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There is ambiguity regarding whether coronavirus disease 2019 (COVID-19) is a boon or bane for the IT services industry. On the one hand, it has created opportunities, especially with the growth of collaborative technologies. On the other hand, many firms have reduced their IT budgets owing to the ongoing recession. This study explores how IT firms have assessed the risk of the pandemic in the early days and informed capital market participants. In addition, it examines the impact of such online disclosures on information asymmetry.

The authors analysed annual reports of publicly listed firms in the USA filed on the Securities and Exchange Commission website in 2020 and examined whether the disclosure scenario of technology firms was different from that of the other industries. Moreover, the risk sentiment of COVID-19-related disclosures was assessed by employing text analytics. Information asymmetry was measured using the bid–ask spread.

Overall, it was found that IT services firms were less likely to discuss the COVID-19 pandemic in their annual reports. Interestingly, it was observed that technology firms that chose to communicate about the pandemic had a lower incidence of words related to risks. Furthermore, communicating about COVID-19 in annual reports calms investors and improves the information asymmetry situation about the firm. Variation in the severity of the pandemic and the responses of state governments was controlled for by employing state-fixed effects in the empirical models.

The authors inform the literature on corporate disclosures and technology and highlight the importance of effectively communicating about the pandemic.

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.

The purpose of the paper is to examine the trade structure of India’s pharmaceutical sector with a focus on intra-industry trade (IIT).

This paper starts with analysing export destinations and import sources using significant trade shares; the study calculates IIT between India and its consistent trade partners at an aggregate level and considers the problem of categorical aggregation at a disaggregate level. To determine the determinants of IIT at different levels, the Vector Error Correction model used production-related data to identify the drivers of IIT. Also, the Granger causality test was used for short-run causality.

This study examining India’s consistent trade partners from 1993 to 2023, finds long-run association and short-run causality. The results show a significant long-run association between total IIT and factors like unskilled labour share, invested capital, fuel consumption, total input and net value added. The key low-vertical IIT (LVIIT) drivers are invested capital, unskilled labour, fixed capital and total inputs. The negative long-run association between the total input and LVIIT obtained implies a rising level of total input cost, leading to a fall in IIT and LVIIT. Also, a negative association is obtained for unskilled labour and total IIT, while a positive association is obtained for LVIIT. In the short run, causality indicates that total IIT is influenced by invested capital and fuel consumption, while unskilled labour shares and total inputs drive LVIIT. Both IIT types impact invested capital, highlighting the need for policy intervention in input markets. It provides insights for improving quality trade expansion and correcting production-related factors.

Unlike other studies on the pharmaceutical trade in India, this study analyses India’s pharmaceutical trade for a longer time period, focusing on destination-wise analysis and calculating the intra-industry trade index while taking care of the problem of categorical aggregation. Further, the study attempted to find the long-run association with production-related drivers.