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This study aims to evaluate the potential of using the in-nozzle impregnation approach to reuse recycled PET (RPET) to develop continuous banana fiber (CBF) reinforced bio-composites. The mechanical properties and fracture morphology behavior are evaluated to establish the relationships between layer spacing–microstructural characteristics–mechanical properties of CBF/RPET composite.

This study uses RPET filament developed from post-consumer PET bottles and CBF extracted from agricultural waste banana sap. RPET serves as the matrix material, while CBF acts as the reinforcement. The test specimens were fabricated using a customized fused deposition modeling 3D printer. In this process, customized 3D printer heads were used, which have a unique capability to extrude and deposit print fibers consisting of a CBF core coated with an RPET matrix. The tensile and flexural samples were 3D printed at varying layer spacing.

The Young’s modulus (E), yield strength (sy) and ultimate tensile strength of the CBF/RPET sample fabricated with 0.7 mm layer spacing are 1.9 times, 1.25 times and 1.8 times greater than neat RPET, respectively. Similarly, the flexural test results showed that the flexural strength of the CBF/RPET sample fabricated at 0.6 mm layer spacing was 47.52 ± 2.00 MPa, which was far greater than the flexural strength of the neat RPET sample (25.12 ± 1.94 MPa).

This study holds significant social implications highlighting the growing environmental sustainability and plastic waste recycling concerns. The use of recycled PET material to develop 3D-printed sustainable structures may reduce resource consumption and encourages responsible production practices.

The key innovation lies in the concept of in-nozzle impregnation approach, where RPET is reinforced with CBF to develop a sustainable composite structure. CBF reinforcement has made RPET a superior, sustainable, environmentally friendly material that can reduce the reliance on virgin plastic material for 3D printing.

Recycling is associated with positive social and environmental impact, but previous studies have overlooked the cost of recycling operations. Based on the dynamic capability view, the purpose of this study was to identify and evaluate risk factors and resilience strategies within the recycling industry, prioritize these factors and identify the optimal combination of resilience strategies and risk factors to improve market performance.

The research questions were addressed in three subsequent studies. In Study 1, qualitative interviews were conducted to identify risk factors and strategies to mitigate those risks. In Study 2, quality function deployment methodologies were implemented via case studies derived from three different companies. Based on the results of Studies 1 and 2, in addition to the use of fuzzy set qualitative comparative analysis, Study 3 aimed to determine the optimal combination of risk factors and strategies impacting market performance.

The results across the three studies revealed a number of risk factors as well as which risk factors and resilience strategies have the greatest impact on market performance. Specifically, it was found that higher levels of readiness, response and recovery strategies lead to greater market performance, whereas weak readiness, response and recovery strategies, along with low societal, environmental and health and safety risk factors, significantly inhibit performance.

This research extends current understandings of market performance in relation to recycling industry management and offers insight for decision-makers toward combating significant risk factors in business-to-business settings.

This study investigates augmented reality (AR) retailing and attempts to develop a profound understanding of consumer decision-making processes in AR-enabled e-retailing.

The study is grounded in rich informational cues and information processing mechanisms by incorporating the elaboration likelihood model (ELM) and trust transfer theory. This study employs a mixed analytic method that incorporates structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to provide a complete picture of individual information process mechanisms in AR retailing under the tenet of ELM.

The SEM analysis results confirm the relationships between the central and peripheral route factors, information processing outcomes and eventual behavioral intentions. Moreover, all configurations revealed by the fsQCA include both central and peripheral factors. Hence, the dual routes proposed in the ELM are verified by using two distinct analytical approaches.

This study is pioneering in validating and contextualizing ELM theory in AR retailing. In addition, this study offers a methodological paradigm by demonstrating the application of multi-analysis in exploring consumers’ information process mechanisms in AR retailing, which offers a holistic and comprehensive view to understand consumers’ decision-making mechanisms.

In recent times, the field of corporate intelligence has gained substantial prominence, employing advanced data analysis techniques to yield pivotal insights for instantaneous strategic and tactical decision-making. Expanding beyond rudimentary post observation and analysis, social media analytics unfolds a comprehensive exploration of diverse data streams encompassing social media platforms and blogs, thereby facilitating an all-encompassing understanding of the dynamic social customer landscape. During an extensive evaluation of social media presence, various indicators such as popularity, impressions, user engagement, content flow, and brand references undergo meticulous scrutiny. Invaluable intelligence lies within user-generated data stemming from social media platforms, encompassing valuable customer perspectives, feedback, and recommendations that have the potential to revolutionize numerous operational facets, including supply chain management. Despite its intrinsic worth, the actual business value of social media data is frequently overshadowed due to the pervasive abundance of content saturating the digital realm. In response to this concern, the present study introduces a cutting-edge system known as the Enterprise Just-in-time Decision Support System (EJDSS).

Leveraging deep learning techniques and advanced analytics of social media data, the EJDSS aims to propel business operations forward. Specifically tailored to the domain of marketing, the framework delineates a practical methodology for extracting invaluable insights from the vast expanse of social data. This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

To substantiate the efficacy of the EJDSS, a detailed case study centered around reverse logistics resource recycling is presented, accompanied by experimental findings that underscore the system’s exceptional performance. The study showcases remarkable precision, robustness, F1 score, and variance statistics, attaining impressive figures of 83.62%, 78.44%, 83.67%, and 3.79%, respectively.

This scholarly work offers a comprehensive overview of fundamental principles, pertinent challenges, functional aspects, and significant advancements in the realm of extensive social data analysis. Moreover, it presents compelling real-world scenarios that vividly illustrate the tangible advantages companies stand to gain by incorporating social data analytics into their decision-making processes and capitalizing on emerging investment prospects.

This study investigates the impact of augmented reality (AR) on consumers’ shopping process, focusing on value creation, brand engagement and purchasing intents.

This quantitative deductive study analyzed 393 questionnaires through non-probability sampling. A cross-sectional timeframe was applied; data collection took place in May 2023 to provide a snapshot of Egyptian consumers’ responses to AR. Once the needed data were collected, statistical analyses were conducted through the statistical package for social science (SPSS).

This study shows that in the shopping process, AR vividness, interactivity and informativeness lead consumers to develop utilitarian values toward the technology, and AR interactivity generates hedonic values towards the technology. When consumers sense the hedonic and utilitarian values, it impacts consumers purchase intention. Hedonic value in AR leads to consumers’ brand content consumption and creation; utilitarian value in AR leads to consumers’ brand content consumption, contribution and creation.

AR developers, brand managers and marketers may use this study to understand the current AR landscape and consumer responses toward AR (value development, purchase intention and brand engagement). With these insights, they can make better strategic decisions that include AR usage in brand marketing tactics.

This study develops conclusions to grasp more clearly and in-depth why and how AR impacts consumers’ shopping process. This study addresses various literature gaps, enhancing the understanding of AR: the salient AR attributes that offer consumers hedonic and utilitarian values and how AR influences consumer behavior, specifically when it comes to the development of brand engagement and purchase intents.