The transition to a Circular Economy (CE) depends on several factors, such as the implementation of circular business models. Although circular business models were developed, there are socio-cultural distinctions that need to be considered. One such distinction is the sachet culture of the Philippines. The country's consumers use single-use sachets for daily needs, including body care and hygiene. As the local sachet culture lead to significant waste emission to the ocean, designing circular business model for switching to a circular product (e.g., refillable shampoo bottle) is a key measure towards sustainable societies. The design of this business model, however, will need to consider consumer preference. In this sense, modeling consumer preference provides insights on how enterprises can design consumer-centric circular business models. The present study modeled consumer preference between single-use plastic shampoo sachet and refillable shampoo bottles through binary logistic regression, considering responses from 457 consumers of Metro Manila, Philippines. The independent variables used were the socioeconomic and demographic characteristics, product channel, and usage. The dependent variable was consumer preference between using sachet or refillable bottles shampoo. The model indicated a good fit with a McFadden R2 of 0.255. The model identified age, gender, education, environmental awareness, budget, daily use, and retailer as statistically significant independent variables. The variable 'environmental awareness' attained the highest significance for socioeconomic and demographic characteristics. Meanwhile, the variable 'retailer' attained the highest significance for product channel and usage. The two variables had the highest influence on consumer preference. This study recommends enterprises to focus on utilizing malls as the product channel and on ecolabelling of products. Future research works may use the model in integrating consumer preference on circular economy scenarios. Enterprises may also use the model in designing circular business models suitable for the target market.

Engineering students face challenges of on-time successful degree completion. Universities incorporate academic advising as a solution to these challenges. Decision support systems enhance the effectivity of academic advising. Combined with machine learning, it can predict future student performance providing useful information. Compared to common ‘black box’ models, linguistic rules provide better interpretation and insight discovery. However, existing models often use positive predictors of academic excellence, with limited consideration on factors of negative effect. This work, therefore, generates linguistic rules for academic advising based on three predictors using rough set theory (RST) and then compared with artificial neural network (ANN) for benchmarking. Forty-eight samples of mechanical engineering students taking up machine design were considered. RST attained accuracy of 72.92% while ANN attained 66.66%. The model generated 13 linguistic rules, having reflected unrealized insights. The findings from this study may be utilized by academic advisers for pattern recognition, in identifying ‘at-risk’ students.

In this paper, the analysis of metal and plastic straw choice of individuals residing in Metro Manila is conducted using discrete choice modelling. The mathematical model was correlated with the key factors determined in the study. Using the RStudio software as tool, a discrete choice model is generated. The key factors for straw choice of plastic and metal were hygiene, comfortability, trend, habituation, value, and willingness to buy their choice of straw.

Attaining high retention rates among engineering institutions is a predominant is-sue. A significant portion of engineering students face challenges of retention. Academic advising was implemented to resolve the issue. Decision support sys-tems were developed to support the endeavor. Machine learning have been inte-grated among such systems in predicting student performance accurately. Most works, however, rely on a black box model approach. Rule induction generates simpler if-then rules, exhibiting clearer understanding. As most research works considered attributes for positive academic performance, there is the need to con-sider ‘negative’ attributes. ‘Negative’ attributes are critical indicators to possibility of failure. This work applied rule induction techniques for course grade predic-tion using ‘negative’ attributes. The dataset is the academic performance of 48 mechanical engineering students taking a machine design course. Students’ at-tributes on workload, course repetition, and incurred absences are the predictors. This work implemented two rule induction techniques, rough set theory (RST) and adaptive neuro fuzzy inference system (FIS). Both models attained a classifi-cation accuracy of 70.83% with better performance for course grades of ‘Pass’ and ‘High’. RST generated 16 crisp rules while ANFIS generated 27 fuzzy rules, yielding significant insights. Results of this study can be used for comparative analysis of student traits between institutions. The illustrated framework can be used in formulating linguistic rules of other institutions.