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In this digital age, the rapid technological innovation and adoption, with the increasing use of big data analytics, has raised concerns about the ability of small and medium enterprises (SMEs) to sustain the competition and innovation performance (IP). To narrow the research gap, this paper investigates the role of big data analytics capability (BDAC) in moderating the relationship between digital innovation (DI) and SME innovation performance.

This research has been carried forward through a detailed theory and literature analysis. Data were analyzed through confirmatory factor analysis and structural equation models using a two-stage approach in smartPLS-4.

Results highlight that digital service capability (DSC) significantly mediates the relationship between DI and IP. Additionally, value co-creation (VCC) directly affects digital transformation (DT), while DI has a stronger effect on DSC than IP. Furthermore, BDAC significantly moderates the relation between DSC → IP and DT → IP, whereas it has a detrimental effect on the relation between DI and IP. In addition to that, VCC, DSC, DT, DI and BDAC have a direct, significant and positive effect on IP.

This research was motivated by the practical relevance of supporting SMEs in adopting DT and the resource-based view (RBV) and technology acceptance model (TAM). This study shows that all direct and indirect measures significantly affect innovation performance, including BDAC as moderator. These findings refresh the perspective on what DT, DI, VCC, DSC and BDAC can bring to a firm's innovation performance.

This paper has contributed to DT by empirically validating a theoretical argument that suggests the acceptance and adoption of new technology. This paper aims to fill theoretical gaps in understanding BDAC and DT by incorporating the RBV and TAM theories on BDAC and DT.

There has been limited research that has explored the connection between digital supply chain (DSC) and SC innovation and SC dynamic capabilities. This paper aims to examine the mediating effect of SC innovation on the relationship between DSC and SC dynamic capabilities.

The research model and hypotheses were tested, employing (Statistical Package of Social Sciences) SPSS 25.0 and (Analysis of Moment Structures) AMOS 24.0 on data drawn from the Chinese manufacturing enterprises.

The study reveals that DSC has a significant positive effect on SC innovation and SC dynamic capabilities. SC innovation also has a significant positive effect on SC dynamic capabilities. Besides, the authors' research illustrates that SC innovation partially mediates the relationship between DSC and SC dynamic capabilities.

Since the results are derived from the data collected from China, it may not, therefore, be generalized to other settings. Moreover, future research could consider other contextual variables such as “environmental uncertainty” and “Government's Reward-Penalty Mechanism,” which may influence SC dynamic capabilities.

The study provides practical insights for senior executives and managers in the manufacturing industry. Managers should emphasize the investment of advanced digital technologies and tools (DTTs) and improvement of SC visibility and collaboration. In the digital age, companies should pay attention to the introduction of advanced technologies, tools and processes and focus on cultivating an innovative spirit to promote SC dynamic capabilities, thereby enhancing competitive advantages.

The paper illustrates that DSC is of great significance to improving SC dynamic capabilities. This study reveals compelling insights for firms to enhance SC innovation and dynamic capabilities by using DSC as an enabler.

Successful digital transformation involves all areas which bring new impacts and challenges to the leadership of the enterprise. From the perspective of organizational identification, the authors construct a theoretical model of digital leadership–digital strategic consensus–digital transformation and explore the different moderated mediation effects of diversity types.

This paper obtains data from 351 Chinese science and technology enterprises and uses regression analysis and bootstrap analysis to test the research hypotheses.

The results demonstrate that digital leadership has a positive impact on digital transformation. Digital strategic consensus partially mediates the linkage between digital leadership and digital transformation. Disparity diversity and variety diversity positively moderate the mediating role of digital strategic consensus between digital leadership and digital transformation, respectively; and separation diversity negatively moderates the mediating role of digital strategic consensus between digital leadership and digital transformation.

The research innovatively measures digital leadership and digital transformation. It expands the application of leadership, strategic consensus, diversity and other related theories in a digital context and provides a decision-making basis for enterprises' digital transformation.

With the current wave of modernization in the dairy industry, the global dairy market has seen significant shifts. Making the most of inventory planning, machine learning (ML) maximizes the movement of commodities from one site to another. By facilitating waste reduction and quality improvement across numerous components, it reduces operational expenses. The focus of this study was to analyze existing dairy supply chain (DSC) optimization strategies and to look for ways in which DSC could be further improved. This study tends to enhance the operational excellence and continuous improvements of optimization strategies for DSC management

Preferred reporting items for systematic reviews and meta-analyses (PRISMA) standards for systematic reviews are served as inspiration for the study's methodology. The accepted protocol for reporting evidence in systematic reviews and meta-analyses is PRISMA. Health sciences associations and publications support the standards. For this study, the authors relied on descriptive statistics.

As a result of this modernization initiative, dairy sector has been able to boost operational efficiency by using cutting-edge optimization strategies. Historically, DSC researchers have relied on mathematical modeling tools, but recently authors have started using artificial intelligence (AI) and ML-based approaches. While mathematical modeling-based methods are still most often used, AI/ML-based methods are quickly becoming the preferred method. During the transit phase, cloud computing, shared databases and software actually transmit data to distributors, logistics companies and retailers. The company has developed comprehensive deployment, distribution and storage space selection methods as well as a supply chain road map.

Many sorts of environmental degradation, including large emissions of greenhouse gases that fuel climate change, are caused by the dairy industry. The industry not only harms the environment, but it also causes a great deal of animal suffering. Smaller farms struggle to make milk at the low prices that large farms, which are frequently supported by subsidies and other financial incentives, set.

This paper addresses a need in the dairy business by giving a primer on optimization methods and outlining how farmers and distributors may increase the efficiency of dairy processing facilities. The majority of the studies just briefly mentioned supply chain optimization.

Mouth segmentation is one of the challenging tasks of development in lip reading applications due to illumination, low chromatic contrast and complex mouth appearance. Recently, deep learning methods effectively solved mouth segmentation problems with state-of-the-art performances. This study presents a modified Mobile DeepLabV3 based technique with a comprehensive evaluation based on mouth datasets.

This paper presents a novel approach to mouth segmentation by Mobile DeepLabV3 technique with integrating decode and auxiliary heads. Extensive data augmentation, online hard example mining (OHEM) and transfer learning have been applied. CelebAMask-HQ and the mouth dataset from 15 healthy subjects in the department of rehabilitation medicine, Ramathibodi hospital, are used in validation for mouth segmentation performance.

Extensive data augmentation, OHEM and transfer learning had been performed in this study. This technique achieved better performance on CelebAMask-HQ than existing segmentation techniques with a mean Jaccard similarity coefficient (JSC), mean classification accuracy and mean Dice similarity coefficient (DSC) of 0.8640, 93.34% and 0.9267, respectively. This technique also achieved better performance on the mouth dataset with a mean JSC, mean classification accuracy and mean DSC of 0.8834, 94.87% and 0.9367, respectively. The proposed technique achieved inference time usage per image of 48.12 ms.

The modified Mobile DeepLabV3 technique was developed with extensive data augmentation, OHEM and transfer learning. This technique gained better mouth segmentation performance than existing techniques. This makes it suitable for implementation in further lip-reading applications.

This study aims to synthesize two different benzoxazines (Bz) monomers using bio-based and petroleum-based primary amines, respectively, and they have been compared to study their thermal and mechanical performances.

A bio-based bisphenol, Divanillin (DiVa), was formed by reacting two moles of vanillin with one mole of ethylenediamine (EDA) which was then reacted firstly with paraformaldehyde and EDA to form the benzoxazine DiVa-EDA-Bz, and secondly with paraformaldehyde and furfuryl amine (FFA) to form the benzoxazine DiVa-FFA-Bz. The molecular structure and thermal properties of the benzoxazines were characterized by fourier transform infrared spectroscopy and nuclear magnetic resonance (1H,13C) spectroscopies, differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), respectively. The benzoxazines were further coated on mild steel panels to evaluate their mechanical properties and chemical resistance.

The DSC results of DiVa-FFA-Bz showed two exothermic peaks related to crosslinking compared to the one in DiVa-EDA-Bz. The DiVa-FFA-Bz also showed a higher heat of polymerization than DiVa-EDA-Bz. The TGA results showed that DiVa-FFA-Bz exhibited higher thermal stability with a residual char of 54.10% than 43.24% for DiVa-EDA-Bz. The chemical resistance test results showed that DiVa-FFA-Bz showed better chemical resistance and mechanical properties due to its higher crosslinking density.

This study shows the use of bio-based materials, vanillin and FFA, for synthesizing a benzoxazine resin and its application at high temperatures.

This paper aims to explore the impact of live streamer authenticity (LSA) on purchase intention in tourism e-commerce live streaming, with a focus on boundary conditions and underlying mechanisms.

The data collected from 451 participants were analyzed using structural equation modeling.

This paper found that four dimensions of LSA – sincerity, truthfulness endorsement, expertise and uniqueness – positively influenced purchase intention, while visibility did not. In addition, sincerity, truthfulness endorsement and uniqueness had an indirect influence on purchase intention through flow experience, while sincerity, truthfulness endorsement, expertise and uniqueness had an indirect effect through perceived trust. Furthermore, self-construal moderated the effect of sincerity and truthfulness endorsement on purchase intention, with the positive effect being stronger for the dependent self-construal.

To the best of the authors’ knowledge, it is the first study to examine LSA dimensions and their consequences. This paper not only provides a better and more detailed understanding of the complexity of LSA but also contributes to the development of authenticity theory by responding to individual authenticity studies.

本文旨在研究旅游直播电商中主播真实性对旅游购买意向的影响, 及其内在机制和边界条件。

本文采用问卷调查的方法收集451名参与者的数据。然后用结构方程模型（SEM）对收集的数据进行分析。

本文发现, 主播真实性的四个维度–真诚性、真实背书、专业性和独特性–对购买意愿有积极影响, 而主播的透明性没有影响。此外, 真诚性、真实背书和独特性通过心流体验对购买意愿产生间接影响, 此外真诚性、真实背书、专业性和独特性还通过感知信任产生间接影响。此外, 自我建构调节了真诚性和真实背书对购买意向的影响。对于依赖型自我建构, 真诚性和真实背书对购买意愿的影响更强。

本文是最早探讨主播真实性维度及其后果影响的研究之一。本文不仅对主播真实性的复杂性有了更好更详细的了解, 而且响应了对个体真实性研究的呼吁, 为真实性理论的发展做出了贡献。

Este artículo explora el impacto de la autenticidad del streamer en la intención de compra en la retransmisión en directo de comercio electrónico turístico, centrándose en las limitaciones y los procesos subyacentes.

Se analizaron los datos recopilados de 451 participantes utilizando un modelo de ecuaciones estructurales (SEM).

Este trabajo halló que cuatro dimensiones de la autenticidad de los streamers en directo -la sinceridad, el respaldo a la veracidad, la pericia y la singularidad- influían positivamente en la intención de compra, mientras que la visibilidad no lo hacía. Además, la sinceridad, el respaldo de veracidad y la singularidad influyeron indirectamente en la intención de compra a través de la experiencia de flujo, mientras que la sinceridad, el respaldo de veracidad, la pericia y la singularidad tuvieron un efecto indirecto a través de la confianza percibida. Además, la autoconstrucción moderó el efecto de la sinceridad y el respaldo de veracidad sobre la intención de compra, siendo el efecto positivo más fuerte para la autoconstrucción dependiente.

Es el primer estudio que examina las dimensiones de la autenticidad de los retransmisores en directo y sus consecuencias. Este trabajo no sólo proporciona una comprensión mejor y más detallada de la complejidad de la autenticidad de los streamers en directo, sino que también contribuye al desarrollo de la teoría de la autenticidad al responder a los estudios sobre autenticidad individual.

This study aims to comparatively reveal the physical, thermal and mechanical properties of horse tail and mane hairs, which have the potential to be used in many areas.

Physical properties of horsehairs such as diameter, density, moisture and water absorption were measured. Fourier transform infrared, field emission scanning electron microscopy and amino acid analyzes were applied to the hairs. Thermal stability of horsehair was investigated by thermogravimetric analysis and differential scanning calorimetry analysis. In addition, breaking strength and elongation values of mane and tail hairs were measured.

As a result of morphological analysis of horsehair, it was observed that there are usually gaps in the internal structure of horsehair, but the size and continuity of these gaps vary. It has been determined that there is a significant difference between the tenacity values of tail and mane hairs as well as geometric characteristics. In addition, amino acid analysis has shown that the amino acid contents of horse tail and mane hairs are similar and not much different from sheep’s wool.

Horsehair has been used for various purposes such as clothing, accessories, brushes, upholstery and reinforcement material. To use horsehair in accordance with its application area and performance characteristics, it is necessary to know its physical, thermal and mechanical properties. It is considerable to reveal the similar and different aspects of tail and mane hairs to determine whether they are alternatives to each other. Therefore, revealing the characteristics of tail and mane hairs comparatively constitutes the originality of this study.

Gillette’s historically successful “razor and blade” business model (RBM) has been a promising benchmark for multiple businesses across diverse industries worldwide in the past several decades. The extant literature deals with very few nuances of this business model notwithstanding the fact that there are several variants of this business model being put to practical use by firms in diverse industries in grossly metaphorically equivalent situations.

This study adopts the 2 × 2 truth table framework from the domains of mathematical logic and combinatorics in fleshing out all possible (four logical possibilities) variants of the razor and blade business model for further analysis. This application presents four mutually exclusive yet collectively exhaustive possibilities on any chosen dimension. Two major dimensions (viz., provision of subsidy and intra- or extra-firm involvement in the making of razors or blades or both) form part of the discussion in this paper. In addition, this study synthesizes and streamlines entrepreneurial wisdom from multiple intra-industry and inter-industry benchmarks in terms of real-time firms explicitly or implicitly adopting several variants of the RBM that suit their unique context and idiosyncratic trajectory of evolution in situations that are grossly reflective of the metaphorically equivalent scenario of razor and recurrent blades. Inductive method of research is carried out with real-time cases from diverse industries with a pivotally common pattern of razor and blade model in some form or the other.

Several new variants of the razor and blade model (much beyond what the extant literature explicitly projects) have been discovered from the multiple metaphorically equivalent cases of RBM across industries. All of these expand the portfolio of options that relevant entrepreneurial firms can explore and exploit the best possible option chosen from them, given their unique context and idiosyncratic trajectory of growth.

This study has enriched the literature by presenting and analyzing a more inclusive or perhaps comprehensive palette of explicit choices in the form of several variants of the RBM for the relevant entrepreneurial firms to choose from. Future research can undertake the task of comparing these variants of RBM with those of upcoming servitization business models such as guaranteed availability, subscription and performance-based contracting and exploring the prospects of diverse combinations.

Smart entrepreneurial firms identify and adopt inspiring benchmarks (like razor and blade model whenever appropriate) duly tweaked and blended into a gestalt benchmark for optimal profits and attractive market shares. They target diverse market segments for tied-goods with different variants or combinations of the relevant benchmarks in the form of variegated customer value propositions (CVPs) that have unique and enticing appeal to the respective market segments.

Value-sensitive customers on the rise globally choose the option that best suits them from among multiple alternatives offered by competing firms in the market. As long as the ratio of utility to price of such an offer is among the highest, even a no-frills CVP may be most appealing to one market segment while a plush CVP may be tempting to yet another market segment simultaneously. While professional business firms embrace resource leverage practices consciously, amateur customers do so subconsciously. Each party subliminally desires to have the maximum bang-to-buck ratio as the optimal return on investment, given their priorities ceteris paribus.

Prior studies on the RBM have explicitly captured only a few variants of the razor and blade model. This study is perhaps the first of its kind that ferrets out many other variants (more than ten) of the razor and blade model with due simplification and exemplification, justification and demystification.

The purpose of this study is to look into the hygroscopic and tribo-mechanical properties of a polypropylene/polyamide-6 (PP/PA6) blend and a PP/PA6/Boron sesquioxide composite.

The hygroscopic behaviour of the PP/PA6 blend and PP/PA6/Boron sesquioxide composite was studied using a water contact angle goniometer in this study. To validate the hygroscopic behaviour of the blend and composite, water contact angles and surface energy of the materials were investigated. Tensile strength and hardness tests were used to determine mechanical characteristics, and tribological experiments on a pin-on-disc tribometer were used to demonstrate the friction and wear rates of dry and water-conditioned blends and composites. The melting temperature of dry and water-conditioned composites was determined using DSC analysis.

The hygroscopic effect of the PP/PA6 blend was found to be minimal in the experiment, while it was relatively dominating in the PP/PA6/Boron sesquioxide composite. Tensile strength was found to be somewhat lower in blend and composite compared to virgin PP, whereas hardness was found to be higher in both blend and composite. The composite’s tribological testing findings were fairly outstanding, with the coefficient of friction (COF) and wear rates significantly reduced due to boron sesquioxide reinforcement. The reaction between boron sesquioxide and water molecules produced boric acid, which increased the tribological characteristics of the composite even further. Following 30 days of water conditioning, the weight of the blend increased by 3.64% and the weight of the composite increased by 6.45% as compared to the dry materials. After water conditioning, tensile strength reduced by 0.8% for the blend and 14.16% for the composite. Hardness was determined to be the same in the dry state and after water-conditioning for blend but dropped 1% for composite. As compared to blend, the COF and wear resistance of composite were 15.52% and 25.16% higher, respectively. After absorbing some water, the results increased to 28.57% and 34.9%, respectively.

The mechanical and thermal behaviour of polymer composites (particularly polyamide composites) vary depending on the surrounding environment. Tests were carried out to explore the effect of water treatment on the tribo-mechanical and thermal characteristics of PP/PA6/Boron sesquioxide composite. Water treatment caused polyamides to bind with water molecules, resulting in voids in the material. The interaction between boron sesquioxide and water molecules produced boric acid, which increased the tribological characteristics of the composite.