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Decontamination following chemical, biological, radiological and nuclear (CBRN)/Hazmat incidents is a critical activity carried out in order to mitigate and contain the risk posed by any hazardous materials involved. Human behaviour plays a crucial role in such incidents, as casualties will have little understanding of the situation they find themselves in, leading to uncertainty in what actions to take. This will result in very difficult circumstances within which first responders must operate. However, the importance of human behaviour appears to be a fundamental element being missed in the preparation, training and planning assumptions being made by emergency services and planners in preparation for these events.

This paper looks to understand the scope of this omission by reviewing relevant literature on the subject and engaging with Fire and Rescue Service personnel and managers in the UK. This study utilised semi-structured interviews with 10 Fire and Rescue Service Mass Decontamination Operatives, four Fire and Rescue Service Hazardous Material Advisers and three Fire and Rescue Service Strategic Officers participating. These interviews were then analysed using a thematic framework to identified key themes from the research which were then validated using two independent researchers to provide an inter-rater reliability measure. Finally, a follow-up validation questionnaire was also developed to test the validity of the themes identified and this was completed by another with 36 Fire and Rescue Service Mass Decontamination Operatives.

Both the literature review and interviews undertaken with emergency responders and mangers demonstrated the crucial importance of accounting for behavioural aspects in these situations especially in regards to the likely levels of compliance to be expected by responders and the potential problem of casualties not remaining at the scene of an incident to undergo decontamination.

This research identifies a number of key themes so far not recognized through any other research and in doing so offers insights into potential flaws in the UK Fire and Rescue Service response planning for CBRN/Hazmat incidents requiring mass decontamination. It is intended that this research will inform further study into the areas identified in order to ensure gaps in planning, training and strategies for mass decontamination operations can be more fully informed and if required allow for a more effective response.

Literature, spanning industrial organization and strategic management disciplines, uses variance decomposition to understand the relative importance of firm, industry and business group effects in shaping profitability variations. Some literature analyzes firm profitability under transition to liberalization. Previous research has taken a static before-and-after view on institutional change. This paper aims to focus on the dynamic process of liberalization in India, analyzing how different institutional regime changes alter firm behavior leading to changes in profitability patterns.

Based on a panel data set of several thousand Indian firms, spanning the 26-year period between 1980-1981 and 2005-2006, the authors determine the relative importance of firm, industry and business group effects in explaining manufacturing firms’ profitability variances across different institutional phases. The authors evaluate three propositions that help assess transition dynamics between phases. They determine the quantum of catch-up or falling behind by firms.

Different industries emerge as profitability leaders, as the economy progresses through different liberalization phases. Business groups that have been more effective in resource appropriation, rent-seeking, politician management and non-market activities in a controlled regime are replaced as profit leaders by those that, in a free-market economy, can be capable of intra-business resource allocation tasks and leveraging corporate capabilities.

The approach demonstrates how to analyze the underlying detailed structure of firm-level data, and performance outcomes, to derive nuanced interpretation of factors giving rise to the effects that explain profitability variances, and how to assess the way these effects behave over time. The dynamic evidence-based approach highlights what factors matter, where, when and why, in influencing profitability variances, which are a key dimension of industrial and economic performance.

In the context of sharing economy, the superhost program of Airbnb emerges as a phenomenal success story that has transformed the tourism industry and garnered humongous popularity. Proper performance evaluation and classification of the superhosts are crucial to incentivize superhosts to maintain higher service quality. The main objective of this paper is to design an integrated multicriteria decision-making (MCDM) method-based performance evaluation and classification framework for the superhosts of Airbnb and to study the variation in various contextual factors such as price, number of listings and cancelation policy across the superhosts.

This work considers three weighting techniques, mean, entropy and CRITIC-based methods to determine the weights of factors. For each of the weighting techniques, an integrated TOPSIS-MOORA-based performance evaluation method and classification framework have been developed. The proposed methodology has been applied for the performance evaluation of the superhosts (7,308) of New York City using real data from Airbnb.

From the perspective of performance evaluation, the importance of devising an integrated methodology instead of adopting a single approach has been highlighted using a nonparametric Wilcoxon signed-rank test. As per the context-specific findings, it has been observed that the price and the number of listings are the highest for the superhosts in the topmost category.

The proposed methodology facilitates the design of a leaderboard to motivate service providers to perform better. Also, it can be applicable in other accommodation-sharing economy platforms and ride-sharing platforms.

This is the first work that proposes a performance evaluation and classification framework for the service providers of the sharing economy in the context of tourism industry.

In the context of India, this article proposes an integrated multicriteria decision-making (MCDM) regression-based methodology to evaluate input-level performance of the schools and investigate the impact of this performance along with contextual factors, i.e. medium of instruction and location of the school, on the school's output level performance, i.e. student pass rate.

First, Shannon entropy-based approach is applied for the weight assignment to different parameters. Then, integrated VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique for order preference by similarity to an ideal solution (TOPSIS)-based methodology is devised to measure the input-level performance of a school. Finally, multiple linear regression (MLR) analysis is incorporated to study the effect of input-level performance and above-mentioned contextual factors on the school's output-level performance.

Proposed methodology is applied to assess the input-level performance of 82,930 primary and secondary schools of West Bengal, India. All the factors have a significant impact on boys' pass rate, whereas only input-level performance and location of the school have a significant influence on the girls' pass rate.

The entropy-based approach highlights the importance of scientific weight assignment. Integrated MCDM demonstrates the significance of aggregation due to the variation in scores related to input-level performance across the methods. Regression analysis facilitates the exploration of determinants influencing the output-level performance of the schools.

This work depicts a holistic picture of the performance measurement system of the schools. It encompasses scientific weight assignment to the evaluation criteria, integrated input-level performance assessment of the schools and investigation into the effect of this performance, as well as other contextual factors on the output level performance.

This paper aims to determine the implications of Covid-19 on the livelihood of marine fishermen. It gives a concrete picture of how vulnerable communities like marine fishermen are affected due to the lockdown policies. The paper examines these communities' present status and the extent of vulnerability during the post-Covid period.

The study uses an exploratory research design to find the solution to the research problem. 298 samples were collected and analysed within a sustainable livelihood theoretical framework. The scope of the study is limited to marine fishermen in Kerala, residing in six districts out of the nine coastal districts. The impact of the lockdown on income was analysed using paired t-test and results linked with the theory.

The study has done an empirical analysis for three periods: before lockdown, lockdown and after lockdown, to identify the impact of lockdown on marine fishermen. The study's significant findings are that these fishermen's livelihood is at risk during the post-lockdown period, and many families are moving into a “debt-trap”.

Policymakers can develop appropriate policy strategies to enhance the livelihood assets of vulnerable communities to include them in a sustainable framework.

Only a few studies are highlighting the impact of Covid-19 on vulnerable communities in India. The effects of climate change on the marine ecosystem are already endangering marine fisher folks' livelihoods. In this light, it is vital to study the extent of the impact of income shock on the livelihood assets of marine fishermen due to the lockdown policy implemented in the State to prevent the spread of Covid-19.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0192

The purpose of this study is to present a novel approach for the evaluation of tribological properties of brake friction materials (BFM).

In this study, a BFM was newly formulated with 16 different ingredients and produced using an industrial hot compression molding process. Experimentation was carried out on the brake tester, which was developed for this purpose according to SAE J661 standards. The braking applications, sliding speed and braking pressure were considered as performance parameters, whereas coefficient of friction (CoF) and wear rate as output parameters. The influence of the performance parameters on the output was studied using response surface plots. Analysis of variance and regression analysis was accomplished for post-experimental evaluation of results. Multi-criteria decision-making (MCDM) and multi-objective genetic algorithm (MOGA) were applied for estimating the most critical performance parameter combination to evaluate the BFM.

The present experimental model was significant and effectively used to predict the performance. MCDM generates the optimal values for the parameters braking applications, braking pressure (Bar) and sliding speed (rpm) as 1000, 30 and 915, whereas MOGA as 1008, 10.503 and 462.8202, respectively.

An efficient model for performance evaluation of the BFM considering maximum CoF and minimum wear rate was experimentally presented and statistically verified. Also, the two multi-objective optimization methodologies were implemented and compared. A comparison between the results of MCDM and MOGA reveals that MOGA yields 30% better results than MCDM.