Recent times have seen significant advancements in AI and NLP technologies, poised to revolutionize logistical decision-making across industries. This study investigates integrating ChatGPT, an advanced AI language model, into strategic, tactical, and operational logistics. Examining its applicability, benefits, and limitations, the study delves into ChatGPT’s capacity for strategic logistics planning, facilitating nuanced decision-making through natural language interactions. At the tactical level, it explores ChatGPT’s role in optimizing route planning and enhancing real-time decision support. The operational aspect scrutinizes ChatGPT’s capabilities in micro-level logistics and emergency response. Ethical implications, encompassing data security and human-AI trust dynamics, are also analyzed. This report furnishes valuable insights for the logistics sector, emphasizing AI’s potential in reshaping decision-making while underscoring the necessity for foresight, evaluation, and ethical considerations in AI integration. In this publication, it is assumed that ChatGPT is not entirely reliable for decision-making in the logistics field: at the strategic level, it can be effectively used for “brainstorming” in preparing decisions, but at the tactical and operational level, the depth of the knowledge is not sufficient to make appropriate decisions. Therefore, the answers provided by ChatGPT to the defined logistic tasks are compared with real logistic solutions. The article highlights ChatGPT’s effectiveness at different levels of logistics and clarifies its potential and limitations in the logistics field.

This study investigated the impact of social media on purchasing decision-making using data from a questionnaire survey of 257 randomly sampled students from the College of Business at Imam Muhammad Ibn Saud Islamic University. The study items were selected from the study community through a random sample, where several (257) students were surveyed. To achieve its objectives, the study follows the descriptive analytical approach in addressing its topic. The questionnaire was adopted as a tool for collecting data. The questionnaire collected data on the independent variable social media—and the dimensions of the dependent variables representing the stages of purchasing decision-making: Feeling the need for the advertised goods, collecting information about alternatives, evaluating available options, buying decisions, and post-purchase evaluation of the purchase decision. Then, the data were analyzed based on regression analysis using SPSS and AMOS. The important findings are summarized below: Social media use is directly related to feeling the need for and searching for information on advertised goods. Social communication and the evaluation of alternatives to advertised goods, in addition to the existence of a moral effect and a direct correlation between social media use and making the purchasing decision for advertised goods. Providing honest, sufficient, and accurate information via social media to the buyer can help them make the purchasing decision.

This study aims to identify key strategies and tactics necessary to effectively implement national social security in a democratic Indonesia. Indonesia established the Law on the National Social Security System in 2004. However, the national social security programs did not commence until 2014. The national social security implementation has faced significant obstacles. These challenges include recurring delays, legal disputes, appeals, judicial reviews, and deviations from the original policy objectives, all threatening the long-term viability of the national social security programs. This article applies a qualitative approach by critically analyzing regulations, government reports, and publicly available data and observing open public meetings and hearings concerning implementing national social security programs. Our findings indicate that implementing national social security policies in a democratic Indonesia depends on effectively managing the dynamic processes involved in policy formulation and adoption. We propose a risk-based decision-making model to assist policymakers in mitigating policy-related risks and enhance the effectiveness of future policy agendas in social security.

This study explored the relationships between college students’ indecisiveness, anxiety, and career decision-making ability. Using the convenience sampling method, 1072 college students at a college in Hunan Province, China completed a questionnaire online that included the Indecisiveness Scale, Career Exploration and Decision Self-Efficacy Scale, and Generalized Anxiety Scale-7. Participants reported their gender and place of origin (rural or city). They indicated whether they were an only child, were left behind, and liked the major they were studying. The t-test was used to identify differences in indecisiveness, career decision-making ability, and anxiety according to demographic characteristics. Correlations were calculated between the main variables of interest. Regression analysis was conducted to test the mediation model. Participants who liked their major were significantly more indecisive than those who did not like their major. Career decision-making ability was significantly higher among men than women, participants from urban areas than those from rural areas, participants who were an only child than those with siblings, and among non-left-behind participants than those who were left behind. Anxiety was significantly lower in participants who liked their major than those who did not like their major. In addition, anxiety partially mediated the relationship between indecisiveness and career decision-making ability. College students’ indecisiveness and career decision-making ability are affected by sociocultural background, gender, family background, and career interest. Anxiety partially mediates the relationship between indecisiveness and career decision-making ability. Implications of the findings for counseling college students are discussed.

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.