Consumer Perception of Robotic Mobile Fulfillment Systems: A Netnographic Case Study of Amazon

Introduction

In the current era, robotics offers several technical and business opportunities. Some companies have chosen robotization to increase the cost-effectiveness of their products and services. Several industries are adopting robotics to perform various tasks. Service companies in general and retail companies, in particular, have followed these technological advancements to evolve their business and logistics systems (Mende et al., 2019). To gain a competitive edge and trigger innovation processes in retail, they have chosen to opt for the technological advances offered by robotization and the integration of these technologies in several phases of the value chains.

Warehouse order picking is a set of operations that requires a large amount of manual work. These are several related tasks that aim to prepare customer orders in warehouses. Its role is becoming more and more important, especially in the distribution and e-commerce logistic chains. Automation of these chains is possible. Indeed, several companies have already implemented automated or robotized systems to facilitate the preparation of customer orders. Changing lifestyles, exponential advances in technology, and innovation in retail are encouraging service companies to adopt robots to perform their daily operations.

The acceleration of global demand for online commerce and ongoing changes in transportation and telecommunication mechanisms have increased the number of transactions conducted and created new requirements, particularly for warehousing and storage systems (Bentalha et al., 2023). Manual order-picking systems were relatively operational and reliable with large and predictable orders. However, e-commerce orders are generally small and unpredictable. In addition, warehouse sizes are often large with huge variations that tire operators and require them to travel repetitive distances every day. Automated order-picking systems aim to reduce delivery times and ensure certain ergonomics for the order pickers.

The Robotic Mobile Filling System (RMFS) is an automated order-picking system. It is based on robotic technology and seems to be very suitable for online retail companies especially given their large warehouse sizes (Merschformann et al., 2019). In this system, robots, not operators, pick orders using mobile shelves of different sizes and dimensions, called pods, and bring the requested goods to the workstations. The goal is to minimize time loss, reduce picker distances, and consequently increase productivity rates.

Amazon is a global online shopping giant. It offers remote sales of several products on an international scale. It is also an innovative company given the introduction of several technological or organizational innovations (Amazon go, Amazon Key, etc.). In this sense, it is a very advanced company in terms of the use of robots in different parts of the value chain. The use of these robots comes in response to several issues concerning product delivery, labor shortages, and situational breaks between supply and demand. There are approximately 100,000 in its warehouses (Wingfield, 2017).

Thus, the analysis of the introduction of robotization in supply chains seems to be a highly topical theoretical theme. Indeed, there are currently several gaps in the understanding of the motives and effects of robotization in supply chains. Also, from a more practical point of view, we have identified an empirical gap concerning the perception of customers on the introduction of robotization that could serve as a question to the identified research problem. Robotic Fulfillment Systems for Smart Service Supply Chains represent a phenomenon that deserves the attention of service scholars and practitioners.

Therefore, how do customers perceive the introduction of robots in Amazon's corporate storage areas? To what extent does digital feedback vary in content and discourse in terms of opportunities and threats of using Robotic Mobile Fulfillment Systems for Amazon?

To deepen our reflection, we will first analyze the process and possible interrelationships between the Smart Service Supply chain, IoT, and IoRT. Then, we will talk about our netnographic methodological approach and the possibilities offered by this method to discuss the issues of interference of the optimization logic and customer perception. Finally, we will support our reflection on the results obtained by our qualitative netnographic study to show the different relationships and possibilities.