Knowledge sharing in interest online communities: A comparison of posters and lurkers

Lai, Hui-Min; Chen, Tsung Teng

doi:10.1016/j.chb.2014.02.004

Cited by 187 publications

(180 citation statements)

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“…This personal inventory reveals that extraversion, agreeableness, and conscientiousness are positively related to knowledge sharing intention. Lai and Chen (2014) examine the differences between posters and lurkers in knowledge-sharing. They find that posters are influenced by intrinsic motivational factors (i.e., enjoyment and knowledge self-efficacy), while lurkers are affected by extrinsic motivational factors (i.e., reciprocity).…”

Section: Personal Attributes and Online Knowledge Sharingmentioning

confidence: 99%

“…The topic has received growing research attention in recent years (e.g. Heo & Toomey, 2016;Jadin, Gnambs, & Batinic, 2013;Lai & Chen, 2014;Wei, Lin, Chen, An, & Yeh, 2015). Prior studies have focused on constructs associated with social capital theory (Chumg, Cooke, Fry, & Hung, 2015;Wasko & Faraj, 2005), social cognitive theory (Fang & Chiu, 2010), resource exchange theory (Chan & Li, 2010), uses and gratification theory (Chen, Yang, & Tang, 2013), and organizational citizenship behaviors (Chiu, Hsu, & Wang, 2006) and self-efficacy (Jin, Zhou, Lee, & Cheung, 2013;Papadopoulos, Stamati, & Nopparuch, 2013).…”

Section: Introductionmentioning

confidence: 99%

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What drives consumer knowledge sharing in online travel communities?: Personal attributes or e-service factors?

Yuan

Lin

Zhuo

2016

Computers in Human Behavior

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Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractConsumer voluntary sharing of e-service knowledge in an online community is of great value to both business and consumers. This study develops and tests a research model integrating two personal attributes (consumer innovativeness and subjective knowledge)and two e-service factors (perceived ease of use and usefulness) in predicting consumer voluntary knowledge sharing in an online community. Data were collected using a webbased survey of 364 airline travelers recruited through an online travel community in China. Partial Least Squares Structural Equation Modeling (PLS-SEM) technique wasused to analyze the data. The results reveal that personal factors are stronger drivers of knowledge sharing than e-service factors. This study advances consumer knowledge sharing in online communities, and has implications for enhancing a firm's e-service strategies.

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Section: Personal Attributes and Online Knowledge Sharingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What drives consumer knowledge sharing in online travel communities?: Personal attributes or e-service factors?

Yuan

Lin

Zhuo

2016

Computers in Human Behavior

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“…But there could also be underlying strategic reasons operating, due to the willingness to push towards the improvement in service/ product quality by posting a bad online review. Hence, the analysis of the difference between posters and lurkers in terms of motivation and incentives is another topic the literature is currently tackling (Lai and Chen, 2014;Liao and Chou, 2012). Differently, we consider the strand of literature investigating the content of reviews and those factors affecting ratings.…”

Section: The Drivers Of Individual Ratingmentioning

confidence: 99%

Social Influence Bias in Online Ratings: A Field Experiment

2016

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The aim of this paper is to study the empirical phenomenon of rating bubbles, i.e. clustering on extremely positive values in e-commerce platforms and rating web sites. By means of a field experiment that exogenously manipulates prior ratings for a hotel in an important Italian tourism destination, we investigate whether consumers are influenced by prior ratings when evaluating their stay (i.e., social influence bias). Results show that positive social influence exists, and that herd behavior is asymmetric: information on prior positive ratings has a stronger influence on consumers' rating attitude than information on prior mediocre ratings. Furthermore, we are able to exclude any brag-or-moan effect: the behavior of frequent reviewers, on average, is not statistically different from the behavior of consumers who have never posted ratings online. Yet, non-reviewers exhibit a higher influence to excellent prior ratings, thus lending support to the social influence bias interpretation. Finally, also repeat customers are affected by prior ratings, although to a lesser extent with respect to new customers.

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“…The proposed questionnaire takes into account all issues connected with virtual knowledge sharing. Lai and Chen (2014) used the scale of reputation by Kankanhalli et al (2005) to identify motivating factors, mutuality by Davenport and Prusak (1998), joy in helping others by Wasko and Faraj (2000), knowledge of own efficiency by Spreitzer (1996), enthusiasm by Koh and Kim (2003), online activity by Jang and Ko (2010), joy by Koh and Kim (2003), and intention of sharing knowledge by Ajzen (1991). The research covered 324 users of the Mobile01 platform.…”

Section: Measurement Of Virtual Knowledge Sharing In Crowdsourcingmentioning

confidence: 99%

Virtual Knowledge Sharing in Crowdsourcing: Measurement Dilemmas

Lenart-Gansiniec

2017

JEMI

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Knowledge sharing in interest online communities: A comparison of posters and lurkers

Cited by 187 publications

References 50 publications

What drives consumer knowledge sharing in online travel communities?: Personal attributes or e-service factors?

What drives consumer knowledge sharing in online travel communities?: Personal attributes or e-service factors?

Social Influence Bias in Online Ratings: A Field Experiment

Virtual Knowledge Sharing in Crowdsourcing: Measurement Dilemmas

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