مقاله انگلیسی رایگان در مورد سیستم های پیچیده: مرز جدید بازاریابی – اسپرینگر 2018

Complex systems approaches are emerging as new methods that complement conventional analytical and statistical approaches for analyzing marketing phenomena. These methods can provide researchers with tools to understand and predict marketing outcomes that emerge at the aggregate level by modeling feedback between heterogeneous agents and agent interaction with various marketing environmental variables. While the benefits of complex systems approaches often come with a high computational cost, steady advances in access to better computational resources has allowed more researchers to adopt complex systems approaches as part of their portfolio of methods. In this paper, we will provide a description of the key concepts, benefits, and tools of complex systems. The goal of this work is to encourage marketing researchers and practitioners who are not familiar with these approaches to consider the adoption of these methods. We end with a discussion of the future research opportunities that this powerful methodology enables.

Analyzing marketing phenomena is often complex, and two particular aspects often make analysis difficult: (1) interactions between heterogeneous individuals, and (2) many elements of the marketing environment operate simultaneously. Together these features can mean that to truly understand a marketing phenomenon, researchers need to adopt increasingly advanced methods. Complex systems is one framework that can help deal with these difficult aspects of marketing research. The basic idea behind complex systems analysis is that individuals are modeled from the ground up, and the patterns of behavior of the system are observed as a result of the interactions of those individuals. For instance, individuals in the marketing environment, such as consumers, sellers and distributors, are far from being homogeneous entities and they are not isolated from the influence of other agents. In fact, these individuals, often called agents, constantly affect each others’ behaviors and choices. These constant interactions gradually lead to various emerging patterns at the aggregate level that are not obtained by simply summing properties of individual agents and elements at the micro-level of the system. For instance, consumers create and share word-of-mouth about goods and services (Berger 2014). The extent to which consumers interact with one another determines the speed and the strength of word-of-mouth, which leads to various adoption patterns of goods and services among the consumer population. Marketing researchers who are interested in experimenting with multiple factors that can affect the word-of-mouth need appropriate tools to capture the aspects of interactions between heterogeneous agents over time. Another interesting marketing phenomenon driven by social interactions between heterogeneous groups of consumers is the adoption of fashion (Rust 2015). One way to conceptualize fashion is that there are two different groups of consumers: an Bin-group^ and an Bout-group^. The Bin-group^ can be classified as a group of influential consumers such as celebrities, vloggers, or rich. When a certain fashion gains popularity among the Bin-group^, it is attractive to both the Bin-group^ and the Bout-group^. However, as the Bout-group^ picks up the fashion initially shared by the Bin-group^, the Bin-group^ loses their interest in pursuing the fashion and exhibits disadoption behaviors to differentiate themselves. In this context, one of marketers’ questions is how quickly adoption and disadoption behaviors emerge and peak depending on the strength of the two different groups in a market.