Supply chain (SC) innovation has become a competitive source for hotels to enhance performance in the turbulent business environment. Drawing on the resource orchestration (RO) and information sharing (IS) theories, we propose an integrated theoretical framework delineating how strategic information exchange (SIE) and RO capabilities and digital orientation of a hotel foster innovation and enhance the performance of its SC. Based on PLS-SEM analysis of data collected from 281 hotels in the United Arab Emirates, our findings indicate that RO and SIE capabilities of hotels influence the performance of SCs, whereas SC innovation mediates the said relationships. However, contrary to the expectations, digital orientation did not moderate the relationships between RO and SIE capabilities and SC innovation of hotels.

PurposeThis study provides a comprehensive analysis of the potential contagion of Bitcoin on financial markets and sheds light on the complex interplay between technological advancements, accounting regulatory and financial market stability.Design/methodology/approachThe study employs a multi-faceted approach to analyze the impact of BTC systemic risk, technological factors and regulatory variables on Asia–Pacific financial markets. Initially, a single-index model is used to estimate the systematic risk of BTC to financial markets. The study then uses ordinary least squares (OLS) to assess the potential impact of systemic risk, technological factors and regulatory variables on financial markets. To further control for time-varying factors common to all countries, a fixed effect (FE) panel data analysis is implemented. Additionally, a multinomial logistic regression model is utilized to evaluate the presence of contagion.FindingsResults indicate that Bitcoin's systemic risk to the Asia–Pacific financial markets is relatively weak. Furthermore, technological advancements and international accounting standard adoption appear to indirectly stabilize these markets. The degree of contagion is also found to be stronger in foreign currencies (FX) than in stock index (INDEX) markets.Research limitations/implicationsThis study has several limitations that should be considered when interpreting the study findings. First, the definition of financial contagion is not universally accepted, and the study results are based on the specific definition and methodology. Second, the matching of daily financial market and BTC data with annual technological and regulatory variable data may have limited the strength of the study findings. However, the authors’ use of both parametric and nonparametric methods provides insights that may inspire further research into cryptocurrency markets and financial contagions.Practical implicationsBased on the authors analysis, they suggest that financial market regulators prioritize the development and adoption of new technologies and international accounting standard practices, rather than focusing solely on the potential risks associated with cryptocurrencies. While a cryptocurrency crash could harm individual investors, it is unlikely to pose a significant threat to the overall financial system.Originality/valueTo the best of the authors knowledge, they have not found an asset pricing approach to assess a possible contagion. The authors have developed a new method to evaluate whether there is a contagion from BTC to financial markets. A simple but intuitive asset pricing method to evaluate a systematic risk from a factor is a single index model. The single index model has been extensively used in stock markets but has not been used to evaluate the systemic risk potentials of cryptocurrencies. The authors followed Morck et al. (2000) and Durnev et al. (2004) to assess whether there is a systemic risk from BTC to financial markets. If the BTC possesses a systematic risk, the explanatory power of the BTC index model should be high. Therefore, the first implied contribution is to re-evaluate the findings from Aslanidis et al. (2019), Dahir et al. (2019) and Handika et al. (2019), using a different method.

Although tsunamis occur less frequently compared to some other natural disasters, they can be extremely devastating in the nearshore environment if they occur. An earthquake of magnitude 6.9 Mw occurred on 30 October 2020 at 12:51 p.m. UTC (2:51 p.m. GMT+03:00) and its epicenter was approximately 23 km south of İzmir province of Turkey, off the Greek island of Samos. The tsunami event triggered by this earthquake is known as the 30 October 2020 İzmir-Samos (Aegean) tsunami, and in this paper, we study the hydrodynamics of this tsunami using some of these artificial intelligence (AI) techniques applied to observational data. More specifically, we use the tsunami time series acquired from the UNESCO data portal at different stations of Bodrum, Syros, Kos, and Kos Marina. Then, we investigate the usage and shortcomings of the Long Short Term Memory (LSTM) DL technique for the prediction of the tsunami time series and its Fourier spectra. More specifically we study the predictability of the offshore water surface elevation dynamics, their spectral frequency and amplitude features, possible prediction success and enhancement of the accurate early prediction time scales. The uses and applicability of our findings and possible research directions are also discussed.

The visual-motor illusion (VMI) induces a kinesthetic illusion by watching one’s physically-moving video while the body is at rest. It remains unclear whether the early stages (immediately to one hour later) of motor learning are promoted by VMI. This study investigated whether VMI changes the early stages of motor learning in healthy individuals. Thirty-six participants were randomly assigned to two groups: the VMI or action observation condition. Each condition was performed with the left hand for 20 min. The VMI condition induced a kinesthetic illusion by watching one’s ball-rotation task video. The action observation condition involved watching the same video as the VMI condition but did not induce a kinesthetic illusion. The ball-rotation task and brain activity during the task were measured pre, post1 (immediately), and post2 (after 1 h) in both conditions, and brain activity was measured using functional near-infrared spectroscopy. The rate of the ball-rotation task improved significantly at post1 and post2 in the VMI condition than in the action observation condition. VMI condition lowers left dorsolateral prefrontal cortex and right premotor area activity from post1 to pre compared to the action observation condition. In conclusion, VMI effectively aids early stages of motor learning in healthy individuals.

In an era of one-off funding, entrepreneurs and small businesses struggle to find sustainable funding sources. A novel solution – the crowd patronage model, has emerged in recent years. This model incorporates elements from traditional crowdfunding but is systematically different due to the lack of a fixed funding goal or duration. To capitalise on the long-term nature of crowd patronage, this article explores whether factors that have an impact in traditional crowdfunding will have a similar impact within an extended duration beyond what traditional crowdfunding typically offers. We posit that incentivising mechanisms will have a long-term impact on crowd patronage, whereas project characteristic measures will exhibit short-term effects. Using data from 3229 crowd patronage projects, we find that our results largely support our hypotheses. Given our findings, we propose using project characteristics to attract contributors before shifting focus to incentivising mechanisms as the project matures in order to maximise crowdfunding returns.

Soccer players with chronic ankle instability (CAI) may stabilize their supporting leg by the proximal joint to compensate for the ankle instability during kicking motion. This study aimed to investigate the characteristics of leg and joint stiffness of the supporting leg during side-foot kicking in soccer players with CAI. Twenty-four male collegiate-level soccer players with and without CAI participated in this study. The kinematic and kinetic data were obtained using a three-dimensional motion analysis system. Leg stiffness and joint (hip, knee, and ankle) stiffness in the sagittal and frontal planes were calculated and analyzed. The results clarified that soccer players with CAI (0.106 ± 0.053 Nm/°) had greater knee stiffness in knee adduction during the kicking cycle compared to those without CAI (0.066 ± 0.030 Nm/°; p = 0.046), whereas no characteristic differences were observed in knee stiffness in knee flexion and hip and ankle stiffness (p > 0.05). Knee stiffness is believed to occur to compensate for ankle joint instability in the supporting leg. Therefore, adjusting knee stiffness to accommodate ankle joint instability is crucial for maintaining kicking performance. Based on results of this study, it may be important to consider training and exercises focused on joint coordination to improve knee stiffness in soccer players with CAI.

This article explores the Indonesian Constitutional Court’s views through its decisions in shaping the practice of outsourcing in Indonesia based on the 1945 Constitution. The study was first conducted by analyzing Decision No. 012/PUU-I/2003 and later Decision No. 27/PUU-IX/2011. The novelty of this research is evident from the involvement of perspective in analyzing the outsourcing scheme in Indonesia as newly regulated in several laws, namely Job Creation Law and the Government Regulation in lieu of Job Creation Law. Using the normative legal research method, the authors used statutory, case, and conceptual approaches. Based on the research conducted, the authors found that the Constitutional Court aims to uphold the balance of companies’ efficiency and outsourced workers’ rights protection. The findings are evidenced by the Constitutional Court’s stance in a decision that implies that outsourcing is constitutional to enhance the State’s economy while simultaneously protecting the outsourced workers’ rights to ensure the fulfillment of their constitutional rights by setting two-fold models of protection. Therefore, even if new outsourcing scheme regulations are issued, the criterion of legality in future judicial reviews must involve determining whether such balance has been sufficiently met.

In a first study, this paper argues and demonstrates that spiking neural networks (SNN) can be successfully used for predictive and explainable modelling of multimodal streaming data. The paper proposes a new method, where both time series and on-line news are integrated as numerical streaming data in the same time domain and then used to train incrementally a SNN model. The connectivity and the spiking activity of the SNN are then analyzed through clustering and dynamic graph extraction to reveal on-line interaction between all input variables in regard to the predicted one. The paper answers the main research question of how to understand the dynamic interaction of time series and on-line news through their integrative modelling. It offers a new method to evaluate the efficiency of using on-line news on the predictive modelling of time series. Results on financial stock time series and online news are presented. In contrast to traditional machine learning techniques, the method reveals the dynamic interaction between stock variables and news and their dynamic impact on model accuracy when compared to models that do not use news information. Along with the used financial data, the method is applicable to a wide range of other multimodal time series and news data, such as economic, medical, environmental and social. The proposed method, being based on SNN, promotes the use of massively parallel and low energy neuromorphic hardware for multivariate on-line data modelling.