期刊：
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS,2024年67:101421 ISSN：1567-4223
通讯作者：
Zhang, JN
作者机构：
[Zhang, Jinao; Zheng, Wenqing; Wang, Xuelin; Lu, Xinyuan] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
通讯机构：
[Zhang, JN ] C;Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
关键词：
Information Service Failure;System Service Failure;Artificial Intelligence Chatbot
摘要：
Artificial intelligence (AI) chatbot have become increasingly popular as a tool for improving employee productivity over the last few years. In the early stages of AI chatbot development, exploring the impact of AI chatbot service failures on user reusage intention is useful for coordinating human-computer interaction and optimizing AI chatbot service mechanisms. The extant literature on AI service failures focuses on service recovery and anthropomorphism. There is less literature comparing different types of service failures and their effects. The article includes three studies. First, a randomized group experiment was conducted with 120 respondents. The results showed significant differences in the impact of different AI chatbot service failures on user reusage intentions. Second, an online questionnaire was completed by 386 respondents, the results found specific impact mechanisms of service failures on user reusage intentions. Third, an interview survey was conducted with 15 customers using AI chatbots to verify the findings of Study 1 and Study 2. Furthermore determine the boundary conditions for the unsupported hypotheses through meta-inference. The research enriches the literature on relationship marketing and expands the attribution theory of service failures. In addition, which provides theoretical basis and practical support for companies to reduce adverse effects of service failures.
作者机构：
[Fan, Lijun] Harbin Normal Univ, Sch Econ & Management, Harbin 150025, Peoples R China.;[Guo, Yang] Henan Police Coll, Dept Network Secur, Zhengzhou 450046, Peoples R China.;[Wang, Yiwen; Guo, Yang] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Wang, Wei] Chongqing Univ, Sch Econ & Business Adm, Chongqing 400044, Peoples R China.
通讯机构：
[Wang, YW ] C;Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
关键词：
green innovation;customer concentration;digital transformation;resource allocation;industrial transformation;sustainable development
摘要：
The increasingly environmental issues pose challenges to the economic development of countries, particularly hindering industrial transformation in developing nations. This study, grounded in the Resource-Based View, examines factors influencing green innovation in high-tech manufacturing firms. Market interactions and digital technologies significantly impact resource investments in green innovation. Using data from Chinese high-tech manufacturing firms from 2007 to 2021, the study reveals that customer concentration negatively affects green innovation, while digital transformation promotes it and mitigates the inhibitory effect of customer concentration. To explain this mechanism, green innovation is divided into green process innovation and green product innovation, and the effect of customer concentration is more pronounced in green product innovation. Further testing discusses the roles of the external environment, internal governance, and manager characteristics. Specifically, product market competition and political resources influence firms' reliance on major customers, allowing digital technologies to optimize resource allocation for green innovation. In terms of internal governance, flexibility and regulatory strength alter the emphasis firms place on green innovation, with higher governance efficiency reducing dependency on major customers. Managerial characteristics, particularly managers' rationality, determine the importance placed on digital technologies versus customer demands, leading to varied investment decisions in green innovation. Our findings provide valuable insights for optimizing resource allocation and enhancing green innovation investment, thereby effectively promoting sustainable regional economic development.
期刊：
JOURNAL OF COMPUTER ASSISTED LEARNING,2024年 ISSN：0266-4909
通讯作者：
Wang, L
作者机构：
[Dai, Zhicheng; Xiong, Junxia] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Res & Engn Ctr Elearning, Wuhan, Hubei, Peoples R China.;[Wang, Ling] Cent China Normal Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.;[Zhao, Liang; Peng, Xian] Cent China Normal Univ, Fac Artificial Intelligence Educ, Natl Engn Res Ctr Educ Big Data, Wuhan, Hubei, Peoples R China.;[Wang, Ling] Cent China Normal Univ, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Wang, L ] C;Cent China Normal Univ, 152 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China.
关键词：
ACSI model;higher education;smart classroom environment;student satisfaction;TAM model
摘要：
BackgroundSmart classroom environment has drawn worldwide attention, however, there is still a lack of studies that can explore and analyse potential factors, which affect students' satisfaction with smart classrooms in higher education.ObjectivesTo assess students' satisfaction with smart classrooms in higher education, this study proposed the smart classroom environment satisfaction model based on TAM and ACSI models.MethodsA sample of 1979 Chinese college students who studied in a smart classroom environment completed a survey assessing student satisfaction with the smart classroom environment. And a structural equation modelling analysis was used to further analyse students' preferences for smart classroom environments.Results and conclusionsThe results showed that student satisfaction with smart classroom environment is significantly correlated with subjective perception factors after use, such as students' perceived value (PV), user experience and perceived expectations (PE). It indicated that user experience indirectly affected overall satisfaction (OS) through its positive impact on PV, while PE indirectly affected OS through its positive impact on user experience. Second, user experience had the greatest impact on student satisfaction, followed by PV and PE. This study is a theoretical supplement to future research on smart classroom environment in higher education, and can also provide reference for relevant government departments, universities and enterprises to build and manage smart classroom environment. What is already known about this topic? Smart classrooms can combine technology with the goal of providing students with the highest educational services. This technology-rich smart classroom environment provides unique opportunities for students to search, acquire, analyse and apply digital learning resources and tools in autonomous learning, collaborative learning, and exploratory learning activities. Although smart classrooms have played a certain role in the above aspects, it has invested a large amount of money to build the most advanced facilities, without considering student perspectives.What the paper adds? This study investigates the satisfaction factors and preferences of 1979 college students to evaluate the smart classroom environment supported by technology. This study proposed the smart classroom environment satisfaction model, and a structural equation modelling analysis was used to further analyse students' preferences for smart classroom environments Student satisfaction with smart classroom environment is significantly correlated with subjective perception factors after use, such as students' perceived value, user experience and perceived expectations.Implications for practice and/or policy In order to improve students' satisfaction with the smart classroom environment, more attention needs to be paid to improving their actual user experience. Whether students can receive timely learning support and whether there is interaction in the classroom is worth special consideration to improve their user experience.
作者机构：
[Li, Duantengchuan; Li, Bing; Xia, Tao] Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.;[Wang, Jing] Chongqing Univ Posts & Telecommun, Sch Automat, Chongqing 400065, Peoples R China.;[Shi, Fobo] Cent China Normal Univ, Natl Engn Res Ctr Elearning, Wuhan 430079, Peoples R China.;[Zhang, Qi; Zhang, Q] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Li, Bing] Hubei Luojia Lab, Wuhan 430079, Peoples R China.
通讯机构：
[Li, DTC; Li, B ] W;[Zhang, Q ] C;Wuhan Univ, Sch Comp Sci, Wuhan 430072, Peoples R China.;Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;Hubei Luojia Lab, Wuhan 430079, Peoples R China.
关键词：
Link prediction;Knowledge graph embedding;Shallow interaction;Deep interaction;Attention mechanism;Vector tokenization
摘要：
Inferring missing information from current facts in a knowledge graph (KG) is the target of the link prediction task. Currently, existing methods embed the entities and relations of KG as a whole into a low-dimensional vector space. Nonetheless, they ignore the multi-level interactions (shallow interactions, deep interactions) among the finer-grained sub-features of entities and relations. To overcome these limitations, we present a shallow-to-deep feature interaction for knowledge graph embedding (SDFormer). It takes into account the interpretability of sub-feature tokens of entities and relations and learns shallow-to-deep interaction information between entities and relations at a more fine-grained level. Specifically, entity and relation vectors are decomposed into sub-features to represent multi-dimensional information. Then, a shallow-to-deep feature interaction method is designed to capture multi-level interactions between entities and relations. This process enriches the feature representation by modeling the interaction between sub-features. Finally, a 1-X scoring function is utilized to calculate the score of each knowledge triplet. The experimental results on several benchmark datasets show that SDFormer obtains competitive performance results and more efficient training efficiency on other comparative models and because of the shallow-to-deep feature interaction between entities and relations.
作者机构：
[Cheng, Xiufeng; Yang, Jinqing; Ye, Guanghui] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Liu, Zhifeng] Peking Univ, Dept Informat Management, Beijing 100871, Peoples R China.
通讯机构：
[Yang, JQ ] C;Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
关键词：
Keyword adoption behavior;Functional structure;Researcher behavior;Keyword spatial distribution
摘要：
Researchers adopt keywords to signify the core content of papers, and the spatial distribution of these keywords within the paper can provide insight into researchers' adoption behavior patterns. In this study, the primary purpose was to investigate how keyword adoption patterns affect academic papers' perceived value. First, we collected 5,739 papers from the China National Knowledge Infrastructure (CNKI) to extract the first-level subtitles for statistically characterizing the functional structure of papers in the Library and Information Science (LIS) field. Second, we introduce a balance degree indicator to measure the keywords' spatial distribution. Next, we identify researchers' keyword adoption behavior patterns based on the keyword spatial distribution in the functional structure. Finally, we investigate the effect of keyword adoption behavior patterns on paper impact. The findings of our study reveal that: (1) In the Library and Information Science field, the balance degree values exhibit a normal distribution and are verified to be valid. (2) Depending on the keyword distribution across the four segments, the keyword adoption behaviors of researchers can be categorized into 24 distinct types. (3) The balance degree is positively correlated with both the citation and download count, and notably, the keyword spatial distribution of the Introduction and Results & Discussion sections have a significant effect on a paper's impact. These findings have significant implications for keyword selection and the early prediction of a paper's citation and download frequency.
作者机构：
[Junren Ming; Qiuyu Zhu; Yu Cheng] School of Management, Wuhan Institute of Technology, Wuhan, 430205, China;[Ruide Tu] School of Information Management, Central China Normal University, Wuhan, 430079, China;[Rong Chen] School of Information Management, Sun Yat-sen University, Guangzhou, 510006, China
关键词：
Online learning platforms;user behavior;quality of service;intention to continue using;Interpretative Structural Modeling Method
摘要：
The rapid development of information technology and various information terminals has contributed to the advancement of online learning platforms, and the "Internet + education" has greatly contributed to the development of online learning platforms, which have become a new way for people to receive education. However, many online learning platforms also suffer from low user satisfaction, high dropout rates and low willingness to continue using them. This paper presents an in-depth analysis of both the platform and user perceptions to explore the factors that influence users’ willingness to use. This paper uses a combination of exploratory and validation analyses to verify the factors influencing users’ intention to continue using online learning platforms, construct an Interpretative Structural Model, further analyze the mechanism of the influencing factors and clarify the relationship between them, which is of great significance for improving the service experience of users’ online learning platforms and promoting the sustainable development of online learning platforms.
摘要：
This paper explored the temporal characteristics of clinical citations of biomedical papers, including how long it takes to receive its first clinical citation (the initial stage) and how long it takes to receive two or more clinical citations after its first clinical citation (the build-up stage). Over 23 million biomedical papers in PubMed between 1940 and 2013 and their clinical citations are used as the research data. We divide these biomedical papers into three groups and four categories from clinical citation level and translational science perspectives. We compare the temporal characteristics of biomedical papers of different groups or categories. From the perspective of clinical citation level, the results show that highly clinically cited papers had obvious advantages of receiving clinical citations over medium and lowly clinically cited papers in both the initial and build-up stages. Meanwhile, as the number of clinical citations increased in the build-up stage, the difference in the length of time to receive the corresponding number of clinical citations among the three groups of biomedical papers significantly increased. From the perspective of translational science, the results reveal that biomedical papers closer to clinical science more easily receive clinical citations than papers closer to basic science in both the initial and build-up stages. Moreover, we found that highly clinically cited papers had the desperate advantage of receiving clinical citations over even the clinical guidelines or clinical trials. The robustness analysis of the two aspects demonstrates the reliability of our results. The indicators proposed in this paper could be useful for pharmaceutical companies and government policy-makers to monitor the translational progress of biomedical research. Besides, the findings in this study could be interesting for young scholars in biomedicine to get more attention from clinical science and to obtain success in their scientific careers, especially for those in basic science.
期刊：
ISA Transactions,2024年147:304-327 ISSN：0019-0578
通讯作者：
Xiao, XP
作者机构：
[Xiao, Xinping; Chen, Rongxing] Wuhan Univ Technol, Sch Sci, Wuhan 430070, Peoples R China.;[Gao, Mingyun] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Ding, Qi] Nanjing Univ, Sch Business, Nanjing 210008, Peoples R China.
通讯机构：
[Xiao, XP ] W;Wuhan Univ Technol, Sch Sci, Wuhan 430070, Peoples R China.
关键词：
Chimp optimization algorithm;Hard disk drive failure forecasting;Mixed data sampling;Mixed frequency sampling grey model
摘要：
The mixed data sampling (MIDAS) model has attracted increasing attention due to its outstanding performance in dealing with mixed frequency data. However, most MIDAS model extension studies are based on statistical methods or machine learning models, which suffer from insufficient prediction performance and stability in small sample environments. To solve this problem, this paper proposes a novel mixed frequency sampling discrete grey model (MDGM(1, N)), which is a coupled form of the MIDAS model and discrete grey multivariate model. By adjusting the structure parameters, the model can be adapted to different sampling frequencies data, and degenerate into several types of grey models. Then, the unbiasedness and stability of the model are proved using the mathematical analysis method and numerical random experiment. The meta-heuristic algorithm is introduced to obtain the optimal weight parameters and the maximum lag order, improving the model's fitting ability to mixed frequency data. To demonstrate the effectiveness of the new model, a model evaluation system consisting of traditional evaluation metrics and a monotonicity test is established. Taking four hard disk drive failure datasets as research cases, the performance of the proposed model is compared with seven mainstream benchmark models. The results show that the proposed model has excellent applicability and outperforms other competition models in terms of validity, stability, and robustness. Furthermore, it is observed that the reported uncorrectable errors and the command timeout have a greater impact on hard disk drive failure. Finally, the new model is employed to forecast the failure of four hard disk drives. The forecasting results indicate that in the next four time points with a cycle of 21 days beginning in April 2023, the failure of the smaller capacity hard disk drives (0055 and 0086, corresponding to 8TB and 10TB) show a decreasing trend, reaching 67.442% and 89.7683%, respectively. The failure of the other larger capacity hard disk drives (0007 and 0138, corresponding to 12TB and 14TB) has increased, with a growth rate of 17.1016% and 123.7899%.
