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The purpose of this paper is to examine the extent to which the capital control measures implemented by the Malaysian central bank in late 1998 had an influence on segmenting the Malaysian equity market from other major equity markets.

The S&P 500, the Nikkei 225 Index, the STI Index and the KLSE Composite Index are considered. The discrete wavelet transform technique – “Haar” is employed to decompose the series into various time scales during the pre‐ and post‐capital control periods in Malaysia. The decomposed series are then used to estimate the interdependence between KLSE Composite Index with the other three markets at various time scales.

The empirical findings support three conclusions. First, in the pre‐capital control period, Singapore is the most influential market followed by the US across all time scales in transmitting news into Malaysia. Second, after the imposition of capital controls, the spillover effects from Singapore to Malaysia have declined substantially, suggesting a reduced integration between these two markets. Finally, in the post‐capital control period, all three markets appear to be imparting a similar but moderate level of influence on the Malaysian market.

To explore the return and volatility spillovers, the use of return and volatility series at different time scales provided a greater level of insight into the dynamics than the standard approaches which employ only one series in the time domain.

The results from this paper will have potential implications for asset allocation, the pricing of domestic securities, the implementation of global hedging and trading strategies and the evaluation of regulatory proposals to restrict international capital flows.

This paper aims to study the weak form of efficiency of Indian capital market during the period of global financial crisis in the form of random walk.

The study considered daily closing prices of S&P CNX Nifty, BSE, CNX100, S&P CNX 500 from April 1, 2005 to March 31, 2010. The data source is the equity market segment of NSE and BSE. Both parametric and nonparametric tests (“ex‐posts” in nature) are applied for the purpose of testing weak‐form efficiency. The parametric tests include Augmented Dickey‐Fuller (ADF) unit root tests and nonparametric tests include Phillips‐Perron (PP) unit root tests and Run test. ADF tests use a parametric autoregressive structure to capture serial correlation and PP tests use non‐parametric corrections based on estimates of the long‐run variance of ΔY_t.

The results suggested that the Indian stock market was efficient in its weak form during the period of recession. It means that investors should not be able to consistently earn abnormal gains by analysing the historical prices. Hence one should not be able to make a profit from using something that everybody else knows.

The study reports that all the stocks in these selected indices are fundamentally strong and their prices are not influenced largely by historical prices and other relevant factors which came from industry and any other information that is publically available. Thus it can be concluded that the Indian stock market was informationally efficient and no investor can usurp any privileged information to make abnormal profits.

Where past studies have examined the weak‐form of efficiency of various markets and the effect of globalisation and global financial crisis on the various sectors of developing and emerging economies, this paper attempts to study the weak form of efficiency of the Indian capital market in the period of recession in the form of random walk.

Narendra Modi’s Bharatiya Janata Party made fighting corruption as a major election plank, and won the 2014 elections, and formed the National Democratic Alliance government – a majority party government in India in nearly 30 years. Modi was re-elected in May 2019 with a bigger majority. As his government celebrates the second term, it is time to assess the last five years’ accomplishments in fighting corruption. This chapter deals with the subject under four major headings. The first examines the efficacy of existing anti-corruption agencies. The second explains the office of Lokpal (Ombudsman). The third analyses the initial challenges and efforts of the Modi government. The fourth draws some conclusions.

This study contributes to the limited and rather fragmented research on mobile payment (m-payment) within a post-adoption context by identifying individual characteristics (personal innovativeness and m-payment self-efficacy) and m-payment quality characteristics (system quality, information quality and service quality) that can influence expectations about performance and effort of this technology. These two outcome expectations may affect user satisfaction with m-payment, which will in turn influence users' continued intention to use this technology.

A survey was carried out among university students in the United Arab Emirates who have used the m-payment method. Structural equation modeling technique was used to test and analyze both the measurement model and the structural model.

Individual characteristics are important in establishing users' expectations of the ease of use and usefulness of m-payment. However, of the quality characteristics, only system quality significantly affected users' expectations of the ease of use and usefulness of m-payment.

The findings of this study provide unique insights into the individual and quality characteristics that will eventually lead to continued intention to use m-payment. This will help businesses to develop appropriate marketing strategies to increase adoption and use of m-payment.

Mobile payment (MP), near field communication-based particularly, has become one of the future payment tools. This study aims to indicate the difference between consumer users and non-users by examining the effect of MP users’ perceived value on their satisfaction and on MP non-users behavioural intention. Once those MP users’ have achieved satisfaction, the study then investig ates how consumers bring about the need recognition and are more desirable to buy more products.

This study develops a research model, which examines the effect of consumer perceived value, including utilitarian value, technicality and perceived risk, on MP users’ satisfaction and MP non-users behavioural intention. In methodology, it adopts structural equation modelling to verify the proposed research model for empirical studies. The data set consists of 161 MP users and 277 non-users of consumers.

The findings show that consumers, both MP users and non-users included, are encouraged to improve utilitarian value if they are favourable to adopt MP services. Relatively, female users emphasize more on perceived risk, whilst male users emphasize more on technicality. Moreover, MP non-users and female users still have much concern about perceived risk such that they are inclined to avoid adopting MP services.

Past research has examined the adoption of MP services from the perspective of consumers with a focus on the perceived value, satisfaction and behavioural intention. However, only limited research examined the post-purchase evaluation of MP users. The study fills this gap by clarifying the difference between consumer users and non-users in the effect of MP users’ perceived value on their satisfaction and on their non-users behavioural intention.

The purpose of this paper is to develop a methodology for knowledge discovery in emergency response service databases based on police occurrence reports, generating information to help law enforcement agencies plan actions to investigate and combat criminal activities.

The developed model employs a methodology for knowledge discovery involving text mining techniques and uses latent Dirichlet allocation (LDA) with collapsed Gibbs sampling to obtain topics related to crime.

The method used in this study enabled identification of the most common crimes that occurred in the period from 1 January to 31 December of 2016. An analysis of the identified topics reaffirmed that crimes do not occur in a linear manner in a given locality. In this study, 40 per cent of the crimes identified in integrated public safety area 5, or AISP 5 (the historic centre of the city of RJ), had no correlation with AISP 19 (Copacabana – RJ), and 33 per cent of the crimes in AISP 19 were not identified in AISP 5.

The collected data represent the social dynamics of neighbourhoods in the central and southern zones of the city of Rio de Janeiro during the specific period from January 2013 to December 2016. This limitation implies that the results cannot be generalised to areas with different characteristics.

The developed methodology contributes in a complementary manner to the identification of criminal practices and their characteristics based on police occurrence reports stored in emergency response databases. The generated knowledge enables law enforcement experts to assess, reformulate and construct differentiated strategies for combating crimes in a given locality.

The production of knowledge from the emergency service database contributes to the government integrating information with other databases, thus enabling the improvement of strategies to combat local crime. The proposed model contributes to research on big data, on the innovation aspect and on decision support, for it breaks with a paradigm of analysis of criminal information.

The originality of the study lies in the integration of text mining techniques and LDA to detect crimes in a given locality on the basis of the criminal occurrence reports stored in emergency response service databases.