| Abstract: |
To achieve great performance and ensure their long-term survival,
organizations must successfully act in and adapt to the reality that surrounds
them, which requires organizations to learn effectively. For decades,
organizations have relied exclusively on human learning for this purpose. With
today’s rise of machine learning (ML) systems as a modern form of artificial
intelligence (AI) and their ability to autonomously learn and act, ML systems
can now also contribute to this vital process, offering organizations an
alternative way to learn. Although organizations are increasingly adopting ML
systems within a wide range of processes, we still know surprisingly little
about how the learning of humans and ML systems affects each other and how
their mutual learning affects organizational performance. Although a
significant amount of research has addressed ML, existing research leaves it
largely unclear whether and when humans and ML systems act as beneficial
complementarities or as mutual impediments within the context of learning.
This is problematic, as the (mis)use of ML systems may corrupt an
organization’s central process of learning and thus impair the organizational
adaptation that is crucial for organizational survival. To help organizations
facilitate useful synergies of humans and ML systems, this dissertation
explores humans’ and ML systems’ idiosyncrasies and their bilateral interplay.
As research on organizational learning has demonstrated, the key to managing
such dynamics is the effective coordination of the ones who learn. The studies
that were conducted for this dissertation therefore aim to uncover virtuous
and vicious dynamics between humans and ML systems and how these dynamics can
be managed to increase organizational performance. To take a holistic
perspective, this dissertation explores three central levels of analysis. The
first level of analysis deals with performance impacts on the individual
level. Here, the analysis focuses on two essential issues. First, the
availability of ML systems as an alternative to humans requires organizations
to rethink their problem delegation strategies. Organizations can benefit the
most from the relative strengths of humans and ML systems if they are able to
delegate problems to those whose expertise and capabilities best fit the
problem. This requires organizations to develop an understanding of the
problem characteristics that point to problems that are better (or less)
suited to being solved by ML systems than by humans. Using a qualitative
interview approach, the first study identifies central criteria and procedural
artifacts and synthesizes these into a framework for identifying and
evaluating problems in ML contexts. The framework provides a theoretical basis
to help inform research about delegation decisions between humans and ML
systems by unpacking problem nuances that decisively render problems suitable
for ML systems. Building on these insights, a subsequent qualitative analysis
explores how the dependency between a human and an ML system with respect to
the delegated problem affects performance outcomes. The theoretical model that
is proposed explains individual performance gains that result from ML systems’
use as a function of the fit between task, data, and technology
characteristics. The model highlights how idiosyncrasies of an ML system can
affect a human expert’s task execution performance when the expert bases
her/his task execution on the ML system’s contributions. This study provides
first empirical evidence on controllable levers for managing involved
dependencies to increase individual performance. The second level of analysis
focuses on performance impacts on the group level. In contrast to traditional
(non-ML) information systems, ML systems’ unique learning ability enables them
to contribute independently to team endeavors, joining groups as active
members that can affect group dynamics through their own contributions. Thus,
in a third study, a digital trace analysis is conducted to explore the
dynamics of a real-world case in which a group of human traders and a
productively trading reinforcement ML system collaborate during trading. The
studied case reveals that bilateral learning between multiple humans and an ML
system can increase trading performance, which appears to be the result of an
emerging virtuous cycle between the humans and the ML system. The findings
demonstrate that the interactions between the humans and the ML system can
lead to group performance that outperforms the individual trading of either
the humans or the ML system. However, in order to achieve this, organizations
must effectively coordinate the knowledge transfer and the roles of the
involved humans and the ML system. The third level of analysis focuses on
performance impacts on the organization level. As ML systems increasingly
contribute to organizational processes in all areas of the organization,
changes in the organization’s fundamental concepts are likely to occur, and
these may affect the organization’s overall performance. In a fourth study, a
series of agent-based simulations are therefore used to explore the dynamics
of organization-wide interactions between humans and ML systems. The results
imply that ML systems can help stimulate the pursuit of innovative directions,
liberating humans from exploring unorthodox ideas. The results also show that
the alignment of human learning and ML is largely beneficial but can, under
certain conditions, become detrimental to organizations. The findings
emphasize that effective coordination of humans and ML systems that takes
environmental conditions into account can determine the positive and negative
impacts of ML systems on organization-level performance. The analyses included
in this dissertation highlight that it is precisely the unique differences
between humans and ML systems that often seem to make them better complements
than substitutes for one another. The secret to unleashing the true potential
of ML systems may therefore lie in effectively coordinating the differences
between humans and ML systems within their bilateral relationship to produce
virtuous cycles of mutual improvement. This dissertation is a first step
toward developing theory and guidance on coordinating the dynamics between
humans and ML systems, with the aim of helping to rethink collaboration theory
in the era of AI. |