Online reviews contain a great deal of information about consumers' purchasing preferences, which seriously affects potential consumers' purchasing decisions. Using the online review data to help customers make purchasing decisions has become a concern of customers, which has theoretical and practical application value. Therefore, a product selection model is presented based on sentiment analysis combined with an intuitionistic fuzzy TODIM method. Firstly, the product features are extracted by the Apriori algorithm based on online reviews. The sentiment orientation and intensity of the sentiment words for the product features are identified by the lexicon-based sentiment analysis approach. Next, the sentiment orientation of the product features is represented by an intuitionistic fuzzy value. Then the intuitionistic fuzzy TODIM method is used to determine the ranking results of the alternative products. Finally, the case study of mobile phone selection is given to illustrate the proposed approach. The results show that the proposed method considers the online reviews’ sentiment orientation and intensity and the consumers’ gain and loss in the purchasing product process and is more reasonable than the previous research.

Mobile application innovation diffusion can be used to enhance the reputation and competitiveness of Internet enterprises. However, few works have explored the process of mobile application innovation diffusion from the individual perspective. Therefore, this paper employs multi-agent methods to simulate the innovation diffusion of mobile applications from the perspective of social networks. Specifically, we combine psychology, sociology, game theory and network effect theory to model user behaviors of adoption and rejection decisions for mobile applications. The multi-agent model was built in Anylogic 8 to simulate the communication and interaction between individual users. Then, this paper discusses the evolution of decision-making of social network user groups with different network structures and network effects. We also investigate the impact of different firms’ promotion on innovation diffusion. Our findings suggest firms could make better strategies and achieve better diffusion effects from mobile applications.

The classification of multidimensional time series holds significant importance across various domains, including action classification, medical diagnosis, and credit assessment. Within multidimensional time series data, features pertinent to classification exhibit variance in their positional distribution along the entirety of the sequence. Moreover, the relative significance of features across distinct dimensions also fluctuates, contributing to suboptimal performance in multidimensional time series classification. Consequently, the proposition of tailored deep learning models for feature extraction specific to multidimensional time series data becomes imperative. This paper introduces attention mechanisms applied to the temporal dimension, graph attention mechanisms for inter-dimensional relationships within multidimensional data, and attention mechanisms applied between channels post-convolutional calculations. These mechanisms are deployed for feature extraction across temporal, variational, and channel dimensions of multidimensional time series data, respectively. Furthermore, attention is directed towards inter-channel interactions within the squeeze-and-excitation network to enhance the model’s representational capacity. Experimental findings substantiate the viability of integrating attention mechanisms into multidimensional time series classification endeavors.

Purpose: Employees play an essential role in interactive innovation activities in Open Innovation Communities (OICs). Nevertheless, the factors influencing employees' innovation behavior in OICs have not been studied in depth. This study selects personality traits and social network characteristics to explain why and how these two factors affect employees' innovation behavior in OICs.Design/methodology/approach: Three regression models were constructed to test the relationship between personality traits, social network characteristics, and interactive innovation behaviors. The authors examined how employees' personality traits (Big Five personality traits) influence employees' innovative behavior (initiating and supporting innovation) directly in OICs and explored whether social network characteristics (social group) mediate the relationship between employees' personality traits and employees' innovation behavior.Findings: Using empirical data on 162 employees from Salesforce's IdeaExchange, the authors found that extraversion and openness to experience have significant positive effects on employees' interactive innovation behaviors, while conscientiousness has a significant negative effect on employees' interactive innovation behaviors in OICs. Furthermore, the mediation effect test results indicated that social network characteristics have a mediating effect on the relationship between extraversion and innovative behavior, and between openness and innovative behavior.Originality/value: This study analyzes how personality traits influence innovation behavior in an open innovation environment, thus enriching research related to the factors influencing interactive innovation behavior. Meanwhile, the study integrates personality, social network, and innovative behavior research streams and clearly explains the relationship between the three variables. The research findings assist firms in selecting suitable employees to participate in interactive innovation behaviors in OICs.