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This paper examines the impact of Russian invasion of Ukraine on the intraday efficiency of four major energy markets, namely, diesel oil, Brent oil, light oil and natural gas.

This study applies the multifractal detrended fluctuation analysis (MFDFA) to high-frequency returns (30-min intervals) for the period from October 21, 2021, to May 20, 2022. The data sample of 5,141 observations is divided into two sub-samples, before and after the invasion of 24th February 2022. Additionally, the magnitude of long memory index is employed to investigate the presence of herding behavior around the invasion period.

Results confirm the presence of multifractality in energy markets and reveal significant changes of multifractal strength due to the invasion, indicating a decline of intraday efficiency for oil markets. Surprisingly, the natural gas market, being the least efficient before the invasion, turns out to be more efficient after the invasion. The findings also suggest that investors in these energy markets are likely to show herding, more prominently after the invasion.

The multifractal patterns, in particular the long memory property of energy markets, can help investors develop profitable investment strategies. Furthermore, the improved efficiency observed in the natural gas market, after the invasion, highlights its unique traits and underlying complexity.

This study is the first attempt to assess the impact of the Russia–Ukraine war on the efficiency of global commodity markets. This is quite important because the adverse effects of the war on financial markets may potentially cause destabilizing outcomes and negative effects on social welfare on a global scale.

COVID-19 evolved from a local health crisis to a pandemic and affected countries worldwide accordingly. Similarly, the impacts of the pandemic on the performance of global stock markets could be time-varying. This study applies a dynamic network analysis approaches to evaluate the evolution over time of the impact of COVID-19 on the stock markets' network.

Daily closing prices of 55 global stock markets from August 1, 2019 to September 10, 2020 were retrieved. This sample period was further divided into nine subsample periods for dynamic analysis purpose. Distance matrix based on long-range correlations was calculated, using rolling window's length of 100 trading days, rolled forward at an interval of one month's working days. These distance matrices than used to construct nine minimum spanning trees (MSTs). Network characteristics were figured out, community detection and network rewiring techniques were also used for extracting meaningful from these MSTs.

The findings are, with the evolution of COVID-19, a change in co-movements amongst stock markets' indices occurred. On the 100th day from the date of reporting of the first cluster of cases, the co-movement amongst the stock markets become 100% positively correlated. However, the international investor can still get better portfolio performance with such temporal correlation structure either avoiding risk or pursuing profits. A little change is observed in the importance of authoritative node; however, this central node changed multiple times with change of epicenters. During COVID-19 substantial clustering and less stable network structure is observed.

It is confirmed that this work is original and has been neither published elsewhere, nor it is currently under consideration for publication elsewhere.

The investigation of the fractal nature of financial data has been growing in the literature. The purpose is to investigate the multifractal behavior of frontier markets using multifractal detrended fluctuation analysis (MFDFA).

This study used daily closing prices of nine frontier stock markets up to 31-Aug-2020. A preliminary analysis reveals that these markets exhibit fat tails and clustering patterns. For a more robust analysis, a combination of Seasonal and Trend Decomposition using Loess (STL) and MFDFA has been employed. The former method is used to decompose daily stock returns, where later detected the long rang dependence in the series.

The results confirm varying degree of multifractality in frontier stock markets, implying that they exhibit long-range dependence. Based on these multifractality levels, Serbian and Romanian stock markets are the ones exhibiting least long-range dependence, while Slovenian and Mauritius stock markets indicating highest dependence in their series. Furthermore, the markets of Kenya, Morocco, Romania and Serbia exhibit mean reversion (anti-persistent) behavior while the remaining frontier markets show persistent behaviors.

The information given by the detection of the fractal measure of data can support for investment and policymaking decisions.

Frontier markets are of great potential from the perspective of international diversification. However, most of the research focused on other emerging and developed markets, especially in the context of multifractal analysis. This study combines the STL method and a physics-based robust technique, MFDFA to detect the multifractal behavior of frontier stock markets.

The purpose of this study is to develop a precise Islamic securities index forecasting model using artificial neural networks (ANNs).

The data of daily closing prices of KMI-30 index span from Aug-2009 to Oct-2019. The data of 2,520 observations are divided into training and test data sets by using the 80:20 ratio, which corresponds to 2016 and 504 observations, respectively. In total, 25 features are used; however, in model selection step, based on maximum accuracy, top ten indicators are selected from several iterations of predictive models.

The results of feature selection show that top five influencing indicators on Islamic index include Bollinger Bands, Williams Accumulation Distribution, Aroon Oscillator, Directional Movement and Forecast Oscillator while Mesa Sine Wave is the least important. The findings show that the model captures much of the trend and some of the undulations of the original series.

The findings of this study may have important implications for investment and risk management by using index-based products.

Numerous studies proved that traditional econometric techniques face significant challenges in out-of-sample predictability due to model uncertainty and parameter instability. Recent studies show an upsurge of interest in machine learning algorithms to improve the prediction accuracy.

The purpose of this study aims to investigate the nature of the relationship between workers' remittances and financial development (FD) in Sri Lanka for the period from 1975 to 2017.

This study used both the exploratory data analysis and inferential data analysis (IDA) techniques to test the objective of this study. The IDA technique consisted of the augmented Dickey–Fuller (ADF) and Phillips–Perron unit root tests, the autoregressive distributed lag (ARDL) bounds cointegration technique, the Granger causality test and impulse response function analysis.

The unit root test results show that the variables are in mixed order. The empirical results of cointegration confirm that workers' remittances have a beneficial long-run relationship with FD in Sri Lanka. The Granger causality test result indicates that there is a bidirectional relationship between workers' remittances and FD. The impulse response analysis indicates that a positive shock to workers' remittance has an immediate significant positive impact on the FD of up to 10 years.

The analytical techniques used in this study explain how workers' remittances induce FD in Sri Lanka.

This study fills an important gap in the academic literature by using newly developed ARDL bounds cointegration techniques in Sri Lanka, by using impulse response function analysis, and by studying the dynamic relationship between workers' remittances and FD using time series data.

This study aims to investigate a randomized 3 (endorser type: celebrity vs CEO vs expert) × 2 (product type: hedonic vs utility) between-respondents factorial experiment to inspect the effects of the endorsers and product types on consumers’ engagement in BRM through brand-relationship variables [i.e. self-brand connection (SBC), perceived product attachment (PPA) and source credibility (SC)]. Marketing in a digital era is witnessing a rising trend of “brand resurrection as revolution” led by consumer power. The successful revitalization of various dead brands provides some new opportunities for companies to engage millennial consumers in brand resurrection movements (BRM) through the right choice of brand endorsers. The new-found love of companies for the revitalization of long-forgotten brands has attracted considerable interest among scholars and marketing practitioners. Despite the brand resurrection’s high practical relevance, little is known in marketing research about how to revive failing brands back to life.

Using source credibility theory (SCT) as a lens, this study conducted two studies (i.e. Study 1, N = 300; Study 2, N = 300) and builds on an analysis of data from Pakistani millennials. The hypotheses were inspected using both structural equation modeling and SPSS’s PROCESS macro.

Through two studies, the authors find that the match between endorser types and product types affects customer motivation to engage in BRM via SBC, PPA and SC (i.e. attractiveness, trustworthiness and expertise).[AQ2] The results showed that for utilitarian products, both SBC and PPA mediate the link between endorser types and BRM, but for hedonic products, PPA does not play a role. Similarly, the authors’ results indicate that for hedonic products, attractiveness, trustworthiness and expertise mediate the link between endorser types and BRM, but for utilitarian products, trustworthiness does not play a role.

The results of this research will help marketing managers devise effective brand endorsers strategies in reviving failing brands. Specifically, this endeavor highlights that understanding brand advertisements merely in terms of celebrity endorsement restricts the full potential that brand advertisements could have and also that a comprehensive understanding must include expert and chief executive officers (CEO) endorsers. Therefore, one of the central contributions of this research is the introduction of expert and CEO endorsers and the evidence that both celebrity (i.e. celebrity and CEO) and non-celebrity endorsers (i.e. experts) have an impact on consumers’ motivation to engage in BRM.

To the best of the authors’ knowledge, this is one of the first endeavors of its kind to empirically explore consumer attitude/motivation behind participation in reviving failing brands. The significance of this work is underscored by the fact that numerous dead brands are being brought back by companies because of consumer–brand co-creation movements.

The present study aims to analyze the effect of workplace incivility and coworker deviant behavior on turnover intentions and nurses' job performance.

Data were obtained from 318 nurses using a structured questionnaire. The multistage sampling technique was used to distribute the questionnaire and analysis was performed through structural equation modeling.

The findings show association between workplace incivility and coworker deviant behavior on turnover intentions and nurses' job performance. Further, the findings state that coworker deviant behavior has impact on turnover intentions and nurses' job performance.

Results of the study show critical situation for healthcare sector. Findings show that negative behaviors influence nurses' performance and escalate their intentions to leave this profession. These findings can help authorities to take some actions and use interventions to suppress or control these negative behaviors to improve the nursing performance at workplace.

The nursing literature is devoid of evidence about how workplace incivility and coworker deviant behavior influence employee outcomes.

In wireless sensor networks, improving the network lifetime is considered as the prime objective that needs to be significantly addressed during data aggregation. Among the traditional data aggregation techniques, cluster-based dominating set algorithms are identified as more effective in aggregating data through cluster heads. But, the existing cluster-based dominating set algorithms suffer from a major drawback of energy deficiency when a large number of communicating nodes need to collaborate for transferring the aggregated data. Further, due to this reason, the energy of each communicating node is gradually decreased and the network lifetime is also decreased. To increase the lifetime of the network, the proposed algorithm uses two sets: Dominating set and hit set.

The proposed algorithm uses two sets: Dominating set and hit set. The dominating set constructs an unequal clustering, and the hit set minimizes the number of communicating nodes by selecting the optimized cluster head for transferring the aggregated data to the base station. The simulation results also infer that the proposed optimized unequal clustering algorithm (OUCA) is greater in improving the network lifetime to a maximum amount of 22% than the existing cluster head selection approach considered for examination.

In this paper, lifetime of the network is prolonged by constructing an unequal cluster using the dominating set and electing an optimized cluster head using hit set. The dominator set chooses the dominator based on the remaining energy and its node degree of each node. The optimized cluster head is chosen by the hit set to minimize the number of communicating nodes in the network. The proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit set. The simulation result confirms that the proposed algorithm prolonging the lifetime of the network efficiently when compared with the existing algorithms.

The proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit sets. The simulation result confirms that the proposed algorithm is prolonging the lifetime of the network efficiently when compared with the existing algorithms.

This study aims to analyze the effect of religiosity (RE), halal knowledge (HK) and halal certification (HC), attitudes, subjective norms and vaccine quality on the Muslim community’s intention to use halal vaccines in Indonesia.

This study involved 725 Muslim respondents in 32 Indonesian provinces. The model used was based on the theory of reasoned action development with the partial least squares structural equation modeling as the data processing tool.

The study discovered that RE, HK and HC did not affect the intention of Muslims to use halal vaccines. In addition, the MUI (Majelis Ulama Indonesia – Indonesia Council of Ulama) fatwa permitting the use of the AstraZeneca vaccine despite the haram ingredients was considered an excuse for Muslims to administer non-halal vaccines. However, several parties disagreed on the fatwa because the emergency legal standing for its permissibility did not apply to all regions. The reason was that each had different rates of confirmed cases, ranging from high-risk areas to those with zero confirmed Covid-19 cases.

This study examines the Muslim community’s intention toward using halal vaccines in several regions in Indonesia. In addition, this study conducted in-depth interviews as samples in several regions. This study also conducted interviews to determine the public’s views on government obligation about the Covid-19 vaccine. Finally, this study proposes a vaccine to avoid the possibility of controversy over the use of non-halal vaccines.

Employee work engagement has become a major concern for managers as hardly 21% of employees are engaged in their work. Therefore, this study aims to unveil the association between ethical leadership and employee engagement. Specifically, the study explores the mediating role of trust in leader between ethical leadership and employee work engagement and moderating role of harmonious work passion in the association between trust in leader and employee work engagement.

This study collected data from 491 employees and their immediate supervisors working in various organizations (in Pakistan) through “Google Forms”. The data were analyzed through analysis of moment structure (AMOS) and structural equation modeling (SEM) was applied to examine measurement model (for unidimensionality) and structural model (for hypotheses testing).

The study noted that ethical leaders positively influence their subordinates to engage in their work. In addition, employees' trust in leader was noted to mediate the association between ethical leadership and employee work engagement. Finally, employees high in harmonious work passion are more likely to engage in their work when perceived their leaders ethical style.

The study suggests to management that fair dealing and involvement in decision-making (ethical leadership) improve employee work engagement as such practices build employees' level of trust in their leaders. In addition, management is suggested to give freedom to employees while selecting their tasks as it positively contributes to their harmonious work passion which ultimately benefits the organization.

Drawing upon social exchange and self-determination theory, this study is the first of its kind that explored the moderating role of harmonious work passion and mediating role of trust in leader between ethical leadership and employee work engagement.