Research on social e-commerce reputation formation and state-introduced model

Abstract

Purpose This paper aims to solve the problem of information overload and reduce search costs. It proposes a social e-commerce online reputation formation model and community state-introduced model. A system dynamics trend simulation has been run to capture the relationship among the sellers, buyers, social e-commerce platforms and external environment to obtain an online reputation. Design/methodology/approach Empirical research relating to social e-commerce reputation has been used to confirm the influencing factors in social e-commerce, and a conceptual framework is developed for social e-commerce reputation formation. Thereafter, a trend simulation is generated to classify the relationship among the factors based on system dynamics. Also, the improved algorithm for community detection and a state-introduced model based on a Markov network are proposed to achieve better network partition for better online reputation management. Findings The empirical model captures the interaction effect of social e-commerce reputation and the state-introduced model to guide community public opinion and improve the efficiency of social e-commerce reputation formation. This helps minimize searching cost thereby improving social e-commerce reputation construction and management. Research limitations/implications There is no appropriate online reputation system to be constructed to test the relationship proposed in the study for a field experiment. Also, deeper investigation for the nodes’ attributes in social networks should be made in future research. Besides, researchers are advised to explore measurement for the reputation of a given seller by using social media data as from Twitter or micro blogs. Originality/value Investigations that study online reputation in the social e-commerce are limited. The empirical research figured out the factors which can influence the formation of online reputation in social e-commerce. An SD model was proposed to explain the factors interaction and trend simulation was run. Also, a state-introduced model was proposed to highlight the effect of nodes’ attributes on communities’ detection to give a deeper investigation for the online reputation management.

A Reputation Bootstrapping Model for E-Commerce Based on Fuzzy DEMATEL Method and Neural Network

“…Inspired by several pioneering studies [4], [19], we confirm the reputation-related factors for new vendors in e-commerce by comprehensively considering four aspects: vendor, consumer, transaction platform and external factors. The factors are listed in Table 1.…”

“…Reputation management has been studied in many computer science domains including e-commerce [12], multi-agent [13], peer-to-peer [14], and social network [15] systems. Reputation management can help entities choose a trustworthy and suitable partner so that risk can be minimized in future defective transactions [4]. Most existing studies [5], [12]- [15] concentrate on gathering and aggregating the ratings of consumers and encouraging honest feedback.…”

“…As early researchers have declared, ''trust, more than technology, promotes the development of e-commerce in all its forms'' [3]. It is generally believed that reputation and feedback systems promote the trust, which makes consumers feel secure when purchasing through social e-commerce [4]. The basic idea of traditional reputation management is to encourage consumers to evaluate vendors, provide feedback after each transaction and aggregate ratings…”

Online Reputation and Stress: Discovering the Dark Side of Social Media

“…Reputation on Facebook is the number of likes or comments posted; on Linkedin, it’s about endorsements from other users; and on YouTube, reputation is based on ‘view counts’ or ratings (Kietzmann et al, 2011). Therefore, online reputation is viewed as credibility of an individual or a firm, based on the ratings on virtual networks by other members/users (Wei et al, 2017). Social media has become an innovative and economical tool for firms and individuals to build their reputations online (Spanbauer, 2006; Tasențe, 2020).…”

Sentiment analysis of social commerce: a harbinger of online reputation management

“…Due to the multifarious possibilities of online reputation management, the extant studies have examined online reputation management in different perspectives. Wei, Yu, and Chen, (2017) found a gap in online reputation in the social e-commerce and discovered the factors that are responsible for online reputation formation in social commerce. Medeiros, (2017) focused on reputation management industry with legal undertone and try to bridge the gap between legal protection and the perceived harm and rebuff the idea of the online reputation management industry to retard the freedom of speech.…”