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In most economies, there are rules from the market regulators or government to sell at an equal wholesale price (EWP). But when one upstream channel is facing a negative demand disruption and another positive, EWP can create extra pressure on the disadvantageous supply chain partner, which faces negative disruption. The purpose of this study is to analyse the impact of EWP and the scope of the discriminatory wholesale price (DWP) during disruptions.

For the study, the authors used a dual-channel supply chain consisting of a manufacturer, online retailer (OR) and traditional brick-and-mortar (BM) retailer. Stackelberg game is used to model the interaction between the upstream and downstream channel partners, and the horizontal Nash game to analyse the interaction within downstream channel partners. For modelling asymmetric disruption, the authors took instances from the lock-down and post-lock-down periods of the COVID-19 pandemic, where consumers flow from BM retailer to OR store.

By analysing the disruption period, the authors found that this asymmetric disruption is detrimental to the BM channel, favourable to OR and has no impact on the manufacturer. But with DWP, the authors found that the profit of the BM channel and manufacturer can be increased during disruption. Though the profit of the OR decreased, it was found to be higher than in the pre-disruption period. Under DWP, the consumer surplus increased during disruption, making it favourable for the customers also. Thus, DWP can aid in creating a win-win strategy for all the supply chain partners during asymmetric disruption. Later as an extension to the study, the authors analysed the impact of the consumer transfer factor and found that it plays a crucial role in the optimal decisions of the channel partner during DWP.

Very scant literature analyses the intersection of DWP and disruptions. To the best of the authors’ knowledge, this study, for the first time uses DWP as a tool to help the disadvantageous supply chain partner during asymmetric disruptions. The study findings will assist the government, market regulators and manufacturers in revamping the wholesale pricing policies and strategies to help the disadvantageous supply chain partner during asymmetric disruption.

The purpose of this paper is to highlight e-recruitment and training comprehensiveness as the untapped antecedents of employer branding (EB) in the relevant literature, which might enhance the employer’s knowledge and lead to organisational development.

This study adopts an exploratory conceptual modelling approach based on the extant literature from 1964 to 2017 using the databases of Emerald, EBSCO, Scopus, Proquest, JSTOR and search engines such as Google Scholar to ensure the reliability of the literature.

This paper suggests that e-recruitment and training comprehensiveness might be the untapped antecedents of EB as compared to traditional recruitment and training process explored in earlier studies.

The viewpoint can be further refined through academic conceptualisation and empirical validation.

This paper lays a conceptual foundation in the emerging area of EB. Ideas expressed herein can be approached by academicians.

Past studies have not explored e-recruitment and training comprehensiveness as the antecedents of EB. This work provides knowledge that candidly contributes to the conceptualisation of e-recruitment and training comprehensiveness. Further, this research has the potential to help academicians to understand the antecedents of EB leading to organisational development.

Electric cars have very little market share in developing countries despite their environmental benefits. Thus, governments have started promoting electric cars by providing financial incentives to consumers. The current article aims to examine the direct and indirect effects of government financial incentives on consumer electric car adoption in India.

The study followed a quantitative research method that employed a self-administered survey questionnaire. Structural Equation Modelling and Multi-Group Analysis were followed for data analysis.

The study revealed that financial incentives have an indirect effect on electric car adoption intention rather than a direct effect. In addition, financial incentives were found to have a direct effect on attitude and Perceived Behavioural Control (PBC). Attitude and PBC positively influenced consumer adoption intention.

The insights and implications from the present study would help policymakers and marketers to formulate better incentive policies and market strategies to increase consumer acceptance of electric cars in developing countries.

The study contributes to the literature by analysing the underlying mechanism that links government financial incentives to electric car adoption intention. This study also explored the direct effect of financial incentives on attitude and PBC, which are less investigated in electric vehicle literature. In addition, the present article also assessed the moderating role of age in electric car adoption, which has mixed evidence in the literature, and such studies are scarce in the Indian context.

This paper aims to review existing literature about both e-recruitment and training comprehensiveness to find out if there was a link to perceptions of the organization among employees. The authors felt this would lead to more positive beliefs and higher levels of employer branding (EB).

To test their beliefs, they analyzed literature published between 1964 and 2017. The strategy was to use the databases of Emerald, EBSCO, Scopus, ProQuest and JSTOR, and search engines like Google Scholar. They searched for key words and came up with 51 articles, 17 dealing with employer branding, 23 about e-recruitment and employer branding, and 11 about training comprehensiveness and employer branding.

The authors felt the literature review confirmed their beliefs that e-recruitment was a good way to create a more positive view of organizations, and training comprehensiveness helped to develop both employee skills and levels of commitment.

Very little research has previously addressed e-recruitment and training comprehensiveness as drivers of EB.

The authors constructed an automatic essay scoring (AES) model in a discussion forum where the result was compared with scores given by human evaluators. This research proposes essay scoring, which is conducted through two parameters, semantic and keyword similarities, using a SentenceTransformers pre-trained model that can construct the highest vector embedding. Combining these models is used to optimize the model with increasing accuracy.

The development of the model in the study is divided into seven stages: (1) data collection, (2) pre-processing data, (3) selected pre-trained SentenceTransformers model, (4) semantic similarity (sentence pair), (5) keyword similarity, (6) calculate final score and (7) evaluating model.

The multilingual paraphrase-multilingual-MiniLM-L12-v2 and distilbert-base-multilingual-cased-v1 models got the highest scores from comparisons of 11 pre-trained multilingual models of SentenceTransformers with Indonesian data (Dhini and Girsang, 2023). Both multilingual models were adopted in this study. A combination of two parameters is obtained by comparing the response of the keyword extraction responses with the rubric keywords. Based on the experimental results, proposing a combination can increase the evaluation results by 0.2.

This study uses discussion forum data from the general biology course in online learning at the open university for the 2020.2 and 2021.2 semesters. Forum discussion ratings are still manual. In this survey, the authors created a model that automatically calculates the value of discussion forums, which are essays based on the lecturer's answers moreover rubrics.

The email is an important medium for sharing information rapidly. However, spam, being a nuisance in such communication, motivates the building of a robust filtering system with high classification accuracy and good sensitivity towards false positives. In that context, this paper aims to present a combined classifier technique using a committee selection mechanism where the main objective is to identify a set of classifiers so that their individual decisions can be combined by a committee selection procedure for accurate detection of spam.

For training and testing of the relevant machine learning classifiers, text mining approaches are used in this research. Three data sets (Enron, SpamAssassin and LingSpam) have been used to test the classifiers. Initially, pre-processing is performed to extract the features associated with the email files. In the next step, the extracted features are taken through a dimensionality reduction method where non-informative features are removed. Subsequently, an informative feature subset is selected using genetic feature search. Thereafter, the proposed classifiers are tested on those informative features and the results compared with those of other classifiers.

For building the proposed combined classifier, three different studies have been performed. The first study identifies the effect of boosting algorithms on two probabilistic classifiers: Bayesian and Naïve Bayes. In that study, AdaBoost has been found to be the best algorithm for performance boosting. The second study was on the effect of different Kernel functions on support vector machine (SVM) classifier, where SVM with normalized polynomial (NP) kernel was observed to be the best. The last study was on combining classifiers with committee selection where the committee members were the best classifiers identified by the first study i.e. Bayesian and Naïve bays with AdaBoost, and the committee president was selected from the second study i.e. SVM with NP kernel. Results show that combining of the identified classifiers to form a committee machine gives excellent performance accuracy with a low false positive rate.

This research is focused on the classification of email spams written in English language. Only body (text) parts of the emails have been used. Image spam has not been included in this work. We have restricted our work to only emails messages. None of the other types of messages like short message service or multi-media messaging service were a part of this study.

This research proposes a method of dealing with the issues and challenges faced by internet service providers and organizations that use email. The proposed model provides not only better classification accuracy but also a low false positive rate.

The proposed combined classifier is a novel classifier designed for accurate classification of email spam.

This research aims to find which components of Decent Work are associated with Subjective Well-Being.

With data from 2021 from the states of Mexico, econometric models are carried out.

It is found that disposable income and satisfaction with leisure time have a significant positive relationship with employees' Subjective Well-Being. Likewise, the rate of critical occupancy conditions and informality rate have a significant negative relationship with Subjective Well-Being. The research suggests that influencing the Decent Work conditions of the population in Mexico could favour their Subjective Well-Being.

Share guidelines that enable employers and governments to establish strategies and policies that promote Decent Work to increase the Subjective Well-being of employees.

This article evaluates different variables that make up the Decent Work construct in their level of influence on Subjective Well-being. These relationships and variables considered have not been identified in previous studies as a whole.

This paper aims to measure peak temperatures and cooling rates for distinct locations of thermocouples in the butt weld joint of mild steel plates. For experimental measurement of peak temperatures, K-type thermocouples coupled with a data acquisition system were used at predetermined locations. Thereafter, Rosenthal’s analytical models for thin two-dimensional (2D) and thick three-dimensional (3D) plates were adopted to predict peak temperatures for different thermocouple positions. A finite element model (FEM) based on an advanced prescribed temperature approach was adopted to predict time-temperature history for predetermined locations of thermocouples.

Comparing experimental and Rosenthal analytical models (2D and 3D) findings show that predicted and measured peak temperatures are in close agreement, while cooling rates predicted by analytical models (2D, 3D) show significant variation from measured values. On the other hand, 3D FEM simulation predicted peak temperatures and cooling rates for different thermocouple positions are close to experimental findings.

The inclusion of filler metal during simulation of welding rightly replicates the real welding situation and improves outcomes of the analysis.

The present study is an original contribution to the field of welding technology.

The purpose of this paper is to identify and analyze the agile manufacturing barriers (AMBs) for establishing a cause and effect relationship among them.

A methodology is proposed using fuzzy decision-making trial and evaluation laboratory (DEMATEL) to capture multiple experts’ qualitative judgments for mitigating the impact of the AMBs. In order to validate the proposed methodology, it is applied to an Indian automobile manufacturing company.

Out of 36 AMBs identified through literature review, 20 AMBs are found to be relevant to the case company. Five AMBs (i.e. lack of resource reconfiguration, inefficient conflicting management styles, imperfect market knowledge, inadequate information handling and improper strategic plan) were identified as significant cause group where the case company has to put efforts and resources. Also the impact relationship matrix for each AMB has been developed to visualize its interactions (i.e. influencing and influenced) among other AMBs.

The results obtained are specific to the Indian automobile manufacturing company and it cannot be generalized for every manufacturing company or any other sector. However, the proposed approach can be a basis and provide a platform to understand and analyze the interactions between AMBs.

The proposed methodology will show the appropriate areas for allocating efforts and resources to mitigate the impact of AMBs for successful implementation of agile manufacturing.

According to the authors’ knowledge, no work is reported in the literature that proposes a framework using fuzzy DEMATEL for the analysis of AMBs in Indian automobile manufacturing company.

The purpose of this paper is to develop a set of key performance indicators (KPIs) for agile manufacturing (AM) and to propose a methodology for its performance evaluation.

The proposed methodology was developed using fuzzy analytic hierarchy process (FAHP) and performance value analysis (PVA) to evaluate and analyze the AM performance. The FAHP is applied to determine the importance of KPIs, and PVA is used to evaluate AM performance.

The proposed methodology is applied to an Indian auto component manufacturer, and it is observed that there is an improvement of performance along the timeline.

The proposed approach is generic in nature and can be applied to different agile business environments for performance evaluation.

This study provides insights into the AM performance evaluation. The managers can establish the impact of each significant area (SA) on AM and each KPI on its corresponding SA by capturing their manufacturing environments.

Although many issues related to AM have been widely researched, only a few studies have been carried out to quantify, analyze and evaluate the AM performance in the Indian manufacturing environment. The proposed model has the ability to capture the performance of AM along the KPIs to draw fruitful conclusions.