Xinxue Zhou, Jian Tang and Tianmei Wang
Customers' co-design behavior is an important source of knowledge for product innovation. Firms can regulate the focus of information interaction with customers to set goals and…
Abstract
Purpose
Customers' co-design behavior is an important source of knowledge for product innovation. Firms can regulate the focus of information interaction with customers to set goals and motivate their co-design behavior. Drawing on regulatory fit theory and construal level theory, the authors build a research model to study whether the fit between the regulatory focus of firms' task invitations (promotion focus vs prevention focus) and their feedback focus (self-focused vs other-focused) can enhance co-design behavior by improving customers' experiences (perceived meaning, active discovery and perceived empowerment).
Design/methodology/approach
The authors conducted two online between-subjects experiments to validate the proposed research model.
Findings
The two online experiments reveal that customers' experiences are enhanced when the feedback focus is congruent with the regulatory focus of the firm's task invitations. Specifically, self-focused feedback has a stronger positive effect on customers' experiences in the prevention focus context. Other-focused feedback has a stronger positive effect on customers' experiences in the promotion focus context. Moreover, customers' experience significantly and positively affects co-design behavior (i.e. co-design effort and knowledge contribution).
Originality/value
This work provides theoretical and practical implications for firms to improve the effectiveness of information interaction with their customers and eventually ensure the sustainability of co-design.
Details
Keywords
Tuotuo Qi, Tianmei Wang, Yanlin Ma and Xinxue Zhou
Knowledge sharing has entered the stage of knowledge payment with the typical models of paid Q&A, live session, paid subscription, course column and community service. Numerous…
Abstract
Purpose
Knowledge sharing has entered the stage of knowledge payment with the typical models of paid Q&A, live session, paid subscription, course column and community service. Numerous knowledge suppliers have begun to pour into the knowledge payment market, and users' willingness to pay for premium content has increased. However, the academic research on knowledge payment has just begun.
Design/methodology/approach
In this paper, the authors searched several bibliographic databases using keywords such as “knowledge payment”, “paid Q&A”, “pay for answer”, “social Q&A”, “paywall” and “online health consultation” and selected papers from aspects of research scenes, research topics, etc. Finally, a total of 116 articles were identified for combing studies.
Findings
This study found that in the early research, scholars paid attention to the definition of knowledge payment concept and the discrimination of typical models. With the continuous enrichment of research literature, the research direction has gradually been refined into three main branches from the perspective of research objects, i.e. knowledge provider, knowledge demander and knowledge payment platform.
Originality/value
This paper focuses on discussing and sorting out the key research issues from these three research genres. Finally, the authors found out conflicting and contradictory research results and research gaps in the existing research and then put forward the urgent research topics.
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Yuning Zhao, Xinxue Zhou and Tianmei Wang
Following Hovland’s persuasion theory, this paper aims to develop a conceptual model and analyzes characteristics of online political deliberation behavior from three aspects…
Abstract
Purpose
Following Hovland’s persuasion theory, this paper aims to develop a conceptual model and analyzes characteristics of online political deliberation behavior from three aspects (i.e. information, situation and manager). Based on the whole interactive process of online political deliberation, this paper aims to reveal the key points that affect the response effect of the government from the persuasive perspective of online political consultation.
Design/methodology/approach
Based on more than 40,000 netizens’ posts and government responses from 2011 to the first half of 2019 of the Chinese political platform, this paper used the text analysis and machine learning methods to extract measurement variables of online political deliberation characteristics and the econometrics analysis method to conduct empirical research.
Findings
The results showed that the textual information, political environment and identity of the political objects affect the effectiveness of government response. Furthermore, for different position categories of political officials, the length of political texts, topic categories and emotional tendencies have different effects on the response effectiveness. Additionally, the effect of political time on the effectiveness of response differs.
Originality/value
The findings will help ascertain the characteristics of online political deliberation behavior that affect how effective government response is and provide a theoretical basis for why the public should express their political concerns.
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Xue Xin, Yuepeng Jiao, Yunfeng Zhang, Ming Liang and Zhanyong Yao
This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic…
Abstract
Purpose
This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.
Design/methodology/approach
The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.
Findings
The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.
Originality/value
The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.