Research has often overlooked the role of digital innovation in driving social transformation, especially in underserved rural areas, but the integration of digital technology is promoting rural high-quality development through the establishment of digital entrepreneurial ecosystems. Approaching from a complex systems perspective, this study contends that these ecosystems navigate multiple routes to enhance total factor productivity (TFP) in rural settings. Performing a configurational analysis of a sample of 60 demonstration counties for rural revitalization in China, this study identifies three primary pathways yielding high TFP: an investment-led model under government stewardship, a collaborative model steered by both government and social capital, and a talent-centric model governed by digital market forces. Conversely, this study also pinpoints a pathway that does not yield high TFP. Theoretical and practical insights are offered for researchers and practitioners exploring digital innovation and its implications for rural entrepreneurial ecosystems.
{"title":"High-Quality Growth in Rural China","authors":"Xiaotong Liu, Chengshuang Qi, Yu Liu, Yuhuan Xia, Haili Wu","doi":"10.4018/joeuc.332063","DOIUrl":"https://doi.org/10.4018/joeuc.332063","url":null,"abstract":"Research has often overlooked the role of digital innovation in driving social transformation, especially in underserved rural areas, but the integration of digital technology is promoting rural high-quality development through the establishment of digital entrepreneurial ecosystems. Approaching from a complex systems perspective, this study contends that these ecosystems navigate multiple routes to enhance total factor productivity (TFP) in rural settings. Performing a configurational analysis of a sample of 60 demonstration counties for rural revitalization in China, this study identifies three primary pathways yielding high TFP: an investment-led model under government stewardship, a collaborative model steered by both government and social capital, and a talent-centric model governed by digital market forces. Conversely, this study also pinpoints a pathway that does not yield high TFP. Theoretical and practical insights are offered for researchers and practitioners exploring digital innovation and its implications for rural entrepreneurial ecosystems.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135918086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chong Li, Jinjie Zhang, Anyu Wang, Xuemin Liu, Yunchsun Sun, Shibo Zhang, Zhixia Ji, Justin Z. Zhang
An enormous challenge for project management is to identify similar research projects accurately and efficiently among numerous proposals. To address this challenge, this paper proposes an algorithm to calculate the similarity between research projects using an improved generating method for fused word order sentence vectors based on USIF (unsupervised random walk sentence embeddings). The experimental results show that the proposed algorithm is about 15.8% more accurate than the existing approaches. The authors also propose a pre-checking algorithm by introducing a complex research cooperation graph to enhance query efficiency. The results show the pre-checking method reduces the query time cost by 96% on average.
{"title":"Similarity Discriminating Algorithm for Scientific Research Projects","authors":"Chong Li, Jinjie Zhang, Anyu Wang, Xuemin Liu, Yunchsun Sun, Shibo Zhang, Zhixia Ji, Justin Z. Zhang","doi":"10.4018/joeuc.332008","DOIUrl":"https://doi.org/10.4018/joeuc.332008","url":null,"abstract":"An enormous challenge for project management is to identify similar research projects accurately and efficiently among numerous proposals. To address this challenge, this paper proposes an algorithm to calculate the similarity between research projects using an improved generating method for fused word order sentence vectors based on USIF (unsupervised random walk sentence embeddings). The experimental results show that the proposed algorithm is about 15.8% more accurate than the existing approaches. The authors also propose a pre-checking algorithm by introducing a complex research cooperation graph to enhance query efficiency. The results show the pre-checking method reduces the query time cost by 96% on average.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136013036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Research on cloud computing (CC) has gained a lot of momentum owing to its massive adoption. It has moved beyond the exploration of inherent capabilities to understand its disruptiveness and transformative value. In this vein, the authors conducted a comparative literature review of 101 articles to better understand the developments from previous reviews. This article serves as a replication study to evaluate the growth of the business perspective of CC. The authors identify 126 factors guiding the characteristics, adoption, governance, and business impact of the cloud. Further, they employ a rigorous analysis that situates our review at the intersection of these factors and applies a multidimensional scaling technique. The developed matrix (a) helps to clarify the current state of research, (b) identifies research gaps, and (c) identifies potential further research avenues. Unlike previous reviews, this developed multidimensional view of each article uncovers numerous perspectives that can guide future research.
{"title":"Understanding the Developments in the Business Perspective of Cloud Computing","authors":"Harsh Parekh, Huai-Tzu Cheng, Andrew Schwarz","doi":"10.4018/joeuc.330751","DOIUrl":"https://doi.org/10.4018/joeuc.330751","url":null,"abstract":"Research on cloud computing (CC) has gained a lot of momentum owing to its massive adoption. It has moved beyond the exploration of inherent capabilities to understand its disruptiveness and transformative value. In this vein, the authors conducted a comparative literature review of 101 articles to better understand the developments from previous reviews. This article serves as a replication study to evaluate the growth of the business perspective of CC. The authors identify 126 factors guiding the characteristics, adoption, governance, and business impact of the cloud. Further, they employ a rigorous analysis that situates our review at the intersection of these factors and applies a multidimensional scaling technique. The developed matrix (a) helps to clarify the current state of research, (b) identifies research gaps, and (c) identifies potential further research avenues. Unlike previous reviews, this developed multidimensional view of each article uncovers numerous perspectives that can guide future research.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135579920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid progress of the digital economy has brought forth a myriad of complexities in economic governance, particularly in the domains of stocks and network finance. The authors propose the exploration of an innovative economic management model founded on the compound neural network framework. Central to this approach is the utilization of the deep bidirectional long and short-term memory neural network model (Bi-LSTM) as the primary instrument for predictive analysis, complemented by the refinement and enhancement provided by the Markov chain model. Through comparative analysis of experiments, it is found that although the forecast price of this model has a certain lag, it has a more accurate judgment than other prediction models, and the accuracy and recall rate reach 87.66% and 86.31%. At the same time, the error evaluation index R2 is very close to the upper limit 1 of the index, and the mean absolute error MAE Hill inequality coefficient; TIC root; mean square error; RMSE; and symmetric mean percentage error (SMAPE) are 0.2654, 0.0124, 0.3481, and 0.3531, respectively.
{"title":"The Design of a Compound Neural Network-Based Economic Management Model for Advancing the Digital Economy","authors":"Ke Shang, Muhammad Asif","doi":"10.4018/joeuc.330678","DOIUrl":"https://doi.org/10.4018/joeuc.330678","url":null,"abstract":"The rapid progress of the digital economy has brought forth a myriad of complexities in economic governance, particularly in the domains of stocks and network finance. The authors propose the exploration of an innovative economic management model founded on the compound neural network framework. Central to this approach is the utilization of the deep bidirectional long and short-term memory neural network model (Bi-LSTM) as the primary instrument for predictive analysis, complemented by the refinement and enhancement provided by the Markov chain model. Through comparative analysis of experiments, it is found that although the forecast price of this model has a certain lag, it has a more accurate judgment than other prediction models, and the accuracy and recall rate reach 87.66% and 86.31%. At the same time, the error evaluation index R2 is very close to the upper limit 1 of the index, and the mean absolute error MAE Hill inequality coefficient; TIC root; mean square error; RMSE; and symmetric mean percentage error (SMAPE) are 0.2654, 0.0124, 0.3481, and 0.3531, respectively.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"328 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135769797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Computer vision has made significant advancements in emotional design. Designers can now utilize computer vision to create emotionally captivating designs that deeply resonate with people. This article aims at enhancing emotional design selection by separating appearance and color. A two-stage emotional design method is proposed, which yields significantly better results compared to classical single-stage methods.. In the Radboud face dataset (RaFD), facial expressions primarily rely on appearance, while color plays a relatively smaller role. Therefore, the two-stage model presented in this article can focus on shape design. By utilizing the SSIM image quality evaluation index, our model demonstrates a 31.63% improvement in generation performance compared to the CGAN model. Additionally, the PSNR image quality evaluation index shows a 10.78% enhancement in generation performance. The proposed model achieves superior design results and introduces various design elements.This article exhibits certain improvements in design effectiveness and scalability compared to conventional models.
{"title":"A Two-Stage Emotion Generation Model Combining CGAN and pix2pix","authors":"Yuanqing Wang, Dahlan Abdul Ghani, Bingqian Zhou","doi":"10.4018/joeuc.330647","DOIUrl":"https://doi.org/10.4018/joeuc.330647","url":null,"abstract":"Computer vision has made significant advancements in emotional design. Designers can now utilize computer vision to create emotionally captivating designs that deeply resonate with people. This article aims at enhancing emotional design selection by separating appearance and color. A two-stage emotional design method is proposed, which yields significantly better results compared to classical single-stage methods.. In the Radboud face dataset (RaFD), facial expressions primarily rely on appearance, while color plays a relatively smaller role. Therefore, the two-stage model presented in this article can focus on shape design. By utilizing the SSIM image quality evaluation index, our model demonstrates a 31.63% improvement in generation performance compared to the CGAN model. Additionally, the PSNR image quality evaluation index shows a 10.78% enhancement in generation performance. The proposed model achieves superior design results and introduces various design elements.This article exhibits certain improvements in design effectiveness and scalability compared to conventional models.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136130842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The COVID-19 pandemic has accelerated the trend of digital transformation (DT) among businesses. DT redefines business models, which significantly changes employees' work practices. If employees lack an appropriate mindset for DT, it can result in DT failure. However, little research has explored the intention of employees to embrace DT. This study proposes a dilemmatic dual-factor research model to examine the factors influencing employees' acceptance of DT, including management support and resistance to change in the outer/explicit aspect and perceived benefits and inertia in the inner/tacit aspect. The study found that the perceived benefits of DT positively impact employees' intention to accept DT, but resistance to change and perceived inertia are significant barriers. Moreover, management support alone is insufficient to encourage employees to accept DT. This study is distinct from prior research, which typically focuses on successfully implementing DT from the firm's perspective. Instead, the study offers valuable insights into promoting employee acceptance of DT.
{"title":"The Duality Determinants of Adoption Intention in Digital Transformation Implementation","authors":"Cheng-Kui Huang, Chueh-An Lee, Ying-Ni Chen","doi":"10.4018/joeuc.330534","DOIUrl":"https://doi.org/10.4018/joeuc.330534","url":null,"abstract":"The COVID-19 pandemic has accelerated the trend of digital transformation (DT) among businesses. DT redefines business models, which significantly changes employees' work practices. If employees lack an appropriate mindset for DT, it can result in DT failure. However, little research has explored the intention of employees to embrace DT. This study proposes a dilemmatic dual-factor research model to examine the factors influencing employees' acceptance of DT, including management support and resistance to change in the outer/explicit aspect and perceived benefits and inertia in the inner/tacit aspect. The study found that the perceived benefits of DT positively impact employees' intention to accept DT, but resistance to change and perceived inertia are significant barriers. Moreover, management support alone is insufficient to encourage employees to accept DT. This study is distinct from prior research, which typically focuses on successfully implementing DT from the firm's perspective. Instead, the study offers valuable insights into promoting employee acceptance of DT.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136130677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Guobin Fang, Yaoxun Deng, Huimin Ma, Jun Zhang, Li Pan
Effective energy financial risk management is crucial to ensure that China's economic system can remain stable. This article utilizes the quantile vector autoregressive spillover index model, complex networks, and deep learning methods to simultaneously assess both the internal and external energy financial market risks in China. Spillover effects under different market conditions are also examined. The research findings indicate that: (1) Under extreme market conditions, static total spillover values between internal and external markets exceed 70%, while under normal market conditions, they are only around 53% and 13%, respectively; (2) Crude oil and fuel oil as well as energy and stocks are important nodes in both internal and external markets; and (3) The attention-convolutional neural network-long short-term memory model outperforms the second-best performing model, and achieves an improvement of 12.9% and 21.4% in terms of mean absolute error and root mean square error, respectively; inclusion of early warning indicators leads to further improvements of 19.8% and 31.9%, respectively.
{"title":"Energy Financial Risk Management in China Using Complex Network Analysis","authors":"Guobin Fang, Yaoxun Deng, Huimin Ma, Jun Zhang, Li Pan","doi":"10.4018/joeuc.330249","DOIUrl":"https://doi.org/10.4018/joeuc.330249","url":null,"abstract":"Effective energy financial risk management is crucial to ensure that China's economic system can remain stable. This article utilizes the quantile vector autoregressive spillover index model, complex networks, and deep learning methods to simultaneously assess both the internal and external energy financial market risks in China. Spillover effects under different market conditions are also examined. The research findings indicate that: (1) Under extreme market conditions, static total spillover values between internal and external markets exceed 70%, while under normal market conditions, they are only around 53% and 13%, respectively; (2) Crude oil and fuel oil as well as energy and stocks are important nodes in both internal and external markets; and (3) The attention-convolutional neural network-long short-term memory model outperforms the second-best performing model, and achieves an improvement of 12.9% and 21.4% in terms of mean absolute error and root mean square error, respectively; inclusion of early warning indicators leads to further improvements of 19.8% and 31.9%, respectively.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135395680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Na Li, Chongyuan Guan, Xuefeng Huang, Qinfang Zhen, Anni Wang, Xin Dai, Yuanyuan Zhang
With the continuous development of information technology and information management systems, they are widely and successfully used in practice. Health behavior and psychological monitoring systems are a breakthrough in the technological transformation of the therapeutic field. It is widely used in the field of healing and health care with its characteristics of convenience, quickness, accuracy, and timeliness. In this study, 466 valid samples were collected by questionnaires from students. The results show that effort expectation, community influence, and convenience have a positive effect on the health behaviors monitored by the health behavior and psychological monitoring systems of school students, and the three factors indirectly influence the behavioral intention through influencing the perceived value, learning willingness, and perceived trust and then affect the actual behavior. Based on the analysis results of this research, the authors provide research and management recommendations for health behavior and psychological monitoring systems operators and researchers in related fields.
{"title":"Factors Influencing the Willingness to Accept Health Behavior and Psychological Monitoring Systems in the Milieu of Information Management Technology","authors":"Na Li, Chongyuan Guan, Xuefeng Huang, Qinfang Zhen, Anni Wang, Xin Dai, Yuanyuan Zhang","doi":"10.4018/joeuc.330020","DOIUrl":"https://doi.org/10.4018/joeuc.330020","url":null,"abstract":"With the continuous development of information technology and information management systems, they are widely and successfully used in practice. Health behavior and psychological monitoring systems are a breakthrough in the technological transformation of the therapeutic field. It is widely used in the field of healing and health care with its characteristics of convenience, quickness, accuracy, and timeliness. In this study, 466 valid samples were collected by questionnaires from students. The results show that effort expectation, community influence, and convenience have a positive effect on the health behaviors monitored by the health behavior and psychological monitoring systems of school students, and the three factors indirectly influence the behavioral intention through influencing the perceived value, learning willingness, and perceived trust and then affect the actual behavior. Based on the analysis results of this research, the authors provide research and management recommendations for health behavior and psychological monitoring systems operators and researchers in related fields.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48313216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Maican, S. Sumedrea, A. Tecău, Eliza Nichifor, I. Chițu, R. Lixăndroiu, G. Brătucu
Motivated by the need to better understand the ongoing role of artificial intelligence in businesses and to shift the focus from a purely technological and algorithmic perspective to one that encompasses human-computer interaction, this article aims to investigate people's intention to use AI for generating images in a business context. The present study employed structural equation modelling to analyse how factors from UTAUT2 such as perceived customer value, effort expectancy, social influence, and facilitating conditions affect behavioural intention. The research introduces new moderators (creativity and English language proficiency), in the context of generative AI. Language proficiency and gender impact AI usage, while the impact of effort expectancy is more pronounced in cases of low creativity.
{"title":"Factors Influencing the Behavioural Intention to Use AI-Generated Images in Business","authors":"C. Maican, S. Sumedrea, A. Tecău, Eliza Nichifor, I. Chițu, R. Lixăndroiu, G. Brătucu","doi":"10.4018/joeuc.330019","DOIUrl":"https://doi.org/10.4018/joeuc.330019","url":null,"abstract":"Motivated by the need to better understand the ongoing role of artificial intelligence in businesses and to shift the focus from a purely technological and algorithmic perspective to one that encompasses human-computer interaction, this article aims to investigate people's intention to use AI for generating images in a business context. The present study employed structural equation modelling to analyse how factors from UTAUT2 such as perceived customer value, effort expectancy, social influence, and facilitating conditions affect behavioural intention. The research introduces new moderators (creativity and English language proficiency), in the context of generative AI. Language proficiency and gender impact AI usage, while the impact of effort expectancy is more pronounced in cases of low creativity.","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44369548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
For employees, work involves taking breaks as well as engaging in specific required duties, and sometimes that break taking is construed as “cyberslacking” by employers. After historical treatments of cyberslacking concepts, this article analyzes ways that artificial intelligence (AI) methodologies and the “bossware” platform genre are aiding management to counter the cyberslacking phenomena directly exhibited by employees or projected from previous activities and profiles. It contrasts straightforward “policing” methods that aim toward the identification of cyberslacking instances for selective punishment through surveillance, with “predictive cyberslacking” approaches that profile certain trends and patterns in employee behavior. Such identified inclinations can be used to engage or nudge workers into specific, individualized patterns of work and approved recreational or developmental activity. A medicalization-style approach is often used in bossware to entice employees toward particular mental health-themed activities (including mindfulness and meditation activities).
{"title":"The Shape of Workbreaks to Come","authors":"Jo Ann Oravec","doi":"10.4018/joeuc.329596","DOIUrl":"https://doi.org/10.4018/joeuc.329596","url":null,"abstract":"For employees, work involves taking breaks as well as engaging in specific required duties, and sometimes that break taking is construed as “cyberslacking” by employers. After historical treatments of cyberslacking concepts, this article analyzes ways that artificial intelligence (AI) methodologies and the “bossware” platform genre are aiding management to counter the cyberslacking phenomena directly exhibited by employees or projected from previous activities and profiles. It contrasts straightforward “policing” methods that aim toward the identification of cyberslacking instances for selective punishment through surveillance, with “predictive cyberslacking” approaches that profile certain trends and patterns in employee behavior. Such identified inclinations can be used to engage or nudge workers into specific, individualized patterns of work and approved recreational or developmental activity. A medicalization-style approach is often used in bossware to entice employees toward particular mental health-themed activities (including mindfulness and meditation activities).","PeriodicalId":49029,"journal":{"name":"Journal of Organizational and End User Computing","volume":" ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46751088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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