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This paper presents an in-depth analysis of a Quantum-inspired Multi-objective Optimization Algorithm (QMOA) applied to a unique problem: maximizing trading profits while minimizing energy costs. Previous investigations have explored the profitability of Bitcoin, yet our research delves into its relationship with energy costs. Regarding the trade-offs, the Pareto analysis reveals that trading profit and energy cost do not strongly inversely correlate. The range of outcomes shows a relatively uniform trading profit (from 1.302,85 to 1.310,22$), but a broader variation in energy costs (from 1.141,66 to 5.657,94$). While the trading profit remains stable, there is a wide array of options for minimizing energy cost, which is influenced by various constraints and market conditions. Solutions tend to cluster more in areas of higher energy costs. However, the variability in energy costs offers Bitcoin miners choices, allowing them to tailor strategies, whether that involves prioritizing energy efficiency, profit maximization or striking a balance.

This paper presents an empirical analysis of e-commerce data obtained through Google Analytics (GA) from two small businesses’ perspectives: an IT components company and a tourism agency website located within the same county in Romania. The objective of our study is to examine the enduring effects of the COVID-19 pandemic and seasonal variations over the last four years. The data collection spanned from January 2019, predating the onset of the COVID-19 pandemic, until mid-February 2023. To facilitate our analysis, we categorize the GA metrics into groups that encompassed website performance, site accessibility, and user behavior for the IT company. As for the tourism agency, we focus on website accessibility, user behavior, and marketing campaigns. Our goal is to empirically group or associate GA metrics according to their intrinsic meaning and check if each group reflects a certain latent concept (such as user behavior or site accessibility). Furthermore, our study aims to formulate and test five hypotheses regarding the immediate and long-lasting impact of the COVID-19 pandemic on the operations of small businesses. Our contribution consists of formulating and verifying the five hypotheses by providing descriptive data from the results of the Pearson correlation test, empirically grouping the GA metrics and verifying whether they reflect certain latent factors or topics, interpreting the results from the application of the ANOVA technique and Scarpello’s adaptation of the one factor test, respectively.