Browsing by Subject "Artificial intelligence"

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Artificial intelligence and robots in services : theory and management of (future) human–robot service interactions
(2023) Blaurock, Marah Karin; Büttgen, Marion
During the past decade, service robots have increasingly been deployed in a wide variety of services, where they co-produce service outcomes with and for the benefit of internal or external customers within human–robot service interactions (HRSI). Although the introduction of different service robot types into the marketplace promises efficiency gains, it changes premises of service encounter theory and practice fundamentally. Moreover, introducing service robots without considering external or internal customers’ needs can lead to negative service outcomes. This thesis aims to generate knowledge on how the introduction of different service robot types (i.e., embodied and digital service robots) in internal and external service encounters changes fundamental premises of service encounter theory and impacts HRSI outcomes. In doing so, it leverages different scientific methods and focuses on external service encounters with digital and embodied service robots, as well as internal service encounters with digital service robots. Chapter 2 aims to advance service encounter theory in the context of HRSI in external service encounters by conceptually developing a service encounter theory evaluation scheme to assess a theory’s fit to explain HRSI-related phenomena. The scheme includes individual and contextual factors that bound theoretical premises and, hence, supports scholars in assessing standing service encounter theories. The chapter also puts forth an exemplary assessment of role theory and provides detailed avenues for future research. Chapter 3 aims to synthesize the great wealth of knowledge on HRSI related to external service encounters with embodied service robots. By conducting a comprehensive systematic literature review, the chapter identifies 199 empirical research articles across scientific fields that can inform service research on how to successfully introduce service robots into the organizational frontline. To organize the plethora of research findings, this chapter develops a new structuring framework (D3: design, delegate, deploy). It utilizes this framework to provide a comprehensive overview of the empirical HRSI literature, delineates practical implications, and identifies gaps in literature to identify promising future research avenues. Chapter 4 also addresses HRSI in external service encounters but focuses specifically on the transformative potential of embodied service robots to enhance vulnerable consumers’ (i.e., children and older adults) well-being in social isolation. To identify how different robots can enhance well-being, this chapter follows a conceptual approach and integrates findings from service research, social robotics, social psychology, and medicine. The chapter develops a typology of robotic transformative service (i.e., entertainer, social enabler, mentor, and friend) as a function of consumers state of social isolation, well-being focus, and robot capabilities and a future research agenda for robotic transformative service research (RTSR). This work guides service consumers and providers, as well as robot developers, in identifying and developing the most appropriate robot type for advancing the well-being of vulnerable consumers in social isolation. Finally, Chapter 5 focuses on HRSI research in the context of interactions with digital service robots in internal service encounters. Based on a comprehensive literature review paired with a qualitative study, it conceptionally develops a new concept of a collaborative, digital service robot: a collaborative intelligence system (i.e., CI system) that co-produces service with employees. Drawing from service encounter needs theory, the chapter also empirically tests the effect of CI systems on employee need fulfillment (i.e., need for control, cognition, self-efficacy, and justice) and, in turn, on responsibility taking in two scenario-based experiments. The results uncover divergent mechanisms of how the fulfillment of service encounter needs drives the effect of CI systems on outcome responsibility for different employee groups. Service scholars and managers benefit from a blueprint for designing collaborative digital service robots and an understanding of their effects on employee outcomes in service co-production. In summary, this thesis contributes to literature by providing new insights into different types of HRSI by consolidating HRSI knowledge, developing and advancing HRSI concepts and theory, and empirically investigating HRSI-related phenomena. The new insights put forth in this thesis are discussed and implications for service theory and practice are delineated.
Behind the scenes of emerging technologies – Opportunities, challenges, and solution approaches along a socio-technical continuum
(2021) Bayer, Sarah; Gimpel, Henner
Digitalization is a socio-technical phenomenon that shapes our lives as individuals, economies, and societies. The perceived complexity of technologies continues to increase, and technology convergence makes a clear separation between technologies impossible. A good example of this is the Internet of Things (IoT) with its embedded Artificial Intelligence (AI). Furthermore, a separation of the social and the technical component has become near enough impossible, for which there is increasing awareness in the Information Systems (IS) community. Overall, emerging technologies such as AI or IoT are becoming less understandable and transparent, which is evident for instance when AI is described in terms of a “black box”. This opacity undermines humans’ trust in emerging technologies, which, however, is crucial for both its usage and spread, especially as emerging technologies start to perform tasks that bear high risks for humans, such as autonomous driving. Critical perspectives on emerging technologies are often discussed in terms of ethics, including such aspects as the responsibility for decisions made by algorithms, the limited data privacy, and the moral values that are encoded in technology. In sum, the varied opportunities that come with digitalization are accompanied by significant challenges. Research on the negative ramifications of AI is crucial if we are to foster a human-centered technological development that is not simply driven by opportunities but by utility for humanity. As the IS community is positioned at the intersection of the technological and the social context, it plays a central role in finding answers to the question as to how the advantages outweigh the challenges that come with emerging technologies. Challenges are examined under the label of “dark side of IS”, a research area which receives considerably less attention in existing literature than the positive aspects (Gimpel & Schmied, 2019). With its focus on challenges, this dissertation aims to counterbalance this. Since the remit of IS research is the entire information system, rather than merely the technology, humanistic and instrumental goals ought to be considered in equal measure. This dissertation follows calls for research for a healthy distribution along the so-called socio-technical continuum (Sarker et al., 2019), that broadens its focus to include the social as well as the technical, rather than looking at one or the other. With that in mind, this dissertation aims to advance knowledge on IS with regard to opportunities, and in particular with a focus on challenges of two emerging technologies, IoT and AI, along the socio-technical continuum. This dissertation provides novel insights for individuals to better understand opportunities, but in particular possible negative side effects. It guides organizations on how to address these challenges and suggests not only the necessity of further research along the socio-technical continuum but also several ideas on where to take this future research. Chapter 2 contributes to research on opportunities and challenges of IoT. Section 2.1 identifies and structures opportunities that IoT devices provide for retail commerce customers. By conducting a structured literature review, affordances are identified, and by examining a sample of 337 IoT devices, completeness and parsimony are validated. Section 2.2 takes a close look at the ethical challenges posed by IoT, also known as IoT ethics. Based on a structured literature review, it first identifies and structures IoT ethics, then provides detailed guidance for further research in this important and yet under-appreciated field of study. Together, these two research articles underline that IoT has the potential to radically transform our lives, but they also illustrate the urgent need for further research on possible ethical issues that are associated with IoTs’ specific features. Chapter 3 contributes to research on AI along the socio-technical continuum. Section 3.1 examines algorithms underlying AI. Through a structured literature review and semi-structured interviews analyzed with a qualitative content analysis, this section identifies, structures and communicates concerns about algorithmic decision-making and is supposed to improve offers and services. Section 3.2 takes a deep dive into the concept of moral agency in AI to discuss whether responsibility in human-computer interaction can be grasped better with the concept of “agency”. In section 3.3, data from an online experiment with a self-developed AI system is used to examine the role of a user’s domain-specific expertise in trusting and following suggestions from AI decision support systems. Finally, section 3.4 draws on design science research to present a framework for ethical software development that considers ethical issues from the beginning of the design and development process. By looking at the multiple facets of this topic, these four research articles ought to guide practitioners in deciding which challenges to consider during product development. With a view to subsequent steps, they also offer first ideas on how these challenges could be addressed. Furthermore, the articles offer a basis for further, solution-oriented research on AI’s challenges and encourage users to form their own, informed, opinions.
A comparison of seven innovative robotic weeding systems and reference herbicide strategies in sugar beet (Beta vulgaris subsp. vulgaris L.) and rapeseed (Brassica napus L.)
(2023) Gerhards, Roland; Risser, Peter; Spaeth, Michael; Saile, Marcus; Peteinatos, Gerassimos
More than 40 weeding robots have become commercially available, with most restricted to use in crops or fallow applications. The machines differ in their sensor systems for navigation and weed/crop detection, weeding tools and degree of automation. We tested seven robotic weeding systems in sugar beet and winter oil‐seed rape in 2021 and 2022 at two locations in Southwestern Germany. Weed and crop density and working rate were measured. Robots were evaluated based on weed control efficacy (WCE), crop stand loss (CL), herbicide savings and treatment costs. All robots reduced weed density at least equal to the standard herbicide treatment. Band‐spraying and inter‐row hoeing with RTK‐GPS guidance achieved 75%–83% herbicide savings. When hoeing and band spraying were applied simultaneously in one pass, WCE was much lower (66%) compared to the same treatments in two separate passes with 95% WCE. Hoeing robots Farmdroid‐FD20®, Farming Revolution‐W4® and KULTi‐Select® (+finger weeder) controlled 92%–94% of the weeds. The integration of Amazone spot spraying® into the FD20 inter‐row and intra‐row hoeing system did not further increase WCE. All treatments caused less than 5% CL except for the W4‐robot with 40% CL and the combination of conventional inter‐row hoeing and harrowing (21% CL). KULT‐Vision Control® inter‐row hoeing with the automatic hydraulic side‐shift control resulted in 80% WCE with only 2% CL. Due to the low driving speed of maximum 1 km h−1 of hoeing robots with in‐row elements, treatment costs were high at 555–804 € ha−1 compared to camera‐guided inter‐row hoeing at 221 € ha−1 and broadcast herbicide application at 307–383 € ha−1. Even though the costs of robotic weed management are still high, this study shows that robotic weeding has become a robust, and effective weed control method with great potential to save herbicides in arable and vegetable crops.