Design Principles for Online Platforms Fostering Deliberative Political Discourse

2022 | International Conference on Business Informatics | Citations: 0

Authors: Vebrova, Michaela; Petrosyan, Lusine; Volkmer, Lucas Alexander; Distel, Bettina

Abstract: The design choices behind online participatory platforms, intended to facilitate ... Expand

Abstract: The design choices behind online participatory platforms, intended to facilitate interaction between citizens and government representatives, frequently undermine the potential for genuine democratic deliberation. This article presents a set of six success criteria for publicly owned online participatory platforms designed to facilitate the process of deliberation: political privacy, discursive diversity, reciprocity, reflexivity, availability of information, and perceived impact. In addition, 12 design principles that support these success criteria are formulated, whose use might increase the effective implementation and take-up of publicly owned online participatory platforms fostering democratic deliberation. Collapse

Topics: anonymity privacy digital platform digital media filter bubble

Methods: design principle literature sample design requirement archival research design artifact

Theories: socio technical theory

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

Personalized Recommendation for Balancing Content Generation and Usage on Two-Sided Entertainment Platforms

2022 | International Conference on Information Systems | Citations: 0

Authors: Zhang, Hao; Guo, Zhiling; Wang, Mingzheng

Abstract: Online entertainment platforms such as Youtube host a vast amount of user-gener ... Expand

Abstract: Online entertainment platforms such as Youtube host a vast amount of user-generated content (UGC). The unique feature of two-sided UGC entertainment platforms is that creators' content generation and users' content usage can influence each other. However, traditional recommender systems often emphasize content usage but ignore content generation, leading to a misalignment between these two goals. To address the challenge, this paper proposes a prescriptive uplift framework to balance content generation and usage through personalized recommendations. Specifically, we first predict the heterogeneous treatment effects (HTEs) of recommended contents on creators' content generation and users' content usage, then consider these two predicted HTEs simultaneously in an optimization model to determine the recommended contents for each user. Using a large-scale real-world dataset, we demonstrate that the proposed recommendation method better balances content generation and usage and brings a 42% increase in participants' activity compared to existing benchmark methods. Collapse

Topics: recommender system cloud computing digital service music streaming service information system use

Methods: optimization model hidden Markov model regression analysis method machine learning causality analysis

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

Support Forums and Software Vendor’s Pricing Strategy

2021 | Information Systems Research | Citations: 0

Authors: Dey, Debabrata; Ghoshal, Abhijeet; Lahiri, Atanu

Abstract: This research develops a microeconomic model to study the impact of support foru ... Expand

Abstract: This research develops a microeconomic model to study the impact of support forums on the software industry. Motivated by the rapid growth of forums such as Stack Exchange, Quora, and Yahoo! Answers, we examine what impact they could have on a software vendor’s pricing strategy. Because these forums provide an alternative, free source for obtaining software support, one might suspect that the shadow competition from them would eat into the profit the vendor makes from selling support services. We find that, although such a conclusion could be right at the forum’s initial and formative stages, it may not hold later, especially if the forum generates enough momentum. This is because forums, besides playing a substitutive role, also have a complementary effect. At the initial stages, the substitutive role of the forum dominates. However, if and when the forum grows sufficiently, the complementary role eventually takes control. In the meantime, the vendor can try to mitigate the immediate losses and find temporary relief by adopting a strategy of bundling support services with the product. Collapse

Topics: price management IS economics network effect consumer surplus economic surplus

Methods: experiment economic model design methodology

Theories: theory of bounded rationality

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

Human-machine collaboration in online customer service – a long-term feedback-based approach

2021 | Electronic Markets | Citations: 0

Authors: Graef, Roland; Klier, Mathias; Kluge, Kilian; Zolitschka, Jan Felix

Abstract: The rising expectations of customers have considerably contributed to the need f ... Expand

Abstract: The rising expectations of customers have considerably contributed to the need for automated approaches supporting employees in online customer service. Since automated approaches still struggle to meet the challenge to fully grasp the semantics of texts, hybrid approaches combining the complementary strengths of human and artificial intelligence show great potential for assisting employees. While research in Case-Based Reasoning (CBR) already provides well-established approaches, they do not fully exploit the potential of CBR as hybrid intelligence. Against this background, we follow a design-oriented approach and develop an adapted textual CBR cycle that integrates employees’ feedback on semantic similarity, which is collected during the Reuse phase, into the Retrieve phase by means of long-term feedback methods from information retrieval. Using a real-world data set, we demonstrate the practical applicability and evaluate our approach regarding performance in online customer service. Our novel approach surpasses human-based, machine-based, and hybrid approaches in terms of effectiveness due to a refined retrieval of semantically similar customer problems. It is further favorable regarding efficiency, reducing the average time required to solve a customer problem. Collapse

Topics: customer service information retrieval Quora website artificial intelligence

Methods: literature study design science computational algorithm design process artificial neural network

Theories: theory of case-based reasoning

Using Data Analytics to Filter Insincere Posts from Online Social Networks A case study: Quora Insincere Questions

2020 | HICSS | Citations: 0

Authors: Al-Ramahi, Mohammad; Alsmadi, Izzat

Abstract: The internet in general and Online Social Networks (OSNs) in particular continue ... Expand

Abstract: The internet in general and Online Social Networks (OSNs) in particular continue to play a significant role in our life where information is massively uploaded and exchanged. With such high importance and attention, abuses of such media of communication for different purposes are common. Driven by goals such as marketing and financial gains, some users use OSNs to post their misleading or insincere content. Collapse

Topics: Quora social media website email spam analytical information system

Methods: experiment machine learning logistic regression term frequency–inverse document frequency random forest classification

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