System-based autonomous and flexible interfaces that interact, communicate, and give service to an organization's customers” is how service robots are characterized. They can exist in both physical and virtual forms, which can be more human-like or more machine-like.
The fast rise of service robots in frontline labor is gaining traction in marketing, hospitality, and other industries. In the public sector, social care is the most often used application.
Such focus shines a light on what makes human connection, or human touch, so valuable. The concept of bringing robots into the workplace to give service or help people who offer frontline service raises a slew of fears and concerns among the current workforce. Similarly, the frequent depiction of robots in Hollywood films contributes to controversy when robots are used, such as at Japan's Henn-na Hotel, which is the world's first hotel staffed entirely by robots.
Masahiro Mori's discoveries that familiarity with robots rises with human similarity to a point when the human becomes sensitive to the imperfection of near human-like forms—with discomfort aggravated by movement—are perhaps the most extensively discussed notion. According to the "uncanny valley" thesis, "as a robot's looks and actions get more humanlike, a human's emotional response to the robot grows progressively positive and empathetic, until a limit is reached beyond which the response swiftly becomes one of strong repulsion."
This phenomena has been extensively studied, with consumers being shown video clips of more human-like robots in prominent studies. Bartneck et al(2009) .'s five "godspeed dimensions": anthropomorphism, animacy, likeability, perceived intellect, and perceived safety, have subsequently been developed as frameworks for analyzing human acceptance of such service robots. Human-like or machine-like, dead or alive, friendly or despised, extremely intelligent, fear-inducing, and so on are all possible perceptions of service robots.
As technology has progressed, attention in this field has shifted to constructing service robot typologies. Wirtz et al. (2018) adopt a framework from Lovelock (1983) in which different sorts of robots are plotted in a 2 2 table based on whether the task is tangible or intangible on the Y axis, and whether the task is oriented towards a human or an item on the X axis. Table 8.1 adopts Wirtz's typology and charts in a number of technologies. Unlike Wirtz, who concentrated on robots that serviced objects (such as a porter robot that transports items from one area of a building to another), the focus here is on robots that serve citizens directly or support frontline public officials in their work. Pepper, Nao, and Dinsow, three humanoid robots used in a variety of social care, medical, and educational contexts; and K5, a Dalek-like surveillance robot used in security and policing.
Physical robots that assist the agent rather than the consumer are housed in cell 2, which also includes a bear-like robot for moving patients into and out of beds and chairs, as well as a lie detector system for border guards to check passenger accounts. Cell 3 of Wirtz's typology depicts a spectrum of media forms ranging from holograms through video, audio, and text. Although their intelligence is based in the cloud, voice assistants are deployed in an ever-increasing range of physical devices, including smartphones (e.g. Siri) and smart speakers (e.g. Alexa), headphones, TVs, car dashboards, and so on.
We'll need to examine how voice assistants are used in social care robots. Face recognition, robotic process automation, which uses bots to carry out monotonous tasks on computers, and user record analytics, which detects crucial aspects in a patient's or offender's prior medical or criminal record, are all examples of technology that aid human agents in their job. The thought of robots replacing people is causing a lot of dread, which the media is helping to spread with their attention-getting headlines.
In the example of "Amelia", despite the fact that the word "robot" was not used in the press release, it was the term of choice in the main media headlines (e.g. Meet Amelia, your new robot worker). According to Wirtz et al. (2018), robots only influence one sort of frontline service employee at this time. They differentiate between "professional" and "subordinate" service jobs. The former carry out complicated cognitive activities that need creative and emotional intelligence, as well as “a great degree of flexibility, out-of-the-box thinking, and innovative problem solving... a divorce lawyer, a PhD supervisor, or a surgeon” (2018: 911).
For many customer service employees in so-called subordinate service roles (SSRs), the scenario is substantially different: Employees are frequently underpaid, have a lack of knowledge, receive little training, lack decision-making discretion and empowerment, are disengaged, and are unmotivated... Employees in such jobs are more likely to participate in surface acting (if at all). Robots may be able to give greater service than humans in such situations, and they may even be better at showing surface-acted emotions. That is, robots may beat humans in everyday service interactions (e.g., as a ticketing clerk or bank teller) because to their continuously pleasant surface acting, which is unaffected by moods, health, or stereotyped prejudices.
As a result, robots may become the favored form of frontline service delivery for low-level, low-pay SSRs. Unlike Wirtz, who favors robots above people in occupations that are generally basic and repetitive, Daugherty and Wilson (2018) see a symbiotic connection between people and machines, with humans teaching and overseeing machines and machines complementing or amplifying human ability. Their work focuses on what they refer to as the "missing middle," which states that as machines advance and take on roles previously performed by humans, there will be roles that humans excel at, but there is also a third space where humans can greatly enhance machines and machines can greatly enhance humans.
They claim that humans can improve machines by teaching, explaining, and maintaining them. To begin, training entails assisting robots in learning from tacit information, displaying empathy, and developing a personality. However, whether contemporary technology are capable doing this is disputed. While Alexa may be programmed to provide words of consolation when specific intentions are suspected, others may argue that such phony sympathy is meaningless and worthless. Second, explaining entails keeping track of how machine agents make choices.
This is an oversight and monitoring function with the goal of maintaining algorithmic openness. Unsupervised machine learning raises additional difficulties, since the computer educates itself how to finish a task, when previously writers were concerned about bias from a single programmer. DeepMind's AlphaGo, which defeated the world's greatest Go players, is an example of this (see Lee 2018). Sustaining is the third step, which entails routine maintenance and system security.
Furthermore, Daugherty and Wilson propose three ways in which machines might assist humans: Machines may
(1) augment human abilities,
(2) provide a way of interacting with consumers, and
(3) function as an agent in specific scenarios.
We need to go through each of these in detail, with examples of robots and virtual agents to show how they might help public workers on the front lines.
We need to look at the function of facial recognition technology, lie detection, evaluation and feedback, and user record analytics, starting with the amplification of capabilities.
Humanoid robots, care and therapeutic robots, and voice assistants all play a part in increasing engagement.
Finally, two sorts of robots are considered in the position of public servant embodiment: power-intensive job robots and patrol robots.
We have a collection of tools here that aren't service robots per per, but they operate to augment the job of frontline agents.
Although there are already instances of these technologies in use in the public sector, I wanted to see how frontline public workers reacted to them—whether they believed they could be beneficial, if they were a danger, or whether they were thrilled about working with them.