Search results

This paper aims to explore the significance of knowledge transfer (KT) from universities to organizations by employing in-service employees, who are working in organizations and at the same time studying in the universities, as the channel.

By using survey methodology, data were collected from 216 in-service business students via self-administered questionnaires and partial least square structural equation modeling (PLS-SEM) was applied.

The results revealed that intrinsic motivation, psychological hardiness, functional value and innovative culture significantly predict the acquired knowledge which henceforth positively affects KT.

Data from business students of the single university were extracted which limits the findings, and hence directed future researchers to explore further.

Interactive, case study-based training and others implications are discussed to effectively transfer the knowledge from universities to organizations.

The role of in-service employed students is of significant importance, as students exist as an intermediary between organizations and universities, and so both universities and organizations should make maximum use of the said channel for an effective KT.

This study aims to examine the house price fluctuations in G7 countries by using the multifractal detrended fluctuation analysis (MF-DFA) for the years 1970–2019. The study examined the market efficiency between the short-term and long-term in the full sample period, before and after the global financial crisis period.

This study uses the MF-DFA to analyze house price fluctuations.

The findings confirmed that the housing market series are multifractal. Furthermore, all the markets showed long-term persistence in both the short and long-term. The USA is identified as the most persistent house market in the short run and Japan in the long run. Moreover, in terms of efficiency, Canada is identified as the most efficient house market in the long run and the UK in the short run. Finally, the result of before and after the financial crisis period is consistent with the full sample result.

The contribution of this study in the literature is fourfold. This is the first study that has examined the house prices efficiency by using the MF-DFA technique given by Kantelhardt et al. (2002). Previously, the house market prices and efficiency has been investigated using generalized Hurst exponent (Liu et al., 2019), Quantile Regression Approach (Chae and Bera, 2019; Tiwari et al., 2019) but no study to the best of the knowledge has been done that has used the MF-DFA technique on the housing market. Second, this is the first study that has focused on the house markets of G7 countries. Third, this study explores the house market efficiency by dividing the market into two periods i.e. before and after the financial crisis. The study strives to investigate if the financial crisis determines the change in the degree of market efficiency or not. Finally, the study gives valuable insights to the investors that will help them in their investment decisions.

The purpose of this study is to examine the factors which affect mobile banking (M-banking) acceptance in Islamic banks of Pakistan by using the modified unified theory of acceptance and use of technology (UTAUT) model. The performance expectancy, facilitating conditions, social influence, effort expectancy, perceived value, habit and hedonic motivation are taken as independent variables. Similarly, the intention to adopt M-banking is taken as the mediator, and actual usage is used as the dependent variable.

The data are collected by using the survey method, and the five-point Likert scale is used for this purpose. The statistical techniques applied to the dataset were confirmatory factor analysis and partial least square structure equation modeling.

The empirical evidence shows that all the variables except for social influence have a significant positive effect on the intention which results in actual usage.

This study will help the Islamic banks in boosting the M-banking growth and decision-makers in crafting those strategies that increase the M-banking acceptance.

This paper makes a unique contribution to the literature with reference to Pakistan, being a pioneering attempt to investigate the factors which affect M-banking acceptance in Islamic banks of Pakistan by using the modified UTAUT model.

This article aims to uncover the impact of Google Trends on cryptocurrency markets beyond Bitcoin during the time of increased attention to altcoins, especially during the COVID-19 pandemic.

This paper analyses the nexus among the Google Trends and six cryptocurrencies, namely Bitcoin, New Economy Movement (NEM), Dash, Ethereum, Ripple and Litecoin by utilizing the causality-in-quantiles technique on data comprised of the years January 2016–March 2021.

The findings show that Google Trends cause the Litecoin, Bitcoin, Ripple, Ethereum and NEM prices at majority of the quantiles except for Dash.

The findings will help investors to develop more in-depth understanding of impact of Google Trends on cryptocurrency prices and build successful trading strategies in a more matured digital assets ecosystem.

The purpose of this study is twofold: first is to examine the nonlinear relationship between tourism development and housing prices, and secondly, the role of economic growth in facilitating the materialization of tourism development and housing prices nexus in G7 countries.

The authors used the newly introduced econometric technique panel smooth transition regression (PSTR) model with two regimes on annual panel data from 1995 to 2018.

Results confirmed that the nexus between the tourism development and housing prices is nonlinear and regime dependent. Moreover, the results showed that the threshold level of economic growth above which tourism development increases the housing prices is 2.63%. The relationship above the threshold value is positive and growth enhancing, while below the threshold, tourism development has a negative effect on housing prices. The economic growth and housing prices also showed the U-shape relationship implying that at a certain level increase in economic growth decreases the housing prices but after a certain level increase in economic growth increases the housing prices.

This paper makes a unique contribution to the literature with reference to developed economies, being a pioneering attempt to investigate the nonlinear relationship between tourism development and housing prices and applying more rigorous and advanced econometric techniques like PSTR.

In the past few years, numerous economic uncertainty challenges have occurred globally. These uncertainties grasp the attention of the researchers and they examine the role of economic policy uncertainties in several aspects. Therefore, this study contributes to the literature by exploring the house prices volatility and economic policy uncertainty nexus in G7 countries.

The authors applied the newly introduced econometric technique, the GARCH-MIDAS model, to the sample size of January 1998–May 2021.

The result shows a significant relationship between house prices volatility and economic policy uncertainty. Moreover, economic policy uncertainty acts as a significant determinant of house prices volatility. In addition, the out-of-sample also shows that the economic policy uncertainty is an effective predictor and the GARCH-MIDAS has a better predictive ability.

This paper makes a unique contribution to the literature with reference to developed economies, being a pioneering attempt to investigate the GARCH-MIDAS model to analyze the relationship between housing prices volatility and economic policy uncertainty by applying more rigorous and advanced econometric techniques.

This chapter examines the nexus between the between tourism growth and income inequality in the top 10 tourist destinations in the world by using the advanced econometric technique namely quantile-on-quantile (QnQ). This approach combines the two approaches, that is, the nonparametric estimation and quantile regression and regresses the quantile of the tourism growth onto income inequality quantiles, thus enabling the effect of the income inequality on across different conditional tourism growth distribution. It also allows to explain a comprehensive picture of the overall interdependence and nonlinear relationship between the examined variables. The result from QnQ approach shows a negative association between income inequality and tourism growth, however, the country-specific analysis shows wide variations within and across different quantiles of variables. Notably, on the one hand, a strong negative association between the variables is found in China, France, Spain, Italy, Russia and the USA implying that tourism expansion minimizes the income inequality. On the other hand, a strong positive association is noted in Germany, Turkey, Mexico and the UK, which means that growth in tourism widens the income inequality. These outcomes provide important policy direction for tourism management in the respective countries.