System use is one of the most researched areas in Information Systems (IS) scholarship. In the context of social media (SM), scholars differentiate between active and passive SM use, yet the factors that influence these different types of SM use are less well understood. We draw on relationship science and media synchronicity theory (MST) to conceptualize SM use types as a function of users' goal-striving behavior for relationship management. In a multi-wave mixed-methods study, we examined two social-relational goals (i.e., connecting and deepening) and five media capabilities as factors influencing active and passive SM use. We tested our model quantitatively (in two data collections) with SM users and expanded it in a subsequent qualitative study. The findings illustrate an asymmetry: Connecting goals influence passive SM use, whereas deepening goals influence active SM use. Four of the five MST capabilities influence both SM use types, although in different ways. Based on our findings, we propose reconceptualizing connecting goals from users' "wanting to connect" to "feeling connected". We also refine the theoretical reach of the MST capability of rehearsability to include post-transmission modifications as influential for SM use.
Integrating artificial intelligence (AI) into financial institutions requires more than deploying technical solutions. It requires data management capabilities (DMCs) aligned with regulatory constraints and strategic priorities. This clinical study investigates how a German bank evolved from fragmented, Excel-based processes to a cloud-enabled data environment supporting AI enablement. Using a longitudinal embedded clinical case study, we trace the development of DMCs across three transformation phases and show how executives institutionalized interdependent DMCs through iterative cycles of experimentation, negotiation, and learning. We identify five mutually reinforcing capability domains: (1) Technology and Infrastructure, (2) Data Governance and Quality, (3) Cultural and Organizational Shifts, (4) Regulatory Compliance and Risk Management, and (5) AI Enablement. We contribute to IS research by (1) developing a grounded DMC maturity model that translates these insights into a practical diagnostic tool, (2) providing a process-based lens on cumulative DMC development that highlights sociocultural readiness and recurring managerial missteps as central mechanisms, explaining how readiness builds toward scaled AI enablement in regulated environments, and (3) extending clinical IS research by demonstrating how embedded inquiry can generate context-aware artifacts to support AI transformation. For practitioners, the maturity model provides diagnostic signals, minimum viable actions, and readiness evidence for scaling trustworthy AI.
