Jiang Daqi, Wang Hong, Zhou Bin and Wei Chunfeng
This paper aims to save time spent on manufacturing the data set and make the intelligent grasping system easy to deploy into a practical industrial environment. Due to the…
Abstract
Purpose
This paper aims to save time spent on manufacturing the data set and make the intelligent grasping system easy to deploy into a practical industrial environment. Due to the accuracy and robustness of the convolutional neural network, the success rate of the gripping operation reached a high level.
Design/Methodology/Approach
The proposed system comprises two diverse kinds of convolutional neuron network (CNN) algorithms used in different stages and a binocular eye-in-hand system on the end effector, which detects the position and orientation of workpiece. Both algorithms are trained by the data sets containing images and annotations, which are generated automatically by the proposed method.
Findings
The approach can be successfully applied to standard position-controlled robots common in the industry. The algorithm performs excellently in terms of elapsed time. Procession of a 256 × 256 image spends less than 0.1 s without relying on high-performance GPUs. The approach is validated in a series of grasping experiments. This method frees workers from monotonous work and improves factory productivity.
Originality/Value
The authors propose a novel neural network whose performance is tested to be excellent. Moreover, experimental results demonstrate that the proposed second level is extraordinary robust subject to environmental variations. The data sets are generated automatically which saves time spent on manufacturing the data set and makes the intelligent grasping system easy to deploy into a practical industrial environment. Due to the accuracy and robustness of the convolutional neural network, the success rate of the gripping operation reached a high level.
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Shuyi Kong, Mengling Xie, Wei Zhang, Chunfeng Xia, Xie Yi, Tamirat Solomon, Xinan Yin, Haifei Liu and Changhai Wang
This article aims to explore the key role of community participation in the protection and development of national parks under the global trend of national park development and…
Abstract
Purpose
This article aims to explore the key role of community participation in the protection and development of national parks under the global trend of national park development and provide reference for the construction of China’s national park system by analyzing international successful cases and experiences.
Design/methodology/approach
The study on “International Experience of Community Support for National Park Development” integrates multimethods, from data mining reports, journals and policy docs from WB, UNEP, to case analyses. In-depth interviews with policymakers, academics and farmers reveal needs, challenges and best practices. Comparative analysis tailors findings to China’s context, offering recommendations for enhancing community support. This hybrid approach ensures practical insights for China’s application.
Findings
The results of this study underscore the paramount significance of community participation as a cornerstone in advancing sustainable development and safeguarding national parks amidst a growing global awareness of environmental stewardship. Through a thorough examination of international National Parks such as Yellowstone, Maasai Mara, Uluru-Kata Tjuta and Sanjiangyuan, we reveal a blueprint of success that hinges on robust policy support, empowerment of local communities, strategic economic incentives and multifaceted cross-sectoral collaborations. In the context of domestic hurdles, including inadequate legal frameworks, narrow participation avenues and resource scarcities, our analysis outlines actionable recommendations aimed at fortifying policy and legal frameworks, establishing efficient engagement modalities, bolstering community capacity-building initiatives and fostering economic sustainability. This comprehensive approach presents a visionary roadmap for World’s national park system, guiding it towards achieving an optimal equilibrium where ecological integrity and community prosperity coexist harmoniously.
Originality/value
The article underscores the originality in illuminating the pivotal role of community participation in national park protection and development amidst a global shift. By delving into international exemplars like Yellowstone, Maasai Mara, Uluru-Kata Tjuta, Sanjiangyuan and Panda, it uncovers novel insights on policy frameworks, community empowerment, economic incentives and collaborative models. This work contributes to the burgeoning discourse on balancing ecological conservation with socioeconomic development, providing a blueprint for sustainable national park management of all the world.
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Hongming Gao, Hongwei Liu, Weizhen Lin and Chunfeng Chen
Purchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially…
Abstract
Purpose
Purchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially for e-retailers. To date, little is known about how e-retailers can scientifically detect users' intents within a purchase conversion funnel during their ongoing sessions and strategically optimize real-time marketing tactics corresponding to dynamic intent states. This study mainly aims to detect a real-time state of the conversion funnel based on graph theory, which refers to a five-class classification problem in the overt real-time choice decisions (RTCDs)—click, tag-to-wishlist, add-to-cart, remove-from-cart and purchase—during an ongoing session.
Design/methodology/approach
The authors propose a novel graph-theoretic framework to detect different states of the conversion funnel by identifying a user's unobserved mindset revealed from their navigation process graph, namely clickstream graph. First, the raw clickstream data are identified into individual sessions based on a 30-min time-out heuristic approach. Then, the authors convert each session into a sequence of temporal item-level clickstream graphs and conduct a temporal graph feature engineering according to the basic, single-, dyadic- and triadic-node and global characteristics. Furthermore, the synthetic minority oversampling technique is adopted to address with the problem of classifying imbalanced data. Finally, the authors train and test the proposed approach with several popular artificial intelligence algorithms.
Findings
The graph-theoretic approach validates that users' latent intent states within the conversion funnel can be interpreted as time-varying natures of their online graph footprints. In particular, the experimental results indicate that the graph-theoretic feature-oriented models achieve a substantial improvement of over 27% in line with the macro-average and micro-average area under the precision-recall curve, as compared to the conventional ones. In addition, the top five informative graph features for RTCDs are found to be Transitivity, Edge, Node, Degree and Reciprocity. In view of interpretability, the basic, single-, dyadic- and triadic-node and global characteristics of clickstream graphs have their specific advantages.
Practical implications
The findings suggest that the temporal graph-theoretic approach can form an efficient and powerful AI-based real-time intent detecting decision-support system. Different levels of graph features have their specific interpretability on RTCDs from the perspectives of consumer behavior and psychology, which provides a theoretical basis for the design of computer information systems and the optimization of the ongoing session intervention or recommendation in e-commerce.
Originality/value
To the best of the authors' knowledge, this is the first study to apply clickstream graphs and real-time decision choices in conversion prediction and detection. Most studies have only meditated on a binary classification problem, while this study applies a graph-theoretic approach in a five-class classification problem. In addition, this study constructs temporal item-level graphs to represent the original structure of clickstream session data based on graph theory. The time-varying characteristics of the proposed approach enhance the performance of purchase conversion detection during an ongoing session.
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Jun Wen, Carol Chunfeng Wang, Edmund Goh, Zhaohui Su and Tianyu Ying
This paper explores the role of traditional Chinese medicine (TCM) as a tourism recovery drawcard to boost China's inbound tourism after COVID-19.
Abstract
Purpose
This paper explores the role of traditional Chinese medicine (TCM) as a tourism recovery drawcard to boost China's inbound tourism after COVID-19.
Design/methodology/approach
This paper employed a mixed method involving a cross-disciplinary literature review along with reflections from experts in TCM and health communication to inform tourism management. Specifically, this paper examines TCM and its potential benefits as a medical tourism drawcard to combat COVID-19. The selected literature focusses on the image and merits of TCM to frame how this medical philosophy can be used to position China as a tourist destination. Reflections on the use of TCM as a tourism marketing tool can guide promotional strategies from the Chinese government and destination managers during and after COVID-19.
Findings
The Chinese government, the tourism industry (e.g. destination managers), the media and tourists must focus on three aspects of the role of TCM: to provide medical benefits to travellers amid COVID-19 and beyond, elevate China as a destination for global medical tourists and be leveraged as a tool for economic recovery.
Practical implications
The paper builds a tourism recovery framework for stakeholders to adopt tailored TCM communication strategies to boost its inbound tourism programme.
Originality/value
This paper is the first academic paper to review TCM comprehensively and critically in relation to China tourism and post-COVID-19 recovery measures.
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Chunfeng Chen and Depeng Zhang
Negative word-of-mouth has a variety of negative effects on companies. Thus, how consumers process and evaluate negative word-of-mouth is an important issue for companies. This…
Abstract
Purpose
Negative word-of-mouth has a variety of negative effects on companies. Thus, how consumers process and evaluate negative word-of-mouth is an important issue for companies. This research aims to investigate the effect of emotional intensity of negative word-of-mouth on consumers' perceived helpfulness.
Design/methodology/approach
The research model was developed based on attribution theory. A four-study approach involving two field experiments and two online experiments was employed to examine the proposed hypotheses.
Findings
The results show that the emotional intensity of negative word-of-mouth negatively affects altruistic motive attributions, while altruistic motive attributions positively affect perceived helpfulness and plays a mediating role in the relationship between the emotional intensity of negative word-of-mouth and perceived helpfulness. Consumers' self-construal moderates the effects of emotional intensity of negative word-of-mouth on altruistic motive attributions and perceived helpfulness, with the negative effects of emotional intensity of negative word-of-mouth on altruistic motive attributions and perceived helpfulness being weaker for consumers with high interdependent self-construal than for those with high independent self-construal.
Originality/value
The findings not only have a significant theoretical contribution, deepening the understanding of the effects of negative word-of-mouth but also have useful implications for practitioners to improve the management of negative word-of-mouth.
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Dan Ma, Chunfeng Wang, Zhenming Fang and Ziwei Wang
The purpose of this paper is to empirically examine the impact of closing mechanism changes on market quality, investor trading behavior and market manipulation in the Shanghai…
Abstract
Purpose
The purpose of this paper is to empirically examine the impact of closing mechanism changes on market quality, investor trading behavior and market manipulation in the Shanghai stock market.
Design/methodology/approach
A dummy variable is constructed indicating whether the closing mechanism is call auction or continuous auction. Market quality is measured from aspects of liquidity, volatility and price continuity; investor trading behavior is scaled by order timing and order aggressiveness, and a price deviation indicator is the proxy of manipulation. Using panel regression, this study examines the impact of closing mechanism changes based on intraday transaction data from the Shanghai stock market.
Findings
The conclusions are as follows: First, market quality improves after the closing mechanism is reformed in terms of liquidity, volatility and price continuity. Second, order strategy changes significantly in the closing call market, and investors trade more aggressively in the continuous trading period before closing. Third, the closing call mechanism restrains the closing price manipulation and thus prompts an efficient closing price.
Originality/value
This paper examines the policy effects of closing mechanism changes from aspects of market quality, trading behavior and price manipulation, providing pieces of evidence for trading mechanism design and market supervision in emerging markets.
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Chunfeng Chen and Depeng Zhang
This research aims to investigate the effects of innovation types (exploratory innovation vs. exploitative innovation) on users' psychological perceptions (perceived…
Abstract
Purpose
This research aims to investigate the effects of innovation types (exploratory innovation vs. exploitative innovation) on users' psychological perceptions (perceived self-improvement and prosocial impact) and continuous knowledge sharing intention and the moderating effects of monetary incentives.
Design/methodology/approach
The research model was developed based on the self-determination theory. A two-study approach involving an online survey (n = 338) and an online experiment (n = 160) was employed to collect quantitative data. Structural equation modeling and variance analysis were adapted to analyze the data.
Findings
The results show that exploratory innovation leads to higher perceived self-improvement among users than exploitative innovation, whereas exploitative innovation leads to higher perceived prosocial impact than exploratory innovation. The perceived self-improvement and perceived prosocial impact positively affects users' continuous knowledge sharing intention. Monetary incentives moderate the relationships among perceived self-improvement, perceived prosocial impact and continuous knowledge sharing intention.
Originality/value
This research highlights the role of users' experience of initial participation in forming continuous knowledge sharing intentions and also reveals the effectiveness of monetary incentives in different types of innovation activities. The findings provide a more comprehensive understanding of the antecedents of users' continuous knowledge sharing behavior, offering new insights and recommendations for managerial practitioners.
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Wenfeng Yuan, Sifeng Liu and Chaoqing Yuan
The paper attempts to establish a two‐dimension frame to analyze dynamic supplier risks of large‐scale and complex equipment and study the probability distribution of the…
Abstract
Purpose
The paper attempts to establish a two‐dimension frame to analyze dynamic supplier risks of large‐scale and complex equipment and study the probability distribution of the occurring risks.
Design/methodology/approach
Starting from the dynamic and correlated supplier risks of large‐scale and complex equipment, a two‐dimension frame to analyze these risks is established. A maximum entropy model is also built to research the probability distribution of the risks; then, K‐T conditions are proved to solve the model. A real case is also studied in the last part of the paper.
Findings
The results are convincing: in order to analyze the supplier risk dynamically of large‐scale and complex equipment development project, a two‐dimension analysis frame is established and a maximum entropy model is worked out. The case study shows that they are valuable.
Practical implications
The two‐dimension frame gives a new viewpoint for risk management, but also the maximum entropy model supplies a valuable method for risk management.
Originality/value
The paper succeeds in creating a dynamic analysis frame to study risks and building a new method to research the disciplines of the dynamic risks.
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The profound impact of the COVID-19 pandemic on the film industry has underscored the growing significance of online movies. However, there is limited research available on the…
Abstract
Purpose
The profound impact of the COVID-19 pandemic on the film industry has underscored the growing significance of online movies. However, there is limited research available on the factors that influence the viewership of online films. Therefore, this study aims to use the signaling theory to investigate how signals of varying qualities affect online movie viewership, considering both signal transmission costs and prices.
Design/methodology/approach
This study uses a sample of 1,071 online movies released on the iQiyi from July 2020 to July 2022. It uses OLS regression and instrumental variable method to examine the impact of various quality indicators on the viewership of online movies, as well as the moderating effect of price.
Findings
After conducting a thorough analysis of this study, it can be deduced that the varying impacts on online movie viewership are attributed to disparities in signal transmission costs. Specifically, star influence and rating exhibit a positive effect on the viewership of online movies, whereas the number of raters has a detrimental impact. Furthermore, there exists an “inverted U-shaped” relationship between the number of reviews and online movie viewership. Additionally, within the consumer decision-making process, both price-cost and price-quality relationships coexist. This is evident as prices negatively affect online movie viewership but positively moderate the relationship between rating, number of reviews and online movie viewership.
Originality/value
The research findings of this study offer valuable insights for online film producers to effectively leverage quality signals and pricing, thereby capturing market attention and enhancing film profitability.
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Dingguo Yu, Nan Chen and Xu Ran
With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing…
Abstract
Purpose
With the development and application of mobile internet access, social media represented by Weibo, WeChat, etc. has become the main channel for information release and sharing. High-impact users in social networks are key factors stimulating the large-scale propagation of information within social networks. User influence is usually related to the user’s attention rate, activity level, and message content. The paper aims to discuss these issues.
Design/methodology/approach
In this paper, the authors focused on Sina Weibo users, centered on users’ behavior and interactive information, and formulated a weighted interactive information network model, then present a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc., the model incorporated the time dimension, through the calculation of users’ attribute influence and interactive influence, to comprehensively measure the user influence of Sina Weibo users.
Findings
Compared with other models, the model reflected the dynamics and timeliness of the user influence in a more accurate way. Extensive experiments are conducted on the real-world data set, and the results validate the performance of the approach, and demonstrate the effectiveness of the dynamics and timeliness. Due to the similarity in platform architecture and user behavior between Sina Weibo and Twitter, the calculation model is also applicable to Twitter.
Originality/value
This paper presents a novel computational model for Weibo user influence, which combined multiple indexes such as the user’s attention rate, activity level, and message content influence, etc.