Published Scholarly Output

Recent years have seen an increase in online purchasing, particularly during the global pandemic lockdown. This study aims to gain a deeper understanding of the factors influencing the adoption of augmented reality in online purchasing applications and its effect on purchase intent. Specifically, we investigate the influence of virtual presence, devices, and enjoyment on the purchase intentions of consumers. In total, 103 respondents who made purchases on the e-commerce and resided in Jakarta have completed the survey. The data was subsequently analyzed using Structural Equation Modeling - Smart PLS (PLS-SEM). Both virtual presence and devices utilized in the online purchasing experience have a positive effect on the level of enjoyment felt by users, according to the findings of this study. Despite this positive influence on enjoyment, we discovered that enjoyment has no direct effect on purchase intention. In addition, we examined whether enjoyment functions as a moderator between virtual presence, devices, and purchase intention. Our findings indicate that enjoyment does not mediate the relationship between virtual presence and devices and purchase intent. Similar to previous research, our findings indicate that enjoyment has no direct or indirect effect on the relationship between virtual presence, devices, and purchase intent. This study illuminates the factors that influence the adoption of augmented reality in online purchasing applications and their implications for purchase intent. Contributing to the existing corpus of knowledge in this field, the results provide valuable insights into the relationship between virtual presence, devices, enjoyment, and consumers' intentions to purchase.

=This qualitative research study investigates the effectiveness of gamified handicrafts as an inspiration for teenagers to practice recycling and upcycling. The study utilises focus group interviews and thematic analysis to explore the perceptions and experiences of 15 teenagers who participated in an educational programme called Edcraft, which combines gamification and handicrafts to promote sustainable practices among youth. The findings reveal that Edcraft successfully motivates teenagers to engage in recycling and upcycling activities through its gamified approach, which includes challenges, rewards and social interaction. Themes such as ‘social connections are vital’, ‘convenience and rewards are significant motivators’, ‘gamified activities help attract and engage teens’ and ‘environmental knowledge is crucial to prolonging recycling’ emerged from the thematic analysis. The results also highlight the positive impact of Edcraft on teenagers’ attitudes towards the environment and their willingness to adopt sustainable behaviours beyond the programme. The implications of these findings for promoting environmental education and sustainability among teenagers are discussed, and recommendations for future research and practice are provided.=

PurposeUtilizing the Social Cognitive Theory framework, this research aims to illuminate how self-efficacy acts as a mediating factor, enhancing employability among working university students by fostering essential capabilities in digital leadership and intercultural competence.Design/methodology/approachQuantitative study with 206 working undergraduates from an Indonesian university. Questionnaires and structural equation modeling (SEM) with SPSS and SmartPLS were used.FindingsFindings showed that working undergraduate students possess a high level of digital leadership capability and intercultural competence and tend to display increased confidence in their ability to perform challenging tasks and attain anticipated outcomes, thereby enhancing their employability. The integration of these skills, coupled with a solid self-efficacy belief, emerges as a powerful combination in the modern employment landscape in the digital transformation and Artificial Intelligence age.Research limitations/implicationsThis research is conducted in one university in Indonesia, potentially limiting its generalizability. Future studies could expand its regions to multiple universities across different countries for a more comprehensive implication.Practical implicationsThis research answered the call for strategies from the university level on enhancing the development of essential competencies for working undergraduate students’ employability. It further highlights the necessity for shaping the curriculum to prioritize digital leadership capability, intercultural competence, and the cultivation of self-efficacy among working students.Social implicationsThis insight of the research has profound social implications, indicating that educational institutions must adapt their curricula to prioritize these competencies. By doing so, universities can better prepare students to navigate the complexities of the modern workplace, thereby contributing to a workforce that is not only technologically proficient but also culturally adept and resilient in the face of challenges. This adaptation is essential for fostering a generation of graduates who are well-equipped to contribute positively to a diverse and rapidly evolving labor market, ultimately benefiting both the individuals and the wider society by promoting economic development and cultural understanding.Originality/valueAdopting a quantitative approach, this research offers a data-driven lens of employability determinants, deepening the understanding of how intertwined competencies shape employment outcomes in the AI age.

Obtaining reliable flow data is essential for the fluid mechanics analysis and control, and various measurement techniques have been proposed to achieve this goal. However, imperfect data can occur in experimental scenarios, particularly in the particle image velocimetry technique, resulting in insufficient flow data for accurate analysis. To address this issue, a novel machine learning-based multi-scale autoencoder (MS-AE) framework is proposed to reconstruct missing flow fields from imperfect turbulent flows. The framework includes two missing flow reconstruction strategies: complementary flow reconstruction and non-complementary flow reconstruction. The former requires two independent measurements of complementary paired flow fields, posing challenges for real-world implementation, whereas the latter requires only a single measurement, offering greater flexibility. A benchmark case study of channel flow with ordinary missing configuration is used to assess the performance of the MS-AE framework. The results demonstrate that the MS-AE framework outperforms the traditional fused proper orthogonal decomposition method in reconstructing missing turbulent flow, irrespective of the availability of complementary paired faulty flow fields. Furthermore, the robustness of the proposed MS-AE approach is assessed by exploring its sensitivity to various factors, such as latent size, overlap proportion, reconstruction efficiency, and suitability for multiscale turbulent flow structures. The new method has the potential to contribute to more effective flow control in the future, thanks to its characteristic that eliminates the requirement for complementary flow fields.