Intention for enhancing metaverse-based learning using gamification among university students: a study using Delphi and structural equation modelling approaches

Figures & data

Figure 1. Conceptual framework.

Table 1. Respondents’ demographic profile.

Demographic	Number	Percentage
Gender
Male	254	50.60
Female	248	49.40
Age
18 to 21 years	127	25.30
22 to 25 years	203	40.44
Above 25 years	172	34.26
Educational Qualifications
B.A.	30	5.97
B. Arch	20	3.98
B. Com	36	7.17
B. Sc	44	8.76
B. Tech	47	9.36
BBA	78	15.53
MA.	37	7.37
M.Com	40	7.96
M.Sc	31	6.17
M.Tech	60	11.95
MBA	79	15.73
Gamification and its application awareness
Yes	206	41.03
No	296	58.97
Knowledge about Metaverse
Yes	236	47.01
No	266	52.99
Metaverse applications awareness
Yes	241	48.01
No	261	51.99

Table 2. Values of Cronbach alpha and factor loadings.

Latest variables	Indicators	Cronbach’s alpha	Factor loadings	Tolerance	VIF
Perceived Ease of Use (PEOU)	PEOU 1: Learning based on Metaverse using gamification is advantageous	0.857	.826	0.852	1.174
	PEOU 2: Gamification can be considered one of the best technologies for Metaverse-based learning		.874
	PEOU 3: Understanding of Metaverse and Gamification is a pre-requisite for this type of learning		.887
	PEOU 4: Usage of gamification is user-friendly for Metaverse-based learning		.693
Perceived Usefulness (PU)	PU 1: Using gamification, my Metaverse-based learning will improve	0.92	.865	0.65	1.47
	PU 2: Using apps makes things easier for me		.856
	PU 3: My productivity will be enhanced once I am acquainted with Metaverse-based learning		.854
	PU 4: I can adapt my Metaverse-based learning enhancement due to gamification		.827
Trust (TRU)	TRU 1: Use of gamification to boost Metaverse-based learning is reliable	0.872	.838	0.776	1.286
	TRU 2: Use of gamification is secured for Metaverse-based learning		.867
	TRU 3: I am assured of the gamification technology in developing Metaverse-based learning		.838
Attitude (ATT)	ATT 1: I believe that employing gamification to improve Metaverse-based learning is beneficial	0.874	.812	0.74	1.352
	ATT 2: Technological user-friendliness will affect the growth of Metaverse-based learning		.851
	ATT 3: It helps in creating awareness about metaverse and gamification		.856
	ATT 4: I feel assured that the technological benefits of gamification are adequate for improving Metaverse-based learning		.740
Discomfort (DIS)	DIS 1: I try to avoid using technology if I am uncomfortable with it	0.85	.845	0.837	1.194
	DIS 2: My confidence goes down if I am having high discomfort		.893
	DIS 3: I believe technology is less useful for me if my discomfort is high		.819
Insecurity (INS)	INS 1: I am concerned about technology’s productivity if I can’t believe in it	0.901	.840	0.745	1.342
	INS 2: Providing personal information is unsafe and can be misused over the internet		.891
	INS 3: Dependency on technology too much can be harmful sometimes		.881
Subjective Norms (SN)	SN1: I was encouraged to improve my Metaverse-based learning using gamification by my friends, classmates, seniors, and juniors	0.935	.896	0.661	1.513
	SN2: People who influence me in improving Metaverse-based learning prefer to use gamification		.880
	SN3: Metaverse-based learning enhancement through gamification is my status symbol		.850
Perceived Behavioural Control (PBC)	PBC1: I am confident that gamification will boost my Metaverse-based learning	0.923	.838	0.644	1.553
	PBC2: I can understand that my Metaverse-based learning is enhanced due to gamification		.840
	PBC3: I have control over performing the defined behaviour		.854
Gamification (GAM)	GAM1: Metaverse-based learning became better due to gamification	0.951	.867	0.655	1.528
	GAM2: Knowledge about gamification increased because of the development of Metaverse-based learning		.872
	GAM3: I would most likely use the technology again if fun and entertainment were added to the learning		.862
	GAM4: I would prefer using this technology more if it gave me points, incentives, etc.		.878
Adoption of Metaverse-based learning (MBL)	MBL1: My Metaverse-based learning is boosted due to the implementation of gamification	0.871	.800
	MBL2: I can understand how gamification influenced the improvement of my Metaverse-based learning		.839
	MBL3: Now, I know that many factors are required to enhance my Metaverse-based learning along with gamification technology		.875
	MBL4: I plan to grow my Metaverse-based learning in future		.807

Table 3. Divergent validity matrix.

	CR	AVE	MSV	MaxR(H)	TRU	MBL	GAM	PU	PEoU	SN	ATT	INS	DIS	PBC
TRU	0.907	0.71	0.362	0.921	0.842
MBL	0.862	0.612	0.235	0.884	0.462***	0.782
GAM	0.872	0.631	0.369	0.883	0.522***	0.400***	0.794
PU	0.888	0.667	0.362	0.903	0.601***	0.472***	0.547***	0.816
PEoU	0.855	0.597	0.286	0.857	0.478***	0.457***	0.473***	0.534***	0.773
SN	0.829	0.618	0.163	0.833	0.241***	0.263***	0.288***	0.292***	0.292***	0.786
ATT	0.849	0.652	0.369	0.86	0.514***	0.380***	0.607***	0.561***	0.394***	0.166**	0.808
INS	0.819	0.603	0.188	0.834	0.351***	0.296***	0.344***	0.432***	0.433***	0.404***	0.345***	0.777
DIS	0.787	0.555	0.169	0.809	0.226***	0.263***	0.227***	0.331***	0.279***	0.235***	0.199**	0.412***	0.745
PBC	0.799	0.57	0.262	0.807	0.512***	0.485***	0.367***	0.458***	0.502***	0.319***	0.444***	0.379***	0.242***	0.755

Table 4. HTMT analysis.

	GAM	PU	MBL	ATT	PEOU	SN	INS	PBC	TRU	DIS
GAM
PU	0.194
MBL	0.354	0.206
ATT	0.143	0.515	0.123
PEOU	0.195	0.292	0.112	0.074
SN	0.381	0.008	0.033	0.059	0.207
INS	0.422	0.039	0.101	0.011	0.018	0.454
PBC	0.543	0.237	0.278	0.116	0.015	0.489	0.361
TRU	0.001	0.302	0.218	0.359	0.034	0.153	0.141	0.124
DIS	0.089	0.036	0	0.216	0.034	0.181	0.121	0.189	0.432

Figure 2. Structural equation model.

Table 5. Hypothesis results.

Hypothesis	Parameters	Estimate	SE	CR	P	Hypotheses
H 1	TRU <--- PEOU	−0.014	0.066	−0.206	0.837	Not Supported
H 2	TRU <--- PU	0.38	0.073	5.234	***	Supported
H 3	ATT <--- PEOU	−0.023	0.056	−0.422	0.673	Not Supported
H 4	ATT <--- PU	0.541	0.065	8.345	***	Supported
H 5	GAM <--- ATT	0.161	0.056	2.884	0.004	Supported
H 6	PU <--- DIS	0.047	0.063	0.744	0.457	Not Supported
H 7	PEOU <--- DIS	0.056	0.068	0.825	0.409	Not Supported
H 8	PU <--- INS	−0.057	0.073	−0.779	0.436	Not Supported
H 9	PEOU <--- INS	−0.027	0.079	−0.342	0.732	Not Supported
H 10	MBL <--- SN	−0.153	0.05	−3.073	0.002	Supported
H 11	MBL <--- PBC	0.161	0.047	3.414	***	Supported
H 12	MBL <--- GAM	0.247	0.047	5.231	***	Supported

Data availability

Data available on request from the author (Name: Tarinmoy Das; Email: [email protected])