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Burr distribution has been proved to be a useful failure model. It can assume different shapes which allow it to be a good fit for various lifetimes data. Hybrid censoring is an important way of generating lifetimes data. The purpose of this paper is to estimate an unknown parameter of the Burr type XII distribution when data are hybrid censored.

The problem is dealt with through both the classical and Bayesian point of view. Specifically, the methods of estimation used to tackle the problem are maximum likelihood estimation method and Bayesian method. Empirical Bayesian approach is also considered. The performance of all estimates is compared through their mean square error values. The paper employs Monte Carlo simulation to evaluate the mean square error values of all estimates.

The key findings of the paper are that the Bayesian estimates are superior to the maximum likelihood estimates (MLE).

This work has practical importance. Indeed, the proposed methods are applied to real life data.

The paper is original and is quite applicable in lifetimes data analysis.

The present study examines the foremost determinants of teachers' perception, i.e. teachers' satisfaction, attitude and continuance intention towards adopting e-learning in Higher Educational Institutions (HEIs) in India during the COVID-19 pandemic.

Data were collected through online Google forms from 1,111 (1,060 considered useable) teachers of different HEIs in India using the purposive sampling technique and was analyzed by PLS-SEM (performing partial least squares-structural equation modeling).

Results of this study show that perceived usefulness (PU) followed by institutional support, perceived ease of use (PEOU), and teacher-student interaction positively and significantly impact teachers' satisfaction. Results also revealed that perceived usefulness (PU), institutional support, and satisfaction significantly affect teachers' attitude. Finally and most notably, teachers' continuance intention towards using online teaching in HEIs is most significantly influenced by teachers' satisfaction than perceived usefulness (PU), perceived ease of use (PEOU), and attitude.

The authors anticipate that this study brings a significant and valuable input to the existing literature by providing inclusive research in a more harmonizing understanding of the teachers' satisfaction, attitude, and continuance intention with online teaching-learning practices in diverse educational institutions.

In the era of digitalization and technology, tremendous changes have taken place in the taxi industry worldwide. The traditional taxi service has transformed into the latest innovative technology-based e-hailing service. There are innumerable factors that drive the user adoption of e-hailing apps. This study aims to primarily concentrate on identifying, analyzing and ranking these factors which have an impact on the user intention toward using e-hailing apps.

The e-hailing app users in the state of Punjab and Chandigarh are the target population for the study. A fuzzy analytical hierarchy process technique has been applied to analyze and codify the determinants that influence the user intention of adopting e-hailing apps. The primary factors that have been considered for the study are social influence, perceived usefulness, facilitating conditions, perceived ease of use, self-efficacy, perceived risk, compatibility and trust.

The study revealed that “Perceived Usefulness” is the factor that influences user intention to use e-hailing apps the most, while “Perceived Risk” the least. The sub-criteria codified in the top priority was as follows: “Overall, I find the e-hailing app useful in booking a taxi (C15)”; “I do not need some people to use e-hailing apps (C52); “I believe e-hailing app is compatible with existing technology (C61).” The sub-criterion “E-hailing app service provider keeps its promise (C72)” was demonstrated to have the least impact on the user intention of adopting e-hailing apps.

The study has been confined to only eight factors selected from the extended technological acceptance model framework and some related technology acceptance theories. Some more other factors may have an impact on user adoption of e-hailing apps, which need to be added further. Also, the scope of the study should be enhanced by expanding the geographical area beyond the selected region.

The findings of the study enable the e-hailing service providers and marketers to understand the users’ intention in a better way, to make improvements in e-hailing apps and formulate strategies accordingly.

The previous literature provides the base to the present study for identifying the factors affecting user behavioral intention toward e-hailing apps and information technology. The findings and results of the present research make value addition to the existing knowledge base.

The present study delves into the causes of relatively lower retail participation in the Indian REIT market. Specifically, it investigates investors' attitudes and perceptions towards REITs as a unique asset class. This paper provides a comprehensive understanding of the perception and factors influencing Indian retail investors' reluctance to participate in the REIT market.

Qualitative research was conducted through semi-structured interviews to gather insights from non-investors in REITs. The data were transcribed and analyzed using content analysis techniques. Finally, coding techniques were used to identify broad study themes.

According to the study results, many retail investors are unfamiliar with REITs. Even among those knowledgeable about REITs and with a favorable view, it is not commonly seen as a feasible investment option due to its early stage, unattractive returns and limited number of REITs.

Developed countries have established REIT markets, while it is still in its infancy in developing countries such as India. Financial advisors, fund houses and the media should focus on educating investors to increase awareness.

The study is the first qualitative investigation into the perception of retail investors to understand the reasons for lower retail engagement in the Indian REIT market.

The study aims to facilitate the medical tourists visiting emerging countries for various kinds of ailments by ranking the possible destinations to avail medical treatments.

A Fuzzy Analytical Hierarchical Process (FAHP) with a mixed-method approach is applied to analyze data collected from patients and substantiate it with medical tour operators in India to gain managerial insights on the choice-making patterns of the patients.

India is a preferred emerging market location due to the low cost and high medical staff quality. India offers value for money, whereas Singapore and Thailand are preferred destinations for quality and technology.

The study will facilitate the emerging markets' governments, hospitals and medical tourists to understand the importance of various determinants responsible for availing medical treatment outside their country.

The study recommends that cost and quality care are the patients' prime focus; government policies must provide clear guidelines on what the hospitals and country environment can offer and accordingly align the marketing strategies.

This study is the first attempt to rank various factors affecting medical tourism using the FAHP approach.

Knowledge management during the pandemic has been a challenging task due to the sudden intervention of technology in the organisational environment and the unexpected shift to the work-from-home culture. This study aims to investigate the role of technology intervention in the relationship between knowledge diffusion and knowledge application.

A cross-sectional study was conducted and data were collected from 541 employees who were working from home during the pandemic in India.

This study found significant relationships between knowledge diffusion and technology intervention. This study also observed the mediating role of technology intervention in the relationship between knowledge diffusion and knowledge application.

Tis study stands with other pioneering studies that have explored the role of technology intervention in the knowledge diffusion–application relationship using the job demand-resource model.