Background

The technological evolution of service robotics is very vast and extensive. Its technological development will boost economic interest in different areas of growth and future market niches. There are some emerging visions and opportunities to develop new technologies and services, such as the Internet of Robotic Things [18] as a merge of pervasive sensors with robotic and autonomous systems. Another example is Robot Process Automation (RPA) as an emergent technology that mimics the steps taken by a human to complete a task [19]. RPA implies the automation of repetitive processes that involve routine tasks, structured data, and deterministic outcomes [20]. The optimization of RPA routines uses different techniques grouped in Robotic Process Mining (RPM) [21]. Its function is to determine the importance and prioritization of a routine or task, converting the RPM in a functional tool to RPA routines. Despite the progress made so far, it is important to state that to keep researching and developing new service robots and automating tasks, there must be at least one economic reason to do so. Complementing the above, the banking and financial industry is a fundamental pillar and one of the main drivers of digital disruption [22]. In the near future, new technologies, trends, and applications of service robots will emerge. However, is there a way to forecast a service robot's impact and the future trends that service robotics will have? To answer this question, we need to look into technological evolution to predict disruptive innovations and identify which service robotics technologies are likely to experiment with fast growth and development. Due to this reason, we are going to look into several theories related to technology evolution.

The theory of technological parasitism for the measurement of the evolution of technology and technological forecasting [23] aims to measure the evolution of technology. It takes the ecology of parasites and their evolvement as a reference to estimate technological growth and its dynamics. This approach foretells which innovations and developments are likely to have fast progress and an easy society acceptance. This research, to achieve the measurement of the evolution of technology, takes several approaches (hedonic, RAND, functional and structural, wholistic and holistic approaches) [23]. It concludes it is possible to have a measure on the technological evolution, but is challenging and complex to foretell which technological innovations are going to have fast growth.

A theory of classification and evolution of technologies within a generalized Darwinism [24]. Following the previous theory, the synergy between humans and technologies propagates and generates a parasitic ecosystem. Thus, this idea implies that all the agents participating in the ecosystem are supposed to benefit. In this theory, a taxonomy is presented to differentiate the possible human–technologies interactions (parasitism, commensalism, mutualism, symbiosis, amensalism, or competition) [24]. This classification between humans and different technologies proposes an explanation of how the technology evolves, how complex systems are going to be socially implemented, and the impacts the different interactions are going to have on the economics of innovation.

A theory of the evolution of technology: Technological parasitism and the implications for innovation management [25]. The adaptive behavior derived by high competition between firms and nations impels the technology evolution. In this theory, it is stated that host technologies that have a high number of technological parasites are more likely to have an accelerated evolution, rather than the ones who have low technological parasites. This condition results since having more "parasites" involves having more complex systems, with more interactions between technologies and more benefits driven by those interactions. It also considers that because a specific technology has more "parasites," more humans are focused on developing new operations and uses (could be performed through a host technology or a parasite technology).

The above can be a very general view of technological development. However, it allows us to lay the foundations to estimate future disruptive inventions and the impacts of innovations on social dynamics. Moreover, the change generated by disruptive technologies highly tends to change competitive advantages that a firm could have in a determined market. Some examples of firms that implement disruptive technologies are Apple Inc. (introducing wireless headphones to the market) and AstraZeneca (generating innovative treatments treat lung cancer) [26]. However, these new technologies imply a change in industrial behavior, leading companies to destroy (directly or indirectly) older products, goods, or technologies, to keep their leadership on their market, maximize profits, and/or protect competitive advantages [27]. Nevertheless, constantly generating innovations can be a problem for companies if they are not planned incrementally. To solve this, Coccia

presented a conceptual framework of problem-driven innovation to explain industrial and technological change and the importance of solving problems by researching and developing radical innovations, either to maintain competitive advantages, maximize profits, or stay leaders in their sector [28]. Moreover, firms must consider the behavior that technologies have in the market since there is an asymmetry in the technological cycle of disruptive innovations (having a down phase shorter than the up phase) [29]. Empirically speaking, this behavior depends on the offer and demand on the markets, the grade of acceptance of a particular product that uses disruptive technologies, and by what firms want to sell.

Given all the above, it is crucial to consider the impact that service robots will have on the economics of innovation, its life-cycle, and that they could be a radical innovation in some fields that destroys or replaces the operation worth of preliminarily established technologies applied and used in markets [30]. There is a variety of tasks that researchers must develop for the use and implementation of service robotics, such as object detection, task/motion planning, activity recognition, navigation and localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques [31].

There are many areas of opportunity to apply service robots; however, there may be countries that have very incipient markets for the use of these new technologies, so one option may be to resort to foreign markets [32]. Besides, giving robots with cognitive and affective faculties, by working out infrastructures that allow them to establish compassionate connections with people, is a priority task [33]. Definitively, due to the constantly changing social mindset and current status quo of humans, the discussion on social, ethical implications, and concerns of using service robots is open, but the economic impact and social changes that service robots produce will likely accelerate their technological evolution.
