Status: ongoing
Description
Artificial Intelligence (AI) refers to a set of computational technologies which enable systems carrying out tasks usually requiring human intelligence (Stone et al., 2016). Due to exponentially increasing computer processing power, connectivity, ubiquitous computing, and especially big data, AI-based technology becomes more sophisticated by the minute.
AI and AI-based service robots are increasingly adopted in service encounters (Teixeira et al., 2017) which have significant impact on customer experience during service provision (Larivière et al., 2017; Ostrom et al., 2015). For instance, AI enables individually tailored, more efficient and effective services which will change customer expectations (Stock & Merkle, 2018) and the roles of employees.
In many cases, AI is implemented in service firms as internal support software for employees (Berlucchi et al., 2016). Very intelligent and self-learning AI with the ability to interact with an employee may be seen as a service provider (Hofacker, 2019), thus, providing service to customers (i.e. employees) in an internal service encounter (Gremler, Bitner, & Evans, 1994). In order to provide this internal service successfully, employee and AI need to collaborate (Subramony et al., 2018).
Recently, service scholars have called for research that focuses on how service tasks will be effectively delivered by human-robot/AI teams since few empirical studies have been conducted thus far (Larivière et al., 2017; Subramony et al., 2018). We address this call by investigation the effects of a collaboratively designed artificial intelligence system. Epstein (2015, p. 40) coined the term collaborative intelligence (CI) as a software that “partners with a person to achieve the person’s goals. (…) The assumption is that some subtasks are more reasonably delegated to the person, and others to the computer. (…) A CI is intended not to substitute for a human employee, but to engage in a task with one.” Wilson and Daugherty defined the term in 2018 as humans and AI actively enhancing each other’s complementary strengths to achieve the organization’s goals. Thus, creating the most value for the organization as a whole making it collaborative intelligent. The larger impact of AI lies in its ability to augment, and not substitute, employee capabilities (Wilson & Daugherty, 2018) as it helps to “resolve the long-standing tension between service efficiency and effectiveness” (Marinova, de Ruyter, Huang, Meuter, & Challagalla, 2017, p. 1). In essence, by collaborating, AI and employees can thus become more effective in their respective roles and in co-creating service delivery. Building on previous work, we define collaborative intelligence as a special form of AI that interacts with customers (i.e. employees) in an internal service encounter to achieve the employee’s and thus the organization’s goals. For an AI to be perceived as collaborative we propose five sub-dimensions of CI.
We address the following research questions:
- Does CI lead to employees’ service encounter need fulfillment and which dimensions of CI in particular drive this effect?
- Which variables moderate the positive effect of CI on service encounter need fulfillment?
- How do high risk situations effect employee’s perceptions of a CI and their need fulfillment?
With this research project, we aim to make a two-fold contribution to literature. First, we introduce collaborative intelligence as a new construct to service literature and examine which dimensions of CI determine employees’ service encounter need fulfillment. Second, we further investigate potential moderating effects and situational factors on the relationship of CI on employee need fulfillment. Hence, the findings of our study not only contribute to current research but also offer valuable insights for practitioners to design technology-based internal service encounters and thus improve the collaboration of employees with smart technology.
Involved Persons
