Search results

This study aims to empirically investigate the relationship between artificial intelligence (AI) in marketing (AIM) and business performance from the resource-based view (RBV) perspective.

A survey strategy was used in this study to collect data from 225 small and medium enterprises (SMEs) respondents who were on the registered list of the Ghana Enterprise Agency in the Eastern Region of Ghana. Structural equation modeling – path analysis was used to estimate the impact of AIM on the performance of SMEs.

The analyzed data shows that AIM has significant impact on the financial performance, customer performance, internal business process performance and learning and growth performance in the case of SMEs in Ghana. This study establishes the significance of AIM approach in achieving financial performance, customer performance, internal business process performance and learning and growth performance through the application of AIM determinants including, Internet of Things (IoT), collaborative decision-making systems (CDMS), virtual and augmented reality (VAR) and personalization.

Aside the aforementioned significance of this research study, this study has limitations. The sample size of this research study can be expanded to include SME respondents in other geographical areas that were not considered in this study. Future research studies should concentrate on how AIM can analyze customer communications and information such as posts on social media to develop future communications that may enhance customer engagement.

The practical implications comprise of two key items. First, this research study encourages SME owners and managers to develop an AIM method as a fundamental strategic goal in their pursuit to improve SME performance. Second, SME owners and managers should increasingly implement the four determinants of AIM indicated in this research study (i.e., IOT, CDMS, VAR and personalization) to develop essential resources for effective application of AIM to improve their performance.

The results of this study provide a strong support to RBV theory and the proposition that AIM and its determinants (i.e., IOT, CDMS, VAR and personalization) should be recognized as an essential strategic resource for improving the performance (i.e., financial performance, customer performance, internal business process performance and learning and growth performance) of SMEs. This study also contributes to the current body of knowledge on AIM and management, particularly in the context of an emerging economy.

The purpose of this study is to unfold the existing information channel in the higher moments of currency futures for different time horizons.

The authors use a quasi-Bayesian local likelihood approach within a time-varying parameter vector autoregression (TVP-VAR) framework and a dynamic connectedness measure to study the volatility, skewness and kurtosis of most traded currency futures.

The authors’ results suggest a time-varying presence of dynamic connectedness within higher moments of currency futures. Most spillovers pertain to shorter time horizons. The authors find that in net terms, CHF, EUR and JPY are the most important contributors to the system, while the authors emphasize that the role of being a transmitter or a receiver varies for pairwise interactions and time windows.

To the best of the authors’ knowledge, this is the first study that looks upon the connectivity vis-á-vis uncertainty, asymmetry and fat tails in currency futures within a dynamic Bayesian paradigm. The authors extend the current literature by proposing new insights into asset distributions.

This study examines the extreme quantile connectedness and spillovers between West Texas Intermediate (WTI) crude oil futures and ten Vietnamese stock market sectors. Knowledge of such links is important to both investors and policymakers in understanding the transmission of shocks across markets.

The authors employ the extreme quantile connectedness methodology of Ando et al. (2022).

Initial results show that the size of spillovers is higher during bearish markets than bullish markets and under major financial, political, energy and pandemic events. The oil market is a net receiver of spillovers during downward markets and net contributors during upward markets. The banking sector is a net contributor of spillovers, whereas consumer discretionary and consumer staples are net receivers for different quantiles. The role of the remaining sectors as net receivers/contributors is sensitive to the quantiles. Oil has a large spillover effect on the electricity sector for all quantiles. Comparing all crises, oil offers the best hedging effectiveness to the Vietnamese sector during the coronavirus disease 2019 (COVID-19) crisis. Moreover, oil was a cheap hedge asset during oil crises. Finally, oil provides the highest hedging effectiveness for healthcare during the global financial crisis (GFC) and consumer staples during the European debt crisis (EDC), oil crisis and COVID-19.

Acknowledging the presence of heterogeneity in the relation between oil and economic sectors under different market conditions, this study is the first to examine the extreme quantile connectedness between oil and Vietnamese sectors.

Internationalization and financial deregulation have caused market participants and policymakers to consider the significance of financial connectedness and the spillover effects of shocks. In this context, this research is a pioneering effort to investigate the direction and magnitude of return volatility spillovers between Pakistan’s financial markets and those of its key trade partners. This paper examines the relationship between return and volatility spillover in the financial markets of Pakistan and its major trading partners.

Ten countries are selected for empirical examination of dynamic connectedness among Pakistan and its major trading partner’s stock markets. This study utilizes a spillover index approach model and considers daily, weekly and monthly datasets spanning 25 years from 1995 to 2019.

The results indicate that stock markets provide efficient channels for return and volatility spillovers. Moreover, it is found that the intensity of spillovers during the financial crisis is more intense as these crises are major determinants of contagion; consequently, investors, speculators and policymakers use these events for their respective purposes.

Researchers, practitioners, policymakers and investors may all benefit from the findings in areas including risk management, portfolio diversification and trading methods.

The crude oil market plays a key role in addressing the issue of energy economics. This paper aims to detect the causality relationship between the crude oil market and economy based on the financial system.

This paper used the static and dynamic Hatemi-J Bootstrap Toda–Yamamoto and Diebold–Yilmaz connectedness index. The Hatemi-J Bootstrap Toda-Yamamoto approach allows researchers to use nonstationary data and that method is robust to nonnormal distribution and heteroscedasticity. The Diebold–Yilmaz connectedness index model provides researchers to detect the power of connectedness besides linkage direction. The analyzed period is the span from January 3, 2005 to October 3, 2022.

The results show bidirectional causality in the full sample but unidirectional causality before and after the 2008 financial crisis. During the 2008 financial crisis period and the COVID-19 period, there was a bidirectional and unidirectional causality, respectively. The connectedness approach indicates that the crude oil market affects financial stress through investors’ risk preferences.

The Diebold–Yilmaz spillover index model is based on vector autoregression methods with a stationarity precondition. However, some of the five dimensions that constitute the financial stress index (FSI) are nonstationary in level. Therefore, the authors takes the first difference of the nonstationary data.

The linkage between the crude oil market and the FSI provides useful information for investors and policymakers. For instance, this paper indicates that an investor wanted to forecast future value of the crude oil (financial stress) should consider the current and past values of financial stress (crude oil). Moreover, policymaker should consider the crude oil market (FSI) to make a policy proposal for financial system (crude oil market).

Recently, indicators of economic activity levels (economic policy uncertainty, implied volatility index) have begun to be considered to analyze the relationship between energy and the economy but very little is known in the literature about the leading and lagging roles of data in subsample periods and the linkage channel. The other originality of this research is using the new econometric approaches.

The purpose of this paper is to explore the dynamic interdependence structure and risk spillover effect between the Chinese stock market and the US stock market.

This paper mainly uses the multivariate R-vine copula-complex network analysis and the multivariate R-vine copula-CoVaR model and selects stock price indices and their subsector indices as samples.

The empirical results indicate that the Energy, Materials and Financials sectors have leading roles in the interdependent structure of the Chinese and US stock markets, while the Utilities and Real Estate sectors have the least important positions. The comprehensive influence of the Chinese stock market is similar to that of the US stock market but with smaller differences in the influence of different sectors of the US stock market on the overall interdependent structure system. Over time, the interdependent structure of both stock markets changed; the sector status gradually equalized; the contribution of the same sector in different countries to the interdependent structure converged; and the degree of interaction between the two stock markets was positively correlated with the degree of market volatility.

This paper employs the methods of nonlinear cointegration and the R-vine copula function to explore the interactive relationship and risk spillover effect between the Chinese stock market and the US stock market. This paper proposes the R-vine copula-complex network analysis method to creatively construct the interdependent network structure of the two stock markets. This paper combines the generalized CoVaR method with the R-vine copula function, introduces the stock market decline and rise risk and further discusses the risk spillover effect between the two stock markets.

Standard economic theory predicts that any increase in public spending is accompanied by a rise in inflation in an economy. This paper presents empirical proof that prices do not always rise with an increase in public expenditure but only up to a certain threshold level. The primary aim of this paper is to unearth the government size-inflation nexus in India for the period from 1971 to 2019.

The logistic STAR (smooth transition autoregression) model is employed to unravel the government size-inflation nexus for the Indian economy from a non-linear perspective.

The finding of our study confirm the non-linear relationship between the size of the government and inflation in India. The estimated threshold level for government size is precisely found to be 9.27%. The size of the government exerts a negative influence on inflation until it reaches the optimal or threshold level. Any further increase in the size of government beyond this threshold level would result in a rise in inflation.

The findings have implications for the conduct of fiscal policy. Policymakers can increase government spending in a regime of small government size without having any inflationary impacts by generating revenues from taxes and other sources instead of relying much on the central bank. In the regime of a large-sized government, adhering strictly to the discipline in the conduct of fiscal and monetary policies would help curb inflation and enhance growth synchronously, hence alleviating any loss of welfare.

To the best of the authors’ knowledge, this study is an attempt to revisit the government size-inflation nexus in India from a non-linear perspective using the Smooth Transition Autoregression (STAR) model for the first time.

Throughout the 21st century, US households have experienced unprecedented levels of leverage. This dynamic has been exacerbated by income shortfalls during the COVID-19 crisis. Leveraging and deleveraging decisions affect household consumption. This study investigates the effect of the dynamics of household leverage and consumption on the stock market.

The authors explore the relation between household leverage and consumption in the context of the consumption capital asset pricing model (CCAPM). The authors test the model's implication that leverage has a negative risk premium by transforming the asset pricing restriction into an unconditional linear factor model and estimate the model using the general method of moments procedure. The authors run time-series regressions to estimate individual stocks' exposures to leverage, and cross-sectional regressions to investigate the leverage risk premium.

The authors show that shocks to household debt have strong and lasting effects on consumption growth. The authors extend the CCAPM to accommodate this effect and find, using various test assets, a negative risk premium associated with household deleveraging. Looking at individual stocks the authors show that the deleveraging risk premium is not explained by well-known risk factors.

This paper contributes to the literature on the role of leverage in economics and finance by establishing a relation between household leverage and spending decisions. The authors provide novel evidence that households' leveraging and deleveraging decisions can be a fundamental and influential force in determining asset prices. Further, this paper argues that household leverage might explain the small, persistent, and predictable component in consumption growth hypothesised in the long-run risk asset pricing literature.

This paper examines the time-varying efficiency of nine major Middle East and North Africa (MENA) stock markets namely Egypt, Bahrain, UAE, Jordan, Saudi Arabia, Oman, Qatar, Morocco and Tunisia during times of COVID-19 pandemic outbreak and vaccines.

The authors use two econometric approaches: (1) autocorrelation tests including the wild bootstrap automatic variance ratio test, the automatic portmanteau test and the Generalized spectral test, and (2) a non-Bayesian generalized least squares-based time-varying model with statistical inferences.

The results show that the degree of stock market efficiency of Egyptian, Bahraini, Saudi, Moroccan and Tunisian stock markets is influenced by the COVID-19 pandemic crisis. Furthermore, the authors find a tendency toward efficiency in most of the MENA markets after the announcement of the COVID-19's vaccine approval. Finally, the Jordanian, Omani, Qatari and UAE stock markets remain globally efficient during the three sub-periods of the COVID-19 pandemic outbreak.

The results have important implications for asset allocations and financial risk management. Portfolio managers may maximize the benefit of arbitrage opportunities by taking strategic long and short positions in these markets during downward trend periods. Policymakers should implement the action plans and reforms to protect the stock markets from global shocks and ensure the stability of the stock markets.

The aim of this paper is to provide a narrative review of previous research on tourism demand modelling and forecasting and potential future developments.

A narrative approach is taken in this review of the current body of knowledge.

Significant methodological advancements in tourism demand modelling and forecasting over the past two decades are identified.

The distinct characteristics of the various methods applied in the field are summarised and a research agenda for future investigations is proposed.

本文旨在对先前关于旅游需求建模和预测的研究进行叙述性回顾并对未来潜在发展进行展望。

本文采用叙述性回顾方法对当前知识体系进行了评论。

本文确认了过去二十年旅游需求建模和预测方法论方面的重要进展。

本文总结了该领域应用的各种方法的独特特征, 并对未来研究提出了建议。

El objetivo de este documento es ofrecer una revisión narrativa de la investigación previa sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros.

En esta revisión del marco actual de conocimientos sobre modelización y previsión de la demanda turística y los posibles desarrollos futuros,se adopta un enfoque narrativo.

Se identifican avances metodológicos significativos en la modelización y previsión de la demanda turística en las dos últimas décadas.

Se resumen las características propias de los diversos métodos aplicados en este campo y se propone una agenda de investigación para futuros trabajos.