Employee engagement has become a strategic concern in human resource management, particularly among Generation Z employees who are expected to dominate the future workforce. However, maintaining high engagement remains challenging due to increasing job stress and suboptimal psychosocial work environment. This study examines the effects of the non physical work environment and job stress on employee engagement among Generation Z employees in Bandung, Indonesia. A Quantitative explanatory approach was employed using survey data collected from 100 Generation Z employees selected through purposive sampling. Data were analyzed using descriptive statistics, validity and reliability testing, classical assumption test and multiple linear regression analysis to assess both partial and simultaneous effect among variables. The findings indicate that employee engagement was at a moderately high level, while the non-physical work environment was perceived as moderately favorable and job stress was relatively high. Multiple regression analysis revealed that the non-physical work environment had a positive and significant effect on employee engagement, whereas job stress had a negative and significant effect. The non-physical work environment explained 28.9% of the variance in employee engagement, job stress explained 17.6%, and both variables simultaneously accounted for 47.95% of the variance (R² = 0.4795, p < 0.001). These findings support the Job Demands–Resources (JD-R) Theory, suggesting that employee engagement is shaped by the balance between job resources and job demands. The study concludes that supportive psychosocial workplace conditions and effective stress management are essential for enhancing employee engagement, employee retention and long term organizational performance among Generation Z employees.
Dynamic pricing in ride-sharing platforms must balance revenue generation with stable pricing decisions under changing demand and supply. This study aims to develop and evaluate a reinforcement learning-based dynamic pricing policy that maximizes expected revenue while reducing abrupt policy-level price adjustments. A stochastic contextual environment was constructed from 1,000 historical ride records and evaluated using a leakage-safe 70/15/15 train-validation-test split. The agent was trained with Proximal Policy Optimization (PPO) using five discrete price adjustments from -10% to +10%. Expected revenue was combined with a multiplier-based stability penalty, where stability was measured from changes in the price multiplier rather than nominal price variation across heterogeneous rides. Across 30 paired test episodes, the PPO policy achieved a cumulative reward of 103,316.25 +/- 3,243.99 and expected revenue of 103,449.18 +/- 3,241.27, significantly exceeding static pricing (p < 0.001). Relative to rule-based surge pricing, PPO produced statistically indistinguishable cumulative reward (p = 0.808) while reducing multiplier volatility by 24.83%, mean absolute multiplier change by 24.21%, and action switch rate by 10.97% (all p < 0.001). These results indicate that PPO can preserve near-surge revenue while producing smoother dynamic pricing decisions within the simulated environment.
Financial systems are highly nonlinear and often influenced by investment cycles, market fluctuations, and external financing activities, which can generate complex dynamic behaviors. This paper proposes a new three-dimensional chaotic financial firm model by extending the classical Bouali financial system through the inclusion of a cyclic reinvestment perturbation represented by a sinusoidal nonlinear term. The proposed modification aims to capture state-dependent nonlinear reinvestment feedback in reinvestment decisions caused by changing economic conditions and business cycles. The dynamical characteristics of the model are investigated using equilibrium analysis, local stability theory, Lyapunov exponents, the Kaplan–Yorke dimension, bifurcation analysis, and multistability analysis. Numerical results show that the system possesses three equilibrium points, all of which are unstable under the selected parameter setting. The system exhibits chaotic dynamics confirmed by a positive maximum Lyapunov exponent and a fractal attractor characterized by a Kaplan–Yorke dimension greater than two. Comparative results indicate that the new model demonstrates richer nonlinear dynamical behavior than existing financial firm systems reported in the literature. Furthermore, bifurcation and Lyapunov spectrum analyses disclose multiple transitions between periodic and chaotic states as system parameters vary, while multistability analysis reveals the coexistence of symmetric periodic and chaotic attractors under identical parameter values but different initial conditions. Finally, total amplitude control and offset boosting techniques are implemented to regulate attractor size and position while preserving the underlying chaotic behavior. These findings demonstrate that cyclic reinvestment perturbations significantly enrich the nonlinear dynamics and symmetry properties of financial firm systems, providing a useful framework for studying complex financial behaviors and chaos control.
The rapid development of digitization has driven a fundamental shift in the travel industry, particularly through the emergence of shared economy platforms like Airbnb. Thus, the requirement to have a prediction model for travel industry is really crucial point and will give huge benefit to travel industry to solve their problem to finding host to rent their building for the industry and guest to rent for property rented from the platform. From this study we have several prediction models which developed from 10 machine learning algorithm. Each model has a distinct of its own accuracy f1 score and ROC AUC which each score would reveal how good is each model to be utilized for booking likelihood prediction. Some Algorithm like XGboost, Random Forest, Logistic Regression, SVM has huge of accuracy for each score to extend from 90% accuracy after we did a hyperparameter tunning to boost each model’s performances. However, these three score (accuracy, f1 score, and roc auc) aren’t sufficient to make the model work efficient and to be reliable to be utilized for our prediction model. Hence, more analytical methods are required to make sure our models are perform well for our aim to create a reliable prediction model and less bias on output we desired. In this study we shall commit curve learning analysist to make our models surely become a more reliable models which give numerous benefits to model accuracy and which many studies are still overturned the methods that very unfortunate. We shall dismantle our discovery and reveal which machine learning algorithm, optimization technique and how to analyst learning curve for each model on its own chapter hopefully our research is useful for everyone to gain new knowledge to developing prediction model using machine learning algorithm.
The increasing digitisation of stock markets and the growing diversity of financial data sources have intensified the need for accurate, robust, and risk-aware stock market forecasting. This systematic literature review synthesises recent evidence to examine the effectiveness of forecasting methods under different data and market conditions, the characteristics of commonly used benchmark datasets, the contribution of preprocessing strategies, and the evaluation and validation practices applied in stock market forecasting. Following the PRISMA framework, 71 peer-reviewed studies retrieved from the Scopus database were systematically screened, classified, and analysed. The evidence mapping shows that sequence-based deep learning models, including LSTM, GRU, and CNN–LSTM, represent the largest methodological group at approximately 41%, followed by transformer- and attention-based approaches at around 16%. Volatility-oriented econometric and classical statistical models account for approximately 18% and 14%, respectively, while probabilistic and quantile-based approaches remain limited. The findings indicate that forecasting performance is strongly context-dependent: classical models remain effective for relatively stationary univariate series, volatility-oriented models are particularly relevant when clustering and spillover effects are present, and deep learning and transformer-based approaches are more suitable for multivariate, nonlinear, and feature-rich settings. Overall, the review highlights the need for greater integration of uncertainty-aware evaluation, regime-sensitive validation, and risk-oriented forecasting frameworks.
