Understanding the emotional and informational influence on customer knowledge contribution through quantitative content analysis

Wang, Xiaolei; Ow, Terence T.; Feng, Yong; Liu, Luning

doi:10.1016/j.im.2020.103426

Cited by 37 publications

(23 citation statements)

References 151 publications

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“…Therefore, knowledge contribution can have social effects on members; OKCs can be characterized as goal-oriented, and members can obtain a sense of task completion by posting valuable information and then gain a sense of self-worth. Therefore, knowledge contribution can also have psychological effects on members [18].…”

Section: Knowledge Contributionmentioning

confidence: 99%

The Formation Mechanism of Social Identity Based on Knowledge Contribution in Online Knowledge Communities: Empirical Evidence from China

Cheng

Cui

2022

Sustainability

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Social identity is a key factor in the sustainable development of online knowledge communities (OKCs). The purpose of this research is to explore the formation mechanism of the respective social identities of lurkers and posters, based on knowledge contribution behavior. To evaluate the research model, an online survey was conducted in the WeChat group and QQ group, which yielded 469 usable questionnaire responses. Structural equation modeling was then adapted to examine the model. We found that knowledge contribution can produce social and psychological outcomes (i.e., cognitive communication, parasocial interaction, a sense of self-worth, social support, and social identity). The posters’ social identity arises through the mediating effects of information support and cognitive communication, while the lurkers’ social identity arises through the mediating effect of parasocial interaction. In addition, this research reveals that personalized behaviors and social identity can coexist in OKCs. Our findings may provide theoretical and practical enlightenment for managers to achieve sustainable and successful operations in OKCs.

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Section: Knowledge Contributionmentioning

confidence: 99%

The Formation Mechanism of Social Identity Based on Knowledge Contribution in Online Knowledge Communities: Empirical Evidence from China

Cheng

Cui

2022

Sustainability

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“…Social support includes multiple dimensions. Existing studies believe that social support in general contains informational, emotional, and companionship support ( Wang et al, 2021 ), esteem support, tangible support, network support ( Cutrona and Suhr, 1992 ), and experiential support ( Lin and Kishore, 2021 ). However, online social support includes informational support and emotional support in the forms of kindness and sympathy ( Chen et al, 2020 ; Mirzaei and Esmaeilzadeh, 2021 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Informational support is one of the dimensions of social support theory ( Wang et al, 2021 ). Personal experience, recommendation, suggestion, and feedback are all behaviors associated with information support ( Zhao et al, 2020 ).…”

Section: Research Model and Hypothesesmentioning

confidence: 99%

Why should I consult? The impact of social support on patient consultation in online healthcare communities

Liu

et al. 2022

Front. Psychol.

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The COVID-19 epidemic put the traditional healthcare system and offline consultation method under strain. Patient consultations through online healthcare communities (OHCs) provide patients and physicians with a more convenient and secure route. Based on the social support theory, this study explores the impact of three dimensions of social support from physicians—information diagnosticity, source credibility, and emotional support—on patient consultations in OHCs and their moderating effect on patients’ compliments. We utilized Python Spiders to retrieve data from Haodf.com and gathered 2,982 physician reports. The model uses OLS regression with fixed effect estimations. The results show that these three dimensions of social support are positively impacted by consultation. Furthermore, patients’ compliments weaken the positive relationship between the three dimensions of physicians’ social support and patient consultations. This study contributes to the literature on social support theory in OHCs by exploring the physicians’ social support dimension and its impact on patient consultation. Moreover, this study makes practical contributions to physicians and platform administrators in OHCs.

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“…In addition, emotional information can also be used as a fine-grained emotional analysis technology to describe the human mental state in a multidimensional way from the perspective of human psychology combined with the traditional coarse-grained emotional analysis [ 8 , 9 ]. In recent years, text emotion analysis has attracted extensive attention in academia and industry [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Microblog User Emotion Analysis Method Based on Improved Hierarchical Attention Mechanism and BiLSTM

Chen

Xiao

2022

Computational Intelligence and Neuroscience

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The goal of Chinese fine-grained emotion analysis is to identify the target words corresponding to fine-grained elements from sentences and determine the corresponding emotional polarity for the target words. Aiming at the problem that the current Sina Microblog user emotion analysis methods have low accuracy and are difficult to effectively predict and manage, a Sina Microblog user emotion analysis method based on the Bidirectional Long Short-Term Memory algorithm (BiLSTM) and improved hierarchical attention mechanism is proposed. Firstly, an emotion analysis model is constructed based on text-level analysis and subjective and objective analysis, and the dimensionality curse problem of one-hot representation is solved by integrating the weighted word vector of TF-IDF. Then, by constructing a bidirectional long short-term memory neural network, the full acquisition of context information is realized, which increases the fine-grained elements of emotion analysis. Finally, by introducing an improved hierarchical attention mechanism, the network model can focus on different parts to achieve text classification and emotion analysis. Through simulation experiments, the proposed emotion analysis method and the other two methods are compared and analyzed under the condition of using the same database. The results show that the precision, recall, and F1 value of the method proposed in this paper are the best under 7 different emotion classifications, with the highest reaching 95.8%, 95.9%, and 96.1%, respectively, and the algorithm performance is better than the other two comparisons algorithm. It is proved that the proposed model has excellent performance.

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Understanding the emotional and informational influence on customer knowledge contribution through quantitative content analysis

Cited by 37 publications

References 151 publications

The Formation Mechanism of Social Identity Based on Knowledge Contribution in Online Knowledge Communities: Empirical Evidence from China

The Formation Mechanism of Social Identity Based on Knowledge Contribution in Online Knowledge Communities: Empirical Evidence from China

Why should I consult? The impact of social support on patient consultation in online healthcare communities

Microblog User Emotion Analysis Method Based on Improved Hierarchical Attention Mechanism and BiLSTM

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