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The purpose of this paper is to focus on how to automatically generate the individualized patterns for women’s suits based on the 3D body point-cloud images.

With the software Imageware, the point-cloud data of the female body were measured according to the female body feature to obtain the heights, widths, depths and girths at various landmarks. Then the relationship between the height of each landmark and the body height was analyzed to build the height calculation rules by software SPSS, and the prediction models of body girths were established from the body widths and depths using regression analysis for pattern generation.

The pattern generation rules were built with the relationships between a human body and the garment patterns using the graphic flattening method. Based on the above rules, the final patterns were drafted automatically by using these dimensions to fit the subjects. The try-on experiment also showed that the individualized suits could fit the subjects’ body well at some feature landmarks.

In order to realize tailor-made and meet the consumers’ demands for individualized clothes, the development of garment CAD system has become inevitable in the garment industry. This paper could provide the foundation for automatic pattern generation, and technical support for tailor-made.

The purpose of this paper is to explore how sustainability can become a source of competitive advantage for mature manufacturing sectors where technologies are standardized, and innovation is mainly generated across the value chain and not by individual companies.

From the methodological point of view, this research estimates the sustainability status of ceramic production in the Sassuolo district (Italy), using the Life Cycle Sustainability Assessment (LCSA) model, and changing the observation point for the analysis, from the enterprise (micro level) to the entire sector (meso level).

This paper provides an analysis of the environmental, economic and social impacts of the four main types of ceramic tiles manufactured in Italy, both in aggregate terms for the entire sector and per square meter of product.

The methodological approach used in this research is easy to replicate both for companies when designing their sustainability strategies and for public decision makers when assessing the sustainability performance of a sector or supply chain.

For the first time, a socio-economic impact assessment is proposed for the ceramic sector, conducted in parallel with the environmental impact assessment through stakeholder mapping and prioritization.

This paper conceptualizes the theme of relations and interdependencies between ceramic producers organized in industrial districts and the territories in which they operate in order to determine empirically the sustainability performance of Italian ceramic sector, using the LCSA model with a territorial extension that presupposes an innovative contribution to current literature and practice.

This study has two main objectives. First, to examine the indirect effects of digital platform capability and digital resilience on digital transformation (DT) outcomes for small- and medium-sized enterprises (SMEs), and second, to investigate how digital business model maturity influences these indirect effects.

The study adopts a quantitative design and collects data through a self-reporting survey from individuals in the technological industries. The Partial Least Squares-Structural Equation Modelling (PLS-SEM) and PLS multi-group analysis examine the measurement and structural models and the significance of differences in indirect paths based on the digital business model maturity level, serving as a moderator.

The findings of this study provide valuable insights into the internationalisation of digital SMEs. They indicate that digital platform capability and resilience fully mediate, connecting digital resources to SME growth. The study also confirms the digital business model maturity’s positive and significant moderating effect on these indirect relationships.

This research contributes to the existing literature by focusing on the international outcomes of platform ecosystems in developing markets. It explores how digital platform capability and resilience support the digital transformation of SMEs, considering their vulnerability due to their small size. The study also fills a research gap by investigating the relationship between big data, digital leadership and the international growth of digital platforms. Lastly, it explores the role of digital maturity in the relationships between antecedents, determinants and outcomes of digitalisation.

Nanotechnology (NT) advancements in personal protective textiles (PPT) or personal protective equipment (PPE) have alleviated spread and transmission of this highly contagious viral disease, and enabled enhancement of PPE, thereby fortifying antiviral behavior.

Review of a series of state of the art research papers on the subject matter.

This paper expounds on novel nanotechnological advancements in polymeric textile composites, emerging applications and fight against COVID-19 pandemic.

As a panacea to “public droplet prevention,” textiles have proven to be potentially effective as environmental droplet barriers (EDBs).

PPT in form of healthcare materials including surgical face masks (SFMs), gloves, goggles, respirators, gowns, uniforms, scrub-suits and other apparels play critical role in hindering the spreading of COVID-19 and other “oral-respiratory droplet contamination” both within and outside hospitals.

When used as double-layers, textiles display effectiveness as SFMs or surgical-fabrics, which reduces droplet transmission to <10 cm, within circumference of ∼0.3%.

NT advancements in textiles through nanoparticles, and sensor integration within textile materials have enhanced versatile sensory capabilities, robotics, flame retardancy, self-cleaning, electrical conductivity, flexibility and comfort, thereby availing it for health, medical, sporting, advanced engineering, pharmaceuticals, aerospace, military, automobile, food and agricultural applications, and more. Therefore, this paper expounds on recently emerging trends in nanotechnological influence in textiles for engineering and fight against COVID-19 pandemic.

Despite an increasing interest in online brand advocacy (OBA) and the importance of online brand conversations, OBA’s conceptualization, dimensionality and measurement are unclear, which has created confusion. This paper aims to answer calls from researchers and practitioners for a better understanding and measurement of OBA. The development and validation of a parsimonious and practical OBA scale is outlined in this paper.

A multi-methods, multi-stage approach was followed to develop a parsimonious OBA scale. From an initial pool of 96 items obtained from qualitative research and from items used in prior general brand advocacy scales, a test-retest reliability study is followed. Academic judges were consulted to verify dimensionality, followed by two separate online surveys to further purify the scale and assess criterion-related validity. Programs including SPSS, AMOS and WarpPLS were used.

This research extends the knowledge of OBA by developing and testing a parsimonious and practical 16-item, four-dimensional OBA scale. Unlike previous attempts to measure OBA, this study suggested OBA as a multidimensional construct with four dimensions (i.e. brand defense, brand information sharing, brand positivity and virtual positive expression). Further, this study showed that OBA is conceptually different from consumer–brand engagement and electronic word-of-mouth.

Future research is encouraged to validate the OBA scale in various contexts and locations. Researchers can use the new OBA scale to examine potential brand-related antecedents and consequences of OBA.

This study provides brand and marketing practitioners with a better understanding of brand advocacy occurring online. The OBA scale offers clear markers or trademarks that will be useful in assessing any brand’s health online and to track and better manage online brand communications and performance.

This research provides the first empirical investigation of Wilk et al.’s (2018) exploratory insights into OBA. The resulting parsimonious scale has furthered OBA as a new area for academic enquiry and presented practitioners with a practical way of measuring OBA.

This paper aims to examine how high involvement HRM (HIHRM), technology adaptation and innovativeness influence employees to respond to technology innovation performance – that is, radical innovation performance and incremental innovation performance – in distinct ways.

The purposive judgmental sample technique is used to assess the perceptions of 545 IT employees in Bangladesh's ready-made garment (RMG) industry. The partial least square-structural equation modeling (SEM-PLS) method is applied to analyze and interpret the data.

Findings of the analysis show that HIHRM, technology adaptation and innovativeness influence employees to exhibit technology innovation performance. Furthermore, both technology adaptation and innovativeness show the significant mediating effects between HIHRM and technology innovation performance.

First, this study’s findings contribute to HIHRM and technology innovation performance within the context of the resource-based theory. Second, the RMG organizations would acknowledge the role of HIHRM on employee technology adaptability and innovation capability in this regard. A future study might point to the models' ability to create the best work environments, which can help organizations to boost employee productivity through adaptation and innovation.

The study would offer a distinctive perspective on higher-order HIHRM and how they affect IT employees in RMG organizations in Bangladesh that could be approached in other labor-intensive and developing nations. This study also expands the research on technology adaptation and innovativeness by exploring the mediating roles between HIHRM and employee technology innovation performance in the organization.

This study aims to find the key drivers of green innovation in family firms by examining firm characteristics and geographical factors. It seeks to develop a conceptual framework that explains how internal resources and external environments influence environmental innovation practices in these businesses.

Using machine learning (ML) methods, this study develops a predictive model for green innovation in family firms, drawing on data from 3,289 family businesses across 27 EU Member States and 12 additional countries. The study integrates the Resource-Based View (RBV) and Location Theory to analyze the impact of firm-level resources and geographical contexts on green innovation outcomes.

The results show that both firm-specific resources, such as size, digital capabilities, years of operation and geographical factors, like country location, significantly influence the likelihood of family firms engaging in environmental innovation. Larger, technologically advanced firms are more likely to adopt sustainable practices, and geographic location is crucial due to different regulatory environments and market conditions.

The findings reinforce the RBV by showing the importance of firm-specific resources in driving green innovation and extend Location Theory by emphasizing the role of geographic factors. The study enriches the theoretical understanding of family businesses by showing how noneconomic goals, such as socioemotional wealth and legacy preservation, influence environmental innovation strategies.

Family firms can leverage these findings to enhance their green innovation efforts by investing in technology, fostering sustainability and recognizing the impact of geographic factors. Aligning innovation strategies with both economic and noneconomic goals can help family businesses improve market positioning, comply with regulations and maintain a strong family legacy.

This research contributes a new perspective by integrating the RBV and Location Theory to explore green innovation in family firms, highlighting the interplay between internal resources and external environments. It also shows the effectiveness of machine learning methods in predicting environmental innovation, providing deeper insights than traditional statistical techniques.

Family businesses often lack sufficient knowledge about digital business model innovation digital business model innovation (BMI). This study's purpose was to analyze how and when organizational readiness for digital innovation exerts a positive impact on family businesses' digital BMI. To do so, the authors examined the mediating effect of the familiness learning mechanism and the moderating effect of family involvement on this relationship.

A quantitative survey method was used to collect the data for this study. Using a sample of 282 family businesses involved in manufacturing in China, the authors conducted hierarchical regression analyses to evaluate the authors' theoretical model.

The results of this work demonstrate a positive relationship between organizational readiness for digital innovation and family businesses' digital BMI, and the find that the familiness learning mechanism mediates this relationship. The findings also show that second-generation family involvement in management moderates the direct effect of organizational readiness for digital innovation on the familiness learning mechanism, as well as the indirect effect of organizational readiness for digital innovation on digital BMI via the familiness learning mechanism. Moreover, the results establish that family involvement in ownership moderates the direct effect of the familiness learning mechanism on digital BMI, as well as the indirect effect of organizational readiness for digital innovation on digital BMI via the familiness learning mechanism.

This study provides practical contributions to the literature on family businesses and to public policy, providing concrete suggestions for fostering digital innovation in family enterprises. This study also enriches our understanding of the unique conditions by which family businesses can successfully implement digital BMI.

This research confirms that organizational readiness for digital innovation is an antecedent of digital BMI. This finding offers a new perspective that helps explain what might lead family businesses to engage in digital BMI. This study also places the familiness learning mechanism into a theoretical framework, which expands the current understanding of how organizational readiness for digital innovation facilitates digital BMI. Moreover, this work provides new insights into the boundary conditions by which organizational readiness for digital innovation affects the digital BMI of family businesses in terms of second-generation family involvement in management and family involvement in ownership.

In the present work, we focus on developing an in-house parallel meshless local Petrov-Galerkin (MLPG) code for the analysis of heat conduction in two-dimensional and three-dimensional regular as well as complex geometries.

The parallel MLPG code has been implemented using open multi-processing (OpenMP) application programming interface (API) on the shared memory multicore CPU architecture. Numerical simulations have been performed to find the critical regions of the serial code, and an OpenMP-based parallel MLPG code is developed, considering the critical regions of the sequential code.

Based on performance parameters such as speed-up and parallel efficiency, the credibility of the parallelization procedure has been established. Maximum speed-up and parallel efficiency are 10.94 and 0.92 for regular three-dimensional geometry (343,000 nodes). Results demonstrate the suitability of parallelization for larger nodes as parallel efficiency and speed-up are more for the larger nodes.

Few attempts have been made in parallel implementation of the MLPG method for solving large-scale industrial problems. Although the literature suggests that message-passing interface (MPI) based parallel MLPG codes have been developed, the OpenMP model has rarely been touched. This work is an attempt at the development of OpenMP-based parallel MLPG code for the very first time.

The purpose of this paper is to understand how Design for Additive manufacturing Knowledge has been developing and its significance to both academia and industry.

In this paper, the authors use a bibliometric approach to analyse publications from January 2010 to December 2020 to explore the subject areas, publication outlets, most active authors, geographical distribution of scholarly outputs, collaboration and co-citations at both institutional and geographical levels and outcomes from keywords analysis.

The findings reveal that most knowledge has been developed in DfAM methods, rules and guidelines. This may suggest that designers are trying to learn new ways of harnessing the freedom offered by AM. Furthermore, more knowledge is needed to understand how to tackle the inherent limitations of AM processes. Moreover, DfAM knowledge has thus far been developed mostly by authors in a small number of institutional and geographical clusters, potentially limiting diverse perspectives and synergies from international collaboration which are essential for global knowledge development, for improvement of the quality of DfAM research and for its wider dissemination.

A concise structure of DfAM knowledge areas upon which the bibliometric analysis was conducted has been developed. Furthermore, areas where research is concentrated and those that require further knowledge development are revealed.