textPrior studies have emphasized that structural attributes are crucial to simultaneously pursuing exploration and exploitation, yet our understanding of antecedents of ambidexterity is still limited. Structural differentiation can help ambidextrous organizations to maintain multiple inconsistent and conflicting demands; however, differentiated exploratory and exploitative activities need to mobilized, coordinated, integrated, and applied. Based on this idea, we delineate formal and informal senior team integration mechanisms (i.e. contingency rewards and social integration) and formal and informal organizational integration mechanisms (i.e. cross-functional interfaces and connectedness) and examine how they mediate the relationship between structural differentiation and ambidexterity. Overall, our findings suggest that the previously asserted direct effect of structural differentiation on ambidexterity operates through informal senior team (i.e. senior team social integration) and formal organizational (i.e. cross-functional interfaces) integration mechanisms. Through this richer explanation and empirical assessment, we contribute to a greater clarity and better understanding of how organizations may effectively pursue exploration and exploitation simultaneously to achieve ambidexterity.

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Structural Differentiation and Ambidexterity: The Mediating Role of Integration Mechanisms

Artificial intelligence (AI) characterizes a new generation of technologies capable of interacting with the environment and aiming to simulate human intelligence. The success of integrating AI into...

Human Trust in Artificial Intelligence: Review of Empirical Research

Much of the prior research on interorganizational learning has focused on the role of absorptive capacity, a firm's ability to value, assimilate, and utilize new external knowledge. However, this definition of the construct suggests that a firm has an equal capacity to learn from all other organizations. We reconceptualize the firm-level construct absorptive capacity as a learning dyad-level construct, relative absorptive capacity. One firm's ability to learn from another firm is argued to depend on the similarity of both firms' (1) knowledge bases, (2) organizational structures and compensation policies, and (3) dominant logics. We then test the model using a sample of pharmaceutical–biotechnology R&D alliances. As predicted, the similarity of the partners' basic knowledge, lower management formalization, research centralization, compensation practices, and research communities were positively related to interorganizational learning. The relative absorptive capacity measures are also shown to have greater explanatory power than the established measure of absorptive capacity, R&D spending. © 1998 John Wiley & Sons, Ltd.

Relative absorptive capacity and interorganizational learning

https://research-api.cbs.dk/ws/files/58401014/rony_medaglia_et_al_mapping_the_challenges_acceptedmanuscript.pdf

Mapping the Challenges of Artificial Intelligence in the Public Sector: Evidence from Public Healthcare

This field study aims at understanding the influence of direct experience of an automated vehicle (AV, Level 3) and explaining and predicting public acceptance of AVs through a psychological model. The model includes behavioral intention (BI) to use self-driving vehicles (SDVs, Level 5), willingness to re-ride (WTR) in our AV (Level 3), and their four potential determinants, namely perceived usefulness (PU), perceived ease of use (PEU), trust related to SDVs, and perceived safety (PS) while riding in our AV. The last two determinants are largely ignored, but we consider them critical in the context of AVs. Three-hundred students were invited as participants (passengers) to experience the AV. The trust, PU, PEU, and BI of the participants were recorded prior to their experiencing the AV; after this experience, all the constructs of the psychological model were recorded. The participants’ experience with the AV was found to increase their trust, PU and PEU (but not BI), the consistency between PU/PEU and BI, and the explanatory power of BI. The model explained 55% of the variance in BI and 40% in WTR. PU, trust, and PS were found to be steady and direct predictors of both the acceptance measures; PEU predicted BI only after the participants’ AV experience. Mediation analysis showed that trust also can indirectly affect AV acceptance through other determinants. Out-of-sample prediction confirmed the model’s predictive capability for AV acceptance. The theoretical contributions and practical implications of the findings are discussed.

What drives people to accept automated vehicles? Findings from a field experiment

Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices

Exploratory and exploitative innovation: To what extent do the dimensions of individual level absorptive capacity contribute?

To survive and thrive in the long-term, firms are constantly challenged to create new business. In this context, firms are increasingly attracted to the concept of innovation ecosystem. The essence...

Monika Hengstler

Papers

Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices

Exploratory and exploitative innovation: To what extent do the dimensions of individual level absorptive capacity contribute?

Orchestrating an Innovation Ecosystem: an Exploratory Study of Distinctive Dynamic Capabilities