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This study aims to analyse the structure of the Indian vaccine supply chain (SC) during the Covid-19 crisis and explore the underlying challenges at each stage in the network. It also brings out the difference in performance of various constituent states.

This study relied on both primary and secondary data for the analyses. For the primary data, the study gathered experts’ opinions to validate the authors’ inferences. For the secondary data, it relies on government data provided in websites.

Based on the quartile analysis and cluster analysis of the secondary data, the authors find that the constituent states responded differently during the first and second waves. This was due to the differences in SC characteristics attributed to varied demographics and administrative efficiency.

This paper’s analyses is primarily limited to secondary information and inferences are based on them. The study has important implications for implementing the large-scale vaccination drives by government and constituent states for better coordination and last-mile delivery.

The contribution is unique in studying the performance of constituent states using statistical techniques, with secondary data from authentic sources. It is also unique in combining this observation with validation from experts.

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.

Vicarious abusive supervision (VAS) has recently garnered the attention of hospitality researchers. VAS is prevalent in hospitality work settings characterized by long production chains and open operating environments. Based on the conservation of resources (CORs) theory, this study aims to examine how VAS influences hospitality employees’ work behaviours (i.e. supervisor-directed deviance, silence and helping behaviour) via affective rumination, with the moderating role of industry tenure as an individual contingency on the relationship between VAS and affective rumination.

The data were gathered from 233 restaurant frontline employees and their supervisors in Turkey. The authors tested the proposed model using partial least squares method through SmartPLS 3.

The results reveal that VAS triggers affective rumination, which, in turn, is positively related to supervisor-directed deviance and silence, and negatively related to helping behaviour. Moreover, industry tenure, as a buffer resource, significantly moderates the relationship between VAS and affective rumination.

To reduce the occurrence of VAS and mitigate its negative effects, managers should establish a work environment that embraces understanding and respect, pay attention to how they communicate with employees, implement appropriate interventions when VAS occurs and conduct stress management training and improve employees’ emotion regulation skills in ways that correspond to the employees’ industry experience.

This study advances research on VAS by offering insight into how VAS impacts employees’ work behaviours via the underlying mechanism of affective rumination through a COR lens. The findings also shed light on the salient buffering effect of industry tenure as an individual contingency.

The South African vocational education and training (VET) sector is required by legislation to redefine postsecondary education, advance industrialisation and expand the job market to address unemployment in the country. Yet, VET leaders' heterogenous educational and occupational backgrounds do not enable them to address the needs of the VET sector. Continuous professional development of leaders in the education sector needs to include support structures such as mentoring.

The present study sought to investigate how VET managers in South Africa perceive three different types of mentoring – individual, peer group and expert-based key performance area (KPA) mentoring – during a part-time professional leadership development programme. Using interactive qualitative analysis (IQA), the authors collected and analysed data from focus group discussions (n = 24) and individual interviews (n = 21) from two cohorts of the programme.

The results revealed that individual mentoring represented the most important driving mechanism, followed by peer group mentoring and expert-based KPA mentoring. Participants identified leadership as a prerequisite for their development. Emotions formed the final outcome of the mentoring framework.

Based on the findings, the authors suggest investigating the role played by leaders' interpersonal competences such as emotional competence in the workplace. Additionally, research needs to clarify if and how mentoring can support leaders to develop interpersonal competences in formal and informal settings.

The study offers empirical evidence on a three-pillar mentoring framework adopted in a professional development programme for leaders in VET in South Africa. It highlights the importance of individual, social and emotional factors.

The maintainability of urban spaces has become critical with rapid urbanization to create an effective and safe environment for the increasing population. The absence of scientific studies exploring the factors that affect urban space maintainability (USM) has hindered the incorporation of maintainability aspects during the urban space planning and designing stages. This paper aims to establish critical factors for USM.

Qualitative content analysis is performed under an abductive approach to developing USM factors. A bibliometric search is conducted using databases including Scopus Elsevier, Emerald Insight, Science Direct, IEEE XPLORE and the American Society of Civil Engineers. The selected primary data set comprises journal papers on USM published after 2000. Seventy-three journal articles are selected through a comprehensive screening procedure and subjected to further analysis. The literature findings are processed via a software-assisted systematic coding and visualizing of the key data using NVivo 12 software. The coded USM factors are validated based on experts’ consensus statements by conducting an expert focus group discussion.

Twelve critical factors are established for USM; they include six design stage-related factors, one construction stage-related factor and five operational stage-related factors.

Established USM factors give an insight into the main focus areas when incorporating maintainability into urban spaces.

The authors establish a set of maintainability factors for urban spaces based on the life cycle stages. USM factors such as vegetation management, interdepartmental coordination and work zone safety draw attention to context-specific aspects of USM.

The purpose of this paper, an experimental study, is to investigate the optimal machining parameters for turning of nickel-based superalloy Inconel 718 under eco-friendly nanofluid minimum quantity lubrication (NMQL) environment to minimize cutting tool flank wear (Vb) and machined surface roughness (Ra).

The central composite rotatable design approach under response surface methodology (RSM) is adopted to prepare a design of experiments plan for conducting turning experiments.

The optimum value of input turning parameters: cutting speed (A), feed rate (B) and depth of cut (C) is found as 79.88 m/min, 0.1 mm/rev and 0.2 mm, respectively, with optimal output response parameters: Vb = 138.633 µm and Ra = 0.462 µm at the desirability level of 0.766. Feed rate: B and cutting speed: A2 are the leading model variables affecting Vb, with a percentage contribution rate of 12.06% and 43.69%, respectively, while cutting speed: A and feed rate: B are the significant factors for Ra, having a percentage contribution of 38.25% and 18.03%, respectively. Results of validation experiments confirm that the error between RSM predicted and experimental observed values for Vb and Ra is 3.28% and 3.75%, respectively, which is less than 5%, thus validating that the formed RSM models have a high degree of conformity with the obtained experimental results.

The outcomes of this research can be used as a reference machining database for various metal cutting industries to establish eco-friendly NMQL practices during the turning of superalloy Inconel 718 to enhance cutting tool performance and machined surface integrity.

No study has been communicated till now on the turning of Inconel 718 under NMQL conditions using olive oil blended with multi-walled carbon nanotubes-based nanofluid.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2023-0317/