Carbonated soft drinks (CSDs) have long been a mainstay of the beverage business but changing consumer tastes and rising health awareness have necessitated a thorough study of the variables impacting consumer choices. This study intends to explore the complex web of customer preferences, purchasing behaviour, and perceptions related to carbonated soft drinks. This research analyses how numerous variables, including gender, affect these preferences and choices via careful examination. The purpose of thepresent research is to determine the perception of consumer influencing customer choice preferences for the consumption of carbonated soft drinks, influence of gender and the role of advertisement in finalizing the choice. It would be helpful to do further research to better understand how these highlighted variables affect purchasing choices, especially gender-based variances. The important influence of gender on consumer behaviour has been acknowledged. For this study, a structured questionnaire was distributed through online social media to individuals of 12–45 years of age from the period of April–May 2023. For analysis of the data collected, SPSS 22.0 was used. The study has confirmed that consumption of Coca-Cola is higher than any other soft drink in almost the entire country. The factors like youthfulness, tradition, status symbol and level of carbonation have different influences on the buying behavior of male and female consumers.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

This research aims to investigate the factors shaping the investment choices of individuals in Saudi Arabia concerning cryptocurrencies, particularly focusing on the influence of the Fear of Missing Out (FOMO) psychological phenomenon. This study employs a mixed-methods approach to comprehend the factors influencing Saudi investors' decisions in the cryptocurrency realm. Quantitative surveys are conducted to gauge perceptions of risk, return, regulatory factors, and social influence. Additionally, qualitative interviews delve into the nuanced interplay of these elements and the impact of FOMO on decision-making. Integrating the Theory of Planned Behavior and Behavioral Finance theories, this research offers a holistic understanding of cryptocurrency investment determinants. The combined quantitative and qualitative methods provide a comprehensive view, enabling an in-depth analysis of the subject matter. The study reveals that Saudi Arabian investors' decisions regarding cryptocurrencies are significantly influenced by multiple factors, including perceived risk, potential return, regulatory environment, and social dynamics. FOMO emerges as a crucial psychological factor, interacting with these influences and driving decision-making. This research underscores the intricate interplay between these factors and FOMO, shedding light on the dynamics of cryptocurrency investment choices in the Saudi Arabian market. The findings hold implications for policymakers, financial institutions, and investors seeking deeper insights into this evolving landscape. Drawing from the Theory of Planned Behavior and Behavioral Finance, it examines perceived risk, return, regulatory factors, and social influence in influencing cryptocurrency investment choices among Saudi investors, focusing on the influence of Fear of Missing Out (FOMO).  The research outcome provides insights for policymakers, financial institutions, and investors seeking to understand cryptocurrency investment dynamics in Saudi Arabia.

The business life cycle is examined through a comprehensive literature review in this academic study. Our initial approach involves searching for relevant articles on firm life cycle and strategy using the Web of Science and Scopus databases. We conduct bibliometric analyses to identify key contributors and recurring keywords. Subsequently, we select twenty-seven research papers to explore the Theory Development, Characteristics, Context, and Methodology (TCCM) framework for firm life cycle and strategy. Our analysis summarizes corresponding business strategies for each stage, including the use of Initial Management Control Systems (MCS) in the introduction phase. As companies grow, a high inventory-to-sales ratio may hinder effectiveness, but it proves beneficial in the growth and revival stages. Mature companies excel in green process innovation and engage more in Corporate Social Responsibility (CSR) activities. In the decline stage, firms use cost efficiencies, asset retrenchment, and core activity focus for recovery, signaling commitment to a successful turnaround. However, there is a research gap in exploring appropriate global strategies for various life cycle stages, providing an opportunity for additional articles to thoroughly investigate this relationship and assess multinational enterprises’ success trajectories throughout their life cycles.

This article using thematic and content analysis investigated the contribution of innovation in achieving sustainable economic development. The objective of the bibliometric research was to assess the literature on this subject it identified research trends, ideas, and authors who contributed to this area so that future research and policy directions could be suggested. The data was derived from the Scopus database and was extracted between January 2020 and February 2024 by applying inclusion and exclusion criteria. The Scopus database search yielded 66 articles, published between 2020 and February 2024. Scopus analytics and Microsoft Excel were used for descriptive analysis and VOS Viewer software was used for network visualization of keywords. The descriptive analysis showed the trajectory of research, the prolific authors, their publication outlets, authors affiliation, and county of origin of the documents. The prolific visualization showed five clusters: red, green, blue, purple, and yellow. The main clusters are economic development, alternative energy, sustainable development, and innovation. This research showed where consideration should be given to drive sustainability and sustainable economic development. This research outcome will assist government agencies, corporations, and non-profit organizations in planning appropriate action and policies to support innovative and renewable energy initiatives so that participation in those fields could enhance the opportunity to achieve sustainable economic development.