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Chatbots are increasingly engaged in service marketing. Some academics and managers think using anthropomorphism chatbots will improve positive attitudes and behaviors in the customer journey, but at a high degree of anthropomorphism, consumers may experience negative emotions such as fear and disgust due to the feeling that the robots resemble humans too much, which is known as the uncanny valley effect. Therefore, the authors aim to explore whether chatbot anthropomorphism will promote or limit the development of the customer journey and explore the moderating factors and the antecedent factors affecting consumers' perceptions of chatbot anthropomorphism.

The authors collected 72,782 unique data points from 42 articles and 82 samples using a meta-analysis. Based on the stimuli-organism-response (SOR) model, the impact of anthropomorphic chatbots on the consumer journey was discussed.

The authors’ findings show that chatbot anthropomorphism positively impacts the customer journey but not their negative attitudes. Further moderator analysis reveals that the impact depends on service result, chatbot gender and sample source. The chatbot anthropomorphism is significantly influenced by social presence cues, emotional message cues and mixed cues.

This research contributes to the chatbot anthropomorphism literature and offers guidance for managers on whether and how to enhance chatbot anthropomorphism to facilitate the customer journey and improve service sustainability.

The majority of existing studies about named entity recognition (NER) concentrate on the prediction enhancement of deep neural network (DNN)-based models themselves, but the issues about the scarcity of training corpus and the difficulty of annotation quality control are not fully solved, especially for Chinese ancient corpora. Therefore, designing a new integrated solution for Chinese historical NER, including automatic entity extraction and man-machine cooperative annotation, is quite valuable for improving the effectiveness of Chinese historical NER and fostering the development of low-resource information extraction.

The research provides a systematic approach for Chinese historical NER with a three-stage framework. In addition to the stage of basic preprocessing, the authors create, retrain and yield a high-performance NER model only using limited labeled resources during the stage of augmented deep active learning (ADAL), which entails three steps—DNN-based NER modeling, hybrid pool-based sampling (HPS) based on the active learning (AL), and NER-oriented data augmentation (DA). ADAL is thought to have the capacity to maintain the performance of DNN as high as possible under the few-shot constraint. Then, to realize machine-aided quality control in crowdsourcing settings, the authors design a stage of globally-optimized automatic label consolidation (GALC). The core of GALC is a newly-designed label consolidation model called simulated annealing-based automatic label aggregation (“SA-ALC”), which incorporates the factors of worker reliability and global label estimation. The model can assure the annotation quality of those data from a crowdsourcing annotation system.

Extensive experiments on two types of Chinese classical historical datasets show that the authors’ solution can effectively reduce the corpus dependency of a DNN-based NER model and alleviate the problem of label quality. Moreover, the results also show the superior performance of the authors’ pipeline approaches (i.e. HPS + DA and SA-ALC) compared to equivalent baselines in each stage.

The study sheds new light on the automatic extraction of Chinese historical entities in an all-technological-process integration. The solution is helpful to effectively reducing the annotation cost and controlling the labeling quality for the NER task. It can be further applied to similar tasks of information extraction and other low-resource fields in theoretical and practical ways.

China has been going through a “museum boom” paralleling the domestic tourism boom since 2000; such growth changed the cultural landscape; museums became a vital characteristic of some Chinese cities for both residents and tourists. Encouraged by this growth, the more ambitious “All-for-one Museum (全域博物馆)” was proposed. The physical boundary between museums and living spaces is infinite ambiguity, challenging the idea of museums as “heterotopias.” This study aims to explore the musealization of urban spaces in the context of anthropology and museology, scrutinizing the cultural-political intentions and meanings of these developments, and seeks to ignite further investigation into the reconstruction of historical imaginaries for tourists and urban populations across related disciplines.

This paper examines two cases in Chinese metropolises, Beijing and Shanghai, to illustrate this development of musealization, that is, how the cities actively leverage museological values and methods to connect with their past. In the Beijing case, the authors explore how the local government is leading the effort to musealize the city; in the Shanghai case, they will see how tourists, especially dweller-tourists, navigate through a curated past story in the city and connect their own experience, memory and identity with the place.

The all-for-one museum creates a museal layer projected onto the bigger urban space, even though the authenticity of the “past” is challenged by the modernization development of the city. The authors also find out that for some tourists (especially dweller-tourists), an existential sense of authenticity plays a more significant role as they not only seek to sightsee the past of the city but also to take part in its creation.

This paper discusses two kinds of musealization in cosmopolitan cities of Beijing and Shanghai: top-down and bottom-up. It approaches questions about the musealization of urban spaces from the perspectives of anthropology and museology, and discusses musealization in the specific historical context of China’s modernization process.

The purpose of this paper is to investigate the friction and wear mechanisms in copper-based self-lubricating composites with MoS₂ as the lubricating phase, which provides a theoretical basis for subsequent research on high-performance copper-based self-lubricating materials.

Friction tests were performed at a speed of 100 r/min, a load of 10 N, a friction radius of 5 mm and a sliding speed of 30 min. Friction experiments were carried out at RT-500°C. The phase composition of the samples was characterized by X-ray diffraction of Cu Ka radiation, and the microstructure, morphology and elemental distribution were characterized by scanning electron microscopy and energy dispersive spectroscopy. Reactants and valences formed during the wear process were analyzed by X-ray photoelectron spectroscopy.

The addition of MoS₂ can effectively improve friction-reducing and anti-wear action of the matrix, which is beneficial to form a lubricating film on the sliding track. After analyzing different changing mechanism of the sliding tracks, the oxides and sulfides of MoS₂, MoO₂, Cu₂O, CuO and Ni(OH)₂ were detected to form a synergetic lubricating film on the sliding track, which is responsible for the excellent tribological properties from room to elevated temperature.

For self-lubrication Cu–Sn–Ni–MoS₂ material in engineering field, there are still few available references on high-temperature application.

This paper provides a theoretical basis for the following research on copper-based self-lubricating materials with high performance.

With this statement, the authors hereby certify that the manuscript is the results 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 exits in the submission of this manuscript.

Financial services industry is increasingly showing interest in automated financial advisors, or robo-advisors, with the aim of democratizing access to financial advice and stimulating investment behavior among populations that were previously less active and less served. However, the extent to which consumers trust this technology influences the adoption of rob-advisors. The resemblance to a human, or anthropomorphism, can provide a sense of social presence and increase trust.

In this paper, we conduct an experiment (N = 223) to test the effect of anthropomorphism (low vs medium vs high) and gender (male vs female) of the robo-advisor on social presence. This perception, in turn, enables consumers to evaluate personality characteristics of the robo-advisor, such as competence, warmth, and persuasiveness, all of which are related to trust in the robo-advisor. We separately conduct an experimental study (N = 206) testing the effect of gender neutrality on consumer responses to robo-advisory anthropomorphism.

Our results show that consumers prefer human-alike robo-advisors over machinelike or humanoid robo-advisors. This preference is only observed for male robo-advisors and is explained by perceived competence and perceived persuasiveness. Furthermore, highlighting gender neutrality undermines the positive effect of robo-advisor anthropomorphism on trust.

We contribute to the body of knowledge on robo-advisor design by showing the effect of robot’s anthropomorphism and gender on consumer perceptions and trust. Consequently, we offer insightful recommendations to promote the adoption of robo-advisory services in the financial sector.