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The purpose of this study is to analyze the impacts of the COVID-19 pandemic on the performance of companies using a hybrid Multi-Criteria Decision-Making (MCDM) approach. Specifically, the study examines Türkiye’s Top 500 Industrial Enterprises to analyze their performance before and during the pandemic, and to capture their performance in determining investment and production strategy.

To achieve the study’s objectives, the Fuzzy Best-Worst Method (F-BWM) was used to obtain importance levels of performance indicators, decreasing the vagueness in experts’ decision-making preferences. The Measurement Alternatives and Ranking According to Compromise Solution (MARCOS) method was used to rank enterprises based on their performance.

The COVID-19 pandemic has clearly had a substantial impact on the performance of Türkiye’s top 500 industrial enterprises. While some companies suffered decreased sales, others reported that their revenues increased or remained constant during the outbreak. The results reveal that the pandemic caused a shift in the initial ranking outcomes for the first two enterprises.

The study’s limitations include the sample size and the time period under consideration, which may have an impact on the generalizability of the findings.

Decision-makers’ investment, employment and operational decisions were influenced by the impact of the COVID-19 pandemic. The results provide insights for decision-makers on how to achieve higher growth and performance under the pressure of the pandemic.

The study’s practical consequences help decision-makers understand how to attain higher growth and performance in the face of the epidemic.

The originality of this study lies in using a hybrid MCDM approach to examine the impact of the COVID-19 pandemic on company performance. A hybrid MCDM approach is proposed to help decision-makers make the best possible investment and implementation decisions.

This study aims to understand how brand personality affects purchase intention through brand love and perceived quality in e-commerce.

Three hundred forty-eight users of e-commerce sites in New Delhi, India, were surveyed for the study. The data set was examined using confirmatory factor analysis, and the research hypotheses were assessed using structural equation modeling.

Two important conclusions emerged from the study. First, brand love and perceived quality have been considerably and favorably influenced by all six dimensions of brand personality of e-commerce brands. Second, the purchase intention toward the e-commerce sites is significantly and positively impacted by brand love and perceived quality.

This study by exploring various dimensions of brand personality, will assist e-commerce executives in increasing purchase intention toward the e-retailing sites.

This research is supposed to be the foremost to look at how brand personality, through brand love and perceived quality affects purchase intention toward e-commerce websites. The attachment theory is used in this study as a theoretical foundation for linking e-commerce brand personality to customers’ purchase intentions via brand love and perceived quality.

This study aims to examine the patterns of and factors influencing agricultural diversification among farmers in the Kaithal district of Haryana, India.

The authors analysed the data collected from a sample of 400 agricultural households from five blocks of Kaithal, Haryana. Agricultural diversification is measured through the Herfindahl Index. Determinants of agricultural diversification have been evaluated using the two-stage least square method.

The average value of the diversification index among farmers in Kaithal is 0.51, indicating that there is room for diversification. In the study area wheat and rice are the dominant crops, while vegetables emerge as the most common non-staple crop. Regression analysis indicates that farmers of Scheduled Caste (SC) are more inclined to diversify crops. Factors such as larger land ownership, utilization of tractors, government subsidies, education and loans positively impact agricultural diversification. The availability of irrigation facilities, and employing labour negatively influence it.

Focus on SC within the diversification policies for equitable access to resources. Special programmes aimed at educating small landowners and casual labourers about diversified crops. Implementing an “Uberisation” model for tractors, providing subsidies on seeds and equipment, customized financing programmes tailored to the needs of small-scale farmers can contribute to boosting agricultural diversification.

This study contributes by providing insights into the farm-level determinants of agricultural diversification in Kaithal district, Haryana. It adds to the understanding of factors influencing agricultural diversification in developing economies at the micro level.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2023-0963.

Instrumental and emotional information influence paradoxically on people’s decision-making, and similar influences are more evident in e-commerce scenarios where physical information is limited. This study aims to construct a systematic explanatory framework for the influence of multidimensional recommendation information diversity (RID) on users' click and purchase decisions based on the social support theory (SST).

This study analyses 453,176 data from 67,079 users of a Chinese e-commerce platform, applying lasso algorithmic techniques and cross-fit partialling-out (XPO) regression for empirical analysis.

The study finds that instrumental support information diversity (ISID) and emotional support information diversity (ESID) play divergent roles, and that the effects of both on user decision-making are inconsistent with mode-flip and marginal change. Differences in users' information craving and information overload processing mechanisms for instrumental and emotional information, leading to an inverted U-shaped effect of ISID on consumption decisions, while ESID has a U-shaped effect. Additionally, supplier certification eliminates the marginal change in ESID, and products with a high information standardisation degree eliminate the marginal change in ISID.

Research results reveal the opposing roles of the two types of RID and the application boundaries of their roles, providing empirical evidence for academic research.

The present study aims to deepen the understanding of the relationship between nostalgic brand positioning, nostalgic brand relationship dimensions and brand love.

This study is based on the data collected from 401 citizens of Tehran aged over 18 years old. Respondents admitted that they have felt love for at least one Iranian brand in their lives. The data collected from a questionnaire and the hypothesized relationships were analyzed using the partial least squares approach using Smart PLS.

The results showed that nostalgic brand positioning positively and significantly impacts nostalgic brand relationship dimensions. Also, there was a positive and significant relationship between nostalgic brand relationship dimensions and brand love. Nostalgic brand positioning has a significant effect on brand love through the mediating role of the nostalgic brand relationship.

The major contribution of this research is that, based on the construal level theory and literature review, the authors developed a conceptual model in which nostalgic brand relationship dimensions, i.e. emotional attachment, brand local iconness, and brand authenticity, explain how nostalgic brand positioning results in brand love.

Student open feedback is an essential element to improve the teaching service. Comprehending the feedback collected daily may not be possible especially in a large classroom. There is needed an automated system that processes feedback and helps to recommend focused, precise points to the teacher stating the positives and negatives of a class. Also, the feedback texts are neither going to be grammatically correct nor going to consist only of English. Hence, an automated feedback processing system is essential that processes the mixed-language language text that provides crisp clear insights to the teachers, thus making effective student–teacher interaction.

This research is designed to analyse daily feedback from the students in grammarless English-Tamil mixed feedback and creates a dashboard that displays concise keywords regarding positive and negative aspects of the class. An ML-based system architecture is proposed for processing English-Tamil mixed grammarless feedback texts and validates the same with an experimental prototype and compares the results with other state-of-the-art models. This prototype classifies the text into different categories and provides the concise view with topic modelling techniques. This system is useful in progressive improvement of teaching learning process, subsequently leading to better teaching learning environment.

The proposed web-based architecture is validated with a prototype by comparing the results with other state-of-the-art models. The accuracy of the results is higher (>90%) in the proposed architecture than other models (<60%). The created teacher dashboard is highly recommendable and provides day-to-day recommendation for finetuning teaching and learning process. The web-based dashboard created for teachers enables them to interpret the student feedback with much ease due to the Machine learning algorithms used in implementing the web-based solution.

This system is designed to help the teachers to improve themselves in the teaching learning process with the feedback. The proposed system is a prototype that is initially tested with sample feedback texts obtained in sessions in postgraduate classrooms. The implementation of the prototype and analysis of teacher and student experience are presented as the immediate scope of this research work. This helps the teachers to get an overall view on the best teaching practices and what to improve. This work currently uses Bidirectional Encoder Representations from Transformers (BERT) uncased and in the increase of native language text the system may work with BERT multilingual.

This prototype will be implemented as a web-mobile based application. Students can submit their daily feedback through a mobile app, while teachers will access a dashboard that presents a concise overview generated by the proposed system architecture. The dashboard will also provide trend analysis, highlighting positive and negative aspects of the sessions. The system's effectiveness will be evaluated through a qualitative study, incorporating feedback from teachers and insights from students. This evaluation will help teachers gain a comprehensive understanding of the most effective teaching practices and areas needing improvement, thereby enhancing the teaching-learning process. The web-mobile application aims to Streamline the feedback process, making it easy for students to share their thoughts and for teachers to receive actionable insights. This study offers a clear and concise summary of student feedback and trend analysis from which the teachers can quickly identify patterns and make necessary adjustments to their teaching methods. Ultimately, this approach will foster a more responsive and effective educational environment, supporting continuous improvement and better student–teacher interactions. Further, the proposed system requires lesser technical knowledge and can be used by anyone.

A literature review has identified a critical need for a feedback processing system that functions at short intervals. Such a system is essential for providing teachers with concise, periodic summaries of students' open-ended feedback, which is vital for fostering continuous improvement in the teaching-learning process. The immediate processing of feedback, particularly when it contains English-mixed texts, is crucial for making timely adjustments that enhance both student performance and experience. By swiftly addressing concerns and reinforcing positive feedback, the system will improve student–teacher interactions, provide meaningful insights that contribute to progressive educational growth. This will help implement a feedback system that operates in these short intervals and allows for real-time monitoring and response to students' needs and experiences. Additionally, by highlighting areas of success, teachers can build on effective strategies and practices.

This research paper proposes a system architecture PSFAS: Progressive Student Feedback Analysis System with Multi Level Classification and Clustering that enables effective interaction between the student and the teacher with the findings from feedback and presenting an experimental prototype that can be incorporated into the regular teaching learning process, whether online or offline. It has been found from the literature review, that feedback processing is mostly done in the English language. This work proposes a system architecture that gives higher accuracy than other state-of-the art models for feedback texts having English-mix grammarless sentences.

This study identified the determinants of improved rice variety adoption and measured their impact on farm productivity and the income of smallholder farmers in Indonesia.

We used a multistage sampling procedure and data from household surveys in four districts in the Banten region of Indonesia. An endogenous switching regression (ESR) model was used to estimate the impact of adoption, and the propensity score matching (PSM) non-parametric method tested the strength of the ESR findings.

The farm productivity of adoption increased by 11.45% and the income of smallholder farmers increased by 12.10% when compared to that of traditional methods.

The positive and significant effects of improved rice variety adoption indicated that research programs that develop improved rice varieties can optimize the productivity and income of smallholder farmers.

Adopting improved rice varieties increases the productivity and income of smallholder farmers, and the level of education, rice farming experience, access to extension workers, off-farm work, and mobile phone ownership have positive and significant effects on the adoption of improved rice varieties.

The study aims to explore the relationship between the SDGs and the environmental and social impacts of university classes. It evaluates the potential contributions of different teaching models to the SDGs, providing insights into sustainable teaching systems.

In-person classes exhibited hotspots in transportation and energy consumption. Scenarios I and II, involving virtual and hybrid classes, increased eutrophication potential and water scarcity due to higher food consumption, negatively impacting SDGs. However, all scenarios showed positive contributions to SDGs 2, 3, 6, 9, 11, 12, 13, 14 and 15.

This research integrates Life Cycle Assessment (LCA) with the United Nations Sustainable Development Goals (SDGs) to assess the environmental and social impacts of classes at the Federal University of São Carlos, Sorocaba campus in Brazil. Three scenarios were analyzed: (I) virtual classes, (II) hybrid classes and (III) in-person classes with improved energy efficiency.

The study primarily focuses on environmental and social impacts, excluding other factors like class quality. Integrating Life Cycle Costing and Life Cycle Sustainability Assessment could provide a more holistic evaluation in the future.

The methodology adopted offers valuable insights for managing the impacts of university performance and aligning teaching systems with the SDGs. It enables institutions to make informed decisions for sustainability in education.

The research emphasizes the importance of considering social impacts alongside environmental ones when assessing sustainability in educational institutions. It encourages universities to engage stakeholders in sustainability efforts.

This research innovatively combines LCA and the SDGs in the context of university education providing a replicable methodology for evaluating and enhancing sustainability in teaching systems and from a more quantitative perspective.

This study examines the moderating role of female directors on the relationship between the firms’ characteristics and tax avoidance in an emerging economy.

This study employs the second-generation unit root test and the generalised method of moments (GMM) techniques. The Kao residual cointegration test corroborates a long-run cointegration among variables.

Female directors demonstrate mixed and unusual findings. No significant impact of female directors on tax avoidance is found. In addition, the presence of female directors does not show any negative or significant moderating impacts on the relationship between leverage, firm age, board size and tax avoidance. However, having more female directors can negatively and significantly moderate the relationship between more profitable firms, larger firms and tax avoidance. These findings show that the board of directors could use the presence of female directors to maximise their opportunistic behaviour, such as to avoid tax.

Research limitations – The study is limited by considering only 62 listed firms. The scope could be extended to include non-listed firms.

Research implications – There is increasing pressure for female directors on boards from diverse stakeholders, such as the European Commission, national governments, politicians, employer lobby groups, shareholders, and Fortune and Financial Times Stock Exchange (FTSE) rankings. This study provides input to decision-makers putting gender quota laws into practice. Our findings can help policy-makers adopt regulatory reforms to control tax avoidance practices and enhance organisational legitimacy. Policymakers can change their policy to include female directors up to the threshold suggested by the critical mass theory.

This is the first attempt in Bangladesh to explore the role of female directors in the relationship between the firms' characteristics and tax avoidance. The current study has significant ramifications for bringing gender diversity into practice as a component of good corporate governance.

For successful management of construction projects, a precise analysis of the balance between time and cost is imperative to attain the most effective results. The aim of this study is to present an innovative approach tailored to tackle the challenges posed by time-cost trade-off (TCTO) problems. This objective is achieved through the integration of the multi-verse optimizer (MVO) with opposition-based learning (OBL), thereby introducing a groundbreaking methodology in the field.

The paper aims to develop a new hybrid meta-heuristic algorithm. This is achieved by integrating the MVO with OBL, thereby forming the iMVO algorithm. The integration enhances the optimization capabilities of the algorithm, notably in terms of exploration and exploitation. Consequently, this results in expedited convergence and yields more accurate solutions. The efficacy of the iMVO algorithm will be evaluated through its application to four different TCTO problems. These problems vary in scale – small, medium and large – and include real-life case studies that possess complex relationships.

The efficacy of the proposed methodology is evaluated by examining TCTO problems, encompassing 18, 29, 69 and 290 activities, respectively. Results indicate that the iMVO provides competitive solutions for TCTO problems in construction projects. It is observed that the algorithm surpasses previous algorithms in terms of both mean deviation percentage (MD) and average running time (ART).

This research represents a significant advancement in the field of meta-heuristic algorithms, particularly in their application to managing TCTO in construction projects. It is noteworthy for being among the few studies that integrate the MVO with OBL for the management of TCTO in construction projects characterized by complex relationships.