Paper Details

Name: Irrinki Mohan Krishna, Dharba V K S S Tulasinadh, Garikparthi Sasank & Nettem Chakridhar
Country: India

Year: 2025
Volume: Volume-12, Issue-2 (July-December)
Page Number: 132-144
DOI: https://doi.org/10.5281/zenodo.17304937

ABSTRACT
A sweeping analytical framework has been developed in this study in order to analyze and
understand the critical barriers that give rise to the difficulty of adoption of Self-Service
Business Intelligence (SSBI) tools in Small and Medium-sized Enterprises (SMEs) and for
the subsequent evaluation of firm performance. Nowadays, data determines decision-making
for survival and growth, yet SMEs always lag behind their counterparts in using advanced
analytics. This paper argues that this gap is not just due to reasons of resource limitations but
is rather because of a more complex interaction of technological, organizational, and human
factors all of which existing technology adoption models do not sufficiently explain. Thus, an
integrated theoretical model is developed that merges the micro-level behavioral constructs of
the Technology Acceptance Model (TAM) with the macro-level contextual environment
discussed by the TechnologyOrganization-Environment (TOE) framework. This synthesis is
necessary to capture the unique interplay of user perception and organizational constraints
present in SMEs. A sequential explanatory mixed-methods research design is proposed to
ascertain the validity of this model empirically. The quantitative phase will begin with a
large-scale SME survey. The data collected shall be analyzed within a PLS-SEM framework
with the aim to testing the integrated adoption model and identifying the significant causal
paths. change). This work seeks to close a critical gap in the existing one by providing
nuance.
Keywords: Self-Service Business Intelligence (SSBI), Small and Medium-sized Enterprises
(SMEs), Technology Adoption, Adoption Barriers, Impact Analysis, Technology Acceptance
Model (TAM), Technology-Organization-Environment (TOE) Framework, Data
Democratization, Data Literacy.