Accounting Information Systems Implementation and Data Reliability Outcomes

Abstract

This research investigates the relationship between the implementation methodologies of Accounting Information Systems (AIS) and the subsequent reliability of the
financial data they produce, a linkage that remains underexplored despite its critical importance to organizational integrity and decision-making. Moving beyond conventional
technical evaluations of system success, this study posits that the process of implementation—specifically the sequence of module activation, the integration of legacy data
validation protocols, and the structural alignment of the AIS with pre-existing internal
control frameworks—fundamentally shapes the epistemological quality of accounting
data. We develop a novel conceptual model, the Data Reliability Pathway (DRP),
which frames implementation not merely as a technical installation but as a constitutive process that embeds reliability characteristics into the data generation lifecycle.
The methodology employs a longitudinal, multi-case study design across four organizations undergoing AIS transitions, utilizing a mixed-methods approach that combines
qualitative process tracing of implementation decisions with quantitative analysis of
post-implementation data anomaly rates, reconciliation discrepancies, and audit adjustment frequencies. Our findings reveal that implementations characterized by a
’control-first’ module sequencing—where modules governing internal controls and data
validation are activated prior to core transactional modules—yield significantly higher
data reliability metrics compared to traditional ’transaction-first’ approaches. Furthermore, the research identifies a previously unrecognized phenomenon: ’reliability decay’
in phased rollouts, where delays between module implementations erode the integrity
benefits of structured sequencing. The study concludes that AIS implementation strategy is a primary determinant of data reliability, offering a new paradigm for system
design that prioritizes data integrity architecture from the outset. This contribution
reorients the field from a focus on system functionality and user adoption toward a
deeper concern for the inherent trustworthiness of the informational output, with significant implications for practice, auditing standards, and the theoretical understanding
of information systems as epistemic infrastructures.