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The adoption of climate-smart agricultural practices (CSAPs) is important for sustaining Indian agriculture in the face of climate change. Despite considerable effort by both national and international agricultural organizations to promote CSAPs in India, adoption of these practices is low. This study aims to examine the elements that affect the likelihood and intensity of adoption of multiple CSAPs in Bihar, India.

The probability and intensity of adoption of CSAPs are analyzed using multivariate and ordered probit models, respectively.

The results show significant correlations between multiple CSAPs, indicating that their adoptions are interrelated, providing opportunities to exploit the complementarities. The results confirm that both the probability and intensity of adoption of CSAPs are affected by numerous factors, such as demographic characteristics, farm plot features, access to market, socio-economics, climate risks, access to extension services and training. Farmers who perceive high temperature as the major climate risk factor are more likely to adopt crop diversification and minimum tillage. Farmers are less likely to adopt site-specific nutrient management if faced with short winters; however, they are more likely to adopt minimum tillage in this case. Training on agricultural issues is found to have a positive impact on the likelihood and the intensity of CSAPs adoption.

The major policy recommendations coming from of our results are to strengthen local institutions (public extension services, etc.) and to provide more training on CSAPs.

By applying multivariate and ordered probit models, this paper provides some insights on the long-standing discussions on whether farmers adopt CSAPs in a piecemeal or in a composite way.

The link between confidence and economic decisions has been widely covered in the economic literature, yet it is still an unexplored field in tourism. The purpose of this paper is to address this gap, and investigate benefits in forecast accuracy that can be achieved by combining the UNWTO Tourism Confidence Index (TCI) with statistical forecasts.

Research is conducted in a real-life setting, using UNWTO unique data sets of tourism indicators. UNWTO TCI is pooled with statistical forecasts using three distinct approaches. Forecasts efficiency is assessed in terms of accuracy gains and capability to predict turning points in alternative scenarios, including one of the hardest crises the tourism sector ever experienced.

Results suggest that the TCI provides meaningful indications about the sign of future growth in international tourist arrivals, and point to an improvement of forecast accuracy, when the index is used in combination with statistical forecasts. Still, accuracy gains vary greatly across regions and can hardly be generalised. Findings provide meaningful directions to tourism practitioners on the use opportunity cost to produce short-term forecasts using both approaches.

Empirical evidence suggests that a confidence index should not be collected as input to improve their forecasts. It remains a valuable instrument to supplement official statistics, over which it has the advantage of being more frequently compiled and more rapidly accessible. It is also of particular importance to predict changes in the business climate and capture turning points in a timely fashion, which makes it an extremely valuable input for operational and strategic decisions.

The use of sentiment indexes as input to forecasting is an unexplored field in the tourism literature.

Social sustainability is a concept frequently referred to in public debates concerning how to construct the governance of future societies. The interpretations of its meaning, however, are ambiguous, and practices often dubious. Confronting top-down technocratic governance structures, this paper aims to argue for for tripartite collaborations between residents, higher education institutions (HEIs) and local government, as an approach toward social sustainability that involves residents’ interests in local governance.

This study argues that a specific time-spatial method of analysis can benefit the co-creation of knowledge as it passes through the spectrum of resident–HEI–local government. It provides a way for resident perceptions to become structured knowledge that originates from the residents, effectively engendering a bottom-up governance structure.

This study shows how to include residents in policymaking and implementation processes as co-creators of knowledge, and thereby displays the possibility of examining knowledge and competence within municipal projects for social sustainability.

The model developed in this study can be used as a methodological instrument to analyze and expand resident participation in local social sustainability work. It thereby provides a toolbox for inclusive policymaking and strategies.

The purpose of this study is to examine the effect of bank-specific, industry-specific and macroeconomic determinants of bank profitability amongst domestic UK commercial banks.

This study used an empirically driven single equation framework that incorporates the traditional structure–conduct–performance (SCP) hypothesis. A generalised method of moments technique was applied to a panel of UK banks covering the period 1998–2018 to account for profit persistence.

The estimation results show that all bank-specific determinants, with the exception of credit risk, significantly affect bank profitability in the anticipated way. However, no evidence was found in support of the SCP hypothesis. Interest rates, especially longer-term interest rates, and the rate of inflation has a significant effect on bank profitability, with the business cycle having a symmetric insignificant effect once other variables have been accounted for. Profitability persists to a moderate extent within the UK banking market, indicating that there exists a departure from a perfectly competitive market structure.

The literature that examines the actual underlying determinants of UK domestic bank profitability is limited.

The stock market anomalies have been studied across the globe with intermingled results for individual markets. The present study has investigated the financial year effect for Indian stock markets by testing month-of-the-year-effect anomalies.

The oldest stock exchange's index returns (Bombay Stock Exchange [BSE]) have been tested using ordinary least squares (OLS) and autoregressive conditional heteroskedasticity in mean (ARCH-M) models with Student's t and Student's t-fixed distributions for the period between 1991 and 2019. The Glosten, Jagannathan and Runkle-generalised autoregressive conditional heteroskedasticity (GJR-GARCH) model has been further used to find out existence of the leverage effect in returns.

The findings indicated no evidence for anomalies in the Indian stock market which may be used by investors for making unusual returns. However, the volatility in returns has shown weak but significant results due to the financial year impact. The leverage effect has not been found in the financial year cycle change over. The Indian market may be said to be moving towards a state of efficiency, leaving no scope for investors to gauge bizarre profits.

The study has incorporated the Indian context for testing anomalies during the start and end of the financial year cycle. The model may be extended further to developed and developing nations’ markets for testing efficiency in their stock markets during the same cycle.

The paper may be the first of its kind to test for the financial year effect on standalone basis for Indian markets. The paper also adds to the existing literature on testing events’ effect.

Experiments are the backbone of the development process of data-driven predictive models for scientific applications. The quality of the experiments directly impacts the model performance. Uncertainty inherently affects experiment measurements and is often missing in the available data sets due to its estimation cost. For similar reasons, experiments are very few compared to other data sources. Discarding experiments based on the missing uncertainty values would preclude the development of predictive models. Data profiling techniques are fundamental to assess data quality, but some data quality dimensions are challenging to evaluate without knowing the uncertainty. In this context, this paper aims to predict the missing uncertainty of the experiments.

This work presents a methodology to forecast the experiments’ missing uncertainty, given a data set and its ontological description. The approach is based on knowledge graph embeddings and leverages the task of link prediction over a knowledge graph representation of the experiments database. The validity of the methodology is first tested in multiple conditions using synthetic data and then applied to a large data set of experiments in the chemical kinetic domain as a case study.

The analysis results of different test case scenarios suggest that knowledge graph embedding can be used to predict the missing uncertainty of the experiments when there is a hidden relationship between the experiment metadata and the uncertainty values. The link prediction task is also resilient to random noise in the relationship. The knowledge graph embedding outperforms the baseline results if the uncertainty depends upon multiple metadata.

The employment of knowledge graph embedding to predict the missing experimental uncertainty is a novel alternative to the current and more costly techniques in the literature. Such contribution permits a better data quality profiling of scientific repositories and improves the development process of data-driven models based on scientific experiments.

This paper aims to empirically indicate the factors influencing stock liquidity premium (i.e. the relationship between liquidity and stock returns) in one of the leading European emerging markets, namely, the Polish one.

Various firms’ characteristics and market states are analysed as potentially affecting liquidity premiums in the Polish stock market. Stock returns are regressed on liquidity measures and panel models are used. Liquidity premium has been estimated in various subsamples.

The findings vividly contradict the common sense that liquidity premium raises during the periods of stress. Liquidity premium does not increase during bear markets, as investors lengthen the investment horizon when market liquidity decreases. Liquidity premium varies with the firm’s size, book-to-market value and stock risk, but these patterns seem to vanish during a bear market.

This is one of the first empirical papers considering conditional stock liquidity premium in an emerging market. Using a unique methodological design it is presented that liquidity premium in emerging markets behaves differently than in developed markets.

A transition toward sustainable development requires engagement of university students in transformative learning. Therefore, quality frameworks and processes should support deep approaches to sustainable development in higher education. Research and initiatives that connect sustainable development, higher education and quality assurance (QA) are lacking. This study aims to explore to what extent quality assurance agencies in Europe support transformative learning for sustainable development in their frameworks.

The authors conducted a qualitative analysis of national QA frameworks in the European Higher Education Area (EHEA) to assess whether they support transformative learning for sustainable development. First, frequency analysis was undertaken; second, a blended coding approach was used to investigate whether and how transformative learning for sustainable development is addressed.

Overall, the authors found little support for transformative learning for sustainable development in most QA frameworks. One exception is the framework of the United Kingdom, which includes a specific guide on education for sustainable development wherein transformative learning is prominently mentioned. To a lesser extent, some support exists in the frameworks of Estonia, Holy See, Romania, Sweden, Switzerland and Ukraine. Although the transformative learning for sustainable development approach is not explicitly mentioned in most QA frameworks, many of them contain opportunities to highlight it. France and The Netherlands offer guidelines and criteria for acquiring a sustainable development label, while Andorra suggests including the sustainable development goals in institutional quality assessment.

The research provides the first map of how countries within the EHEA support transformative learning for sustainable development in national QA systems.

Immigrants, asylum seekers and refugees living in Europe face a number of challenges in accessing or using health information and healthcare services available in their host countries. To resolve these issues and deliver the necessary services, providers must take a comprehensive approach to better understand the types of health information and healthcare services that these individuals need, seek and use. Therefore, the purpose of this paper is to develop that comprehensive approach.

In this paper, a systematic literature review of peer-reviewed publications was performed, with 3.013 articles collected from various databases. A total of 57 qualifying papers on studies conducted in Europe were included in the review after applying the predefined inclusion and exclusion requirements, screening processes and eliminating duplicates. The information seeking and communication model (ISCM) was used in the analysis.

The findings revealed that while many health information and healthcare services are accessible in Europe for immigrants, asylum seekers and refugees, many of these individuals are unaware of their existence or how to access them. While our findings do not specify what health-related information these groups need, use or seek, they do suggest the importance and value of providing mental health, sexual health and HIV, as well as pregnancy and childbirth information and services. Furthermore, according to our results, health information services should be fact-based, easy to understand and raise awareness about healthcare structure and services available in Europe for this vulnerable population.

This study has a range of practical implications, including (1) highlighting the need for mental health and behavioural health services and (2) stressing the value of addressing cultural context and religious values while investigating (health) information seeking of people with foreign background.

This is one of the first studies to systematically review and examine the behaviour of immigrants, asylum seekers and refugees in relation to health information and healthcare services in the European context.

Artificial intelligence (AI)-based chatbots have brought unprecedented business potential. This study aims to explore consumers' trust and response to a text-based chatbot in e-commerce, involving the moderating effects of task complexity and chatbot identity disclosure.

A survey method with 299 useable responses was conducted in this research. This study adopted the ordinary least squares regression to test the hypotheses.

First, the consumers' perception of both the empathy and friendliness of the chatbot positively impacts their trust in it. Second, task complexity negatively moderates the relationship between friendliness and consumers' trust. Third, disclosure of the text-based chatbot negatively moderates the relationship between empathy and consumers' trust, while it positively moderates the relationship between friendliness and consumers' trust. Fourth, consumers' trust in the chatbot increases their reliance on the chatbot and decreases their resistance to the chatbot in future interactions.

Adopting the stimulus–organism–response (SOR) framework, this study provides important insights on consumers' perception and response to the text-based chatbot. The findings of this research also make suggestions that can increase consumers' positive responses to text-based chatbots.

Extant studies have investigated the effects of automated bots' attributes on consumers' perceptions. However, the boundary conditions of these effects are largely ignored. This research is one of the first attempts to provide a deep understanding of consumers' responses to a chatbot.