期刊：
International Journal of Computational Intelligence Systems,2021年14(1):808-817 ISSN：1875-6891
通讯作者：
Meng, Qiuqing
作者机构：
[Xiong, Huixiang; Meng, Qiuqing] Cent China Normal Univ, Sch Informat Management, Wuhan 403792, Peoples R China.;[Meng, Qiuqing] Guizhou Univ, Sch Informat Financial & Econ, Guiyang 550025, Peoples R China.
通讯机构：
[Meng, Qiuqing] C;[Meng, Qiuqing] G;Cent China Normal Univ, Sch Informat Management, Wuhan 403792, Peoples R China.;Guizhou Univ, Sch Informat Financial & Econ, Guiyang 550025, Peoples R China.
关键词：
Doctor recommendation;LDA topic model;Eigenvector centrality;Graph computing;word2vec
摘要：
Doctor recommendation technology can help patients filter out large number of irrelevant doctors and find doctors who meet their actual needs quickly and accurately, helping patients gain access to helpful personalized online healthcare services. 'co address the problems with the existing recommendation methods, this paper proposes a hybrid doctor recommendation model based on online healthcare platform, which utilizes the word2vec model, latent Dirichlet allocation (LDA) topic model, and other methods to find doctors who best suit patients' needs with the information obtained from consultations between doctors and patients. Then, the model treats these doctors as nodes in order to construct a doctor tag cooccurrence network and recommends the most important doctors in the network via an eigenvector centrality calculation model on the graph. This method identifies the important nodes in the entire effective doctor network to support the recommendation from a new graph computing perspective. An experiment conducted on the Chinese healthcare website Chunyuyisheng.com proves that the proposed method a good recommendation performance. (C) 2021 The Authors. Published by Atlantis Press B.V.
期刊：
International Journal of Computational Intelligence Systems,2021年14(1):734-743 ISSN：1875-6891
通讯作者：
Li, Yating
作者机构：
[Dong, Ting; Chen, Ye; Ban, Qunwei] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Li, Yating] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
通讯机构：
[Li, Yating] C;Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Peoples R China.
关键词：
Hypertension information needs;Social media platform;Topic modeling;Biterm topic model
摘要：
In this paper, the Biterm topic modeling method and comparative analysis were employed to identify consumers' information needs on hypertension and their differences between the Online Health Community and the Q&A Forum. There are common information needs on both platforms but consumers on MedHelp discussed more about pathology and pharmacology, and mental health of hypertension than those on Quora. The results can help consumers, social media platform designers, and medical professionals better understand consumers' information needs on hypertension. (C) 2021 The Authors. Published by Atlantis Press B.V.
摘要：
This paper considers a supply chain consisting of one supplier and one retailer who jointly invest in item-level radio frequency identification (RFID) by considering their demands and inventories. Since the supplier can be negative to adopt RFID, the main objective of this study is to design an effective mechanism that maximizes the profits of both players and the whole supply chain by implementing item-level RFID. For this objective, we propose a novel revenue-cost-sharing (RCS) contract based on the bargaining game as the incentive to encourage the implementation, and numerical results show that the RCS contract is considerably more effective than the wholesale-price contract for supply chain coordination. Moreover, we show that the retail supply chain can be coordinated perfectly under the RCS contract when the thresholds of the revenue/cost sharing rates are set appropriately. This study provides significant managerial insight into the incentive and coordination strategy to implement item-level RFID in the retail supply chain.
摘要：
Recent research has shifted to investigating knowledge integration in an interdisciplinary field and measuring the interdisciplinarity. Conventional citation analysis does not consider the context of citations, which limits the understanding of interdisciplinary knowledge integration. This study introduces a novel analytical framework to characterize interdisciplinary knowledge integration by both the content, i.e., integrated knowledge phrases (IKPs), and location of citances (i.e., citing sentences) in addition to citations. Seven knowledge categories are used to classify IKPs, including Research Subject, Theory, Research Methodology, Technology, Human Entity, Data, and Others. The eHealth field is explored as an exemplar interdisciplinary field in the case study. The result reveals that the ranks of source disciplines quantified by the integrated knowledge phrases are different from those by citations, especially in terms of average knowledge integration density. The distributions of the IKPs over the knowledge categories differ among source disciplines, indicating their different contributions to knowledge integration of eHealth field. The knowledge from adjacent disciplines is integrated into the field faster than that from other disciplines. Knowledge distributions over sections of articles are also different among source disciplines, and a correlation between knowledge categories and the sections they were used is observed. The analytical framework offers a way to better understand an interdisciplinary field by disclosing the characteristics of interdisciplinary knowledge integration from the perspective of knowledge content and usage.
作者机构：
[Chi, Maomao; Li, Weiqing] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Dan, Qianyi] Cent China Normal Univ, Sch Comp, Wuhan 430079, Peoples R China.;[Wang, Weijun] Cent China Normal Univ, Key Lab Adolescent Cyberpsychol & Behav, Minist Educ, Wuhan 430079, Peoples R China.
通讯机构：
[Chi, Maomao; Wang, Weijun] C;Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Key Lab Adolescent Cyberpsychol & Behav, Minist Educ, Wuhan 430079, Peoples R China.
关键词：
consumer information search behavior;price level;perceived price dispersion;durables and consumables;moderating effect
摘要：
The methods consumers use to reduce their perceived risk and make reasonable purchase decisions can be synthesized under the umbrella term “consumer information search behavior” (CISB). As one key factor that conveys a product’s value and quality, price has a significant impact on CISB. There are few studies that comprehensively consider the impact of price level (PL) and perceived price dispersion (PPD) on CISB, and there is a certain disagreement about the impact of PPD specific to the online shopping environment. To address this research gap, we construct a model using the data from 5515 consumers’ purchasing and browsing behavior on a B2C e-commerce website, selecting six products as our research objects. We use a hierarchical regression analysis method to study the influence of product PL and PPD on CISB, and to explore the moderating effect of product categories (durables and consumables) on the relationship between PL, PPD and CISB. The results show that PL significantly affects CISB, and that product categories have a significant moderating effect on the relationship between PL and CISB. For durable goods, when the PL is high, consumers tend to increase their search behavior, both in depth and in breadth, and for consumables with low PL but higher purchase frequency, consumers likewise tend to increase their search behavior. In the B2C online shopping environment, PPD has a significant positive effect on CISB, and product category has a moderating effect on the relationship between PPD and CISB. When consumers purchase consumables, the higher the PPD, the higher the depth of CISB. The findings have several implications for marketing practitioners and enterprises advertising, also can help customers save time and energy in their search behaviors.
作者机构：
[Yan, Xiaoyan] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Sun, Bo; Jian, Fanghong] Cent China Normal Univ, Natl Engn Res Ctr ELearning, Wuhan 430079, Peoples R China.
通讯机构：
[Yan, X.] S;School of Information Management, China
摘要：
Sentiment analysis of online reviews is an important task in natural language processing. It has received much attention not only in academia but also in industry. Data have become an important source of competitive intelligence. Various pretraining models such as BERT and ERNIE have made great achievements in the task of natural language processing, but lack domain-specific knowledge. Knowledge graphs can enhance language representation. Furthermore, knowledge graphs have high entity / concept coverage and strong semantic expression ability. We propose a sentiment analysis knowledge graph (SAKG)-BERT model that combines sentiment analysis knowledge and the language representation model BERT. To improve the interpretability of the deep learning algorithm, we construct an SAKG in which triples are injected into sentences as domain knowledge. Our investigation reveals promising results in sentence completion and sentiment analysis tasks.
摘要：
Order picking is the process of retrieving products from the storage locations to meet customer orders, which accounts for more than 55% of the total warehouse cost. The joint order batching and picker routing problem (JOBPRP) is an effective way to improve picking efficiency. Although many warehouses face the physical constraints of products that have impact on the picking sequence, such as weight, size, shape, and fragility, JOBPRP with such physical constraints has not been widely studied in the literature. This paper is inspired by a practical case observed in an online-to-offline grocery store in China, where food products should not be carried under nonfood products in the picking container to maintain food safety, called category constraint. Therefore, JOBPRP with category constraint is studied. The JOBPRP optimization models with and without category constraint are formulated to minimize the total processing time, and the modified seed algorithms, with new seed addition rules and modified near-optimal routing methods are proposed to solve the models. The performance of the proposed algorithms is evaluated in different seed addition rules, routing methods, sort time scenarios, and storage assignment strategies (SASs) in a case study. We found that considering category constraint in JOBPRP can reduce the total processing time, and the modified seed algorithms perform better than the traditional first-come-first-serve benchmark algorithms and the seed algorithms with traditional seed addition rules and S-shape routing method. The SASs where nonfood and food products are separately in fewer number of zones are recommended.
摘要：
Background: More and more females are diagnosed with and die of cancers. Acquiring cancer-related information and enriching one & rsquo;s knowledge of cancers are important to cancer prevention and treatment. Effective online health headlines are indispensable to encouraging the reading of the hyperlinked health articles, especially those on daunting topics such as cancers. Objective: This study aims to reveal how message framing, i.e., gain- or loss-framing, influences female users & rsquo; selection of cancer-related health headlines at two levels, i.e., attention and behavior. Methods: An eye-tracking experiment was conducted to capture female participants & rsquo; attention and clicking behavior in response to cancer-related headlines manipulated in terms of message framing. The StimulusOrganism-Response (S- O-R) framework was introduced to develop the research model that also took approach/avoidance motivation into account as moderator. Results: Compared with loss-framed headlines, gain-framed ones attracted more and longer fixations (13 = .09, p < .01; 13 = .12, p < .01) as well as more clicks (exp(B) = 1.76, p < .001), and they additionally evoked a higher level of pleasure (13 = .50, p = .00) yet a lower level of arousal (13=-.16, p = .00). Arousal partially mediated the relationship between message framing and headline selection (13 = .16, p = .00; 13 = .16, p = .00; exp(B) = 1.8, p = .00). The participants high in approach motivation devoted more attention to gain-framed headlines than to loss-framed ones (F(1,1333) = 15.74, p < .001; F(1,1333) = 31.94, p < .001). Conclusion: Gain-framing is a preferred technique over loss-framing for online health information providers to create effective headlines of cancer-related information. Using gain-framed headlines helps alleviate cancer information avoidance and enrich people & rsquo;s knowledge of fatal diseases.
摘要：
This paper aims to provide a comprehensive understanding of the evolution of major research themes and trends in e-learning research. A co-word analysis is applied for the analysis of the 21,656 keywords collected from 7214 articles published in 10 journals in the field of e-learning from the years 1999 to 2018. Specifically, a cluster analysis, social network analysis, strategic diagram, and graph theory were applied in the analysis for two time periods: 1999-2008 and 2009-2018. The study detects the bridging, popular, and core topics in e-learning research for the two periods. The research results indicate that e-learning research has undergone a health evolution over the past two decades. There is a temporal continuity of e-learning research because some research topics have kept their continuity over the studied 20 years. Meanwhile, the research traditions in the e-learning field are also continuously evolving with the development of new technologies. The results also offer useful hints on the future direction of how the field may evolve.
期刊：
Mitigation and Adaptation Strategies for Global Change,2020年25(7):1325-1343 ISSN：1381-2386
通讯作者：
Xiao, Yi
作者机构：
[Li, Keying; Xiao, Yi] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.;[Hu, Yi] Univ Chinese Acad Sci, Sch Econ & Management, Beijing 100190, Peoples R China.;[Xiao, Jin] Sichuan Univ, Sch Business, Chengdu 610064, Peoples R China.;[Wang, Shouyang] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China.
通讯机构：
[Xiao, Yi] C;Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
关键词：
Natural gas consumption forecasting;Emission reduction;Deep learning;STRIPAT model;Gated recurrent unit model;Bagging
摘要：
With the orderly advancement of China Energy Development Strategic Action Plan, clean energy has become a major trend in the energy market. As a major industry of clean energy, natural gas industry plans to consume at least 10% of the total primary energy by 2020. The energy structure will be improved in an orderly manner to achieve the goal of energy conservation, consumption reduction, and emission reduction. To achieve energy saving and emission reduction, and using clean energy effectively, accurate prediction of natural gas consumption is of great importance. Because of the many influencing factors affecting natural gas demand, this paper first utilizes STRIPAT to analyze the factors affecting natural gas consumption and then uses a deep learning ensemble approach to analyze and predict China’s natural gas consumption. One is an advanced deep neural network model named gated recurrent unit model which is used to model the nonlinear and complex relationships of natural gas consumption with its factors. The other is a powerful ensemble method named bootstrap aggregation which generates multiple data sets for training a set of base models. Our approach combines the advantages of these two technologies to forecast the demand for China’s natural gas market. In empirical research, our method has been tested by some competitive methods and has shown superiority.
期刊：
Frontiers in Genetics,2020年11:546210 ISSN：1664-8021
通讯作者：
Jiang, Xingpeng
作者机构：
[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Pan, Min; Zhu, Qiang; Zhu, Qing] Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Pan, Min; Zhu, Qing] Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.
通讯机构：
[Jiang, Xingpeng] C;Cent China Normal Univ, Sch Comp, Wuhan, Peoples R China.;Cent China Normal Univ, Hubei Prov Key Lab Artificial Intelligence & Smar, Wuhan, Peoples R China.
摘要：
Purpose Social coding platforms (SCPs) have been adopted by scores of developers in building, testing and managing their codes collaboratively. Accordingly, this type of platform (site) enables collaboration between enterprises and universities (c-EU) at a lower cost in the form of online team-building projects (repositories). This paper investigates the open collaboration patterns between these two parties on GitHub by measuring their online behaviours. The purpose of this investigation is to identify the most attractive collaboration features that enterprises can offer to increase university students' participation intentions. Design/methodology/approach The research process is divided into four steps. First, the authors crawled for numerical data for each interactive repository feature created by employees of Alibaba on GitHub and identified the student accounts associated with these repositories. Second, a categorisation schema of feature classification was proposed on a behavioural basis. Third, the authors clustered the aforementioned repositories based on feature data and recognised four types of repositories (popular, formal, normal and obsolete) to represent four open collaboration patterns. The effects of the four repository types on university students' collaboration behaviour were measured using a multiple linear regression model. An ANOVA test was implemented to examine the robustness of research results. Finally, the authors proposed some practical suggestions to enhance collaboration between both sides of SCPs. Findings Several counterintuitive but reasonable findings were revealed, for example, those based on the "star" repository feature. The actual coding contribution of the repositories had a negative correlation with student attention. This result indicates that students were inclined to imitate rather than innovate. Originality/value This research explores the open collaboration patterns between enterprises and universities on GitHub and their impact on student coding behaviour. According to the research analysis, both parties benefit from open collaboration on SCPs, and the allocation or customisation of online repository features may affect students' participation in coding. This research brings a new perspective to the measurement of users' collaboration behaviour with output rates on SCPs.
期刊：
Journal of Cleaner Production,2020年260:120962 ISSN：0959-6526
通讯作者：
Chi, Maomao
作者机构：
[Chi, Maomao; Wang, Ping] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[George, Joey F.] Iowa State Univ, Ivy Coll Business, Ames, IA USA.;[Huang, Rui] Univ Massachusetts Dartmouth, Charlton Coll Business, Dartmouth, MA USA.
通讯机构：
[Chi, Maomao] C;Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
关键词：
Sharing economy;Bicycle-sharing;Sustainable behavior;Stimulus-organism-response framework;Self-determination theory
摘要：
Sustainable behaviors in the sharing economy have attracted attentions from both researchers and practitioners. Prior literature has mostly focused on sustainable development in the traditional economy and treated sustainability as an influential factor of participation in the sharing economy. Based on the self-determination theory and the stimulus-organism-response (S-O-R) framework, this paper explores the formation mechanisms of sustainable behaviors in the bicycle-sharing economy. An online survey was conducted in China and data were collected from 387 shared-bicycle users. Structural equation modeling was employed to examine the research model and research hypotheses. The study presents three critical findings. First, environmental stimulation (including government laws, enterprise regulations, and social ethics) have a significant and positive impact on user's sense of self-determination (including perceived autonomy, perceived competence, and perceived relatedness). Second, both perceived competence and perceived relatedness can help promote user sustainable behaviors. Third, this study also supports that both perceived competence and perceived relatedness have a mediating effect on the relationship between environmental stimuli and sustainable behaviors of shared-bicycle users. The paper offers insights to both government and firms supporting bicycle-sharing on how to effectively elicit user sustainable behaviors in the bicycle-sharing economy. (C) 2020 Published by Elsevier Ltd.
