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This research focuses on the role of product acquisition cues in positive word-of-mouth (PWOM) content on social media, comparing the characteristics of different sources of product acquisition (purchased vs. gifted) and exploring whether and how they affect consumers' reliance on word-of-mouth (WOM).

The research model was developed based on the mental imagery theory. Two offline experiments and two online experiments were used to test the proposed hypotheses.

The results show that, compared to the purchased source, the gifted source evokes more positive mental imagery and greater emotional attachment to the product, resulting in greater consumer reliance on PWOM. In addition, the effect of the source of product acquisition on reliance on PWOM was stronger for experiential (vs. material) products and for consumers with higher interdependent (vs. independent) self-construal.

This research highlights the role of product acquisition cues in PWOM in influencing consumers' evaluation of WOM, while also revealing the processes inherent in how consumers process information through mental imagery. The findings provide a more comprehensive understanding of the antecedents of reliance on WOM and offer new insights and recommendations for management practitioners.

This paper aims to examine the effects of design sources (user design vs. company design) on customers’ perceived value (perceived self-improvement and perceived uncertainty) and consequently purchase intention, as well as the moderating effect of brand strength in the context of purchasing utilitarian products.

Two studies were conducted. Study 1 used a laboratory experiment (n = 160) to test the effects of design sources on perceived self-improvement, perceived uncertainty and purchase intention. Study 2 used an online experiment (n = 312) to examine the moderating effect of brand strength.

The results showed that user design is a double-edged sword for companies. Compared with company design, user design is associated with stronger self-improvement and uncertainty as perceived by customers. Perceived self-improvement is positively related to purchase intention, while perceived uncertainty undermines purchase intention. Moreover, for weak brands, perceived self-improvement is significantly stronger in user design than company design, while for strong brands, this relationship is not significant.

This paper draws on mental accounting theory to study the perceived benefits and risks of user design of utilitarian products, and highlights the double-edged effects of user design on customers’ perceived value and purchase decision. The findings provide more rounded insights on user design of utilitarian products, complementing the one-sided view of customers’ positive perceives of user design in unclassified product categories.

The rapid development of live-streaming commerce has increased companies’ marketing effectiveness. While previous studies have explored the effects of its technical features on consumers, the effects of marketing-related factors remain unknown. This study aims to investigate the effects of the marketing elements of live-streaming commerce on consumers’ purchase intentions.

The research model is derived from the Yale model and the benefit–risk framework. To test the study hypotheses, data were collected through a questionnaire survey of 392 live-streaming shoppers and analyzed using SmartPLS.

The empirical results indicate that broadcaster competence and online crowding increase consumers’ perception of price attractiveness while reducing their perceived uncertainty. Information diagnosticity also reduces consumers’ perceived uncertainty. Furthermore, purchase intention is positively and negatively affected by perceived price attractiveness and perceived uncertainty, respectively. Finally, product scarcity moderates the relationships between broadcaster competence, online crowding, information diagnosticity, perceived price attractiveness and perceived uncertainty.

The study identifies the different marketing elements in live-streaming commerce and their effects on consumers’ value evaluations and purchase intentions. The findings provide comprehensive insights into the antecedents of live-streaming shopping and offer new perceptions and recommendations for practitioners.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

The purpose of this paper is to handle the problem of the radial disturbance caused by rotor mass unbalance and load change in a bearingless induction motor (BIM).

The active disturbance rejection controller (ADRC) is used to replace the traditional PI controller, and a cubic interpolation method is used to fit the nonlinear function of ADRC, so as to improve the control performance. Meanwhile, a disturbance observer is applied to the suspension system, and the observed disturbance acceleration is compensated to the suspension system in the form of current; thus, the suppression of the rotor radial disturbance is realized.

The proposed method can effectively suppress the radial disturbance of the rotor, meliorate the suspension performance of the motor and enhance the anti-interference ability of the system. Besides, it has excellent dynamic and static performance.

A radial disturbance control strategy of the BIM based on improved ADRC is proposed is to suppress the radial disturbance of the rotor. The improved ADRC is to enhance the control performance of the system, and the disturbance observer is designed to observe and compensate the disturbance.

This paper explores the role of traditional Chinese medicine (TCM) as a tourism recovery drawcard to boost China's inbound tourism after COVID-19.

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.

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.

The paper builds a tourism recovery framework for stakeholders to adopt tailored TCM communication strategies to boost its inbound tourism programme.

This paper is the first academic paper to review TCM comprehensively and critically in relation to China tourism and post-COVID-19 recovery measures.

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.

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.

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.

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.

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.

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.

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.

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.