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This study aims to address a significant gap in the literature by exploring the individual nature and microfoundation perspective of cultural friction during the integration phase following cross-border mergers and acquisitions (CBMAs). It focuses on the role of face, a pivotal facet of interactions within Asian organizations, elucidating its importance in post-M&A integration outcomes.

Using a conceptual approach, this study draws on three bodies of literature, namely, cultural friction, microfoundations and face concerns. It reconsiders cultural friction as a microfounded construct and introduces face concerns as a pivotal element to understanding the challenges faced by managers at the forefront of CBMAs involving Asian companies. The conceptual approach is illustrated with examples for the world of practice.

This research makes two significant contributions to the fields of CBMAs and cultural friction. First, it demonstrates the relevance of the concept of cultural friction at the individual level, shedding light on the complex post-CBMA integration process. Second, this study demonstrates the critical role of face concerns in the sociocultural integration following CBMAs. This is underexplored in extant literature.

The crucial role of face is well known to those working in Asia. Yet academic inquiry remains underdeveloped on this issue for CBMAs. The friction derived from face concerns provides additional insights into the nature of the cultural challenges confronting managers during sociocultural integration and elucidates the micro-mechanisms influencing individuals’ responses to cultural friction. This research responds to calls to examine the human side of M&As uncovering previously neglected issues within intercultural managerial encounters.

Amidst compounding crises and increasing global population’s nutritional needs, food supply chains are called to address the “diet–environment–health” trilemma in a sustainable and resilient manner. However, food system stakeholders are reluctant to act upon established protein sources such as meat to avoid potential public and industry-driven repercussions. To this effect, this study aims to understand the meat supply chain (SC) through systems thinking and propose innovative interventions to break this “cycle of inertia”.

This research uses an interdisciplinary approach to investigate the meat supply network system. Data was gathered through a critical literature synthesis, domain-expert interviews and a focus group engagement to understand the system’s underlying structure and inspire innovative interventions for sustainability.

The analysis revealed that six main sub-systems dictate the “cycle of inertia” in the meat food SC system, namely: (i) cultural, (ii) social, (iii) institutional, (iv) economic, (v) value chain and (vi) environmental. The Internet of Things and innovative strategies help promote sustainability and resilience across all the sub-systems.

The study findings demystify the structure of the meat food SC system and unveil the root causes of the “cycle of inertia” to suggest pertinent, innovative intervention strategies.

This research contributes to the SC management field by capitalising on interdisciplinary scientific evidence to address a food system challenge with significant socioeconomic and environmental implications.

A major source of external funding for US airports comes from issuing municipal bonds. Credit rating agencies evaluate the bonds using multiple factors, but the judgments behind the ratings are not well understood. This paper examines if airport rate-setting methods affect the bond ratings of US airports.

Using a set of unbalanced panel data for 58 hub airports from 2010 to 2019, we examine the effect of the rate-setting methods and other airport characteristics on Fitch’s airport bond rating.

We find that compensatory airports consistently receive a very high bond rating from Fitch. The probability of getting a very high Fitch rating increases by ∼28 percentage points for a compensatory airport. Additionally, the probability of getting a very high rating is about 33 percentage points higher for a legacy hub.

The study uses Fitch bond ratings. Future studies could examine if S&P’s and Moody’s ratings are also influenced by airport rate-setting methods and legacy hub status.

The results uncover the linkage between bond ratings and their determinants for US airports. This information is important for investors when assessing airport creditworthiness and for airport operators as they manage capital project financing.

This is the first study to evaluate the effects of rate-setting methods on airport bond rating and also the first to document a statistically significant relationship between airports’ legacy hub status and bond ratings.

Wire-arc-based additive manufacturing (WAAM) is a promising technology for the efficient and economical fabrication of medium-large components. However, the anisotropic behavior of the multilayered WAAM-fabricated components remains a challenging problem.

The purpose of this paper is to conduct a comprehensive study of the grain morphology, crystallographic orientation and texture in three regions of the WAAM printed component. Furthermore, the interdependence of the grain morphology in different regions of the fabricated component with their mechanical and tribological properties was established.

The electron back-scattered diffraction analysis of the top and bottom regions revealed fine recrystallized grains, whereas the middle regions acquired columnar grains with an average size of approximately 8.980 µm. The analysis revealed a higher misorientation angle and an intense crystallographic texture in the upper and lower regions. The investigations found a higher microhardness value of 168.93 ± 1.71 HV with superior wear resistance in the bottom region. The quantitative evaluation of the residual stress detected higher compressive stress in the upper regions. Evidence for comparable ultimate tensile strength and greater elongation (%) compared to its wrought counterpart has been observed.

The study found a good correlation between the grain morphology in different regions of the WAAM-fabricated component and their mechanical and wear properties. The Hall–Petch relationship also established good agreement between the grain morphology and tensile test results. Improved ductility compared to its wrought counterpart was observed. The anisotropy exists with improved mechanical properties along the longitudinal direction. Moreover, cylindrical components have superior tribological properties compared with cuboidal components.

This research investigates Hong Kong academic librarians' attitudes toward robotic process automation (RPA) and their willingness to learn this technology.

This qualitative study collected data through one-on-one semi-structured interviews conducted with video conferencing software. After participants received basic RPA information and three existing library application cases, they answered questions based on the interview guide. This research used the inductive thematic analysis method to analyze the collected data.

Regarding Hong Kong academic librarians' attitudes towards RPA, 19 themes were identified. Although all participants did not have previous knowledge of RPA, most showed positive attitudes toward implementing RPA in their libraries and some willingness to learn it. Besides, among all identified themes, negative attitudes mainly comprised “Affect” and “Cognition” factors, hindering RPA deployment in academic libraries.

This research helps librarians and RPA vendors make better decisions or strategies for implementing RPA for libraries, which has not been explored, especially in East Asia.

This study examines the relationship between multi-layer supply chain flexibility (MSCF) and Supply chain resilience (SCR). Further, it looks at the moderating effect of environmental dynamism (ED) and supply chain risks (SCRI) on the relationship between MSCF and SCR.

Executives from the pharmaceutical, agri-food, electronics, automobile and textile industries were invited to complete a self-administered questionnaire. We received feedback from a total of 302 participants. Prior to conducting the primary analysis, we addressed the potential for nonresponse bias and verified the assumptions of homoscedasticity and normal distribution of the data. The reliability and validity of the constructs were established through confirmatory factor analysis. Structural equation modelling is employed for the purpose of conducting hypothesis testing.

The results demonstrate a notable influence of MSCF on SCR, particularly in settings characterized by high levels of ED and SCRI. The study highlights the importance of flexibility in multiple aspects of the supply chain to build resilience against a range of disruptions and uncertainties.

The study presents the fundamental role of Multi-Layer Flexibility in building up SCR. The results of this study reinforce the existing literature and offers empirical evidence for how ED, SCRI moderates the influence between MSCF to SCR. These results offer valuable information to both supply chain specialists and researchers for building comprehensive strategy to bring resilience in supply chains.

This study aimed to compare the performance of sustainable shoes made with bacterial cellulosic composite and commercial leather shoes using an experimental research design. The two specific research objectives were: (1) to examine the basic material properties of multi-layered bacterial cellulosic materials (MBC), which include green tea-based cellulosic (GBC) mats, hemp fabrics, and denim fabrics, in comparison with those of two-layered leathers (MCP) consisting of calf-skin and pig-skin – commonly used in shoe manufacturing; and (2) to explore wearers’ performance in the two types of shoes by assessing quantitative kinematic and kinetic parameters of lower body movements.

This study focused on assessing the basic materials testing and performance of sustainable shoes through a biomechanical approach, in contrast to commercially available leather shoes, through human wear trials. In this study, green tea-based cellulosic (GBC) mats were developed using the optimal combination of ingredients for cellulose growth. Subsequently, the GBC, denim fabric (100% cotton), and 100% hemp fabric were combined to create multi-layered bacterial cellulosic materials (MBC) as an alternative to leather. Additionally, calf-skin and pig-skin leathers were utilized to produce a commercially available two-layered leather (MCP), commonly employed in shoe manufacturing. 37 of the 42 human subjects who participated in wear testing were collected. A paired t-test was conducted to determine whether significant mean differences existed between the two shoe types, a paired t-test was conducted.

To develop a biodegradable and compostable material that could be used as a leather alternative for the footwear industry, we proposed MBC and examined its properties compared with those of MCP, a product often used when making shoes. These findings confirmed the similar properties of MBC and MCP from the material testing and the possibility of using a men’s sustainable shoe prototype as a leather alternative, in terms of kinematics and kinetics.

The new multi-layered bacterial cellulosic materials (MBC) could be an alternative to commercial leathers such as innovative sustainable material construction, advanced design, and advanced techniques to optimize the overall performance of sustainable footwear.

Investigating the integration of smart textile technologies, ergonomic design principles, and personalized customization will contribute to developing MBC and making sustainable shoes using MBC compared with commercial leather shoes. This study provides valuable insights into further refinement and innovation in the sustainable footwear industry.

This study aims to demonstrate a modified wire arc additive manufacturing (AM) named non-transferring arc and wire AM (NTA-WAM). Here, the build plate has no electrical arc attachment, and the system’s arc is ignited between tungsten electrode and filler wire.

The effect of various deposition conditions (welding voltage, travel speed and wire feed speed [WFS]) on bead characteristics is studied through response surface methodology (RSM). Under optimum deposition condition, a single-bead and thin-layered part is fabricated and subjected to microstructural, tensile testing and X-ray diffraction study. Moreover, bulk texture analysis has been carried out to illustrate the effect of thermal cycles and tensile-induced deformations on fibre texture evolutions.

RSM illustrates WFS as a crucial deposition parameter that suitably monitors bead width, height, penetration depth, dilution, contact angle and microhardness. The ferritic (acicular and polygonal) and lath bainitic microstructure is transformed into ferrite and pearlitic micrographs with increasing deposition layers. It is attributed to a reduced cooling rate with increased depositions. Mechanical testing exhibits high tensile strength and ductility, which is primarily due to compressive residual stress and lattice strain development. In deposits, ϒ-fibre evolution is more resilient due to the continuous recrystallisation process after each successive deposition. Tensile-induced deformation mostly favours ζ and ε-fibre development due to high strain accumulations.

This modified electrode arrangement in NTA-WAM suitably reduces spatter and bead height deviation. Low penetration depth and dilution denote a reduction in heat input that enhances the cooling rate.

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.