New technologies create a network of complementary producers and consumers around themselves. Researchers have been investigating the effects of such networks on end-users and called them “Network Externalities”. These network externalities are either direct or indirect. There have been no prior researches on negative and indirect consequences of these networks. The aim of this paper is to observe negative effects of the network on the market from both macro and micro level. Therefore, our main research question is: “Does expansion of network lead to Negative Indirect Network Effects?” The objectives of the present research were to investigate the nature of the network and to acquire more insight into the causes of Negative Indirect Network Effects (NINE) and their impact on network growth, to develop an empirical model for it, and simulate this model in order to have a better understanding of underlying network dynamics. The focus of the present research was on the laptop operation system network. To start with, we reviewed different aspects of this particular network in order to reform a new conceptual model about consumers’ behaviour. Then, we studied three major operating systems (OS) existing in the market today (Windows, Mac OS, and Linux). First, we reconstructed the history of these technologies, their life cycle, network characteristics, network size, and then, we continued with a short history of unwanted complementary products like bugs, viruses, worms, and malware. We surveyed user attitudes for the two main operating systems, Windows vs. Mac OS, and used those insights to make an agent-based simulation derived from our empirical model and mathematical equation. This simulation was applied to different scenarios. Specifically, we found that a network becomes more attractive for unwanted complementary goods when it expands in size. Also, we discovered that an increase in problems for the dominant design will lead to NINE. Importantly, security measures play an important role in the satisfaction of end-users. When users distrust security measures of the dominant technology, they may decide to switch to a smaller and saver network, and create herd behaviour in NINE, leading other end-users or even new consumers to choose for the secondary technology. However, expansion of the secondary design could also attract unwanted actors in its network and same procedure would emerge for the secondary technology as well. Our model is able to explain this recursive S-curve mechanism. Therefore, the present research provides additional understanding for industries to implement dynamic strategies in order concern to NINE. That is, security is important and industries have to be conscious about it and maintain safety measures with the intention of preventing NINE to happen. But also, industries must be aware that end-users should be involved in the feedback process.