Diffusion of Innovation in Technological Platforms: The Uber Case.
| Autor | de Souza Ferreira, Wilquer Silvano |
| Cargo | Research Article |
INTRODUCTION
Diffusion, defined as the transmitting process of a given innovation or novelty to a given group or social system, is an essential part of any successful innovation (Ali, Raza, Puah, & Amin, 2019; Bhardwaj, 2020; Hadengue, Marcellis-Warin, & Warin, 2017; Zvolska, Palgan, & Mont, 2019). "Diffusion is a process where innovation is communicated to the social system" (Ali et al., 2019, p. 624). This process takes place over time through various channels. It usually occurs from person to person through the influencing and convincing capacity that early adopters have on people close to them. This ability to convince people leads others to adopt these novelties, replacing previous practices and procedures (Rogers, 1962; 2003). In other words, when an innovation appears and spreads, the number of adopters increases, the user experiences accumulate, and the risks of adoption tend to decrease, which results in an increase in diffusion (Bhardwaj, 2020; Dedehayir, Ortt, Riverola, & Miralles, 2017). Several technological platforms are spreading rapidly as a result of diffusion, including Uber.
The platform appeared in 2009 in San Francisco, California, and has since changed the transport market's functioning, affected urban mobility, and even how people report and use the available resources (Azevedo, Pongeluppe, Morgulis, & Ito, 2015; Barbour & Luiz, 2019). For example, "the Uber network is now available in 475 cities in 75 countries" (Barbour & Luiz, 2019, p. 38). For Uber and other consumer platforms based on 'peer-to-peer' systems, i.e., platforms that directly connect people who transact with each other, the diffusion process seeks a balance between tradeoffs and implies the winning over of two types of adopters or users: the end consumers (passengers); and the service providers (drivers). This characteristic makes the diffusion of this kind of innovation more complex (Matzler, Veider, & Kathan, 2015). The greatest challenge for peer-to-peer technological platforms is the necessity of designing intermediation systems capable of integrating organic and economically independent entities (Pu & Pathranarakul, 2019). For Chen, Yang, and Liu (2004), if suppliers' and clients' needs are not balanced, the system will become unbalanced and eventually collapse.
There is already comprehensive literature on the diffusion of innovation focusing on adopting novel technologies (Currie & Spyridonidis, 2019; Lai 2017; Marques, Lontra, Wanke, & Antunes, 2021). However, such literature has been limited to the adoption of novel technologies only by consumers and focuses on describing the variables involved in user adoption (Cheng, 2016; Chu & Chen, 2016; Marangunic & Granic, 2015; Rahman, Lesch, Horrey, & Strawderman, 2017; Rondan-Cataluna, Arenas-Gaitan, & Ramirez-Correa, 2015; Scherer, Siddiq, & Tondeur, 2019; Williams, Rana, & Dwivedi, 2015). Although still incipient, there is also some literature on the specific field of diffusion in novel technologies and technological platforms, including Uber (Borowiak & Ji, 2019; Geissinger, Laurell, & Sandstrom, 2020; Guda & Subramanian, 2019; Hall & Krueger, 2018; Laurell & Sandstrom, 2016; Min, So, & Jeong, 2019; Peticca-Harris, Gama, & Ravishankar, 2020; Shokoohyar, Sobhani, & Nargesi, 2020). However, these studies have disregarded the diffusion curve focusing on the peer-to-peer technological platforms process, its different user technological readiness in each phase of the process, and how the P2P balance, necessary for platform diffusion (Bresnahan & Greenstein, 2014; Matzler et al., 2015), occurs along the diffusion process, which are topics that will be discussed in the present paper. Recently, some scholars have emphasized the need for practical studies that address this aspect of diffusion (Barbour & Luiz, 2019; Currie & Spyridonidis, 2019; Guda & Subramanian, 2019; Lai, 2017), especially regarding the balance between peer-to-peer relationships (Bresnahan & Greenstein, 2014; Matzler et al., 2015). Indeed, Atkin, Hunt, and Lin (2015) highlighted the relevance of research that addresses adopters' characteristics in the process stage, and Piscicelli, Ludden, and Cooper (2018) emphasize that broader adoption and diffusion is necessary to tackle pressing societal problems. How they are implemented and what determines their success (or lack thereof) in the market is not yet well understood. However, there remains a lack of understanding regarding the potential differences or similarities between the various types of adopters and users.
This gap becomes more evident when, from a theoretical point of view, we consider the marked lack of research seeking to understand the balance of the peer-to-peer diffusion process considering Rogers' diffusion of innovation theory, one of the first (and now considered seminal) authors on the theme. Dedehayir, Ortt, Riverola, and Miralles (2017) are among the few researchers who have recently sought to reflect on Rogers' diffusion of innovation theory, albeit using a systematic literature review. Rogers' theory explains how novel technologies and ideas spread through the environment, i.e., how a given group's or social system's members come to know about the innovation over time. According to Rogers (1962; 2003), five groups of users make up an adoption curve. The author used risk predisposition and the ability to use innovation in advance to classify users into innovators, early adopters, the early majority, the late majority, and laggards.
No recent study has used Rogers' reflections to understand the balance of the peer-to-peer platform on each phase of the diffusion process. In other words, research is still lacking regarding investigating the platform diffusion curve's evolution considering the different user profiles (peer-to-peer process) and their technological readiness among innovators, early adopters, early majority, late majority, and laggards. A recent study by Min, So, and Jeong (2019) focused on consumer adoption of the Uber mobile application, using insights from diffusion of innovation theory and technology acceptance model, but they only addressed the factors that influenced the use of the platform. Further, few studies have addressed whether innovation occurs in a convergent way among them (Rogers, 1962; 2003). A search carried out by the authors of the present paper using international databases did not reveal any research seeking to understand the evolution of the innovation diffusion curve, considering all the profiles investigated here.
Despite the existence of several studies that aim to explain the adoption models, e.g., technology acceptancemodel--TAM, theory of reasoned action--TRA, and theory of planned behavior--TPB (Venkatesh, Morris, Davis, & Davis, 2003), there is a lack of studies that measure the balance of peer-to-peer platforms and the level of users' technological readiness in the diffusion process.
Platforms are flexible structures of business networks created around central coordination and can include thousands of independent offers and consumers (Gawer, 2014), facilitating transactions between them (Parker, Van Alstyne, & Choudary, 2016). However, such platforms need to balance peer-to-peer relationships (Bresnahan & Greenstein, 2014; Matzler et al., 2015). This balance is necessary to ensure the proper fit between offers (drivers) and consumers (passengers) and ensure peer-to-peer platform diffusion (Chen, Yang, & Liu, 2004; Pu & Pathranarakul, 2019). Otherwise, the system will unbalance and eventually collapse (Parker et al., 2016; Rochet & Tirole, 2003; Sundararajan, 2016). Therefore, based on Rogers' (1962; 2003) categories, the following research question is proposed:
RQ1: Does the innovation diffusion process occur with balanced peer-to-peer relationships among Uber drivers and passengers?
The diffusion process includes offers and consumers (Chen et al., 2004). Based on Parasuraman and Colby's (2007) and Bernstein and Singh's (2008) studies, we expect distinct degrees of technological readiness in the continuum of the Rogers' curve (2003), greater degrees into innovators and early adopters, followed by decreasing degrees for the initial majority, late majority, and latecomers. In considering this backdrop, the following research question is proposed:
RQ2: Is there a difference between drivers' and users' technological readiness levels?
The research was probabilistic and stratified and took place in Belo Horizonte city, Brazil. The country is a large developing economy (Raziq, Rodrigues, Borini, Malik, & Saeed, 2020). The municipality was the third to have access to Uber in Brazil, which is currently the second-largest platform market globally, after the US. Representing the entire adult population (18 to 65 years) in Belo Horizonte (1.6 million people--about the population of West Virginia), the sample comprised 843 Uber users, including 397 drivers and 446 customers, that used the platform from 2014 to 2019. They were identified and researched in 32 randomly selected census regions.
THEORETICAL BACKGROUND
Innovation and diffusion
Innovation is a kind of capital (Migdadi, 2021). It refers to a new object, a new method, or a new idea or perception, different from the previously existing standard (Ammirato, Sofo, Felicetti, & Raso, 2019; Migdadi, 2021). According to Rogers (1962; 2003, p.12), innovation is an idea or practice "perceived as new by an individual or other unit of adoption." According to Lazaretti, Giotto, Sehnem, and Bencke (2019, p. 2168), "innovation is the practical implementation of knowledge, ideas, or discoveries, resulting in the introduction of new products, production methods, organizational process changes and the opening of new markets or resources." In turn, diffusion is the "process by which an innovation is communicated by certain channels during a certain time, among the members of a social system" (Rogers, 2003, p. 5). Several other authors have also...
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