Location
Kumasi, Ghana and Virtual
Start Date
6-8-2026 11:30 AM
End Date
6-8-2026 12:00 PM
Description
Across public and private sector institutions in Ghana’s developing economy, accelerating digital transformation is reshaping organizational decision-making under conditions of uncertainty. This study examines how AI-enabled analytics reconfigures organizational information processing and improves decision quality through the lens of Organizational Information Processing Theory (OIPT). It investigates the relationships between AI-enabled analytics, information processing capacity, information processing requirements, information processing alignment, and decision quality. Using survey data from 410 respondents drawn from mixed public and private sector organizations in Ghana and PLS-SEM analysis, the findings reveal a dual role of information processing requirements: they strengthen alignment between informational demands and processing capability, yet they directly and negatively affect decision-making quality, exposing a tension between adaptive restructuring and information overload that prior OIPT research has not fully theorized. AI-enabled analytics also significantly enhances information processing capacity, which in turn strengthens alignment. Information processing alignment emerges as the strongest predictor of decision quality and mediates the effect of capacity on outcomes, confirming OIPT's founding alignment premise. The study extends OIPT by foregrounding this dual mechanism of information processing requirements and demonstrating that decision quality is largely shaped through structural alignment rather than direct technological effects in data-intensive organizational contexts.
Included in
Reconfiguring Organizational Information Processing: The Role of AI-Enabled Analytics in Shaping Information Processing Alignment and Decision Quality
Kumasi, Ghana and Virtual
Across public and private sector institutions in Ghana’s developing economy, accelerating digital transformation is reshaping organizational decision-making under conditions of uncertainty. This study examines how AI-enabled analytics reconfigures organizational information processing and improves decision quality through the lens of Organizational Information Processing Theory (OIPT). It investigates the relationships between AI-enabled analytics, information processing capacity, information processing requirements, information processing alignment, and decision quality. Using survey data from 410 respondents drawn from mixed public and private sector organizations in Ghana and PLS-SEM analysis, the findings reveal a dual role of information processing requirements: they strengthen alignment between informational demands and processing capability, yet they directly and negatively affect decision-making quality, exposing a tension between adaptive restructuring and information overload that prior OIPT research has not fully theorized. AI-enabled analytics also significantly enhances information processing capacity, which in turn strengthens alignment. Information processing alignment emerges as the strongest predictor of decision quality and mediates the effect of capacity on outcomes, confirming OIPT's founding alignment premise. The study extends OIPT by foregrounding this dual mechanism of information processing requirements and demonstrating that decision quality is largely shaped through structural alignment rather than direct technological effects in data-intensive organizational contexts.
