Economic competition, Free-rider problem, Dealers (Retail trade), Social influence, and Online social networks
Online social networks are increasingly being used to conduct commercial activities, and many online social networking platforms allow users to sell products to their online connections. Although extensive research has been conducted on the interactions among buyers within a social network, interactions among sellers have rarely been explored. Using seller data from a company that sells on a major online social networking platform in China, we empirically examine how sellers' efforts and sales performance are affected by the efforts and sales performance of other sellers they are connected to (i.e., their inviters and invitees) and the commissions they themselves have received. We find evidence for social influence and competition effects in the "inviter-to-invitee" direction and sellers' free-riding behavior driven by the commissions they receive from their invitees' sales. These results extend the social network literature that has largely focused on connected buyers (or users) to connected sellers and offer implications for social networking platforms to promote seller participation. [ABSTRACT FROM AUTHOR]
Haverila, Matti J., McLaughlin, Caitlin, and Haverila, Kai
International Journal of Healthcare Management. Apr2023, Vol. 16 Issue 1, p145-156. 12p.
Social impact, Social influence, Theory of reasoned action, Social forces, and Technology Acceptance Model
Purpose: Against the backdrop of the technology acceptance model (TAM), theory of reasoned action, and social impact theory the purpose of this research is to examine the validity of the TAM and assess the impact of social influence on the usage of NPIs in order to determine how best to encourage people to engage in the use of NPIs. Design/methodology/approach: A survey instrument was used to gather data with a snowball sampling method from Canadian respondents. The survey questionnaire items were adapted from existing literature. Data analysis was done using PLS-SEM. Findings: The results indicate that the TAM framework is applicable in the context of the use of NPIs with the COVID-19 outbreak as all TAM relationships were positive and significant. In addition, the results show a positive and significant impact of social influence on perceived usefulness, attitudes, and behavioral intentions towards the usage of NPIs. Thus, social forces can be considered relevant when understanding the adoption of technology. Originality/value: This research gives a better understanding of how social influence impacts adoption of behavior, such as the use of NPIs, and can be used to support the use of NPIs to decrease the spreading of viruses. [ABSTRACT FROM AUTHOR]
In this paper, we investigate the optimal subscription strategy for network video platforms and show how it is affected by social influence. The strategy decision is made among paid strategies, free strategies, and trial strategies, and revenue models are presented in two cases: positive social influence and negative social influence. We show that regardless of which strategy a platform adopts, positive social influence always makes a platform better. Results run counter to the conventional wisdom that positive social influence has an adverse effect on subscription demand without a free trial, which is benefited under negative social influence. A platform can always benefit from offering trial clips in the presence of positive social influence. A paid strategy is optimal if a video generates less social influence and advertising becomes more of a nuisance for consumers. A free strategy, otherwise, is dominant. In the presence of negative social influence, however, a free strategy is always the worst choice for a platform. Moreover, we found that positive social influence expands a consumer's tolerance of advertising when compared to a video with no social influence. [ABSTRACT FROM AUTHOR]
Brent D. Ruben, Ralph A. Gigliotti, Brent D. Ruben, and Ralph A. Gigliotti
Can you identify five political leaders whose ideas you don't share but for whom you still have respect? Or multiple media channels and news outlets you tend to disagree with but still listen to? In an age of heightened and polarized ideologies and viewpoints, it is becoming increasingly important to engage in critical self-reflection about the dynamics of social influence in our personal and professional lives, and the responsibility we each bear as agents of social influence in local and global groups, teams, organizations, and communities. Ruben and Gigliotti challenge readers to bring a more nuanced understanding of communication and social influence to the decisions they make as aspiring leaders and followers. Throughout the book, the authors explore vexing questions, such as how some leaders in the workplace, community, or national political scene succeed in amassing large amounts of dedicated followers, and yet seemingly fail to exhibit the characteristics and competencies described by most experts in leadership? Or why certain social influence efforts seem to connect immediately and quite automatically with some audiences, while possibilities for influence with other constituencies may only develop over a longer period—or not at all? By exploring the convergence of leadership and communication, Ruben and Gigliotti evaluate the ways in which the perspectives, messages, and behaviors of a sender/leader and receiver/follower can resonate and the impact of this resonance on the responses and reactions of people around them. Designed for leadership and communication students, scholars, and practitioners, Leadership, Communication, and Social Influence: A Theory of Resonance, Activation, and Cultivation offers a timely exploration into the evolution of leadership, communication, and social influence, and sheds light on how we can all become more responsible leaders, followers, and citizens.
Physics - Physics and Society, Mathematics - Optimization and Control, and 91D30
We present an opinion dynamics model framework discarding two common assumptions in the literature: (a) that there is direct influence between beliefs of neighbouring agents, and (b) that agent belief is static in the absence of social influence. Agents in our framework learn from random experiences which possibly reinforce their belief. Agents determine whether they switch opinions by comparing their belief to a threshold. Subsequently, influence of an alter on an ego is not direct incorporation of the alter's belief into the ego's but by adjusting the ego's decision making criteria. We provide an instance from the framework in which social influence between agents generalises majority rules updating. We conduct a sensitivity analysis as well as a pair of experiments concerning heterogeneous population parameters. We conclude that the framework is capable of producing consensus, polarisation and fragmentation with only assimilative forces between agents which typically, in other models, lead exclusively to consensus. Comment: 19 pages, 14 figures