Evaluating factors influencing customers' intention to eat Korean cuisine "Samgyeopsal" in the Philippines: A structural equation model forest classifier approach.

Samgyeopsal has become a widely popular cuisine in the Philippines since 2014. The rise of Samgyeopsal is evident worldwide as it is available in countries such as the United States, Northern, and Southern Asia. This study aimed to explore the intention to eat Samgyeopsal during the COVID-19 pandemic utilizing structural equation modeling and random forest classifier. With a total of 1014 responses collected online, the result showed that utilitarian and hedonic motivation, Korean influence, and attitude led to very high actual behavior in east Samgyeopsal in the Philippines. Moreover, subjective norm, perceived behavioral control, and intention led to significant results influencing intention to actual behavior. Lastly, the COVID-19 safety protocol showed the least significant result. This study is the first study that evaluated the intention of consumers to eat Samgyeopsal in the Philippines during the COVID-19 pandemic. The results of this study would be beneficial to Korean BBQ restaurateurs and the further development of their marketing strategies even in other countries. Finally, the model construct of this study can be extended and applied in evaluating the consumers' eating intention toward other varieties of food or cuisines worldwide.

Improving the sustainability of a reverse supply chain system under demand uncertainty by using postponement strategies.

In recent decades, issues of resource depletion and waste piling have grown at an alarming rate, which are happening in the cases of product wastes with significant residual values, such as e-waste. To address these issues, stakeholders have focused to develop a reverse supply chain (RSC) system that can sustain profitable takeback, reuse, and recycling operations in the long-term. Such a system requires efficiency in handling complex operations involving various players while being responsive to demand uncertainty and changes. One way in realizing these capabilities is by incorporating postponement concepts to the integrated RSC network, allowing the delay of operations susceptible to demand uncertainty. This study pioneers the formulation of a two-stage stochastic mixed-integer model of a multi-player RSC with speculation-postponement strategies. The sample average approximation method is used to solve and verify the proposed model that has an uncertain demand. Various speculation-postponement strategies, namely, disassembly, reconditioning, and reassembly strategies are developed to configure forecast and demand-driven RSC operations, including the purchasing, product takeback, production planning, inventory, and item speculation decisions. Numerical examples of the notebook computer RSC demonstrate that utilizing the right operation postponement can increase the network's flexibility, allowing better economic performances even under high demand uncertainty risks and stricter environmental regulations. In various cases, the RSC performs better with speculation-postponement strategies than without postponement strategy, demonstrating the proposed model's superiority. This study can provide insight to decision-makers to improve RSC sustainability through postponement. Moreover, the model is generic and can be applied to other products as well.