Purpose: This paper aims to discuss the necessity of building a knowledge management system in today’s knowledge economy by focusing on human capital management, choice of tools, and how knowledge processes affect an organization’s strategic capabilities. Design/methodology/approach: Analyzing several theoretical models in the area of knowledge management and explaining how motivation and choice of tools can improve utilization of knowledge management system. Findings: Building a knowledge management system is recommended for transitioning into data analytics to capture business trends in the knowledge economy. Motivation and choice of tools are important to determine the utilization of a knowledge management system. Practical implications: This paper presents practical issues and provides insights into building and using a knowledge management system in today’s organizations. Originality/value: This paper justifies the need of knowledge management system and presents the issues and solutions to integrate knowledge management system into an organization.

Pursuing sustainability in the production inventory model has motivated researchers to reconsider carbon emission costs. In this study, we develop an economic production quantity (EPQ) inventory model to consider greenhouse gas and wastewater emission costs. This study aims to optimize the production quantity, the total costs per production cycle, and the waste emissions. To validate and test the model, data from a pulp and paper mill industry was used. The effectiveness of the model was determined by comparing the costs generated under three (3) scenarios: (1) an EPQ model that considers the costs of greenhouse gas (GHG) emissions; (2) an EPQ model considering shortages; and (3) a multiproduct EPQ model considering shortages. Our proposed sustainable EPQ models can provide decision-makers with insights to visualize how certain factors affect the manufacturing system. Future research can be conducted to consider the product life cycle as well as deteriorating items.

Sustainable consumption has become a global priority, yet the factors that encourage people to adopt environmentally friendly purchasing behavior differ across cultures and technologies. This study explores how environmental knowledge, environmental attitude, and the perception of AI-driven personalization influence green purchasing intention and actual purchasing behavior among young consumers in Java, Indonesia. A survey of 517 university students was conducted, and the relationships among these factors were analyzed using structural equation modeling. The findings reveal that environmental knowledge strongly shapes environmental attitudes, which in turn enhance the intention and behavior to purchase green products. Perception of AI-driven personalization also strengthens green purchasing intention, although its direct effect on behavior is limited. These results suggest that digital platforms and marketers can promote sustainable consumption by combining environmental education with transparent and value-based AI personalization. The study contributes to understanding how psychological readiness and technological engagement together encourage greener consumption among youth in emerging economies.

Generative AI offers promising potential to promote environmental sustainability through personalized recommendations that influence individual behavior. This study examines the factors influencing the adoption and actual use of generative AI recommendations for environmental sustainability among Gen Z users in the Philippines by integrating the Theory of Planned Behavior (TPB) and the Technology–Environmental, Economic, and Social Sustainability Theory (T-EESST) with key generative AI attributes, together with trust and perceived risk. Survey data were collected from 531 Gen Z users in higher education institutions in the National Capital Region (NCR), Philippines, and analyzed using a hybrid SEM and ANN approach. Results from SEM indicate that key AI attributes, namely perceived anthropomorphism, perceived intelligence, and perceived animacy, significantly influenced users’ attitude towards generative AI recommendations. Attitude, perceived behavioral control, and trust emerged as significant predictors of behavioral intention, which have an eventual positive relation to actual use and environmental sustainability outcomes. In contrast, subjective norms and perceived risk did not significantly affect behavioral intention, which may suggest that Gen Z users’ engagement with generative AI for environmental sustainability is primarily driven by internal evaluations, perceived capability, and trust rather than social pressure or risk concerns. Complementing these findings, the ANN analysis identified perceived behavioral control, attitude, and trust as the most important factors, reinforcing the robustness of the SEM results. Overall, this study integrates existing sustainability and technology-adoption literature by demonstrating how generative AI recommendations can support environmental sustainability among Gen Z users by combining behavioral theory, sustainability theory, and AI attributes through a hybrid SEM–ANN approach in the context of a developing country.

Artificial intelligence (AI) has reshaped various aspects of human lives, particularly through its capabilities to address complex sustainability challenges. Despite the rapid expansion of AI applications, their contribution to cleaner production and sustainable development remains underexplored, especially in developing nations. In Southeast Asia (SEA), where AI adoption has grown substantially across environmental, economic, and social dimensions, research that examines its role in cleaner production outcomes remains fragmented. In view of this gap, this study conducts a systematic literature review (SLR) of AI applications related to cleaner production and sustainable development by examining relevant themes, application areas, and sustainability dimensions addressed by AI, while evaluating the maturity of AI methodologies, alignment with cleaner production outcomes, and integration with circular economy and resource efficiency goals. Moreover, it investigates the barriers and challenges that constrain AI application and offers future research directions to advance AI deployment for cleaner production and sustainable development across SEA countries.

Financial literacy has grown significantly in recent years, leading to increased accountability among individuals in managing their spending, investments, and financial planning. To effectively develop new technologies, how potential users respond to them beforehand must be understood. In this study, we developed a model based on the unified theory of acceptance and use of technology (UTAUT), encompassing five primary components: performance expectations, social influence, facilitating conditions, satisfaction, and continuation intentions. A total of 250 participants’ data were analyzed using an artificial neural network (ANN) to evaluate the integrated model. The results showed variables affecting lower-middle-class users of a digital financial literacy application’s acceptance of new technology.