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This study identifies a methodology to explore the issues of enterprise innovation ecosystem health (EIEH).

First, this study constructs the indicator system of EIEH based on the research objective; second, the dynamic vertical projection method (DVPM) and entropy weight method are proposed to analyze the status and influencing factors of EIEH; finally, the future development of EIEH is analyzed using GM (1,1).

In terms of methodology, the DVPM can effectively analyze EIEH, which can not only analyze the development status and potential of EIEH every year but also analyze the comprehensive state of EIEH for many years. In terms of practice, the value and grade of EIEH in China have been gradually increasing from 2016 to 2020, but the overall development is unbalanced, and five key factors affecting EIEH have been identified. The EIEH in China is predicted to steadily grow from 2021 to 2025.

The analytical method employed in this study can effectively analyze EIEH, which provides a new analytical perspective for the evaluation of EIEH and enriches the research content of the enterprise innovation ecosystem (EIE). By analyzing the results, we can gain a comprehensive understanding of the state of different EIEs, enabling each EIE to design tailored remedial measures to enhance EIEH and achieve sustainable development.

Current multi-source image fusion methods frequently overlook the issue of detailed features when employing deep learning technology, resulting in inadequate target feature information. In real-world mission scenarios, such as military information acquisition or medical image enhancement, the prominence of target feature information is of paramount importance. To address these challenges, this paper introduces a novel infrared-visible light fusion model.

Leveraging the foundational architecture of the traditional DenseFuse model, this paper optimizes the backbone network structure and incorporates a Unique Feature Encoder (UFE) to meticulously extract the distinctive features inherent in the two images. Furthermore, it integrates the Convolutional Block Attention Module (CBAM) and the Squeeze and Excitation Network (SE) to enhance and replace the original spatial and channel attention mechanisms.

Compared to other methods such as IFCNN, NestFuse, DenseFuse, etc., the values of entropy, standard deviation, and mutual information index of the method presented in this paper can reach 6.9985, 82.6652, and 13.6022, respectively, which are significantly improved compared with other methods.

This paper presents a UFEFusion framework that synergizes with the CBAM attention mechanism to markedly augment the extraction of detailed features relative to other methods. Moreover, the framework adeptly extracts and amplifies unique features from disparate images, thereby elevating the overall feature representation capability.

This paper aims to study the possibility of electroplating copper coatings on chemically and chemical-galvanically nickel-plated acrylic fibers, to be further processed into yarn, fabrics, knitwear and nonwoven materials.

Electrically conductive fibers with different copper contents have been obtained, and the effect of electrolyte pH, its composition, current strength at the first and second cathodes, as well as the metallization time on the electrophysical, physical and mechanical properties of copper-containing fibers, has been studied.

The studies have shown that with an increase in the copper content, the electrical conductivity, the uniformity of the coating and the uniformity of the electrophysical properties (for chemical-galvanically nickel-plated fiber) increase. In the case of copper plating of chemically nickel-plated fiber, the coefficient of variation in electrical resistance increases with increasing plating time, even though the copper content increases, and the coefficient of variation in copper content and electrical resistance decreases. The physical and mechanical properties of copper-containing fibers differ slightly from the original (subjected to copper plating) and industrial Nitron fibers. With copper plating, the strength of the fiber practically does not decrease, and the elongation decreases somewhat, compared with the mass-produced Nitron fiber.

The physical and mechanical properties of copper-containing fibers are quite high, which makes it possible to be successfully further processed into yarn, fabrics, knitwear and nonwoven materials.

This paper aims to regulate the oil retention rate and tribological properties of pored polytetrafluoroethylene (PPTFE) using polyvinyl alcohol (PVA)-based oil gel.

PPTFE was first prepared by using citric acid (CA) as an efficient pore-making agent. Subsequently, PVA and chitosan solution was introduced into the pores and experienced a freezing-thawing process, forming PVA-based gels inside the pores. Then, the PPTFE/PVA composite was impregnated with polyethylene glycol 200 (PEG200), yielding an oil-impregnated PPTFE/PVA/PEG200 composite.

It was found that the oil-impregnated PPTFE/PVA/PEG200 composite exhibited advanced tribological properties than neat PTFE with reductions of 53% and 70% in coefficient of friction and wear rate, respectively.

This study shows an efficient strategy to regulate the tribological property of PTFE using a PVA-based oil-containing gel.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0432/

This study aims to gain a better understanding of artificial serendipity – pre-planned surprises intentionally crafted through deliberate designs – in online marketplaces. By exploring the key features of artificial serendipity, this study investigates whether serendipity can be intentionally designed, particularly with the use of artificial intelligence (AI). The findings from this research broaden the scope of serendipity studies, making them more relevant and applicable in the context of the AI era.

A narrative study was conducted, gathering insights from 32 Chinese online consumers through diaries and interviews. The data were analysed in close collaboration with participants, ensuring an authentic reflection of their perceptions regarding the features of artificial serendipity in online marketplaces.

Findings reveal that artificial serendipity, particularly when designed by AI, is still regarded by online consumers as genuine serendipity. It provides a sense of real surprise and encourages deeper reflection on personal knowledge, affording the two central qualities of genuine serendipity: unexpectedness and valuableness. However, since artificial serendipity is pre-planned through intentional design, consumers cannot have entire control over it. Therefore, compared to natural serendipity – fortune surprises arising from accidental correspondence between individuals and contexts – artificial serendipity tends to be more surprising yet less valuable.

For research, it highlights the potential of intelligent technologies to facilitate genuine serendipity, updating our understanding of serendipity.

Also, the study provides practical insights into designing serendipity, especially in online markets. These contributions enrich both the theoretical framework and practical strategies surrounding serendipity in the era of AI.

This study stands out as one of the few to provide a nuanced understanding of artificial serendipity, offering valuable insights for both research and practice. For research, it highlights the potential of intelligent technologies to facilitate genuine serendipity, updating our understanding of serendipity. Also, the study provides practical insights into designing serendipity, especially in online markets. These contributions enrich both the theoretical framework and practical strategies surrounding serendipity in the era of AI.

This research aims to introduce the concept of information snowballing, which shows the process of how individuals with a specific interest in food sustainability acquire information about and remain abreast of sustainability-related information in everyday life and food practices.

This study employed constructivist grounded theory to investigate the information acquisition practices of consumers of sustainable food around topics related to sustainability. The author collected data through pre-screening phone calls, photo diaries and in-depth semi-structured interviews by using theoretical sampling with nine sustainable food consumers; analyzed data through constant comparison and memoing; and validated findings via methodological jotting, data triangulation and peer-debriefing throughout the process.

The findings identify three stages of information snowballing: (a) learning by lifelong exposure and serendipitous information encountering; (b) constructing information landscapes; and (c) snowballing or gradually accumulating information.

This concept advocates for spreading sustainability-related information and awareness outside the context of formal education.

This study originates the concept of “information snowballing,” which evolves from the idea of snowball sampling, a common strategy for participant recruitment. Both of these terms lean on the idea that a snowball rolling downhill picks up more and more momentum and more and more snow as it travels; recruitment and information gathering show the same process of accretion through building momentum. Information snowballing highlights the individual’s agency in seeking relevant information sources, expressing one’s interests, constructing information landscapes—all activities that precede the serendipitous encountering and snowballing of information based on a particular interest. It also connects everyday information practices to information landscapes within the context of everyday food practices.

The extensive brand associations research lacks organisation when it comes to the used information cues. This paper aims to systematically map and categorise the brand knowledge associations’ components and develop a typology applicable to any brand.

Using the restaurant and hotel industries in four different European cultural clusters as contexts, this work uses well-established systematic qualitative analysis approaches to categorise, code and model pictorial content in two studies. A four-stage sampling process identified Instagram brand-posted signals (photos), 243 from 26 restaurants in Madrid, Paris and Rome for study one and 390 from 29 hotels in Moscow, Berlin and Stockholm for study two. Adhering to relevant guidelines, the manual coding procedures progressed from 246 for restaurants and 231 for hotels initially generated free information coding inductive codes to a theory-informed categorisation. Quantitative analysis complemented the qualitative analysis, revealing the information cues relative utilisation.

For both studies, the analysis produced a typology consisting of two high-level and five lower-level brand knowledge association categories, namely: (a) brand characteristics consisting of the brand as a symbol, the brand as a product and the brand as a person, and (b) brand imagery consisting of user imagery and experience imagery. The five lower-level categories comprise of sub-categories and dimensions, providing a more comprehensive understanding of the brand associations conceptual structure relevant to brands operating in any industry.

Researchers can use this typology to holistically encapsulate brand associations or design projects aiming to deepen brand knowledge association aspects/dimensions understanding.

Managers can use this typology to portray brands. Some of the identified lower-level categories and/or sub-categories and dimensions are likely to need customisation to fit specific contexts.

The suggested categorisation offers a solid, comprehensive framework for effectively categorising and coding brand knowledge associations and proposes a new theory in the form of a typology.

This study aims to minimize the warpage issue in memory-computing integrated chiplets with 2.5D packaging on a large-scale wafer subjected to multistress through synergistic optimization of key structure paraeters.

In this study, memory-computing integrated chiplet-based 2.5D packaging was designed and the warpage optimization under electro-thermal-vibration coupling was conducted with finite element analysis simulation. Compared studies were also conducted with the imposing condition of single electrical and thermal stress.

The research results indicated that electrical and thermal stress had a significant impact on the packaging warpage while that of vibration on warpage was minimal. For structure parameters, the chiplet thickness had a significant effect on the warpage of the model, while the influence of chiplet size was relatively small. When the chiplet thickness decreased to 100 µm, the warpage was reduced by 8.96%. Under thermal stress loading, the impact of packaging density on the overall warpage of the chiplet-based 2.5D packaging model is relatively small. However, under the loading of electrical stress or electro-thermal coupling, the packaging density has a severe impact on warpage, with the maximum approaching 1.3011 µm for just one chiplet. Compared with vibration alone, electro-thermal-vibration coupling slightly increased the warpage, which is primarily evident in the exacerbation of warpage in stacked chiplets and edge locations. Lower packaging density with 150 µm chiplet thickness contributed to a minimal warpage in stacked chiplet, the edge locations of which exhibited a relatively severe warpage.

The research provides a theoretical basis for warpage optimization of memory-computing integrated chiplets with 2.5D packaging subjected to multistress coupling.

When designing the layout of chips for large-sized high-reliability silicon substrates, this method can be used to control the most severely warped areas at the edges, thus making it easier to optimize warpage.

With the approaching of physical limits dictated by Moore’s Law, advanced packaging technologies, primarily centered around 2.5D packaging and three-dimensional packaging, were attached importance to. It can integrated multifunctional chips, which have become increasingly complex, leading to greater difficulty in design and implementation, and an obvious increase in overall manufacturing costs. In this context, chiplets offered a viable approach for future chip designs. With the high demand for high-performance computing, memory-computing integrated chiplets were designed. The warpage of 2.5D packaging by altering structural parameters under multiphysical field coupling conditions was studied to provide a theoretical basis for warpage optimization of memory-computing integrated chiplets with 2.5D packaging.

Drawing on the tenets of institutional theory, the purpose of this study is to examine the impact of Confucianism on technology for social good, while also considering the moderating influence of extrinsic informal institutions (foreign culture) and intrinsic formal institutions (property rights).

This study constructs a comprehensive database comprising 9,759 firm-year observations in China by using a sample of Chinese A-share listed firms from 2016 to 2020. Subsequently, the hypotheses are examined and confirmed, with the validity of the results being upheld even after conducting endogenous and robustness tests.

The findings of this study offer robust and consistent evidence supporting the notion that Confucianism positively affects technology for social good through both incentive effect and normative effect. Moreover, this positive influence is particularly prominent in organizations with limited exposure to foreign culture and in nonstate-owned enterprises.

The findings contribute to the literature by fostering a deep understanding of technology for social good and Confucianism research, and further provide a nuanced picture of the role of foreign culture and property rights in the process of technology for social good in China.

This study aims to provide a new perspective on the impact of mortality threats on consumer behavior through the lens of brand anthropomorphism. It examines the mediating effects of control and connectedness motives and the moderating effects of brand roles on the relationship between mediators and brand attitudes.

A preliminary study explored the relationship between pandemic-induced mortality threats and attitudes toward anthropomorphized brands. Study 1 investigated the underlying mechanism, and Study 2 examined the moderating effects of servant or partner roles. Study 3 confirmed the mortality threat effect on anthropomorphic brand attitudes in the absence of the pandemic.

The study revealed that mortality threats enhanced the desire for control and connectedness, which strengthened attitudes toward anthropomorphized brands. The results also indicated matching effects between the motivations for anthropomorphism and brand roles.

This research offers novel insights into the effects of pandemic-induced mortality threats and mortality threats in non-pandemic contexts on anthropomorphic motives. It highlights the influence of these psychological needs on consumer responses to brand roles and provides insights for brand management during a crisis.