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This study aims to further the understanding of employees’ engagement by explaining their organizational commitment through their perception of the availability of work-life benefits in the organization. This study also investigates the mediating role of job satisfaction in this context.

The model was tested on the primary data collected in two phases from 270 teaching professionals in higher education institutes in Northern India. Barren and Kenny’s algorithm and hierarchical regression analysis were used to test the hypotheses.

The results reveal that employees’ perception of work-life benefits strongly influences their organizational commitment. Also, the results support that employees’ job satisfaction mediates the above-mentioned relationship.

Self-reported data could be considered as a key limitation of this study and for more accurate results supervisors’ (line managers) perspective could also be included in future studies. Also, in addition to perceived work-life benefits, supervisors’ support could also have an impact on employees’ commitment, thus its inclusion in the model could draw a clearer picture.

This research has two key contributions: first, it adds to the limited literature examining the employees’ engagement issues in the academic sector. Second, this research is one of, if not the first, to investigate perceived work-life benefits among third-level teaching staff in India to explain employees’ commitment to their organizations.

While natural disasters cannot be prevented, measures need to be taken to reduce the extent of damage, especially in a vast country like India with a huge population base and scarce facilities. Proper physical planning is an important tool, which one should utilize to regulate urban development as per the extent of damage anticipated. The article is a brief account of a study done on the city of Patna, in Bihar, a flood‐affected city of India, where strategies were developed to link the process of urban planning to the risk and damage of flooding in the area.

Recently, a powerful earthquake of the magnitude of 6.9 on the Richter Scale that rocked the western Indian state of Gujarat has caused extensive damage to life and property. Apart from human misery, it has devastated the state physically, socially and economically. Discusses some issues which emerged from this disaster on search and rescue, relief and rehabilitation, scientific and technical, communication and earthquake preparedness. Also describes the lessons learned from this disaster in managing future earthquakes in India and also in other parts of the world as well.

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.

In the context of the negotiations for apportionment of emission reduction post‐Kyoto regime, issues of equity and fairness have emerged. The purpose of this paper is to generate a model for equitable emission reduction apportionment.

The mathematical model has been designed utilizing mitigation capacity (based on gross domestic product (GDP)) and cumulative excess emissions as the criteria for apportionment. Quantitative results have been arrived at, using cumulative γ and parabolic mitigation emission reduction trajectories to demonstrate the impact on stakeholders.

The apportionment outcomes are independent of the specific trajectory fine‐tuned in the feasibility region. Since the apportionment takes into account entitlements and the mitigation capacity, Africa and India have negligible reduction targets in tune with the development goals in these economies. Substantial reduction commitments would fall on the USA and the EU countries. China gets a moderate target due to higher emissions and GDP.

The approach is in consonance with the principle of common but differentiated responsibility enunciated in the UNFCCC and the Kyoto Protocol. The method can easily incorporate emissions trading. The issue of population as a driving factor of emissions has been partially accounted for by considering the entire national GDP as an emission reduction responsibility factor, without considering population based GDP entitlements.

The generalized framework can be extended to situations involving responsibility apportionment in public policies dealing with externalities. The framework is original and will be useful to policymakers and other stakeholders dealing with climate change, as well as researchers looking at externalities of a global or national dimension.

The sharing economy, supported by digital platforms, efficiently matches the demand and supply of underused resources. Expanding globally and impacting different industries is offering a new path to sustainable resource consumption. The United Nations endorsed the sustainable development goals (SDGs), responding to a global call to end poverty, protect the environment and ensure that by 2030, everyone lives in peace and prosperity. Researchers have linked the sharing economy with SGDs, and extensive efforts have been exerted to quantify its impact.

Due to its evolving nature, especially in developing countries, there is a need for research to assess its promises and challenges from the SDG perspective. This research fills in this gap and contributes to the sharing economy studies by exploring its evolution within the framework of sustainable development goals, followed by an assessment of various methodologies for measuring the sharing economy and highlighting the significance of the web mining technique.

The proper implementation of this decentralized business model within a country is encouraging optimal resource use, lowering energy consumption and increasing long-term economic development. The sharing economy is a disruptive force that addresses the pillars of sustainability.

The research holds importance in addressing the achievement of the SDGs through the sharing economy, necessitating focus from policymakers and scholarly discourse on its merits. The transformative impact of the sharing economy poses questions about its encouragement or regulation, with the potential to disrupt established monopolies and possibly create new ones. Its successful implementation underscores the significance of effective data sharing and governance.

Corrosion loop development is an integral part of the risk-based inspection (RBI) methodology. The corrosion loop approach allows a group of piping to be analyzed simultaneously, thus reducing non-value adding activities by eliminating repetitive degradation mechanism assessment for piping with similar operational and design characteristics. However, the development of the corrosion loop requires rigorous process that involves a considerable amount of engineering man-hours. Moreover, corrosion loop development process is a type of knowledge-intensive work that involves engineering judgement and intuition, causing the output to have high variability. The purpose of this paper is to reduce the amount of time and output variability of corrosion loop development process by utilizing machine learning and group technology method.

To achieve the research objectives, k-means clustering and non-hierarchical classification model are utilized to construct an algorithm that allows automation and a more effective and efficient corrosion loop development process. A case study is provided to demonstrate the functionality and performance of the corrosion loop development algorithm on an actual piping data set.

The results show that corrosion loops generated by the algorithm have lower variability and higher coherence than corrosion loops produced by manual work. Additionally, the utilization of the algorithm simplifies the corrosion loop development workflow, which potentially reduces the amount of time required to complete the development. The application of corrosion loop development algorithm is expected to generate a “leaner” overall RBI assessment process.

Although the algorithm allows a part of corrosion loop development workflow to be automated, it is still deemed as necessary to allow the incorporation of the engineer’s expertise, experience and intuition into the algorithm outputs in order to capture tacit knowledge and refine insights generated by the algorithm intelligence.

This study shows that the advancement of Big Data analytics and artificial intelligence can promote the substitution of machines for human labors to conduct highly complex tasks requiring high qualifications and cognitive skills, including inspection and maintenance management area.

This paper discusses the novel way of developing a corrosion loop. The development of corrosion loop is an integral part of the RBI methodology, but it has less attention among scholars in inspection and maintenance-related subjects.

The sudden outbreak of COVID-19 has led in a very short time to a worldwide pandemic that has had severe effects on both businesses and daily lives. In a scenario characterised by the social distancing imperative, the sharing economy found itself struggling to survive, deprived of its core asset, namely, peer (human) relationships. The purpose of this paper is to reveal the coping strategies that have been implemented by sharing economy platforms to navigate the pandemic.

An explorative empirical investigation of 20 sharing economy platforms was carried out during the first lockdown phase (March–June 2020). To drive the analysis, a theoretical framework was developed by merging the literature on the business model and crisis management strategies.

The answers provided by sharing economy platforms to the health crisis caused by the COVID-19 pandemic have been classified into nine typologies of intervention according to the developed framework, with illustrative examples.

This paper provides an empirical investigation into the impact of the COVID-19 pandemic on the sharing economy field and it offers evidence about the immediate reactions of sharing platforms to the health crisis effects. The paper also introduces a framework about business model and crisis management strategies as a conceptual tool that could be adopted by firms from other industries.

The purpose of the study is to provide insights into the process of decision making. Indian Railways as an organization has to use the available resources based on thorough analysis and proper application of available evaluation methodologies.

This study uses an integrated group discussion – analytical hierarchy process (AHP) to prioritize capital-intensive Indian Railways projects.

Through an email-based survey, six important criteria for ranking upcoming domestic freight corridors were selected, and weights for these criteria were calculated through AHP. The sensitivity analysis of the decision model suggests that the results of this study are significant, reliable and robust.

This study lays a foundation for the decision-makers of Indian Railways to consider a scientific approach while finalizing the big investment projects. This paper provides essential insights on prioritizing capital-intensive transport projects and is readily applicable to any case study.