Purpose: This study aims to assess the relationships among quality dimensions in higher education (HE) and to determine the effect of each quality dimension on students' satisfaction.Design/methodology/approach: A questionnaire was developed and distributed to 270 final year students of an engineering faculty in an Indonesian state university. Confirmatory factor analysis (CFA) was used to test the validity of the conceptual model and structural equation modeling (SEM) was conducted to measure the relationships that lie within the model.Findings: The study reveals the relationships among quality dimensions in HE in the engineering faculty of this Indonesian state university. The results show that students' satisfaction was positively influenced by commitment of faculty management, the quality of course delivery, and the ease of giving feedback for quality improvement.Research limitations/implications: This study has limited scope because it was only conducted based on students' perceptions of an engineering faculty in one institution in Indonesia. However, the methods, models and instruments applied could serve as a basis for developing a measurement for evaluating quality in higher education more generally.Practical implications: The instrument in this study will be useful to help policy makers in engineering HE institutions to assess the level of quality dimensions in their institution and the effectiveness of their quality program based on students' satisfaction. Furthermore, the conceptual relationships model can give deeper understanding of the quality dimensions that should be prioritized by top management. Lastly, top management should pay attention to their commitment to quality, course activities, and customer feedback and improvement.Originality/value: This research promotes a methodology using SEM to assess relationships among quality dimensions in HE that can be helpful for top management when making decisions. The conceptual relationships model can also be easily reproduced to assess other engineering institutions' characteristics.

Waste of electrical and electronic equipment, better known as electronic waste (ewaste) shows an increasing trend from time to time. By 2016, the total amount of e-waste generated globally has reached 44.7 million metric tons (Mt), i.e. 24.4% of growth over the past five years. It should not be a problem if all of this waste is being collected and recycled properly. Statistics show that only around 20% of the e-waste generated in the world was recycled properly. The situation is even worse in the developing countries where the population has not yet covered by e-waste legislation, such as Indonesia. The lack of reliable ewaste data is the main reason as no statistics are available to show that e-waste in Indonesia is growing rapidly and will cause problems in the future. This study attempts to quantitatively estimate the e-waste in Indonesia by using the population balance model (PBM). PBM enhances the estimation results by involving all three data points, i.e. sales, stocks and lifespans in the calculation step. The e-waste estimated is for four most common e-products found in the e-waste stream in Indonesia. These four products are categorized into two groups based on their market's characteristics. Washing machines, refrigerators, and television represent part of saturated market products, while mobile phones lie in the unsaturated market category. The results show that the average growth rate of e-waste in Indonesia is 14.91% annually. The total amount of electronic waste generated in Indonesia is estimated to reach ±49,627,917 units (±487,416 ton) by 2028.

Purpose: This study aims to assess the factors that can affect the turnover intention of millennial talents working in the Indonesian manufacturing industry. Design/methodology/approach: This work analyzes 115 responses from millennial talents working in the Indonesian manufacturing industry on the issue of turnover intention. Data were collected through an online questionnaire. The collected data were then tested using partial least square-structural equation modeling. Findings: The findings suggest that competency development, work–life balance and meaningful work have a direct impact on the employee satisfaction of millennials in the Indonesian manufacturing industry. However, compensation and benefits have no significant effect on job satisfaction. Research limitations/implications: This study has limitations in data collection, concentrated merely in the Jakarta Greater Area. Hence, it would potentially influence the generalization of the results. Originality/value: This study reveals the potential lack of awareness within Indonesian manufacturing companies in providing a valuable compensation and benefits program for millennials.

While end-of-life mobile phones make up a growing fraction of the total volume of waste electrical and electronic equipment (WEEE) generated worldwide, their collection rate remains low. In order to address this problem, it is necessary to understand individual consumers’ disposal behaviors, especially in the context of developing countries. Accordingly, the aim of this study is to assess the determinants of disposal behaviors regarding waste mobile phones in a country lacking well-established systems. This study conceptualizes a model based on the theory of planned behavior and proposes five types of disposal behaviors: keeping, reselling, donating, recycling, and discarding. Models were tested using a survey administered to residents of Jakarta, Indonesia. Through use of structural equation modeling, this study shows that improving environmental awareness can stimulate subsequent responsible disposal behaviors. It also reveals that throwing waste mobile phones into mixed bins is common practice in Indonesia.

The growth of construction industries in Indonesia creates an increasing level of generated construction waste. The limited implementation of ISO 14001 in the Indonesian construction industry might indicate the limited implementation of waste management based on sustainability principles. Therefore, this study aims to explore the drivers and barriers to implementing integrated waste management in construction industries. The Content Validity Index (CVI) and Modified Kappa methods were utilized to validate the indicators from the literature review. A total of 18 driver factors and 21 barrier factors in six dimensions were assessed by seven experts. This study then employs the decision-making evaluation and laboratory-based analytical network process (DEMATEL-based ANP) to find the weight and relation between each indicator and dimension. The results show that environmental awareness is the most important factor that drives construction industries to implement waste management in their company. In contrast, a lack of education about waste management is the most significant factor that inhibits construction industries from implementing sound waste management systems.