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This paper aims to discuss an integrated paradigm that aligns the measures of knowledge management (KM) performance to attain corporate goals in organisations. It presents the main findings of an exploratory study on the use of the paradigm and the accompanied self-assessment scheme in industrial enterprises in Trinidad and Tobago (T&T).

An integrated knowledge management (IKM) model was derived, incorporating the guiding principles of the Total Quality Management/Business Excellence Models. A host of 20 elements was advocated under 5 assessment criteria, namely, Senior management leadership, KM processes, people development, continuous improvement and results orientation. A four-level self-assessment scheme was developed for facilitating users to determine the maturity status of IKM performance in organisations. An exploratory study was conducted with respondents of 18 companies in T&T. A results-oriented methodology with a self-assessment instrument (includes a set of questionnaire and facilitative tools) was used to acquire the industry practitioners’ views on the potential applicability of the IKM model. The study compared the current with the expected organisational performance and explored the relevance of integrating KM and PM practices in these participating organisations.

The findings provided some useful data sources and managerial insights in integrating KM/PM initiatives with reference to groups of large enterprises vs small- and medium-sized companies in T&T. Empirical evidence showed that the self-assessment analysis could help participating organisations utilise their resources and keep up with improvement progress. The objectives, emphasis and administrative context of the KM/PM integration could be changed with varying resources, constraints and maturity status of organisations.

Future research could validate the self-assessment paradigm of KM performance in enterprises across various industry sectors, with the emphasis on human–technology–organisation interactions.

It is anticipated that adapting the IKM model and using it for regular self-assessments could help industrial enterprises to enhance their KM and PM capabilities for attaining improvement goals. The results could facilitate information sharing of best practices and create conditions conducive to continuous performance improvement.

Online education has emerged as one of the major channels for dissemination of learning materials. As more and more organizations offer online distance learning courses, the security concerns of these online education systems become more and more critical, especially when the organizations rely on the registration fees of students to maintain the smooth running of the courses. Provides a mechanism, the Secure e‐Course eXchange (eCX), to protect the learning material from unauthorized dissemination, and shows how this mechanism can be integrated in the operation model of online learning course providers. The design of eCX is general enough to fit two operating models, namely the Institutional Server Model and the Corporate Server Model.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

In e-commerce, consumers have begun to rely on the opinions of fellow consumers who posted through online consumer reviews to a reputation management system. An ethical concern has arisen in the use and abuse of these new systems. We examine the underlying ethical issues that entrepreneurs are confronting in this time of surging e-commerce. Using 32 vignettes, one for each cross-section of our research construct framework, followed by two Likert scales for respondents to indicate their agreement with the action described from both the perspective of ethicality and professional acceptability, we received responses for 1,252 vignettes, which generated a dataset of 2,504 data points. The results of our pilot study suggest that the ethical considerations for business professionals conducting business online are more nuanced and complex than conventional wisdom on the subject might suggest. While 60 research subjects are small, the use of paired vignettes in our survey allowed us to measure at least 1,000 paired responses for each research construct. The results have the potential of revealing how young professionals have been conditioned by the prevalence of web-based interactions and the anonymity they afford participants, as well as the degree to which they rationalize the misrepresentation of information by business professionals for the purpose of manipulating consumers’ purchasing decisions in order to drive sales. If consumers’ trust in reputation management systems erodes, the result could be a collapse of the entire system as a meaningful source of information. We also demonstrate the tolerance of what is deemed ethical versus professionally acceptable with online business practices.

The purpose of this paper is twofold. First, the authors investigate the main drivers of house prices in the Hong Kong housing market. Second, further research is undertaken to confirm the existence of house price overvaluation, which has driven the market into a bubble episode.

First, the authors propose a theoretical framework to identify the fundamentals of the market. In the second step, they decompose house prices into fundamentals, frictions and bubble episodes for a better understanding of the evolution of house prices during the period 1996(Q3)-2013(Q3).

The results of this paper suggest an eventual possible correction of up to 46 per cent of house prices with respect to their 2013(Q3) level.

The originality of this paper is to use the procedure developed by Glindro and Delloro (2010) to analyse the Hong Kong housing market. The contribution of this paper also modifies the original Glindro and Delloro’s (2010) approach by including the Christiano and Fitzgerald (2003) filter to decompose house prices.

The authors aim to report empirical linkages between the US and Brazil, Russia, India and China (BRIC) financial stress indices catalyzing catalyzing dependent economic policy initiatives (an extended version of Singh and Singh, 2017a).

Initially, the study develops financial stress indices for the respective BRIC financial markets. Later, it captures linkages among the said US-BRIC indices by using Johansen cointegration, vector autoregression/vector error correction models (VECM), generalized impulse response functions, Toda–Yamamoto Granger causality, variance decomposition analyses and bivariate generalized autoregressive conditional heteroskedasticity (GARCH) model under constant conditional correlation framework, in general. Markov regime switching and efficient causality tests proposed by Hill (2007) are also used.

Overall, there are both short-run and long-run dynamic interactions observed between the US and Indian financial stress indices. For rest of the markets, only short-run interactions are found to be in existence. The time-varying co-movement coefficients report financial contagion impact of the US financial crisis on Russian and Indian financial systems only. Contrary to this, Brazilian and Chinese financial systems are largely exhibiting interdependence with the US financial system. Efficient causality tests report indirect impact of the Russian financial system on Brazilian via auxiliary Indian financial system.

The present study is the first of its kind capturing linkages among the US-BRIC financial stress indices by using diverse econometric models. The results support different market participants and policymakers in understanding effectiveness and implementation of economic policies while considering their cross-market interactions as well.

This chapter presents several approaches for identifying and dating the speculative bubble on real estate market. Using the real estate price index (IPAI), statistical and structural approaches were combined in order to detect the existence of a bubble on the Moroccan real estate market. The results obtained affirm that the Moroccan real estate market experienced a speculative bubble during the period 2006–2008 explained mainly by the boom of credit during the same period. The use of the Markov switching model affirmed that the speculative bubble on Morocco is cyclic and consequently corroborates the critic formulated by Evans (1991) concerning the traditional approaches for the detection of financial bubbles. Thus, the analysis of the series of the bubble, extracted using the Kalman filter, affirms the existence of two regimes, namely an explosive regime and a normal regime. The first regime describes the periods of explosion of the bubble and lasts for about 9 quarters, while the second, lasting for 14 quarters, describes the periods of return to the average cycle.

This chapter investigates the determinants of the volatility of spread in the over-the-counter foreign exchange market and examines whether the relationships differ in the crisis periods. We compute the measures for the volatility of liquidity by using bid-ask spread data sampled at a high frequency of five minutes. By examining 11 currencies over a 13-year sample period, we utilize a balanced dynamic panel regression to investigate whether the risk associated with the currencies quoted or trading activity affects the variability of liquidity provision in the FX market and examine whether the crisis periods have any effect. We find that both the level of spread and volatility of spread increases during the crisis periods for the currencies of emerging countries. In addition, we find increases in risks associated with the currencies proxied by realized volatility during the crisis periods. We also show risks associated with the currency are the major determinants of the variability of liquidity and that these relationships strengthen during periods of uncertainty. First, we develop measures to capture the variability of liquidity. Our measures to capture the variability of liquidity are non-parametric and model-free variable. Second, we contribute to the debate of whether variability of liquidity is adverse to market participants by examining what drives the variability of liquidity. Finally, we analyze seven crisis periods, allowing us to document the effect of the crises on determinants of variability of liquidity over time.

This paper aims to investigate the presence of a housing bubble using Swedish data from 1986Q1-2016Q4 by using various methods.

First, the authors use affordability indicators and asset-pricing approaches, including the price-to-income ratio, price-to-rent ratio and user cost, supplemented by a qualitative discussion of other factors affecting house prices. Second, the authors use cointegration techniques to compute the fundamental (or long-run) price, which is then compared with the actual price to test the degree of Sweden’s housing price bubble during the studied period. Third, they apply the univariate right-tailed unit root test procedure to capture bursting bubbles and to date-stamp bubbles.

The authors find evidence for rational housing bubbles with explosive behavioral components beginning in 2004. These bubbles do not continuously diverge but instead periodically revert to their fundamental value. However, the deviation is persistent, and without any policy correction, it takes decades for real house prices to return to equilibrium.

The policy implication is that monetary policy designed to contain mortgage demand and thereby prevent burst episodes in the housing market must address external imbalances, as revealed in real exchange rate undervaluation. It is unlikely that current policies will stop the rise of house prices, as the growth of mortgage credit, improvement in Sweden’s international competitiveness and the path of interest rates are much more important factors.

Virtual learning environments (VLE) have in the past 15 years radically changed the way learning objects are spread among students and the way learners communicate with teachers. In parallel, social networks are now widely used by users with all kinds of profiles. Among social networks, Facebook is the one offering the largest array of functions and possibilities to be used as an open platform for various kinds of applications. That is why, in this chapter, we provide an overview of the possible use of Facebook as a VLE supporting a Massive Open Online Course (MOOC). The authors distinguish generic functionalities that can be found in the three most popular xMOOC platforms as well as the relevant concepts needed for effective learning that are manipulated by such platforms. Then the authors evaluate how, within Facebook groups, features can be used to create a fully working MOOC environment. The authors distinguish notably class management, communication, collaboration, assessment, learning activity management and the possibilities for learning object management. Overall, the authors highlight that, except for the assessment, Facebook can support all of the functionalities required by a modern MOOC platform while offering a high social presence. While not vital for most MOOC followers, further developments can nevertheless be made to customise Facebook for ensuring this assessment functionality. The chapter can be used as guidance for an implementation of the social network as MOOC through the use of a Facebook group.