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This paper aims to develop flame-retardant (FR) polyamide 12 (PA12) nanocomposite from regenerated powder via selective laser sintering (SLS), an additive manufacturing technique.

First, the morphology, processibility, thermal and mechanical properties of PA12 regenerated powder, consisting of 50 wt% new and 50 wt% recycled powder, as well as corresponding printed specimens, were evaluated to characterize the effects of previous SLS processing. Second, flame-retardant PA12 was developed by incorporating both single and binary halogen-free flame retardants into the regenerated powder.

It was found that the printed specimens from regenerated powder had much higher tensile and impact properties compared to specimens made from new powder, which is attributed to better particulate fusion and coalescence realized in higher temperature SLS printing. The effect of FRs on thermal, mechanical and flame retardant properties of the PA12 composites/nanocomposites was investigated systematically. It was found that the nanoclay, as a synergist, improved both flame-retardant and mechanical properties of PA12. UL94 standard rating of V-0 was achieved for the printed nanocomposite by incorporating 1 wt% nanoclay into 15 wt% phosphinates FR. Moreover, on average, the tensile and impact strength of the nanocomposite were increased by 26.13% and 17.09%, respectively, in XY, YZ and Z printing orientations as compared to the equivalent flame retardant composite with 20 wt% of the phosphinates FR.

This paper fulfills the need to develop flame retardant parts via SLS technology with waste feedstock. It also addresses the challenge of developing flame retardant materials without obviously compromising the mechanical properties by making use of the synergistic effect of nanoclay and organic phosphinates.

This study aims to propose a theoretical model to explain mobile payment (MP) continuance usage in a physical store in Indonesia from a habit perspective. In detail, continuance usage was argued to be a consequence of habitual behavior which is related to specific actions conducted automatically, repeatedly and frequently. Therefore, the theoretical model was constructed on the theory of habit establishment.

In total, 220 Indonesian respondents were used to examine the theoretical model. Furthermore, a cross-sectional study was used through the use of a descriptive statistical approach to preparing data and descriptive analyses and structural equation modeling method for analysis.

Satisfaction was found to have the most substantial direct influence on the establishment of habit to use MP followed by perceived usefulness and perceived compatibility. Meanwhile, deal proneness and social ties were discovered to have a significant indirect effect on habit through the mediation of usefulness.

This study used the theory of habit formation to understand how user develops repeated behavior in MP usage which leads to continuance usage of the platform. There is limited explicit exploration and development of a theory based on this concept, therefore, this study is a contribution to the body of knowledge with respect to habit formation and its impacts on MP continuance usage.

Traditional Chinese Medicine (TCM) is a complete medical healthcare system that encompasses acupuncture, acupressure, moxibustion, herbal medicine, diet, tui na massage, and exercises (tai chi and qigong) among other traditional therapies. It uses herbs and natural resources to produce the traditional medicines and focuses on maintaining the balance between body and mind. As such, many aspects of TCM can be considered as green and sustainable. While there is market demand for TCM in some countries and among some communities, some others are still not aware of TCM. Moreover, there are fewer discussions in the academic platforms on TCM. This case highlights the scenario of TCM based on Malaysia’s perspective and discusses its challenges and prospects.

In recent years, the traditional pastries industry has gained popularity among tourists due to the advantage of the pastries location at UNESCO Heritage city, Penang. However, the little research focussed on this particular industry, and there is lack of evidence of the tourists' experience with the traditional pastries and how these attributes affect their revisit decision.

The study utilizes a qualitative research design to gain in-depth understanding on tourists' thought and their repurchase decision. Secondary data were collected via TripAdvisor with 68 tourists who visited the most popular three pastries shops namely, Him Heang, Ghee Hiang and Min Xiang Tai, which are later analysed using qualitative content analysis.

The findings revealed that tourists generally concerned about the service quality, value, brand image and atmospherics that could affect their repurchasing decision. Particularly, the staff service quality has been viewed as the upmost important attribute to influence the tourists' decision. Therefore, the pastries shops shall ensure that the staff who serve the tourists shall be well trained to satisfy the tourists' enquiry.

The limitation concerning the interpretation of the secondary data based on the feedbacks and comments of the tourists may derive the bias possibility. Future research might consider the large-scale primary data to extend the findings.

Limited research exists on the tourists' experience which affects the repurchasing decision in pastries industry. This study provides valuable information for pastries shops and researchers interested in this area.

There have been significant developments in the field of retailing with digitalization. One of these developments is the emergence of omnichannel retailing. Although this has affected both firms and consumers considerably, the literature is dominated by the studies dealing with omnichannel retailing from the firms’ perspectives. The studies dealing with omnichannel retailing from the consumers’ perspectives have recently begun to attract the attention of researchers. For this reason, this study conducted a literature review to examine various consumer behaviors mentioned in the studies aimed at explaining consumer behaviors in the omnichannel retailing context. The distribution of these studies according to years and journals, research methods used, theories adopted, and the related five-stage consumer decision-making stages are summarized. Additionally, this review addresses future research avenues.

After having drawn lessons from the recent COVID-19 pandemic for retailers in the previous chapters, in this last chapter we provide an outline on retailing over a longer time horizon. We start with projections of how the phygitalization trend in retailing will further evolve and what role data plays as a basis for a competitive advantage – on the condition of smart and ethical use. Besides looking at customers (downstream), we address the upstream in the value delivery network, focusing on how to succeed in balancing between efficiency and sustainability in the retail supply chain. Retailers face huge challenges. This chapter contributes to setting the scene for retailers to thrive in the brand-new post-pandemic aftermath.

Many manufacturers are exploring adopting smart technologies in their operations, also referred to as the shift towards “Industry 4.0”. Employees' contribution to high-tech initiatives is key to successful Industry 4.0 technology adoption, but few studies have examined the determinants of employee acceptance. This study, therefore, aims to explore how managers affect employees' acceptance of Industry 4.0 technology, and, in turn, Industry 4.0 technology adoption.

Rooted in the unified theory of acceptance and use of technology model and social exchange theory, this inductive research follows an in-depth comparative case study approach. The two studied Dutch manufacturing firms engaged in the adoption of Industry 4.0 technologies in their primary processes, including cyber-physical systems and augmented reality. A mix of qualitative methods was used, consisting of field visits and 14 semi-structured interviews with managers and frontline employees engaged in Industry 4.0 technology adoption.

The cross-case comparison introduces the manager's need to adopt a transformational leadership style for employees to accept Industry 4.0 technology adoption as an organisational-level factor that extends existing Industry 4.0 technology user acceptance theorising. Secondly, manager's and employee's recognition and serving of their own and others' emotions through emotional intelligence are proposed as an additional individual-level factor impacting employees' acceptance and use of Industry 4.0 technologies.

Synthesising these insights with those from the domain of Organisational Behaviour, propositions were derived from theorising the social aspects of effective Industry 4.0 technology adoption.

This study aims to investigate the latest breakthroughs in social commerce by examining the characteristics of consumers' social self-identity factors in the food and beverage sector. The objective is to examine how social self-identities factors contribute to exchanging information on social commerce platforms for food and beverage products.

This study employed partial least squares structural equation modeling (PLS-SEM) to test the measurement and structural model. A total of 238 food and beverage website and apps users were questioned using a structural survey.

Contrary to expectations, one self-inner driver does not influence consumers' intentions to exchange social commerce information, while up-to-date information, validation and like-minded discussion have a strong impact on the social commerce exchange model.

The findings of this research offer new insights into the brand's abilities to induce social commerce. The findings also assist marketing managers in better managing brand content on these websites.

The purpose of this study is to explore the relationship between big data and supply chain platform in China’s retail industry. With the emergence of big data resources and technologies, the business pattern of new retail advocates the combination of online and offline channels. Supply chain platform plays a key role in the implementation of retail activities, which has gradually become a research hotspot in the cross field of operations management and information system.

Through the method of literature review and case study, this study empirically explores how big data shapes supply chain platform to support new forms of online retail by grounded theory.

The model framework is validated by reliability test and coding method to process survey materials. The results identify the overall antecedents of supply chain platform and reveal positive effects between big data and new retail. The findings help firm managers build a big data-driven supply chain to support new retail.

There are insufficient studies on theoretical frameworks and interaction relationships among big data, supply chain platform and new retail.

This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface.

The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on “how exactly the matching process is done for evaluating the document.” Finally, the e-learners are classified based on their grades.

On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively.

This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors’ knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners.