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This paper describes the development of a user interface to a composite database, i.e. one containing records for library‐held monographs as well as for journal articles, conference documents, and non‐conventional literature, downloaded from global databases. The user environment and characteristics of users for whom the interface is targeted are discussed. The features of BASIS, the retrieval software in use at ICRISAT to create, maintain and search the library's database and the need to build an interface for the occasional user are discussed. The objectives of building the user interface, software options considered, and the development of the interface using the BASIS menu language are presented.

Coronavirus disease (COVID-19) was declared as a pandemic since COVID-19's widespread outbreak and the hospitality industry has been the hardest hit due to lockdown. Consequently, hospitality workers are suffering from the negative aspects of mental health. In the event of such a crisis, this study aims to explore the link between unemployment and home isolation to the willingness to choose electronic consultation (e-consultation) by exploiting psychological ill-being and behavioural intention (BI) with marital status as a moderator.

A quantitative methodology is applied to primary data collected from 310 workers from the hospitality industry through an online survey.

Findings of this study suggest that the usage of the e-consultation service can be adopted using three levels. There are valid reasons to conclude unemployment and home isolation are linked to higher rates of psychological health behaviours, which can result in stigma, loss of self-worth and increased mortality. The adverse effect is higher for single individuals than for married people.

The study focussed on e-consultation, BI coupled with the Fishbein scale and a classification model for the prediction of willingness to choose e-consultation with the extension of Theory of Planned Behaviour (TPB).

Deep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.

The integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.

By responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.

There is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

The purpose of this paper, according to the evolutionary perspective of resilience, is to provide a revised adaptive cycle model that explains how organisations that are embedded in a local system can foster their resilience.

An exploratory case study analysis was carried out. The study adopted the methods and principles proposed by Eisenhardt (1989). Case studies were selected according the match-pair method and consist of two Italian wineries operating into the same wine cluster. Qualitative and quantitative data were collected and analysed through descriptive statistics and qualitative data analysis techniques.

The study proposes a revised model for the resilience strategies of small and medium-sized enterprises (SMEs) which combines firm and cluster level. Findings show that the resilience of SMEs is primarily driven by internal resilience strategies, and their surviving and adapting capacity, from a certain point of the evolutionary cycle, is fostered by internal decisions rather than by the influence of the external environment.

The study has some limitations. In particular, the exploratory survey does not permit the generalisation of results, and further empirical evidence is required. This research represents an initial step toward the development of a more exhaustive understanding of how the relationship SMEs-cluster can positively or negatively affect the resilience of organisations.

The proposed model for the resilience strategies of SMEs offers also insights for managers and entrepreneurs.

This study significantly contributes to theory on resilience in the management field, that is largely related to economic geography, while investigations about the resilience at the firm level are limited and inconclusive.