Investigating critical success factors in driving artificial intelligence-based accounting system acceptance and firm performance among SME users in Ghana: Employing a modified information system success model

Buabeng-Andoh, Felix; Buabeng-Andoh, Charles; Pavelkova, Drahomira

doi:10.2478/mmcks-2025-0017

Figures & Tables

j_mmcks-2025-0017_tab_007

Construct	Measurement items	References
SQ	AI-based AS must be easily understandable	(Liu et al., 2022)
	AI-based AS must be explainable
	AI-based AS must be transparent
IQ	Information derived from AI-based AS must be complete	(Chatterjee et al., 2018; DeLone & McLean, 2003)
	Information derived from AI-based AS must be easy to understand
	Information derived from AI-based AS must be relevant
	Information derived from AI-based AS must be secured
ServQ	The service providers of AI-based AS must be reliable	(Chatterjee et al., 2018; DeLone & McLean, 2003)
	The service providers of AI-based AS must be empathetic
	The service providers of AI-based AS must be responsive
	The service providers of AI-based AS must be competent
ICQ	AI-based AS must identify risks that affect achievement of firm’s objectivesAI-based AS must allow the company to present quality financial information	(Gyamerah et al., 2024; Monteiro et al., 2023)
	AI-based AS must improve a firm’s operational effectiveness and efficiency
	AI-based AS must ensure effective monitoring and evaluation
Use	Our firm will be using AI-based AS on daily basis	(Chatterjee et al., 2018; DeLone & McLean, 2003)
	Our firm will be using AI-based AS to meet our daily requirements	(Lutfi et al., 2022c)
	Our firm will be using AI-based AS to execute number of business transactions
	Our firm will depend highly on AI-based AS
SAT	Our firm will use data generated by AI-based AS for accurate and quick decision making	(Chatterjee et al., 2018; Lutfi et al., 2022a)
	AI-based AS will provide us with IQ
	AI-based AS will make our work easy
	AI-based AS will free us from routine tasks to focus on more strategic tasks
FP	Using AI-based AS will lead to cost savings	(Angelina et al., 2019; Lutfi et al., 2022b)
	Using AI-based AS will lead to time savings
	Using AI-based AS will lead to productivity
	Using AI-based AS will lead to profitability

Demographics of respondents_

Demographics	Category	Frequency	Percent (%)
Gender	Male	185	74.30
	Female	64	25.70
Age of respondents	18–25	48	19.28
	26–35	94	37.75
	36–45	42	16.87
	46–55	32	12.85
	More than 55	33	13.25
Position in company	Owner	54	21.69
	Manager	60	24.10
	Chief finance Officer	42	16.87
	Accountant	82	32.93
	Others	11	4.42
Industry sector	Manufacturing	23	9.24
	Retail	49	19.68
	Service	42	16.87
	Agriculture	28	11.24
	Banking and Finance	36	14.46
	Hospitality	31	12.45
	Mining and construction	24	9.65
	Others	16	6.43
Firm size	Micro	42	16.87
	Small	96	38.55
	Medium	111	44.58
Years in operation	Less than 1 year	23	9.24
	1–5 years	55	22.09
	6–10 years	89	35.74
	More than 10 years	82	32.93

HTMT_

	FP	ICQ	IQ	SAT	SQ	ServQ
FP
ICQ	0.840
IQ	0.850	0.767
SAT	0.824	0.773	0.815
SQ	0.693	0.695	0.763	0.837
ServQ	0.807	0.849	0.816	0.798	0.745
Usage	0.794	0.762	0.740	0.794	0.725	0.789

Fornell-Larcker_

	FP	ICQ	IQ	SAT	SQ	ServQ	Usage
FP	0.855
ICQ	0.740	0.854
IQ	0.725	0.652	0.803
SAT	0.703	0.657	0.669	0.805
SQ	0.601	0.598	0.636	0.698	0.871
ServQ	0.697	0.727	0.676	0.663	0.626	0.820
Usage	0.701	0.670	0.633	0.677	0.629	0.680	0.860

CR and validity_

Construct	Indicator	VIF	Load	CA	rho_A	CR	AVE
FP	FP1	2.103	0.846	0.877	0.880	0.916	0.731
	FP2	2.287	0.857
	FP3	2.127	0.830
	FP4	2.618	0.886
ICQ	ICQ1	2.322	0.864	0.876	0.878	0.915	0.730
	ICQ2	2.534	0.872
	ICQ3	2.622	0.885
	ICQ4	1.753	0.793
IQ	IQ1	1.702	0.774	0.816	0.824	0.879	0.645
	IQ2	1.988	0.853
	IQ3	1.730	0.788
	IQ4	1.713	0.796
SAT	SAT1	1.689	0.806	0.817	0.825	0.880	0.647
	SAT2	1.416	0.711
	SAT3	2.044	0.841
	SAT4	2.168	0.852
SQ	SQ1	1.858	0.869	0.841	0.848	0.904	0.759
	SQ2	2.210	0.890
	SQ3	2.007	0.853
ServQ	ServQ1	2.218	0.824	0.838	0.839	0.892	0.673
	ServQ2	2.406	0.847
	ServQ3	1.854	0.824
	ServQ4	1.691	0.786
Usage	Use1	2.265	0.860	0.883	0.885	0.919	0.740
	Use2	2.154	0.837
	Use3	2.429	0.852
	Use4	2.852	0.891

Hypothesis testing_

Hypothesis	Path	Beta	T-stats	P-values	Results
H1	SQ → Usage	0.219	3.209	0.001	Sustained
H2	SQ → SAT	0.300	4.464	0.000	Sustained
H3	IQ → Usage	0.159	2.488	0.006	Sustained
H4	IQ → SAT	0.183	2.896	0.002	Sustained
H5	ServQ → Usage	0.254	3.175	0.001	Sustained
H6	ServQ → SAT	0.112	1.633	0.051	Unsustained
H7	ICQ → Usage	0.252	3.772	0.000	Sustained
H8	ICQ → SAT	0.143	2.147	0.016	Sustained
H9	Usage → SAT	0.200	2.981	0.001	Sustained
H10	Usage → FP	0.415	5.835	0.000	Sustained
H11	SAT → FP	0.423	6.444	0.000	Sustained

Direct, indirect, and total impacts of the research model_

	Direct impact			Indirect impact			Total impact
	Use	SAT	FP	Use	SAT	FP	Use	SAT	FP
ICQ	0.252	0.143			0.050	0.186	0.253	0.193	0.186
IQ	0.159	0.183			0.032	0.156	0.159	0.214	0.156
SAT			0.423						0.423
SQ	0.219	0.300			0.044	0.236	0.219	0.344	0.236
ServQ	0.254	0.112			0.051	0.174	0.254	0.163	0.174
Use		0.200	0.415			0.085		0.200	0.500

Investigating critical success factors in driving artificial intelligence-based accounting system acceptance and firm performance among SME users in Ghana: Employing a modified information system success model

Figures & Tables

Figure 1

j_mmcks-2025-0017_tab_007

Demographics of respondents_

HTMT_

Fornell-Larcker_

CR and validity_

Hypothesis testing_

Direct, indirect, and total impacts of the research model_

Paradigm

My account