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With the development of science and technology, more creators are trying to use new crafts to represent the cultural trends of the social media era, which makes cultural heritage innovative and new genres emerge. This compels the academic community to examine craft from a new perspective. It is very helpful to understand the hidden representational structure of craft more deeply and improve the craft innovation system of cultural and creative products that we deconstruct the craft based on Complex Network and discover its intrinsic connections.

The research crawled and cleaned the craft information of the top 20% products on the Forbidden City’s cultural and creative products online and then performed Complex Network modeling, constructed three craft representation networks among function, material and technique, quantified and analyzed the inner connections and network structure of the craft elements, and then analyzed the cultural inheritance and innovation embedded in the craft representation networks.

The three dichotomous craft representation networks constructed by combining function, material and technique: (1) the network density is low and none of them has small-world characteristics, indicating that the innovative heritage of the craft elements in the Forbidden City’s cultural and creative products is at the stage of continuous exploration and development, and multiple coupling innovation is still insufficient; (2) all have scale-free characteristics and there is still a certain degree of community structure within each network, indicating that the coupling innovation of craft elements of the Forbidden City’s cultural and creative products is seriously uneven, with some specific “grammatical combinations” and an Island Effect in the network structure; (3) the craft elements with high network centrality emphasize the characteristics of decorative culture and design for the masses, as well as the pursuit of production efficiency and economic benefits, which represent the aesthetic purport of contemporary Chinese society and the ideological trend of production and life.

The Forbidden City’s cultural and creative products should continue to develop and enrich the multi-coupling innovation of craft elements, clarify and continue their own brand unique craft genes, and make full use of the network important nodes role.

This paper aims to develop an optimal maintenance and spare parts policy for an urban micro wind power system, focusing on two urban micro wind farms (UMWF). The reliability and efficiency of these systems are sought to be enhanced by considering the relationship between urban wind parameters and wind turbine degradation.

A proportional hazards (PH) model is utilized to describe how urban wind conditions impact turbine degradation. The maintenance strategy includes preventive maintenance (PM), corrective maintenance (CM) and opportunistic maintenance (OM). A multi-objective optimization algorithm is developed to optimize the joint policy of OM plans and spare parts resource allocation.

The proposed maintenance and spare parts policy effectively balances the trade-offs between PM, CM and OM strategies. Numerical experiments demonstrate that the policy improves the reliability of UMWF, reducing downtime and maintenance costs while ensuring the availability of spare parts when needed. The results show a significant enhancement in system performance compared to traditional maintenance approaches.

A novel maintenance policy and spare parts management approach for urban micro wind power systems is proposed. A multi-objective optimization algorithm is developed to optimize the OM schedule and maintenance spare parts resource management strategy for wind farms in urban wind environments.

The current study aims to investigate how young smartphone users in Bangladesh relate their brand experiences to brand loyalty. In addition, we want to visualize the direct and mediating effects of brand satisfaction, brand love and brand advocacy in our model.

The researchers examined the hypotheses by employing structural equation modeling (SEM) in AMOS and Decision Analyst STATS, version 2.0, with a sample size of 470 Bangladeshi smartphone users. The authors constructed the conceptual model by drawing upon both theoretical and empirical foundations. The researchers obtained data by utilizing an adopted and self-administered pre-structured questionnaire distributed via an online platform.

The results showed that brand experience greatly influences brand satisfaction, love, advocacy and loyalty, all of which have a significant impact on users’ brand loyalty across the country. The findings also suggested that the function of brand satisfaction as a critical mediator in the link between brand experience and brand loyalty was significant.

This experiment contributes to the body of knowledge by focusing on emotional brand attachments like brand satisfaction, love and advocacy and proposing that they can mediate experience and loyalty in the mobile market. The study also helps managers and executives better understand the primary drivers of smartphones, which are essential for generating and sustaining consumers’ happiness and loyalty in today’s highly competitive consumer market.

The rapid global changes we are witnessing pose a pressing challenge that necessitates reevaluating conventional supply chain practices. Consequently, the integration of digital technologies into supply chain operations, often referred to as digital supply chain (DSC), has emerged as a strategic shift that aims to empower organizations to proactively seize new opportunities rather than being caught off guard by unforeseen disruptions arising from economic volatility, global pandemics and regional conflicts. Thus, this study embraces a knowledge-centric approach to explore the direct and indirect impact of knowledge management, innovation and learning capabilities on DSC adoption in an emerging economy context. Furthermore, it aims to shed light on the moderating role of environmental dynamism in this intricate interplay.

Employing a cross-sectional survey, the research data were collected from 354 managers representing Egyptian manufacturing and service firms utilizing a structured questionnaire. Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM).

The results unveiled that knowledge management capability (KMC) has the highest path coefficient value among all endogenous variables. It accounts for a significant portion of the variance in innovation and learning capabilities, which play pivotal roles in adopting DSC. Notably, learning capability appears to exert a more powerful influence on DSC adoption than innovation capability through direct and mediating effects. Furthermore, the findings underscore the moderating effect of environmental dynamism on the relationship between learning capability and DSC adoption. However, this moderating role is not observed in the link between innovation capability and DSC adoption.

There is a growing trend among firms to adopt DSC in response to significant environmental shifts. This study offers valuable insights for managers and policymakers, providing them with a deeper understanding of the DSC adoption process. The study’s findings assist in identifying crucial factors that boost DSC adoption and offer guidance on successfully leveraging digital technologies for managing supply chain practices. Moreover, the study offers stimulating directions for future DSC research directions.

The study contributes to the existing literature by expanding our understanding of the adoption of DSC by utilizing knowledge, innovation and learning capabilities within the context of emerging economies.

Agriculture commodity price forecasts have long been important for a variety of market players. The study we conducted aims to address this difficulty by examining the weekly wholesale price index of green grams in the Chinese market. The index covers a ten-year period, from January 1, 2010, to January 3, 2020, and has significant economic implications.

In order to address the nonlinear patterns present in the price time series, we investigate the nonlinear auto-regressive neural network as the forecast model. This modeling technique is able to combine a variety of basic nonlinear functions to approximate more complex nonlinear characteristics. Specifically, we examine prediction performance that corresponds to several configurations across data splitting ratios, hidden neuron and delay counts, and model estimation approaches.

Our model turns out to be rather simple and yields forecasts with good stability and accuracy. Relative root mean square errors throughout training, validation and testing are specifically 4.34, 4.71 and 3.98%, respectively. The results of benchmark research show that the neural network produces statistically considerably better performance when compared to other machine learning models and classic time-series econometric methods.

Utilizing our findings as independent technical price forecasts would be one use. Alternatively, policy research and fresh insights into price patterns might be achieved by combining them with other (basic) prediction outputs.

In today’s competitive business landscape, organizations are increasingly recognizing the strategic advantage of implementing sustainable practices to gain a competitive edge. This study aims to investigate the effect of green artificial intelligence (AI) on achieving a green competitive advantage, examining the mediating roles of green organizational learning, green product innovation and green process innovation. Additionally, the research explores the moderating role of perceived green climate in the relationship between green AI and these mediating factors.

This research examined companies in Isfahan, Iran, that have varying levels of artificial intelligence adoption within their business processes. The target population consisted of 148 senior managers from these companies. This study uses structural equation modeling to examine the proposed model.

Green AI positively impacted green organizational learning and green process innovation but not green product innovation. In addition, the results showed that green organizational learning, green product innovation and green process innovation had positive effects on green competitive advantage. Finally, the results showed that the perceived green climate did not play a moderating role in the relationship between green AI and these mediating factors.

Organizations should prioritize green AI initiatives, foster a culture of green learning and invest in green innovation to achieve sustainable growth and outpace competitors in the environmentally conscious marketplace.

This study positions itself at the forefront of research on green AI and green competitive advantage. It offers a unique framework by examining the combined effects of green AI, green learning and both product and process innovation on achieving a sustainable competitive advantage.

This study aimed to identify a specific taxonomy of entry modes for international construction contractors and to develop a decision-making mechanism based on case-based reasoning (CBR) to facilitate the selection of the most suitable entry modes.

According to the experience orientation of the construction industry, a CBR entry mode decision model was established, and based on successful historical cases, a two-step refinement process was carried out to identify similar situations. Then the validity of the model is proved by case analysis.

This study identified an entry mode taxonomy for international construction contractors (ICCs) and explored their decision-making mechanisms. First, a two-dimension model of entry mode for ICCs was constructed from ownership and value chain dimensions; seven common ICC entry modes were identified and ranked according to market commitment. Secondly, this study reveals the impact mechanism of the ICC entry mode from two aspects: the external environment and enterprise characteristics. Accordingly, an entry mode decision model is established.

Firstly, sorting out the categories of entry mode in the construction field, which provide an entry mode list for ICCs to select. Secondly, revealing the impact mechanism of ICC entry mode, which proposes a systematic decision-making system for the selection of ICC entry mode. Thirdly, constructing a CBR entry mode decision-making model from an empirical perspective, which offers tool support and reduces transaction costs in the decision-making process.

The study on entry modes for ICCs is still in the preliminary exploratory stage. The authors investigate the entry mode categories and decision-making mechanisms for ICCs based on Uppsala internationalization process theory. It widens the applied scope of Uppsala and promotes cross-disciplinary integration. In addition, the authors creatively propose a two-stage retrieval mechanism in the CBR model, which considers the order of decision variables. It refines the influence path of the decision variables on ICCs' entry mode.

This study investigates the effect of entrepreneurial orientation and government support on the innovation openness of South Korean manufacturing firms. By compartmentalizing entrepreneurial orientation and government support, this study seeks to elucidate their effects on the breadth and depth of firms' knowledge searches.

The study utilizes Poisson regression analysis with empirical data from 2,353 South Korean manufacturing firms collected from Korean Innovation Survey data.

Analysis reveals nuanced influences of various factors on firms' open innovation activities. Innovativeness positively affects knowledge search breadth and depth, highlighting the role of innovation-oriented processes, while competitive aggressiveness influences depth, thereby underscoring the pursuit of specialized knowledge acquisition. Proactiveness positively affects breadth but negatively affects depth, suggesting the importance of maintaining broad perspectives. Government support scope positively affects both breadth and depth, reflecting the significance of external support in encouraging inclusive knowledge exploration. The importance of government support positively influences depth, emphasizing its vital role in establishing collaborative relationships to enhance in-depth knowledge acquisition.

This study provides novel insights by exploring the nuanced implementation of open innovation in latecomer economies, especially South Korea, which experienced rapid economic development by leveraging innovation. The identification of specific factors influencing knowledge search breadth and depth contributes to the literature on open innovation by providing strategic guidelines for firms and policymakers. This practical implications of this study suggest that firms should align their strategies with entrepreneurial orientation while leveraging pertinent government support.

This study aims to explore the impact of emotional cues in knowledge product descriptions on users’ willingness to gather information. It specifically focuses on how different types of textual emotional cues, including heuristic cues like “emotional titles” and systematic cues like “emotional synopses,” influence users’ information-gathering willingness and examines the mediating role of emotional arousal in this process.

A conceptual model was developed by integrating the heuristic-systematic model with cue utilization theory. The experimental design employed knowledge product descriptions from the “Knowledge Column” section of the Zhihu platform. A controlled experiment was conducted to investigate the effect of varying emotional cues in these descriptions on participants’ willingness to gather information.

The study identified two types of emotional cues – heuristic cues, such as “emotional titles,” and systematic cues, such as “emotional synopses” – that significantly and positively influence users' information-gathering willingness. Additionally, emotional arousal was found to mediate the relationship between emotional cues and users’ willingness to gather information in the context of knowledge payments.

This study confirms that emotional cues in knowledge product descriptions, mediated by emotional arousal, can enhance the information-gathering willingness of knowledge payment users. The research deepens the theoretical exploration of information behavior among online knowledge payment users, providing valuable insights for knowledge producers on effectively leveraging emotional cues to attract potential customers as well as offering guidance for knowledge payment users in their information-gathering practices.

How to support the rapid internationalization of multinational enterprises (MNEs) is a hot topic in academia and industry. The main purpose of this work is to study the role of relational assets (R-assets) in promoting the speed of internationalization of MNEs, and to explore the moderating effect of environmental uncertainty (institutional environment and industry environment) on the relationship between R-assets and internationalization speed of MNEs.

This study uses the outward foreign investment data of China’s A-share listed enterprises from 2009 to 2021, and employs the Cox proportional hazards model to empirically test the research hypothesis.

The empirical results revealed that R-assets can promote enterprise internationalization speed. In addition, the study also finds that the institutional uncertainty of host countries weakens the promotion effect of R-assets on internationalization speed of MNEs, while the industry uncertainty strengthens the promotion effect of those. Heterogeneity analysis illustrates that, compared with state-owned enterprises, non-state-owned enterprises have a more significant effect on the above conclusions.

This study enriches the literature on internationalization speed of MNEs by focusing on the determinants of internationalization speed through R-assets. From the perspective of knowledge, the work also provides a theoretical reference whereby MNEs can use host country R-assets to accelerate knowledge acquisition and then internationalization practice. In conclusion, this study provides valuable insights for managers aiming to develop effective strategies through R-assets to achieve rapid internationalization, contributing to an emerging literature stream on catch-up for emerging-market MNEs.