What factors influence grassroots knowledge supplier performance in online knowledge platforms? Evidence from a paid Q&A service

2022 | Electronic Markets | Citations: 0

Authors: Zeng, Qingfeng; Zhuang, Wei; Guo, Qian; Fan, Weiguo

Abstract: The popularity of online paid knowledge platforms offers opportunities for massi ... Expand

Abstract: The popularity of online paid knowledge platforms offers opportunities for massive grassroots knowledge suppliers to participate in knowledge sharing services and get financial rewards, but little is known about the determinants influencing users’ payment decisions in the particular knowledge transaction such as paid Q&A. This study examines the factors that influence the performance of grassroots knowledge supplier in paid Q&A platforms. We develop a research model integrating reputation, experience, and authority signal to explain the knowledge payment behavior based on signaling theory. Using a panel data analysis of 12,419 records from Zhihu, the largest online Q&A platform in China, our empirical study reveals that user payment behavior is significantly influenced by reputation signal and experience signal of a knowledge supplier. Interestingly, different from previous conclusions on professional knowledge payment platforms, authority signal of grassroots knowledge supplier has no significant impact on the payment behavior of online Q&A platform users. Collapse

Topics: knowledge sharing electronic authentication user acceptance online community Quora

Methods: descriptive statistic Poisson regression conceptual modelling longitudinal research experiment

Theories: signaling theory lemon market theory

Social Media Moderations, User Ban, and Content Generation: Evidence from Zhihu

2022 | HICSS | Citations: 0

Authors: Zhang, Xiaohui; Wei, Zaiyan; Du, Qianzhou; Zhang, John

Abstract: Social media platforms have evolved as major outlets for many entities to distri ... Expand

Abstract: Social media platforms have evolved as major outlets for many entities to distribute and consume information. The content on social media sites, however, are often considered inaccurate, misleading, or even harmful. To deal with such challenges, the platforms have developed rules and guidelines to moderate and regulate the content on their sites. In this study, we explore user banning as a moderation strategy that restricts, suspends, or bans a user who the platform deems as violating community rules from further participation on the platform for a predetermined period of time. We examine the impact of such moderation strategy using data from a major Q&A platform. Our analyses indicate that user banning increases a user’s contribution after the platform lifts the ban. The magnitude of the impact, however, depends on the user’s engagement level with the platform. We find that the increase in contributions is smaller for a more engaged user. Additionally, we find that the quality of the user-generated content (UGC) decreases after the user ban is lifted. Our research is among the first to empirically evaluate the effectiveness of platform moderations. The findings have important implications for platform owners in managing the content on their sites. Collapse

Topics: social media social norm Quora website design misinformation

Methods: statistical hypothesis test propensity score matching regression analysis method longitudinal research computational algorithm

Theories: reactance theory

Impacts of Hiding Friends’ Liked Content on User-Content Engagement across Newsfeed Channels

2022 | International Conference on Information Systems | Citations: 0

Authors: Zhang, Xiaohui; He, Qinglai; Zhang, Zhongju

Abstract: Social media platforms often distribute content through different newsfeed chan ... Expand

Abstract: Social media platforms often distribute content through different newsfeed channels, most commonly, social networks, algorithmic recommendations and trending content. Prior literature has investigated each channel's impact on user-content engagement. However, little is known about the relationships between these channels. We investigate the impacts of limiting content display from the social network channel on the quantity and diversity of user-engaged content across channels. We leverage a natural experiment, where a social media platform hides friends' liked content from the social network channel, to identify the impacts. Results show that hiding friends' liked content reduces the quantity of users' content engagement on the entire platform. Across channels, users increase their engagement with trending content but decrease their engagement with algorithmic recommendations. Further, restricting exposure to friends' liked content reduces the diversity of users' content engagement. Our results highlight the intercorrelation of usercontent engagement across newsfeed channels and provide insights for newsfeed designs. Collapse

Topics: social network social media mobile application IT skill societal evaluation criteria

Methods: computational algorithm longitudinal research natural experiment experimental group k-means clustering

Theories: cognitive attention theory

Social influence on endorsement in social Q&A community: Moderating effects of temporal and spatial factors

2021 | International Journal of Information Management | Citations: 0

Authors: Dong, Lingfeng; Zhang, Jie; Huang, Liqiang; Liu, Yuan

Abstract: This study examines the impacts of social influence, which are manifested by pop ... Expand

Abstract: This study examines the impacts of social influence, which are manifested by popularity and source credibility, on social endorsement in social Q&A community; and how the relationship is impacted by temporal-spatial factors. By collecting panel data from a large platform, the results of an econometric model demonstrate that popularity and source credibility are positively associated with social endorsement. With respect to the moderation effects, the results further show that time distance strengthens the effect of popularity on the social endorsement, but undermines the effect of source credibility; while crowdedness plays the role that strengthens the impact of popularity on the social endorsement, it has no significant moderating effect on the relationship between source credibility and social endorsement. Both theoretical and practical implications are discussed. Collapse

Topics: social influence decision making social media Facebook marketing management

Methods: longitudinal research descriptive statistic ordinary least square regression analysis method conceptual modelling

Invested or Indebted: Ex-ante and Ex-post Reciprocity in Online Knowledge Sharing Communities

2020 | ACM Transactions on Management Information Systems | Citations: 0

Authors: Li, Hongfei; Shankar, Ramesh; Stallaert, Jan

Abstract: Online communities that curate knowledge critically depend on high-quality contr ... Expand

Abstract: Online communities that curate knowledge critically depend on high-quality contributions from anonymous expert users. Understanding users’ motivation to contribute knowledge helps practitioners design such websites for optimal user contribution and user benefits. Researchers have studied reciprocity as a motivation for users to share knowledge online. In this study, we focus on two different types of reciprocity as drivers of online contribution: ex-post and ex-ante reciprocity. Ex-post reciprocity refers to users who received help from others in the past and pay back by helping others at present. Using a quasi-experiment performed via the instrumental variable method as the identification strategy, we test whether users who received more answers last week answer more questions in the current week on StackOverflow.com. We find a significant positive relationship between ex-post reciprocity and knowledge contribution, and such a reciprocal motivation diminishes with time. Ex-ante reciprocity refers to people helping others in expectation of future help from others. Using data from StackOverflow.com, we take advantage of a natural experiment with a difference-in-differences analysis and find evidence supporting the existence of ex-ante reciprocity. This study offers a new taxonomy for reciprocity and new insights on how reciprocity drives online knowledge sharing. Collapse

Topics: knowledge sharing online community user behavior website Quora

Methods: experimental group autocorrelation analysis longitudinal research instrumental variables estimation regression analysis method

Harnessing Artificial Intelligence to Improve the Quality of Answers in Online Question-answering Health Forums

2020 | Journal of Management Information Systems | Citations: 0

Authors: Mousavi, Reza; Raghu, T. S.; Frey, Keith

Abstract: Quality of answers in health-related community-based question answering (HCQA) f ... Expand

Abstract: Quality of answers in health-related community-based question answering (HCQA) forums has been a concern for both users and forum administrators. We conducted a two-phase study to better understand the quality of answers in HCQA forums. First, we employed machine learning to examine the quality of health content. We validated our algorithmic quality ratings by comparing them with those of two physicians. Second, using data from Yahoo! Answers Health section, we examined the effect of the quality of the first answer on the quality of the subsequent answers. Our results suggest that the quality of the subsequent answers is impacted by the quality of the first displayed answer. We further show that the impact of the first displayed answer is larger when the answerers are more familiar with the forum but smaller when the forum provides tips for answering questions. Our study helps HCQA forums to improve the overall quality of answers by 1- creating an algorithmic solution that reliably measures the quality of answers, and 2- adjusting the order of existing answers to encourage higher quality subsequent answers. Our findings also extend the applicability of the order effect to online forums and provide evidence that experienced users would be more influenced by the order effect in such forums. Collapse

Topics: Python Apache Spark logistics management online community application programming interface

Methods: natural language processing machine learning computational algorithm supervised learning descriptive statistic

Reinventing the Wheel: Explaining Question Duplication in Question Answering Communities

2019 | International Conference on Information Systems | Citations: 1

Authors: Liu, Xiaohui; Li, Yijing; Liu, Fei; Cai, Zhao; Lim, Eric

Abstract: Duplicate questions are common occurrences in Question Answering Communities (QA ... Expand

Abstract: Duplicate questions are common occurrences in Question Answering Communities (QACs) and impede the development of efficacious problem-solving communities. Yet, there is a dearth of research that has sought to shed light on the mechanisms underlying question duplication. Building on the information adoption model, we advance a research model that posits information quality and source credibility as factors deterring users from asking redundant questions within QACs. Furthermore, considering the questionanswer dichotomy intrinsic to QACs, we distinguish the quality and credibility of questions from those of answers as distinctive inhibitors of question duplication. We empirically validate our hypotheses on a leading QAC platform by harnessing a deep learning algorithm to detect duplications on over 9,380,000 question pairs. Results revealed that while the credibility of both questions and answers could alleviate question duplication, visual and actionable elements are more effective in preventing question duplication by boosting the quality of questions and answers respectively. Collapse

Topics: data quality online reputation system Quora word of mouth

Methods: deep learning convolutional neural network question answering computational algorithm partial least squares regression

Inducing Personalities and Values from Language Use in Social Network Communities

2018 | Information Systems Frontiers | Citations: 2

Authors: Kumar, Upendra; Reganti, Aishwarya N.; Maheshwari, Tushar; Chakroborty, Tanmoy; Gambäck, Björn; Das, Amitava

Abstract: A community in social networks is generally assumed to be composed of a group of ... Expand

Abstract: A community in social networks is generally assumed to be composed of a group of individuals with similar characteristics. Although there has been a plethora of work on understanding network topologies (edge density, clustering coefficient, etc.) within an online community, the psycho-sociological compositions of social network communities have hardly been studied. The present paper aims to analyse the communities as composition of induced psycholinguistic and sociolinguistic variables (Personalities, Values and Ethics) across individuals in social media networks. The motivation behind this analysis is to understand the behavioural characteristics at individual as well as societal level in social networks. To this end, three studies were carried out on six different datasets: three Twitter corpora, two Facebook corpora, and an Essay corpus, annotated with Values and Ethics of the users. First, experiments on creating automatic models to determine the Personality and Values of individuals by analysing their language usage and social media behaviour. Second, experiments on understanding the characteristics or blend of characteristics of individuals within an online community. Finally, generation of a map of values and ethics for India, a multi-lingual and multi-cultural country. Striking similarities to general intuitive perception could be observed, i.e., the results obtained in the study resemble our general perception about the cities/towns of India. Collapse

Topics: personality Twitter Facebook social media social network

Methods: experiment survey machine learning topic model support vector machine

Theories: speech act theory