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The purpose of this paper is to apply Maslow's hierarchy of needs to extend the expectation‐confirmation model of information systems continuance (ECM‐IS) to analyze users' continuance intention of social networking services (SNSs).

A survey is conducted on 202 users of social networking services in China.

Fulfillment of self‐actualization needs has a significant impact on continuance intention; however, the direct impact of fulfillment of social needs on continuance intention is not significant but fully mediated by satisfaction.

The first limitation is that the participants in the sample are undergraduates. Second, this study has used cross‐sectional survey data to empirically test the model. Third, the survey is conducted in a single country.

The results of this paper provide several marketing implications to better manage SNSs. First, SNS managers should enhance instant communication functions, develop a platform that is convenient for users to express themselves and provide more entertainment functions. Second, SNS managers should focus on users' expectations and experiences about website functions and adjust or update website functions accordingly.

This paper contributes to the research on continuance intention of social networking services from the perspective of Maslow's hierarchy of needs to capture motivations of continuance intention. The authors believe their conceptualizations of fulfillment of self‐actualization needs and fulfillment of social needs, as well as their substantial findings, would be useful to researchers and practitioners alike to better study and manage continuance intention of socially‐oriented online services.

In the presence of coronavirus disease 2019 (COVID-19), due to the social distance restriction, consumers' regular consumption behaviors and patterns have been changing fundamentally. Thereafter, an innovative group buying model has emerged and developed explosively with a specific focus on consumer's location, known as community-based group buying (CGB). The purpose of this paper is to investigate the transfer mechanism of user's trust in dyadic contexts of social and commercial role-playing in the CGB program.

This study adopts an empirical research method, with an online and offline questionnaire survey, a total of 382 responses have been obtained. Then, both descriptive analysis and hierarchical regression analysis are conducted to explore the dual roles of group leader and its corresponding effects on consumers' trust (i.e. emotional trust and behavioral trust) and engagement actions (i.e. purchase and share) in the CGB program.

Results indicate that resident's trust and their perception of group leader's friend role can positively enhance their engagement actions in the CGB programs. Meanwhile, for the purpose of profit maximization, the group leader is more willing to play a friend role in transactions no matter whether the role conflict exists.

Research findings provide some managerial insights for CGB platform on the selection and training of group leaders and the incentive mechanism design.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

Recently, sharing economy is gradually accepted by people, and it has expanded from life to knowledge. It is important to encourage people to produce high quality content in knowledge sharing area, and knowledge payment is one of the most effective ways to achieve it. Therefore, the knowledge payment has been regarded as a huge business opportunity, and it is of great meaning to study the development trend and feasibility of knowledge payment. This chapter, through big data methods, analyzes the business model of Zhihu (a Chinese platform of knowledge sharing) after it introduced knowledge payment projects, such as Zhihu Live and Pay Consultation. According to data of Zhihu users’ Q&A, concerned fields and others, this chapter tries to outline its user profile to find out the target groups of different topics, the proper form of knowledge payment and the hot topics of Zhihu Live. Through the analysis of knowledge graph, this chapter finds that Zhihu Live is expected to be the mainstream knowledge payment form in the future, and the most potential topics are mainly focused on science, law, and business. Meanwhile, it establishes a pricing model for Zhihu Live, and provides suggestions for the development of knowledge payment.

The purpose of the study is to explore how technological capability and exogenous pressure interactively influence business model (BM) dynamics over time in new technology-based ventures.

The study adopts a longitudinal case study of the BM innovations of a Chinese financial technology venture. The structural approach and temporal bracket are used to analyze and theorize the data.

The findings indicate that distinct contextual changes impel a firm to refine or abandon existing BMs over time. In different stages, the antecedents interactively influence BM dynamics with three successive patterns, namely pressure dominance, parallel influence and hybrid influence. While both antecedents trigger changes during the initiation and implementation of new BMs, they also serve as the filter and the enabler, respectively, during the ideation and integration of BMs.

The study inductively develops three propositions regarding the relationship between BM dynamics and its antecedents, which is based on the data collected from one single firm. Future research should test the propositions in other domains and take more cross-level antecedents into consideration.

The study contributes to the nascent research stream of BM dynamics by offering in-depth insights into the interaction of internal and external antecedents and by linking the differentiated roles of antecedents to the BM innovation process. The research offers some practical implications for new technology-based ventures seeking to develop BMs in a fast-changing environment.

This paper aims to explore the impact of enterprise niche on dual innovation performance and the moderating role of innovation openness on the relationship between enterprise niche and dual innovation performance.

This study uses the panel data of the enterprise technology patents of China's Top 100 Electronic Information Enterprises from 2009 to 2018. Multiple regression analyses were used to test the hypotheses.

Niche width has a significant positive impact on exploitative and exploratory innovation performance. Niche overlap has an inverted U-shaped effect on exploitative innovation performance and significantly positively affects exploratory innovation performance. Innovation openness negatively moderates the impact of niche width on exploitative innovation performance and positively moderates the impact of niche overlap on exploitative innovation performance.

This study provides new insights into the effects of enterprise niche on dual innovation performance by showing the moderating role of innovation openness. The study finds a strategic logic of moderate niche overlap, clarifies the innovative effect of different innovation openness modes and reveals the construction and management mechanisms of enterprise niche and innovation openness strategy.

The purpose of this paper is to present a novel method for fabric defect detection.

The method based on joint low-rank and spare matrix recovery, since patterned fabric is manufactured by a set of predefined symmetry rules, and it can be seen as the superposition of sparse defective regions and low-rank defect-free regions. A robust principal component analysis model with a noise term is designed to handle fabric images with diverse patterns robustly. The authors also estimate a defect prior and use it to guide the matrix recovery process for accurate extraction of various fabric defects.

Experiments on plain and twill, dot-, box- and star-patterned fabric images with various defects demonstrate that the method is more efficient and robust than previous methods.

The authors present a RPCA-based model for fabric defects detection, and show how to incorporate defect prior to improve the detection results. The authors also show that more robust detection and less running time can be obtained by introducing a noise term into the model.