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The purpose of this paper is to present and solve the problem of adaptive beamforming (ABF) for a uniform linear array (ULA) as an optimization problem. ABF mainly concerns with estimation of weights of antenna array so as to direct the major lobe in the direction of desired user and nulls in the direction of interfering signals with reduced side lobe level (SLL).

The potential of gravitational search algorithm is explored to optimize multi-objective fitness function for ABF using MATLAB software.

The performance of the algorithm has been compared by considering different number of interference signals at different power levels. The proposed algorithm presents good convergence rate and accurate steering of main lobe and nulls with reduced SLL compared to the well-known ABF technique, namely, minimum variance distortionless response (MVDR) and previously reported results. The simulation results are presented in tabular form.

The present work is limited to simulation. The researchers are encouraged to solve the problem of ABF using the proposed approach in hardware.

The application of proposed algorithm is to optimize multi-objective function for ABF with reduced SLL in linear antenna arrays.

The primary objective of this study is to propose a robust multiobjective solution search approach for a mean-variance multiple correlated quality characteristics optimisation problem, so-called “multiple response optimisation (MRO) problem”. The solution approach needs to consider response surface (RS) model parameter uncertainties, response uncertainties, process setting sensitivity and response correlation strength to derive the robust solutions iteratively.

This study adopts a new multiobjective solution search approach to determine robust solutions for a typical mean-variance MRO formulation. A fine-tuned, non-dominated sorting genetic algorithm-II (NSGA-II) is used to derive efficient multiobjective solutions for varied mean-variance MRO problems. The iterative search considers RS model uncertainties, process setting uncertainties and response correlation structure to derive efficient fronts. The final solutions are ranked based on two different multi-criteria decision-making (MCDM) techniques.

Five different mean-variance MRO cases are selected from the literature to verify the efficacy of the proposed solution approach. Results derived from the proposed solution approach are compared and contrasted with the best solution(s) derived from other approaches suggested in the literature. Comparative results indicate significant superiorities of the top-ranked predicted robust solutions in nondominated frequency, closeness-to-target and response variabilities.

The solution approach depends on RS modelling and considers continuous search space.

In this study, promising robust solutions are expected to be more suitable for implementation than point estimate-based MOO solutions for a real-life MRO problem.

No evidence of earlier research demonstrates the superiority of a MOO-based iterative solution search approach for mean-variance MRO problems by simultaneously considering model uncertainties, response correlation and process setting sensitivity.

To provide a dynamic view on accommodation choice behaviors during the pandemic, this study aims to examine the impact of recent trends on prospective travelers’ preferences for hotels and Airbnb.

The paper adopts a mixed methods approach that incorporates three independent studies (experimental analysis, online search pattern analysis and an econometric event study) to understand customer decision-making behaviors.

The findings indicate that travelers prefer Airbnb entire flats/apartments to hotels when the pandemic is trending upward. This result externally validates travelers’ preference toward Airbnb during periods of high risk. Interestingly, when the trends go downward, however, the same behavioral pattern was not identified.

This study provides important empirical insights into how the evolution of health crises influence customer decision-making for hotels and Airbnb. Future research needs to consider the role of socio-demographic factors in accommodation selection behaviors and examine how travelers react to cleanliness levels between Airbnb and hotels.

As one of initial studies that empirically examine Airbnb customers’ decision-making behaviors in the context of the COVID-19 pandemic’s trends, this study provides a dynamic view on how the evolution of the pandemic influences accommodation choice behaviors.

The purpose of this paper is to address three key objectives. The first is the proposal of an enhanced multiobjective optimisation (MOO) solution approach for the mean and mean-variance optimisation of multiple “quality characteristics” (or “responses”), considering predictive uncertainties. The second objective is comparing the solution qualities of the proposed approach with those of existing approaches. The third objective is the proposal of a modified non-dominated sorting genetic algorithm-II (NSGA-II), which improves the solution quality for multiple response optimisation (MRO) problems.

The proposed solution approach integrates empirical response surface (RS) models, a simultaneous prediction interval-based MOO iterative search, and the multi-criteria decision-making (MCDM) technique to select the best implementable efficient solutions.

Implementation of the proposed approach in varied MRO problems demonstrates a significant improvement in the solution quality in worst-case scenarios. Moreover, the results indicate that the solution quality of the modified NSGA-II largely outperforms those of two existing MOO solution strategies.

The enhanced MOO solution approach is limited to parametric RS prediction models and continuous search spaces.

The best-ranked solutions according to the proposed approach are derived considering the model predictive uncertainties and MCDM technique. These solutions (or process setting conditions) are expected to be more reliable for satisfying customer specification compared to point estimate-based MOO solutions in real-life implementation.

No evidence exists of earlier research that has demonstrated the suitability and superiority of an MOO solution approach for both mean and mean-variance MRO problems, considering RS uncertainties. Furthermore, this work illustrates the step-by-step implementation results of the proposed approach for the six selected MRO problems.

Ostensibly the case is about an employee resigning from the organization due to lack of support, job clarity, and information about reporting structure. It addresses issues of socialization process, performance appraisal, and communication issue between colleagues in a consulting organization. The case serves as medium for diagnosis and action planning around integration of new employees into the organization, effective performance appraisal, and resolving communication barriers.

Marketing, retail.

The case study is specific to the marketing demographics of Indian shoppers with respect to organized retail stores, and therefore, the inter-relationships between various design elements and the relative importance of certain parameters discussed in the text may not follow the same pattern elsewhere in the world.

The case emulates the real-life situation of an organized retail store, Super Mart, to understand the inculcation of voice of the customer in the design of organized retail stores in India. It gives insights about factors which influence the shopping intent of customers while giving information about the inter-relationships among various design characteristics. It also gives an idea about inter-dependence between design characteristics and customer requirements. This is followed by certain questions, the responses to which can be interpreted from the text and the data provided therein.

The case aims to educate its audience about the following aspects of organized retail business: factors influencing offline shopping intent of customers; relative order of importance of customer requirements with respect to organized retail stores; inter-relationships between various design elements; and future trends in the organized retail space. Such a knowledge would help hone the skills of the next generation of business leaders in the retail space.

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The study aims to profile the scientific retractions in the top five global universities and provide descriptive statistics on specific subjects.

The data for reasons behind retractions is manually extracted from the Retraction Watch Database. The top five global universities according to the Times Higher Education global ranking of 2024 are selected for this study.

The study found that Stanford University emerged with the highest number of retractions in the assessment across institutions in the field of basic life sciences and health sciences. Notably, the predominant reasons for these retractions were identified, with “unreliable results” being the most prevalent, accounting for 53 retractions. Following closely was the category of “errors in results and/or conclusions”, contributing to 51 retractions. MIT has the longest time between publication and retraction of any subject group, with an average of 1,701 days.

This study has some limitations, as it only analysed the retractions of the top five global universities.

The study provides a comprehensive analysis of retractions in academic publishing, focusing on reasons, time gaps, article types and accessibility categories across prestigious universities. The paper underscores the critical role of retractions in maintaining the integrity of scientific literature, emphasizing the importance of transparent correction and responsible peer review to ensure the reliability and trustworthiness of published research. Results show that common reasons for retractions include duplication, fake peer review and plagiarism, underlining the need for ethical research standards.

India is a rapidly developing economy with a rapidly expending internet infrastructure and among the largest Generation Z population. This generation is tech savvy and the access to technology and network creates a conducive environment for such usage. Internet banking for the same reasons is growing leaps and bounds. The introduction of artificial intelligence (AI) has created disruptions in the traditional banking also. This paper aims to analyze the comfort level and usage of AI-enabled banking services by Generation Z.

The data is collected from 272 Generation Z members. The differential aspects, that is, the relationship of independent variables with dependent variables (AI-enabled internet banking), were analyzed using the structural equation modeling approach.

Defining factors for AI-enabled internet banking were identified. The results of factors were consistent with previous studies. It was found that the usage of AI-enabled internet banking services is insignificant, indicating that Generation Z does not perceive any advantage in using AI-enabled internet banking services.

This paper does not incorporate age groups other than Generation Z. Further research could throw light on the difference based on age groups. Further research is required to deeply understand why Generation Z does not perceive AI-enabled internet services as very important.

It has been observed that internet banking is important for Generation Z, but they also place greater importance on interpersonal communication. Banks need to consider this in designing their internet banking services.

This paper addresses the gap between comfort with and usage of AI-enabled internet banking services, by Generation Z. This paper indicates that the comfort with AI-enabled internet banking services does not translate to usage.

The paper aims to identify and report service quality dimensions critical to distributors’ perception of the quality of services their suppliers provide (Manufactures).

This research used unstructured interviews and focused group discussions. The authors have interviewed ten distributors and ten frontline managers of three mid-size Consumer Packaged Goods companies operating in India. Two focused group discussions were conducted involving academicians and practitioners in the service quality domain.

Seven quality dimensions critical to a distributor’s service quality evaluation have been identified. Except for tangibility, all other SERVQUAL dimensions have been found relevant. Three additional dimensions, namely “Fairness,” “Accessibility” and “Image quality,” have been identified. The authors made a theoretical contribution by not only identifying the relevant dimension but also proving their context-specific definition. The authors also present managerial implications and recommendations to improve the service experience of distributors.

This paper investigates the distributor–manufacturer dyadic relationship from a service quality perspective for the first time. This study made a theoretical contribution by explicitly identifying service quality dimensions for a manufacturer-to-distributor (M2D) service relationship.

The purpose of this paper is to explore the moderating influence of attitude toward offshore call centers (OCCs) and brand image of the service provider firm on the relationships among perceived service quality (SQ), customer satisfaction (CS), complaint (CI) and repeat purchase (RP) intentions.

A total of 285 adult shoppers with prior experience with OCCs were recruited using mall‐intercept approach in a large mid‐Western city in the USA. A structured questionnaire was used to record their perceptions about their service provider using OCCs. Structural equation modeling was used to test the measurement and basic structural model, along with multi‐group analysis to test the moderating effects. Multiple moderated regression analysis was also used to verify the moderating effects.

As expected, SQ was found positively associated with CS, and CS was negatively associated with CI but positively with RP. Moreover, attitude toward OCC positively moderated the link between SQ and CS; brand image negatively moderated the link between CS and CI and positively moderated the link between CS and RP. All the hypotheses were supported.

It was found that negative attitudes towards OCC result in feelings of dissatisfaction, increase in complaints and decrease in repeat purchases. However, customers are likely to complain less and continue to patronize for a more reputed service firm compared to a less reputed firm. This finding contradicts recent market reports, hence future research should investigate actual customer complaints and repeat purchases to confirm the moderating role of brand image.

Services firms using OCC should be aware that their customers have varying attitudes toward OCC. They should track these attitudes and their impact on customer complaint and repeat purchase behavior. They should also track and benchmark the service levels of their OCC with their own local call centers and those of the competitors. They could improve customer attitudes toward OCC by customer relationship management and improve the service standards through more training and empowerment of their OCC representatives.

This study is one of the first few empirical investigations of customer reactions to OCCs exploring the moderating influence of “Attitude toward OCC” and “Brand image” on the relationships among perceived service quality, customer satisfaction, complaint and repeat purchases. The findings provide useful insights to managers and researchers alike.