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When we talk about digital companies, the issue of adaptation, transformation, and/or replacement of the business model is widely contemplated by studies on the subject (Burton et al., 2018; Dorner and Edelman, 2015; Swanton and Lehong, 2017).
According to Veit et al. (2014), in a content where business and society undergo extensive digitalization, the logic offered by the business model becomes essential for success, and a subject of great interest to the academic community.
With the digital age, what becomes critical to the success of the business is the availability of adequate levels of information and knowledge. Organizations need to adapt to survive and succeed as their domains, processes, and business technologies change in a world of increasing environmental complexity. Improving your competitive positions, improving your ability to respond quickly to rapid environmental changes with high-quality business decisions, can be supported by the adoption of business models appropriate for this new digital business world (Al-Debi et al., 2008).
The importance of the business model has also been highlighted in the study by Zott et al. (2011) where the authors state that since 1995 there have been at least 1,177 articles published in peer-reviewed academic journals, in which the notion of business model is addressed.
Osterwalder and Pigneur (2010) describe the business model as something that clarifies the logic of creating, delivering, and capturing value in an organization’s vision, translated through nine core components segregated into four main areas: business, customers, infrastructure, and financial:
In more complex and sometimes unique digital businesses, the business model needs to be explicit and provide a new layer of information and knowledge essential to support digital business managers (Al-Debi et al., 2008).
According to Brousseau and Penard (2007), with the evolution of digital business, it becomes easier to identify the commonalities between the business models that exploded with the growth of the Internet and those that existed before. The new models combine new and innovative ways of organizing the relationship between demand and supply with a pricing strategy that considers network externalities, information specificity, and the ability to differentiate and discriminate through digital technologies.
These new business models contradict the prediction of a massive disintermediation caused by the strong development of digital technologies and the Internet, due to the fact that the intermediaries in this context combine demand and supply plans, then carry out transactions that remain costly within the process. They also perform the combination of various digital products to take advantage of their interoperation - as is the case when content is processed by software running a technical interface - is certainly much easier than it was in the past, thanks to standardized interfaces. However, resource and time expenditures are still required to ensure effective interoperability between digital products to generate a service that adds value to customers. Another reason is related to the availability of goods and services both “on-” and “off-line,” making it still a challenge, ensuring the users’ access to the information or the specific knowledge they need. Those who can provide the information should receive appropriate incentives as well as potential users should have access to that information.
Kuebel and Zarnekow (2014) have implemented and described a framework for platform business models based on the concepts presented by Al-Debei and Avison (2010) where they identified the value proposition, architecture, network, and finance as the main elements to be examined in the design, analysis, and evaluation of business models (Kuebel and Zarnekow, 2014; see Figure 2.1).
Parker et al. (2017) still define two types of business models for digital companies: pipelines and platforms. For authors, pipelines are more traditional systems employed in most companies that follow a step-by-step
Figure 2.1 Derived analysis framework for platform business models.
scheme that creates value and transfers that value to producers at one end and consumers at another, that is, a linear product chain.
The platform is described as a business that enables an interaction between suppliers and external buyers, creating value for both sides. It provides an environment that creates an infrastructure that encourages interactions, facilitating the exchange of goods, services, or “social currencies.”
Many of the Internet success stories - E-Bay, Amazon, Google, Yahoo, Autobytel - have developed business models based on the concept of platforms, assembling components, then grouping them into packages that match complex and specific consumer needs.
To support and better understand the subject, bibliographical research was elaborated on the main themes involved that were digital companies, digital transformation, agile methodology, and business models, which composed the phase of the theoretical framework (foundation). For this stage, national and international scientific knowledge bases such as Web of Science, Elsevier, IEEE, among others, as well as books and other information sites were considered.
In relation to the instruments and protocols used, that is, the form and the mechanisms used to construct and collect the empirical data, interviews with specialists of companies that act strongly in projects of digital transformation in clients of various sizes and segments were used as sources of data. The interviews were conducted with open questions, with a semi-structured script.
The main instrument for organizing and extracting the information chosen is the content analysis performed in the transcript of interviews.
Bardin (1977) describes content analysis as a communication analysis technique, studying the context of what was exposed in the interviews or observed by the researcher, and classifying the material into large subjects or categories, helping in a deep understanding of the exposed material. For the author, content analysis has the following objectives: overcoming uncertainty, hoping with this method to see more clearly the content of materials, and the enrichment of reading, bringing from the detailed analysis, relevant and more enlightening information.
A proposed analysis, suggested by Bardin (1977), can be divided into pre-analysis, material exploration, and treatment of results.
"Die material must be organized, selected, and eventually hypotheses must be formulated that can be confirmed or refuted during the studies. Some rules must be followed; for example, exhaustiveness, that is, the subject must be exhausted without omissions; representativeness, that is, a sample should represent the chosen universe; homogeneity, that is, subjects should be related to the same theme and collected using similar and equivalent technique and individuals; pertinence, that is, choice of documents should be related to the research objective and exclusivity; and elements should be classified into more than one category.
As a method, Bardin (1977) suggests a general reading and coding of data (absolute or relative frequency), choice of record units, either in word or phrase. Analysis of the frequency at which these registration units appear, and categorization and schematization is aimed at understanding their relevance to the researched subject.
Tbe following sequence was used in the process of this research, composing of:
The data obtained from the content analysis are aligned with the theoretical foundation. The categories - difficulties, processes, and technologies - offer insight into characteristics, while business model meets the peculiarities of strategy, referring to the framework presented by Kuebel and Zarnekow (2014) that identifies the value proposition, architecture, network, and finance as the “main elements to be examined in the design, analysis, and evaluation of business models” (Al-Debei and Avison, 2010).