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The Soybean Producer Subsidy Policy (SPSP), an agricultural support policy enacted in China within the past few years, is designed to optimise crop planting structure. This study analyses the impact of SPSP on the crop planting structure in terms of absolute and comparative incomes and elucidates the mechanisms involved.

Utilising balanced county-level panel data from 966 counties in China’s major soybean-producing regions, spanning from 2008 to 2021, we investigate the impacts of SPSP on crop planting structure by applying a difference-in-difference (DID) model.

The findings reveal several crucial insights. First, SPSP optimises the crop planting structure in Northeast China, primarily through an expansion in the area sown to soybeans and a simultaneous reduction in the area sown to maize. Second, the impacts of SPSP gradually strengthen over time but begin to weaken by 2021. Third, heterogeneity analysis indicates that the effects of SPSP are most pronounced in Eastern Inner Mongolia, followed by Heilongjiang, Jilin, and Liaoning. Finally, SPSP incentivises farmers to expand soybean sown areas by improving absolute rather than comparative incomes from soybean cultivation.

Addressing structural contradictions within China’s food supply chain necessitates the adjustment of support policies for different crops to mitigate market distortions. Establishing a holistic agricultural support system encompassing various crops could promote sustainable agricultural practices in the future.

Our findings are valuable for policy makers in China and globally who aim to establish support systems for regional linkages that include a variety of crops.

The purpose of this study is to investigate whether the convergence of linguistic features between physicians and patients with chronic diseases facilitates the effectiveness of physician–patient communication in online health communities (OHCs). Drawing on communication accommodation theory (CAT), the authors develop a research model that illustrates how the convergence of semantic features (language concreteness and emotional intensity) and stylistic features (language style) influence patient satisfaction and compliance. The model also incorporates the moderating effects of the physician's social status and the patients' complications.

The data, collected from a prominent online health platform in China, include 15,448 consultation records over five years. The logistic regression is leveraged to test the hypotheses.

The findings reveal that convergent semantic features, such as language concreteness and emotional intensity, along with stylistic features like language style, enhance patient satisfaction, which in turn leads to increased compliance. Additionally, the physician’s social status strengthens the effect of convergent emotional intensity but weakens the effect of convergent language concreteness. The physician’s social status has no significant impact on the link between convergent language style and satisfaction. Patients' complications weaken the effect of satisfaction on their compliance.

This study contributes to the CAT and OHC literature by enhancing the understanding of the role of linguistic convergence in the effectiveness of online physician–patient communication and provides managerial implications for physicians on how to accommodate their communicative styles toward chronic patients to improve patient satisfaction and compliance.

For start-ups or growing firms, to effectively navigate the unpredictable nature of digital development and achieve superior innovative performance, it is crucial to have a workforce comprised of creative and innovative employees. Drawing upon the principles of social information processing theory, this study aims to investigate whether specific combinations of organizational internal and external environments, as well as work characteristics in the digital age, can foster a high level of employee innovative behavior.

By collecting a multilevel and multisource data set comprising 693 employees and 88 CEOs from 88 start-ups or growing firms, this study used fuzzy-set qualitative comparative analysis to examine the distinctive configurations associated with achieving a high level of employee innovative behavior.

The study found that six solutions enabled employees to innovate more effectively, but six solutions led to the absence of employee innovative behavior.

The findings of this study offer important theoretical and practical implications to motivate employee innovative behavior in Chinese enterprises.

First, this study contributes to the literature on employee innovative behavior by addressing the need to explore the impact of the digital context on promoting innovation among employees. Second, this study adds to the existing literature on employee innovation and entrepreneurship by examining multiple organizational contexts and their influence on innovative behavior. Third, this study makes a significant contribution to the field of employee innovative behavior by examining the macroenvironment surrounding digital transformation within enterprises and integrating both internal and external organizational factors.

Research on the impact of the engagement of online medical teams (OMTs) on patient evaluation, for example, satisfaction, remains insufficient. This study attempts to recognize the underlying mechanism of how OMTs’ engagement influences patient satisfaction by adopting social support as the mediator. This study also scrutinizes the moderating effects of the transactive memory system (TMS) on the link between OMTs’ engagement and social support.

We utilized a linear model that had fixed effects controlled at the team level for analysis. A bootstrapping approach using 5,000 samples was employed to test the mediation effect.

Our results reveal that OMTs’ engagement improves informational and emotional support, thereby promoting patient satisfaction. Specialization and credibility strengthen the impact of OMTs’ engagement on informational and emotional support. Simultaneously, coordination has an insignificant influence on the link between OMTs’ engagement and social support.

This study contributes to the literature on OMTs, social support, and TMS, providing insights into patients’ perceptions of OMTs’ engagement during online team consultation. This study also generates several implications for the practice of online health communities and OMTs.

Online medical teams (OMTs) have emerged as an innovative healthcare service mode that relies on the collaboration of doctors to produce comprehensive medical recommendations. This study delves into the relationship between knowledge collaboration and team performance in OMTs and examines the complex effects of participation patterns.

The analysis uses a dataset that consists of 2,180 OMTs involving 8,689 doctors. Ordinary least squares regression with robust standard error is adopted for data analysis.

Our findings demonstrate a positive influence of knowledge collaboration on OMT performance. Leader participation weakens the relationship between knowledge collaboration and team performance, whereas multidisciplinary participation strengthens it. Passive participation and chief doctor participation have no significant effect on the association between knowledge collaboration and OMT performance.

This study provides valuable insights into how knowledge collaboration shapes OMTs' performance and reveals how the participation of different types of members affects outcomes. Our findings offer important practical implications for the optimization of online health platforms and for enhancing the effectiveness of collaborative healthcare delivery.

The purpose of this paper is to examine the influence of intelligent manufacturing on audit quality and its underlying mechanism as well as the variation in this influence across different types of organizations.

This research utilizes a difference-in-differences (DID) method to examine how enterprises that apply intelligent manufacturing choose auditors and impact their audit work. The study is based on 15,228 observations of Chinese-listed A-shares from 2011 to 2020.

(1) There is a strong correlation between intelligent manufacturing and audit quality. (2) This positive correlation is statistically significant only in state-owned enterprises (SOEs), those that have steady institutional investors and where the roles of the CEO and chairman are distinct. (3) Enterprises that have implemented intelligent manufacturing are more inclined to employ auditors who possess extensive industry expertise. The auditor's industry expertise plays a crucial role in ensuring audit quality. (4) The adoption of intelligent manufacturing also leads to higher audit fees and longer audit delay periods.

This paper validates the beneficial impact of intelligent manufacturing on improving corporate governance. In addition, it is recommended that managers prioritize the involvement of skilled auditors with specialized knowledge in the industry to ensure the high audit quality and the transparency of information in intelligent manufacturing enterprises.

This study builds upon previous research that has shown the importance of artificial intelligence in enhancing audit procedures. It contributes to the existing body of knowledge by examining how enterprise intelligent manufacturing systems (IMS) enhance audit quality. Additionally, this study provides valuable information on how to improve audit quality in the field of intelligent manufacturing by strategically selecting auditors based on resource dependency theory.

Performance optimization algorithms based on node attributes are of great importance for sharding blockchain systems. Currently, existing studies on blockchain sharding algorithms consider only random selection sharding strategies. However, the random selection strategy does not perfectly utilize the performance of a node to break the bottleneck of blockchain performance.

This paper proposes a blockchain sharding algorithm called TOPSIS Optimization Sharding System (TOSS), which is based on entropy weight method, relative Euclidean distance and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). It defines a multi-attribute matrix to assess node performance and applies TOPSIS for scoring nodes. Then, an algorithm based on the TOPSIS method is proposed to calculate the performance score of each data node. In addition, an entropy weighting method is introduced to obtain the weights of each attribute to balance the impact of dimensional differences of attributes on the attribute weights. Nodes are ranked by composite scores to guide partitioning.

The effectiveness of the proposed algorithm in this paper is verified by comparing it with various comparative algorithms. The experimental results show that the TOSS algorithm outperforms the comparison algorithms in terms of performance improvement for the blockchain system, and the throughput metrics are improved by about 20% in comparison.

This study introduces a novel approach to blockchain sharding by incorporating the entropy weight method and relative Euclidean distance TOPSIS into the sharding process. This approach allows for a more effective utilization of node performance attributes, leading to significant improvements in system throughput and overall performance, addressing the limitations of the random selection strategy commonly used in existing algorithms.

This paper aims to critically review the intersection of searching and learning among children in the context of voice-based conversational agents (VCAs). This study presents the opportunities and challenges around reconfiguring current VCAs for children to facilitate human learning, generate diverse data to empower VCAs, and assess children’s learning from voice search interactions.

The scope of this paper includes children’s use of VCAs for learning purposes with an emphasis on conceptualizing their VCA use from search as learning perspectives. This study selects representative works from three areas of literature: children’s perceptions of digital devices, children’s learning and searching, and children’s search as learning. This study also includes conceptual papers and empirical studies focusing on children from 3 to 11 because this age spectrum covers a vital transitional phase in children’s ability to understand and use VCAs.

This study proposes the concept of child-centered voice search systems and provides design recommendations for imbuing contextual information, providing communication breakdown repair strategies, scaffolding information interactions, integrating emotional intelligence, and providing explicit feedback. This study presents future research directions for longitudinal and observational studies with more culturally diverse child participants.

This paper makes important contributions to the field of information and learning sciences and children’s searching as learning by proposing a new perspective where current VCAs are reconfigured as conversational voice search systems to enhance children’s learning.

This paper aims to explore how thermal activation enhances the oxidation complexation of the titanium alloy, aiming to enhance surface quality and processing efficiency.

The titanium alloys were chemically mechanically polished under various temperatures. The removal rate and surface roughness were characterized using a three-dimensional topography tester. The surface composition, content and valence state were characterized by X-ray photoelectron spectroscopy. The abrasion performance of the surface reaction layers was conducted using a friction wear testing machine.

The thermal activation temperature can enhance the chemical-mechanical polishing effect of titanium alloy. The thermal activation temperature can enhance the oxidation complexation synergistic effect of K₂S₂O₈ and KF on titanium alloy, thereby improving the polishing effect. With the increase in temperature, the wear resistance of titanium alloy decreases after oxidation corrosion, making it more susceptible to removal through friction. By promoting the oxidation and corrosion of K₂S₂O₈ and KF on the titanium alloy, higher temperatures can facilitate the formation of easily removable film layers on the surface, thereby enhancing the polishing effect.

This research contributes to enriching the theoretical framework of precision machining of titanium alloy and enhancing surface quality and machining efficiency.

With this statement, the authors hereby certify that the manuscript is the result of their own effort and ability. They have indicated all quotes, citations and references. Furthermore, the authors have not submitted any essay, paper or thesis with similar content elsewhere. No conflict of interest exists in the submission of this manuscript.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0167/

Maximum k vertex cover problem is a significant combinatorial optimization problem with various applications, such as transportation planning, social networks and sensor placement. Up to now, no practical algorithm has ever been proposed to solve this problem. Therefore, this paper aims to present an efficient local search algorithm LSKVC combining three methods for it.

First, the quick incremental evaluation method is proposed to update the related vertex scores following each addition or removal incrementally rather than recalculating them, which can speed up the algorithm. Second, the configuration checking method forbids vertices whose configuration has not changed since the last removal from being added into the candidate solution again, which can avoid the cycling problem effectively. Third, the two-stage exchange method swaps the pairs of inside and outside vertices separately rather than simultaneously, which can guarantee the tradeoff between the accuracy and complexity of the algorithm.

The proposed algorithm LSKVC is compared with the traditional GRASP algorithm and the well-known commercial solver CPLEX on DIMACS and BHOSLIB benchmarks. For the best solutions, the LSKVC algorithm is significantly superior to GRASP and CPLEX on DIMACS instances and the CPLEX solver fails, and the LSKVC algorithm slightly outperforms GRASP on the BHOSLIB instances. In addition, we undertake comparative studies of the offered methodologies and demonstrate their efficacy.

In previous research, the focus on the maximum k-vertex cover problem primarily centered around exact algorithms and approximation algorithms, with limited application of heuristic algorithms. While heuristic algorithms have been well-explored for the closely related Minimum Vertex Cover problem, they have seen limited application in the context of the maximum k-vertex cover problem. Consequently, existing algorithms designed for the Minimum Vertex Cover problem do not exhibit satisfactory performance when applied to the maximum k-vertex cover problem. In response to this challenge, we have undertaken algorithmic improvements specifically tailored to address this issue.