Buqing Ma, Xiaoyan Xu, Yanhong Sun and Yiwen Bian
Consumers are increasingly using search-based advertising in e-Business platforms to seek their desirable products. Platforms will choose a centralized advertising mechanism (CAM…
Abstract
Purpose
Consumers are increasingly using search-based advertising in e-Business platforms to seek their desirable products. Platforms will choose a centralized advertising mechanism (CAM) or decentralized advertising mechanism (DAM) to offer a search advertising service to lower consumer search cost, as represented by using search time length. It is important for the platform to decide how to choose advertising mechanisms, and how to determine the optimal advertising price and search time length. To address these issues, this study aims to develop a theoretical approach under each mechanism to examine the platform’s optimal search-based advertising strategy by considering search cost.
Design/methodology/approach
In this study, two models are developed to examine the optimal search-based advertising strategy by considering consumer search cost (i.e. search time length). By comparing the platform’s profits under two models, the optimal advertising strategy, search time length and price are explored.
Findings
It is found that when the seller’s reserve benefit is sufficiently large, the platform benefits from choosing the DAM; otherwise, the CAM is a better choice. The advertising service is usually offered with a shorter search time length accompanied by a higher charge, and a longer search time length accompanied by a lower charge. Specifically, when the seller’s reserve benefit is substantially high, a DAM that benefits both the platform and seller is a better choice. This can explain why many platforms offer advertising services with a DAM.
Originality/value
This paper is the first theoretical study on addressing the search-based advertising strategy, especially the choice of advertising mechanisms, in the online advertising context. It is also the first piece of analytical research that considers the effect of consumer search cost on product demand, and then examines the optimal advertising price and search cost (i.e. search time length) for online platforms.
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Lu Han, Hao Wu, Buqing Fang and Wangyue Zhou
This paper aims to study whether rural collective construction land marketization (RCCLM) affects pension industry and analyze its impact mechanism.
Abstract
Purpose
This paper aims to study whether rural collective construction land marketization (RCCLM) affects pension industry and analyze its impact mechanism.
Design/methodology/approach
Based on the theory of planned behavior (TPB) and social cognition theory (SCT), this paper constructs a theoretical framework for the purchase behavior of rural retirement housing with the influence of RCCLM. A mixed-methods investigation combining qualitative and quantitative study is adopted in this paper.
Findings
The research results indicate that the purchase intention of rural retirement housing has a significant positive impact on the purchase behavior. However, RCCLM has a significant negative impact on the purchase intention of rural retirement housing. A logical framework of “land system participant behavior” has been constructed from three main bodies: government, developers and elderly urban and rural residents.
Practical implications
This paper provides suggestions for the three entities from the perspectives of macro, medium and micro-level to improve transaction system for rural collective construction land use right (TSRCCLUR), providing references for the collective construction land marketization policy and the development of the pension industry.
Originality/value
This paper deepens the study of behavior intention in planned behavior, enriches TPB model in the study of rural retirement housing security and clarifies the influence mechanism of the rural retirement housing purchase intention with the theoretical and empirical test of the model.
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Yanxinwen Li, Ziming Xie, Buqing Cao and Hua Lou
With the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification…
Abstract
Purpose
With the introduction of graph structure learning into service classification, more accurate graph structures can significantly improve the precision of service classification. However, existing graph structure learning methods tend to rely on a single information source when attempting to eliminate noise in the original graph structure and lack consideration for the graph generation mechanism. To address this problem, this paper aims to propose a graph structure estimation neural network-based service classification (GSESC) model.
Design/methodology/approach
First, this method uses the local smoothing properties of graph convolutional networks (GCN) and combines them with the stochastic block model to serve as the graph generation mechanism. Next, it constructs a series of observation sets reflecting the intrinsic structure of the service from different perspectives to minimize biases introduced by a single information source. Subsequently, it integrates the observation model with the structural model to calculate the posterior distribution of the graph structure. Finally, it jointly optimizes GCN and the graph estimation process to obtain the optimal graph.
Findings
The authors conducted a series of experiments on the API data set and compared it with six baseline methods. The experimental results demonstrate the effectiveness of the GSESC model in service classification.
Originality/value
This paper argues that the data set used for service classification exhibits a strong community structure. In response to this, the paper innovatively applies a graph-based learning model that considers the underlying generation mechanism of the graph to the field of service classification and achieves good results.
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Abstract
Purpose
This paper traces the incorporation of western educational histories in the development of normal-school curricula during the late Qing Dynasty and the Republic of China (1901–1944). It uses publication networks to show how the study of comparative educational history facilitated the international circulation of knowledge in the teaching profession, and how the “uses” of educational history were shaped by larger geopolitical forces.
Design/methodology/approach
This paper analyzes the international exchange of texts between normal schools in China and Japan and, subsequently, between normal schools in China and the United States. A database of 107 publications in the field of western educational history that were adopted in China reveals specific patterns of textual citation, cross-reference, and canon-formation in the field of educational historiography.
Findings
With conclusions derived from a combination of social network analysis and clustering analysis, this paper identifies three broad stages in China's development of normal-school curricula in comparative educational history: “Japan as Teacher,” “transitional period” and “America as Teacher.”
Research limitations/implications
Statistical analysis can reveal citation and reference patterns but not readers' understanding of the deeper meaning of texts – in this case, textbooks on the subject of western educational history. In addition, the types of publications analyzed in this study are relatively limited, the articles on the history of education in journals have not become the main objects of this study.
Originality/value
This paper uses both quantitative and qualitative methods to uncover the transnational circulation of knowledge in the field of comparative educational history during its formative period in China.