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Based on the stimuli-organism-response model and relationship marketing theory, the effect of different dimensions of Servicescape (Ambience, Cleanliness, Functionality, Spatial Layout, Employee Service Quality) on Customer Satisfaction and Behavioral Intention in hospitals during the COVID-19 pandemic are considered.

The study takes a quantitative approach, applying structural equation model using partial least square structural equation modeling to test the hypotheses. A total of 360 responses were collected using questionnaires distributed to different individuals who visited private hospitals in the past two months in India.

Contradicting previous research, this study found that among servicescape dimensions, employee service quality had the maximum influence on customer satisfaction and cleanliness does not have any significant impact on customer satisfaction as hypothesized. Mediation results show that customer satisfaction has a partial mediation effect for all servicescape dimensions except ambience, as both direct and indirect effects are significant. Importance-performance map analysis was performed on the responses collected, and it was found that employee service quality is the most important dimension affecting servicescape, followed by functionality and spatial layout. Thus, health-care institutions should focus on these factors to keep their customers satisfied.

Past studies have focused on the roles of servicescape and customer satisfaction separately. The authors have extended the literature by examining the combined effects of both servicescape and customer satisfaction. The findings from the study, therefore, help in developing a deeper understanding of the literature on the behavior intention relationship in the context of health care, as well as in service marketing.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.