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The purpose of this study is to formulate a new class of vehicle routing problem with an objective to minimise the total cost of raw material collection and derive a new approach to solve optimization problems. This study can help to select the optimum number of suppliers based on cost.

To model the raw material vehicle routing problem, a mixed integer linear programming (MILP) problem is formulated. An interesting phenomenon added to the proposed problem is that there is no compulsion to visit all suppliers. To guarantee the demand of semiconductor industry, all visited suppliers should reach a given raw material capacity requirement. To solve the proposed model, the authors developed a novel hybrid approach that is a combination of block and edge recombination approaches. To avoid bias, the authors compare the results of the proposed methodology with other known approaches, such as genetic algorithms (GAs) and ant colony optimisation (ACO).

The findings indicate that the proposed model can be useful in industries, where multiple suppliers are used. The proposed hybrid approach provides a better sequence of suppliers compared to other heuristic techniques.

The data used in the proposed model is generated based on previous literature. The problem derives from the assumption that semiconductor industries use a variety of raw materials.

This study provides a new model and approach that can help practitioners and policymakers select suppliers based on their logistics costs.

This study provides two important contributions in the context of the supply chain. First, it provides a new variant of the vehicle routing problem in consideration of raw material collection; and second, it provides a new approach to solving optimisation problems.

Artificial intelligence (AI)-based human capital management (HCM) software solutions represent a potentially effective way to leverage and streamline a bank’s human resources. However, despite the attractiveness of AI-based HCM solutions to improve banks’ effectiveness, to the best of the authors’ knowledge, there are no current studies that identify critical success factors (CSFs) for adopting AI-based HCM in the banking sector. This study aims to fill this gap by investigating CSFs for adopting AI-based HCM software solutions in the banking sector.

Full consistency method methodology and technology–organization–environment, economic and human framework are used for categorizing and ranking CSFs.

The study identifies the technological and environmental dimensions as the most and least important dimensions for AI-based HCM adoption in banks. Among specific CSFs, compatible technology facilities, sufficient privacy and security and relative advantages of technology over competing technologies were identified as the most important. Implementation of AI-based HCM solutions requires significant outlays of resources, both human and financial, for banks.

The study provides bank administrators a set of objective parameters and criterion to evaluate the feasibility of adopting a particular AI-based HCM solution in banks.

Healthcare workers’ (HCWs’) job-related high exposure to Covid-19 virus arouses fear of Covid-19 among them. Based on the Theory of Mind (ToM), the study predicts that fears will lead to negative psychological (psychological distress) and behavioral (withdrawal intentions) outcomes. ToM is also used to identify social intelligence as a means to counter fear of Covid-19 on heightened psychological distress and increased withdrawal intentions.

To investigate the study design, a sample of 262 HCWs, including doctors, nurses and technicians, were surveyed using standardized questionnaires.

As predicted, Covid-19 fear led to increased withdrawal intentions with heightened psychological distress partially mediating the relationship. The alleviating role of social intelligence on the effects of Covid-19 was supported as high social intelligence reduced HCWs’ turnover intentions, with decreased psychological distress partially mediating the relationship.

Given the universality of the Theory of Mind (ToM), the findings of this study are likely to be generalizable to all pandemics. The study results support the increased application of ToM in organizational settings and have both theoretical and practical implications for health administrators. Based on study results, health administrators are exhorted to develop ToM-based mental models to understand and deal with the fear of contagious diseases. Health administrators can also increase HCWs’ social intelligence to deal with the negative perceptual and behavioral outcomes arising from the emotions aroused by the nature of their work.

Decision support systems developed using machine learning classifiers have become a valuable tool in predicting various diseases. However, the performance of these systems is adversely affected by the missing values in medical datasets. Imputation methods are used to predict these missing values. In this paper, a new imputation method called hybrid imputation optimized by the classifier (HIOC) is proposed to predict missing values efficiently.

The proposed HIOC is developed by using a classifier to combine multivariate imputation by chained equations (MICE), K nearest neighbor (KNN), mean and mode imputation methods in an optimum way. Performance of HIOC has been compared to MICE, KNN, and mean and mode methods. Four classifiers support vector machine (SVM), naive Bayes (NB), random forest (RF) and decision tree (DT) have been used to evaluate the performance of imputation methods.

The results show that HIOC performed efficiently even with a high rate of missing values. It had reduced root mean square error (RMSE) up to 17.32% in the heart disease dataset and 34.73% in the breast cancer dataset. Correct prediction of missing values improved the accuracy of the classifiers in predicting diseases. It increased classification accuracy up to 18.61% in the heart disease dataset and 6.20% in the breast cancer dataset.

The proposed HIOC is a new hybrid imputation method that can efficiently predict missing values in any medical dataset.

The study approaches the social problem of overconsumption by examining how dispositional characteristics (Trigunas) affect self-control capacity and buying impulsiveness.

A survey of 181 Indian students was conducted to collect data on Trigunas, self-control and impulse buying tendency (IBT). Partial least squares-based structure equation modeling package ADANCO was used for data analysis.

The results indicate that two out of the three gunas were related to impulsive buying tendency. As hypothesized, self-control mediated these relationships. The findings prove that Trigunas carry differential influence on self-control capacity and impulsive buying behavior of individuals.

The results of this study offer new insights and ideas to practitioners and researchers pursuing the problem of overconsumption. This study delves into ancient Hindu knowledge of mindfulness and offers fresh psychological constructs that broaden scholarly understanding on personality-related drivers of overconsumption.

Most research on overconsumption and related issues has been conducted using western personality models. Additionally, many of these findings are inconsistent. This article broadens this discussion by applying indigenous Indian psychology constructs to the study of consumer behavior and provides empirical support for the same.

The study's objective is to examine the consumers' intention to buy counterfeit brand shoes. A conceptual model is developed to test the risk-taking and word-of-mouth (WOM) as a moderator in the relationship between status consumption, brand image, and consumer intention to buy counterfeit shoes.

Based on the theory of reasoned action (TRA) and signaling theory (ST), this research was conducted in the Indian National Capital Region. Using a structured instrument, the data was collected from 240 respondents. After checking the psychometric properties of the survey instrument using the Lisrel package of structural equation modeling, Hayes's PROCESS macros were used for testing the hypotheses.

The findings from the study indicate that (1) status consumption and brand image are positively associated with purchase intention of counterfeit brand shoes, and (2) risk-taking moderates the relationship between (1) status consumption and purchase intention, and (2) brand image and purchase intension, (3) significant three-way interaction between WOM, risk-taking and status consumption on purchase intention, and (4) significant three-way interaction between brand image, WOM, and risk-taking on purchase intention of counterfeit brand shoes.

As with any survey research, this study has common method variance as a potential problem. However, through the latent variable method and Harman's single-factor analysis, the common method variance was checked. The study has several implications for managers, e-marketers, and consumers.

The study has several implications for marketers selling counterfeit products and managers intending to protect their branded products.

A conceptual model showing two-way and three-way interactions between status consumption, risk-taking, and WOM influencing the consumer purchase intention of counterfeit products was discussed. This is the first of its kind in India to explore such relationships.

The importance of story‐telling in organizational life has often been overlooked in contemporary organizational and leadership literature. Throughout history, leaders ‐ political and religious ‐ have used story‐telling as a powerful motivational tool, particularly during times of uncertainty, change and upheaval or in response to crises. This article looks at the role of story‐telling as an integral part of the human experience and at its applications in modern organizational life. The article concludes by suggesting that the art of story‐telling is still, despite recent advances in communication technologies, an essential managerial skill ‐ particularly for leaders of organizations.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.