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In Massive Open Online Courses (MOOCs), online reviews serve as a basis for teachers to improve their courses. The disconfirmation effect of online reviews, i.e. the inconsistency between the level of attention paid to a course factor and the actual weight of that factor’s influence on learner satisfaction, leads to erroneous judgments by teachers. Based on the two-factor theory of emotion, 4,070 courses and 165,705 online reviews are adopted as a corpus to identify the effect of learner sentiment on the disconfirmation effect. The empirical results show that there is a significant disconfirmation effect for negative reviews, but not for positive ones. A fine-grained analysis on negative sentiment finds that reviews containing more sadness and anger sentiments have a stronger disconfirmation effect. A comparison of course types reveals that the disconfirmation effect is stronger for instrument-based courses than that for knowledge-based and practice-based ones. In addition, negative word-of-mouth weakens the disconfirmation effect of sadness and anger reviews and enhances the disconfirmation effect of positive reviews. Further, learner’s reputation weakens the disconfirmation effect of sadness reviews and enhances the disconfirmation effect of positive and anger reviews. [ABSTRACT FROM AUTHOR]

Social networks can provide massive amounts of information for communication among users and communities. The trust relationships in social networks can be utilized to reveal user preferences for improving the quality of social recommendation, which aims to mitigate information overload and provide users with the most attractive and relevant items or services. However, the data sparsity and cold-start issue degrade recommendation performance significantly. To address these issues, a novel trust-embedded collaborative deep generative model (TCDG) is proposed for exploiting multisource information (content, rating and trust) to predict ratings. TCDG employs deep generative model to unsupervisedly learn deep latent representations for item content through an inference network in latent space instead of observation space. Meanwhile, TCDG adopts probabilistic matrix factorization to map users into low-dimensional latent feature spaces by trust relationships, which can reflect users' mutual influence on the formation of users' opinions more accurately and learn better implicit relationships between items and users from content, rating and trust. In addition, an approach with an annealing parameter to calculate the maximum a posteriori estimates is proposed to learn model parameters. Experiments using four real-world datasets are conducted to evaluate the prediction and top-ranking performance of our model. The results indicate that TDCG has better accuracy and robustness than other methods for making recommendations. [ABSTRACT FROM AUTHOR]

Abstract Entrepreneurship has gained increasing research attention in recent years. Although researchers largely agree the importance of innovation in the process of entrepreneurship, little research explores how the emerging innovation approaches can facilitate the above process. Furthermore, there is a lack of studies on entrepreneurship adopting the open platform strategy in light of the digital evolution. To address this research gap, this study investigates how entrepreneurs can take advantage of the platform-based innovation ecosystem in the entrepreneurial process. First, the study discusses various platform-based innovations for entrepreneurs and elaborates on the implementation with examples and practices. Accordingly, the study demonstrates a framework which categorizes these innovation platforms to fulfill the needs at two pivotal stages of the innovation process (i.e. invention and commercialization) for entrepreneurs. The study also discusses the concerns and issues an innovation platform ecosystem may bring forth for entrepreneurs. Finally, the study proposes a number of topics that merit further research. This study represents the first effort to categorize online platforms and develop an innovation process-based framework of platforms for entrepreneurs. Highlights • We explore how entrepreneurs can take advantage of the platform strategy. • We classify innovation platforms for entrepreneurs. • Classification is based on NPD/NSD and commercialization capabilities. • We discuss the concerns and issues innovation platforms bringing forth for entrepreneurs. • The study proposes a number of future research topics. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization, effective smart city programs need to consider both daily management and crisis management. In this process, to enable health information technology to better contribute to the construction of smart cities, the government and firms need to pay attention to the public's intention to adopt technology. Based on the context of China's response to the COVID-19 pandemic in smart cities, we analyze the influencing factors of the behavioral intention to use health information technology using an extended unified theory of acceptance and use of technology model. Data for this study were collected from 721 inhabitants of 290 smart cities in China. The empirical results showed that performance expectations, effort expectations, social influence, and facilitating conditions positively affected their behavioral intention to use health information technology, whereas perceived risk had the opposite effect. This study found that the positive effects of social influence and effort expectations on the behavioral intention to use health information technology increased with improvement in the perceived level of city smartness and technical information transparency. Finally, we discuss theoretical and practical implications. • We analyze the public's acceptance intention for health information technology in public crisis management by smart cities. • The perceived city smartness level and technical information transparency are added to the UTAUT model. • The boundary conditions affecting intention to use health information technology are examined. • The applicability of the UTAUT model is empirically supported in the context of a sudden public crisis in smart cities. [ABSTRACT FROM AUTHOR]

AbstractNowadays, numerous enterprises are actively adopting electronic performance monitoring (EPM) systems with the goal of enhancing organizational performance by stimulating employee proactive behavior. However, some companies have reported that the EPM system didn't achieve the expected results and even led to the opposite. To address this paradoxical phenomenon, we employed psychological reactance theory to investigate the impacts of employee differentiate perceptions of EPM (developmental EPM [DEPM] and preventive EPM [PEPM]) on individual psychological and behavior reactance. By conducting a three-wave survey questionnaire among 446 corporate employees, we find that DEPM facilitates proactive behavior, while PEPM inhibits such behaviors; Second, psychological reactance mediates the relationships among DEPM, PEPM, and proactive behavior; Third, individual trait mindfulness positively (negatively) moderates the impact of DEPM (PEPM) on psychological reactance. This research elucidates the effects of EPM on proactive behavior, providing valuable information to support attempts to refine digitally driven monitoring models within organizations. [ABSTRACT FROM AUTHOR]

New media technology can effectively support teacher–student rapport. In this study, Chinese students befriended their teachers on social media, and we compared their blended learning (N=455) based on this social media interaction with face-to-face learning (N=450). The results indicate that affective learning and cognitive learning in blended learning were superior to face-to-face learning. However, no significant differences in social presence, academic self-concept, or grade point average were observed between blended learning and face-to-face learning. According to a multi-group structural equation modelling analysis, the mediating effect of affective learning in blended learning was superior to that in face-to-face learning; in addition, academic self-concept significantly influenced cognitive learning. No significant difference was found for grade point average. Finally, we propose blended learning related suggestions regarding how to trigger students' connection need and inspire their wisdom and potential. [ABSTRACT FROM AUTHOR]

The capability of learners to work cooperatively and to coordinate efforts in a team is becoming more important to the success of any job and advancement of knowledge. In cooperative learning, the literature regarding the impact of social technologies is controversial. Because of this controversy, the object of this study is to examine students' experiences and feedback regarding teamwork in cooperative simulation interactions. The research object was addressed in a two-phase study with an experimental design. The participants were 328 students of 6 classes from a large city in China. In this experiment, communication through face-to-face encounters, mobile instant messaging, and combinations thereof were executed based on qualitative and quantitative data. Our research results describe the differences between experimental treatments. Various relevant teaching implications are discussed as well. • Cooperative simulation is enabled by social interaction. • This study examines student's experience in cooperative simulation interactions. • Participants advocated for face-to-face interaction. [ABSTRACT FROM AUTHOR]

The transportation problems faced by the elderly and people with disabilities have received attention from policymakers and practitioners. Eden Social Welfare Foundation (Eden) has observed the challenges and expectations from the elderly and people with disabilities while operating Rehabuses and long-term care services in Taiwan. This study presents a social business model for people with disabilities based on Eden's mobile-based service platform for barrier-free transportation. This social business model illustrates how information and communication technology (ICT) is integrated with transport service providers and government resources to satisfy the transportation needs of disabled people. • Propose a social business model for barrier-free transportation. • Integrate service providers and government resources to build a sharing platform. • Satisfy the transportation needs of people with disabilities. [ABSTRACT FROM AUTHOR]

The correlation analysis of commodity sales is very important in cross-marketing. A means of undertaking dynamic-model-based time series data-mining was proposed to analyze the sales correlations among different commodities. A dynamic model comprises some distance models in different observation windows for a time series database that is transformed from a commodities transaction database. There are sales correlations in two time series at different times, and this may produce valuable rules and knowledge for those who wish to practice cross-marketing and earn greater profits. It means that observation time points denoting the time at which the sales correlation occurs constitute important information. The dynamic model that leverages the techniques inherent in time series data-mining can uncover the kinds of commodities that have similar sales trends and how those sales trends change within a particular time period, which indicates that the "right" commodities can be commended to the "right" customers at the "right" time. Moreover, some of the time periods used to pinpoint similar sales patterns can be used to retrieve much more valuable information, which can in turn be used to increase the sales of the correlated commodities and improve market share and profits. Analysis results of retail commodities datasets indicate that the proposed method takes into consideration the time factor, and can uncover interesting sales patterns by which to improve cross-marketing quality. Moreover, the algorithm can be regarded as an intelligent component of the recommendation and marketing systems so that human–computer interaction system can make intelligent decision. • Right commodities can be recommended to right customers at right time by the proposed method. • Dynamic model reflects the relationships of the goods at the different observation time points. • The beginning time and duration of correlation among goods can improve the sales quantity. [ABSTRACT FROM AUTHOR]

As a new financing model, crowdfunding has been developed rapidly in recent years and has attracted the attention of investors and small- and medium-sized enterprises and entrepreneurs. However, many projects fail to be funded; thus, crowdfunding project fundraising outcomes forecasting and multimodel comparisons are meaningful ways to identify project quality and reduce market risk. It is important to reduce participation risk through automated methods, which is of great significance to the sustainable development of Internet finance. First, based on the data from the Kickstarter, preprocessing and exploratory analysis are conducted. Then, we introduce a deep learning algorithm (multilayer perceptron) and apply it to the prediction of crowdfunding financing performance. We compare deep learning with other commonly used machine learning algorithms, including decision tree, random forest, logistic regression, support vector machine, and K-nearest neighbors algorithm. We tune each machine learning algorithm to get the best parameters. The experimental results show that the deep learning model can obtain the best prediction results, with an accuracy of 92.3% when predicting the fundraising outcomes of crowdfunding financing, followed by the decision tree. Deep learning shows significant advantages in many evaluation criteria, which demonstrates the potential for crowdfunding project financing predictions. This study combines machine learning with Internet finance, providing inspiration for future research and resulting in many practical implications. [ABSTRACT FROM AUTHOR]

In recent years, the tourism industry has expanded rapidly and China will enter the mass tourism era comprehensively, with tourism becoming an important leisure lifestyle for people. However, natural disasters have the characteristics of high frequency, multiple types, wide distribution and large post-disaster losses, which often cause huge impact on tourism industry development. It is of great practical significance to explore the key factors for successful rescue of tourists after disasters. Taking the Wenchuan earthquake as an example, the article selects the Fuzzy Delphi Method and the Analytical Hierarchy Process as the data analysis method, and also randomly selects the local residents of 20 affected scenic spots as the research subjects. The results show that among the assessment dimensions, the most important dimension is prevention, followed by preparedness, response and recovery respectively. Meanwhile, among the 16 assessment indicators, the top five most valued indicators were regional emergency planning, management systems, prompt notification, publicity and education, training and emergency drill in order. Plain Language Summary: In recent years, the tourism industry has expanded rapidly and China will enter the mass tourism era comprehensively, with tourism becoming an important leisure lifestyle for people. This article selects the Fuzzy Delphi Method and the Analytical Hierarchy Process as the data analysis method, and also randomly selects the local resi-dents of 20 affected scenic spots as the research subjects. The results show that among the assessment dimensions, the most important dimension is prevention, followed by preparedness, response and re-covery respectively. Meanwhile, among the 16 assessment indicators, the top five most valued indi-cators were regional emergency planning, management systems, prompt notification, publicity and education, training and emergency drill in order. [ABSTRACT FROM AUTHOR]

This study investigated 150 university lecturers from the perspective of their emotional labour, focusing on the effect of a playful climate on lecturers' innovation intention and job engagement. A structural equation model analysis determined the following: is positively related to lecturers' strategies of deep acting and expression of natural emotions, but its effect on surface action is not significant; deep acting partially mediates between university playfulness climate and innovation intention, and the expression of natural emotions partially mediates between university playfulness climate and job engagement. This study expands our knowledge regarding the effect of emotional labour on lecturers' individual positive performance and enriches our understanding of the effect of playfulness climate on teaching activities. [ABSTRACT FROM AUTHOR]

This paper builds a dynamic supply chain network, where firms can select suppliers according to the trust, the selling price or just randomly. Simulation results show that the trust-based rule can significantly increase the aggregated working capital and decrease firm's likelihoods of bankruptcy. Moreover, firms' sizes under trust-based and price-based rules follow power-law distributions. The degree distribution of supply chain network under price-based rule follows a power-law distribution, while those under trust-based and randomly-choosing rules are similar to that of random network. Furthermore, results also indicate that trust-based rule is the most robust one against random and targeted disruptions. [ABSTRACT FROM AUTHOR]

Purpose: Previous studies overemphasize the negative effects of social media usage (SMU) within organizations and underestimate its positive influences on employees' behavior. This study attempts to link employees' social media use at work to their creativity performance. Design/methodology/approach: Based on the bounded generalized reciprocity theory and unbounded indirect reciprocity (UIR) theory, the authors developed a research model. To test the model, the authors collected a set of 172 paired data of organizations and employees from 31 knowledge-intensive enterprises in China to test the hypothesis. Findings: This research found that the social, cognitive and hedonic uses of social media all directly affect employee creativity. Relational energy fully mediates the effects of the cognitive and hedonic usages on creativity. Moreover, job autonomy moderates the effects of the relationships among the social, cognitive and hedonic uses on employee creativity. Originality/value: The conclusions not only enriched authors' understanding of the effectiveness of interpersonal interaction but also extended the research boundary of the relationship between SMU and employee creativity. [ABSTRACT FROM AUTHOR]

Innovation is believed to be a key driver of societal and economic well-being. In many cases, it has also led to more sustainable lifestyles and a more efficient use of natural resources. But despite the relevance of innovation as a tool to support sustainable development, there is a need for research that analyses current trends in order to guide future efforts. The present paper addresses this research need. It examines existing methods and tools for fostering social innovation in matters related to sustainable development, within the framework of the SDGs. By means of an online survey undertaken among sustainability and innovation experts in a sample of industrialised and developing nations, this study has identified some of the means via which social innovation is being pursued, along with its applications and limitations. A number of case studies showing examples of sustainable innovation have been mapped to demonstrate its usefulness. A causal loop diagram, which links the factors associated with social innovation within the context of the UN Sustainable Development Goals, is also presented, offering a greater understanding of their interconnections. The paper concludes by outlining some measures that may help to take better advantage of the many opportunities offered by social innovation that put the principles of sustainable development into practice. [ABSTRACT FROM AUTHOR]

A multivariate time series is one of the most important objects of research in data mining. Time and variables are two of its distinctive characteristics that add the complication of the algorithms applied to data mining. Reduction in the dimensionality is often regarded as an effective way to address these issues. In this paper, we propose a method based on principal component analysis (PCA) to effectively reduce the dimensionality. We call it "piecewise representation based on PCA" (PPCA), which segments multivariate time series into several sequences, calculates the covariance matrix for each of them in terms of the variables, and employs PCA to obtain the principal components in an average covariance matrix. The results of the experiments, including retained information analysis, classification, and a comparison of the central processing unit time consumption, demonstrate that the PPCA method used to reduce the dimensionality in multivariate time series is superior to the prior methods. [ABSTRACT FROM AUTHOR]

A crowdfunding narrative often has a title, abstract, detailed description, reward statements, and the creators' biographies, which carry elements of subjectivity and objectivity in their wordings. The subjectivity and objectivity facets of an entrepreneurial narrative influence the signal's observability, thus affecting the fundraising outcomes. This study contributes to the understanding of the subjectivity versus objectivity debate in economic exchange in online crowdfunding, by examining how and if the crowdfunding outcomes can be better achieved through the deliberate positioning of the subjectivity versus objectivity claims in the various text sections and through carefully determining the prioritization of the subjective and objective contents. This study employs text mining and Bayesian inference to identify and differentiate the subjective and objective attributes in an entrepreneurial narrative. Taking 328,974 campaigns from Kickstarter , from April 2009 to April 2019, we observe a statistical difference between the text sections in a narrative. Some level of objective appeal should reside in the abstract and the reward statement. To court investors, the title, detailed textual description, and biography should be phrased subjectively. For the detailed textual description, the objective contents should be positioned at the start of the narrative, followed by the subjective statements, to enhance the success of online fundraising. Further, the overall prediction accuracy of funding support in the presence of subjective versus objective factors improves by 1.76%, from 76.37%, as shown through machine learning prediction analytics. • Measure the subjectivity vs. objectivity for large entrepreneurial corpus. • Evaluate the impact of subjectivity vs. objectivity among the text sections. • Objective appeal should reside in abstract and reward statement. • Title, detail textual description, and biography can be worded subjectively. [ABSTRACT FROM AUTHOR]

In online learning, the high dropout ratio is a serious problem and reflects a poor level of motivation in e-learning programmes. Social-interactive engagement may greatly affect users' attitudes and choices in many fields; among these, online learning is inevitably impacted by factors such as social connections. To study the impact of social-interactive engagement on the dropout ratio and learning progress, iMOOC was employed as the study object using data from 619 courses and 2,071,147 learners, as well as 19,451,428 learning records. As engagement is a process of collecting experiences, the learner's experience plays a significant role in reducing the dropout ratio. Social-interactive engagement helps to reduce the dropout ratio; thus, learners should be encouraged to engage in online activities, such as discussions, note sharing, commenting and Q&A, to alleviate the feelings of being disconnected and isolated. Through an empirical study, we also find that the longer a learner's registered time is, the lower the dropout ratio. From the perspective of the courses themselves, the dropout ratios of shorter or more difficult courses are lower than those of longer or less-difficult courses. This paper provides theoretical and practical recommendations for reducing the dropout ratio in online learning and improving learning efficiency. [ABSTRACT FROM AUTHOR]