Search results

As a potentially disruptive technology, blockchain technology ensures that all the data cannot be merely tampered with once they are recorded on-chain. However, the fake source information may be input into the blockchain, which is mistaken for truthful data and results in a trust divide between the on-chain and the actual world. One missing perspective from previous studies is information manipulation at the source still exists under the blockchain mode. The authors’ goal was to analyze how blockchain technology affects the information deception of the agricultural product supply chain (APSC) under this premise. Also, the authors further analyzed some factors that influence the effectiveness of blockchain technology.

The authors build an APSC game model consisting of a farmer and an agricultural product broker, which employs the principal–agent game model to explore the conditions for achieving the mutual trust equilibrium between the two parts. Then, through numerical simulation, the authors further analyze how the quality of on-chain information and the numbers of on-chain firms affect blockchain’s effect on deception in APSC and examine the circumstances in which blockchain technology is more suitable.

The authors demonstrate that only by meeting the threshold of high-quality on-chain information and having a sufficient number of on-chain firms, can the blockchain-based supply chain initiate a better information ecosystem, which helps eradicate deception in the APSC.

This paper provides valuable insights for participants in supply chains as well as is probably generalizable to other industrial products that require similar services in the early stage of blockchain.

As an important form of the e-commerce industry, online group buying is under the spotlight from with two sides: cheaper price but longer waiting time. The purpose of this paper is to adequately investigate the interaction between saving and waiting time of group buying comprehensively.

To fill the research gap, the authors elaborate a dual-channel supply chain (SC) with regular retail (individual buying) and group-buying channel, and formulate the demand based on the consumer utility with the positive effect of saving money and the negative effect of wasting time.

The authors find that power structure only changes the optimal prices, instead of the waiting time. The selling price mainly influences consumer demands, instead of the price discount of group buying. The SC profits are only positive to the channel preference, and it is the decisive parameter of consumers' choice. The price sensitivity lays a more remarkable impact on the SC compared to the time sensitivity. Above all, the price is the main factor of group buying, instead of time.

These results underscore the improvement for the dual-channel SC of group buying, providing managerial insights for the group-buying industry.

The purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.

To achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.

Experimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.

The research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.

The paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.

This study aims to identify the attitudes of Chinese residents towards tourists and tourism development. Based on periods of observation, temporary residency and several visits for more than a decade, and supplemented by data collected from 478 residents, the study examines to what extent the rural villagers identify the tourism induced changes as being an outcome of official Chinese policies. The villages, Xidi, Hongcun and Nanping, are three heritage villages in Anhui Province and represent appropriate case studies for such an examination due to their differing histories of tourism administrative procedures. Findings contribute to scholarly knowledge by putting pro-poor tourism and community participation under scrutiny in Chinese context. A change of residents’ perceptions towards tourism could potentially be consequential for tourists’ experience and the sustainability of tourism development, particularly in emerging rural destinations.

This paper aims to regulate the oil retention rate and tribological properties of pored polytetrafluoroethylene (PPTFE) using polyvinyl alcohol (PVA)-based oil gel.

PPTFE was first prepared by using citric acid (CA) as an efficient pore-making agent. Subsequently, PVA and chitosan solution was introduced into the pores and experienced a freezing-thawing process, forming PVA-based gels inside the pores. Then, the PPTFE/PVA composite was impregnated with polyethylene glycol 200 (PEG200), yielding an oil-impregnated PPTFE/PVA/PEG200 composite.

It was found that the oil-impregnated PPTFE/PVA/PEG200 composite exhibited advanced tribological properties than neat PTFE with reductions of 53% and 70% in coefficient of friction and wear rate, respectively.

This study shows an efficient strategy to regulate the tribological property of PTFE using a PVA-based oil-containing gel.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2024-0432/

This paper aims to investigate the crucial roles of textured surfaces on oil-impregnated polytetrafluoroethylene (PTFE) created by a facile tattoo strategy in improving tribological properties.

Pored PTFE (PPTFE) was prepared by mixing powder PTFE and citric acid and experienced a cold-press sintering molding process. Subsequently, textured surfaces were obtained with using a tattoo strategy. Surface-textured PPTFE was thus impregnated with polyethylene glycol 200, yielding oil-impregnated and pore-connected PPTFE.

This study found that oil-impregnated and surface-textured PPTFE exhibited excellent tribological performances with an 82% reduction in coefficient of friction and a 72.5% lowering in wear rate comparing to PPTFE.

This study shows an efficient strategy to improve the tribological property of PTFE using a tattoo-inspired surface texturing method.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0378/

This paper aims to prepare an oil-impregnated porous polytetrafluoroethylene (PTFE) composite with advanced tribological properties using citric acid as a novel pore-forming agent.

Citric acid (CA) was used to form pores in PTFE, and then oil-impregnated PTFE composites were prepared. The pore-forming efficiency of CA was evaluated. The possible mechanism of lubrication was proposed according to the tribological properties.

The results show CA is an efficient pore-forming agent and completely removed, and the porosity of the PTFE increases with the increase of the CA content. The oil-impregnated porous PTFE exhibits an excellent tribological performance, an increased wear resistance of 77.29% was realized in comparison with neat PTFE.

This study enhances understanding of the lubrication mechanism of oil-impregnated porous polymers and guides for their tribological applications.

The purpose of this study is to examine the variables affecting college students’ consumption behaviour in the context of the internet celebrity economy and the We Media environment.

In this study, five independent variables − perceived ease of use, perceived usefulness, attitude, We Media environment and internet celebrity marketing, as well as one mediating variable, consumption intention, are used to analyse college students’ consumption behaviour.

This study concluded that all five independent variables have positive relationships with the consumption intention and that the consumption intention also has a positive relationship with the consumption behaviour.

This study expanded the technology acceptance model and theory of planned behaviour model, which could provide insights for future research on consumption intention and behaviour. In addition, this study gives guidance for businesses considering to join this new industry in the internet celebrity economy.