Introduction

Informal news diffusion in organizations has positive and negative consequences. It affects productivity but enhances familiarity with organizational norms. Thus, managers must understand the diffusion process to impede negative aspects and promote positive dissemination. This research primarily focuses on hindering news spread.

Theory

Grosser et al. (2010) posit that negative news diffusion relies on two factors: Shared connections between nodes, and Trust. Here, the first factor is measured by Node Redundancy and the second by Thresholds.

Data

The study uses network data from SG&R law firm (1988-1991), comprising a one-mode network with 34 partners across three offices (Lazega, 2001). It contains the following nodal attributes: gender, age, tenure years, practice, and law school.

Diffusion Models

The first model is a simple SI Model. I selected 1, 4, 8, 12, and 16 random nodes as seeds, depicting scenarios where up to approximately 50% of employees convey rumors. The subsequent plot illustrates seed numbers ascending from 1 to 16.

Regardless of seed count, in each scenario, all nodes become infected after about 10 steps. The networks significant embeddedness (14.26) enabled complete diffusion. To introduce control mechanisms, thresholds were incorporated; for each seeding, they incrementally rose to a minimum value that ensured spread containment.


Minimum thresholds for each seeding strategy are compiled below:

1 4 8 12 16
2 5 6 8 9

Excluding the scenario with one seed node, in the remaining 4 cases, company trust must be so low that, on average, each employee hears news from at least 53% of their neighbours to believe it. In the last scenario, even with the average neighbours as the threshold, the spread isnt contained. To test an alternative strategy, the recovery factor was added to the original five models, starting with 0.01 and increasing to 0.02 and 0.04.