Legal technology acceptance in Vietnam’s courts

Figures & data

Figure 1. Conceptual proposal for the study based on TAM3 of Venkatesh and Bala (Venkatesh & Bala, 2008).

The developed framework TAM3 revealed a comprehensive conceptual model of the predictors of user adoption of innovation technology.

Read the detailed description of this figure

The conceptual proposal for the study investigated the effects of trust, risk perception, equality aspirations, legaltech insights, and individual inventiveness.

Table 1. Study constructs, components, and references.

Constructs	Items	References	Described
TRT: Trust in legaltech	TRT1	Lee et al., 2019; Vimalkumar et al., 2021; Zhang et al., 2022	I believe that legal technology might aid us in making just judicial decisions.
	TRT2		I believe it could be perfectly safe to entrust legal technologies to certain aspects of lawsuits.
	TRT3		Generally, I might have trust in court technology.
PEU: Perceived usefulness of adoption of legaltech in court proceedings	PEU1	Davis, 1989	Using legal technology would enhance the effectiveness of the courts from my perspective.
	PEU2		I believe that using legal technology would boost the efficiency of the courts.
	PEU3		Adopting legal technology would boost the efficiency of the court, in my opinion.
PES: Perceived simplicity of adoption legaltech in court proceedings	PES1	Davis, 1989	I believe it would be simple to understand how to run legal technology.
	PES2		I believe it would be simple to become proficient at employing legal technologies.
	PES3		I believe that legal technology might be simple from the beginning.
BEI: Behavior intention to adopt legaltech in court proceedings	BEI1	Lu et al., 2005; Xu & Huang, 2019	I expect the adoption of legal technology in the court system.
	BEI2		If offered the option, I would employ legal technology in the court.
INI: Individual innovation in IT	INI1	Lu et al., 2005; Patil et al., 2020	If I knew of relatively newer technology and information, I would explore opportunities to experience it.
	INI2		I am typically the earliest among several of my colleagues to discover new advances in technology.
	INI3		I enjoy experimenting with cutting-edge information technology.
PER: Perception risk of adoption of legaltech in court proceedings	PER1	Featherman & Pavlou, 2003; Wynne, 2019	I believe that the performance of legal technology in court would be subpar and problematic.
	PER2		In my perspective, legal technology may expose sensitive data managed by courts.
	PER3		I believe that technical errors caused using legal technology in court may slow down court proceedings.
	PER4	Lee et al., 2019	I am afraid that court technology could be abused.
EQE: Equality expectations for legaltech in court proceedings	EQE1	Colquitt, 2001	I anticipate that court proceedings utilizing legal technology were founded on proper information.
	EQE2		I anticipate that judicial proceedings employing legal technology did not go against moral and ethical criteria.
	EQE3		I anticipate that court proceedings utilizing legal technologies will permit the expressing of a Person’s viewpoints and attitudes.
	EQE4		I anticipate that court procedures utilizing legal technologies will treat individuals with dignity.
	EQE5		I anticipate a thorough overview of the functioning and usefulness of legal technologies in court proceedings when they will be utilized.
LTK: Legaltech knowledge	LTK1	Clothier et al., 2015; Davis, 1989; Featherman & Pavlou, 2003; Wynne, 2019, proposed by the authors	Some nations employ a program that classifies reported problems, petitions, and other documents filed to the court as having no legal validity.
	LTK2		A system has been developed to automate the following legal processes: analyzing the claim, determining what information is lacking, and drafting the requisite response document.
	LTK3		In some nations, judges had access to a software that offers a comprehensive case study, evaluates the evidence, and identifies the potential results.
	LTK4		In some nations, a software provides the judge the appropriate penalties for the defendant in the legal sense; however, the judge determines the final decision.
	LTK5		In some nations, an algorithm is employed to generate relevant reasons for the case, which the judge might use to explain his or her verdict.
	LTK6		In certain nations, claims court disputes were adjudicated by algorithms.

Table 2. Respondents’ profile.

Item	Category	Number	Percentage (%)
Respondent’s occupation	Law students	33	15.87%
	Lawyers	41	19.71%
	Other	125	60.09%
	Missing	9	4.33%
	Total	208	100%
Experiences in the industry	Yes	125	60.10%
	No	83	39.90%
	Missing	0	0%
	Total	208	100%
Gender	Male	119	57.19%
	Female	89	42.81%
	Missing	0	0%
	Total	208	100%
Age	>17–40	77	37.21%
	>40	131	62.79%
	Missing	0	0%
	Total	208	100%

Table 3. Measurement model results of the constructs [Means (M), standard deviation (SD), factor loading, Cronbach’s Alpha (α), composite reliability (CR), and average variance extracted (AVE)].

Constructs	Item	M	SD	Factor loading	α	CR	AVE
BEI	BEI1	4.60	1.58	0.934	0.871	0.955	0.884
	BEI2	5.15	1.48	0.936
EQE	EQE1	6.18	1.19	0.840	0.929	0.958	0.770
	EQE2	6.19	1.18	0.872
	EQE3	6.29	1.10	0.875
	EQE4	6.33	1.19	0.897
	EQE5	6.33	1.09	0.877
PES	PES1	4.30	1.48	0.878	0.824	0.911	0.749
	PES2	4.57	1.44	0.910
	PES3	3.16	1.51	0.771
PER	PER1	3.50	1.55	0.874	0.837	0.908	0.675
	PER2	4.36	1.66	0.833
	PER3	3.85	1.53	0.837
	PER4	4.61	1.66	0.718
PEU	PEU1	5.25	1.45	0.860	0.868	0.936	0.794
	PEU2	5.48	1.39	0.888
	PEU3	5.42	1.37	0.910
INI	INI1	5.32	1.42	0.797	0.842	0.922	0.763
	INI2	4.22	1.57	0.909
	INI3	4.59	1.50	0.907
TRT	TRT1	4.83	1.43	0.854	0.863	0.934	0.788
	TRT2	4.68	1.51	0.878
	TRT3	4.54	1.48	0.916
LTK	LTK1	1.92	0.97	0.765	0.888	0.933	0.654
	LTK2	1.82	0.91	0.765
	LTK3	1.70	0.93	0.832
	LTK4	1.76	0.93	0.849
	LTK5	1.68	0.91	0.845
	LTK6	1.77	0.97	0.776

Table 4. Discriminant reliability of structures: ratio of Heterotraits to Monotraits (HTMT).

Constructs	BEI	EQE	LTK	PES	PER	PEU	INI
BEI
EQE	0.262
LTK	0.288	0.090
PES	0.605	0.083	0.225
PER	0.688	0.090	0.191	0.595
PEU	0.840	0.316	0.261	0.535	0.604
INI	0.607	0.154	0.261	0.460	0.466	0.400
TRT	0.826	0.165	0.323	0.579	0.712	0.784	0.451

Table 5. The variation inflation factor (VIF) across variables.

Constructs	BEI	PES	PEU	TRT
BEI
EQE			1.027
LTK			1.089	1.018
PES	1.391		1.437
PER			1.767	1.018
PEU	1.941
INI		1.157
TRT	2.005	1.157	1.868

Figure 2. Findings of the structural equation model evaluation.

The outcomes of the testing hypotheses.

Read the detailed description of this figure

The final assessment and monitoring of the study model’s quality was conducted through an examination of its forecasting performance. The determination coefficient (R2) demonstrated the quality and consistency of the conceptual framework (Hair, Hult, et al., 2019; Hair et al., 2020; Hair & Sarstedt, 2021).

Table 6. Findings from testing the hypothesis.

Hypothesis paths	Standard path coefficient (β)	t-Stat	p-Value	Results
H1 INI → PES	0.245	4.728	0.000***	Supported
H2a LTK → TRT	0.184	5.132	0.000***	Supported
H2b LTK → PEU	0.029	2.639	0.019***	Supported
H3a PER → TRT	0.292	5.627	0.000***	Supported
H3b PER → PEU	0.185	3.961	0.003***	Supported
H4 EQE → PEU	0.210	5.493	0.000***	Supported
H5a TRT → BEI	0.350	6.379	0.000***	Supported
H5b TRT → PES	0.412	8.643	0.000***	Supported
H5c TRT → PEU	0.475	10.001	0.000***	Supported
H6a PES → PEU	0.132	2.855	0.003***	Supported
H6b PES → BEI	0.153	3.484	0.000***	Supported
H6c PEU → BEI	0.442	9.617	0.000***	Supported

PEU: perceived usefulness of adoption of legaltech in court proceedings; PES: perceived simplicity of adoption of legaltech in court proceedings; BEI: behavioral intention to adopt legaltech in court proceedings; TRT: trust in legaltech; LTK: legaltech knowledge; PER: perception risk of adoption of legaltech in court proceedings; EQE: equality expectations for legaltech in court proceedings; INI: individual innovation in IT.

Note: ***, **, and * indicate significant at 1, 5, and 10% level of significance, respectively, based on t-statistics.

Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x

 Web of Science ®Google Scholar

Lee, M. K., Jain, A., Cha, H. J. I. N., Ojha, S., & Kusbit, D. (2019). Procedural justice in algorithmic fairness: Leveraging transparency and outcome control for fair algorithmic mediation. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), 1–26. https://doi.org/10.1145/3359284

 PubMedGoogle Scholar

Vimalkumar, M., Sharma, S. K., Singh, J. B., & Dwivedi, Y. K. (2021). ‘Okay google, what about my privacy?’: User’s privacy perceptions and acceptance of voice based digital assistants. Computers in Human Behavior, 120, 106763. https://doi.org/10.1016/j.chb.2021.106763

 Web of Science ®Google Scholar

Zhang, H., Wei, G., & Chen, X. (2022). SF-GRA method based on cumulative prospect theory for multiple attribute group decision making and its application to emergency supplies supplier selection. Engineering Applications of Artificial Intelligence, 110, 104679. https://doi.org/10.1016/j.engappai.2022.104679

 Web of Science ®Google Scholar

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–339. https://doi.org/10.2307/249008

 Web of Science ®Google Scholar

Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268. https://doi.org/10.1016/j.jsis.2005.07.003

 Web of Science ®Google Scholar

Xu, X., & Huang, Y. (2019). Restaurant information cues, diners’ expectations, and need for cognition: Experimental studies of online-to-offline mobile food ordering. Journal of Retailing and Consumer Services, 51, 231–241. https://doi.org/10.1016/j.jretconser.2019.06.010

 Web of Science ®Google Scholar

Patil, P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, 102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144

 Web of Science ®Google Scholar

Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3

 Web of Science ®Google Scholar

Wynne, C. W. (2019). The influence of perceived risk and trust in adoption of FinTech services in Indonesia. Journal of the Academy of Marketing Science, 13(1), 1039. https://doi.org/10.21512/commit.v13i1.5708

 Google Scholar

Colquitt, J. A. (2001). On the dimensionality of organizational justice: A construct validation of a measure. The Journal of Applied Psychology, 86(3), 386–400. https://doi.org/10.1037/0021-9010.86.3.386

 PubMed Web of Science ®Google Scholar

Clothier, R. A., Greer, D. A., Greer, D. G., & Mehta, A. M. (2015). Risk perception and the public acceptance of drones. Risk Analysis, 35(6), 1167–1183. https://doi.org/10.1111/risa.12330

 PubMed Web of Science ®Google Scholar

Data availability statement

The data used in this study can be obtained from V. P. Nguyen.