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Set-based design (SBD) is a lean tool widely adopted for improving design processes and providing value maximization to clients. The purpose of this paper is to present the development and testing of a lean simulation game that incorporated point-based and SBD principles. The objective of the game was to enhance learning of lean design management among construction students.

After a thorough and comprehensive literature review consisting of secondary data in journal papers, books, thesis references and primary data in the form of interviews with lean practitioners, the simulation game prototype was developed. The testing of the game was carried out with a study group. Data were collected during the gameplay with the help of a questionnaire survey on a confidence scale and Likert scale and assessed using the Wilcoxon signed-rank test, histogram, one-sample t-test and false discovery rate (Benjamini–Hochberg) correction method.

The data collected both pre- and post-simulation showed an increase in average confidence in understanding from 3.33 to 3.89, a 16.7% rise. The data was further interpreted by using Wilcoxon signed-rank test, indicating that the post-simulation learning experience was significantly better than the pre-simulation one. Promising positive results were obtained for the questions on game design, engagement and understanding of point-based design and SBD concepts.

The simulation game helps bridge the gap between knowledge building and real-life by effectively imitating the process. The game facilitates a dynamic and critical approach toward developing new educational simulation games and their successful incorporation in propagating lean principles in the construction industry.

This paper aims to propose a two-element dual fed ultra-wideband (UWB) multiple input multiple output (MIMO) antenna system with no additional decoupling structures. The antenna operates from 3.1 to 10.6 GHz. The antenna finds its usage in on-body wearable device applications.

The antenna system measures 63.80 × 29.80 × 0.7 mm. The antenna radiating element is designed by using a modified dumbbell-shaped structure. Jean cloth material is used as substrate. The isolation improvement is achieved through spacing between two elements.

The proposed antenna has a very low mutual coupling of S₂₁ < −20 dB and impedance matching of S₁₁ < −10 dB. The radiation characteristics are stable in the antenna operating region. It provides as ECC < 0.01, diversity gain >9.9 dB. The antenna offers low average specific absorption rate (SAR) of 0.169 W/kg. The simulated and measured results are found to be in reasonable match.

The MIMO antenna is proposed for on-body communication, hence, a very thin jean cloth material is used as substrate. This negates the necessity of additional material usage in antenna design and the result range indicates good diversity performance and with a low SAR of 0.169 W/kg for on-body performance. This makes it a suitable candidate for textile antenna application.

This research aims to study the recent changes in consumer behaviour and purchase pattern during the Covid-19 pandemic. Covid-19 pandemic has forced consumers to stockpile, which has its own consequences. The article proposes the importance of “minimalism in consumption” to avoid greed in consumer behaviour.

The data are collected from consumers across India using an online survey during the first lockdown from March 2020 to May 2020. A simple random sampling technique is used for data collection, and the collected data are analysed using SPSS version 26.

The study states that there will be a shift in the purchase pattern of the consumers if lockdowns are imposed in the future or during any other crisis. However, at present, consumers have developed a stockpiling mentality fearing the unavailability of essentials.

Pandemic has stimulated a drastic change in consumer behaviour, which is a situational effect. Each crisis affects consumer behaviour in a different way. In this research, we have considered only fear, greed and anxiety in the light of Covid-19. On the other hand, the research intends to draw realistic conclusions based on consumers' experiences during the lockdown.

The study proposes solutions that will help marketers frame exclusive strategies for a future crisis. Analysing the change in consumer behaviour and the shift in purchase patterns will emphasize the importance of market research to know consumer expectations during a crisis situation in order to cater to their new demands.

Consumers who stockpile should realize the unavailability of goods to other consumers who are in need. They also have to understand the importance of “minimalism in consumption” during a crisis.

The data are collected during the most taxing crisis, the Covid-19 pandemic. Data are collected at the peak time of the first wave of Covid-19 in India, during a major shift in consumers' behaviour and purchase pattern. The article brings to the larger consciousness and also preaches a life lesson to all consumers to execute their responsibilities in consumption without over-demands and expectations.

This hypothetical case study aims to revisit the classical model given by Henri Fayol whereby he put forward a set of 14 principles to guide managers in decision-making across organizations. The case study showcases the dilemma in which the top manager of an automobile company finds himself when some of the very basic principles – on which the whole discipline of management is founded – are ignored. It will also serve as an aid for faculty members in B-Schools to teach students the significance of basic management principles postulated many years back which stand relevant even in contemporary times.

This case study is based on a hypothetical scenario in the corporate world. Different incidents in a fictitious automobile manufacturing firm are presented and the corresponding principles given by Henri Fayol are inferred.

This case study highlights that decision-making gets complicated if fundamental principles of management are not complied with. The decision taken during each and every situation which has been discussed in this case study is contrary to the correct course of action as propounded by Fayol. Modern-day managers must acknowledge the relevance and importance of these principles for achieving success in business.

This case study underscores that even in this volatile business environment where most of the management practices are technology-driven, we cannot disregard the most elementary rules of management. The managers working at different levels in the organizational hierarchy may be guided to make the right decisions in situations similar to the ones described.

Indian Railways (IR) has been slow in innovation. The competition from other modes of transport has posed new challenges to IR. Railways worldwide have taken help from startups to develop innovative solutions to improve railway operations. Such collaborations have helped in leveraging the technical expertise of startups in domains which are non-conventional for railways to develop in-house. These collaborations have been made possible by funding startups through various investment channels.

The aim of this study is to perform a systematic literature review on retail agglomeration literature and present an agenda of future research in this domain.

To synthesize and evaluate the retail agglomeration literature, the study adopts a structured systematic literature review approach. Additionally, the study employs the Theory-Context-Characteristics-Methodology (TCCM) framework to present future research directions in the retail agglomeration domain.

This review proposes a conceptual framework showing the relationships between the antecedents, mediators and consequences reported in the retail agglomeration literature. Lexicometric analysis shows that the key themes of retail agglomeration research are retail store selection and retail performance, retail location strategy and store format, customer perceptions, behavior and expectations.

Specific criteria used for the inclusion of literature limits articles considered for the current systematic review. Also, only those articles published in English were considered.

Based on the proposed model, this review presents strategies to enhance the performance of retail agglomeration.

This study has systematically synthesized the retail agglomeration literature to explore its development over time and proposes a research framework which provides a comprehensive understanding of retail agglomeration literature.

Product returns are a major challenge for e-businesses as they involve huge logistical and operational costs. Therefore, it becomes crucial to predict returns in advance. This study aims to evaluate different genres of classifiers for product return chance prediction, and further optimizes the best performing model.

An e-commerce data set having categorical type attributes has been used for this study. Feature selection based on chi-square provides a selective features-set which is used as inputs for model building. Predictive models are attempted using individual classifiers, ensemble models and deep neural networks. For performance evaluation, 75:25 train/test split and 10-fold cross-validation strategies are used. To improve the predictability of the best performing classifier, hyperparameter tuning is performed using different optimization methods such as, random search, grid search, Bayesian approach and evolutionary models (genetic algorithm, differential evolution and particle swarm optimization).

A comparison of F1-scores revealed that the Bayesian approach outperformed all other optimization approaches in terms of accuracy. The predictability of the Bayesian-optimized model is further compared with that of other classifiers using experimental analysis. The Bayesian-optimized XGBoost model possessed superior performance, with accuracies of 77.80% and 70.35% for holdout and 10-fold cross-validation methods, respectively.

Given the anonymized data, the effects of individual attributes on outcomes could not be investigated in detail. The Bayesian-optimized predictive model may be used in decision support systems, enabling real-time prediction of returns and the implementation of preventive measures.

There are very few reported studies on predicting the chance of order return in e-businesses. To the best of the authors’ knowledge, this study is the first to compare different optimization methods and classifiers, demonstrating the superiority of the Bayesian-optimized XGBoost classification model for returns prediction.