications

This study would help the practitioners enable the sustainable warehousing system or convert the existing warehouse into a smart and sustainable warehouse by developing an appropriate strategy. This study would also help reduce the impact of different barriers that would strengthen the chance of technology adoption in the warehouses.

Originality/value

The literature related to adopting smart and sustainable practices in the warehouse is scarce. Modeling of adoption barrier for smart and sustainable warehouse using an integrated research approach is the uniqueness of this study that have added value in the existing scientific knowledge.