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This study conceptualizes a form of luxury consumption in which luxury brands collaborate with unconventional non-luxury partners. These unconventional luxury brand collaborations are growing in popularity among Chinese luxury consumers of the post-1990s generation. Luxury brands are exploring new branding strategies due to the growing commercial importance of Chinese luxury consumers.

An in-depth qualitative study informs this paper. Interviews with young adult luxury consumers self-identifying as Chinese reveal a growing interest for luxury brands that collaborate with odd partners in social media and online culture.

Unconventional collaborations between luxury brands and non-luxury partners catalyze shifting meanings of luxury through the following juxtapositions: ephemeral instead of timeless, trendy rather than inaccessible, and playful in contrast with traditional. First, young Chinese consumers construct luxury meanings through ephemerality, like digital possessions, social media fame and fleeting experiences. Second, luxury meanings emerge in trendiness among social media influencers and online culture rather than in the seemingly inaccessible taste regimes of the upper class. Third, younger consumers appreciate fun, rebellious and over-the-top aesthetics in luxury brands.

The study contributes to the nascent field of unconventional luxury by conceptualizing how unusual, odd and unexpected collaborations constitute new forms of luxury consumption. The shifting meanings of luxury consumption that this study conceptualizes raise new opportunities and challenges for luxury brands. One of such is the release of limited collections with non-luxury partners seemingly at the opposite spectrum of design, image and values. Moreover, the study adds nuance to the understanding of luxury consumption among young Chinese consumers.

The number of patents in China has grown rapidly in recent years. The purpose of this paper is to investigate how patents impact economic development in China.

This paper developed an empirical model by using panel data of 42 China's patent-intensive industries to investigate the economic contribution made by Chinese patent-intensive manufacturing industries.

This paper found that the intensity of valid patents is strongly positively related to economic growth. The intensity of yearly added patents presented an inverse U-shaped and a U-shaped curve with the economy made by China’s patent-intensive industries. The correlativity mainly depended on whether the patent intensity converges near the economic indicators. Meanwhile, from the perspective of input–output efficiency, for China’s patent-intensive industries, R&D institutes were overinvested, followed by R&D intensity and R&D staff.

Investigating patent influence on economic development is quite complex research. Existing studies have mainly focused on patent protection in legal systems, but have not provided a definitive answer to what the real influence is. This study sought to narrow this gap from the patent economy perspective.

This study aims to compare the purchase intention of counterfeit outdoor products between Taiwan and Hong Kong consumers.

A total of 584 respondents from Hong Kong (n = 247, 42%) and Taiwan (n = 337, 58%) were recruited for the study. Data analysis was performed by using structural equation modelling techniques.

The results showed that consumers' perceived risk had a negative influence on attitude and intention to purchase counterfeit outdoor products. Moreover, attitude towards buying counterfeit outdoor products, perceived behavioural control and subjective norm had positive impacts on purchase intention. Brand consciousness, however, had a negative influence on purchase intention. The multi-group analysis identified significant differences between Hong Kong and Taiwanese respondents.

This study provides a better understanding of how these factors affect purchase intention of counterfeit outdoor products across different cultures.

This study aims to explore how and why self-discrepancy affects materialism and impulsive buying and the extent to which subjective well-being mediates the relationship between self-discrepancy, materialism and impulsive buying.

The authors have tested the hypothesis with a convenience sample (N = 434) from Lithuania. Descriptive analysis, principal components analysis (PCA), serial mediation hypothesis tested with model 81 from regression-based path analysis modeling tool PROCESS Macro for IBM® SPSS® Statistics 24.7 statistical software.

The serial and parallel mediation analysis results indicated that greater self-discrepancy was related to poorer life satisfaction, which was related to greater materialism centrality, which promoted greater impulsive buying. Also, the greater the self-discrepancy, caused more occurrence of negative affect, which relates to increased materialism happiness, which triggers impulsive buying. Self-discrepancy was negatively associated with the frequency of positive affect, which was positively related to materialism, which stimulates impulsive buying.

The study was dominated by younger respondents. The survey was conducted during the lockdown of the Covid-19 virus pandemic.

There is little empirical evidence to support the reasoning behind why self-discrepancy predicts a higher degree of materialism, which increases impulsive buying. This study suggests the mechanism of how subjective well-being affects relationships of self-discrepancy on materialism and impulsive buying.

The purpose of the paper is to propose and demonstrate a novel approach for addressing the challenges of path planning and obstacle avoidance in the context of mobile robots (MR). The specific objectives and purposes outlined in the paper include: introducing a new methodology that combines Q-learning with dynamic reward to improve the efficiency of path planning and obstacle avoidance. Enhancing the navigation of MR through unfamiliar environments by reducing blind exploration and accelerating the convergence to optimal solutions and demonstrating through simulation results that the proposed method, dynamic reward-enhanced Q-learning (DRQL), outperforms existing approaches in terms of achieving convergence to an optimal action strategy more efficiently, requiring less time and improving path exploration with fewer steps and higher average rewards.

The design adopted in this paper to achieve its purposes involves the following key components: (1) Combination of Q-learning and dynamic reward: the paper’s design integrates Q-learning, a popular reinforcement learning technique, with dynamic reward mechanisms. This combination forms the foundation of the approach. Q-learning is used to learn and update the robot’s action-value function, while dynamic rewards are introduced to guide the robot’s actions effectively. (2) Data accumulation during navigation: when a MR navigates through an unfamiliar environment, it accumulates experience data. This data collection is a crucial part of the design, as it enables the robot to learn from its interactions with the environment. (3) Dynamic reward integration: dynamic reward mechanisms are integrated into the Q-learning process. These mechanisms provide feedback to the robot based on its actions, guiding it to make decisions that lead to better outcomes. Dynamic rewards help reduce blind exploration, which can be time-consuming and inefficient and promote faster convergence to optimal solutions. (4) Simulation-based evaluation: to assess the effectiveness of the proposed approach, the design includes a simulation-based evaluation. This evaluation uses simulated environments and scenarios to test the performance of the DRQL method. (5) Performance metrics: the design incorporates performance metrics to measure the success of the approach. These metrics likely include measures of convergence speed, exploration efficiency, the number of steps taken and the average rewards obtained during the robot’s navigation.

The findings of the paper can be summarized as follows: (1) Efficient path planning and obstacle avoidance: the paper’s proposed approach, DRQL, leads to more efficient path planning and obstacle avoidance for MR. This is achieved through the combination of Q-learning and dynamic reward mechanisms, which guide the robot’s actions effectively. (2) Faster convergence to optimal solutions: DRQL accelerates the convergence of the MR to optimal action strategies. Dynamic rewards help reduce the need for blind exploration, which typically consumes time and this results in a quicker attainment of optimal solutions. (3) Reduced exploration time: the integration of dynamic reward mechanisms significantly reduces the time required for exploration during navigation. This reduction in exploration time contributes to more efficient and quicker path planning. (4) Improved path exploration: the results from the simulations indicate that the DRQL method leads to improved path exploration in unknown environments. The robot takes fewer steps to reach its destination, which is a crucial indicator of efficiency. (5) Higher average rewards: the paper’s findings reveal that MR using DRQL receive higher average rewards during their navigation. This suggests that the proposed approach results in better decision-making and more successful navigation.

The paper’s originality stems from its unique combination of Q-learning and dynamic rewards, its focus on efficiency and speed in MR navigation and its ability to enhance path exploration and average rewards. These original contributions have the potential to advance the field of mobile robotics by addressing critical challenges in path planning and obstacle avoidance.

The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.

The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.