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Despite sponsorship disclosure regulations, many influencers circumvent regulations by posting hidden advertising in covert formats. However, the impact of influencer hidden advertising sponsorship disclosure (IHASD) on brand attitudes is complex and contradictory. To understand the influence mechanism clearly, we introduced the operational transparency framework and investigated the mediating effects of perceived manipulative intent and perceived authenticity as well as the relationship between them. The conditions under which the mediation effect occurs were also analyzed.

The authors conducted three experimental studies. Studies 1 and 2 examined the influence mechanism of sponsorship disclosure (present vs absent) and sponsorship disclosure prominence (explicit vs implicit) of influencer hidden advertising on brand attitudes (i.e. the mediating effect of perceived manipulative intent and perceived authenticity). Study 3 explored the moderating effect of consumers’ thinking styles.

The results revealed that sponsorship disclosure and sponsorship disclosure prominence of influencer hidden advertising weakened brand attitudes through perceived manipulative intent while enhancing brand attitudes through perceived authenticity. Perceived authenticity and perceived manipulative intent played a bidirectional chain mediating role. When consumers’ thinking style was experiential, the negative mediating effect of perceived manipulative intent was alleviated and the positive mediating effect of perceived authenticity was enhanced; this effect, though, was the opposite when consumers’ thinking style was rational.

This research contributes to influencer sponsorship disclosure literature through providing an enhanced comprehensive, in-depth theoretical explanation of the competing mechanisms of sponsorship disclosure effects.

This study investigates the inter-relationships among liquidity creation, bank capital and credit risk in selected emerging economies between 2012 and 2016.

A three-step procedure as proposed by Berger and Bouwman (2009) is used to measure liquidity creation. Thereafter, a simultaneous equations model with the generalized method of moments (GMM) estimator is used to examine the links between liquidity creation, bank capital and credit risk.

The findings indicate that bank capital and credit risk affect each other positively after controlling for liquidity creation. Also, the findings show a negative impact of credit risk on liquidity creation while our findings do not find any evidence to confirm the reverse relationship between them. Furthermore, the findings demonstrate a two-way negative relationship between liquidity creation and bank capital in these emerging economies. Finally, the results indicate a positive relationship between capital and credit risk, especially in the case of small banks in the sample.

The findings suggest that the trade-off between the benefits of financial stability induced by tightening capital requirements and those of improved liquidity creation has crucial implications for policymakers and bank regulators in making the banking system more resilient. A positive impact of capital on credit risk emphasizes that the authorities in selected emerging economies should put more attention on small banks to ensure their exposures under target control.

This is the first study that examines the dynamic interrelationships among liquidity creation, bank capital and credit risk in the Asia–Pacific region.

This study seeks to understand the communication factors associated with effective social media for nonprofit organizations (NPOs). Specifically, the study investigated how interactive and emotional communication strategies influence public engagement in different ways, and how the effects differ by service-oriented and other types of NPOs.

Using computer-assisted textual and emotional analyses, the authors examined the functional interactivity, contingency interactivity and emotion elements of 301,559 tweets from the 100 largest US nonprofits. Negative binomial regression was applied to test the relationships among these elements and public engagement on Twitter (i.e. likes and retweets).

Findings revealed negative effects of functional interactivity on likes, negative effects of contingency interactivity on likes and retweets but a positive effect of functional interactivity on retweets. The findings also showed negative effects of emotion valence on likes and retweets but positive effects of emotion strength on likes and retweets. There were varying effects of interactivity and emotion on public engagement for service-oriented and other types of NPOs.

This study advances the nonprofit social media scholarship in several ways. First, this study suggests a clear yet largely ignored distinction in the effects of functional and contingency interactivity on public engagement. Second, this study is an early attempt to examine the role and impact of emotion elements in nonprofit social media success without downplaying the role of interactivity. Third, this study is one of the earliest attempts to include interaction effects for different types of NPOs. Last, this study contributes to the organizational social media use research by demonstrating the benefits of computer-assisted approaches in processing text data on social media. From a practical perspective, this study provides strategic guidelines for NPOs to design effective communication contents and improve their public engagement on social media.

With an increase in the amount of multilingual content on the World Wide Web, users are often striving to access information provided in a language of which they are non-native speakers. The purpose of this paper is to present a comprehensive study of user profile representation techniques and investigate their use in personalized cross-language information retrieval (CLIR) systems through the means of personalized query expansion.

The user profiles consist of weighted terms computed by using frequency-based methods such as tf-idf and BM25, as well as various latent semantic models trained on monolingual documents and cross-lingual comparable documents. This paper also proposes an automatic evaluation method for comparing various user profile generation techniques and query expansion methods.

Experimental results suggest that latent semantic-weighted user profile representation techniques are superior to frequency-based methods, and are particularly suitable for users with a sufficient amount of historical data. The study also confirmed that user profiles represented by latent semantic models trained on a cross-lingual level gained better performance than the models trained on a monolingual level.

Previous studies on personalized information retrieval systems have primarily investigated user profiles and personalization strategies on a monolingual level. The effect of utilizing such monolingual profiles for personalized CLIR remains unclear. The current study fills the gap by a comprehensive study of user profile representation for personalized CLIR and a novel personalized CLIR evaluation methodology to ensure repeatable and controlled experiments can be conducted.

The adoption of energy-efficient systems is crucial for Pakistan to meet its growing energy demand and address its energy challenges. However, adoption of these systems in Pakistan is hindered by several barriers, including economic constraints, lack of awareness and social attitudes toward sustainable development. Therefore, the purpose of this study is to explore adoption of energy-efficient household systems and the associated social influence.

The study incorporates social influence as a mediating factor to examine the relationships between awareness of consequences, perceived consumer effectiveness and attitudes toward the adoption of energy-efficient systems. A quantitative survey method was used to collect data from households from Faisalabad, Pakistan. A total of 203 valid questionnaires were received and data analyzed through SmartPLS 4 for structural equation modeling.

The results revealed that awareness of consequences positively impacts compliance, social identification and internalization, while perceived consumer effectiveness has a positive relationship with social identification and internalization. Moreover, the positive association of social identification and internalization with attitude were supported but relationship of compliance with attitude was unsupported.

The results may also be used to develop compelling marketing campaigns focusing environmental conservation and social influence for positive attitude development.

The study contributes to theoretical literature by examining the empirical relationships between specific individual characteristics and societal pressure that play a critical role in shaping attitudes toward the acceptance of energy-efficient systems. Additionally, the study's findings offer actionable implications for policymakers and marketers, contributing to the development of targeted interventions for promoting sustainable consumption.

Perhaps the most significant development in the global business arena in the post-war period has been the emergence of the Asia-Pacific rim countries as a significant economic force.

By incorporating the defect feature information, an ML-based linkage between defects and fatigue life unaffected by the time scale is developed, the primary focus is to quantitatively assess and elucidate the impact of different defect features on fatigue life.

A machine learning (ML) framework is proposed to predict the fatigue life of LPBF-built Hastelloy X utilizing microstructural defects identified through nondestructive detection prior to fatigue testing. The proposed method combines nondestructive micro-computerized tomography (micro-CT) technique to comprehensively analyze the size, location, morphology and distribution of the defects.

In the test set, SVM-based fatigue life prediction exhibits the highest accuracy. Regarding the defect information, the defect size significantly affects fatigue life, and the diameter of the circumscribed sphere of the largest defect has a critical effect on fatigue life.

This comprehensive approach provides valuable insights into the fatigue mechanism of structural materials in defective states, offering a novel perspective for better understanding the influence of defects on fatigue performance.

The purpose of this paper is to improve the lattice Boltzmann method’s ability to simulate a microflow under constant heat flux.

Develop the thermal lattice Boltzmann method based on double population of hydrodynamic and thermal distribution functions.

The buoyancy forces, caused by gravity, can change the hydrodynamic properties of the flow. As a result, the gravity term was included in the Boltzmann equation as an external force, and the equations were rewritten under new conditions.

To the best of the authors’ knowledge, the current study is the first attempt to investigate mixed-convection heat transfer in an inclined microchannel in a slip flow regime.

In this study, we aim to investigate the connectedness spillovers among major cryptocurrency markets. Moreover, we also explore to identify factors driving this connectedness, particularly focusing on the sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies under Twitter-based economic uncertainties and US economic policy uncertainty. Finally, we investigate the extent to which cryptocurrency markets serve as a safe haven, hedge, and diversifier from news-based uncertainties.

This study employs the connectedness approach following the combination of Ando et al. (2022) QVAR and Baruník and Krehlík's (2018) frequency connectedness methodologies into the framework proposed by Diebold and Yilmaz (2012, 2014). The data covered from November 10, 2017, to April 21, 2023, and the factors driving cryptocurrency connectedness spillovers are identified and examined. The sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies, concerning Twitter-based economic uncertainties and US economic policy uncertainty, are analyzed. We apply the Wavelet quantile correlation (WQC) method developed by Kumar and Padakandla (2022) to explore the effects of Twitter-based economic uncertainties and US economic policy uncertainty on Cryptocurrency market connectedness risk spillovers. Besides, we check and present the robustness of WQC findings with the multivariate stochastic volatility method.

Our findings indicate that Ethereum and Bitcoin are net shock transmitters at the center of the connectedness return network. Ethereum and Bitcoin hold the highest market capitalization and value in the cryptocurrency market, respectively. This suggests that return shocks originating from these two cryptocurrencies have the most significant impact on other cryptocurrencies. Tether and Monero are the net receivers of return shocks, while Cardano and XRP exhibit weak shock-transmitting characteristics through returns. In terms of return spillovers, Ethereum is the most effective, followed by Bitcoin and Stellar. Further analysis reveals that Twitter economic policy uncertainty and US economic policy uncertainty are effective drivers of short-term and total directional spillovers. These uncertainty indices exhibit positive coefficient signs in short-term and total directional spillovers, which turn predominantly negative in different magnitudes and frequency ranges in the long term. In addition, we also document that as the Total Connectedness Index (TCI) value increases, market risk also rises. Also, our empirical findings provide significant evidence of Twitter-based economic uncertainties and US economic policy uncertainty that affect short-term market risks. Hence, we state that risk-connectedness spillovers in cryptocurrency markets enclose permanent or temporary shock variations. Besides, findings of the low value of long-term spillovers suggest that risk shocks in cryptocurrency markets are not permanent, indicating long-term changes require careful monitoring and control over market dynamics.

In this study, we find evidence that Twitter's news-based uncertainty and US economic policy uncertainty have a significant effect on short-term market risk spillovers. Furthermore, we observe that high cryptocurrency market risk spillovers coincide with periods of events such as the US-China trade tensions in January 2018, the Brexit process in February 2019, and the COVID-19 outbreak in November 2019. Next, we observe a decline in cryptocurrency market risk spillovers after March 2020. The reason for this mitigation of market risk spillover may be that the Fed's quantitative easing signals have initiated a relaxation process in the markets. Because the Fed's signal to fight inflation in March 2022 also coincides with the period when risk spillover increased in crypto markets. Based on this, we present evidence that the FED's communication mechanism with the markets can potentially affect both short- and long-term expectations. In this context, we can say that our hypothesis that uncertainty about the news causes short-term risks to increase has been confirmed. Our findings may have investment policy implications for portfolio managers and investors generally in terms of reducing financial risks.

Our paper contributes to the literature by examining the interconnectedness among major cryptocurrencies and the drivers behind them, particularly focusing on the role of news-based economic uncertainties. More broadly, we calculate the utilization of advanced methodologies and the incorporation of real-time economic uncertainty data to enhance the originality and value of the research, which provides insights into the dynamics of cryptocurrency markets.

Owing to the limitations of traditional infectious disease dynamic systems in accurately encapsulating the nuances of China’s dynamic epidemic prevention policies and considering the varying sensitivity of local governments to the unfolding of public health emergencies (PHEs), this paper introduces a novel infectious disease dynamic system.

This system, rooted in the distinct characteristics of infectious diseases and nuanced prevention and control measures, leverages a learning model for enhanced precision. It intricately incorporates factors such as the infectivity in sealed and controlled areas and the role of asymptomatic patients, thereby refining the dynamics of isolation, sealing, control and the transition from asymptomatic to confirmed cases. Employing the Markov Chain Monte Carlo (MCMC) parameter estimation approach significantly augments the accuracy in pinpointing the valid parameters of disease spread. Empirical analysis was meticulously carried out, using data from the Shanghai epidemic from 1 Mar 2022 to 1 Jul 2022.

This analysis not only illuminates the profound impact of control efforts on the trajectory of the epidemic but also underscores the pivotal role of social distancing in curbing the rapid transmission of infectious diseases. Furthermore, it reveals that an accelerated detection rate during the swift spread and peak of the epidemic paradoxically leads to a surge in confirmed cases and a consequent strain on medical resources, thereby impeding the pace of medical intervention.

A stage-wise dissection of the Shanghai epidemic and comparative analyses against the evolution profiles in ASEAN countries elucidates the five stages of PHE risk evolution in alignment with the crisis lifecycle theory. These stages encompass hidden transmission, multi-point dissemination, multi-chain parallelism, rapid spread, fluctuation rebound and multi-community spread, each presenting unique challenges and dynamics in the control and management of the epidemic.