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This paper aims to demonstrate how attractions and experiences associated with coffee can contribute to dissemination and knowledge of its culture and to the development of coffee tourism in Portugal.

A case study methodology is applied, focusing on the Coffee Science Centre – Delta Interpretation, Scientific and Technological Promotion and Tourism Promotion Centre (CCC).

Results indicate that this type of attraction can be used to disseminate coffee culture by providing differentiating and enriching experiences to tourists, and most importantly, by developing tourism in a sustainable way.

This study contributes to expanding the knowledge on coffee tourism, an under-explored field of study, particularly in Portugal, where to the authors knowledge, there is no applied research within this scope.

The purpose of this paper is to argue that human resource development (HRD) needs to embrace and include futures and foresight learning (FFL) as a new addition to its field of theorising and practice. The question to consider is: How can FFL become a new feature of HRD? A key part of the authors’ argument is that the inclusion of FFL will enable HRD to add to the success of any organisation and make a vital contribution to the management of people at work.

This paper firstly considers some of the debates surrounding the meaning of HRD. The authors suggest that instability of the time serves to disturb any comforts that have been created in HRD and that there is a need to consider how there might be different futures for what we still call HRD in research, practice and praxis. This paper then considers how FFL might become one possibility for expanding the existing boundaries of HRD. The authors characterise futures and foresight as a learning process, which provides new but complementary features to what is already considered as HRD. This paper will show how FFL can lead to organisation's success and the way this can be achieved.

There is a wide variety of meanings of the term HRD; however, HRD is still cast as a “weakened profession” which has to play a subservient role to others in the workplace. Over the last 15 years, the expansion of the meaning of HRD has been seen as evidence of its evolving and emerging nature and development based on a co-creation with other disciplines. This creates a space for FFL, defined as an ongoing learning process to find predictable, probable, possible and/or a variety of long-term futures. FFL embraces three key processes of scanning, futuring and reconfiguring, all of which contain a high potential for participants and others to learn as they proceed, providing outcomes at each stage. FFL has been shown to enhance organisation performance and success and HRD interventions can play a key part in implementation. This represents a significant opportunity for the HRD profession to move from weakness towards strength.

For HRD researchers, while FFL is not yet on its radar, the authors would argue that the uncertainties of the future require that more attention be given to what might lie ahead. Indeed, HRD researchers need to ask the question: What is the future of HRD research? In addition, if the authors’ call for FFL to be included in the practice of HRD, such practice will itself provide new pathways for HRD research. Further research questions might include: To what extent is FFL practiced in organisations and what role do HRD practitioners play in delivery? How does FFL impact on organisation behaviour and outcomes? What new products and services emerge from FFL? What new skills are required to deliver FFL? Can FFL enhance the status of HRD practitioners in the work place and its role in decision-making? and How can the HRD profession develop as a hybrid profession with respect to machine learning (ML)/artificial intelligence (AI)?

FFL produces outcomes that have importance for strategy, HRD practitioner can learn to facilitate FFL by action learning and in leadership development programmes. FFL offers a significant opportunity to enhance the importance of HRD in organisations and beyond. FFL offers those involved in HRD a significant opportunity to transfer ideas into practice that have an impact on organisation sustainability. HRD can play a significant role in the design and delivery of ML and AI projects.

This paper concludes with a call for embracing FFL as a challenging but important addition to how we talk about learning at work. The authors argue that FFL offers a significant opportunity to enhance the importance of HRD in organisations and beyond. At its centre, FFL involves learning by people, groups, organisations and machines and this has to be of concern to HRD.

This chapter aims to assess the scope of India's gastronomic tourism post-COVID-19 and discuss the utilisation of vlogs to promote India as a gastronomic destination. First, the evolution of gastronomic tourism is reviewed. Next, opportunities and challenges associated with India's gastronomic offerings, both from international and domestic tourism perspectives, are discussed. Then, the role of vlogging to position and promote India as a gastronomic destination is established. The chapter suggests recommendations for the Ministry of Tourism, Government of India on utilising vlogging to promote gastronomic tourism.

The purpose of this paper is to empirically examine the issue of convergence in the monthly returns, rental growth and yields for ten market segments in the UK direct real estate market, using monthly data over the period from January 1987 to December 2014.

The methodology used to determine convergence is principal component analysis as it provides an assessment of the extent to which the variance of the market segments can be represented by a single common factor, explaining their long-run behaviour, and the degree of independence between the market segments.

The results suggest that there is strong evidence of convergence over the entire sample period in relation to monthly returns and yields but less evidence of convergence in rental growth, which confirms the findings in previous studies in international markets.

The evidence also suggests that convergence has increased over the sample period and that convergence is period specific and was particularly strong during and after the period of the Global Financial Crisis, which implies that the UK direct real estate market is largely integrated and as a consequence the extent of diversification potential in the market is still severely limited.

The convergence in returns has crucial implications for investors as it leaves investors exposed to the same structural shocks and so magnifies the importance of volatility spillover effects, limits their ability to create well-diversified portfolios and make it more difficult for fund managers to outperform the market.

This is the first paper to examine the convergence in the UK direct real estate market.

Internet-enabled commerce activities have evidently been strong in the leisure and tourism industry. The use of the Internet reveals an ever-growing market of millions of business and leisure travelers who use the Internet for travel planning purpose. As segment-based approach is an accepted tool in strategic marketing and helps understand the needs of homogeneous travel planner subpopulations, this study suggests a framework as the guideline and procedure to improve the segmentation approach. The main aim is to increase understanding of the growing Internet travel market by accurately classifying the Internet travel planners. Following the procedures methodically shown in the framework, the study conducted multiple complementary statistical techniques to cross validate statistical results found in each step. The typology of Internet travel planners was therefore identified systematically with great accuracy and validity. The typology consists of four groups of Internet travel planners: sensate, deal, defensive, and totemic. Based on the major characteristics of each group, the study also provided discussions and suggestions for the implementation of the typology to develop successful e-commerce. The findings offer academia and practitioners a paradigm for strategic marketing.

The purpose of this paper is to examine the model of seasonal employee leadership (SEL) in a service management process and to create a multi-dimensional scale to gauge this construct. This is because very recent qualitative research by Arasli and Arici (2019), which is the first stage of this scale, recommended a multi-dimensional SEL model for the hospitality industry.

Making use of data gathered from 1,343 seasonal hotel employees, the authors established a new scale to examine the SEL model. Two separate data sets were collected; the first set was used to perform an exploratory factor analysis, while the second set was processed to confirm the initial factor results using a confirmatory factor analysis.

The results show that the measurement scale developed in this research provides considerable reliability, as well as convergent and discriminant validities. In particular, the findings confirmed a four-dimensional measurement scale of the SEL: seasonal leader’s qualities, core influence, operational influence and terminal influence.

The paper is the first attempt to develop a new scale which measures the SEL approach in the hospitality literature. Therefore, this study contributes to the current literature through developing and testing the four-dimensional SEL scale and shedding light on the importance of an industry-specific leadership in managing seasonal hotel employees effectively.

Aarhus Kommunes Biblioteker (Teknisk Bibliotek), Ingerslevs Plads 7, Aarhus, Denmark. Representative: V. NEDERGAARD PEDERSEN (Librarian).

Learning styles are incorporated more and more in e‐education, mostly in order to provide adaptivity with respect to the learning styles of students. For identifying learning styles, at the present time questionnaires are widely used. While such questionnaires exist for most learning style models, their validity and reliability is an important issue and has to be investigated to guarantee that the questionnaire really assesses what the learning style theory aims at. In this paper, we focus on the Index of Learning Styles (ILS), a 44‐item questionnaire to identify learning styles based on Felder‐ Silverman learning style model. The aim of this paper is to analyse data gathered from ILS by a data‐driven approach in order to investigate relationships within the learning styles. Results, obtained by Multiple Correspondence Analysis and cross‐validated by correlation analysis, show the consistent dependencies between some learning styles and lead then to conclude for scarce validity of the ILS questionnaire. Some latent dimensions present in data, that are unexpected, are discussed. Results are then compared with the ones given by literature concerning validity and reliability of the ILS questionnaire. Both the results and the comparisons show the effectiveness of data‐driven methods for patterns extraction even when unexpected dependencies are found and the importance of coherence and consistency of mathematical representation of data with respect to the methods selected for effective, precise and accurate modelling.

Bankruptcy prediction has attracted a great deal of research in the data mining/machine learning community, due to its significance in the world of accounting, finance, and investment. This chapter examines the influence of different dimension reduction techniques on decision tree model applied to the bankruptcy prediction problem. The studied techniques are principal component analysis (PCA), sliced inversed regression (SIR), sliced average variance estimation (SAVE), and factor analysis (FA). To focus on the impact of the dimension reduction techniques, we chose only to use decision tree as our predictive model and “undersampling” as the solution to the issue of data imbalance. Our computation shows that the choice of dimension reduction technique greatly affects the performances of predictive models and that one could use dimension reduction techniques to improve the predictive power of the decision tree model. Also, in this study, we propose a method to estimate the true dimension of the data.